WorldWideScience

Sample records for clustering based facial

  1. Global classification of human facial healthy skin using PLS discriminant analysis and clustering analysis.

    Science.gov (United States)

    Guinot, C; Latreille, J; Tenenhaus, M; Malvy, D J

    2001-04-01

    Today's classifications of healthy skin are predominantly based on a very limited number of skin characteristics, such as skin oiliness or susceptibility to sun exposure. The aim of the present analysis was to set up a global classification of healthy facial skin, using mathematical models. This classification is based on clinical, biophysical skin characteristics and self-reported information related to the skin, as well as the results of a theoretical skin classification assessed separately for the frontal and the malar zones of the face. In order to maximize the predictive power of the models with a minimum of variables, the Partial Least Square (PLS) discriminant analysis method was used. The resulting PLS components were subjected to clustering analyses to identify the plausible number of clusters and to group the individuals according to their proximities. Using this approach, four PLS components could be constructed and six clusters were found relevant. So, from the 36 hypothetical combinations of the theoretical skin types classification, we tended to a strengthened six classes proposal. Our data suggest that the association of the PLS discriminant analysis and the clustering methods leads to a valid and simple way to classify healthy human skin and represents a potentially useful tool for cosmetic and dermatological research.

  2. Resorcinarene-Based Facial Glycosides

    DEFF Research Database (Denmark)

    Hussain, Hazrat; Du, Yang; Tikhonova, Elena

    2017-01-01

    degradation during extraction and purification, thus necessitating the development of new agents with enhanced properties. In the current study, two classes of new amphiphiles are introduced, resorcinarene-based glucoside and maltoside amphiphiles (designated RGAs and RMAs, respectively), for which the alkyl...

  3. Perceptually Valid Facial Expressions for Character-Based Applications

    Directory of Open Access Journals (Sweden)

    Ali Arya

    2009-01-01

    Full Text Available This paper addresses the problem of creating facial expression of mixed emotions in a perceptually valid way. The research has been done in the context of a “game-like” health and education applications aimed at studying social competency and facial expression awareness in autistic children as well as native language learning, but the results can be applied to many other applications such as games with need for dynamic facial expressions or tools for automating the creation of facial animations. Most existing methods for creating facial expressions of mixed emotions use operations like averaging to create the combined effect of two universal emotions. Such methods may be mathematically justifiable but are not necessarily valid from a perceptual point of view. The research reported here starts by user experiments aiming at understanding how people combine facial actions to express mixed emotions, and how the viewers perceive a set of facial actions in terms of underlying emotions. Using the results of these experiments and a three-dimensional emotion model, we associate facial actions to dimensions and regions in the emotion space, and create a facial expression based on the location of the mixed emotion in the three-dimensional space. We call these regionalized facial actions “facial expression units.”

  4. Regression-based Multi-View Facial Expression Recognition

    NARCIS (Netherlands)

    Rudovic, Ognjen; Patras, Ioannis; Pantic, Maja

    2010-01-01

    We present a regression-based scheme for multi-view facial expression recognition based on 2蚠D geometric features. We address the problem by mapping facial points (e.g. mouth corners) from non-frontal to frontal view where further recognition of the expressions can be performed using a

  5. Prediction of mortality based on facial characteristics

    Directory of Open Access Journals (Sweden)

    Arnaud Delorme

    2016-05-01

    Full Text Available Recent studies have shown that characteristics of the face contain a wealth of information about health, age and chronic clinical conditions. Such studies involve objective measurement of facial features correlated with historical health information. But some individuals also claim to be adept at gauging mortality based on a glance at a person’s photograph. To test this claim, we invited 12 such individuals to see if they could determine if a person was alive or dead based solely on a brief examination of facial photographs. All photos used in the experiment were transformed into a uniform gray scale and then counterbalanced across eight categories: gender, age, gaze direction, glasses, head position, smile, hair color, and image resolution. Participants examined 404 photographs displayed on a computer monitor, one photo at a time, each shown for a maximum of 8 seconds. Half of the individuals in the photos were deceased, and half were alive at the time the experiment was conducted. Participants were asked to press a button if they thought the person in a photo was living or deceased. Overall mean accuracy on this task was 53.8%, where 50% was expected by chance (p < 0.004, two-tail. Statistically significant accuracy was independently obtained in 5 of the 12 participants. We also collected 32-channel electrophysiological recordings and observed a robust difference between images of deceased individuals correctly vs. incorrectly classified in the early event related potential at 100 ms post-stimulus onset. Our results support claims of individuals who report that some as-yet unknown features of the face predict mortality. The results are also compatible with claims about clairvoyance and warrants further investigation.

  6. Frame-Based Facial Expression Recognition Using Geometrical Features

    Directory of Open Access Journals (Sweden)

    Anwar Saeed

    2014-01-01

    Full Text Available To improve the human-computer interaction (HCI to be as good as human-human interaction, building an efficient approach for human emotion recognition is required. These emotions could be fused from several modalities such as facial expression, hand gesture, acoustic data, and biophysiological data. In this paper, we address the frame-based perception of the universal human facial expressions (happiness, surprise, anger, disgust, fear, and sadness, with the help of several geometrical features. Unlike many other geometry-based approaches, the frame-based method does not rely on prior knowledge of a person-specific neutral expression; this knowledge is gained through human intervention and not available in real scenarios. Additionally, we provide a method to investigate the performance of the geometry-based approaches under various facial point localization errors. From an evaluation on two public benchmark datasets, we have found that using eight facial points, we can achieve the state-of-the-art recognition rate. However, this state-of-the-art geometry-based approach exploits features derived from 68 facial points and requires prior knowledge of the person-specific neutral expression. The expression recognition rate using geometrical features is adversely affected by the errors in the facial point localization, especially for the expressions with subtle facial deformations.

  7. Robust facial landmark detection based on initializing multiple poses

    Directory of Open Access Journals (Sweden)

    Xin Chai

    2016-10-01

    Full Text Available For robot systems, robust facial landmark detection is the first and critical step for face-based human identification and facial expression recognition. In recent years, the cascaded-regression-based method has achieved excellent performance in facial landmark detection. Nevertheless, it still has certain weakness, such as high sensitivity to the initialization. To address this problem, regression based on multiple initializations is established in a unified model; face shapes are then estimated independently according to these initializations. With a ranking strategy, the best estimate is selected as the final output. Moreover, a face shape model based on restricted Boltzmann machines is built as a constraint to improve the robustness of ranking. Experiments on three challenging datasets demonstrate the effectiveness of the proposed facial landmark detection method against state-of-the-art methods.

  8. Facial Expression Recognition Based on TensorFlow Platform

    Directory of Open Access Journals (Sweden)

    Xia Xiao-Ling

    2017-01-01

    Full Text Available Facial expression recognition have a wide range of applications in human-machine interaction, pattern recognition, image understanding, machine vision and other fields. Recent years, it has gradually become a hot research. However, different people have different ways of expressing their emotions, and under the influence of brightness, background and other factors, there are some difficulties in facial expression recognition. In this paper, based on the Inception-v3 model of TensorFlow platform, we use the transfer learning techniques to retrain facial expression dataset (The Extended Cohn-Kanade dataset, which can keep the accuracy of recognition and greatly reduce the training time.

  9. Facial expression recognition based on improved deep belief networks

    Science.gov (United States)

    Wu, Yao; Qiu, Weigen

    2017-08-01

    In order to improve the robustness of facial expression recognition, a method of face expression recognition based on Local Binary Pattern (LBP) combined with improved deep belief networks (DBNs) is proposed. This method uses LBP to extract the feature, and then uses the improved deep belief networks as the detector and classifier to extract the LBP feature. The combination of LBP and improved deep belief networks is realized in facial expression recognition. In the JAFFE (Japanese Female Facial Expression) database on the recognition rate has improved significantly.

  10. Facial contour deformity correction with microvascular flaps based on the 3-dimentional template and facial moulage

    Directory of Open Access Journals (Sweden)

    Dinesh Kadam

    2013-01-01

    Full Text Available Introduction: Facial contour deformities presents with varied aetiology and degrees severity. Accurate assessment, selecting a suitable tissue and sculpturing it to fill the defect is challenging and largely subjective. Objective assessment with imaging and software is not always feasible and preparing a template is complicated. A three-dimensional (3D wax template pre-fabricated over the facial moulage aids surgeons to fulfil these tasks. Severe deformities demand a stable vascular tissue for an acceptable outcome. Materials and Methods: We present review of eight consecutive patients who underwent augmentation of facial contour defects with free flaps between June 2005 and January 2011. De-epithelialised free anterolateral thigh (ALT flap in three, radial artery forearm flap and fibula osteocutaneous flap in two each and groin flap was used in one patient. A 3D wax template was fabricated by augmenting the deformity on facial moulage. It was utilised to select the flap, to determine the exact dimensions and to sculpture intraoperatively. Ancillary procedures such as genioplasty, rhinoplasty and coloboma correction were performed. Results: The average age at the presentation was 25 years and average disease free interval was 5.5 years and all flaps survived. Mean follow-up period was 21.75 months. The correction was aesthetically acceptable and was maintained without any recurrence or atrophy. Conclusion: The 3D wax template on facial moulage is simple, inexpensive and precise objective tool. It provides accurate guide for the planning and execution of the flap reconstruction. The selection of the flap is based on the type and extent of the defect. Superiority of vascularised free tissue is well-known and the ALT flap offers a versatile option for correcting varying degrees of the deformities. Ancillary procedures improve the overall aesthetic outcomes and minor flap touch-up procedures are generally required.

  11. Scattered Data Processing Approach Based on Optical Facial Motion Capture

    Directory of Open Access Journals (Sweden)

    Qiang Zhang

    2013-01-01

    Full Text Available In recent years, animation reconstruction of facial expressions has become a popular research field in computer science and motion capture-based facial expression reconstruction is now emerging in this field. Based on the facial motion data obtained using a passive optical motion capture system, we propose a scattered data processing approach, which aims to solve the common problems of missing data and noise. To recover missing data, given the nonlinear relationships among neighbors with the current missing marker, we propose an improved version of a previous method, where we use the motion of three muscles rather than one to recover the missing data. To reduce the noise, we initially apply preprocessing to eliminate impulsive noise, before our proposed three-order quasi-uniform B-spline-based fitting method is used to reduce the remaining noise. Our experiments showed that the principles that underlie this method are simple and straightforward, and it delivered acceptable precision during reconstruction.

  12. Cluster Based Text Classification Model

    DEFF Research Database (Denmark)

    Nizamani, Sarwat; Memon, Nasrullah; Wiil, Uffe Kock

    2011-01-01

    We propose a cluster based classification model for suspicious email detection and other text classification tasks. The text classification tasks comprise many training examples that require a complex classification model. Using clusters for classification makes the model simpler and increases...... the accuracy at the same time. The test example is classified using simpler and smaller model. The training examples in a particular cluster share the common vocabulary. At the time of clustering, we do not take into account the labels of the training examples. After the clusters have been created......, the classifier is trained on each cluster having reduced dimensionality and less number of examples. The experimental results show that the proposed model outperforms the existing classification models for the task of suspicious email detection and topic categorization on the Reuters-21578 and 20 Newsgroups...

  13. A novel human-machine interface based on recognition of multi-channel facial bioelectric signals

    International Nuclear Information System (INIS)

    Razazadeh, Iman Mohammad; Firoozabadi, S. Mohammad; Golpayegani, S.M.R.H.; Hu, H.

    2011-01-01

    Full text: This paper presents a novel human-machine interface for disabled people to interact with assistive systems for a better quality of life. It is based on multichannel forehead bioelectric signals acquired by placing three pairs of electrodes (physical channels) on the Fron-tails and Temporalis facial muscles. The acquired signals are passes through a parallel filter bank to explore three different sub-bands related to facial electromyogram, electrooculogram and electroencephalogram. The root mean features of the bioelectric signals analyzed within non-overlapping 256 ms windows were extracted. The subtractive fuzzy c-means clustering method (SFCM) was applied to segment the feature space and generate initial fuzzy based Takagi-Sugeno rules. Then, an adaptive neuro-fuzzy inference system is exploited to tune up the premises and consequence parameters of the extracted SFCMs. rules. The average classifier discriminating ratio for eight different facial gestures (smiling, frowning, pulling up left/right lips corner, eye movement to left/right/up/down is between 93.04% and 96.99% according to different combinations and fusions of logical features. Experimental results show that the proposed interface has a high degree of accuracy and robustness for discrimination of 8 fundamental facial gestures. Some potential and further capabilities of our approach in human-machine interfaces are also discussed. (author)

  14. Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms

    Directory of Open Access Journals (Sweden)

    Cheng-Yuan Shih

    2010-01-01

    Full Text Available This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state. The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB with effective Gabor features to recognize the facial expressions. Entropy criterion is applied to select the effective Gabor feature which is a subset of informative and nonredundant Gabor features. The proposed RDAB algorithm uses RDA as a learner in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA and quadratic discriminant analysis (QDA. It solves the small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses the particle swarm optimization (PSO algorithm to estimate optimal parameters in RDA. Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.

  15. Radiofrequency facial rejuvenation: evidence-based effect.

    Science.gov (United States)

    el-Domyati, Moetaz; el-Ammawi, Tarek S; Medhat, Walid; Moawad, Osama; Brennan, Donna; Mahoney, My G; Uitto, Jouni

    2011-03-01

    Multiple therapies involving ablative and nonablative techniques have been developed for rejuvenation of photodamaged skin. Monopolar radiofrequency (RF) is emerging as a gentler, nonablative skin-tightening device that delivers uniform heat to the dermis at a controlled depth. We evaluated the clinical effects and objectively quantified the histologic changes of the nonablative RF device in the treatment of photoaging. Six individuals of Fitzpatrick skin type III to IV and Glogau class I to II wrinkles were subjected to 3 months of treatment (6 sessions at 2-week intervals). Standard photographs and skin biopsy specimens were obtained at baseline, and at 3 and 6 months after the start of treatment. We performed quantitative evaluation of total elastin, collagen types I and III, and newly synthesized collagen using computerized histometric and immunohistochemical techniques. Blinded photographs were independently scored for wrinkle improvement. RF produced noticeable clinical results, with high satisfaction and corresponding facial skin improvement. Compared with the baseline, there was a statistically significant increase in the mean of collagen types I and III, and newly synthesized collagen, while the mean of total elastin was significantly decreased, at the end of treatment and 3 months posttreatment. A limitation of this study is the small number of patients, yet the results show a significant improvement. Although the results may not be as impressive as those obtained by ablative treatments, RF is a promising treatment option for photoaging with fewer side effects and downtime. Copyright © 2010 American Academy of Dermatology, Inc. Published by Mosby, Inc. All rights reserved.

  16. Research on facial expression simulation based on depth image

    Science.gov (United States)

    Ding, Sha-sha; Duan, Jin; Zhao, Yi-wu; Xiao, Bo; Wang, Hao

    2017-11-01

    Nowadays, face expression simulation is widely used in film and television special effects, human-computer interaction and many other fields. Facial expression is captured by the device of Kinect camera .The method of AAM algorithm based on statistical information is employed to detect and track faces. The 2D regression algorithm is applied to align the feature points. Among them, facial feature points are detected automatically and 3D cartoon model feature points are signed artificially. The aligned feature points are mapped by keyframe techniques. In order to improve the animation effect, Non-feature points are interpolated based on empirical models. Under the constraint of Bézier curves we finish the mapping and interpolation. Thus the feature points on the cartoon face model can be driven if the facial expression varies. In this way the purpose of cartoon face expression simulation in real-time is came ture. The experiment result shows that the method proposed in this text can accurately simulate the facial expression. Finally, our method is compared with the previous method. Actual data prove that the implementation efficiency is greatly improved by our method.

  17. Biometric identification based on novel frequency domain facial asymmetry measures

    Science.gov (United States)

    Mitra, Sinjini; Savvides, Marios; Vijaya Kumar, B. V. K.

    2005-03-01

    In the modern world, the ever-growing need to ensure a system's security has spurred the growth of the newly emerging technology of biometric identification. The present paper introduces a novel set of facial biometrics based on quantified facial asymmetry measures in the frequency domain. In particular, we show that these biometrics work well for face images showing expression variations and have the potential to do so in presence of illumination variations as well. A comparison of the recognition rates with those obtained from spatial domain asymmetry measures based on raw intensity values suggests that the frequency domain representation is more robust to intra-personal distortions and is a novel approach for performing biometric identification. In addition, some feature analysis based on statistical methods comparing the asymmetry measures across different individuals and across different expressions is presented.

  18. Normalization based K means Clustering Algorithm

    OpenAIRE

    Virmani, Deepali; Taneja, Shweta; Malhotra, Geetika

    2015-01-01

    K-means is an effective clustering technique used to separate similar data into groups based on initial centroids of clusters. In this paper, Normalization based K-means clustering algorithm(N-K means) is proposed. Proposed N-K means clustering algorithm applies normalization prior to clustering on the available data as well as the proposed approach calculates initial centroids based on weights. Experimental results prove the betterment of proposed N-K means clustering algorithm over existing...

  19. MRI-based diagnostic imaging of the intratemporal facial nerve

    International Nuclear Information System (INIS)

    Kress, B.; Baehren, W.

    2001-01-01

    Detailed imaging of the five sections of the full intratemporal course of the facial nerve can be achieved by MRI and using thin tomographic section techniques and surface coils. Contrast media are required for tomographic imaging of pathological processes. Established methods are available for diagnostic evaluation of cerebellopontine angle tumors and chronic Bell's palsy, as well as hemifacial spasms. A method still under discussion is MRI for diagnostic evaluation of Bell's palsy in the presence of fractures of the petrous bone, when blood volumes in the petrous bone make evaluation even more difficult. MRI-based diagnostic evaluation of the idiopatic facial paralysis currently is subject to change. Its usual application cannot be recommended for routine evaluation at present. However, a quantitative analysis of contrast medium uptake of the nerve may be an approach to improve the prognostic value of MRI in acute phases of Bell's palsy. (orig./CB) [de

  20. Likelihood Ratio Based Mixed Resolution Facial Comparison

    NARCIS (Netherlands)

    Peng, Y.; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.

    2015-01-01

    In this paper, we propose a novel method for low-resolution face recognition. It is especially useful for a common situation in forensic search where faces of low resolution, e.g. on surveillance footage or in a crowd, must be compared to a high-resolution reference. This method is based on the

  1. Face detection and facial feature localization using notch based templates

    International Nuclear Information System (INIS)

    Qayyum, U.

    2007-01-01

    We present a real time detection off aces from the video with facial feature localization as well as the algorithm capable of differentiating between the face/non-face patterns. The need of face detection and facial feature localization arises in various application of computer vision, so a lot of research is dedicated to come up with a real time solution. The algorithm should remain simple to perform real time whereas it should not compromise on the challenges encountered during the detection and localization phase, keeping simplicity and all challenges i.e. algorithm invariant to scale, translation, and (+-45) rotation transformations. The proposed system contains two parts. Visual guidance and face/non-face classification. The visual guidance phase uses the fusion of motion and color cues to classify skin color. Morphological operation with union-structure component labeling algorithm extracts contiguous regions. Scale normalization is applied by nearest neighbor interpolation method to avoid the effect of different scales. Using the aspect ratio of width and height size. Region of Interest (ROI) is obtained and then passed to face/non-face classifier. Notch (Gaussian) based templates/ filters are used to find circular darker regions in ROI. The classified face region is handed over to facial feature localization phase, which uses YCbCr eyes/lips mask for face feature localization. The empirical results show an accuracy of 90% for five different videos with 1000 face/non-face patterns and processing rate of proposed algorithm is 15 frames/sec. (author)

  2. Text Clustering Algorithm Based on Random Cluster Core

    Directory of Open Access Journals (Sweden)

    Huang Long-Jun

    2016-01-01

    Full Text Available Nowadays clustering has become a popular text mining algorithm, but the huge data can put forward higher requirements for the accuracy and performance of text mining. In view of the performance bottleneck of traditional text clustering algorithm, this paper proposes a text clustering algorithm with random features. This is a kind of clustering algorithm based on text density, at the same time using the neighboring heuristic rules, the concept of random cluster is introduced, which effectively reduces the complexity of the distance calculation.

  3. Energy-Based Facial Rejuvenation: Advances in Diagnosis and Treatment.

    Science.gov (United States)

    Britt, Christopher J; Marcus, Benjamin

    2017-01-01

    The market for nonsurgical, energy-based facial rejuvenation techniques has increased exponentially since lasers were first used for skin rejuvenation in 1983. Advances in this area have led to a wide range of products that require the modern facial plastic surgeon to have a large repertoire of knowledge. To serve as a guide for current trends in the development of technology, applications, and outcomes of laser and laser-related technology over the past 5 years. We performed a review of PubMed from January 1, 2011, to March 1, 2016, and focused on randomized clinical trials, meta-analyses, systematic reviews, and clinical practice guidelines including case control, case studies and case reports when necessary, and included 14 articles we deemed landmark articles before 2011. Three broad categories of technology are leading non-energy-based rejuvenation technology: lasers, light therapy, and non-laser-based thermal tightening devices. Laser light therapy has continued to diversify with the use of ablative and nonablative resurfacing technologies, fractionated lasers, and their combined use. Light therapy has developed for use in combination with other technologies or stand alone. Finally, thermally based nonlaser skin-tightening devices, such as radiofrequency (RF) and intense focused ultrasonography (IFUS), are evolving technologies that have changed rapidly over the past 5 years. Improvements in safety and efficacy for energy-based treatment have expanded the patient base considering these therapies viable options. With a wide variety of options, the modern facial plastic surgeon can have a frank discussion with the patient regarding nonsurgical techniques that were never before available. Many of these patients can now derive benefit from treatments requiring significantly less downtime than before while the clinician can augment the treatment to maximize benefit to fit the patient's time schedule.

  4. Projection-based curve clustering

    International Nuclear Information System (INIS)

    Auder, Benjamin; Fischer, Aurelie

    2012-01-01

    This paper focuses on unsupervised curve classification in the context of nuclear industry. At the Commissariat a l'Energie Atomique (CEA), Cadarache (France), the thermal-hydraulic computer code CATHARE is used to study the reliability of reactor vessels. The code inputs are physical parameters and the outputs are time evolution curves of a few other physical quantities. As the CATHARE code is quite complex and CPU time-consuming, it has to be approximated by a regression model. This regression process involves a clustering step. In the present paper, the CATHARE output curves are clustered using a k-means scheme, with a projection onto a lower dimensional space. We study the properties of the empirically optimal cluster centres found by the clustering method based on projections, compared with the 'true' ones. The choice of the projection basis is discussed, and an algorithm is implemented to select the best projection basis among a library of orthonormal bases. The approach is illustrated on a simulated example and then applied to the industrial problem. (authors)

  5. Advances in face detection and facial image analysis

    CERN Document Server

    Celebi, M; Smolka, Bogdan

    2016-01-01

    This book presents the state-of-the-art in face detection and analysis. It outlines new research directions, including in particular psychology-based facial dynamics recognition, aimed at various applications such as behavior analysis, deception detection, and diagnosis of various psychological disorders. Topics of interest include face and facial landmark detection, face recognition, facial expression and emotion analysis, facial dynamics analysis, face classification, identification, and clustering, and gaze direction and head pose estimation, as well as applications of face analysis.

  6. Subtypes of autism by cluster analysis based on structural MRI data.

    Science.gov (United States)

    Hrdlicka, Michal; Dudova, Iva; Beranova, Irena; Lisy, Jiri; Belsan, Tomas; Neuwirth, Jiri; Komarek, Vladimir; Faladova, Ludvika; Havlovicova, Marketa; Sedlacek, Zdenek; Blatny, Marek; Urbanek, Tomas

    2005-05-01

    The aim of our study was to subcategorize Autistic Spectrum Disorders (ASD) using a multidisciplinary approach. Sixty four autistic patients (mean age 9.4+/-5.6 years) were entered into a cluster analysis. The clustering analysis was based on MRI data. The clusters obtained did not differ significantly in the overall severity of autistic symptomatology as measured by the total score on the Childhood Autism Rating Scale (CARS). The clusters could be characterized as showing significant differences: Cluster 1: showed the largest sizes of the genu and splenium of the corpus callosum (CC), the lowest pregnancy order and the lowest frequency of facial dysmorphic features. Cluster 2: showed the largest sizes of the amygdala and hippocampus (HPC), the least abnormal visual response on the CARS, the lowest frequency of epilepsy and the least frequent abnormal psychomotor development during the first year of life. Cluster 3: showed the largest sizes of the caput of the nucleus caudatus (NC), the smallest sizes of the HPC and facial dysmorphic features were always present. Cluster 4: showed the smallest sizes of the genu and splenium of the CC, as well as the amygdala, and caput of the NC, the most abnormal visual response on the CARS, the highest frequency of epilepsy, the highest pregnancy order, abnormal psychomotor development during the first year of life was always present and facial dysmorphic features were always present. This multidisciplinary approach seems to be a promising method for subtyping autism.

  7. Dynamic facial expression recognition based on geometric and texture features

    Science.gov (United States)

    Li, Ming; Wang, Zengfu

    2018-04-01

    Recently, dynamic facial expression recognition in videos has attracted growing attention. In this paper, we propose a novel dynamic facial expression recognition method by using geometric and texture features. In our system, the facial landmark movements and texture variations upon pairwise images are used to perform the dynamic facial expression recognition tasks. For one facial expression sequence, pairwise images are created between the first frame and each of its subsequent frames. Integration of both geometric and texture features further enhances the representation of the facial expressions. Finally, Support Vector Machine is used for facial expression recognition. Experiments conducted on the extended Cohn-Kanade database show that our proposed method can achieve a competitive performance with other methods.

  8. Facial Image Compression Based on Structured Codebooks in Overcomplete Domain

    Directory of Open Access Journals (Sweden)

    Vila-Forcén JE

    2006-01-01

    Full Text Available We advocate facial image compression technique in the scope of distributed source coding framework. The novelty of the proposed approach is twofold: image compression is considered from the position of source coding with side information and, contrarily to the existing scenarios where the side information is given explicitly; the side information is created based on a deterministic approximation of the local image features. We consider an image in the overcomplete transform domain as a realization of a random source with a structured codebook of symbols where each symbol represents a particular edge shape. Due to the partial availability of the side information at both encoder and decoder, we treat our problem as a modification of the Berger-Flynn-Gray problem and investigate a possible gain over the solutions when side information is either unavailable or available at the decoder. Finally, the paper presents a practical image compression algorithm for facial images based on our concept that demonstrates the superior performance in the very-low-bit-rate regime.

  9. Spanning Tree Based Attribute Clustering

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Jorge, Cordero Hernandez

    2009-01-01

    Attribute clustering has been previously employed to detect statistical dependence between subsets of variables. We propose a novel attribute clustering algorithm motivated by research of complex networks, called the Star Discovery algorithm. The algorithm partitions and indirectly discards...... inconsistent edges from a maximum spanning tree by starting appropriate initial modes, therefore generating stable clusters. It discovers sound clusters through simple graph operations and achieves significant computational savings. We compare the Star Discovery algorithm against earlier attribute clustering...

  10. Analysis of facial expressions in parkinson's disease through video-based automatic methods.

    Science.gov (United States)

    Bandini, Andrea; Orlandi, Silvia; Escalante, Hugo Jair; Giovannelli, Fabio; Cincotta, Massimo; Reyes-Garcia, Carlos A; Vanni, Paola; Zaccara, Gaetano; Manfredi, Claudia

    2017-04-01

    The automatic analysis of facial expressions is an evolving field that finds several clinical applications. One of these applications is the study of facial bradykinesia in Parkinson's disease (PD), which is a major motor sign of this neurodegenerative illness. Facial bradykinesia consists in the reduction/loss of facial movements and emotional facial expressions called hypomimia. In this work we propose an automatic method for studying facial expressions in PD patients relying on video-based METHODS: 17 Parkinsonian patients and 17 healthy control subjects were asked to show basic facial expressions, upon request of the clinician and after the imitation of a visual cue on a screen. Through an existing face tracker, the Euclidean distance of the facial model from a neutral baseline was computed in order to quantify the changes in facial expressivity during the tasks. Moreover, an automatic facial expressions recognition algorithm was trained in order to study how PD expressions differed from the standard expressions. Results show that control subjects reported on average higher distances than PD patients along the tasks. This confirms that control subjects show larger movements during both posed and imitated facial expressions. Moreover, our results demonstrate that anger and disgust are the two most impaired expressions in PD patients. Contactless video-based systems can be important techniques for analyzing facial expressions also in rehabilitation, in particular speech therapy, where patients could get a definite advantage from a real-time feedback about the proper facial expressions/movements to perform. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. A novel method for human age group classification based on Correlation Fractal Dimension of facial edges

    OpenAIRE

    Yarlagadda, Anuradha; Murthy, J.V.R.; Krishna Prasad, M.H.M.

    2015-01-01

    In the computer vision community, easy categorization of a person’s facial image into various age groups is often quite precise and is not pursued effectively. To address this problem, which is an important area of research, the present paper proposes an innovative method of age group classification system based on the Correlation Fractal Dimension of complex facial image. Wrinkles appear on the face with aging thereby changing the facial edges of the image. The proposed method is rotation an...

  12. Facial expression recognition based on improved local ternary pattern and stacked auto-encoder

    Science.gov (United States)

    Wu, Yao; Qiu, Weigen

    2017-08-01

    In order to enhance the robustness of facial expression recognition, we propose a method of facial expression recognition based on improved Local Ternary Pattern (LTP) combined with Stacked Auto-Encoder (SAE). This method uses the improved LTP extraction feature, and then uses the improved depth belief network as the detector and classifier to extract the LTP feature. The combination of LTP and improved deep belief network is realized in facial expression recognition. The recognition rate on CK+ databases has improved significantly.

  13. Progressive Exponential Clustering-Based Steganography

    Directory of Open Access Journals (Sweden)

    Li Yue

    2010-01-01

    Full Text Available Cluster indexing-based steganography is an important branch of data-hiding techniques. Such schemes normally achieve good balance between high embedding capacity and low embedding distortion. However, most cluster indexing-based steganographic schemes utilise less efficient clustering algorithms for embedding data, which causes redundancy and leaves room for increasing the embedding capacity further. In this paper, a new clustering algorithm, called progressive exponential clustering (PEC, is applied to increase the embedding capacity by avoiding redundancy. Meanwhile, a cluster expansion algorithm is also developed in order to further increase the capacity without sacrificing imperceptibility.

  14. Active AU Based Patch Weighting for Facial Expression Recognition

    Directory of Open Access Journals (Sweden)

    Weicheng Xie

    2017-01-01

    Full Text Available Facial expression has many applications in human-computer interaction. Although feature extraction and selection have been well studied, the specificity of each expression variation is not fully explored in state-of-the-art works. In this work, the problem of multiclass expression recognition is converted into triplet-wise expression recognition. For each expression triplet, a new feature optimization model based on action unit (AU weighting and patch weight optimization is proposed to represent the specificity of the expression triplet. The sparse representation-based approach is then proposed to detect the active AUs of the testing sample for better generalization. The algorithm achieved competitive accuracies of 89.67% and 94.09% for the Jaffe and Cohn–Kanade (CK+ databases, respectively. Better cross-database performance has also been observed.

  15. Facial Asymmetry-Based Age Group Estimation: Role in Recognizing Age-Separated Face Images.

    Science.gov (United States)

    Sajid, Muhammad; Taj, Imtiaz Ahmad; Bajwa, Usama Ijaz; Ratyal, Naeem Iqbal

    2018-04-23

    Face recognition aims to establish the identity of a person based on facial characteristics. On the other hand, age group estimation is the automatic calculation of an individual's age range based on facial features. Recognizing age-separated face images is still a challenging research problem due to complex aging processes involving different types of facial tissues, skin, fat, muscles, and bones. Certain holistic and local facial features are used to recognize age-separated face images. However, most of the existing methods recognize face images without incorporating the knowledge learned from age group estimation. In this paper, we propose an age-assisted face recognition approach to handle aging variations. Inspired by the observation that facial asymmetry is an age-dependent intrinsic facial feature, we first use asymmetric facial dimensions to estimate the age group of a given face image. Deeply learned asymmetric facial features are then extracted for face recognition using a deep convolutional neural network (dCNN). Finally, we integrate the knowledge learned from the age group estimation into the face recognition algorithm using the same dCNN. This integration results in a significant improvement in the overall performance compared to using the face recognition algorithm alone. The experimental results on two large facial aging datasets, the MORPH and FERET sets, show that the proposed age group estimation based on the face recognition approach yields superior performance compared to some existing state-of-the-art methods. © 2018 American Academy of Forensic Sciences.

  16. Classical Music Clustering Based on Acoustic Features

    OpenAIRE

    Wang, Xindi; Haque, Syed Arefinul

    2017-01-01

    In this paper we cluster 330 classical music pieces collected from MusicNet database based on their musical note sequence. We use shingling and chord trajectory matrices to create signature for each music piece and performed spectral clustering to find the clusters. Based on different resolution, the output clusters distinctively indicate composition from different classical music era and different composing style of the musicians.

  17. A dynamic texture based approach to recognition of facial actions and their temporal models

    NARCIS (Netherlands)

    Koelstra, Sander; Pantic, Maja; Patras, Ioannis (Yannis)

    2010-01-01

    In this work, we propose a dynamic texture-based approach to the recognition of facial Action Units (AUs, atomic facial gestures) and their temporal models (i.e., sequences of temporal segments: neutral, onset, apex, and offset) in near-frontal-view face videos. Two approaches to modeling the

  18. Efficient clustering aggregation based on data fragments.

    Science.gov (United States)

    Wu, Ou; Hu, Weiming; Maybank, Stephen J; Zhu, Mingliang; Li, Bing

    2012-06-01

    Clustering aggregation, known as clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a single better clustering. Existing clustering aggregation algorithms are applied directly to data points, in what is referred to as the point-based approach. The algorithms are inefficient if the number of data points is large. We define an efficient approach for clustering aggregation based on data fragments. In this fragment-based approach, a data fragment is any subset of the data that is not split by any of the clustering results. To establish the theoretical bases of the proposed approach, we prove that clustering aggregation can be performed directly on data fragments under two widely used goodness measures for clustering aggregation taken from the literature. Three new clustering aggregation algorithms are described. The experimental results obtained using several public data sets show that the new algorithms have lower computational complexity than three well-known existing point-based clustering aggregation algorithms (Agglomerative, Furthest, and LocalSearch); nevertheless, the new algorithms do not sacrifice the accuracy.

  19. Scalable Density-Based Subspace Clustering

    DEFF Research Database (Denmark)

    Müller, Emmanuel; Assent, Ira; Günnemann, Stephan

    2011-01-01

    For knowledge discovery in high dimensional databases, subspace clustering detects clusters in arbitrary subspace projections. Scalability is a crucial issue, as the number of possible projections is exponential in the number of dimensions. We propose a scalable density-based subspace clustering...... method that steers mining to few selected subspace clusters. Our novel steering technique reduces subspace processing by identifying and clustering promising subspaces and their combinations directly. Thereby, it narrows down the search space while maintaining accuracy. Thorough experiments on real...... and synthetic databases show that steering is efficient and scalable, with high quality results. For future work, our steering paradigm for density-based subspace clustering opens research potential for speeding up other subspace clustering approaches as well....

  20. Learning representative features for facial images based on a modified principal component analysis

    Science.gov (United States)

    Averkin, Anton; Potapov, Alexey

    2013-05-01

    The paper is devoted to facial image analysis and particularly deals with the problem of automatic evaluation of the attractiveness of human faces. We propose a new approach for automatic construction of feature space based on a modified principal component analysis. Input data sets for the algorithm are the learning data sets of facial images, which are rated by one person. The proposed approach allows one to extract features of the individual subjective face beauty perception and to predict attractiveness values for new facial images, which were not included into a learning data set. The Pearson correlation coefficient between values predicted by our method for new facial images and personal attractiveness estimation values equals to 0.89. This means that the new approach proposed is promising and can be used for predicting subjective face attractiveness values in real systems of the facial images analysis.

  1. Facial Video-Based Photoplethysmography to Detect HRV at Rest.

    Science.gov (United States)

    Moreno, J; Ramos-Castro, J; Movellan, J; Parrado, E; Rodas, G; Capdevila, L

    2015-06-01

    Our aim is to demonstrate the usefulness of photoplethysmography (PPG) for analyzing heart rate variability (HRV) using a standard 5-min test at rest with paced breathing, comparing the results with real RR intervals and testing supine and sitting positions. Simultaneous recordings of R-R intervals were conducted with a Polar system and a non-contact PPG, based on facial video recording on 20 individuals. Data analysis and editing were performed with individually designated software for each instrument. Agreement on HRV parameters was assessed with concordance correlations, effect size from ANOVA and Bland and Altman plots. For supine position, differences between video and Polar systems showed a small effect size in most HRV parameters. For sitting position, these differences showed a moderate effect size in most HRV parameters. A new procedure, based on the pixels that contained more heart beat information, is proposed for improving the signal-to-noise ratio in the PPG video signal. Results were acceptable in both positions but better in the supine position. Our approach could be relevant for applications that require monitoring of stress or cardio-respiratory health, such as effort/recuperation states in sports. © Georg Thieme Verlag KG Stuttgart · New York.

  2. CORECLUSTER: A Degeneracy Based Graph Clustering Framework

    OpenAIRE

    Giatsidis , Christos; Malliaros , Fragkiskos; Thilikos , Dimitrios M. ,; Vazirgiannis , Michalis

    2014-01-01

    International audience; Graph clustering or community detection constitutes an important task forinvestigating the internal structure of graphs, with a plethora of applications in several domains. Traditional tools for graph clustering, such asspectral methods, typically suffer from high time and space complexity. In thisarticle, we present \\textsc{CoreCluster}, an efficient graph clusteringframework based on the concept of graph degeneracy, that can be used along withany known graph clusteri...

  3. MPEG-4-based 2D facial animation for mobile devices

    Science.gov (United States)

    Riegel, Thomas B.

    2005-03-01

    The enormous spread of mobile computing devices (e.g. PDA, cellular phone, palmtop, etc.) emphasizes scalable applications, since users like to run their favorite programs on the terminal they operate at that moment. Therefore appliances are of interest, which can be adapted to the hardware realities without loosing a lot of their functionalities. A good example for this is "Facial Animation," which offers an interesting way to achieve such "scalability." By employing MPEG-4, which provides an own profile for facial animation, a solution for low power terminals including mobile phones is demonstrated. From the generic 3D MPEG-4 face a specific 2D head model is derived, which consists primarily of a portrait image superposed by a suited warping mesh and adapted 2D animation rules. Thus the animation process of MPEG-4 need not be changed and standard compliant facial animation parameters can be used to displace the vertices of the mesh and warp the underlying image accordingly.

  4. Facial Emotion Recognition Using Context Based Multimodal Approach

    Directory of Open Access Journals (Sweden)

    Priya Metri

    2011-12-01

    Full Text Available Emotions play a crucial role in person to person interaction. In recent years, there has been a growing interest in improving all aspects of interaction between humans and computers. The ability to understand human emotions is desirable for the computer in several applications especially by observing facial expressions. This paper explores a ways of human-computer interaction that enable the computer to be more aware of the user’s emotional expressions we present a approach for the emotion recognition from a facial expression, hand and body posture. Our model uses multimodal emotion recognition system in which we use two different models for facial expression recognition and for hand and body posture recognition and then combining the result of both classifiers using a third classifier which give the resulting emotion . Multimodal system gives more accurate result than a signal or bimodal system

  5. 3D facial expression recognition based on histograms of surface differential quantities

    KAUST Repository

    Li, Huibin

    2011-01-01

    3D face models accurately capture facial surfaces, making it possible for precise description of facial activities. In this paper, we present a novel mesh-based method for 3D facial expression recognition using two local shape descriptors. To characterize shape information of the local neighborhood of facial landmarks, we calculate the weighted statistical distributions of surface differential quantities, including histogram of mesh gradient (HoG) and histogram of shape index (HoS). Normal cycle theory based curvature estimation method is employed on 3D face models along with the common cubic fitting curvature estimation method for the purpose of comparison. Based on the basic fact that different expressions involve different local shape deformations, the SVM classifier with both linear and RBF kernels outperforms the state of the art results on the subset of the BU-3DFE database with the same experimental setting. © 2011 Springer-Verlag.

  6. ADVANCED CLUSTER BASED IMAGE SEGMENTATION

    Directory of Open Access Journals (Sweden)

    D. Kesavaraja

    2011-11-01

    Full Text Available This paper presents efficient and portable implementations of a useful image segmentation technique which makes use of the faster and a variant of the conventional connected components algorithm which we call parallel Components. In the Modern world majority of the doctors are need image segmentation as the service for various purposes and also they expect this system is run faster and secure. Usually Image segmentation Algorithms are not working faster. In spite of several ongoing researches in Conventional Segmentation and its Algorithms might not be able to run faster. So we propose a cluster computing environment for parallel image Segmentation to provide faster result. This paper is the real time implementation of Distributed Image Segmentation in Clustering of Nodes. We demonstrate the effectiveness and feasibility of our method on a set of Medical CT Scan Images. Our general framework is a single address space, distributed memory programming model. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. The image segmentation algorithm makes use of an efficient cluster process which uses a novel approach for parallel merging. Our experimental results are consistent with the theoretical analysis and practical results. It provides the faster execution time for segmentation, when compared with Conventional method. Our test data is different CT scan images from the Medical database. More efficient implementations of Image Segmentation will likely result in even faster execution times.

  7. A model based method for automatic facial expression recognition

    NARCIS (Netherlands)

    Kuilenburg, H. van; Wiering, M.A.; Uyl, M. den

    2006-01-01

    Automatic facial expression recognition is a research topic with interesting applications in the field of human-computer interaction, psychology and product marketing. The classification accuracy for an automatic system which uses static images as input is however largely limited by the image

  8. Assessing the accuracy of perceptions of intelligence based on heritable facial features

    OpenAIRE

    Lee, Anthony J.; Hibbs, Courtney; Wright, Margaret J.; Martin, Nicholas G.; Keller, Matthew C.; Zietsch, Brendan P.

    2017-01-01

    Perceptions of intelligence based on facial features can have a profound impact on many social situations, but findings have been mixed as to whether these judgements are accurate. Even if such perceptions were accurate, the underlying mechanism is unclear. Several possibilities have been proposed, including evolutionary explanations where certain morphological facial features are associated with fitness-related traits (including cognitive development), or that intelligence judgements are ove...

  9. Multi-Layer Sparse Representation for Weighted LBP-Patches Based Facial Expression Recognition

    Directory of Open Access Journals (Sweden)

    Qi Jia

    2015-03-01

    Full Text Available In this paper, a novel facial expression recognition method based on sparse representation is proposed. Most contemporary facial expression recognition systems suffer from limited ability to handle image nuisances such as low resolution and noise. Especially for low intensity expression, most of the existing training methods have quite low recognition rates. Motivated by sparse representation, the problem can be solved by finding sparse coefficients of the test image by the whole training set. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. We evaluate facial representation based on weighted local binary patterns, and Fisher separation criterion is used to calculate the weighs of patches. A multi-layer sparse representation framework is proposed for multi-intensity facial expression recognition, especially for low-intensity expressions and noisy expressions in reality, which is a critical problem but seldom addressed in the existing works. To this end, several experiments based on low-resolution and multi-intensity expressions are carried out. Promising results on publicly available databases demonstrate the potential of the proposed approach.

  10. Support vector machine-based facial-expression recognition method combining shape and appearance

    Science.gov (United States)

    Han, Eun Jung; Kang, Byung Jun; Park, Kang Ryoung; Lee, Sangyoun

    2010-11-01

    Facial expression recognition can be widely used for various applications, such as emotion-based human-machine interaction, intelligent robot interfaces, face recognition robust to expression variation, etc. Previous studies have been classified as either shape- or appearance-based recognition. The shape-based method has the disadvantage that the individual variance of facial feature points exists irrespective of similar expressions, which can cause a reduction of the recognition accuracy. The appearance-based method has a limitation in that the textural information of the face is very sensitive to variations in illumination. To overcome these problems, a new facial-expression recognition method is proposed, which combines both shape and appearance information, based on the support vector machine (SVM). This research is novel in the following three ways as compared to previous works. First, the facial feature points are automatically detected by using an active appearance model. From these, the shape-based recognition is performed by using the ratios between the facial feature points based on the facial-action coding system. Second, the SVM, which is trained to recognize the same and different expression classes, is proposed to combine two matching scores obtained from the shape- and appearance-based recognitions. Finally, a single SVM is trained to discriminate four different expressions, such as neutral, a smile, anger, and a scream. By determining the expression of the input facial image whose SVM output is at a minimum, the accuracy of the expression recognition is much enhanced. The experimental results showed that the recognition accuracy of the proposed method was better than previous researches and other fusion methods.

  11. Clustering based on adherence data.

    Science.gov (United States)

    Kiwuwa-Muyingo, Sylvia; Oja, Hannu; Walker, Sarah A; Ilmonen, Pauliina; Levin, Jonathan; Todd, Jim

    2011-03-08

    Adherence to a medical treatment means the extent to which a patient follows the instructions or recommendations by health professionals. There are direct and indirect ways to measure adherence which have been used for clinical management and research. Typically adherence measures are monitored over a long follow-up or treatment period, and some measurements may be missing due to death or other reasons. A natural question then is how to describe adherence behavior over the whole period in a simple way. In the literature, measurements over a period are usually combined just by using averages like percentages of compliant days or percentages of doses taken. In the paper we adapt an approach where patient adherence measures are seen as a stochastic process. Repeated measures are then analyzed as a Markov chain with finite number of states rather than as independent and identically distributed observations, and the transition probabilities between the states are assumed to fully describe the behavior of a patient. The patients can then be clustered or classified using their estimated transition probabilities. These natural clusters can be used to describe the adherence of the patients, to find predictors for adherence, and to predict the future events. The new approach is illustrated and shown to be useful with a simple analysis of a data set from the DART (Development of AntiRetroviral Therapy in Africa) trial in Uganda and Zimbabwe.

  12. Robust MST-Based Clustering Algorithm.

    Science.gov (United States)

    Liu, Qidong; Zhang, Ruisheng; Zhao, Zhili; Wang, Zhenghai; Jiao, Mengyao; Wang, Guangjing

    2018-06-01

    Minimax similarity stresses the connectedness of points via mediating elements rather than favoring high mutual similarity. The grouping principle yields superior clustering results when mining arbitrarily-shaped clusters in data. However, it is not robust against noises and outliers in the data. There are two main problems with the grouping principle: first, a single object that is far away from all other objects defines a separate cluster, and second, two connected clusters would be regarded as two parts of one cluster. In order to solve such problems, we propose robust minimum spanning tree (MST)-based clustering algorithm in this letter. First, we separate the connected objects by applying a density-based coarsening phase, resulting in a low-rank matrix in which the element denotes the supernode by combining a set of nodes. Then a greedy method is presented to partition those supernodes through working on the low-rank matrix. Instead of removing the longest edges from MST, our algorithm groups the data set based on the minimax similarity. Finally, the assignment of all data points can be achieved through their corresponding supernodes. Experimental results on many synthetic and real-world data sets show that our algorithm consistently outperforms compared clustering algorithms.

  13. Facial expression recognition in the wild based on multimodal texture features

    Science.gov (United States)

    Sun, Bo; Li, Liandong; Zhou, Guoyan; He, Jun

    2016-11-01

    Facial expression recognition in the wild is a very challenging task. We describe our work in static and continuous facial expression recognition in the wild. We evaluate the recognition results of gray deep features and color deep features, and explore the fusion of multimodal texture features. For the continuous facial expression recognition, we design two temporal-spatial dense scale-invariant feature transform (SIFT) features and combine multimodal features to recognize expression from image sequences. For the static facial expression recognition based on video frames, we extract dense SIFT and some deep convolutional neural network (CNN) features, including our proposed CNN architecture. We train linear support vector machine and partial least squares classifiers for those kinds of features on the static facial expression in the wild (SFEW) and acted facial expression in the wild (AFEW) dataset, and we propose a fusion network to combine all the extracted features at decision level. The final achievement we gained is 56.32% on the SFEW testing set and 50.67% on the AFEW validation set, which are much better than the baseline recognition rates of 35.96% and 36.08%.

  14. Adaptive metric learning with deep neural networks for video-based facial expression recognition

    Science.gov (United States)

    Liu, Xiaofeng; Ge, Yubin; Yang, Chao; Jia, Ping

    2018-01-01

    Video-based facial expression recognition has become increasingly important for plenty of applications in the real world. Despite that numerous efforts have been made for the single sequence, how to balance the complex distribution of intra- and interclass variations well between sequences has remained a great difficulty in this area. We propose the adaptive (N+M)-tuplet clusters loss function and optimize it with the softmax loss simultaneously in the training phrase. The variations introduced by personal attributes are alleviated using the similarity measurements of multiple samples in the feature space with many fewer comparison times as conventional deep metric learning approaches, which enables the metric calculations for large data applications (e.g., videos). Both the spatial and temporal relations are well explored by a unified framework that consists of an Inception-ResNet network with long short term memory and the two fully connected layer branches structure. Our proposed method has been evaluated with three well-known databases, and the experimental results show that our method outperforms many state-of-the-art approaches.

  15. Extreme Facial Expressions Classification Based on Reality Parameters

    Science.gov (United States)

    Rahim, Mohd Shafry Mohd; Rad, Abdolvahab Ehsani; Rehman, Amjad; Altameem, Ayman

    2014-09-01

    Extreme expressions are really type of emotional expressions that are basically stimulated through the strong emotion. An example of those extreme expression is satisfied through tears. So to be able to provide these types of features; additional elements like fluid mechanism (particle system) plus some of physics techniques like (SPH) are introduced. The fusion of facile animation with SPH exhibits promising results. Accordingly, proposed fluid technique using facial animation is the real tenor for this research to get the complex expression, like laugh, smile, cry (tears emergence) or the sadness until cry strongly, as an extreme expression classification that's happens on the human face in some cases.

  16. Cluster Based Vector Attribute Filtering

    NARCIS (Netherlands)

    Kiwanuka, Fred N.; Wilkinson, Michael H.F.

    2016-01-01

    Morphological attribute filters operate on images based on properties or attributes of connected components. Until recently, attribute filtering was based on a single global threshold on a scalar property to remove or retain objects. A single threshold struggles in case no single property or

  17. Facial anatomy.

    Science.gov (United States)

    Marur, Tania; Tuna, Yakup; Demirci, Selman

    2014-01-01

    Dermatologic problems of the face affect both function and aesthetics, which are based on complex anatomical features. Treating dermatologic problems while preserving the aesthetics and functions of the face requires knowledge of normal anatomy. When performing successfully invasive procedures of the face, it is essential to understand its underlying topographic anatomy. This chapter presents the anatomy of the facial musculature and neurovascular structures in a systematic way with some clinically important aspects. We describe the attachments of the mimetic and masticatory muscles and emphasize their functions and nerve supply. We highlight clinically relevant facial topographic anatomy by explaining the course and location of the sensory and motor nerves of the face and facial vasculature with their relations. Additionally, this chapter reviews the recent nomenclature of the branching pattern of the facial artery. © 2013 Elsevier Inc. All rights reserved.

  18. An Algorithm Based on the Self-Organized Maps for the Classification of Facial Features

    Directory of Open Access Journals (Sweden)

    Gheorghe Gîlcă

    2015-12-01

    Full Text Available This paper deals with an algorithm based on Self Organized Maps networks which classifies facial features. The proposed algorithm can categorize the facial features defined by the input variables: eyebrow, mouth, eyelids into a map of their grouping. The groups map is based on calculating the distance between each input vector and each output neuron layer , the neuron with the minimum distance being declared winner neuron. The network structure consists of two levels: the first level contains three input vectors, each having forty-one values, while the second level contains the SOM competitive network which consists of 100 neurons. The proposed system can classify facial features quickly and easily using the proposed algorithm based on SOMs.

  19. Facial Expression Recognition from Video Sequences Based on Spatial-Temporal Motion Local Binary Pattern and Gabor Multiorientation Fusion Histogram

    Directory of Open Access Journals (Sweden)

    Lei Zhao

    2017-01-01

    Full Text Available This paper proposes novel framework for facial expressions analysis using dynamic and static information in video sequences. First, based on incremental formulation, discriminative deformable face alignment method is adapted to locate facial points to correct in-plane head rotation and break up facial region from background. Then, spatial-temporal motion local binary pattern (LBP feature is extracted and integrated with Gabor multiorientation fusion histogram to give descriptors, which reflect static and dynamic texture information of facial expressions. Finally, a one-versus-one strategy based multiclass support vector machine (SVM classifier is applied to classify facial expressions. Experiments on Cohn-Kanade (CK + facial expression dataset illustrate that integrated framework outperforms methods using single descriptors. Compared with other state-of-the-art methods on CK+, MMI, and Oulu-CASIA VIS datasets, our proposed framework performs better.

  20. 3D Facial Similarity Measure Based on Geodesic Network and Curvatures

    Directory of Open Access Journals (Sweden)

    Junli Zhao

    2014-01-01

    Full Text Available Automated 3D facial similarity measure is a challenging and valuable research topic in anthropology and computer graphics. It is widely used in various fields, such as criminal investigation, kinship confirmation, and face recognition. This paper proposes a 3D facial similarity measure method based on a combination of geodesic and curvature features. Firstly, a geodesic network is generated for each face with geodesics and iso-geodesics determined and these network points are adopted as the correspondence across face models. Then, four metrics associated with curvatures, that is, the mean curvature, Gaussian curvature, shape index, and curvedness, are computed for each network point by using a weighted average of its neighborhood points. Finally, correlation coefficients according to these metrics are computed, respectively, as the similarity measures between two 3D face models. Experiments of different persons’ 3D facial models and different 3D facial models of the same person are implemented and compared with a subjective face similarity study. The results show that the geodesic network plays an important role in 3D facial similarity measure. The similarity measure defined by shape index is consistent with human’s subjective evaluation basically, and it can measure the 3D face similarity more objectively than the other indices.

  1. СREATING OF BARCODES FOR FACIAL IMAGES BASED ON INTENSITY GRADIENTS

    Directory of Open Access Journals (Sweden)

    G. A. Kukharev

    2014-05-01

    Full Text Available The paper provides analysis of existing approaches to the generating of barcodes and description of the system structure for generating of barcodes from facial images. The method for generating of standard type linear barcodes from facial images is proposed. This method is based on the difference of intensity gradients, which represent images in the form of initial features. Further averaging of these features into a limited number of intervals is performed; the quantization of results into decimal digits from 0 to 9 and table conversion into the standard barcode is done. Testing was conducted on the Face94 database and database of composite faces of different ages. It showed that the proposed method ensures the stability of generated barcodes according to changes of scale, pose and mirroring of facial images, as well as changes of facial expressions and shadows on faces from local lighting. The proposed solutions are computationally low-cost and do not require the use of any specialized image processing software for generating of facial barcodes in real-time systems.

  2. Personality Trait and Facial Expression Filter-Based Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Seongah Chin

    2013-02-01

    Full Text Available In this paper, we present technical approaches that bridge the gap in the research related to the use of brain-computer interfaces for entertainment and facial expressions. Such facial expressions that reflect an individual's personal traits can be used to better realize artificial facial expressions in a gaming environment based on a brain-computer interface. First, an emotion extraction filter is introduced in order to classify emotions on the basis of the users' brain signals in real time. Next, a personality trait filter is defined to classify extrovert and introvert types, which manifest as five traits: very extrovert, extrovert, medium, introvert and very introvert. In addition, facial expressions derived from expression rates are obtained by an extrovert-introvert fuzzy model through its defuzzification process. Finally, we confirm this validation via an analysis of the variance of the personality trait filter, a k-fold cross validation of the emotion extraction filter, an accuracy analysis, a user study of facial synthesis and a test case game.

  3. Quantitative facial asymmetry: using three-dimensional photogrammetry to measure baseline facial surface symmetry.

    Science.gov (United States)

    Taylor, Helena O; Morrison, Clinton S; Linden, Olivia; Phillips, Benjamin; Chang, Johnny; Byrne, Margaret E; Sullivan, Stephen R; Forrest, Christopher R

    2014-01-01

    subjectively, can be easily and reproducibly measured using three-dimensional photogrammetry. The RMSD for facial asymmetry of healthy volunteers clusters at approximately 0.80 ± 0.24 mm. Patients with facial asymmetry due to a pathologic process can be differentiated from normative facial asymmetry based on their RMSDs.

  4. Automated detection of pain from facial expressions: a rule-based approach using AAM

    Science.gov (United States)

    Chen, Zhanli; Ansari, Rashid; Wilkie, Diana J.

    2012-02-01

    In this paper, we examine the problem of using video analysis to assess pain, an important problem especially for critically ill, non-communicative patients, and people with dementia. We propose and evaluate an automated method to detect the presence of pain manifested in patient videos using a unique and large collection of cancer patient videos captured in patient homes. The method is based on detecting pain-related facial action units defined in the Facial Action Coding System (FACS) that is widely used for objective assessment in pain analysis. In our research, a person-specific Active Appearance Model (AAM) based on Project-Out Inverse Compositional Method is trained for each patient individually for the modeling purpose. A flexible representation of the shape model is used in a rule-based method that is better suited than the more commonly used classifier-based methods for application to the cancer patient videos in which pain-related facial actions occur infrequently and more subtly. The rule-based method relies on the feature points that provide facial action cues and is extracted from the shape vertices of AAM, which have a natural correspondence to face muscular movement. In this paper, we investigate the detection of a commonly used set of pain-related action units in both the upper and lower face. Our detection results show good agreement with the results obtained by three trained FACS coders who independently reviewed and scored the action units in the cancer patient videos.

  5. A Cluster- Based Secure Active Network Environment

    Institute of Scientific and Technical Information of China (English)

    CHEN Xiao-lin; ZHOU Jing-yang; DAI Han; LU Sang-lu; CHEN Gui-hai

    2005-01-01

    We introduce a cluster-based secure active network environment (CSANE) which separates the processing of IP packets from that of active packets in active routers. In this environment, the active code authorized or trusted by privileged users is executed in the secure execution environment (EE) of the active router, while others are executed in the secure EE of the nodes in the distributed shared memory (DSM) cluster. With the supports of a multi-process Java virtual machine and KeyNote, untrusted active packets are controlled to securely consume resource. The DSM consistency management makes that active packets can be parallelly processed in the DSM cluster as if they were processed one by one in ANTS (Active Network Transport System). We demonstrate that CSANE has good security and scalability, but imposing little changes on traditional routers.

  6. Generation of facial expressions from emotion using a fuzzy rule based system

    NARCIS (Netherlands)

    Bui, T.D.; Heylen, Dirk K.J.; Poel, Mannes; Nijholt, Antinus; Stumptner, Markus; Corbett, Dan; Brooks, Mike

    2001-01-01

    We propose a fuzzy rule-based system to map representations of the emotional state of an animated agent onto muscle contraction values for the appropriate facial expressions. Our implementation pays special attention to the way in which continuous changes in the intensity of emotions can be

  7. Spatio-Temporal Pain Recognition in CNN-based Super-Resolved Facial Images

    DEFF Research Database (Denmark)

    Bellantonio, Marco; Haque, Mohammad Ahsanul; Rodriguez, Pau

    2017-01-01

    Automatic pain detection is a long expected solution to a prevalent medical problem of pain management. This is more relevant when the subject of pain is young children or patients with limited ability to communicate about their pain experience. Computer vision-based analysis of facial pain...

  8. Nine-year-old children use norm-based coding to visually represent facial expression.

    Science.gov (United States)

    Burton, Nichola; Jeffery, Linda; Skinner, Andrew L; Benton, Christopher P; Rhodes, Gillian

    2013-10-01

    Children are less skilled than adults at making judgments about facial expression. This could be because they have not yet developed adult-like mechanisms for visually representing faces. Adults are thought to represent faces in a multidimensional face-space, and have been shown to code the expression of a face relative to the norm or average face in face-space. Norm-based coding is economical and adaptive, and may be what makes adults more sensitive to facial expression than children. This study investigated the coding system that children use to represent facial expression. An adaptation aftereffect paradigm was used to test 24 adults and 18 children (9 years 2 months to 9 years 11 months old). Participants adapted to weak and strong antiexpressions. They then judged the expression of an average expression. Adaptation created aftereffects that made the test face look like the expression opposite that of the adaptor. Consistent with the predictions of norm-based but not exemplar-based coding, aftereffects were larger for strong than weak adaptors for both age groups. Results indicate that, like adults, children's coding of facial expressions is norm-based. PsycINFO Database Record (c) 2013 APA, all rights reserved.

  9. Semantic based cluster content discovery in description first clustering algorithm

    International Nuclear Information System (INIS)

    Khan, M.W.; Asif, H.M.S.

    2017-01-01

    In the field of data analytics grouping of like documents in textual data is a serious problem. A lot of work has been done in this field and many algorithms have purposed. One of them is a category of algorithms which firstly group the documents on the basis of similarity and then assign the meaningful labels to those groups. Description first clustering algorithm belong to the category in which the meaningful description is deduced first and then relevant documents are assigned to that description. LINGO (Label Induction Grouping Algorithm) is the algorithm of description first clustering category which is used for the automatic grouping of documents obtained from search results. It uses LSI (Latent Semantic Indexing); an IR (Information Retrieval) technique for induction of meaningful labels for clusters and VSM (Vector Space Model) for cluster content discovery. In this paper we present the LINGO while it is using LSI during cluster label induction and cluster content discovery phase. Finally, we compare results obtained from the said algorithm while it uses VSM and Latent semantic analysis during cluster content discovery phase. (author)

  10. An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network.

    Science.gov (United States)

    Shen, Xiaolei; Zhang, Jiachi; Yan, Chenjun; Zhou, Hong

    2018-04-11

    In this paper, we present a new automatic diagnosis method for facial acne vulgaris which is based on convolutional neural networks (CNNs). To overcome the shortcomings of previous methods which were the inability to classify enough types of acne vulgaris. The core of our method is to extract features of images based on CNNs and achieve classification by classifier. A binary-classifier of skin-and-non-skin is used to detect skin area and a seven-classifier is used to achieve the classification task of facial acne vulgaris and healthy skin. In the experiments, we compare the effectiveness of our CNN and the VGG16 neural network which is pre-trained on the ImageNet data set. We use a ROC curve to evaluate the performance of binary-classifier and use a normalized confusion matrix to evaluate the performance of seven-classifier. The results of our experiments show that the pre-trained VGG16 neural network is effective in extracting features from facial acne vulgaris images. And the features are very useful for the follow-up classifiers. Finally, we try applying the classifiers both based on the pre-trained VGG16 neural network to assist doctors in facial acne vulgaris diagnosis.

  11. Facial expression recognition based on weber local descriptor and sparse representation

    Science.gov (United States)

    Ouyang, Yan

    2018-03-01

    Automatic facial expression recognition has been one of the research hotspots in the area of computer vision for nearly ten years. During the decade, many state-of-the-art methods have been proposed which perform very high accurate rate based on the face images without any interference. Nowadays, many researchers begin to challenge the task of classifying the facial expression images with corruptions and occlusions and the Sparse Representation based Classification framework has been wildly used because it can robust to the corruptions and occlusions. Therefore, this paper proposed a novel facial expression recognition method based on Weber local descriptor (WLD) and Sparse representation. The method includes three parts: firstly the face images are divided into many local patches, and then the WLD histograms of each patch are extracted, finally all the WLD histograms features are composed into a vector and combined with SRC to classify the facial expressions. The experiment results on the Cohn-Kanade database show that the proposed method is robust to occlusions and corruptions.

  12. A comprehensive approach to long-standing facial paralysis based on lengthening temporalis myoplasty.

    Science.gov (United States)

    Labbè, D; Bussu, F; Iodice, A

    2012-06-01

    Long-standing peripheral monolateral facial paralysis in the adult has challenged otolaryngologists, neurologists and plastic surgeons for centuries. Notwithstanding, the ultimate goal of normality of the paralyzed hemi-face with symmetry at rest, and the achievement of a spontaneous symmetrical smile with corneal protection, has not been fully reached. At the beginning of the 20(th) century, the main options were neural reconstructions including accessory to facial nerve transfer and hypoglossal to facial nerve crossover. In the first half of the 20(th) century, various techniques for static correction with autologous temporalis muscle and fascia grafts were proposed as the techniques of Gillies (1934) and McLaughlin (1949). Cross-facial nerve grafts have been performed since the beginning of the 1970s often with the attempt to transplant free-muscle to restore active movements. However, these transplants were non-vascularized, and further evaluations revealed central fibrosis and minimal return of function. A major step was taken in the second half of the 1970s, with the introduction of microneurovascular muscle transfer in facial reanimation, which, often combined in two steps with a cross-facial nerve graft, has become the most popular option for the comprehensive treatment of long-standing facial paralysis. In the second half of the 1990s in France, a regional muscle transfer technique with the definite advantages of being one-step, technically easier and relatively fast, namely lengthening temporalis myoplasty, acquired popularity and consensus among surgeons treating facial paralysis. A total of 111 patients with facial paralysis were treated in Caen between 1997 and 2005 by a single surgeon who developed 2 variants of the technique (V1, V2), each with its advantages and disadvantages, but both based on the same anatomo-functional background and aim, which is transfer of the temporalis muscle tendon on the coronoid process to the lips. For a comprehensive

  13. Analysis of differences between Western and East-Asian faces based on facial region segmentation and PCA for facial expression recognition

    Science.gov (United States)

    Benitez-Garcia, Gibran; Nakamura, Tomoaki; Kaneko, Masahide

    2017-01-01

    Darwin was the first one to assert that facial expressions are innate and universal, which are recognized across all cultures. However, recent some cross-cultural studies have questioned this assumed universality. Therefore, this paper presents an analysis of the differences between Western and East-Asian faces of the six basic expressions (anger, disgust, fear, happiness, sadness and surprise) focused on three individual facial regions of eyes-eyebrows, nose and mouth. The analysis is conducted by applying PCA for two feature extraction methods: appearance-based by using the pixel intensities of facial parts, and geometric-based by handling 125 feature points from the face. Both methods are evaluated using 4 standard databases for both racial groups and the results are compared with a cross-cultural human study applied to 20 participants. Our analysis reveals that differences between Westerns and East-Asians exist mainly on the regions of eyes-eyebrows and mouth for expressions of fear and disgust respectively. This work presents important findings for a better design of automatic facial expression recognition systems based on the difference between two racial groups.

  14. Information Clustering Based on Fuzzy Multisets.

    Science.gov (United States)

    Miyamoto, Sadaaki

    2003-01-01

    Proposes a fuzzy multiset model for information clustering with application to information retrieval on the World Wide Web. Highlights include search engines; term clustering; document clustering; algorithms for calculating cluster centers; theoretical properties concerning clustering algorithms; and examples to show how the algorithms work.…

  15. Weighted voting-based consensus clustering for chemical structure databases

    Science.gov (United States)

    Saeed, Faisal; Ahmed, Ali; Shamsir, Mohd Shahir; Salim, Naomie

    2014-06-01

    The cluster-based compound selection is used in the lead identification process of drug discovery and design. Many clustering methods have been used for chemical databases, but there is no clustering method that can obtain the best results under all circumstances. However, little attention has been focused on the use of combination methods for chemical structure clustering, which is known as consensus clustering. Recently, consensus clustering has been used in many areas including bioinformatics, machine learning and information theory. This process can improve the robustness, stability, consistency and novelty of clustering. For chemical databases, different consensus clustering methods have been used including the co-association matrix-based, graph-based, hypergraph-based and voting-based methods. In this paper, a weighted cumulative voting-based aggregation algorithm (W-CVAA) was developed. The MDL Drug Data Report (MDDR) benchmark chemical dataset was used in the experiments and represented by the AlogP and ECPF_4 descriptors. The results from the clustering methods were evaluated by the ability of the clustering to separate biologically active molecules in each cluster from inactive ones using different criteria, and the effectiveness of the consensus clustering was compared to that of Ward's method, which is the current standard clustering method in chemoinformatics. This study indicated that weighted voting-based consensus clustering can overcome the limitations of the existing voting-based methods and improve the effectiveness of combining multiple clusterings of chemical structures.

  16. Image Based Hair Segmentation Algorithm for the Application of Automatic Facial Caricature Synthesis

    Directory of Open Access Journals (Sweden)

    Yehu Shen

    2014-01-01

    Full Text Available Hair is a salient feature in human face region and are one of the important cues for face analysis. Accurate detection and presentation of hair region is one of the key components for automatic synthesis of human facial caricature. In this paper, an automatic hair detection algorithm for the application of automatic synthesis of facial caricature based on a single image is proposed. Firstly, hair regions in training images are labeled manually and then the hair position prior distributions and hair color likelihood distribution function are estimated from these labels efficiently. Secondly, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood. This energy function is further optimized according to graph cuts technique and initial hair region is obtained. Finally, K-means algorithm and image postprocessing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. Experimental results show that the average processing time for each image is about 280 ms and the average hair region detection accuracy is above 90%. The proposed algorithm is applied to a facial caricature synthesis system. Experiments proved that with our proposed hair segmentation algorithm the facial caricatures are vivid and satisfying.

  17. Facial expression recognition and model-based regeneration for distance teaching

    Science.gov (United States)

    De Silva, Liyanage C.; Vinod, V. V.; Sengupta, Kuntal

    1998-12-01

    This paper presents a novel idea of a visual communication system, which can support distance teaching using a network of computers. Here the author's main focus is to enhance the quality of distance teaching by reducing the barrier between the teacher and the student, which is formed due to the remote connection of the networked participants. The paper presents an effective way of improving teacher-student communication link of an IT (Information Technology) based distance teaching scenario, using facial expression recognition results and face global and local motion detection results of both the teacher and the student. It presents a way of regenerating the facial images for the teacher-student down-link, which can enhance the teachers facial expressions and which also can reduce the network traffic compared to usual video broadcasting scenarios. At the same time, it presents a way of representing a large volume of facial expression data of the whole student population (in the student-teacher up-link). This up-link representation helps the teacher to receive an instant feed back of his talk, as if he was delivering a face to face lecture. In conventional video tele-conferencing type of applications, this task is nearly impossible, due to huge volume of upward network traffic. The authors utilize several of their previous publication results for most of the image processing components needs to be investigated to complete such a system. In addition, some of the remaining system components are covered by several on going work.

  18. Toward a universal, automated facial measurement tool in facial reanimation.

    Science.gov (United States)

    Hadlock, Tessa A; Urban, Luke S

    2012-01-01

    To describe a highly quantitative facial function-measuring tool that yields accurate, objective measures of facial position in significantly less time than existing methods. Facial Assessment by Computer Evaluation (FACE) software was designed for facial analysis. Outputs report the static facial landmark positions and dynamic facial movements relevant in facial reanimation. Fifty individuals underwent facial movement analysis using Photoshop-based measurements and the new software; comparisons of agreement and efficiency were made. Comparisons were made between individuals with normal facial animation and patients with paralysis to gauge sensitivity to abnormal movements. Facial measurements were matched using FACE software and Photoshop-based measures at rest and during expressions. The automated assessments required significantly less time than Photoshop-based assessments.FACE measurements easily revealed differences between individuals with normal facial animation and patients with facial paralysis. FACE software produces accurate measurements of facial landmarks and facial movements and is sensitive to paralysis. Given its efficiency, it serves as a useful tool in the clinical setting for zonal facial movement analysis in comprehensive facial nerve rehabilitation programs.

  19. Membership determination of open clusters based on a spectral clustering method

    Science.gov (United States)

    Gao, Xin-Hua

    2018-06-01

    We present a spectral clustering (SC) method aimed at segregating reliable members of open clusters in multi-dimensional space. The SC method is a non-parametric clustering technique that performs cluster division using eigenvectors of the similarity matrix; no prior knowledge of the clusters is required. This method is more flexible in dealing with multi-dimensional data compared to other methods of membership determination. We use this method to segregate the cluster members of five open clusters (Hyades, Coma Ber, Pleiades, Praesepe, and NGC 188) in five-dimensional space; fairly clean cluster members are obtained. We find that the SC method can capture a small number of cluster members (weak signal) from a large number of field stars (heavy noise). Based on these cluster members, we compute the mean proper motions and distances for the Hyades, Coma Ber, Pleiades, and Praesepe clusters, and our results are in general quite consistent with the results derived by other authors. The test results indicate that the SC method is highly suitable for segregating cluster members of open clusters based on high-precision multi-dimensional astrometric data such as Gaia data.

  20. Reconstruction of various perinasal defects using facial artery perforator-based nasolabial island flaps.

    Science.gov (United States)

    Yoon, Tae Ho; Yun, In Sik; Rha, Dong Kyun; Lee, Won Jai

    2013-11-01

    Classical flaps for perinasal defect reconstruction, such as forehead or nasolabial flaps, have some disadvantages involving limitations of the arc of rotation and two stages of surgery. However, a perforator-based flap is more versatile and allows freedom in flap design. We introduced our experience with reconstruction using a facial artery perforator-based propeller flap on the perinasal area. We describe the surgical differences between different defect subtypes. Between December 2005 and August 2013, 10 patients underwent perinasal reconstruction in which a facial artery perforator-based flap was used. We divided the perinasal defects into types A and B, according to location. The operative results, including flap size, arc of rotation, complications, and characteristics of the perforator were evaluated by retrospective chart review and photographic evaluation. Eight patients were male and 2 patients were female. Their mean age was 61 years (range, 35-75 years). The size of the flap ranged from 1 cm×1.5 cm to 3 cm×6 cm. Eight patients healed uneventfully, but 2 patients presented with mild flap congestion. However, these 2 patients healed by conservative management without any additional surgery. All of the flaps survived completely with aesthetically pleasing results. The facial artery perforator-based flap allowed for versatile customized flaps, and the donor site scar was concealed using the natural nasolabial fold.

  1. Voting-based consensus clustering for combining multiple clusterings of chemical structures

    Directory of Open Access Journals (Sweden)

    Saeed Faisal

    2012-12-01

    Full Text Available Abstract Background Although many consensus clustering methods have been successfully used for combining multiple classifiers in many areas such as machine learning, applied statistics, pattern recognition and bioinformatics, few consensus clustering methods have been applied for combining multiple clusterings of chemical structures. It is known that any individual clustering method will not always give the best results for all types of applications. So, in this paper, three voting and graph-based consensus clusterings were used for combining multiple clusterings of chemical structures to enhance the ability of separating biologically active molecules from inactive ones in each cluster. Results The cumulative voting-based aggregation algorithm (CVAA, cluster-based similarity partitioning algorithm (CSPA and hyper-graph partitioning algorithm (HGPA were examined. The F-measure and Quality Partition Index method (QPI were used to evaluate the clusterings and the results were compared to the Ward’s clustering method. The MDL Drug Data Report (MDDR dataset was used for experiments and was represented by two 2D fingerprints, ALOGP and ECFP_4. The performance of voting-based consensus clustering method outperformed the Ward’s method using F-measure and QPI method for both ALOGP and ECFP_4 fingerprints, while the graph-based consensus clustering methods outperformed the Ward’s method only for ALOGP using QPI. The Jaccard and Euclidean distance measures were the methods of choice to generate the ensembles, which give the highest values for both criteria. Conclusions The results of the experiments show that consensus clustering methods can improve the effectiveness of chemical structures clusterings. The cumulative voting-based aggregation algorithm (CVAA was the method of choice among consensus clustering methods.

  2. Constructing storyboards based on hierarchical clustering analysis

    Science.gov (United States)

    Hasebe, Satoshi; Sami, Mustafa M.; Muramatsu, Shogo; Kikuchi, Hisakazu

    2005-07-01

    There are growing needs for quick preview of video contents for the purpose of improving accessibility of video archives as well as reducing network traffics. In this paper, a storyboard that contains a user-specified number of keyframes is produced from a given video sequence. It is based on hierarchical cluster analysis of feature vectors that are derived from wavelet coefficients of video frames. Consistent use of extracted feature vectors is the key to avoid a repetition of computationally-intensive parsing of the same video sequence. Experimental results suggest that a significant reduction in computational time is gained by this strategy.

  3. CBHRP: A Cluster Based Routing Protocol for Wireless Sensor Network

    OpenAIRE

    Rashed, M. G.; Kabir, M. Hasnat; Rahim, M. Sajjadur; Ullah, Sk. Enayet

    2012-01-01

    A new two layer hierarchical routing protocol called Cluster Based Hierarchical Routing Protocol (CBHRP) is proposed in this paper. It is an extension of LEACH routing protocol. We introduce cluster head-set idea for cluster-based routing where several clusters are formed with the deployed sensors to collect information from target field. On rotation basis, a head-set member receives data from the neighbor nodes and transmits the aggregated results to the distance base station. This protocol ...

  4. I care, even after the first impression: Facial appearance-based evaluations in healthcare context.

    Science.gov (United States)

    Mattarozzi, Katia; Colonnello, Valentina; De Gioia, Francesco; Todorov, Alexander

    2017-06-01

    Prior research has demonstrated that healthcare providers' implicit biases may contribute to healthcare disparities. Independent research in social psychology indicates that facial appearance-based evaluations affect social behavior in a variety of domains, influencing political, legal, and economic decisions. Whether and to what extent these evaluations influence approach behavior in healthcare contexts warrants research attention. Here we investigate the impact of facial appearance-based evaluations of trustworthiness on healthcare providers' caring inclination, and the moderating role of experience and information about the social identity of the faces. Novice and expert nurses rated their inclination to provide care when viewing photos of trustworthy-, neutral-, and untrustworthy-looking faces. To explore whether information about the target of care influences caring inclination, some participants were told that they would view patients' faces while others received no information about the faces. Both novice and expert nurses had higher caring inclination scores for trustworthy-than for untrustworthy-looking faces; however, experts had higher scores than novices for untrustworthy-looking faces. Regardless of a face's trustworthiness level, experts had higher caring inclination scores for patients than for unidentified individuals, while novices showed no differences. Facial appearance-based inferences can bias caring inclination in healthcare contexts. However, expert healthcare providers are less biased by these inferences and more sensitive to information about the target of care. These findings highlight the importance of promoting novice healthcare professionals' awareness of first impression biases. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. A dynamic texture-based approach to recognition of facial actions and their temporal models.

    Science.gov (United States)

    Koelstra, Sander; Pantic, Maja; Patras, Ioannis

    2010-11-01

    In this work, we propose a dynamic texture-based approach to the recognition of facial Action Units (AUs, atomic facial gestures) and their temporal models (i.e., sequences of temporal segments: neutral, onset, apex, and offset) in near-frontal-view face videos. Two approaches to modeling the dynamics and the appearance in the face region of an input video are compared: an extended version of Motion History Images and a novel method based on Nonrigid Registration using Free-Form Deformations (FFDs). The extracted motion representation is used to derive motion orientation histogram descriptors in both the spatial and temporal domain. Per AU, a combination of discriminative, frame-based GentleBoost ensemble learners and dynamic, generative Hidden Markov Models detects the presence of the AU in question and its temporal segments in an input image sequence. When tested for recognition of all 27 lower and upper face AUs, occurring alone or in combination in 264 sequences from the MMI facial expression database, the proposed method achieved an average event recognition accuracy of 89.2 percent for the MHI method and 94.3 percent for the FFD method. The generalization performance of the FFD method has been tested using the Cohn-Kanade database. Finally, we also explored the performance on spontaneous expressions in the Sensitive Artificial Listener data set.

  6. A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization.

    Science.gov (United States)

    Ni, Qingjian; Pan, Qianqian; Du, Huimin; Cao, Cen; Zhai, Yuqing

    2017-01-01

    An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.

  7. Facial paralysis

    Science.gov (United States)

    ... otherwise healthy, facial paralysis is often due to Bell palsy . This is a condition in which the facial ... speech, or occupational therapist. If facial paralysis from Bell palsy lasts for more than 6 to 12 months, ...

  8. Facial-based ethnic recognition: insights from two closely related but ethnically distinct groups

    Directory of Open Access Journals (Sweden)

    S. P. Henzi

    2010-02-01

    Full Text Available Previous studies on facial recognition have considered widely separated populations, both geographically and culturally, making it hard to disentangle effects of familiarity with an ability to identify ethnic groups per se.We used data from a highly intermixed population of African peoples from South Africa to test whether individuals from nine different ethnic groups could correctly differentiate between facial images of two of these, the Tswana and Pedi. Individuals could not assign ethnicity better than expected by chance, and there was no significant difference between genders in accuracy of assignment. Interestingly, we observed a trend that individuals of mixed ethnic origin were better at assigning ethnicity to Pedi and Tswanas, than individuals from less mixed backgrounds. This result supports the hypothesis that ethnic recognition is based on the visual

  9. Interactions between facial emotion and identity in face processing: evidence based on redundancy gains.

    Science.gov (United States)

    Yankouskaya, Alla; Booth, David A; Humphreys, Glyn

    2012-11-01

    Interactions between the processing of emotion expression and form-based information from faces (facial identity) were investigated using the redundant-target paradigm, in which we specifically tested whether identity and emotional expression are integrated in a superadditive manner (Miller, Cognitive Psychology 14:247-279, 1982). In Experiments 1 and 2, participants performed emotion and face identity judgments on faces with sad or angry emotional expressions. Responses to redundant targets were faster than responses to either single target when a universal emotion was conveyed, and performance violated the predictions from a model assuming independent processing of emotion and face identity. Experiment 4 showed that these effects were not modulated by varying interstimulus and nontarget contingencies, and Experiment 5 demonstrated that the redundancy gains were eliminated when faces were inverted. Taken together, these results suggest that the identification of emotion and facial identity interact in face processing.

  10. Management of the Facial Nerve in Lateral Skull Base Surgery Analytic Retrospective study

    Directory of Open Access Journals (Sweden)

    Mohamed A. El Shazly

    2011-01-01

    Full Text Available Background Surgical approaches to the jugular foramen are often complex and lengthy procedures associated with significant morbidity based on the anatomic and tumor characteristics. In addition to the risk of intra-operative hemorrhage from vascular tumors, lower cranial nerves deficits are frequently increased after intra-operative manipulation. Accordingly, modifications in the surgical techniques have been developed to minimize these risks. Preoperative embolization and intra-operative ligation of the external carotid artery have decreased the intraoperative blood loss. Accurate identification and exposure of the cranial nerves extracranially allows for their preservation during tumor resection. The modification of facial nerve mobilization provides widened infratemporal exposure with less postoperative facial weakness. The ideal approach should enable complete, one stage tumor resection with excellent infratemporal and posterior fossa exposure and would not aggravate or cause neurologic deficit. The aim of this study is to present our experience in handling jugular foramen lesions (mainly glomus jugulare without the need for anterior facial nerve transposition. Methods In this series we present our experience in Kasr ElEini University hospital (Cairo–-Egypt in handling 36 patients with jugular foramen lesions over a period of 20 years where the previously mentioned preoperative and operative rules were followed. The clinical status, operative technique and postoperative care and outcome are detailed and analyzed in relation to the outcome. Results Complete cure without complications was achieved in four cases of congenital cholesteatoma and four cases with class B glomus. In advanced cases of glomus jugulare (28 patients (C and D stages complete cure was achieved in 21 of them (75%. The operative complications were also related to this group of 28 patients, in the form of facial paralysis in 20 of them (55.6% and symptomatic vagal

  11. Facial asymmetry correction with moulded helmet therapy in infants with deformational skull base plagiocephaly.

    Science.gov (United States)

    Kreutz, Matthias; Fitze, Brigitte; Blecher, Christoph; Marcello, Augello; Simon, Ruben; Cremer, Rebecca; Zeilhofer, Hans-Florian; Kunz, Christoph; Mayr, Johannes

    2018-01-01

    The recommendation issued by the American Academy of Pediatrics in the early 1990s to position infants on their back during sleep to prevent sudden infant death syndrome (SIDS) has dramatically reduced the number of deaths due to SIDS but has also markedly increased the prevalence of positional skull deformation in infants. Deformation of the base of the skull occurs predominantly in very severe deformational plagiocephaly and is accompanied by facial asymmetry, as well as an altered ear position, called ear shift. Moulded helmet therapy has become an accepted treatment strategy for infants with deformational plagiocephaly. The aim of this study was to determine whether facial asymmetry could be corrected by moulded helmet therapy. In this retrospective, single-centre study, we analysed facial asymmetry of 71 infants with severe deformational plagiocephaly with or without deformational brachycephaly who were undergoing moulded helmet therapy between 2009 and 2013. Computer-assisted, three-dimensional, soft-tissue photographic scanning was used to record the head shape before and after moulded helmet therapy. The distance between two landmarks in the midline of the face (i.e., root of the nose and nasal septum) and the right and left tragus were measured on computer-generated indirect and objective 3D photogrammetry images. A quotient was calculated between the two right- and left-sided distances to the midline. Quotients were compared before and after moulded helmet therapy. Infants without any therapy served as a control group. The median age of the infants before onset of moulded helmet therapy was 5 months (range 3-16 months). The median duration of moulded helmet therapy was 5 months (range 1-16 months). Comparison of the pre- and post-treatment quotients of the left vs. right distances measured between the tragus and root of the nose (n = 71) and nasal septum (n = 71) revealed a significant reduction of the asymmetry (Tragus-Nasion-Line Quotient: 0

  12. Management of the facial nerve in lateral skull base surgery analytic retrospective study.

    Science.gov (United States)

    El Shazly, Mohamed A; Mokbel, Mahmoud A M; Elbadry, Amr A; Badran, Hatem S

    2011-01-01

    Surgical approaches to the jugular foramen are often complex and lengthy procedures associated with significant morbidity based on the anatomic and tumor characteristics. In addition to the risk of intra-operative hemorrhage from vascular tumors, lower cranial nerves deficits are frequently increased after intra-operative manipulation. Accordingly, modifications in the surgical techniques have been developed to minimize these risks. Preoperative embolization and intra-operative ligation of the external carotid artery have decreased the intraoperative blood loss. Accurate identification and exposure of the cranial nerves extracranially allows for their preservation during tumor resection. The modification of facial nerve mobilization provides widened infratemporal exposure with less postoperative facial weakness. The ideal approach should enable complete, one stage tumor resection with excellent infratemporal and posterior fossa exposure and would not aggravate or cause neurologic deficit. The aim of this study is to present our experience in handling jugular foramen lesions (mainly glomus jugulare) without the need for anterior facial nerve transposition. In this series we present our experience in Kasr ElEini University hospital (Cairo-Egypt) in handling 36 patients with jugular foramen lesions over a period of 20 years where the previously mentioned preoperative and operative rules were followed. The clinical status, operative technique and postoperative care and outcome are detailed and analyzed in relation to the outcome. Complete cure without complications was achieved in four cases of congenital cholesteatoma and four cases with class B glomus. In advanced cases of glomus jugulare (28 patients) (C and D stages) complete cure was achieved in 21 of them (75%). The operative complications were also related to this group of 28 patients, in the form of facial paralysis in 20 of them (55.6%) and symptomatic vagal paralysis in 18 of them (50%). Total anterior

  13. Neural bases of different cognitive strategies for facial affect processing in schizophrenia.

    Science.gov (United States)

    Fakra, Eric; Salgado-Pineda, Pilar; Delaveau, Pauline; Hariri, Ahmad R; Blin, Olivier

    2008-03-01

    To examine the neural basis and dynamics of facial affect processing in schizophrenic patients as compared to healthy controls. Fourteen schizophrenic patients and fourteen matched controls performed a facial affect identification task during fMRI acquisition. The emotional task included an intuitive emotional condition (matching emotional faces) and a more cognitively demanding condition (labeling emotional faces). Individual analysis for each emotional condition, and second-level t-tests examining both within-, and between-group differences, were carried out using a random effects approach. Psychophysiological interactions (PPI) were tested for variations in functional connectivity between amygdala and other brain regions as a function of changes in experimental conditions (labeling versus matching). During the labeling condition, both groups engaged similar networks. During the matching condition, schizophrenics failed to activate regions of the limbic system implicated in the automatic processing of emotions. PPI revealed an inverse functional connectivity between prefrontal regions and the left amygdala in healthy volunteers but there was no such change in patients. Furthermore, during the matching condition, and compared to controls, patients showed decreased activation of regions involved in holistic face processing (fusiform gyrus) and increased activation of regions associated with feature analysis (inferior parietal cortex, left middle temporal lobe, right precuneus). Our findings suggest that schizophrenic patients invariably adopt a cognitive approach when identifying facial affect. The distributed neocortical network observed during the intuitive condition indicates that patients may resort to feature-based, rather than configuration-based, processing and may constitute a compensatory strategy for limbic dysfunction.

  14. Morphometric studies on the facial skeleton of humans and pongids based on CT-scans.

    Science.gov (United States)

    Schumacher, K U; Koppe, T; Fanghänel, J; Schumacher, G H; Nagai, H

    1994-10-01

    The changes of the skull, which we can observe during the anthropogenesis, are reflected especially in the different skull proportions. We carried out metric measurements at the median level on 10 adult skulls each of humans, chimpanzees and gorillas as well as 11 skulls of orangutans. All skulls were scanned with a CT at the median level. We measured the lines and angles of the scans and the means and the standard deviations were calculated. We carried out a correlation analysis to observe the relation of their characteristics. We showed that there is a relation between the length of the skull base and the facial length in all species. From the results of the correlation analysis, we can also conclude that a relation exists between the degree of prognathism and the different length measurements of the facial skeleton. We also found a bending of the facial skeleton in relation to the cranial base towards the ventral side, also known as klinorhynchy, in all observed species. The highest degree of klinorhynchy was found in humans and the lowest in orangutans. We will discuss the different definition of the term klinorhynchy and its importance in the evolution of the hominoids.

  15. Cluster Ensemble-Based Image Segmentation

    Directory of Open Access Journals (Sweden)

    Xiaoru Wang

    2013-07-01

    Full Text Available Image segmentation is the foundation of computer vision applications. In this paper, we propose a new cluster ensemble-based image segmentation algorithm, which overcomes several problems of traditional methods. We make two main contributions in this paper. First, we introduce the cluster ensemble concept to fuse the segmentation results from different types of visual features effectively, which can deliver a better final result and achieve a much more stable performance for broad categories of images. Second, we exploit the PageRank idea from Internet applications and apply it to the image segmentation task. This can improve the final segmentation results by combining the spatial information of the image and the semantic similarity of regions. Our experiments on four public image databases validate the superiority of our algorithm over conventional single type of feature or multiple types of features-based algorithms, since our algorithm can fuse multiple types of features effectively for better segmentation results. Moreover, our method is also proved to be very competitive in comparison with other state-of-the-art segmentation algorithms.

  16. Stigmergy based behavioural coordination for satellite clusters

    Science.gov (United States)

    Tripp, Howard; Palmer, Phil

    2010-04-01

    Multi-platform swarm/cluster missions are an attractive prospect for improved science return as they provide a natural capability for temporal, spatial and signal separation with further engineering and economic advantages. As spacecraft numbers increase and/or the round-trip communications delay from Earth lengthens, the traditional "remote-control" approach begins to break down. It is therefore essential to push control into space; to make spacecraft more autonomous. An autonomous group of spacecraft requires coordination, but standard terrestrial paradigms such as negotiation, require high levels of inter-spacecraft communication, which is nontrivial in space. This article therefore introduces the principals of stigmergy as a novel method for coordinating a cluster. Stigmergy is an agent-based, behavioural approach that allows for infrequent communication with decisions based on local information. Behaviours are selected dynamically using a genetic algorithm onboard. supervisors/ground stations occasionally adjust parameters and disseminate a "common environment" that is used for local decisions. After outlining the system, an analysis of some crucial parameters such as communications overhead and number of spacecraft is presented to demonstrate scalability. Further scenarios are considered to demonstrate the natural ability to deal with dynamic situations such as the failure of spacecraft, changing mission objectives and responding to sudden bursts of high priority tasks.

  17. Cosmological constraints with clustering-based redshifts

    Science.gov (United States)

    Kovetz, Ely D.; Raccanelli, Alvise; Rahman, Mubdi

    2017-07-01

    We demonstrate that observations lacking reliable redshift information, such as photometric and radio continuum surveys, can produce robust measurements of cosmological parameters when empowered by clustering-based redshift estimation. This method infers the redshift distribution based on the spatial clustering of sources, using cross-correlation with a reference data set with known redshifts. Applying this method to the existing Sloan Digital Sky Survey (SDSS) photometric galaxies, and projecting to future radio continuum surveys, we show that sources can be efficiently divided into several redshift bins, increasing their ability to constrain cosmological parameters. We forecast constraints on the dark-energy equation of state and on local non-Gaussianity parameters. We explore several pertinent issues, including the trade-off between including more sources and minimizing the overlap between bins, the shot-noise limitations on binning and the predicted performance of the method at high redshifts, and most importantly pay special attention to possible degeneracies with the galaxy bias. Remarkably, we find that once this technique is implemented, constraints on dynamical dark energy from the SDSS imaging catalogue can be competitive with, or better than, those from the spectroscopic BOSS survey and even future planned experiments. Further, constraints on primordial non-Gaussianity from future large-sky radio-continuum surveys can outperform those from the Planck cosmic microwave background experiment and rival those from future spectroscopic galaxy surveys. The application of this method thus holds tremendous promise for cosmology.

  18. Orbit Clustering Based on Transfer Cost

    Science.gov (United States)

    Gustafson, Eric D.; Arrieta-Camacho, Juan J.; Petropoulos, Anastassios E.

    2013-01-01

    We propose using cluster analysis to perform quick screening for combinatorial global optimization problems. The key missing component currently preventing cluster analysis from use in this context is the lack of a useable metric function that defines the cost to transfer between two orbits. We study several proposed metrics and clustering algorithms, including k-means and the expectation maximization algorithm. We also show that proven heuristic methods such as the Q-law can be modified to work with cluster analysis.

  19. Hierarchical video summarization based on context clustering

    Science.gov (United States)

    Tseng, Belle L.; Smith, John R.

    2003-11-01

    A personalized video summary is dynamically generated in our video personalization and summarization system based on user preference and usage environment. The three-tier personalization system adopts the server-middleware-client architecture in order to maintain, select, adapt, and deliver rich media content to the user. The server stores the content sources along with their corresponding MPEG-7 metadata descriptions. In this paper, the metadata includes visual semantic annotations and automatic speech transcriptions. Our personalization and summarization engine in the middleware selects the optimal set of desired video segments by matching shot annotations and sentence transcripts with user preferences. Besides finding the desired contents, the objective is to present a coherent summary. There are diverse methods for creating summaries, and we focus on the challenges of generating a hierarchical video summary based on context information. In our summarization algorithm, three inputs are used to generate the hierarchical video summary output. These inputs are (1) MPEG-7 metadata descriptions of the contents in the server, (2) user preference and usage environment declarations from the user client, and (3) context information including MPEG-7 controlled term list and classification scheme. In a video sequence, descriptions and relevance scores are assigned to each shot. Based on these shot descriptions, context clustering is performed to collect consecutively similar shots to correspond to hierarchical scene representations. The context clustering is based on the available context information, and may be derived from domain knowledge or rules engines. Finally, the selection of structured video segments to generate the hierarchical summary efficiently balances between scene representation and shot selection.

  20. A Classification Method of Normal and Overweight Females Based on Facial Features for Automated Medical Applications

    Directory of Open Access Journals (Sweden)

    Bum Ju Lee

    2012-01-01

    Full Text Available Obesity and overweight have become serious public health problems worldwide. Obesity and abdominal obesity are associated with type 2 diabetes, cardiovascular diseases, and metabolic syndrome. In this paper, we first suggest a method of predicting normal and overweight females according to body mass index (BMI based on facial features. A total of 688 subjects participated in this study. We obtained the area under the ROC curve (AUC value of 0.861 and kappa value of 0.521 in Female: 21–40 (females aged 21–40 years group, and AUC value of 0.76 and kappa value of 0.401 in Female: 41–60 (females aged 41–60 years group. In two groups, we found many features showing statistical differences between normal and overweight subjects by using an independent two-sample t-test. We demonstrated that it is possible to predict BMI status using facial characteristics. Our results provide useful information for studies of obesity and facial characteristics, and may provide useful clues in the development of applications for alternative diagnosis of obesity in remote healthcare.

  1. Comparing clustering models in bank customers: Based on Fuzzy relational clustering approach

    Directory of Open Access Journals (Sweden)

    Ayad Hendalianpour

    2016-11-01

    Full Text Available Clustering is absolutely useful information to explore data structures and has been employed in many places. It organizes a set of objects into similar groups called clusters, and the objects within one cluster are both highly similar and dissimilar with the objects in other clusters. The K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms are the most popular clustering algorithms for their easy implementation and fast work, but in some cases we cannot use these algorithms. Regarding this, in this paper, a hybrid model for customer clustering is presented that is applicable in five banks of Fars Province, Shiraz, Iran. In this way, the fuzzy relation among customers is defined by using their features described in linguistic and quantitative variables. As follows, the customers of banks are grouped according to K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms and the proposed Fuzzy Relation Clustering (FRC algorithm. The aim of this paper is to show how to choose the best clustering algorithms based on density-based clustering and present a new clustering algorithm for both crisp and fuzzy variables. Finally, we apply the proposed approach to five datasets of customer's segmentation in banks. The result of the FCR shows the accuracy and high performance of FRC compared other clustering methods.

  2. CC_TRS: Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life

    Directory of Open Access Journals (Sweden)

    Musaab Riyadh

    2017-01-01

    Full Text Available The rapid spreading of positioning devices leads to the generation of massive spatiotemporal trajectories data. In some scenarios, spatiotemporal data are received in stream manner. Clustering of stream data is beneficial for different applications such as traffic management and weather forecasting. In this article, an algorithm for Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life is proposed. The algorithm consists of two phases. There is the online phase where temporal micro clusters are used to store summarized spatiotemporal information for each group of similar segments. The clustering task in online phase is based on temporal micro cluster lifetime instead of time window technique which divides stream data into time bins and clusters each bin separately. For offline phase, a density based clustering approach is used to generate macro clusters depending on temporal micro clusters. The evaluation of the proposed algorithm on real data sets shows the efficiency and the effectiveness of the proposed algorithm and proved it is efficient alternative to time window technique.

  3. Cluster Based Hierarchical Routing Protocol for Wireless Sensor Network

    OpenAIRE

    Rashed, Md. Golam; Kabir, M. Hasnat; Rahim, Muhammad Sajjadur; Ullah, Shaikh Enayet

    2012-01-01

    The efficient use of energy source in a sensor node is most desirable criteria for prolong the life time of wireless sensor network. In this paper, we propose a two layer hierarchical routing protocol called Cluster Based Hierarchical Routing Protocol (CBHRP). We introduce a new concept called head-set, consists of one active cluster head and some other associate cluster heads within a cluster. The head-set members are responsible for control and management of the network. Results show that t...

  4. A Cross-Sectional Clinic-Based Study in Patients With Side-Locked Unilateral Headache and Facial Pain.

    Science.gov (United States)

    Prakash, Sanjay; Rathore, Chaturbhuj; Makwana, Prayag; Dave, Ankit

    2016-07-01

    To undertake the epidemiological evaluation of the patients presenting with side-locked headache and facial pain in a tertiary neurology outpatient clinic. Side-locked unilateral headache and facial pain include a large number of primary and secondary headaches and cranial neuropathies. A diagnostic approach for the patients presenting with strictly unilateral headaches is important as many of these headache disorders respond to a highly selective drug. Epidemiological data may guide us to formulate a proper approach for such patients. However, the literature is sparse on strictly unilateral headache and facial pain. We prospectively recruited 307 consecutive adult patients (>18 years) with side-locked headache and facial pain presenting to a neurology outpatient clinic between July 2014 and December 2015. All patients were subjected to MRI brain and other investigations to find out the different secondary causes. The diagnosis was carried out by at least two headache specialists together. All patients were classified according to the International Classification of Headache Disorder-third edition (ICHD-3β). The mean age at the time of examination was 42.4 ± 13.6 years (range 18-80 years). Forty-eight percent of patients were male. Strictly unilateral headaches accounted for 19.2% of the total headaches seen in the clinic. Headaches were classified as primary in 58%, secondary in 18%, and cranial neuropathies and other facial pain in 16% patients. Five percent of patients could not be classified. Three percent of patients were classified as per the Appendix section of ICHD-3β. The prevalence of secondary headaches and painful cranial neuropathies increased with age. A total of 36 different diagnoses were made. Only two diseases (migraine and cluster headache) had a prevalence of more than 10%. The prevalence of 13 diseases varied between 6 and 9%. The prevalence of other 14 groups was ≤1%. Migraine was the most common diagnosis (15%). Cervicogenic headache

  5. Communication Base Station Log Analysis Based on Hierarchical Clustering

    Directory of Open Access Journals (Sweden)

    Zhang Shao-Hua

    2017-01-01

    Full Text Available Communication base stations generate massive data every day, these base station logs play an important value in mining of the business circles. This paper use data mining technology and hierarchical clustering algorithm to group the scope of business circle for the base station by recording the data of these base stations.Through analyzing the data of different business circle based on feature extraction and comparing different business circle category characteristics, which can choose a suitable area for operators of commercial marketing.

  6. BioCluster: Tool for Identification and Clustering of Enterobacteriaceae Based on Biochemical Data

    Directory of Open Access Journals (Sweden)

    Ahmed Abdullah

    2015-06-01

    Full Text Available Presumptive identification of different Enterobacteriaceae species is routinely achieved based on biochemical properties. Traditional practice includes manual comparison of each biochemical property of the unknown sample with known reference samples and inference of its identity based on the maximum similarity pattern with the known samples. This process is labor-intensive, time-consuming, error-prone, and subjective. Therefore, automation of sorting and similarity in calculation would be advantageous. Here we present a MATLAB-based graphical user interface (GUI tool named BioCluster. This tool was designed for automated clustering and identification of Enterobacteriaceae based on biochemical test results. In this tool, we used two types of algorithms, i.e., traditional hierarchical clustering (HC and the Improved Hierarchical Clustering (IHC, a modified algorithm that was developed specifically for the clustering and identification of Enterobacteriaceae species. IHC takes into account the variability in result of 1–47 biochemical tests within this Enterobacteriaceae family. This tool also provides different options to optimize the clustering in a user-friendly way. Using computer-generated synthetic data and some real data, we have demonstrated that BioCluster has high accuracy in clustering and identifying enterobacterial species based on biochemical test data. This tool can be freely downloaded at http://microbialgen.du.ac.bd/biocluster/.

  7. Improvements on a simple muscle-based 3D face for realistic facial expressions

    NARCIS (Netherlands)

    Bui, T.D.; Heylen, Dirk K.J.; Nijholt, Antinus; Badler, N.; Thalmann, D.

    2003-01-01

    Facial expressions play an important role in face-to-face communication. With the development of personal computers capable of rendering high quality graphics, computer facial animation has produced more and more realistic facial expressions to enrich human-computer communication. In this paper, we

  8. APPECT: An Approximate Backbone-Based Clustering Algorithm for Tags

    DEFF Research Database (Denmark)

    Zong, Yu; Xu, Guandong; Jin, Pin

    2011-01-01

    algorithm for Tags (APPECT). The main steps of APPECT are: (1) we execute the K-means algorithm on a tag similarity matrix for M times and collect a set of tag clustering results Z={C1,C2,…,Cm}; (2) we form the approximate backbone of Z by executing a greedy search; (3) we fix the approximate backbone...... as the initial tag clustering result and then assign the rest tags into the corresponding clusters based on the similarity. Experimental results on three real world datasets namely MedWorm, MovieLens and Dmoz demonstrate the effectiveness and the superiority of the proposed method against the traditional...... Agglomerative Clustering on tagging data, which possess the inherent drawbacks, such as the sensitivity of initialization. In this paper, we instead make use of the approximate backbone of tag clustering results to find out better tag clusters. In particular, we propose an APProximate backbonE-based Clustering...

  9. Facial motion parameter estimation and error criteria in model-based image coding

    Science.gov (United States)

    Liu, Yunhai; Yu, Lu; Yao, Qingdong

    2000-04-01

    Model-based image coding has been given extensive attention due to its high subject image quality and low bit-rates. But the estimation of object motion parameter is still a difficult problem, and there is not a proper error criteria for the quality assessment that are consistent with visual properties. This paper presents an algorithm of the facial motion parameter estimation based on feature point correspondence and gives the motion parameter error criteria. The facial motion model comprises of three parts. The first part is the global 3-D rigid motion of the head, the second part is non-rigid translation motion in jaw area, and the third part consists of local non-rigid expression motion in eyes and mouth areas. The feature points are automatically selected by a function of edges, brightness and end-node outside the blocks of eyes and mouth. The numbers of feature point are adjusted adaptively. The jaw translation motion is tracked by the changes of the feature point position of jaw. The areas of non-rigid expression motion can be rebuilt by using block-pasting method. The estimation approach of motion parameter error based on the quality of reconstructed image is suggested, and area error function and the error function of contour transition-turn rate are used to be quality criteria. The criteria reflect the image geometric distortion caused by the error of estimated motion parameters properly.

  10. Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity.

    Science.gov (United States)

    Liang, Yin; Liu, Baolin; Li, Xianglin; Wang, Peiyuan

    2018-01-01

    It is an important question how human beings achieve efficient recognition of others' facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition.

  11. Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks.

    Science.gov (United States)

    Taamneh, Madhar; Taamneh, Salah; Alkheder, Sharaf

    2017-09-01

    Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%-16%) more accuracy than without using clustering, while the percentage split achieves (2%-5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.

  12. Facial expression recognition under partial occlusion based on fusion of global and local features

    Science.gov (United States)

    Wang, Xiaohua; Xia, Chen; Hu, Min; Ren, Fuji

    2018-04-01

    Facial expression recognition under partial occlusion is a challenging research. This paper proposes a novel framework for facial expression recognition under occlusion by fusing the global and local features. In global aspect, first, information entropy are employed to locate the occluded region. Second, principal Component Analysis (PCA) method is adopted to reconstruct the occlusion region of image. After that, a replace strategy is applied to reconstruct image by replacing the occluded region with the corresponding region of the best matched image in training set, Pyramid Weber Local Descriptor (PWLD) feature is then extracted. At last, the outputs of SVM are fitted to the probabilities of the target class by using sigmoid function. For the local aspect, an overlapping block-based method is adopted to extract WLD features, and each block is weighted adaptively by information entropy, Chi-square distance and similar block summation methods are then applied to obtain the probabilities which emotion belongs to. Finally, fusion at the decision level is employed for the data fusion of the global and local features based on Dempster-Shafer theory of evidence. Experimental results on the Cohn-Kanade and JAFFE databases demonstrate the effectiveness and fault tolerance of this method.

  13. Cluster-based global firms' use of local capabilities

    DEFF Research Database (Denmark)

    Andersen, Poul Houman; Bøllingtoft, Anne

    2011-01-01

    Purpose – Despite growing interest in clusters role for the global competitiveness of firms, there has been little research into how globalization affects cluster-based firms’ (CBFs) use of local knowledge resources and the combination of local and global knowledge used. Using the cluster......’s knowledge base as a mediating variable, the purpose of this paper is to examine how globalization affected the studied firms’ use of local cluster-based knowledge, integration of local and global knowledge, and networking capabilities. Design/methodology/approach – Qualitative case studies of nine firms...... in three clusters strongly affected by increasing global division of labour. Findings – The paper suggests that globalization has affected how firms use local resources and combine local and global knowledge. Unexpectedly, clustered firms with explicit procedures and established global fora for exchanging...

  14. Improving local clustering based top-L link prediction methods via asymmetric link clustering information

    Science.gov (United States)

    Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

    2018-02-01

    Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

  15. Facial Curvature Detects and Explicates Ethnic Differences in Effects of Prenatal Alcohol Exposure.

    Science.gov (United States)

    Suttie, Michael; Wetherill, Leah; Jacobson, Sandra W; Jacobson, Joseph L; Hoyme, H Eugene; Sowell, Elizabeth R; Coles, Claire; Wozniak, Jeffrey R; Riley, Edward P; Jones, Kenneth L; Foroud, Tatiana; Hammond, Peter

    2017-08-01

    Our objective is to help clinicians detect the facial effects of prenatal alcohol exposure by developing computer-based tools for screening facial form. All 415 individuals considered were evaluated by expert dysmorphologists and categorized as (i) healthy control (HC), (ii) fetal alcohol syndrome (FAS), or (iii) heavily prenatally alcohol exposed (HE) but not clinically diagnosable as FAS; 3D facial photographs were used to build models of facial form to support discrimination studies. Surface curvature-based delineations of facial form were introduced. (i) Facial growth in FAS, HE, and control subgroups is similar in both cohorts. (ii) Cohort consistency of agreement between clinical diagnosis and HC-FAS facial form classification is lower for midline facial regions and higher for nonmidline regions. (iii) Specific HC-FAS differences within and between the cohorts include: for HC, a smoother philtrum in Cape Coloured individuals; for FAS, a smoother philtrum in Caucasians; for control-FAS philtrum difference, greater homogeneity in Caucasians; for control-FAS face difference, greater homogeneity in Cape Coloured individuals. (iv) Curvature changes in facial profile induced by prenatal alcohol exposure are more homogeneous and greater in Cape Coloureds than in Caucasians. (v) The Caucasian HE subset divides into clusters with control-like and FAS-like facial dysmorphism. The Cape Coloured HE subset is similarly divided for nonmidline facial regions but not clearly for midline structures. (vi) The Cape Coloured HE subset with control-like facial dysmorphism shows orbital hypertelorism. Facial curvature assists the recognition of the effects of prenatal alcohol exposure and helps explain why different facial regions result in inconsistent control-FAS discrimination rates in disparate ethnic groups. Heavy prenatal alcohol exposure can give rise to orbital hypertelorism, supporting a long-standing suggestion that prenatal alcohol exposure at a particular time causes

  16. Flowbca : A flow-based cluster algorithm in Stata

    NARCIS (Netherlands)

    Meekes, J.; Hassink, W.H.J.

    In this article, we introduce the Stata implementation of a flow-based cluster algorithm written in Mata. The main purpose of the flowbca command is to identify clusters based on relational data of flows. We illustrate the command by providing multiple applications, from the research fields of

  17. Evaluating visibility of age spot and freckle based on simulated spectral reflectance distribution and facial color image

    Science.gov (United States)

    Hirose, Misa; Toyota, Saori; Tsumura, Norimichi

    2018-02-01

    In this research, we evaluate the visibility of age spot and freckle with changing the blood volume based on simulated spectral reflectance distribution and the actual facial color images, and compare these results. First, we generate three types of spatial distribution of age spot and freckle in patch-like images based on the simulated spectral reflectance. The spectral reflectance is simulated using Monte Carlo simulation of light transport in multi-layered tissue. Next, we reconstruct the facial color image with changing the blood volume. We acquire the concentration distribution of melanin, hemoglobin and shading components by applying the independent component analysis on a facial color image. We reproduce images using the obtained melanin and shading concentration and the changed hemoglobin concentration. Finally, we evaluate the visibility of pigmentations using simulated spectral reflectance distribution and facial color images. In the result of simulated spectral reflectance distribution, we found that the visibility became lower as the blood volume increases. However, we can see that a specific blood volume reduces the visibility of the actual pigmentations from the result of the facial color images.

  18. A Brief Review of Facial Emotion Recognition Based on Visual Information

    Science.gov (United States)

    2018-01-01

    Facial emotion recognition (FER) is an important topic in the fields of computer vision and artificial intelligence owing to its significant academic and commercial potential. Although FER can be conducted using multiple sensors, this review focuses on studies that exclusively use facial images, because visual expressions are one of the main information channels in interpersonal communication. This paper provides a brief review of researches in the field of FER conducted over the past decades. First, conventional FER approaches are described along with a summary of the representative categories of FER systems and their main algorithms. Deep-learning-based FER approaches using deep networks enabling “end-to-end” learning are then presented. This review also focuses on an up-to-date hybrid deep-learning approach combining a convolutional neural network (CNN) for the spatial features of an individual frame and long short-term memory (LSTM) for temporal features of consecutive frames. In the later part of this paper, a brief review of publicly available evaluation metrics is given, and a comparison with benchmark results, which are a standard for a quantitative comparison of FER researches, is described. This review can serve as a brief guidebook to newcomers in the field of FER, providing basic knowledge and a general understanding of the latest state-of-the-art studies, as well as to experienced researchers looking for productive directions for future work. PMID:29385749

  19. A Brief Review of Facial Emotion Recognition Based on Visual Information.

    Science.gov (United States)

    Ko, Byoung Chul

    2018-01-30

    Facial emotion recognition (FER) is an important topic in the fields of computer vision and artificial intelligence owing to its significant academic and commercial potential. Although FER can be conducted using multiple sensors, this review focuses on studies that exclusively use facial images, because visual expressions are one of the main information channels in interpersonal communication. This paper provides a brief review of researches in the field of FER conducted over the past decades. First, conventional FER approaches are described along with a summary of the representative categories of FER systems and their main algorithms. Deep-learning-based FER approaches using deep networks enabling "end-to-end" learning are then presented. This review also focuses on an up-to-date hybrid deep-learning approach combining a convolutional neural network (CNN) for the spatial features of an individual frame and long short-term memory (LSTM) for temporal features of consecutive frames. In the later part of this paper, a brief review of publicly available evaluation metrics is given, and a comparison with benchmark results, which are a standard for a quantitative comparison of FER researches, is described. This review can serve as a brief guidebook to newcomers in the field of FER, providing basic knowledge and a general understanding of the latest state-of-the-art studies, as well as to experienced researchers looking for productive directions for future work.

  20. A Brief Review of Facial Emotion Recognition Based on Visual Information

    Directory of Open Access Journals (Sweden)

    Byoung Chul Ko

    2018-01-01

    Full Text Available Facial emotion recognition (FER is an important topic in the fields of computer vision and artificial intelligence owing to its significant academic and commercial potential. Although FER can be conducted using multiple sensors, this review focuses on studies that exclusively use facial images, because visual expressions are one of the main information channels in interpersonal communication. This paper provides a brief review of researches in the field of FER conducted over the past decades. First, conventional FER approaches are described along with a summary of the representative categories of FER systems and their main algorithms. Deep-learning-based FER approaches using deep networks enabling “end-to-end” learning are then presented. This review also focuses on an up-to-date hybrid deep-learning approach combining a convolutional neural network (CNN for the spatial features of an individual frame and long short-term memory (LSTM for temporal features of consecutive frames. In the later part of this paper, a brief review of publicly available evaluation metrics is given, and a comparison with benchmark results, which are a standard for a quantitative comparison of FER researches, is described. This review can serve as a brief guidebook to newcomers in the field of FER, providing basic knowledge and a general understanding of the latest state-of-the-art studies, as well as to experienced researchers looking for productive directions for future work.

  1. An Efficient Multimodal 2D + 3D Feature-based Approach to Automatic Facial Expression Recognition

    KAUST Repository

    Li, Huibin

    2015-07-29

    We present a fully automatic multimodal 2D + 3D feature-based facial expression recognition approach and demonstrate its performance on the BU-3DFE database. Our approach combines multi-order gradient-based local texture and shape descriptors in order to achieve efficiency and robustness. First, a large set of fiducial facial landmarks of 2D face images along with their 3D face scans are localized using a novel algorithm namely incremental Parallel Cascade of Linear Regression (iPar-CLR). Then, a novel Histogram of Second Order Gradients (HSOG) based local image descriptor in conjunction with the widely used first-order gradient based SIFT descriptor are used to describe the local texture around each 2D landmark. Similarly, the local geometry around each 3D landmark is described by two novel local shape descriptors constructed using the first-order and the second-order surface differential geometry quantities, i.e., Histogram of mesh Gradients (meshHOG) and Histogram of mesh Shape index (curvature quantization, meshHOS). Finally, the Support Vector Machine (SVM) based recognition results of all 2D and 3D descriptors are fused at both feature-level and score-level to further improve the accuracy. Comprehensive experimental results demonstrate that there exist impressive complementary characteristics between the 2D and 3D descriptors. We use the BU-3DFE benchmark to compare our approach to the state-of-the-art ones. Our multimodal feature-based approach outperforms the others by achieving an average recognition accuracy of 86.32%. Moreover, a good generalization ability is shown on the Bosphorus database.

  2. An Efficient Multimodal 2D + 3D Feature-based Approach to Automatic Facial Expression Recognition

    KAUST Repository

    Li, Huibin; Ding, Huaxiong; Huang, Di; Wang, Yunhong; Zhao, Xi; Morvan, Jean-Marie; Chen, Liming

    2015-01-01

    We present a fully automatic multimodal 2D + 3D feature-based facial expression recognition approach and demonstrate its performance on the BU-3DFE database. Our approach combines multi-order gradient-based local texture and shape descriptors in order to achieve efficiency and robustness. First, a large set of fiducial facial landmarks of 2D face images along with their 3D face scans are localized using a novel algorithm namely incremental Parallel Cascade of Linear Regression (iPar-CLR). Then, a novel Histogram of Second Order Gradients (HSOG) based local image descriptor in conjunction with the widely used first-order gradient based SIFT descriptor are used to describe the local texture around each 2D landmark. Similarly, the local geometry around each 3D landmark is described by two novel local shape descriptors constructed using the first-order and the second-order surface differential geometry quantities, i.e., Histogram of mesh Gradients (meshHOG) and Histogram of mesh Shape index (curvature quantization, meshHOS). Finally, the Support Vector Machine (SVM) based recognition results of all 2D and 3D descriptors are fused at both feature-level and score-level to further improve the accuracy. Comprehensive experimental results demonstrate that there exist impressive complementary characteristics between the 2D and 3D descriptors. We use the BU-3DFE benchmark to compare our approach to the state-of-the-art ones. Our multimodal feature-based approach outperforms the others by achieving an average recognition accuracy of 86.32%. Moreover, a good generalization ability is shown on the Bosphorus database.

  3. PROSPECTS OF THE REGIONAL INTEGRATION POLICY BASED ON CLUSTER FORMATION

    Directory of Open Access Journals (Sweden)

    Elena Tsepilova

    2018-01-01

    Full Text Available The purpose of this article is to develop the theoretical foundations of regional integration policy and to determine its prospects on the basis of cluster formation. The authors use such research methods as systematization, comparative and complex analysis, synthesis, statistical method. Within the framework of the research, the concept of regional integration policy is specified, and its integration core – cluster – is allocated. The authors work out an algorithm of regional clustering, which will ensure the growth of economy and tax income. Measures have been proposed to optimize the organizational mechanism of interaction between the participants of the territorial cluster and the authorities that allow to ensure the effective functioning of clusters, including taxation clusters. Based on the results of studying the existing methods for assessing the effectiveness of cluster policy, the authors propose their own approach to evaluating the consequences of implementing the regional integration policy, according to which the list of quantitative and qualitative indicators is defined. The present article systematizes the experience and results of the cluster policy of certain European countries, that made it possible to determine the prospects and synergetic effect from the development of clusters as an integration foundation of regional policy in the Russian Federation. The authors carry out the analysis of activity of cluster formations using the example of the Rostov region – a leader in the formation of conditions for the cluster policy development in the Southern Federal District. 11 clusters and cluster initiatives are developing in this region. As a result, the authors propose measures for support of the already existing clusters and creation of the new ones.

  4. Structure based alignment and clustering of proteins (STRALCP)

    Science.gov (United States)

    Zemla, Adam T.; Zhou, Carol E.; Smith, Jason R.; Lam, Marisa W.

    2013-06-18

    Disclosed are computational methods of clustering a set of protein structures based on local and pair-wise global similarity values. Pair-wise local and global similarity values are generated based on pair-wise structural alignments for each protein in the set of protein structures. Initially, the protein structures are clustered based on pair-wise local similarity values. The protein structures are then clustered based on pair-wise global similarity values. For each given cluster both a representative structure and spans of conserved residues are identified. The representative protein structure is used to assign newly-solved protein structures to a group. The spans are used to characterize conservation and assign a "structural footprint" to the cluster.

  5. Sound-induced facial synkinesis following facial nerve paralysis.

    Science.gov (United States)

    Ma, Ming-San; van der Hoeven, Johannes H; Nicolai, Jean-Philippe A; Meek, Marcel F

    2009-08-01

    Facial synkinesis (or synkinesia) (FS) occurs frequently after paresis or paralysis of the facial nerve and is in most cases due to aberrant regeneration of (branches of) the facial nerve. Patients suffer from inappropriate and involuntary synchronous facial muscle contractions. Here we describe two cases of sound-induced facial synkinesis (SFS) after facial nerve injury. As far as we know, this phenomenon has not been described in the English literature before. Patient A presented with right hemifacial palsy after lesion of the facial nerve due to skull base fracture. He reported involuntary muscle activity at the right corner of the mouth, specifically on hearing ringing keys. Patient B suffered from left hemifacial palsy following otitis media and developed involuntary muscle contraction in the facial musculature specifically on hearing clapping hands or a trumpet sound. Both patients were evaluated by means of video, audio and EMG analysis. Possible mechanisms in the pathophysiology of SFS are postulated and therapeutic options are discussed.

  6. Web-based Visualisation of Head Pose and Facial Expressions Changes:

    DEFF Research Database (Denmark)

    Kalliatakis, Grigorios; Vidakis, Nikolaos; Triantafyllidis, Georgios

    2016-01-01

    Despite significant recent advances in the field of head pose estimation and facial expression recognition, raising the cognitive level when analysing human activity presents serious challenges to current concepts. Motivated by the need of generating comprehensible visual representations from...... and accurately estimate head pose changes in unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data...

  7. Cluster algebras bases on vertex operator algebras

    Czech Academy of Sciences Publication Activity Database

    Zuevsky, Alexander

    2016-01-01

    Roč. 30, 28-29 (2016), č. článku 1640030. ISSN 0217-9792 Institutional support: RVO:67985840 Keywords : cluster alegbras * vertex operator algebras * Riemann surfaces Subject RIV: BA - General Mathematics Impact factor: 0.736, year: 2016 http://www.worldscientific.com/doi/abs/10.1142/S0217979216400300

  8. Seniority-based coupled cluster theory

    International Nuclear Information System (INIS)

    Henderson, Thomas M.; Scuseria, Gustavo E.; Bulik, Ireneusz W.; Stein, Tamar

    2014-01-01

    Doubly occupied configuration interaction (DOCI) with optimized orbitals often accurately describes strong correlations while working in a Hilbert space much smaller than that needed for full configuration interaction. However, the scaling of such calculations remains combinatorial with system size. Pair coupled cluster doubles (pCCD) is very successful in reproducing DOCI energetically, but can do so with low polynomial scaling (N 3 , disregarding the two-electron integral transformation from atomic to molecular orbitals). We show here several examples illustrating the success of pCCD in reproducing both the DOCI energy and wave function and show how this success frequently comes about. What DOCI and pCCD lack are an effective treatment of dynamic correlations, which we here add by including higher-seniority cluster amplitudes which are excluded from pCCD. This frozen pair coupled cluster approach is comparable in cost to traditional closed-shell coupled cluster methods with results that are competitive for weakly correlated systems and often superior for the description of strongly correlated systems

  9. An Intelligent Clustering Based Methodology for Confusable ...

    African Journals Online (AJOL)

    Journal of the Nigerian Association of Mathematical Physics ... The system assigns patients with severity levels in all the clusters. ... The system compares favorably with diagnosis arrived at by experienced physicians and also provides patients' level of severity in each confusable disease and the degree of confusability of ...

  10. A Flocking Based algorithm for Document Clustering Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Cui, Xiaohui [ORNL; Gao, Jinzhu [ORNL; Potok, Thomas E [ORNL

    2006-01-01

    Social animals or insects in nature often exhibit a form of emergent collective behavior known as flocking. In this paper, we present a novel Flocking based approach for document clustering analysis. Our Flocking clustering algorithm uses stochastic and heuristic principles discovered from observing bird flocks or fish schools. Unlike other partition clustering algorithm such as K-means, the Flocking based algorithm does not require initial partitional seeds. The algorithm generates a clustering of a given set of data through the embedding of the high-dimensional data items on a two-dimensional grid for easy clustering result retrieval and visualization. Inspired by the self-organized behavior of bird flocks, we represent each document object with a flock boid. The simple local rules followed by each flock boid result in the entire document flock generating complex global behaviors, which eventually result in a clustering of the documents. We evaluate the efficiency of our algorithm with both a synthetic dataset and a real document collection that includes 100 news articles collected from the Internet. Our results show that the Flocking clustering algorithm achieves better performance compared to the K- means and the Ant clustering algorithm for real document clustering.

  11. Result diversification based on query-specific cluster ranking

    NARCIS (Netherlands)

    He, J.; Meij, E.; de Rijke, M.

    2011-01-01

    Result diversification is a retrieval strategy for dealing with ambiguous or multi-faceted queries by providing documents that cover as many facets of the query as possible. We propose a result diversification framework based on query-specific clustering and cluster ranking, in which diversification

  12. Result Diversification Based on Query-Specific Cluster Ranking

    NARCIS (Netherlands)

    J. He (Jiyin); E. Meij; M. de Rijke (Maarten)

    2011-01-01

    htmlabstractResult diversification is a retrieval strategy for dealing with ambiguous or multi-faceted queries by providing documents that cover as many facets of the query as possible. We propose a result diversification framework based on query-specific clustering and cluster ranking,

  13. Likelihood-based inference for clustered line transect data

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus; Schweder, Tore

    2006-01-01

    The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...

  14. Likelihood-based inference for clustered line transect data

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus Plenge; Schweder, Tore

    The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...

  15. Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

    Directory of Open Access Journals (Sweden)

    Olszewski Kellen L

    2007-07-01

    Full Text Available Abstract Background The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks; these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes (e.g. ribosomes. Results We developed Nearest Neighbor Networks (NNN, a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods. Conclusion The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the

  16. Fuzzy Rules for Ant Based Clustering Algorithm

    Directory of Open Access Journals (Sweden)

    Amira Hamdi

    2016-01-01

    Full Text Available This paper provides a new intelligent technique for semisupervised data clustering problem that combines the Ant System (AS algorithm with the fuzzy c-means (FCM clustering algorithm. Our proposed approach, called F-ASClass algorithm, is a distributed algorithm inspired by foraging behavior observed in ant colonyT. The ability of ants to find the shortest path forms the basis of our proposed approach. In the first step, several colonies of cooperating entities, called artificial ants, are used to find shortest paths in a complete graph that we called graph-data. The number of colonies used in F-ASClass is equal to the number of clusters in dataset. Hence, the partition matrix of dataset founded by artificial ants is given in the second step, to the fuzzy c-means technique in order to assign unclassified objects generated in the first step. The proposed approach is tested on artificial and real datasets, and its performance is compared with those of K-means, K-medoid, and FCM algorithms. Experimental section shows that F-ASClass performs better according to the error rate classification, accuracy, and separation index.

  17. Adaptive density trajectory cluster based on time and space distance

    Science.gov (United States)

    Liu, Fagui; Zhang, Zhijie

    2017-10-01

    There are some hotspot problems remaining in trajectory cluster for discovering mobile behavior regularity, such as the computation of distance between sub trajectories, the setting of parameter values in cluster algorithm and the uncertainty/boundary problem of data set. As a result, based on the time and space, this paper tries to define the calculation method of distance between sub trajectories. The significance of distance calculation for sub trajectories is to clearly reveal the differences in moving trajectories and to promote the accuracy of cluster algorithm. Besides, a novel adaptive density trajectory cluster algorithm is proposed, in which cluster radius is computed through using the density of data distribution. In addition, cluster centers and number are selected by a certain strategy automatically, and uncertainty/boundary problem of data set is solved by designed weighted rough c-means. Experimental results demonstrate that the proposed algorithm can perform the fuzzy trajectory cluster effectively on the basis of the time and space distance, and obtain the optimal cluster centers and rich cluster results information adaptably for excavating the features of mobile behavior in mobile and sociology network.

  18. Realistic Facial Expression of Virtual Human Based on Color, Sweat, and Tears Effects

    Directory of Open Access Journals (Sweden)

    Mohammed Hazim Alkawaz

    2014-01-01

    Full Text Available Generating extreme appearances such as scared awaiting sweating while happy fit for tears (cry and blushing (anger and happiness is the key issue in achieving the high quality facial animation. The effects of sweat, tears, and colors are integrated into a single animation model to create realistic facial expressions of 3D avatar. The physical properties of muscles, emotions, or the fluid properties with sweating and tears initiators are incorporated. The action units (AUs of facial action coding system are merged with autonomous AUs to create expressions including sadness, anger with blushing, happiness with blushing, and fear. Fluid effects such as sweat and tears are simulated using the particle system and smoothed-particle hydrodynamics (SPH methods which are combined with facial animation technique to produce complex facial expressions. The effects of oxygenation of the facial skin color appearance are measured using the pulse oximeter system and the 3D skin analyzer. The result shows that virtual human facial expression is enhanced by mimicking actual sweating and tears simulations for all extreme expressions. The proposed method has contribution towards the development of facial animation industry and game as well as computer graphics.

  19. Realistic facial expression of virtual human based on color, sweat, and tears effects.

    Science.gov (United States)

    Alkawaz, Mohammed Hazim; Basori, Ahmad Hoirul; Mohamad, Dzulkifli; Mohamed, Farhan

    2014-01-01

    Generating extreme appearances such as scared awaiting sweating while happy fit for tears (cry) and blushing (anger and happiness) is the key issue in achieving the high quality facial animation. The effects of sweat, tears, and colors are integrated into a single animation model to create realistic facial expressions of 3D avatar. The physical properties of muscles, emotions, or the fluid properties with sweating and tears initiators are incorporated. The action units (AUs) of facial action coding system are merged with autonomous AUs to create expressions including sadness, anger with blushing, happiness with blushing, and fear. Fluid effects such as sweat and tears are simulated using the particle system and smoothed-particle hydrodynamics (SPH) methods which are combined with facial animation technique to produce complex facial expressions. The effects of oxygenation of the facial skin color appearance are measured using the pulse oximeter system and the 3D skin analyzer. The result shows that virtual human facial expression is enhanced by mimicking actual sweating and tears simulations for all extreme expressions. The proposed method has contribution towards the development of facial animation industry and game as well as computer graphics.

  20. Cluster-based spectrum sensing for cognitive radios with imperfect channel to cluster-head

    KAUST Repository

    Ben Ghorbel, Mahdi

    2012-04-01

    Spectrum sensing is considered as the first and main step for cognitive radio systems to achieve an efficient use of spectrum. Cooperation and clustering among cognitive radio users are two techniques that can be employed with spectrum sensing in order to improve the sensing performance by reducing miss-detection and false alarm. In this paper, within the framework of a clustering-based cooperative spectrum sensing scheme, we study the effect of errors in transmitting the local decisions from the secondary users to the cluster heads (or the fusion center), while considering non-identical channel conditions between the secondary users. Closed-form expressions for the global probabilities of detection and false alarm at the cluster head are derived. © 2012 IEEE.

  1. Cluster-based spectrum sensing for cognitive radios with imperfect channel to cluster-head

    KAUST Repository

    Ben Ghorbel, Mahdi; Nam, Haewoon; Alouini, Mohamed-Slim

    2012-01-01

    Spectrum sensing is considered as the first and main step for cognitive radio systems to achieve an efficient use of spectrum. Cooperation and clustering among cognitive radio users are two techniques that can be employed with spectrum sensing in order to improve the sensing performance by reducing miss-detection and false alarm. In this paper, within the framework of a clustering-based cooperative spectrum sensing scheme, we study the effect of errors in transmitting the local decisions from the secondary users to the cluster heads (or the fusion center), while considering non-identical channel conditions between the secondary users. Closed-form expressions for the global probabilities of detection and false alarm at the cluster head are derived. © 2012 IEEE.

  2. [Facial palsy].

    Science.gov (United States)

    Cavoy, R

    2013-09-01

    Facial palsy is a daily challenge for the clinicians. Determining whether facial nerve palsy is peripheral or central is a key step in the diagnosis. Central nervous lesions can give facial palsy which may be easily differentiated from peripheral palsy. The next question is the peripheral facial paralysis idiopathic or symptomatic. A good knowledge of anatomy of facial nerve is helpful. A structure approach is given to identify additional features that distinguish symptomatic facial palsy from idiopathic one. The main cause of peripheral facial palsies is idiopathic one, or Bell's palsy, which remains a diagnosis of exclusion. The most common cause of symptomatic peripheral facial palsy is Ramsay-Hunt syndrome. Early identification of symptomatic facial palsy is important because of often worst outcome and different management. The prognosis of Bell's palsy is on the whole favorable and is improved with a prompt tapering course of prednisone. In Ramsay-Hunt syndrome, an antiviral therapy is added along with prednisone. We also discussed of current treatment recommendations. We will review short and long term complications of peripheral facial palsy.

  3. Decoding facial expressions based on face-selective and motion-sensitive areas.

    Science.gov (United States)

    Liang, Yin; Liu, Baolin; Xu, Junhai; Zhang, Gaoyan; Li, Xianglin; Wang, Peiyuan; Wang, Bin

    2017-06-01

    Humans can easily recognize others' facial expressions. Among the brain substrates that enable this ability, considerable attention has been paid to face-selective areas; in contrast, whether motion-sensitive areas, which clearly exhibit sensitivity to facial movements, are involved in facial expression recognition remained unclear. The present functional magnetic resonance imaging (fMRI) study used multi-voxel pattern analysis (MVPA) to explore facial expression decoding in both face-selective and motion-sensitive areas. In a block design experiment, participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise) in images, videos, and eyes-obscured videos. Due to the use of multiple stimulus types, the impacts of facial motion and eye-related information on facial expression decoding were also examined. It was found that motion-sensitive areas showed significant responses to emotional expressions and that dynamic expressions could be successfully decoded in both face-selective and motion-sensitive areas. Compared with static stimuli, dynamic expressions elicited consistently higher neural responses and decoding performance in all regions. A significant decrease in both activation and decoding accuracy due to the absence of eye-related information was also observed. Overall, the findings showed that emotional expressions are represented in motion-sensitive areas in addition to conventional face-selective areas, suggesting that motion-sensitive regions may also effectively contribute to facial expression recognition. The results also suggested that facial motion and eye-related information played important roles by carrying considerable expression information that could facilitate facial expression recognition. Hum Brain Mapp 38:3113-3125, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  4. Perioperative antibiotic usage by facial plastic surgeons: national survey results and comparison with evidence-based guidelines.

    Science.gov (United States)

    Grunebaum, Lisa Danielle; Reiter, David

    2006-01-01

    To determine current practice for use of perioperative antibiotics among facial plastic surgeons, to determine the extent of use of literature support for preferences of facial plastic surgeons, and to compare patterns of use with nationally supported evidence-based guidelines. A link to a Web site containing a questionnaire on perioperative antibiotic use was e-mailed to more than 1000 facial plastic surgeons in the United States. Responses were archived in a dedicated database and analyzed to determine patterns of use and methods of documenting that use. Current literature was used to develop evidence-based recommendations for perioperative antibiotic use, emphasizing current nationally supported guidelines. Preferences varied significantly for medication used, dosage and regimen, time of first dose relative to incision time, setting in which medication was administered, and procedures for which perioperative antibiotic was deemed necessary. Surgical site infection in facial plastic surgery can be reduced by better conformance to currently available evidence-based guidelines. We offer specific recommendations that are supported by the current literature.

  5. Tracking subtle stereotypes of children with trisomy 21: from facial-feature-based to implicit stereotyping.

    Directory of Open Access Journals (Sweden)

    Claire Enea-Drapeau

    Full Text Available BACKGROUND: Stigmatization is one of the greatest obstacles to the successful integration of people with Trisomy 21 (T21 or Down syndrome, the most frequent genetic disorder associated with intellectual disability. Research on attitudes and stereotypes toward these people still focuses on explicit measures subjected to social-desirability biases, and neglects how variability in facial stigmata influences attitudes and stereotyping. METHODOLOGY/PRINCIPAL FINDINGS: The participants were 165 adults including 55 young adult students, 55 non-student adults, and 55 professional caregivers working with intellectually disabled persons. They were faced with implicit association tests (IAT, a well-known technique whereby response latency is used to capture the relative strength with which some groups of people--here photographed faces of typically developing children and children with T21--are automatically (without conscious awareness associated with positive versus negative attributes in memory. Each participant also rated the same photographed faces (consciously accessible evaluations. We provide the first evidence that the positive bias typically found in explicit judgments of children with T21 is smaller for those whose facial features are highly characteristic of this disorder, compared to their counterparts with less distinctive features and to typically developing children. We also show that this bias can coexist with negative evaluations at the implicit level (with large effect sizes, even among professional caregivers. CONCLUSION: These findings support recent models of feature-based stereotyping, and more importantly show how crucial it is to go beyond explicit evaluations to estimate the true extent of stigmatization of intellectually disabled people.

  6. Improving Tensor Based Recommenders with Clustering

    DEFF Research Database (Denmark)

    Leginus, Martin; Dolog, Peter; Zemaitis, Valdas

    2012-01-01

    Social tagging systems (STS) model three types of entities (i.e. tag-user-item) and relationships between them are encoded into a 3-order tensor. Latent relationships and patterns can be discovered by applying tensor factorization techniques like Higher Order Singular Value Decomposition (HOSVD),...... of the recommendations and execution time are improved and memory requirements are decreased. The clustering is motivated by the fact that many tags in a tag space are semantically similar thus the tags can be grouped. Finally, promising experimental results are presented...

  7. Evidence-based treatments for cluster headache

    Directory of Open Access Journals (Sweden)

    Gooriah R

    2015-11-01

    Full Text Available Rubesh Gooriah, Alina Buture, Fayyaz Ahmed Department of Neurology, Hull Royal Infirmary, Kingston upon Hull, UK Abstract: Cluster headache (CH, one of the most painful syndromes known to man, is managed with acute and preventive medications. The brief duration and severity of the attacks command the use of rapid-acting pain relievers. Inhalation of oxygen and subcutaneous sumatriptan are the two most effective acute therapeutic options for sufferers of CH. Several preventive medications are available, the most effective of which is verapamil. However, most of these agents are not backed by strong clinical evidence. In some patients, these options can be ineffective, especially in those who develop chronic CH. Surgical procedures for the chronic refractory form of the disorder should then be contemplated, the most promising of which is hypothalamic deep brain stimulation. We hereby review the pathogenesis of CH and the evidence behind the treatment options for this debilitating condition. Keywords: cluster headache, pathogenesis, vasoactive intestinal peptide, suprachiasmatic nucleus

  8. A Web service substitution method based on service cluster nets

    Science.gov (United States)

    Du, YuYue; Gai, JunJing; Zhou, MengChu

    2017-11-01

    Service substitution is an important research topic in the fields of Web services and service-oriented computing. This work presents a novel method to analyse and substitute Web services. A new concept, called a Service Cluster Net Unit, is proposed based on Web service clusters. A service cluster is converted into a Service Cluster Net Unit. Then it is used to analyse whether the services in the cluster can satisfy some service requests. Meanwhile, the substitution methods of an atomic service and a composite service are proposed. The correctness of the proposed method is proved, and the effectiveness is shown and compared with the state-of-the-art method via an experiment. It can be readily applied to e-commerce service substitution to meet the business automation needs.

  9. XML documents cluster research based on frequent subpatterns

    Science.gov (United States)

    Ding, Tienan; Li, Wei; Li, Xiongfei

    2015-12-01

    XML data is widely used in the information exchange field of Internet, and XML document data clustering is the hot research topic. In the XML document clustering process, measure differences between two XML documents is time costly, and impact the efficiency of XML document clustering. This paper proposed an XML documents clustering method based on frequent patterns of XML document dataset, first proposed a coding tree structure for encoding the XML document, and translate frequent pattern mining from XML documents into frequent pattern mining from string. Further, using the cosine similarity calculation method and cohesive hierarchical clustering method for XML document dataset by frequent patterns. Because of frequent patterns are subsets of the original XML document data, so the time consumption of XML document similarity measure is reduced. The experiment runs on synthetic dataset and the real datasets, the experimental result shows that our method is efficient.

  10. Clustering economies based on multiple criteria decision making techniques

    Directory of Open Access Journals (Sweden)

    Mansour Momeni

    2011-10-01

    Full Text Available One of the primary concerns on many countries is to determine different important factors affecting economic growth. In this paper, we study some factors such as unemployment rate, inflation ratio, population growth, average annual income, etc to cluster different countries. The proposed model of this paper uses analytical hierarchy process (AHP to prioritize the criteria and then uses a K-mean technique to cluster 59 countries based on the ranked criteria into four groups. The first group includes countries with high standards such as Germany and Japan. In the second cluster, there are some developing countries with relatively good economic growth such as Saudi Arabia and Iran. The third cluster belongs to countries with faster rates of growth compared with the countries located in the second group such as China, India and Mexico. Finally, the fourth cluster includes countries with relatively very low rates of growth such as Jordan, Mali, Niger, etc.

  11. Local Community Detection Algorithm Based on Minimal Cluster

    Directory of Open Access Journals (Sweden)

    Yong Zhou

    2016-01-01

    Full Text Available In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.

  12. REGIONAL DEVELOPMENT BASED ON CLUSTER IN LIVESTOCK DEVELOPMENT. CLUSTER IN LIVESTOCK SECTOR IN THE KYRGYZ REPUBLIC

    Directory of Open Access Journals (Sweden)

    Meerim SYDYKOVA

    2014-11-01

    Full Text Available In most developing countries, where agriculture is the main economical source, clusters have been found as a booster to develop their economy. The Asian countries are now starting to implement agro-food clusters into the mainstream of changes in agriculture, farming and food industry. The long-term growth of meat production in the Kyrgyz Republic during the last decade, as well as the fact that agriculture has become one of the prioritized sectors of the economy, proved the importance of livestock sector in the economy of the Kyrgyz Republic. The research question is “Does the Kyrgyz Republic has strong economic opportunities and prerequisites in agriculture in order to implement an effective agro cluster in the livestock sector?” Paper focuses on describing the prerequisites of the Kyrgyz Republic in agriculture to implement livestock cluster. The main objective of the paper is to analyse the livestock sector of the Kyrgyz Republic and observe the capacity of this sector to implement agro-cluster. The study focuses on investigating livestock sector and a complex S.W.O.T. The analysis was carried out based on local and regional database and official studies. The results of research demonstrate the importance of livestock cluster for national economy. It can be concluded that cluster implementation could provide to its all members with benefits if they could build strong collaborative relationship in order to facilitate the access to the labour market and implicitly, the access to exchange of good practices. Their ability of potential cluster members to act as a convergence pole is critical for acquiring practical skills necessary for the future development of the livestock sector.

  13. Cluster-based DBMS Management Tool with High-Availability

    Directory of Open Access Journals (Sweden)

    Jae-Woo Chang

    2005-02-01

    Full Text Available A management tool which is needed for monitoring and managing cluster-based DBMSs has been little studied. So, we design and implement a cluster-based DBMS management tool with high-availability that monitors the status of nodes in a cluster system as well as the status of DBMS instances in a node. The tool enables users to recognize a single virtual system image and provides them with the status of all the nodes and resources in the system by using a graphic user interface (GUI. By using a load balancer, our management tool can increase the performance of a cluster-based DBMS as well as can overcome the limitation of the existing parallel DBMSs.

  14. Facial trauma

    Science.gov (United States)

    Maxillofacial injury; Midface trauma; Facial injury; LeFort injuries ... Hockberger RS, Walls RM, eds. Rosen's Emergency Medicine: Concepts and Clinical Practice . 8th ed. Philadelphia, PA: Elsevier ...

  15. Cluster-based analysis of multi-model climate ensembles

    Science.gov (United States)

    Hyde, Richard; Hossaini, Ryan; Leeson, Amber A.

    2018-06-01

    Clustering - the automated grouping of similar data - can provide powerful and unique insight into large and complex data sets, in a fast and computationally efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) has yet to be fully realised. In this paper, we explore the specific application of clustering to a multi-model climate ensemble. We hypothesise that clustering techniques can provide (a) a flexible, data-driven method of testing model-observation agreement and (b) a mechanism with which to identify model development priorities. We focus our analysis on chemistry-climate model (CCM) output of tropospheric ozone - an important greenhouse gas - from the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Tropospheric column ozone from the ACCMIP ensemble was clustered using the Data Density based Clustering (DDC) algorithm. We find that a multi-model mean (MMM) calculated using members of the most-populous cluster identified at each location offers a reduction of up to ˜ 20 % in the global absolute mean bias between the MMM and an observed satellite-based tropospheric ozone climatology, with respect to a simple, all-model MMM. On a spatial basis, the bias is reduced at ˜ 62 % of all locations, with the largest bias reductions occurring in the Northern Hemisphere - where ozone concentrations are relatively large. However, the bias is unchanged at 9 % of all locations and increases at 29 %, particularly in the Southern Hemisphere. The latter demonstrates that although cluster-based subsampling acts to remove outlier model data, such data may in fact be closer to observed values in some locations. We further demonstrate that clustering can provide a viable and

  16. Inhomogeneity of epidemic spreading with entropy-based infected clusters.

    Science.gov (United States)

    Wen-Jie, Zhou; Xing-Yuan, Wang

    2013-12-01

    Considering the difference in the sizes of the infected clusters in the dynamic complex networks, the normalized entropy based on infected clusters (δ*) is proposed to characterize the inhomogeneity of epidemic spreading. δ* gives information on the variability of the infected clusters in the system. We investigate the variation in the inhomogeneity of the distribution of the epidemic with the absolute velocity v of moving agent, the infection density ρ, and the interaction radius r. By comparing δ* in the dynamic networks with δH* in homogeneous mode, the simulation experiments show that the inhomogeneity of epidemic spreading becomes smaller with the increase of v, ρ, r.

  17. Lack of support for the association between facial shape and aggression: a reappraisal based on a worldwide population genetics perspective.

    Directory of Open Access Journals (Sweden)

    Jorge Gómez-Valdés

    Full Text Available Antisocial and criminal behaviors are multifactorial traits whose interpretation relies on multiple disciplines. Since these interpretations may have social, moral and legal implications, a constant review of the evidence is necessary before any scientific claim is considered as truth. A recent study proposed that men with wider faces relative to facial height (fWHR are more likely to develop unethical behaviour mediated by a psychological sense of power. This research was based on reports suggesting that sexual dimorphism and selection would be responsible for a correlation between fWHR and aggression. Here we show that 4,960 individuals from 94 modern human populations belonging to a vast array of genetic and cultural contexts do not display significant amounts of fWHR sexual dimorphism. Further analyses using populations with associated ethnographical records as well as samples of male prisoners of the Mexico City Federal Penitentiary condemned by crimes of variable level of inter-personal aggression (homicide, robbery, and minor faults did not show significant evidence, suggesting that populations/individuals with higher levels of bellicosity, aggressive behaviour, or power-mediated behaviour display greater fWHR. Finally, a regression analysis of fWHR on individual's fitness showed no significant correlation between this facial trait and reproductive success. Overall, our results suggest that facial attributes are poor predictors of aggressive behaviour, or at least, that sexual selection was weak enough to leave a signal on patterns of between- and within-sex and population facial variation.

  18. Local binary pattern variants-based adaptive texture features analysis for posed and nonposed facial expression recognition

    Science.gov (United States)

    Sultana, Maryam; Bhatti, Naeem; Javed, Sajid; Jung, Soon Ki

    2017-09-01

    Facial expression recognition (FER) is an important task for various computer vision applications. The task becomes challenging when it requires the detection and encoding of macro- and micropatterns of facial expressions. We present a two-stage texture feature extraction framework based on the local binary pattern (LBP) variants and evaluate its significance in recognizing posed and nonposed facial expressions. We focus on the parametric limitations of the LBP variants and investigate their effects for optimal FER. The size of the local neighborhood is an important parameter of the LBP technique for its extraction in images. To make the LBP adaptive, we exploit the granulometric information of the facial images to find the local neighborhood size for the extraction of center-symmetric LBP (CS-LBP) features. Our two-stage texture representations consist of an LBP variant and the adaptive CS-LBP features. Among the presented two-stage texture feature extractions, the binarized statistical image features and adaptive CS-LBP features were found showing high FER rates. Evaluation of the adaptive texture features shows competitive and higher performance than the nonadaptive features and other state-of-the-art approaches, respectively.

  19. Managing distance and covariate information with point-based clustering

    Directory of Open Access Journals (Sweden)

    Peter A. Whigham

    2016-09-01

    Full Text Available Abstract Background Geographic perspectives of disease and the human condition often involve point-based observations and questions of clustering or dispersion within a spatial context. These problems involve a finite set of point observations and are constrained by a larger, but finite, set of locations where the observations could occur. Developing a rigorous method for pattern analysis in this context requires handling spatial covariates, a method for constrained finite spatial clustering, and addressing bias in geographic distance measures. An approach, based on Ripley’s K and applied to the problem of clustering with deliberate self-harm (DSH, is presented. Methods Point-based Monte-Carlo simulation of Ripley’s K, accounting for socio-economic deprivation and sources of distance measurement bias, was developed to estimate clustering of DSH at a range of spatial scales. A rotated Minkowski L1 distance metric allowed variation in physical distance and clustering to be assessed. Self-harm data was derived from an audit of 2 years’ emergency hospital presentations (n = 136 in a New Zealand town (population ~50,000. Study area was defined by residential (housing land parcels representing a finite set of possible point addresses. Results Area-based deprivation was spatially correlated. Accounting for deprivation and distance bias showed evidence for clustering of DSH for spatial scales up to 500 m with a one-sided 95 % CI, suggesting that social contagion may be present for this urban cohort. Conclusions Many problems involve finite locations in geographic space that require estimates of distance-based clustering at many scales. A Monte-Carlo approach to Ripley’s K, incorporating covariates and models for distance bias, are crucial when assessing health-related clustering. The case study showed that social network structure defined at the neighbourhood level may account for aspects of neighbourhood clustering of DSH. Accounting for

  20. DSN Beowulf Cluster-Based VLBI Correlator

    Science.gov (United States)

    Rogstad, Stephen P.; Jongeling, Andre P.; Finley, Susan G.; White, Leslie A.; Lanyi, Gabor E.; Clark, John E.; Goodhart, Charles E.

    2009-01-01

    The NASA Deep Space Network (DSN) requires a broadband VLBI (very long baseline interferometry) correlator to process data routinely taken as part of the VLBI source Catalogue Maintenance and Enhancement task (CAT M&E) and the Time and Earth Motion Precision Observations task (TEMPO). The data provided by these measurements are a crucial ingredient in the formation of precision deep-space navigation models. In addition, a VLBI correlator is needed to provide support for other VLBI related activities for both internal and external customers. The JPL VLBI Correlator (JVC) was designed, developed, and delivered to the DSN as a successor to the legacy Block II Correlator. The JVC is a full-capability VLBI correlator that uses software processes running on multiple computers to cross-correlate two-antenna broadband noise data. Components of this new system (see Figure 1) consist of Linux PCs integrated into a Beowulf Cluster, an existing Mark5 data storage system, a RAID array, an existing software correlator package (SoftC) originally developed for Delta DOR Navigation processing, and various custom- developed software processes and scripts. Parallel processing on the JVC is achieved by assigning slave nodes of the Beowulf cluster to process separate scans in parallel until all scans have been processed. Due to the single stream sequential playback of the Mark5 data, some ramp-up time is required before all nodes can have access to required scan data. Core functions of each processing step are accomplished using optimized C programs. The coordination and execution of these programs across the cluster is accomplished using Pearl scripts, PostgreSQL commands, and a handful of miscellaneous system utilities. Mark5 data modules are loaded on Mark5 Data systems playback units, one per station. Data processing is started when the operator scans the Mark5 systems and runs a script that reads various configuration files and then creates an experiment-dependent status database

  1. 3D facial expression recognition based on histograms of surface differential quantities

    KAUST Repository

    Li, Huibin; Morvan, Jean-Marie; Chen, Liming

    2011-01-01

    . To characterize shape information of the local neighborhood of facial landmarks, we calculate the weighted statistical distributions of surface differential quantities, including histogram of mesh gradient (HoG) and histogram of shape index (HoS). Normal cycle

  2. Cosmetics Alter Biologically-Based Factors of Beauty: Evidence from Facial Contrast

    Directory of Open Access Journals (Sweden)

    Alex L. Jones

    2015-01-01

    Full Text Available The use of cosmetics by women seems to consistently increase their attractiveness. What factors of attractiveness do cosmetics alter to achieve this? Facial contrast is a known cue to sexual dimorphism and youth, and cosmetics exaggerate sexual dimorphisms in facial contrast. Here, we demonstrate that the luminance contrast pattern of the eyes and eyebrows is consistently sexually dimorphic across a large sample of faces, with females possessing lower brow contrasts than males, and greater eye contrast than males. Red-green and yellow-blue color contrasts were not found to differ consistently between the sexes. We also show that women use cosmetics not only to exaggerate sexual dimorphisms of brow and eye contrasts, but also to increase contrasts that decline with age. These findings refine the notion of facial contrast, and demonstrate how cosmetics can increase attractiveness by manipulating factors of beauty associated with facial contrast.

  3. Cosmetics alter biologically-based factors of beauty: evidence from facial contrast.

    Science.gov (United States)

    Jones, Alex L; Russell, Richard; Ward, Robert

    2015-02-28

    The use of cosmetics by women seems to consistently increase their attractiveness. What factors of attractiveness do cosmetics alter to achieve this? Facial contrast is a known cue to sexual dimorphism and youth, and cosmetics exaggerate sexual dimorphisms in facial contrast. Here, we demonstrate that the luminance contrast pattern of the eyes and eyebrows is consistently sexually dimorphic across a large sample of faces, with females possessing lower brow contrasts than males, and greater eye contrast than males. Red-green and yellow-blue color contrasts were not found to differ consistently between the sexes. We also show that women use cosmetics not only to exaggerate sexual dimorphisms of brow and eye contrasts, but also to increase contrasts that decline with age. These findings refine the notion of facial contrast, and demonstrate how cosmetics can increase attractiveness by manipulating factors of beauty associated with facial contrast.

  4. COMPARISON AND EVALUATION OF CLUSTER BASED IMAGE SEGMENTATION TECHNIQUES

    OpenAIRE

    Hetangi D. Mehta*, Daxa Vekariya, Pratixa Badelia

    2017-01-01

    Image segmentation is the classification of an image into different groups. Numerous algorithms using different approaches have been proposed for image segmentation. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and objectivity. A review is done on different types of clustering methods used for image segmentation. Also a methodology is proposed to classify and quantify different clustering algorithms based on their consistency in different...

  5. Cluster-based localization and tracking in ubiquitous computing systems

    CERN Document Server

    Martínez-de Dios, José Ramiro; Torres-González, Arturo; Ollero, Anibal

    2017-01-01

    Localization and tracking are key functionalities in ubiquitous computing systems and techniques. In recent years a very high variety of approaches, sensors and techniques for indoor and GPS-denied environments have been developed. This book briefly summarizes the current state of the art in localization and tracking in ubiquitous computing systems focusing on cluster-based schemes. Additionally, existing techniques for measurement integration, node inclusion/exclusion and cluster head selection are also described in this book.

  6. Radiobiological analyse based on cell cluster models

    International Nuclear Information System (INIS)

    Lin Hui; Jing Jia; Meng Damin; Xu Yuanying; Xu Liangfeng

    2010-01-01

    The influence of cell cluster dimension on EUD and TCP for targeted radionuclide therapy was studied using the radiobiological method. The radiobiological features of tumor with activity-lack in core were evaluated and analyzed by associating EUD, TCP and SF.The results show that EUD will increase with the increase of tumor dimension under the activity homogeneous distribution. If the extra-cellular activity was taken into consideration, the EUD will increase 47%. Under the activity-lack in tumor center and the requirement of TCP=0.90, the α cross-fire influence of 211 At could make up the maximum(48 μm)3 activity-lack for Nucleus source, but(72 μm)3 for Cytoplasm, Cell Surface, Cell and Voxel sources. In clinic,the physician could prefer the suggested dose of Cell Surface source in case of the future of local tumor control for under-dose. Generally TCP could well exhibit the effect difference between under-dose and due-dose, but not between due-dose and over-dose, which makes TCP more suitable for the therapy plan choice. EUD could well exhibit the difference between different models and activity distributions,which makes it more suitable for the research work. When the user uses EUD to study the influence of activity inhomogeneous distribution, one should keep the consistency of the configuration and volume of the former and the latter models. (authors)

  7. A Web-based Game for Teaching Facial Expressions to Schizophrenic Patients.

    Science.gov (United States)

    Gülkesen, Kemal Hakan; Isleyen, Filiz; Cinemre, Buket; Samur, Mehmet Kemal; Sen Kaya, Semiha; Zayim, Nese

    2017-07-12

    Recognizing facial expressions is an important social skill. In some psychological disorders such as schizophrenia, loss of this skill may complicate the patient's daily life. Prior research has shown that information technology may help to develop facial expression recognition skills through educational software and games. To examine if a computer game designed for teaching facial expressions would improve facial expression recognition skills of patients with schizophrenia. We developed a website composed of eight serious games. Thirty-two patients were given a pre-test composed of 21 facial expression photographs. Eighteen patients were in the study group while 14 were in the control group. Patients in the study group were asked to play the games on the website. After a period of one month, we performed a post-test for all patients. The median score of the correct answers was 17.5 in the control group whereas it was 16.5 in the study group (of 21) in pretest. The median post-test score was 18 in the control group (p=0.052) whereas it was 20 in the study group (pgames may be used for the purpose of educating people who have difficulty in recognizing facial expressions.

  8. ENERGY OPTIMIZATION IN CLUSTER BASED WIRELESS SENSOR NETWORKS

    Directory of Open Access Journals (Sweden)

    T. SHANKAR

    2014-04-01

    Full Text Available Wireless sensor networks (WSN are made up of sensor nodes which are usually battery-operated devices, and hence energy saving of sensor nodes is a major design issue. To prolong the networks lifetime, minimization of energy consumption should be implemented at all layers of the network protocol stack starting from the physical to the application layer including cross-layer optimization. Optimizing energy consumption is the main concern for designing and planning the operation of the WSN. Clustering technique is one of the methods utilized to extend lifetime of the network by applying data aggregation and balancing energy consumption among sensor nodes of the network. This paper proposed new version of Low Energy Adaptive Clustering Hierarchy (LEACH, protocols called Advanced Optimized Low Energy Adaptive Clustering Hierarchy (AOLEACH, Optimal Deterministic Low Energy Adaptive Clustering Hierarchy (ODLEACH, and Varying Probability Distance Low Energy Adaptive Clustering Hierarchy (VPDL combination with Shuffled Frog Leap Algorithm (SFLA that enables selecting best optimal adaptive cluster heads using improved threshold energy distribution compared to LEACH protocol and rotating cluster head position for uniform energy dissipation based on energy levels. The proposed algorithm optimizing the life time of the network by increasing the first node death (FND time and number of alive nodes, thereby increasing the life time of the network.

  9. Microgrids Real-Time Pricing Based on Clustering Techniques

    Directory of Open Access Journals (Sweden)

    Hao Liu

    2018-05-01

    Full Text Available Microgrids are widely spreading in electricity markets worldwide. Besides the security and reliability concerns for these microgrids, their operators need to address consumers’ pricing. Considering the growth of smart grids and smart meter facilities, it is expected that microgrids will have some level of flexibility to determine real-time pricing for at least some consumers. As such, the key challenge is finding an optimal pricing model for consumers. This paper, accordingly, proposes a new pricing scheme in which microgrids are able to deploy clustering techniques in order to understand their consumers’ load profiles and then assign real-time prices based on their load profile patterns. An improved weighted fuzzy average k-means is proposed to cluster load curve of consumers in an optimal number of clusters, through which the load profile of each cluster is determined. Having obtained the load profile of each cluster, real-time prices are given to each cluster, which is the best price given to all consumers in that cluster.

  10. Simulation-based marginal likelihood for cluster strong lensing cosmology

    Science.gov (United States)

    Killedar, M.; Borgani, S.; Fabjan, D.; Dolag, K.; Granato, G.; Meneghetti, M.; Planelles, S.; Ragone-Figueroa, C.

    2018-01-01

    Comparisons between observed and predicted strong lensing properties of galaxy clusters have been routinely used to claim either tension or consistency with Λ cold dark matter cosmology. However, standard approaches to such cosmological tests are unable to quantify the preference for one cosmology over another. We advocate approximating the relevant Bayes factor using a marginal likelihood that is based on the following summary statistic: the posterior probability distribution function for the parameters of the scaling relation between Einstein radii and cluster mass, α and β. We demonstrate, for the first time, a method of estimating the marginal likelihood using the X-ray selected z > 0.5 Massive Cluster Survey clusters as a case in point and employing both N-body and hydrodynamic simulations of clusters. We investigate the uncertainty in this estimate and consequential ability to compare competing cosmologies, which arises from incomplete descriptions of baryonic processes, discrepancies in cluster selection criteria, redshift distribution and dynamical state. The relation between triaxial cluster masses at various overdensities provides a promising alternative to the strong lensing test.

  11. Image-based Analysis of Emotional Facial Expressions in Full Face Transplants.

    Science.gov (United States)

    Bedeloglu, Merve; Topcu, Çagdas; Akgul, Arzu; Döger, Ela Naz; Sever, Refik; Ozkan, Ozlenen; Ozkan, Omer; Uysal, Hilmi; Polat, Ovunc; Çolak, Omer Halil

    2018-01-20

    In this study, it is aimed to determine the degree of the development in emotional expression of full face transplant patients from photographs. Hence, a rehabilitation process can be planned according to the determination of degrees as a later work. As envisaged, in full face transplant cases, the determination of expressions can be confused or cannot be achieved as the healthy control group. In order to perform image-based analysis, a control group consist of 9 healthy males and 2 full-face transplant patients participated in the study. Appearance-based Gabor Wavelet Transform (GWT) and Local Binary Pattern (LBP) methods are adopted for recognizing neutral and 6 emotional expressions which consist of angry, scared, happy, hate, confused and sad. Feature extraction was carried out by using both methods and combination of these methods serially. In the performed expressions, the extracted features of the most distinct zones in the facial area where the eye and mouth region, have been used to classify the emotions. Also, the combination of these region features has been used to improve classifier performance. Control subjects and transplant patients' ability to perform emotional expressions have been determined with K-nearest neighbor (KNN) classifier with region-specific and method-specific decision stages. The results have been compared with healthy group. It has been observed that transplant patients don't reflect some emotional expressions. Also, there were confusions among expressions.

  12. Facial Fractures.

    Science.gov (United States)

    Ricketts, Sophie; Gill, Hameet S; Fialkov, Jeffery A; Matic, Damir B; Antonyshyn, Oleh M

    2016-02-01

    After reading this article, the participant should be able to: 1. Demonstrate an understanding of some of the changes in aspects of facial fracture management. 2. Assess a patient presenting with facial fractures. 3. Understand indications and timing of surgery. 4. Recognize exposures of the craniomaxillofacial skeleton. 5. Identify methods for repair of typical facial fracture patterns. 6. Discuss the common complications seen with facial fractures. Restoration of the facial skeleton and associated soft tissues after trauma involves accurate clinical and radiologic assessment to effectively plan a management approach for these injuries. When surgical intervention is necessary, timing, exposure, sequencing, and execution of repair are all integral to achieving the best long-term outcomes for these patients.

  13. Ontology-based topic clustering for online discussion data

    Science.gov (United States)

    Wang, Yongheng; Cao, Kening; Zhang, Xiaoming

    2013-03-01

    With the rapid development of online communities, mining and extracting quality knowledge from online discussions becomes very important for the industrial and marketing sector, as well as for e-commerce applications and government. Most of the existing techniques model a discussion as a social network of users represented by a user-based graph without considering the content of the discussion. In this paper we propose a new multilayered mode to analysis online discussions. The user-based and message-based representation is combined in this model. A novel frequent concept sets based clustering method is used to cluster the original online discussion network into topic space. Domain ontology is used to improve the clustering accuracy. Parallel methods are also used to make the algorithms scalable to very large data sets. Our experimental study shows that the model and algorithms are effective when analyzing large scale online discussion data.

  14. Event-based cluster synchronization of coupled genetic regulatory networks

    Science.gov (United States)

    Yue, Dandan; Guan, Zhi-Hong; Li, Tao; Liao, Rui-Quan; Liu, Feng; Lai, Qiang

    2017-09-01

    In this paper, the cluster synchronization of coupled genetic regulatory networks with a directed topology is studied by using the event-based strategy and pinning control. An event-triggered condition with a threshold consisting of the neighbors' discrete states at their own event time instants and a state-independent exponential decay function is proposed. The intra-cluster states information and extra-cluster states information are involved in the threshold in different ways. By using the Lyapunov function approach and the theories of matrices and inequalities, we establish the cluster synchronization criterion. It is shown that both the avoidance of continuous transmission of information and the exclusion of the Zeno behavior are ensured under the presented triggering condition. Explicit conditions on the parameters in the threshold are obtained for synchronization. The stability criterion of a single GRN is also given under the reduced triggering condition. Numerical examples are provided to validate the theoretical results.

  15. A novel clustering algorithm based on quantum games

    International Nuclear Information System (INIS)

    Li Qiang; He Yan; Jiang Jingping

    2009-01-01

    Enormous successes have been made by quantum algorithms during the last decade. In this paper, we combine the quantum game with the problem of data clustering, and then develop a quantum-game-based clustering algorithm, in which data points in a dataset are considered as players who can make decisions and implement quantum strategies in quantum games. After each round of a quantum game, each player's expected payoff is calculated. Later, he uses a link-removing-and-rewiring (LRR) function to change his neighbors and adjust the strength of links connecting to them in order to maximize his payoff. Further, algorithms are discussed and analyzed in two cases of strategies, two payoff matrixes and two LRR functions. Consequently, the simulation results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the clustering algorithms have fast rates of convergence. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.

  16. A Data-origin Authentication Protocol Based on ONOS Cluster

    Directory of Open Access Journals (Sweden)

    Qin Hua

    2016-01-01

    Full Text Available This paper is aim to propose a data-origin authentication protocol based on ONOS cluster. ONOS is a SDN controller which can work under a distributed environment. However, the security of an ONOS cluster is seldom considered, and the communication in an ONOS cluster may suffer from lots of security threats. In this paper, we used a two-tier self-renewable hash chain for identity authentication and data-origin authentication. We analyse the security and overhead of our proposal and made a comparison with current security measure. It showed that with the help of our proposal, communication in an ONOS cluster could be protected from identity forging, replay attacks, data tampering, MITM attacks and repudiation, also the computational overhead would decrease apparently.

  17. Multi scales based sparse matrix spectral clustering image segmentation

    Science.gov (United States)

    Liu, Zhongmin; Chen, Zhicai; Li, Zhanming; Hu, Wenjin

    2018-04-01

    In image segmentation, spectral clustering algorithms have to adopt the appropriate scaling parameter to calculate the similarity matrix between the pixels, which may have a great impact on the clustering result. Moreover, when the number of data instance is large, computational complexity and memory use of the algorithm will greatly increase. To solve these two problems, we proposed a new spectral clustering image segmentation algorithm based on multi scales and sparse matrix. We devised a new feature extraction method at first, then extracted the features of image on different scales, at last, using the feature information to construct sparse similarity matrix which can improve the operation efficiency. Compared with traditional spectral clustering algorithm, image segmentation experimental results show our algorithm have better degree of accuracy and robustness.

  18. OMERACT-based fibromyalgia symptom subgroups: an exploratory cluster analysis.

    Science.gov (United States)

    Vincent, Ann; Hoskin, Tanya L; Whipple, Mary O; Clauw, Daniel J; Barton, Debra L; Benzo, Roberto P; Williams, David A

    2014-10-16

    The aim of this study was to identify subsets of patients with fibromyalgia with similar symptom profiles using the Outcome Measures in Rheumatology (OMERACT) core symptom domains. Female patients with a diagnosis of fibromyalgia and currently meeting fibromyalgia research survey criteria completed the Brief Pain Inventory, the 30-item Profile of Mood States, the Medical Outcomes Sleep Scale, the Multidimensional Fatigue Inventory, the Multiple Ability Self-Report Questionnaire, the Fibromyalgia Impact Questionnaire-Revised (FIQ-R) and the Short Form-36 between 1 June 2011 and 31 October 2011. Hierarchical agglomerative clustering was used to identify subgroups of patients with similar symptom profiles. To validate the results from this sample, hierarchical agglomerative clustering was repeated in an external sample of female patients with fibromyalgia with similar inclusion criteria. A total of 581 females with a mean age of 55.1 (range, 20.1 to 90.2) years were included. A four-cluster solution best fit the data, and each clustering variable differed significantly (P FIQ-R total scores (P = 0.0004)). In our study, we incorporated core OMERACT symptom domains, which allowed for clustering based on a comprehensive symptom profile. Although our exploratory cluster solution needs confirmation in a longitudinal study, this approach could provide a rationale to support the study of individualized clinical evaluation and intervention.

  19. Agent-based method for distributed clustering of textual information

    Science.gov (United States)

    Potok, Thomas E [Oak Ridge, TN; Reed, Joel W [Knoxville, TN; Elmore, Mark T [Oak Ridge, TN; Treadwell, Jim N [Louisville, TN

    2010-09-28

    A computer method and system for storing, retrieving and displaying information has a multiplexing agent (20) that calculates a new document vector (25) for a new document (21) to be added to the system and transmits the new document vector (25) to master cluster agents (22) and cluster agents (23) for evaluation. These agents (22, 23) perform the evaluation and return values upstream to the multiplexing agent (20) based on the similarity of the document to documents stored under their control. The multiplexing agent (20) then sends the document (21) and the document vector (25) to the master cluster agent (22), which then forwards it to a cluster agent (23) or creates a new cluster agent (23) to manage the document (21). The system also searches for stored documents according to a search query having at least one term and identifying the documents found in the search, and displays the documents in a clustering display (80) of similarity so as to indicate similarity of the documents to each other.

  20. Risk Probability Estimating Based on Clustering

    DEFF Research Database (Denmark)

    Chen, Yong; Jensen, Christian D.; Gray, Elizabeth

    2003-01-01

    of prior experiences, recommendations from a trusted entity or the reputation of the other entity. In this paper we propose a dynamic mechanism for estimating the risk probability of a certain interaction in a given environment using hybrid neural networks. We argue that traditional risk assessment models...... from the insurance industry do not directly apply to ubiquitous computing environments. Instead, we propose a dynamic mechanism for risk assessment, which is based on pattern matching, classification and prediction procedures. This mechanism uses an estimator of risk probability, which is based...

  1. Carbon based nanostructures: diamond clusters structured with nanotubes

    Directory of Open Access Journals (Sweden)

    O.A. Shenderova

    2003-01-01

    Full Text Available Feasibility of designing composites from carbon nanotubes and nanodiamond clusters is discussed based on atomistic simulations. Depending on nanotube size and morphology, some types of open nanotubes can be chemically connected with different facets of diamond clusters. The geometrical relation between different types of nanotubes and different diamond facets for construction of mechanically stable composites with all bonds saturated is summarized. Potential applications of the suggested nanostructures are briefly discussed based on the calculations of their electronic properties using environment dependent self-consistent tight-binding approach.

  2. Graph-based clustering and data visualization algorithms

    CERN Document Server

    Vathy-Fogarassy, Ágnes

    2013-01-01

    This work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges characterize relations and weights represent similarities or distances) provides a compact representation of the entire complex data set. This text describes clustering and visualization methods that are able to utilize information hidden in these graphs, based on

  3. Association of stress and depression with chronic facial pain: A case-control study based on the Northern Finland 1966 Birth Cohort.

    Science.gov (United States)

    Nevalainen, Netta; Lähdesmäki, Raija; Mäki, Pirjo; Ek, Ellen; Taanila, Anja; Pesonen, Paula; Sipilä, Kirsi

    2017-05-01

    The aim was to study the association between stress level and chronic facial pain, while controlling for the effect of depression on this association, during a three-year follow-up in a general population-based birth cohort. In the general population-based Northern Finland 1966 Birth Cohort, information about stress level, depression and facial pain were collected using questionnaires at the age of 31 years. Stress level was measured using the Work Ability Index. Depression was assessed using the 13-item depression subscale in the Hopkins Symptom Checklist-25. Three years later, a subsample of 52 subjects (42 women) with chronic facial pain and 52 pain-free controls (42 women) was formed. Of the subjects having high stress level at baseline, 73.3% had chronic facial pain, and 26.7% were pain-free three years later. The univariate logistic regression analysis showed that high stress level at 31 years increased the risk for chronic facial pain (crude OR 6.1, 95%, CI 1.3-28.7) three years later. When including depression in a multivariate model, depression associated statistically significantly with chronic facial pain (adjusted OR 2.5, 95%, CI 1.0-5.8), whereas stress level did not (adjusted OR 2.3, 95%, CI 0.6-8.4). High stress level is connected with increased risk for chronic facial pain. This association seems to mediate through depression.

  4. Perceived Sexual Orientation Based on Vocal and Facial Stimuli Is Linked to Self-Rated Sexual Orientation in Czech Men

    Science.gov (United States)

    Valentova, Jaroslava Varella; Havlíček, Jan

    2013-01-01

    Previous research has shown that lay people can accurately assess male sexual orientation based on limited information, such as face, voice, or behavioral display. Gender-atypical traits are thought to serve as cues to sexual orientation. We investigated the presumed mechanisms of sexual orientation attribution using a standardized set of facial and vocal stimuli of Czech men. Both types of stimuli were rated for sexual orientation and masculinity-femininity by non-student heterosexual women and homosexual men. Our data showed that by evaluating vocal stimuli both women and homosexual men can judge sexual orientation of the target men in agreement with their self-reported sexual orientation. Nevertheless, only homosexual men accurately attributed sexual orientation of the two groups from facial images. Interestingly, facial images of homosexual targets were rated as more masculine than heterosexual targets. This indicates that attributions of sexual orientation are affected by stereotyped association between femininity and male homosexuality; however, reliance on such cues can lead to frequent misjudgments as was the case with the female raters. Although our study is based on a community sample recruited in a non-English speaking country, the results are generally consistent with the previous research and thus corroborate the validity of sexual orientation attributions. PMID:24358180

  5. Perceived sexual orientation based on vocal and facial stimuli is linked to self-rated sexual orientation in Czech men.

    Directory of Open Access Journals (Sweden)

    Jaroslava Varella Valentova

    Full Text Available Previous research has shown that lay people can accurately assess male sexual orientation based on limited information, such as face, voice, or behavioral display. Gender-atypical traits are thought to serve as cues to sexual orientation. We investigated the presumed mechanisms of sexual orientation attribution using a standardized set of facial and vocal stimuli of Czech men. Both types of stimuli were rated for sexual orientation and masculinity-femininity by non-student heterosexual women and homosexual men. Our data showed that by evaluating vocal stimuli both women and homosexual men can judge sexual orientation of the target men in agreement with their self-reported sexual orientation. Nevertheless, only homosexual men accurately attributed sexual orientation of the two groups from facial images. Interestingly, facial images of homosexual targets were rated as more masculine than heterosexual targets. This indicates that attributions of sexual orientation are affected by stereotyped association between femininity and male homosexuality; however, reliance on such cues can lead to frequent misjudgments as was the case with the female raters. Although our study is based on a community sample recruited in a non-English speaking country, the results are generally consistent with the previous research and thus corroborate the validity of sexual orientation attributions.

  6. Readability assessment of internet-based patient education materials related to facial fractures.

    Science.gov (United States)

    Sanghvi, Saurin; Cherla, Deepa V; Shukla, Pratik A; Eloy, Jean Anderson

    2012-09-01

    Various professional societies, clinical practices, hospitals, and health care-related Web sites provide Internet-based patient education material (IPEMs) to the general public. However, this information may be written above the 6th-grade reading level recommended by the US Department of Health and Human Services. The purpose of this study is to assess the readability of facial fracture (FF)-related IPEMs and compare readability levels of IPEMs provided by four sources: professional societies, clinical practices, hospitals, and miscellaneous sources. Analysis of IPEMs on FFs available on Google.com. The readability of 41 FF-related IPEMs was assessed with four readability indices: Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease Score (FRES), Simple Measure of Gobbledygook (SMOG), and Gunning Frequency of Gobbledygook (Gunning FOG). Averages were evaluated against national recommendations and between each source using analysis of variance and t tests. Only 4.9% of IPEMs were written at or below the 6th-grade reading level, based on FKGL. The mean readability scores were: FRES 54.10, FKGL 9.89, SMOG 12.73, and Gunning FOG 12.98, translating into FF-related IPEMs being written at a "difficult" writing level, which is above the level of reading understanding of the average American adult. IPEMs related to FFs are written above the recommended 6th-grade reading level. Consequently, this information would be difficult to understand by the average US patient. Copyright © 2012 The American Laryngological, Rhinological, and Otological Society, Inc.

  7. Emotion Index of Cover Song Music Video Clips based on Facial Expression Recognition

    DEFF Research Database (Denmark)

    Kavallakis, George; Vidakis, Nikolaos; Triantafyllidis, Georgios

    2017-01-01

    This paper presents a scheme of creating an emotion index of cover song music video clips by recognizing and classifying facial expressions of the artist in the video. More specifically, it fuses effective and robust algorithms which are employed for expression recognition, along with the use...... of a neural network system using the features extracted by the SIFT algorithm. Also we support the need of this fusion of different expression recognition algorithms, because of the way that emotions are linked to facial expressions in music video clips....

  8. Facial Fractures.

    Science.gov (United States)

    Ghosh, Rajarshi; Gopalkrishnan, Kulandaswamy

    2018-06-01

    The aim of this study is to retrospectively analyze the incidence of facial fractures along with age, gender predilection, etiology, commonest site, associated dental injuries, and any complications of patients operated in Craniofacial Unit of SDM College of Dental Sciences and Hospital. This retrospective study was conducted at the Department of OMFS, SDM College of Dental Sciences, Dharwad from January 2003 to December 2013. Data were recorded for the cause of injury, age and gender distribution, frequency and type of injury, localization and frequency of soft tissue injuries, dentoalveolar trauma, facial bone fractures, complications, concomitant injuries, and different treatment protocols.All the data were analyzed using statistical analysis that is chi-squared test. A total of 1146 patients reported at our unit with facial fractures during these 10 years. Males accounted for a higher frequency of facial fractures (88.8%). Mandible was the commonest bone to be fractured among all the facial bones (71.2%). Maxillary central incisors were the most common teeth to be injured (33.8%) and avulsion was the most common type of injury (44.6%). Commonest postoperative complication was plate infection (11%) leading to plate removal. Other injuries associated with facial fractures were rib fractures, head injuries, upper and lower limb fractures, etc., among these rib fractures were seen most frequently (21.6%). This study was performed to compare the different etiologic factors leading to diverse facial fracture patterns. By statistical analysis of this record the authors come to know about the relationship of facial fractures with gender, age, associated comorbidities, etc.

  9. clusters

    Indian Academy of Sciences (India)

    2017-09-27

    Sep 27, 2017 ... Author for correspondence (zh4403701@126.com). MS received 15 ... lic clusters using density functional theory (DFT)-GGA of the DMOL3 package. ... In the process of geometric optimization, con- vergence thresholds ..... and Postgraduate Research & Practice Innovation Program of. Jiangsu Province ...

  10. clusters

    Indian Academy of Sciences (India)

    environmental as well as technical problems during fuel gas utilization. ... adsorption on some alloys of Pd, namely PdAu, PdAg ... ried out on small neutral and charged Au24,26,27, Cu,28 ... study of Zanti et al.29 on Pdn (n = 1–9) clusters.

  11. Nonuniform Sparse Data Clustering Cascade Algorithm Based on Dynamic Cumulative Entropy

    Directory of Open Access Journals (Sweden)

    Ning Li

    2016-01-01

    Full Text Available A small amount of prior knowledge and randomly chosen initial cluster centers have a direct impact on the accuracy of the performance of iterative clustering algorithm. In this paper we propose a new algorithm to compute initial cluster centers for k-means clustering and the best number of the clusters with little prior knowledge and optimize clustering result. It constructs the Euclidean distance control factor based on aggregation density sparse degree to select the initial cluster center of nonuniform sparse data and obtains initial data clusters by multidimensional diffusion density distribution. Multiobjective clustering approach based on dynamic cumulative entropy is adopted to optimize the initial data clusters and the best number of the clusters. The experimental results show that the newly proposed algorithm has good performance to obtain the initial cluster centers for the k-means algorithm and it effectively improves the clustering accuracy of nonuniform sparse data by about 5%.

  12. Parallel Density-Based Clustering for Discovery of Ionospheric Phenomena

    Science.gov (United States)

    Pankratius, V.; Gowanlock, M.; Blair, D. M.

    2015-12-01

    Ionospheric total electron content maps derived from global networks of dual-frequency GPS receivers can reveal a plethora of ionospheric features in real-time and are key to space weather studies and natural hazard monitoring. However, growing data volumes from expanding sensor networks are making manual exploratory studies challenging. As the community is heading towards Big Data ionospheric science, automation and Computer-Aided Discovery become indispensable tools for scientists. One problem of machine learning methods is that they require domain-specific adaptations in order to be effective and useful for scientists. Addressing this problem, our Computer-Aided Discovery approach allows scientists to express various physical models as well as perturbation ranges for parameters. The search space is explored through an automated system and parallel processing of batched workloads, which finds corresponding matches and similarities in empirical data. We discuss density-based clustering as a particular method we employ in this process. Specifically, we adapt Density-Based Spatial Clustering of Applications with Noise (DBSCAN). This algorithm groups geospatial data points based on density. Clusters of points can be of arbitrary shape, and the number of clusters is not predetermined by the algorithm; only two input parameters need to be specified: (1) a distance threshold, (2) a minimum number of points within that threshold. We discuss an implementation of DBSCAN for batched workloads that is amenable to parallelization on manycore architectures such as Intel's Xeon Phi accelerator with 60+ general-purpose cores. This manycore parallelization can cluster large volumes of ionospheric total electronic content data quickly. Potential applications for cluster detection include the visualization, tracing, and examination of traveling ionospheric disturbances or other propagating phenomena. Acknowledgments. We acknowledge support from NSF ACI-1442997 (PI V. Pankratius).

  13. Crowd Analysis by Using Optical Flow and Density Based Clustering

    DEFF Research Database (Denmark)

    Santoro, Francesco; Pedro, Sergio; Tan, Zheng-Hua

    2010-01-01

    In this paper, we present a system to detect and track crowds in a video sequence captured by a camera. In a first step, we compute optical flows by means of pyramidal Lucas-Kanade feature tracking. Afterwards, a density based clustering is used to group similar vectors. In the last step...

  14. Core Business Selection Based on Ant Colony Clustering Algorithm

    Directory of Open Access Journals (Sweden)

    Yu Lan

    2014-01-01

    Full Text Available Core business is the most important business to the enterprise in diversified business. In this paper, we first introduce the definition and characteristics of the core business and then descript the ant colony clustering algorithm. In order to test the effectiveness of the proposed method, Tianjin Port Logistics Development Co., Ltd. is selected as the research object. Based on the current situation of the development of the company, the core business of the company can be acquired by ant colony clustering algorithm. Thus, the results indicate that the proposed method is an effective way to determine the core business for company.

  15. A Cluster Based Group Signature Mechanism For Secure Vanet Communication

    Directory of Open Access Journals (Sweden)

    Navjot Kaur

    2015-08-01

    Full Text Available Vehicular adhoc network is one of the recent area of research to administer safety to human lives controlling of messages and in disposal of messages to users and passengers. VANETs allows communication of moving vehicular nodes. Movement of nodes leads in changing network size and scenario. Whenever a new node joins the network there is a threat of malicious node attack. So we need an environment that is secure and trust worthy. Therefore a new cluster based secure technique is proposed where cluster head is responsible for providing communication between the vehicular nodes. Performance parameters used in this paper are message drop ratio packet delay ratio and verification time.

  16. Price Formation Based on Particle-Cluster Aggregation

    Science.gov (United States)

    Wang, Shijun; Zhang, Changshui

    In the present work, we propose a microscopic model of financial markets based on particle-cluster aggregation on a two-dimensional small-world information network in order to simulate the dynamics of the stock markets. "Stylized facts" of the financial market time series, such as fat-tail distribution of returns, volatility clustering and multifractality, are observed in the model. The results of the model agree with empirical data taken from historical records of the daily closures of the NYSE composite index.

  17. [Establishment of the database of the 3D facial models for the plastic surgery based on network].

    Science.gov (United States)

    Liu, Zhe; Zhang, Hai-Lin; Zhang, Zheng-Guo; Qiao, Qun

    2008-07-01

    To collect the three-dimensional (3D) facial data of 30 facial deformity patients by the 3D scanner and establish a professional database based on Internet. It can be helpful for the clinical intervention. The primitive point data of face topography were collected by the 3D scanner. Then the 3D point cloud was edited by reverse engineering software to reconstruct the 3D model of the face. The database system was divided into three parts, including basic information, disease information and surgery information. The programming language of the web system is Java. The linkages between every table of the database are credibility. The query operation and the data mining are convenient. The users can visit the database via the Internet and use the image analysis system to observe the 3D facial models interactively. In this paper we presented a database and a web system adapt to the plastic surgery of human face. It can be used both in clinic and in basic research.

  18. Face puzzle—two new video-based tasks for measuring explicit and implicit aspects of facial emotion recognition

    Science.gov (United States)

    Kliemann, Dorit; Rosenblau, Gabriela; Bölte, Sven; Heekeren, Hauke R.; Dziobek, Isabel

    2013-01-01

    Recognizing others' emotional states is crucial for effective social interaction. While most facial emotion recognition tasks use explicit prompts that trigger consciously controlled processing, emotional faces are almost exclusively processed implicitly in real life. Recent attempts in social cognition suggest a dual process perspective, whereby explicit and implicit processes largely operate independently. However, due to differences in methodology the direct comparison of implicit and explicit social cognition has remained a challenge. Here, we introduce a new tool to comparably measure implicit and explicit processing aspects comprising basic and complex emotions in facial expressions. We developed two video-based tasks with similar answer formats to assess performance in respective facial emotion recognition processes: Face Puzzle, implicit and explicit. To assess the tasks' sensitivity to atypical social cognition and to infer interrelationship patterns between explicit and implicit processes in typical and atypical development, we included healthy adults (NT, n = 24) and adults with autism spectrum disorder (ASD, n = 24). Item analyses yielded good reliability of the new tasks. Group-specific results indicated sensitivity to subtle social impairments in high-functioning ASD. Correlation analyses with established implicit and explicit socio-cognitive measures were further in favor of the tasks' external validity. Between group comparisons provide first hints of differential relations between implicit and explicit aspects of facial emotion recognition processes in healthy compared to ASD participants. In addition, an increased magnitude of between group differences in the implicit task was found for a speed-accuracy composite measure. The new Face Puzzle tool thus provides two new tasks to separately assess explicit and implicit social functioning, for instance, to measure subtle impairments as well as potential improvements due to social cognitive

  19. GENERALISED MODEL BASED CONFIDENCE INTERVALS IN TWO STAGE CLUSTER SAMPLING

    Directory of Open Access Journals (Sweden)

    Christopher Ouma Onyango

    2010-09-01

    Full Text Available Chambers and Dorfman (2002 constructed bootstrap confidence intervals in model based estimation for finite population totals assuming that auxiliary values are available throughout a target population and that the auxiliary values are independent. They also assumed that the cluster sizes are known throughout the target population. We now extend to two stage sampling in which the cluster sizes are known only for the sampled clusters, and we therefore predict the unobserved part of the population total. Jan and Elinor (2008 have done similar work, but unlike them, we use a general model, in which the auxiliary values are not necessarily independent. We demonstrate that the asymptotic properties of our proposed estimator and its coverage rates are better than those constructed under the model assisted local polynomial regression model.

  20. Fast gene ontology based clustering for microarray experiments.

    Science.gov (United States)

    Ovaska, Kristian; Laakso, Marko; Hautaniemi, Sampsa

    2008-11-21

    Analysis of a microarray experiment often results in a list of hundreds of disease-associated genes. In order to suggest common biological processes and functions for these genes, Gene Ontology annotations with statistical testing are widely used. However, these analyses can produce a very large number of significantly altered biological processes. Thus, it is often challenging to interpret GO results and identify novel testable biological hypotheses. We present fast software for advanced gene annotation using semantic similarity for Gene Ontology terms combined with clustering and heat map visualisation. The methodology allows rapid identification of genes sharing the same Gene Ontology cluster. Our R based semantic similarity open-source package has a speed advantage of over 2000-fold compared to existing implementations. From the resulting hierarchical clustering dendrogram genes sharing a GO term can be identified, and their differences in the gene expression patterns can be seen from the heat map. These methods facilitate advanced annotation of genes resulting from data analysis.

  1. AES based secure low energy adaptive clustering hierarchy for WSNs

    Science.gov (United States)

    Kishore, K. R.; Sarma, N. V. S. N.

    2013-01-01

    Wireless sensor networks (WSNs) provide a low cost solution in diversified application areas. The wireless sensor nodes are inexpensive tiny devices with limited storage, computational capability and power. They are being deployed in large scale in both military and civilian applications. Security of the data is one of the key concerns where large numbers of nodes are deployed. Here, an energy-efficient secure routing protocol, secure-LEACH (Low Energy Adaptive Clustering Hierarchy) for WSNs based on the Advanced Encryption Standard (AES) is being proposed. This crypto system is a session based one and a new session key is assigned for each new session. The network (WSN) is divided into number of groups or clusters and a cluster head (CH) is selected among the member nodes of each cluster. The measured data from the nodes is aggregated by the respective CH's and then each CH relays this data to another CH towards the gateway node in the WSN which in turn sends the same to the Base station (BS). In order to maintain confidentiality of data while being transmitted, it is necessary to encrypt the data before sending at every hop, from a node to the CH and from the CH to another CH or to the gateway node.

  2. Facial Sports Injuries

    Science.gov (United States)

    ... Marketplace Find an ENT Doctor Near You Facial Sports Injuries Facial Sports Injuries Patient Health Information News ... should receive immediate medical attention. Prevention Of Facial Sports Injuries The best way to treat facial sports ...

  3. Facial Cosmetic Surgery

    Science.gov (United States)

    ... to find out more. Facial Cosmetic Surgery Facial Cosmetic Surgery Extensive education and training in surgical procedures ... to find out more. Facial Cosmetic Surgery Facial Cosmetic Surgery Extensive education and training in surgical procedures ...

  4. A similarity based agglomerative clustering algorithm in networks

    Science.gov (United States)

    Liu, Zhiyuan; Wang, Xiujuan; Ma, Yinghong

    2018-04-01

    The detection of clusters is benefit for understanding the organizations and functions of networks. Clusters, or communities, are usually groups of nodes densely interconnected but sparsely linked with any other clusters. To identify communities, an efficient and effective community agglomerative algorithm based on node similarity is proposed. The proposed method initially calculates similarities between each pair of nodes, and form pre-partitions according to the principle that each node is in the same community as its most similar neighbor. After that, check each partition whether it satisfies community criterion. For the pre-partitions who do not satisfy, incorporate them with others that having the biggest attraction until there are no changes. To measure the attraction ability of a partition, we propose an attraction index that based on the linked node's importance in networks. Therefore, our proposed method can better exploit the nodes' properties and network's structure. To test the performance of our algorithm, both synthetic and empirical networks ranging in different scales are tested. Simulation results show that the proposed algorithm can obtain superior clustering results compared with six other widely used community detection algorithms.

  5. Unsupervised active learning based on hierarchical graph-theoretic clustering.

    Science.gov (United States)

    Hu, Weiming; Hu, Wei; Xie, Nianhua; Maybank, Steve

    2009-10-01

    Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples.

  6. Facial trauma.

    Science.gov (United States)

    Peeters, N; Lemkens, P; Leach, R; Gemels B; Schepers, S; Lemmens, W

    Facial trauma. Patients with facial trauma must be assessed in a systematic way so as to avoid missing any injury. Severe and disfiguring facial injuries can be distracting. However, clinicians must first focus on the basics of trauma care, following the Advanced Trauma Life Support (ATLS) system of care. Maxillofacial trauma occurs in a significant number of severely injured patients. Life- and sight-threatening injuries must be excluded during the primary and secondary surveys. Special attention must be paid to sight-threatening injuries in stabilized patients through early referral to an appropriate specialist or the early initiation of emergency care treatment. The gold standard for the radiographic evaluation of facial injuries is computed tomography (CT) imaging. Nasal fractures are the most frequent isolated facial fractures. Isolated nasal fractures are principally diagnosed through history and clinical examination. Closed reduction is the most frequently performed treatment for isolated nasal fractures, with a fractured nasal septum as a predictor of failure. Ear, nose and throat surgeons, maxillofacial surgeons and ophthalmologists must all develop an adequate treatment plan for patients with complex maxillofacial trauma.

  7. Tracking Subtle Stereotypes of Children with Trisomy 21: From Facial-Feature-Based to Implicit Stereotyping

    OpenAIRE

    Enea-Drapeau , Claire; Carlier , Michèle; Huguet , Pascal

    2012-01-01

    International audience; BackgroundStigmatization is one of the greatest obstacles to the successful integration of people with Trisomy 21 (T21 or Down syndrome), the most frequent genetic disorder associated with intellectual disability. Research on attitudes and stereotypes toward these people still focuses on explicit measures subjected to social-desirability biases, and neglects how variability in facial stigmata influences attitudes and stereotyping.Methodology/Principal FindingsThe parti...

  8. Energy Aware Cluster Based Routing Scheme For Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Roy Sohini

    2015-09-01

    Full Text Available Wireless Sensor Network (WSN has emerged as an important supplement to the modern wireless communication systems due to its wide range of applications. The recent researches are facing the various challenges of the sensor network more gracefully. However, energy efficiency has still remained a matter of concern for the researches. Meeting the countless security needs, timely data delivery and taking a quick action, efficient route selection and multi-path routing etc. can only be achieved at the cost of energy. Hierarchical routing is more useful in this regard. The proposed algorithm Energy Aware Cluster Based Routing Scheme (EACBRS aims at conserving energy with the help of hierarchical routing by calculating the optimum number of cluster heads for the network, selecting energy-efficient route to the sink and by offering congestion control. Simulation results prove that EACBRS performs better than existing hierarchical routing algorithms like Distributed Energy-Efficient Clustering (DEEC algorithm for heterogeneous wireless sensor networks and Energy Efficient Heterogeneous Clustered scheme for Wireless Sensor Network (EEHC.

  9. Collaborative filtering recommendation model based on fuzzy clustering algorithm

    Science.gov (United States)

    Yang, Ye; Zhang, Yunhua

    2018-05-01

    As one of the most widely used algorithms in recommender systems, collaborative filtering algorithm faces two serious problems, which are the sparsity of data and poor recommendation effect in big data environment. In traditional clustering analysis, the object is strictly divided into several classes and the boundary of this division is very clear. However, for most objects in real life, there is no strict definition of their forms and attributes of their class. Concerning the problems above, this paper proposes to improve the traditional collaborative filtering model through the hybrid optimization of implicit semantic algorithm and fuzzy clustering algorithm, meanwhile, cooperating with collaborative filtering algorithm. In this paper, the fuzzy clustering algorithm is introduced to fuzzy clustering the information of project attribute, which makes the project belong to different project categories with different membership degrees, and increases the density of data, effectively reduces the sparsity of data, and solves the problem of low accuracy which is resulted from the inaccuracy of similarity calculation. Finally, this paper carries out empirical analysis on the MovieLens dataset, and compares it with the traditional user-based collaborative filtering algorithm. The proposed algorithm has greatly improved the recommendation accuracy.

  10. A novel grain cluster-based homogenization scheme

    International Nuclear Information System (INIS)

    Tjahjanto, D D; Eisenlohr, P; Roters, F

    2010-01-01

    An efficient homogenization scheme, termed the relaxed grain cluster (RGC), for elasto-plastic deformations of polycrystals is presented. The scheme is based on a generalization of the grain cluster concept. A volume element consisting of eight (= 2 × 2 × 2) hexahedral grains is considered. The kinematics of the RGC scheme is formulated within a finite deformation framework, where the relaxation of the local deformation gradient of each individual grain is connected to the overall deformation gradient by the, so-called, interface relaxation vectors. The set of relaxation vectors is determined by the minimization of the constitutive energy (or work) density of the overall cluster. An additional energy density associated with the mismatch at the grain boundaries due to relaxations is incorporated as a penalty term into the energy minimization formulation. Effectively, this penalty term represents the kinematical condition of deformation compatibility at the grain boundaries. Simulations have been performed for a dual-phase grain cluster loaded in uniaxial tension. The results of the simulations are presented and discussed in terms of the effective stress–strain response and the overall deformation anisotropy as functions of the penalty energy parameters. In addition, the prediction of the RGC scheme is compared with predictions using other averaging schemes, as well as to the result of direct finite element (FE) simulation. The comparison indicates that the present RGC scheme is able to approximate FE simulation results of relatively fine discretization at about three orders of magnitude lower computational cost

  11. Rejuvenecimiento facial

    Directory of Open Access Journals (Sweden)

    L. Daniel Jacubovsky, Dr.

    2010-01-01

    Full Text Available El envejecimiento facial es un proceso único y particular a cada individuo y está regido en especial por su carga genética. El lifting facial es una compleja técnica desarrollada en nuestra especialidad desde principios de siglo, para revertir los principales signos de este proceso. Los factores secundarios que gravitan en el envejecimiento facial son múltiples y por ello las ritidectomías o lifting cérvico faciales descritas han buscado corregir los cambios fisonómicos del envejecimiento excursionando, como se describe, en todos los planos tisulares involucrados. Esta cirugía por lo tanto, exige conocimiento cabal de la anatomía quirúrgica, pericia y experiencia para reducir las complicaciones, estigmas quirúrgicos y revisiones secundarias. La ridectomía facial ha evolucionado hacia un procedimiento más simple, de incisiones más cortas y disecciones menos extensas. Las suspensiones musculares han variado en su ejecución y los vectores de montaje y resección cutánea son cruciales en los resultados estéticos de la cirugía cérvico facial. Hoy estos vectores son de tracción más vertical. La corrección de la flaccidez va acompañada de un interés en reponer el volumen de la superficie del rostro, en especial el tercio medio. Las técnicas quirúrgicas de rejuvenecimiento, en especial el lifting facial, exigen una planificación para cada paciente. Las técnicas adjuntas al lifting, como blefaroplastias, mentoplastía, lipoaspiración de cuello, implantes faciales y otras, también han tenido una positiva evolución hacia la reducción de riesgos y mejor éxito estético.

  12. Reconocimiento facial

    OpenAIRE

    Urtiaga Abad, Juan Alfonso

    2014-01-01

    El presente proyecto trata sobre uno de los campos más problemáticos de la inteligencia artificial, el reconocimiento facial. Algo tan sencillo para las personas como es reconocer una cara conocida se traduce en complejos algoritmos y miles de datos procesados en cuestión de segundos. El proyecto comienza con un estudio del estado del arte de las diversas técnicas de reconocimiento facial, desde las más utilizadas y probadas como el PCA y el LDA, hasta técnicas experimentales que utilizan ...

  13. Facial Resemblance Exaggerates Sex-Specific Jealousy-Based Decisions1

    Directory of Open Access Journals (Sweden)

    Steven M. Platek

    2007-01-01

    Full Text Available Sex differences in reaction to a romantic partner's infidelity are well documented and are hypothesized to be attributable to sex-specific jealousy mechanisms which are utilized to solve adaptive problems associated with risk of extra-pair copulation. Males, because of the risk of cuckoldry become more upset by sexual infidelity, while females, because of loss of resources and biparental investment tend to become more distressed by emotional infidelity. However, the degree to which these sex-specific reactions to jealousy interact with cues to kin are completely unknown. Here we investigated the interaction of facial resemblance with decisions about sex-specific jealousy scenarios. Fifty nine volunteers were asked to imagine that two different people (represented by facial composites informed them about their romantic partner's sexual or emotional infidelity. Consistent with previous research, males ranked sexual infidelity scenarios as most upsetting and females ranked emotional infidelity scenarios most upsetting. However, when information about the infidelity was provided by a face that resembled the subject, sex-specific reactions to jealousy were exaggerated. This finding highlights the use of facial resemblance as a putative self-referent phenotypic matching cue that impacts trusting behavior in sexual contexts.

  14. Artificial Neural Networks and Gene Expression Programing based age estimation using facial features

    Directory of Open Access Journals (Sweden)

    Baddrud Z. Laskar

    2015-10-01

    Full Text Available This work is about estimating human age automatically through analysis of facial images. It has got a lot of real-world applications. Due to prompt advances in the fields of machine vision, facial image processing, and computer graphics, automatic age estimation via faces in computer is one of the dominant topics these days. This is due to widespread real-world applications, in areas of biometrics, security, surveillance, control, forensic art, entertainment, online customer management and support, along with cosmetology. As it is difficult to estimate the exact age, this system is to estimate a certain range of ages. Four sets of classifications have been used to differentiate a person’s data into one of the different age groups. The uniqueness about this study is the usage of two technologies i.e., Artificial Neural Networks (ANN and Gene Expression Programing (GEP to estimate the age and then compare the results. New methodologies like Gene Expression Programing (GEP have been explored here and significant results were found. The dataset has been developed to provide more efficient results by superior preprocessing methods. This proposed approach has been developed, tested and trained using both the methods. A public data set was used to test the system, FG-NET. The quality of the proposed system for age estimation using facial features is shown by broad experiments on the available database of FG-NET.

  15. An emotion-based facial expression word activates laughter module in the human brain: a functional magnetic resonance imaging study.

    Science.gov (United States)

    Osaka, Naoyuki; Osaka, Mariko; Kondo, Hirohito; Morishita, Masanao; Fukuyama, Hidenao; Shibasaki, Hiroshi

    2003-04-10

    We report an fMRI experiment demonstrating that visualization of onomatopoeia, an emotion-based facial expression word, highly suggestive of laughter, heard by the ear, significantly activates both the extrastriate visual cortex near the inferior occipital gyrus and the premotor (PM)/supplementary motor area (SMA) in the superior frontal gyrus while non-onomatopoeic words under the same task that did not imply laughter do not activate these areas in humans. We tested the specific hypothesis that an activation in extrastriate visual cortex and PM/SMA would be modulated by image formation of onomatopoeia implying laughter and found the hypothesis to be true. Copyright 2003 Elsevier Science Ireland Ltd.

  16. Seminal Quality Prediction Using Clustering-Based Decision Forests

    Directory of Open Access Journals (Sweden)

    Hong Wang

    2014-08-01

    Full Text Available Prediction of seminal quality with statistical learning tools is an emerging methodology in decision support systems in biomedical engineering and is very useful in early diagnosis of seminal patients and selection of semen donors candidates. However, as is common in medical diagnosis, seminal quality prediction faces the class imbalance problem. In this paper, we propose a novel supervised ensemble learning approach, namely Clustering-Based Decision Forests, to tackle unbalanced class learning problem in seminal quality prediction. Experiment results on real fertility diagnosis dataset have shown that Clustering-Based Decision Forests outperforms decision tree, Support Vector Machines, random forests, multilayer perceptron neural networks and logistic regression by a noticeable margin. Clustering-Based Decision Forests can also be used to evaluate variables’ importance and the top five important factors that may affect semen concentration obtained in this study are age, serious trauma, sitting time, the season when the semen sample is produced, and high fevers in the last year. The findings could be helpful in explaining seminal concentration problems in infertile males or pre-screening semen donor candidates.

  17. Caricaturing facial expressions.

    Science.gov (United States)

    Calder, A J; Rowland, D; Young, A W; Nimmo-Smith, I; Keane, J; Perrett, D I

    2000-08-14

    The physical differences between facial expressions (e.g. fear) and a reference norm (e.g. a neutral expression) were altered to produce photographic-quality caricatures. In Experiment 1, participants rated caricatures of fear, happiness and sadness for their intensity of these three emotions; a second group of participants rated how 'face-like' the caricatures appeared. With increasing levels of exaggeration the caricatures were rated as more emotionally intense, but less 'face-like'. Experiment 2 demonstrated a similar relationship between emotional intensity and level of caricature for six different facial expressions. Experiments 3 and 4 compared intensity ratings of facial expression caricatures prepared relative to a selection of reference norms - a neutral expression, an average expression, or a different facial expression (e.g. anger caricatured relative to fear). Each norm produced a linear relationship between caricature and rated intensity of emotion; this finding is inconsistent with two-dimensional models of the perceptual representation of facial expression. An exemplar-based multidimensional model is proposed as an alternative account.

  18. FACIAL PAIN·

    African Journals Online (AJOL)

    -As the conditions which cause pain in the facial structures are many and varied, the ... involvement of the auriculo-temporal nerve and is usually relieved by avulsion of that .... of its effects. If it is uspected that a lesion in the po terior fossa ma ...

  19. Multi-documents summarization based on clustering of learning object using hierarchical clustering

    Science.gov (United States)

    Mustamiin, M.; Budi, I.; Santoso, H. B.

    2018-03-01

    The Open Educational Resources (OER) is a portal of teaching, learning and research resources that is available in public domain and freely accessible. Learning contents or Learning Objects (LO) are granular and can be reused for constructing new learning materials. LO ontology-based searching techniques can be used to search for LO in the Indonesia OER. In this research, LO from search results are used as an ingredient to create new learning materials according to the topic searched by users. Summarizing-based grouping of LO use Hierarchical Agglomerative Clustering (HAC) with the dependency context to the user’s query which has an average value F-Measure of 0.487, while summarizing by K-Means F-Measure only has an average value of 0.336.

  20. Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering

    Directory of Open Access Journals (Sweden)

    Jean Marie Vianney Kinani

    2017-01-01

    Full Text Available We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patient’s response to the therapy. We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering. We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR and fluid-attenuated inversion recovery (FLAIR images to facilitate a smoother segmentation. The scalar output obtained is fed into a gravitational fuzzy clustering algorithm, which separates healthy structures from the unhealthy. Finally, the lesion contour is automatically outlined through the initialization-free level set evolution method. An advantage of this lesion detection algorithm is its precision and its simultaneous use of features computed from the intensity properties of the MR scan in a cascading pattern, which makes the computation fast, robust, and self-contained. Furthermore, we validate our algorithm with large-scale experiments using clinical and synthetic brain lesion datasets. As a result, an 84%–93% overlap performance is obtained, with an emphasis on robustness with respect to different and heterogeneous types of lesion and a swift computation time.

  1. Cluster chain based energy efficient routing protocol for moblie WSN

    Directory of Open Access Journals (Sweden)

    WU Ziyu

    2016-04-01

    Full Text Available With the ubiquitous smart devices acting as mobile sensor nodes in the wireless sensor networks(WSNs to sense and transmit physical information,routing protocols should be designed to accommodate the mobility issues,in addition to conventional considerations on energy efficiency.However,due to frequent topology change,traditional routing schemes cannot perform well.Moreover,existence of mobile nodes poses new challenges on energy dissipation and packet loss.In this paper,a novel routing scheme called cluster chain based routing protocol(CCBRP is proposed,which employs a combination of cluster and chain structure to accomplish data collection and transmission and thereafter selects qualified cluster heads as chain leaders to transmit data to the sink.Furthermore,node mobility is handled based on periodical membership update of mobile nodes.Simulation results demonstrate that CCBRP has a good performance in terms of network lifetime and packet delivery,also strikes a better balance between successful packet reception and energy consumption.

  2. Green Clustering Implementation Based on DPS-MOPSO

    Directory of Open Access Journals (Sweden)

    Yang Lu

    2014-01-01

    Full Text Available A green clustering implementation is proposed to be as the first method in the framework of an energy-efficient strategy for centralized enterprise high-density WLANs. Traditionally, to maintain the network coverage, all of the APs within the WLAN have to be powered on. Nevertheless, the new algorithm can power off a large proportion of APs while the coverage is maintained as the always-on counterpart. The proposed algorithm is composed of two parallel and concurrent procedures, which are the faster procedure based on K-means and the more accurate procedure based on Dynamic Population Size Multiple Objective Particle Swarm Optimization (DPS-MOPSO. To implement green clustering efficiently and accurately, dynamic population size and mutational operators are introduced as complements for the classical MOPSO. In addition to the function of AP selection, the new green clustering algorithm has another new function as the reference and guidance for AP deployment. This paper also presents simulations in scenarios modeled with ray-tracing method and FDTD technique, and the results show that about 67% up to 90% of energy consumption can be saved while the original network coverage is maintained during periods when few users are online or when the traffic load is low.

  3. Research on Bridge Sensor Validation Based on Correlation in Cluster

    Directory of Open Access Journals (Sweden)

    Huang Xiaowei

    2016-01-01

    Full Text Available In order to avoid the false alarm and alarm failure caused by sensor malfunction or failure, it has been critical to diagnose the fault and analyze the failure of the sensor measuring system in major infrastructures. Based on the real time monitoring of bridges and the study on the correlation probability distribution between multisensors adopted in the fault diagnosis system, a clustering algorithm based on k-medoid is proposed, by dividing sensors of the same type into k clusters. Meanwhile, the value of k is optimized by a specially designed evaluation function. Along with the further study of the correlation of sensors within the same cluster, this paper presents the definition and corresponding calculation algorithm of the sensor’s validation. The algorithm is applied to the analysis of the sensor data from an actual health monitoring system. The result reveals that the algorithm can not only accurately measure the failure degree and orientate the malfunction in time domain but also quantitatively evaluate the performance of sensors and eliminate error of diagnosis caused by the failure of the reference sensor.

  4. Image Registration Algorithm Based on Parallax Constraint and Clustering Analysis

    Science.gov (United States)

    Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

    2018-01-01

    To resolve the problem of slow computation speed and low matching accuracy in image registration, a new image registration algorithm based on parallax constraint and clustering analysis is proposed. Firstly, Harris corner detection algorithm is used to extract the feature points of two images. Secondly, use Normalized Cross Correlation (NCC) function to perform the approximate matching of feature points, and the initial feature pair is obtained. Then, according to the parallax constraint condition, the initial feature pair is preprocessed by K-means clustering algorithm, which is used to remove the feature point pairs with obvious errors in the approximate matching process. Finally, adopt Random Sample Consensus (RANSAC) algorithm to optimize the feature points to obtain the final feature point matching result, and the fast and accurate image registration is realized. The experimental results show that the image registration algorithm proposed in this paper can improve the accuracy of the image matching while ensuring the real-time performance of the algorithm.

  5. Methods to quantify soft-tissue based facial growth and treatment outcomes in children: a systematic review.

    Directory of Open Access Journals (Sweden)

    Sander Brons

    Full Text Available CONTEXT: Technological advancements have led craniofacial researchers and clinicians into the era of three-dimensional digital imaging for quantitative evaluation of craniofacial growth and treatment outcomes. OBJECTIVE: To give an overview of soft-tissue based methods for quantitative longitudinal assessment of facial dimensions in children until six years of age and to assess the reliability of these methods in studies with good methodological quality. DATA SOURCE: PubMed, EMBASE, Cochrane Library, Web of Science, Scopus and CINAHL were searched. A hand search was performed to check for additional relevant studies. STUDY SELECTION: Primary publications on facial growth and treatment outcomes in children younger than six years of age were included. DATA EXTRACTION: Independent data extraction by two observers. A quality assessment instrument was used to determine the methodological quality. Methods, used in studies with good methodological quality, were assessed for reliability expressed as the magnitude of the measurement error and the correlation coefficient between repeated measurements. RESULTS: In total, 47 studies were included describing 4 methods: 2D x-ray cephalometry; 2D photography; anthropometry; 3D imaging techniques (surface laser scanning, stereophotogrammetry and cone beam computed tomography. In general the measurement error was below 1 mm and 1° and correlation coefficients range from 0.65 to 1.0. CONCLUSION: Various methods have shown to be reliable. However, at present stereophotogrammetry seems to be the best 3D method for quantitative longitudinal assessment of facial dimensions in children until six years of age due to its millisecond fast image capture, archival capabilities, high resolution and no exposure to ionizing radiation.

  6. Human Amygdala Tracks a Feature-Based Valence Signal Embedded within the Facial Expression of Surprise.

    Science.gov (United States)

    Kim, M Justin; Mattek, Alison M; Bennett, Randi H; Solomon, Kimberly M; Shin, Jin; Whalen, Paul J

    2017-09-27

    Human amygdala function has been traditionally associated with processing the affective valence (negative vs positive) of an emotionally charged event, especially those that signal fear or threat. However, this account of human amygdala function can be explained by alternative views, which posit that the amygdala might be tuned to either (1) general emotional arousal (activation vs deactivation) or (2) specific emotion categories (fear vs happy). Delineating the pure effects of valence independent of arousal or emotion category is a challenging task, given that these variables naturally covary under many circumstances. To circumvent this issue and test the sensitivity of the human amygdala to valence values specifically, we measured the dimension of valence within the single facial expression category of surprise. Given the inherent valence ambiguity of this category, we show that surprised expression exemplars are attributed valence and arousal values that are uniquely and naturally uncorrelated. We then present fMRI data from both sexes, showing that the amygdala tracks these consensus valence values. Finally, we provide evidence that these valence values are linked to specific visual features of the mouth region, isolating the signal by which the amygdala detects this valence information. SIGNIFICANCE STATEMENT There is an open question as to whether human amygdala function tracks the valence value of cues in the environment, as opposed to either a more general emotional arousal value or a more specific emotion category distinction. Here, we demonstrate the utility of surprised facial expressions because exemplars within this emotion category take on valence values spanning the dimension of bipolar valence (positive to negative) at a consistent level of emotional arousal. Functional neuroimaging data showed that amygdala responses tracked the valence of surprised facial expressions, unconfounded by arousal. Furthermore, a machine learning classifier identified

  7. A Clustering-Oriented Closeness Measure Based on Neighborhood Chain and Its Application in the Clustering Ensemble Framework Based on the Fusion of Different Closeness Measures

    Directory of Open Access Journals (Sweden)

    Shaoyi Liang

    2017-09-01

    Full Text Available Closeness measures are crucial to clustering methods. In most traditional clustering methods, the closeness between data points or clusters is measured by the geometric distance alone. These metrics quantify the closeness only based on the concerned data points’ positions in the feature space, and they might cause problems when dealing with clustering tasks having arbitrary clusters shapes and different clusters densities. In this paper, we first propose a novel Closeness Measure between data points based on the Neighborhood Chain (CMNC. Instead of using geometric distances alone, CMNC measures the closeness between data points by quantifying the difficulty for one data point to reach another through a chain of neighbors. Furthermore, based on CMNC, we also propose a clustering ensemble framework that combines CMNC and geometric-distance-based closeness measures together in order to utilize both of their advantages. In this framework, the “bad data points” that are hard to cluster correctly are identified; then different closeness measures are applied to different types of data points to get the unified clustering results. With the fusion of different closeness measures, the framework can get not only better clustering results in complicated clustering tasks, but also higher efficiency.

  8. Performance Based Clustering for Benchmarking of Container Ports: an Application of Dea and Cluster Analysis Technique

    Directory of Open Access Journals (Sweden)

    Jie Wu

    2010-12-01

    Full Text Available The operational performance of container ports has received more and more attentions in both academic and practitioner circles, the performance evaluation and process improvement of container ports have also been the focus of several studies. In this paper, Data Envelopment Analysis (DEA, an effective tool for relative efficiency assessment, is utilized for measuring the performances and benchmarking of the 77 world container ports in 2007. The used approaches in the current study consider four inputs (Capacity of Cargo Handling Machines, Number of Berths, Terminal Area and Storage Capacity and a single output (Container Throughput. The results for the efficiency scores are analyzed, and a unique ordering of the ports based on average cross efficiency is provided, also cluster analysis technique is used to select the more appropriate targets for poorly performing ports to use as benchmarks.

  9. Fast Gene Ontology based clustering for microarray experiments

    Directory of Open Access Journals (Sweden)

    Ovaska Kristian

    2008-11-01

    Full Text Available Abstract Background Analysis of a microarray experiment often results in a list of hundreds of disease-associated genes. In order to suggest common biological processes and functions for these genes, Gene Ontology annotations with statistical testing are widely used. However, these analyses can produce a very large number of significantly altered biological processes. Thus, it is often challenging to interpret GO results and identify novel testable biological hypotheses. Results We present fast software for advanced gene annotation using semantic similarity for Gene Ontology terms combined with clustering and heat map visualisation. The methodology allows rapid identification of genes sharing the same Gene Ontology cluster. Conclusion Our R based semantic similarity open-source package has a speed advantage of over 2000-fold compared to existing implementations. From the resulting hierarchical clustering dendrogram genes sharing a GO term can be identified, and their differences in the gene expression patterns can be seen from the heat map. These methods facilitate advanced annotation of genes resulting from data analysis.

  10. Personalized PageRank Clustering: A graph clustering algorithm based on random walks

    Science.gov (United States)

    A. Tabrizi, Shayan; Shakery, Azadeh; Asadpour, Masoud; Abbasi, Maziar; Tavallaie, Mohammad Ali

    2013-11-01

    Graph clustering has been an essential part in many methods and thus its accuracy has a significant effect on many applications. In addition, exponential growth of real-world graphs such as social networks, biological networks and electrical circuits demands clustering algorithms with nearly-linear time and space complexity. In this paper we propose Personalized PageRank Clustering (PPC) that employs the inherent cluster exploratory property of random walks to reveal the clusters of a given graph. We combine random walks and modularity to precisely and efficiently reveal the clusters of a graph. PPC is a top-down algorithm so it can reveal inherent clusters of a graph more accurately than other nearly-linear approaches that are mainly bottom-up. It also gives a hierarchy of clusters that is useful in many applications. PPC has a linear time and space complexity and has been superior to most of the available clustering algorithms on many datasets. Furthermore, its top-down approach makes it a flexible solution for clustering problems with different requirements.

  11. Dissociable roles of internal feelings and face recognition ability in facial expression decoding.

    Science.gov (United States)

    Zhang, Lin; Song, Yiying; Liu, Ling; Liu, Jia

    2016-05-15

    The problem of emotion recognition has been tackled by researchers in both affective computing and cognitive neuroscience. While affective computing relies on analyzing visual features from facial expressions, it has been proposed that humans recognize emotions by internally simulating the emotional states conveyed by others' expressions, in addition to perceptual analysis of facial features. Here we investigated whether and how our internal feelings contributed to the ability to decode facial expressions. In two independent large samples of participants, we observed that individuals who generally experienced richer internal feelings exhibited a higher ability to decode facial expressions, and the contribution of internal feelings was independent of face recognition ability. Further, using voxel-based morphometry, we found that the gray matter volume (GMV) of bilateral superior temporal sulcus (STS) and the right inferior parietal lobule was associated with facial expression decoding through the mediating effect of internal feelings, while the GMV of bilateral STS, precuneus, and the right central opercular cortex contributed to facial expression decoding through the mediating effect of face recognition ability. In addition, the clusters in bilateral STS involved in the two components were neighboring yet separate. Our results may provide clues about the mechanism by which internal feelings, in addition to face recognition ability, serve as an important instrument for humans in facial expression decoding. Copyright © 2016 Elsevier Inc. All rights reserved.

  12. Automatic Human Facial Expression Recognition Based on Integrated Classifier From Monocular Video with Uncalibrated Camera

    Directory of Open Access Journals (Sweden)

    Yu Tao

    2017-01-01

    Full Text Available An automatic recognition framework for human facial expressions from a monocular video with an uncalibrated camera is proposed. The expression characteristics are first acquired from a kind of deformable template, similar to a facial muscle distribution. After associated regularization, the time sequences from the trait changes in space-time under complete expressional production are then arranged line by line in a matrix. Next, the matrix dimensionality is reduced by a method of manifold learning of neighborhood-preserving embedding. Finally, the refined matrix containing the expression trait information is recognized by a classifier that integrates the hidden conditional random field (HCRF and support vector machine (SVM. In an experiment using the Cohn–Kanade database, the proposed method showed a comparatively higher recognition rate than the individual HCRF or SVM methods in direct recognition from two-dimensional human face traits. Moreover, the proposed method was shown to be more robust than the typical Kotsia method because the former contains more structural characteristics of the data to be classified in space-time

  13. An Improved Surface Simplification Method for Facial Expression Animation Based on Homogeneous Coordinate Transformation Matrix and Maximum Shape Operator

    Directory of Open Access Journals (Sweden)

    Juin-Ling Tseng

    2016-01-01

    Full Text Available Facial animation is one of the most popular 3D animation topics researched in recent years. However, when using facial animation, a 3D facial animation model has to be stored. This 3D facial animation model requires many triangles to accurately describe and demonstrate facial expression animation because the face often presents a number of different expressions. Consequently, the costs associated with facial animation have increased rapidly. In an effort to reduce storage costs, researchers have sought to simplify 3D animation models using techniques such as Deformation Sensitive Decimation and Feature Edge Quadric. The studies conducted have examined the problems in the homogeneity of the local coordinate system between different expression models and in the retainment of simplified model characteristics. This paper proposes a method that applies Homogeneous Coordinate Transformation Matrix to solve the problem of homogeneity of the local coordinate system and Maximum Shape Operator to detect shape changes in facial animation so as to properly preserve the features of facial expressions. Further, root mean square error and perceived quality error are used to compare the errors generated by different simplification methods in experiments. Experimental results show that, compared with Deformation Sensitive Decimation and Feature Edge Quadric, our method can not only reduce the errors caused by simplification of facial animation, but also retain more facial features.

  14. Centroid based clustering of high throughput sequencing reads based on n-mer counts.

    Science.gov (United States)

    Solovyov, Alexander; Lipkin, W Ian

    2013-09-08

    Many problems in computational biology require alignment-free sequence comparisons. One of the common tasks involving sequence comparison is sequence clustering. Here we apply methods of alignment-free comparison (in particular, comparison using sequence composition) to the challenge of sequence clustering. We study several centroid based algorithms for clustering sequences based on word counts. Study of their performance shows that using k-means algorithm with or without the data whitening is efficient from the computational point of view. A higher clustering accuracy can be achieved using the soft expectation maximization method, whereby each sequence is attributed to each cluster with a specific probability. We implement an open source tool for alignment-free clustering. It is publicly available from github: https://github.com/luscinius/afcluster. We show the utility of alignment-free sequence clustering for high throughput sequencing analysis despite its limitations. In particular, it allows one to perform assembly with reduced resources and a minimal loss of quality. The major factor affecting performance of alignment-free read clustering is the length of the read.

  15. Dependence of the appearance-based perception of criminality, suggestibility, and trustworthiness on the level of pixelation of facial images.

    Science.gov (United States)

    Nurmoja, Merle; Eamets, Triin; Härma, Hanne-Loore; Bachmann, Talis

    2012-10-01

    While the dependence of face identification on the level of pixelation-transform of the images of faces has been well studied, similar research on face-based trait perception is underdeveloped. Because depiction formats used for hiding individual identity in visual media and evidential material recorded by surveillance cameras often consist of pixelized images, knowing the effects of pixelation on person perception has practical relevance. Here, the results of two experiments are presented showing the effect of facial image pixelation on the perception of criminality, trustworthiness, and suggestibility. It appears that individuals (N = 46, M age = 21.5 yr., SD = 3.1 for criminality ratings; N = 94, M age = 27.4 yr., SD = 10.1 for other ratings) have the ability to discriminate between facial cues ndicative of these perceived traits from the coarse level of image pixelation (10-12 pixels per face horizontally) and that the discriminability increases with a decrease in the coarseness of pixelation. Perceived criminality and trustworthiness appear to be better carried by the pixelized images than perceived suggestibility.

  16. [Facial injections of hyaluronic acid-based fillers for malformations. Preliminary study regarding scar tissue improvement and cosmetic betterment].

    Science.gov (United States)

    Franchi, G; Neiva-Vaz, C; Picard, A; Vazquez, M-P

    2018-02-02

    Cross-linked hyaluronic acid-based fillers have gained rapid acceptance for treating facial wrinkles, deep tissue folds and sunken areas due to aging. This study evaluates, in addition to space-filling properties, their effects on softness and elasticity as a secondary effect, following injection of 3 commercially available cross-linked hyaluronic acid-based fillers (15mg/mL, 17,5mg/mL and 20mg/mL) in patients presenting with congenital or acquired facial malformations. We started injecting gels of cross-linked hyaluronic acid-based fillers in those cases in 2013; we performed 46 sessions of injections in 32 patients, aged from 13-32. Clinical assessment was performed by the patient himself and by a plastic surgeon, 15 days after injections and 6-18 months later. Cross-linked hyaluronic acid-based fillers offered very subtle cosmetic results and supplemented surgery with a very high level of satisfaction of the patients. When injected in fibrosis, the first session enhanced softness and elasticity; the second session enhanced the volume. Cross-linked hyaluronic acid-based fillers fill sunken areas and better softness and elasticity of scar tissues. In addition to their well-understood space-filling function, as a secondary effect, the authors demonstrate that cross-linked hyaluronic acid-based fillers improve softness and elasticity of scarring tissues. Many experimental studies support our observations, showing that cross-linked hyaluronic acid stimulates the production of several extra-cellular matrix components, including dermal collagen and elastin. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

  17. A first packet processing subdomain cluster model based on SDN

    Science.gov (United States)

    Chen, Mingyong; Wu, Weimin

    2017-08-01

    For the current controller cluster packet processing performance bottlenecks and controller downtime problems. An SDN controller is proposed to allocate the priority of each device in the SDN (Software Defined Network) network, and the domain contains several network devices and Controller, the controller is responsible for managing the network equipment within the domain, the switch performs data delivery based on the load of the controller, processing network equipment data. The experimental results show that the model can effectively solve the risk of single point failure of the controller, and can solve the performance bottleneck of the first packet processing.

  18. Clustering-based analysis for residential district heating data

    DEFF Research Database (Denmark)

    Gianniou, Panagiota; Liu, Xiufeng; Heller, Alfred

    2018-01-01

    The wide use of smart meters enables collection of a large amount of fine-granular time series, which can be used to improve the understanding of consumption behavior and used for consumption optimization. This paper presents a clustering-based knowledge discovery in databases method to analyze r....... These findings will be valuable for district heating utilities and energy planners to optimize their operations, design demand-side management strategies, and develop targeting energy-efficiency programs or policies.......The wide use of smart meters enables collection of a large amount of fine-granular time series, which can be used to improve the understanding of consumption behavior and used for consumption optimization. This paper presents a clustering-based knowledge discovery in databases method to analyze...... residential heating consumption data and evaluate information included in national building databases. The proposed method uses the K-means algorithm to segment consumption groups based on consumption intensity and representative patterns and ranks the groups according to daily consumption. This paper also...

  19. Clustering of 18 Local Black Rice Base on Total Anthocyanin

    Directory of Open Access Journals (Sweden)

    Kristamtini Kristamtini

    2017-10-01

    Full Text Available Black rice has a high anthocyanin content in the pericarp layer, which provides a dark purple color. Anthocyanin serve as an antioxidant that control cholesterol level in the blood, prevent anemia, potentially improve the body's resistance to disease, improve damage to liver cells (hepatitis and chirrosis, prevent impaired kidney function, prevent cancer/tumors, slows down antiaging, and prevent atherosclerosis and cardiovascular disease. Exploration results at AIAT Yogyakarta, Indonesia from 2011 to 2014 obtained 18 cultivar of local black rice Indonesia. The names of the rice are related to the color (black, red or purple formed by anthocyanin deposits in the pericarp layer, seed coat or aleuron. The objective of the study was to classify several types of local black rice from explorations based on the total anthocyanin content. The study was conducted by clustering analyzing the total anthocyanin content of 18 local black rice cultivars in Indonesia. Cluster analysis of total anthocyanin content were done using SAS ver. 9.2. Clustering dendogram shows that there were 4 groups of black rice cultivars based on the total anthocyanin content. Group I consists of Melik black rice, Patalan black rice, Yunianto black rice, Muharjo black rice, Ngatijo black rice, short life of Tugiyo black rice, Andel hitam 1, Jlitheng, and Sragen black rice. Group II consists of Pari ireng, Magelang black hairy rice, Banjarnegara-Wonosobo black rice, and Banjarnegara black rice. Group III consists of NTT black rice, Magelang non hairy black rice, Sembada hitam, and longevity Tugiyo black rice. Group IV consist only one type of black rice namely Cempo ireng. The grouping result indicate the existence of duplicate names among the black rice namely Patalan with Yunianto black rice, and short life Tugiyo with Andel hitam 1 black rice.

  20. Facial Action Unit Recognition under Incomplete Data Based on Multi-label Learning with Missing Labels

    KAUST Repository

    Li, Yongqiang

    2016-07-07

    Facial action unit (AU) recognition has been applied in a wild range of fields, and has attracted great attention in the past two decades. Most existing works on AU recognition assumed that the complete label assignment for each training image is available, which is often not the case in practice. Labeling AU is expensive and time consuming process. Moreover, due to the AU ambiguity and subjective difference, some AUs are difficult to label reliably and confidently. Many AU recognition works try to train the classifier for each AU independently, which is of high computation cost and ignores the dependency among different AUs. In this work, we formulate AU recognition under incomplete data as a multi-label learning with missing labels (MLML) problem. Most existing MLML methods usually employ the same features for all classes. However, we find this setting is unreasonable in AU recognition, as the occurrence of different AUs produce changes of skin surface displacement or face appearance in different face regions. If using the shared features for all AUs, much noise will be involved due to the occurrence of other AUs. Consequently, the changes of the specific AUs cannot be clearly highlighted, leading to the performance degradation. Instead, we propose to extract the most discriminative features for each AU individually, which are learned by the supervised learning method. The learned features are further embedded into the instance-level label smoothness term of our model, which also includes the label consistency and the class-level label smoothness. Both a global solution using st-cut and an approximated solution using conjugate gradient (CG) descent are provided. Experiments on both posed and spontaneous facial expression databases demonstrate the superiority of the proposed method in comparison with several state-of-the-art works.

  1. Facial Action Unit Recognition under Incomplete Data Based on Multi-label Learning with Missing Labels

    KAUST Repository

    Li, Yongqiang; Wu, Baoyuan; Ghanem, Bernard; Zhao, Yongping; Yao, Hongxun; Ji, Qiang

    2016-01-01

    Facial action unit (AU) recognition has been applied in a wild range of fields, and has attracted great attention in the past two decades. Most existing works on AU recognition assumed that the complete label assignment for each training image is available, which is often not the case in practice. Labeling AU is expensive and time consuming process. Moreover, due to the AU ambiguity and subjective difference, some AUs are difficult to label reliably and confidently. Many AU recognition works try to train the classifier for each AU independently, which is of high computation cost and ignores the dependency among different AUs. In this work, we formulate AU recognition under incomplete data as a multi-label learning with missing labels (MLML) problem. Most existing MLML methods usually employ the same features for all classes. However, we find this setting is unreasonable in AU recognition, as the occurrence of different AUs produce changes of skin surface displacement or face appearance in different face regions. If using the shared features for all AUs, much noise will be involved due to the occurrence of other AUs. Consequently, the changes of the specific AUs cannot be clearly highlighted, leading to the performance degradation. Instead, we propose to extract the most discriminative features for each AU individually, which are learned by the supervised learning method. The learned features are further embedded into the instance-level label smoothness term of our model, which also includes the label consistency and the class-level label smoothness. Both a global solution using st-cut and an approximated solution using conjugate gradient (CG) descent are provided. Experiments on both posed and spontaneous facial expression databases demonstrate the superiority of the proposed method in comparison with several state-of-the-art works.

  2. Novel density-based and hierarchical density-based clustering algorithms for uncertain data.

    Science.gov (United States)

    Zhang, Xianchao; Liu, Han; Zhang, Xiaotong

    2017-09-01

    Uncertain data has posed a great challenge to traditional clustering algorithms. Recently, several algorithms have been proposed for clustering uncertain data, and among them density-based techniques seem promising for handling data uncertainty. However, some issues like losing uncertain information, high time complexity and nonadaptive threshold have not been addressed well in the previous density-based algorithm FDBSCAN and hierarchical density-based algorithm FOPTICS. In this paper, we firstly propose a novel density-based algorithm PDBSCAN, which improves the previous FDBSCAN from the following aspects: (1) it employs a more accurate method to compute the probability that the distance between two uncertain objects is less than or equal to a boundary value, instead of the sampling-based method in FDBSCAN; (2) it introduces new definitions of probability neighborhood, support degree, core object probability, direct reachability probability, thus reducing the complexity and solving the issue of nonadaptive threshold (for core object judgement) in FDBSCAN. Then, we modify the algorithm PDBSCAN to an improved version (PDBSCANi), by using a better cluster assignment strategy to ensure that every object will be assigned to the most appropriate cluster, thus solving the issue of nonadaptive threshold (for direct density reachability judgement) in FDBSCAN. Furthermore, as PDBSCAN and PDBSCANi have difficulties for clustering uncertain data with non-uniform cluster density, we propose a novel hierarchical density-based algorithm POPTICS by extending the definitions of PDBSCAN, adding new definitions of fuzzy core distance and fuzzy reachability distance, and employing a new clustering framework. POPTICS can reveal the cluster structures of the datasets with different local densities in different regions better than PDBSCAN and PDBSCANi, and it addresses the issues in FOPTICS. Experimental results demonstrate the superiority of our proposed algorithms over the existing

  3. Reliability analysis of cluster-based ad-hoc networks

    International Nuclear Information System (INIS)

    Cook, Jason L.; Ramirez-Marquez, Jose Emmanuel

    2008-01-01

    The mobile ad-hoc wireless network (MAWN) is a new and emerging network scheme that is being employed in a variety of applications. The MAWN varies from traditional networks because it is a self-forming and dynamic network. The MAWN is free of infrastructure and, as such, only the mobile nodes comprise the network. Pairs of nodes communicate either directly or through other nodes. To do so, each node acts, in turn, as a source, destination, and relay of messages. The virtue of a MAWN is the flexibility this provides; however, the challenge for reliability analyses is also brought about by this unique feature. The variability and volatility of the MAWN configuration makes typical reliability methods (e.g. reliability block diagram) inappropriate because no single structure or configuration represents all manifestations of a MAWN. For this reason, new methods are being developed to analyze the reliability of this new networking technology. New published methods adapt to this feature by treating the configuration probabilistically or by inclusion of embedded mobility models. This paper joins both methods together and expands upon these works by modifying the problem formulation to address the reliability analysis of a cluster-based MAWN. The cluster-based MAWN is deployed in applications with constraints on networking resources such as bandwidth and energy. This paper presents the problem's formulation, a discussion of applicable reliability metrics for the MAWN, and illustration of a Monte Carlo simulation method through the analysis of several example networks

  4. MODEL-BASED CLUSTERING FOR CLASSIFICATION OF AQUATIC SYSTEMS AND DIAGNOSIS OF ECOLOGICAL STRESS

    Science.gov (United States)

    Clustering approaches were developed using the classification likelihood, the mixture likelihood, and also using a randomization approach with a model index. Using a clustering approach based on the mixture and classification likelihoods, we have developed an algorithm that...

  5. Facial EMG responses to dynamic emotional facial expressions in boys with disruptive behavior disorders

    NARCIS (Netherlands)

    Wied, de M.; Boxtel, van Anton; Zaalberg, R.; Goudena, P.P.; Matthys, W.

    2006-01-01

    Based on the assumption that facial mimicry is a key factor in emotional empathy, and clinical observations that children with disruptive behavior disorders (DBD) are weak empathizers, the present study explored whether DBD boys are less facially responsive to facial expressions of emotions than

  6. Coherence-based Time Series Clustering for Brain Connectivity Visualization

    KAUST Repository

    Euan, Carolina

    2017-11-19

    We develop the hierarchical cluster coherence (HCC) method for brain signals, a procedure for characterizing connectivity in a network by clustering nodes or groups of channels that display high level of coordination as measured by

  7. Cluster-based centralized data fusion for tracking maneuvering ...

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    In this scheme, measurements are sent to the data fusion centre where the mea- ... using 'clusters' (a cluster by definition is a type of parallel or distributed processing ... working together as a single, integrated computing resource) is proposed.

  8. Coherence-based Time Series Clustering for Brain Connectivity Visualization

    KAUST Repository

    Euan, Carolina; Sun, Ying; Ombao, Hernando

    2017-01-01

    We develop the hierarchical cluster coherence (HCC) method for brain signals, a procedure for characterizing connectivity in a network by clustering nodes or groups of channels that display high level of coordination as measured by

  9. Inpatient treatment of patients with acute idiopathic peripheral facial palsy: A population-based healthcare research study.

    Science.gov (United States)

    Plumbaum, K; Volk, G F; Boeger, D; Buentzel, J; Esser, D; Steinbrecher, A; Hoffmann, K; Jecker, P; Mueller, A; Radtke, G; Witte, O W; Guntinas-Lichius, O

    2017-12-01

    To determine the inpatient management for patients with acute idiopathic facial palsy (IFP) in Thuringia, Germany. Population-based study. All inpatients with IFP in all hospitals with departments of otolaryngology and neurology in 2012, in the German federal state, Thuringia. Patients' characteristics and treatment were compared between departments, and the probability of recovery was tested. A total of 291 patients were mainly treated in departments of otolaryngology (55%) and neurology (36%). Corticosteroid treatment was the predominant therapy (84.5%). The probability to receive a facial nerve grading (odds ratio [OR=12.939; 95% confidence interval [CI]=3.599 to 46.516), gustatory testing (OR=6.878; CI=1.064 to 44.474) and audiometry (OR=32.505; CI=1.485 to 711.257) was significantly higher in otolaryngology departments, but lower for cranial CT (OR=0.192; CI=0.061 to 0.602), cerebrospinal fluid examination (OR=0.024; CI=0.006 to 0.102). A total of 131 patients (45%) showed a recovery to House-Brackmann grade≤II. A pathological stapedial reflex test (Hazard ratio [HR]=0.416; CI=0.180 to 0.959) was the only independent diagnostic predictor of worse outcome. Prednisolone dose >500 mg (HR=0.579; CI 0.400 to 0.838) and no adjuvant physiotherapy (HR=0.568; CI=0.407 to 0.794) were treatment-related predictors of worse outcome. Inpatient treatment of IFP seems to be highly variable in daily practice, partly depending on the treating discipline and despite the availability of evidence-based guidelines. The population-based recovery rate was worse than reported in clinical trials. © 2017 John Wiley & Sons Ltd.

  10. Bootstrap-Based Improvements for Inference with Clustered Errors

    OpenAIRE

    Doug Miller; A. Colin Cameron; Jonah B. Gelbach

    2006-01-01

    Microeconometrics researchers have increasingly realized the essential need to account for any within-group dependence in estimating standard errors of regression parameter estimates. The typical preferred solution is to calculate cluster-robust or sandwich standard errors that permit quite general heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. In applications with few (5-30) clusters, standard asymptotic tests can over-reject consid...

  11. Customer Clustering Based on Customer Purchasing Sequence Data

    OpenAIRE

    Yen-Chung Liu; Yen-Liang Chen

    2017-01-01

    Customer clustering has become a priority for enterprises because of the importance of customer relationship management. Customer clustering can improve understanding of the composition and characteristics of customers, thereby enabling the creation of appropriate marketing strategies for each customer group. Previously, different customer clustering approaches have been proposed according to data type, namely customer profile data, customer value data, customer transaction data, and customer...

  12. Genetic algorithm based two-mode clustering of metabolomics data

    NARCIS (Netherlands)

    Hageman, J.A.; van den Berg, R.A.; Westerhuis, J.A.; van der Werf, M.J.; Smilde, A.K.

    2008-01-01

    Metabolomics and other omics tools are generally characterized by large data sets with many variables obtained under different environmental conditions. Clustering methods and more specifically two-mode clustering methods are excellent tools for analyzing this type of data. Two-mode clustering

  13. Galaxy clusters in the SDSS Stripe 82 based on photometric redshifts

    International Nuclear Information System (INIS)

    Durret, F.; Adami, C.; Bertin, E.; Hao, J.; Márquez, I.

    2015-01-01

    Based on a recent photometric redshift galaxy catalogue, we have searched for galaxy clusters in the Stripe ~82 region of the Sloan Digital Sky Survey by applying the Adami & MAzure Cluster FInder (AMACFI). Extensive tests were made to fine-tune the AMACFI parameters and make the cluster detection as reliable as possible. The same method was applied to the Millennium simulation to estimate our detection efficiency and the approximate masses of the detected clusters. Considering all the cluster galaxies (i.e. within a 1 Mpc radius of the cluster to which they belong and with a photoz differing by less than 0.05 from that of the cluster), we stacked clusters in various redshift bins to derive colour-magnitude diagrams and galaxy luminosity functions (GLFs). For each galaxy with absolute magnitude brighter than -19.0 in the r band, we computed the disk and spheroid components by applying SExtractor, and by stacking clusters we determined how the disk-to-spheroid flux ratio varies with cluster redshift and mass. We also detected 3663 clusters in the redshift range 0.15< z<0.70, with estimated mean masses between 10"1"3 and a few 10"1"4 solar masses. Furthermore, by stacking the cluster galaxies in various redshift bins, we find a clear red sequence in the (g'-r') versus r' colour-magnitude diagrams, and the GLFs are typical of clusters, though with a possible contamination from field galaxies. The morphological analysis of the cluster galaxies shows that the fraction of late-type to early-type galaxies shows an increase with redshift (particularly in high mass clusters) and a decrease with detection level, i.e. cluster mass. From the properties of the cluster galaxies, the majority of the candidate clusters detected here seem to be real clusters with typical cluster properties.

  14. Facial Displays Are Tools for Social Influence.

    Science.gov (United States)

    Crivelli, Carlos; Fridlund, Alan J

    2018-05-01

    Based on modern theories of signal evolution and animal communication, the behavioral ecology view of facial displays (BECV) reconceives our 'facial expressions of emotion' as social tools that serve as lead signs to contingent action in social negotiation. BECV offers an externalist, functionalist view of facial displays that is not bound to Western conceptions about either expressions or emotions. It easily accommodates recent findings of diversity in facial displays, their public context-dependency, and the curious but common occurrence of solitary facial behavior. Finally, BECV restores continuity of human facial behavior research with modern functional accounts of non-human communication, and provides a non-mentalistic account of facial displays well-suited to new developments in artificial intelligence and social robotics. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

  15. A Secure Cluster-Based Multipath Routing Protocol for WMSNs

    Directory of Open Access Journals (Sweden)

    Jamal N. Al-Karaki

    2011-04-01

    Full Text Available The new characteristics of Wireless Multimedia Sensor Network (WMSN and its design issues brought by handling different traffic classes of multimedia content (video streams, audio, and still images as well as scalar data over the network, make the proposed routing protocols for typical WSNs not directly applicable for WMSNs. Handling real-time multimedia data requires both energy efficiency and QoS assurance in order to ensure efficient utility of different capabilities of sensor resources and correct delivery of collected information. In this paper, we propose a Secure Cluster-based Multipath Routing protocol for WMSNs, SCMR, to satisfy the requirements of delivering different data types and support high data rate multimedia traffic. SCMR exploits the hierarchical structure of powerful cluster heads and the optimized multiple paths to support timeliness and reliable high data rate multimedia communication with minimum energy dissipation. Also, we present a light-weight distributed security mechanism of key management in order to secure the communication between sensor nodes and protect the network against different types of attacks. Performance evaluation from simulation results demonstrates a significant performance improvement comparing with existing protocols (which do not even provide any kind of security feature in terms of average end-to-end delay, network throughput, packet delivery ratio, and energy consumption.

  16. Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method

    Directory of Open Access Journals (Sweden)

    Yimei Wang

    2018-04-01

    Full Text Available To meet the increasing wind power forecasting (WPF demands of newly built wind farms without historical data, physical WPF methods are widely used. The computational fluid dynamics (CFD pre-calculated flow fields (CPFF-based WPF is a promising physical approach, which can balance well the competing demands of computational efficiency and accuracy. To enhance its adaptability for wind farms in complex terrain, a WPF method combining wind turbine clustering with CPFF is first proposed where the wind turbines in the wind farm are clustered and a forecasting is undertaken for each cluster. K-means, hierarchical agglomerative and spectral analysis methods are used to establish the wind turbine clustering models. The Silhouette Coefficient, Calinski-Harabaz index and within-between index are proposed as criteria to evaluate the effectiveness of the established clustering models. Based on different clustering methods and schemes, various clustering databases are built for clustering pre-calculated CFD (CPCC-based short-term WPF. For the wind farm case studied, clustering evaluation criteria show that hierarchical agglomerative clustering has reasonable results, spectral clustering is better and K-means gives the best performance. The WPF results produced by different clustering databases also prove the effectiveness of the three evaluation criteria in turn. The newly developed CPCC model has a much higher WPF accuracy than the CPFF model without using clustering techniques, both on temporal and spatial scales. The research provides supports for both the development and improvement of short-term physical WPF systems.

  17. The relationship between supplier networks and industrial clusters: an analysis based on the cluster mapping method

    Directory of Open Access Journals (Sweden)

    Ichiro IWASAKI

    2010-06-01

    Full Text Available Michael Porter’s concept of competitive advantages emphasizes the importance of regional cooperation of various actors in order to gain competitiveness on globalized markets. Foreign investors may play an important role in forming such cooperation networks. Their local suppliers tend to concentrate regionally. They can form, together with local institutions of education, research, financial and other services, development agencies, the nucleus of cooperative clusters. This paper deals with the relationship between supplier networks and clusters. Two main issues are discussed in more detail: the interest of multinational companies in entering regional clusters and the spillover effects that may stem from their participation. After the discussion on the theoretical background, the paper introduces a relatively new analytical method: “cluster mapping” - a method that can spot regional hot spots of specific economic activities with cluster building potential. Experience with the method was gathered in the US and in the European Union. After the discussion on the existing empirical evidence, the authors introduce their own cluster mapping results, which they obtained by using a refined version of the original methodology.

  18. Sympathicotomy for isolated facial blushing

    DEFF Research Database (Denmark)

    Licht, Peter Bjørn; Pilegaard, Hans K; Ladegaard, Lars

    2012-01-01

    Background. Facial blushing is one of the most peculiar of human expressions. The pathophysiology is unclear, and the prevalence is unknown. Thoracoscopic sympathectomy may cure the symptom and is increasingly used in patients with isolated facial blushing. The evidence base for the optimal level...... of targeting the sympathetic chain is limited to retrospective case studies. We present a randomized clinical trial. Methods. 100 patients were randomized (web-based, single-blinded) to rib-oriented (R2 or R2-R3) sympathicotomy for isolated facial blushing at two university hospitals during a 6-year period...... between R2 and R2-R3 sympathicotomy for isolated facial blushing. Both were effective, and QOL increased significantly. Despite very frequent side effects, the vast majority of patients were satisfied. Surprisingly, many patients experienced mild recurrent symptoms within the first year; this should...

  19. Dynamic Facial Prosthetics for Sufferers of Facial Paralysis

    Directory of Open Access Journals (Sweden)

    Fergal Coulter

    2011-10-01

    Full Text Available BackgroundThis paper discusses the various methods and the materialsfor the fabrication of active artificial facial muscles. Theprimary use for these will be the reanimation of paralysedor atrophied muscles in sufferers of non-recoverableunilateral facial paralysis.MethodThe prosthetic solution described in this paper is based onsensing muscle motion of the contralateral healthy musclesand replicating that motion across a patient’s paralysed sideof the face, via solid state and thin film actuators. Thedevelopment of this facial prosthetic device focused onrecreating a varying intensity smile, with emphasis ontiming, displacement and the appearance of the wrinklesand folds that commonly appear around the nose and eyesduring the expression.An animatronic face was constructed with actuations beingmade to a silicone representation musculature, usingmultiple shape-memory alloy cascades. Alongside theartificial muscle physical prototype, a facial expressionrecognition software system was constructed. This formsthe basis of an automated calibration and reconfigurationsystem for the artificial muscles following implantation, soas to suit the implantee’s unique physiognomy.ResultsAn animatronic model face with silicone musculature wasdesigned and built to evaluate the performance of ShapeMemory Alloy artificial muscles, their power controlcircuitry and software control systems. A dual facial motionsensing system was designed to allow real time control overmodel – a piezoresistive flex sensor to measure physicalmotion, and a computer vision system to evaluate real toartificial muscle performance.Analysis of various facial expressions in real subjects wasmade, which give useful data upon which to base thesystems parameter limits.ConclusionThe system performed well, and the various strengths andshortcomings of the materials and methods are reviewedand considered for the next research phase, when newpolymer based artificial muscles are constructed

  20. PBL - Problem Based Learning for Companies and Clusters

    Energy Technology Data Exchange (ETDEWEB)

    Hamburg, I; Vladut, G.

    2016-07-01

    Small and medium sized companies (SMEs) assure economic growth in Europe. Generally many SMEs are struggling to survive in an ongoing global recession and often they are becoming reluctant to release or pay for staff training. In this paper we present shortly the learning methods in SMEs particularly the Problem Based Learning (PBL) as an efficient form for SMEs and entrepreneurship education. In the field of Urban Logistics it was developed four Clusters with potential of innovation and research in four European Regions: Tuscany - Italy, Valencia - Spain, Lisbon and Tagus - Portugal, Oltenia – Romania. Training and mentoring for SMEs, are essential to create competitiveness. Information and communication technologies (ICT) support the tutors by using an ICT platform which is in the development. (Author)

  1. Neuro-fuzzy system modeling based on automatic fuzzy clustering

    Institute of Scientific and Technical Information of China (English)

    Yuangang TANG; Fuchun SUN; Zengqi SUN

    2005-01-01

    A neuro-fuzzy system model based on automatic fuzzy clustering is proposed.A hybrid model identification algorithm is also developed to decide the model structure and model parameters.The algorithm mainly includes three parts:1) Automatic fuzzy C-means (AFCM),which is applied to generate fuzzy rules automatically,and then fix on the size of the neuro-fuzzy network,by which the complexity of system design is reducesd greatly at the price of the fitting capability;2) Recursive least square estimation (RLSE).It is used to update the parameters of Takagi-Sugeno model,which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network.Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.

  2. A Geometric Fuzzy-Based Approach for Airport Clustering

    Directory of Open Access Journals (Sweden)

    Maria Nadia Postorino

    2014-01-01

    Full Text Available Airport classification is a common need in the air transport field due to several purposes—such as resource allocation, identification of crucial nodes, and real-time identification of substitute nodes—which also depend on the involved actors’ expectations. In this paper a fuzzy-based procedure has been proposed to cluster airports by using a fuzzy geometric point of view according to the concept of unit-hypercube. By representing each airport as a point in the given reference metric space, the geometric distance among airports—which corresponds to a measure of similarity—has in fact an intrinsic fuzzy nature due to the airport specific characteristics. The proposed procedure has been applied to a test case concerning the Italian airport network and the obtained results are in line with expectations.

  3. Community Clustering Algorithm in Complex Networks Based on Microcommunity Fusion

    Directory of Open Access Journals (Sweden)

    Jin Qi

    2015-01-01

    Full Text Available With the further research on physical meaning and digital features of the community structure in complex networks in recent years, the improvement of effectiveness and efficiency of the community mining algorithms in complex networks has become an important subject in this area. This paper puts forward a concept of the microcommunity and gets final mining results of communities through fusing different microcommunities. This paper starts with the basic definition of the network community and applies Expansion to the microcommunity clustering which provides prerequisites for the microcommunity fusion. The proposed algorithm is more efficient and has higher solution quality compared with other similar algorithms through the analysis of test results based on network data set.

  4. Operational Numerical Weather Prediction systems based on Linux cluster architectures

    International Nuclear Information System (INIS)

    Pasqui, M.; Baldi, M.; Gozzini, B.; Maracchi, G.; Giuliani, G.; Montagnani, S.

    2005-01-01

    The progress in weather forecast and atmospheric science has been always closely linked to the improvement of computing technology. In order to have more accurate weather forecasts and climate predictions, more powerful computing resources are needed, in addition to more complex and better-performing numerical models. To overcome such a large computing request, powerful workstations or massive parallel systems have been used. In the last few years, parallel architectures, based on the Linux operating system, have been introduced and became popular, representing real high performance-low cost systems. In this work the Linux cluster experience achieved at the Laboratory far Meteorology and Environmental Analysis (LaMMA-CNR-IBIMET) is described and tips and performances analysed

  5. Cost/Performance Ratio Achieved by Using a Commodity-Based Cluster

    Science.gov (United States)

    Lopez, Isaac

    2001-01-01

    Researchers at the NASA Glenn Research Center acquired a commodity cluster based on Intel Corporation processors to compare its performance with a traditional UNIX cluster in the execution of aeropropulsion applications. Since the cost differential of the clusters was significant, a cost/performance ratio was calculated. After executing a propulsion application on both clusters, the researchers demonstrated a 9.4 cost/performance ratio in favor of the Intel-based cluster. These researchers utilize the Aeroshark cluster as one of the primary testbeds for developing NPSS parallel application codes and system software. The Aero-shark cluster provides 64 Intel Pentium II 400-MHz processors, housed in 32 nodes. Recently, APNASA - a code developed by a Government/industry team for the design and analysis of turbomachinery systems was used for a simulation on Glenn's Aeroshark cluster.

  6. Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses.

    Science.gov (United States)

    Kumpf, Alexander; Tost, Bianca; Baumgart, Marlene; Riemer, Michael; Westermann, Rudiger; Rautenhaus, Marc

    2018-01-01

    In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we - a team of visualization scientists and meteorologists-deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of "Tropical Cyclone Karl", guiding the user towards the cluster robustness information required for subsequent ensemble analysis.

  7. MRI-based diagnostic imaging of the intratemporal facial nerve; Die kernspintomographische Darstellung des intratemporalen N. facialis

    Energy Technology Data Exchange (ETDEWEB)

    Kress, B.; Baehren, W. [Bundeswehrkrankenhaus Ulm (Germany). Abt. fuer Radiologie

    2001-07-01

    Detailed imaging of the five sections of the full intratemporal course of the facial nerve can be achieved by MRI and using thin tomographic section techniques and surface coils. Contrast media are required for tomographic imaging of pathological processes. Established methods are available for diagnostic evaluation of cerebellopontine angle tumors and chronic Bell's palsy, as well as hemifacial spasms. A method still under discussion is MRI for diagnostic evaluation of Bell's palsy in the presence of fractures of the petrous bone, when blood volumes in the petrous bone make evaluation even more difficult. MRI-based diagnostic evaluation of the idiopatic facial paralysis currently is subject to change. Its usual application cannot be recommended for routine evaluation at present. However, a quantitative analysis of contrast medium uptake of the nerve may be an approach to improve the prognostic value of MRI in acute phases of Bell's palsy. (orig./CB) [German] Die detaillierte kernspintomographische Darstellung des aus 5 Abschnitten bestehenden intratemporalen Verlaufes des N. facialis gelingt mit der MRI unter Einsatz von Duennschichttechniken und Oberflaechenspulen. Zur Darstellung von pathologischen Vorgaengen ist die Gabe von Kontrastmittel notwendig. Die Untersuchung in der Diagnostik von Kleinhirnbrueckenwinkeltumoren und der chronischen Facialisparese ist etabliert, ebenso wie die Diagnostik des Hemispasmus facialis. In der Diskussion ist die MRI zur Dokumentation der Facialisparese bei Felsenbeinfrakturen, wobei die Einblutungen im Felsenbein die Beurteilung erschweren. Die kernspintomographische Diagnostik der idiopathischen Facialisparese befindet sich im Wandel. In der herkoemmlichen Form wird sie nicht zur Routinediagnostik empfohlen. Die quantitative Analyse der Kontrastmittelaufnahme im Nerv koennte jedoch die prognostische Bedeutung der MRI in der Akutphase der Bell's palsy erhoehen. (orig.)

  8. Canonical PSO Based K-Means Clustering Approach for Real Datasets.

    Science.gov (United States)

    Dey, Lopamudra; Chakraborty, Sanjay

    2014-01-01

    "Clustering" the significance and application of this technique is spread over various fields. Clustering is an unsupervised process in data mining, that is why the proper evaluation of the results and measuring the compactness and separability of the clusters are important issues. The procedure of evaluating the results of a clustering algorithm is known as cluster validity measure. Different types of indexes are used to solve different types of problems and indices selection depends on the kind of available data. This paper first proposes Canonical PSO based K-means clustering algorithm and also analyses some important clustering indices (intercluster, intracluster) and then evaluates the effects of those indices on real-time air pollution database, wholesale customer, wine, and vehicle datasets using typical K-means, Canonical PSO based K-means, simple PSO based K-means, DBSCAN, and Hierarchical clustering algorithms. This paper also describes the nature of the clusters and finally compares the performances of these clustering algorithms according to the validity assessment. It also defines which algorithm will be more desirable among all these algorithms to make proper compact clusters on this particular real life datasets. It actually deals with the behaviour of these clustering algorithms with respect to validation indexes and represents their results of evaluation in terms of mathematical and graphical forms.

  9. Quantative Evaluation of the Efficiency of Facial Bio-potential Signals Based on Forehead Three-Channel Electrode Placement For Facial Gesture Recognition Applicable in a Human-Machine Interface

    Directory of Open Access Journals (Sweden)

    Iman Mohammad Rezazadeh

    2010-06-01

    Full Text Available Introduction: Today, facial bio-potential signals are employed in many human-machine interface applications for enhancing and empowering the rehabilitation process. The main point to achieve that goal is to record appropriate bioelectric signals from the human face by placing and configuring electrodes over it in the right way. In this paper, heuristic geometrical position and configuration of the electrodes has been proposed for improving the quality of the acquired signals and consequently enhancing the performance of the facial gesture classifier. Materials and Methods: Investigation and evaluation of the electrodes' proper geometrical position and configuration can be performed using two methods: clinical and modeling. In the clinical method, the electrodes are placed in predefined positions and the elicited signals from them are then processed. The performance of the method is evaluated based on the results obtained. On the other hand, in the modeling approach, the quality of the recorded signals and their information content are evaluated only by modeling and simulation. In this paper, both methods have been utilized together. First, suitable electrode positions and configuration were proposed and evaluated by modeling and simulation. Then, the experiment was performed with a predefined protocol on 7 healthy subjects to validate the simulation results. Here, the recorded signals were passed through parallel butterworth filter banks to obtain facial EMG, EOG and EEG signals and the RMS features of each 256 msec time slot were extracted.  By using the power of Subtractive Fuzzy C-Mean (SFCM, 8 different facial gestures (including smiling, frowning, pulling up left and right lip corners, left/right/up and down movements of the eyes were discriminated. Results: According to the three-channel electrode configuration derived from modeling of the dipoles effects on the surface electrodes and by employing the SFCM classifier, an average 94

  10. D Partition-Based Clustering for Supply Chain Data Management

    Science.gov (United States)

    Suhaibah, A.; Uznir, U.; Anton, F.; Mioc, D.; Rahman, A. A.

    2015-10-01

    Supply Chain Management (SCM) is the management of the products and goods flow from its origin point to point of consumption. During the process of SCM, information and dataset gathered for this application is massive and complex. This is due to its several processes such as procurement, product development and commercialization, physical distribution, outsourcing and partnerships. For a practical application, SCM datasets need to be managed and maintained to serve a better service to its three main categories; distributor, customer and supplier. To manage these datasets, a structure of data constellation is used to accommodate the data into the spatial database. However, the situation in geospatial database creates few problems, for example the performance of the database deteriorate especially during the query operation. We strongly believe that a more practical hierarchical tree structure is required for efficient process of SCM. Besides that, three-dimensional approach is required for the management of SCM datasets since it involve with the multi-level location such as shop lots and residential apartments. 3D R-Tree has been increasingly used for 3D geospatial database management due to its simplicity and extendibility. However, it suffers from serious overlaps between nodes. In this paper, we proposed a partition-based clustering for the construction of a hierarchical tree structure. Several datasets are tested using the proposed method and the percentage of the overlapping nodes and volume coverage are computed and compared with the original 3D R-Tree and other practical approaches. The experiments demonstrated in this paper substantiated that the hierarchical structure of the proposed partitionbased clustering is capable of preserving minimal overlap and coverage. The query performance was tested using 300,000 points of a SCM dataset and the results are presented in this paper. This paper also discusses the outlook of the structure for future reference.

  11. blockcluster: An R Package for Model-Based Co-Clustering

    Directory of Open Access Journals (Sweden)

    Parmeet Singh Bhatia

    2017-02-01

    Full Text Available Simultaneous clustering of rows and columns, usually designated by bi-clustering, coclustering or block clustering, is an important technique in two way data analysis. A new standard and efficient approach has been recently proposed based on the latent block model (Govaert and Nadif 2003 which takes into account the block clustering problem on both the individual and variable sets. This article presents our R package blockcluster for co-clustering of binary, contingency and continuous data based on these very models. In this document, we will give a brief review of the model-based block clustering methods, and we will show how the R package blockcluster can be used for co-clustering.

  12. Chondromyxoid fibroma of the mastoid facial nerve canal mimicking a facial nerve schwannoma.

    Science.gov (United States)

    Thompson, Andrew L; Bharatha, Aditya; Aviv, Richard I; Nedzelski, Julian; Chen, Joseph; Bilbao, Juan M; Wong, John; Saad, Reda; Symons, Sean P

    2009-07-01

    Chondromyxoid fibroma of the skull base is a rare entity. Involvement of the temporal bone is particularly rare. We present an unusual case of progressive facial nerve paralysis with imaging and clinical findings most suggestive of a facial nerve schwannoma. The lesion was tubular in appearance, expanded the mastoid facial nerve canal, protruded out of the stylomastoid foramen, and enhanced homogeneously. The only unusual imaging feature was minor calcification within the tumor. Surgery revealed an irregular, cystic lesion. Pathology diagnosed a chondromyxoid fibroma involving the mastoid portion of the facial nerve canal, destroying the facial nerve.

  13. A Coupled User Clustering Algorithm Based on Mixed Data for Web-Based Learning Systems

    Directory of Open Access Journals (Sweden)

    Ke Niu

    2015-01-01

    Full Text Available In traditional Web-based learning systems, due to insufficient learning behaviors analysis and personalized study guides, a few user clustering algorithms are introduced. While analyzing the behaviors with these algorithms, researchers generally focus on continuous data but easily neglect discrete data, each of which is generated from online learning actions. Moreover, there are implicit coupled interactions among the data but are frequently ignored in the introduced algorithms. Therefore, a mass of significant information which can positively affect clustering accuracy is neglected. To solve the above issues, we proposed a coupled user clustering algorithm for Wed-based learning systems by taking into account both discrete and continuous data, as well as intracoupled and intercoupled interactions of the data. The experiment result in this paper demonstrates the outperformance of the proposed algorithm.

  14. Facial Symmetry: An Illusion?

    Directory of Open Access Journals (Sweden)

    Naveen Reddy Admala

    2013-01-01

    Materials and methods: A sample of 120 patients (60 males and 60 females; mean age, 15 years; range, 16-22 years who had received orthodontic clinical examination at AME′s Dental College and Hospital were selected. Selection was made in such a way that following malocclusions with equal sexual distribution was possible from the patient database. Patients selected were classified into skeletal Class I (25 males and 25 females, Class II (25 males and 25 females and Class III (10 males and 10 females based on ANB angle. The number was predecided to be the same and also was based on the number of patients with following malocclusions reported to the department. Differences in length between distances from the points at which ear rods were inserted to the facial midline and the perpendicular distance from the softtissue menton to the facial midline were measured on a frontofacial photograph. Subjects with a discrepancy of more than three standard deviations of the measurement error were categorized as having left- or right-sided laterality. Results: Of subjects with facial asymmetry, 74.1% had a wider right hemiface, and 51.6% of those with chin deviation had left-sided laterality. These tendencies were independent of sex or skeletal jaw relationships. Conclusion: These results suggest that laterality in the normal asymmetry of the face, which is consistently found in humans, is likely to be a hereditary rather than an acquired trait.

  15. A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks

    Science.gov (United States)

    Gui, Jinsong; Zhou, Kai; Xiong, Naixue

    2016-01-01

    Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude. PMID:27681731

  16. A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Jinsong Gui

    2016-09-01

    Full Text Available Multi-Input Multi-Output (MIMO can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs, clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO, which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude.

  17. A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks.

    Science.gov (United States)

    Gui, Jinsong; Zhou, Kai; Xiong, Naixue

    2016-09-25

    Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude.

  18. Cluster-based Data Gathering in Long-Strip Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    FANG, W.

    2012-02-01

    Full Text Available This paper investigates a special class of wireless sensor networks that are different from traditional ones in that the sensor nodes in this class of networks are deployed along narrowly elongated geographical areas and form a long-strip topology. According to hardware capabilities of current sensor nodes, a cluster-based protocol for reliable and efficient data gathering in long-strip wireless sensor networks (LSWSN is proposed. A well-distributed cluster-based architecture is first formed in the whole network through contention-based cluster head election. Cluster heads are responsible for coordination among the nodes within their clusters and aggregation of their sensory data, as well as transmission the data to the sink node on behalf of their own clusters. The intra-cluster coordination is based on the traditional TDMA schedule, in which the inter-cluster interference caused by the border nodes is solved by the multi-channel communication technique. The cluster reporting is based on the CSMA contention, in which a connected overlay network is formed by relay nodes to forward the data from the cluster heads through multi-hops to the sink node. The relay nodes are non-uniformly deployed to resolve the energy-hole problem which is extremely serious in the LSWSN. Extensive simulation results illuminate the distinguished performance of the proposed protocol.

  19. Web-based visualisation of head pose and facial expressions changes: monitoring human activity using depth data

    OpenAIRE

    Kalliatakis, Grigorios; Vidakis, Nikolaos; Triantafyllidis, Georgios

    2017-01-01

    Despite significant recent advances in the field of head pose estimation and facial expression recognition, raising the cognitive level when analysing human activity presents serious challenges to current concepts. Motivated by the need of generating comprehensible visual representations from different sets of data, we introduce a system capable of monitoring human activity through head pose and facial expression changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor)...

  20. A comparison of heuristic and model-based clustering methods for dietary pattern analysis.

    Science.gov (United States)

    Greve, Benjamin; Pigeot, Iris; Huybrechts, Inge; Pala, Valeria; Börnhorst, Claudia

    2016-02-01

    Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward's method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data. The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice. The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward's methods, the performance of k-means was better in 64-100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a 'non-processed' cluster with a high consumption of fruits, vegetables and wholemeal bread, a 'balanced' cluster with only slight preferences of single foods and a 'junk food' cluster. The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.

  1. Efficient similarity-based data clustering by optimal object to cluster reallocation.

    Science.gov (United States)

    Rossignol, Mathias; Lagrange, Mathieu; Cont, Arshia

    2018-01-01

    We present an iterative flat hard clustering algorithm designed to operate on arbitrary similarity matrices, with the only constraint that these matrices be symmetrical. Although functionally very close to kernel k-means, our proposal performs a maximization of average intra-class similarity, instead of a squared distance minimization, in order to remain closer to the semantics of similarities. We show that this approach permits the relaxing of some conditions on usable affinity matrices like semi-positiveness, as well as opening possibilities for computational optimization required for large datasets. Systematic evaluation on a variety of data sets shows that compared with kernel k-means and the spectral clustering methods, the proposed approach gives equivalent or better performance, while running much faster. Most notably, it significantly reduces memory access, which makes it a good choice for large data collections. Material enabling the reproducibility of the results is made available online.

  2. A model-based clustering method to detect infectious disease transmission outbreaks from sequence variation.

    Directory of Open Access Journals (Sweden)

    Rosemary M McCloskey

    2017-11-01

    Full Text Available Clustering infections by genetic similarity is a popular technique for identifying potential outbreaks of infectious disease, in part because sequences are now routinely collected for clinical management of many infections. A diverse number of nonparametric clustering methods have been developed for this purpose. These methods are generally intuitive, rapid to compute, and readily scale with large data sets. However, we have found that nonparametric clustering methods can be biased towards identifying clusters of diagnosis-where individuals are sampled sooner post-infection-rather than the clusters of rapid transmission that are meant to be potential foci for public health efforts. We develop a fundamentally new approach to genetic clustering based on fitting a Markov-modulated Poisson process (MMPP, which represents the evolution of transmission rates along the tree relating different infections. We evaluated this model-based method alongside five nonparametric clustering methods using both simulated and actual HIV sequence data sets. For simulated clusters of rapid transmission, the MMPP clustering method obtained higher mean sensitivity (85% and specificity (91% than the nonparametric methods. When we applied these clustering methods to published sequences from a study of HIV-1 genetic clusters in Seattle, USA, we found that the MMPP method categorized about half (46% as many individuals to clusters compared to the other methods. Furthermore, the mean internal branch lengths that approximate transmission rates were significantly shorter in clusters extracted using MMPP, but not by other methods. We determined that the computing time for the MMPP method scaled linearly with the size of trees, requiring about 30 seconds for a tree of 1,000 tips and about 20 minutes for 50,000 tips on a single computer. This new approach to genetic clustering has significant implications for the application of pathogen sequence analysis to public health, where

  3. In search of Leonardo: computer-based facial image analysis of Renaissance artworks for identifying Leonardo as subject

    Science.gov (United States)

    Tyler, Christopher W.; Smith, William A. P.; Stork, David G.

    2012-03-01

    One of the enduring mysteries in the history of the Renaissance is the adult appearance of the archetypical "Renaissance Man," Leonardo da Vinci. His only acknowledged self-portrait is from an advanced age, and various candidate images of younger men are difficult to assess given the absence of documentary evidence. One clue about Leonardo's appearance comes from the remark of the contemporary historian, Vasari, that the sculpture of David by Leonardo's master, Andrea del Verrocchio, was based on the appearance of Leonardo when he was an apprentice. Taking a cue from this statement, we suggest that the more mature sculpture of St. Thomas, also by Verrocchio, might also have been a portrait of Leonardo. We tested the possibility Leonardo was the subject for Verrocchio's sculpture by a novel computational technique for the comparison of three-dimensional facial configurations. Based on quantitative measures of similarities, we also assess whether another pair of candidate two-dimensional images are plausibly attributable as being portraits of Leonardo as a young adult. Our results are consistent with the claim Leonardo is indeed the subject in these works, but we need comparisons with images in a larger corpora of candidate artworks before our results achieve statistical significance.

  4. Comparative analysis of the anterior and posterior length and deflection angle of the cranial base, in individuals with facial Pattern I, II and III

    Directory of Open Access Journals (Sweden)

    Guilherme Thiesen

    2013-02-01

    Full Text Available OBJECTIVE: This study evaluated the variations in the anterior cranial base (S-N, posterior cranial base (S-Ba and deflection of the cranial base (SNBa among three different facial patterns (Pattern I, II and III. METHOD: A sample of 60 lateral cephalometric radiographs of Brazilian Caucasian patients, both genders, between 8 and 17 years of age was selected. The sample was divided into 3 groups (Pattern I, II and III of 20 individuals each. The inclusion criteria for each group were the ANB angle, Wits appraisal and the facial profile angle (G'.Sn.Pg'. To compare the mean values obtained from (SNBa, S-N, S-Ba each group measures, the ANOVA test and Scheffé's Post-Hoc test were applied. RESULTS AND CONCLUSIONS: There was no statistically significant difference for the deflection angle of the cranial base among the different facial patterns (Patterns I, II and III. There was no significant difference for the measures of the anterior and posterior cranial base between the facial Patterns I and II. The mean values for S-Ba were lower in facial Pattern III with statistically significant difference. The mean values of S-N in the facial Pattern III were also reduced, but without showing statistically significant difference. This trend of lower values in the cranial base measurements would explain the maxillary deficiency and/or mandibular prognathism features that characterize the facial Pattern III.OBJETIVO: o presente estudo avaliou as variações da base craniana anterior (S-N, base craniana posterior (S-Ba, e ângulo de deflexão da base do crânio (SNBa entre três diferentes padrões faciais (Padrão I, II e III. MÉTODOS: selecionou-se uma amostra de 60 telerradiografias em norma lateral de pacientes brasileiros leucodermas, de ambos os sexos, com idades entre 8 anos e 17 anos. A amostra foi dividida em três grupos (Padrão I, II e III, sendo cada grupo constituído de 20 indivíduos. Os critérios de seleção dos indivíduos para cada grupo

  5. Development of the Korean Facial Emotion Stimuli: Korea University Facial Expression Collection 2nd Edition

    Directory of Open Access Journals (Sweden)

    Sun-Min Kim

    2017-05-01

    Full Text Available Background: Developing valid emotional facial stimuli for specific ethnicities creates ample opportunities to investigate both the nature of emotional facial information processing in general and clinical populations as well as the underlying mechanisms of facial emotion processing within and across cultures. Given that most entries in emotional facial stimuli databases were developed with western samples, and given that very few of the eastern emotional facial stimuli sets were based strictly on the Ekman’s Facial Action Coding System, developing valid emotional facial stimuli of eastern samples remains a high priority.Aims: To develop and examine the psychometric properties of six basic emotional facial stimuli recruiting professional Korean actors and actresses based on the Ekman’s Facial Action Coding System for the Korea University Facial Expression Collection-Second Edition (KUFEC-II.Materials And Methods: Stimulus selection was done in two phases. First, researchers evaluated the clarity and intensity of each stimulus developed based on the Facial Action Coding System. Second, researchers selected a total of 399 stimuli from a total of 57 actors and actresses, which were then rated on accuracy, intensity, valence, and arousal by 75 independent raters.Conclusion: The hit rates between the targeted and rated expressions of the KUFEC-II were all above 80%, except for fear (50% and disgust (63%. The KUFEC-II appears to be a valid emotional facial stimuli database, providing the largest set of emotional facial stimuli. The mean intensity score was 5.63 (out of 7, suggesting that the stimuli delivered the targeted emotions with great intensity. All positive expressions were rated as having a high positive valence, whereas all negative expressions were rated as having a high negative valence. The KUFEC II is expected to be widely used in various psychological studies on emotional facial expression. KUFEC-II stimuli can be obtained through

  6. Clustering-based urbanisation to improve enterprise information systems agility

    Science.gov (United States)

    Imache, Rabah; Izza, Said; Ahmed-Nacer, Mohamed

    2015-11-01

    Enterprises are daily facing pressures to demonstrate their ability to adapt quickly to the unpredictable changes of their dynamic in terms of technology, social, legislative, competitiveness and globalisation. Thus, to ensure its place in this hard context, enterprise must always be agile and must ensure its sustainability by a continuous improvement of its information system (IS). Therefore, the agility of enterprise information systems (EISs) can be considered today as a primary objective of any enterprise. One way of achieving this objective is by the urbanisation of the EIS in the context of continuous improvement to make it a real asset servicing enterprise strategy. This paper investigates the benefits of EISs urbanisation based on clustering techniques as a driver for agility production and/or improvement to help managers and IT management departments to improve continuously the performance of the enterprise and make appropriate decisions in the scope of the enterprise objectives and strategy. This approach is applied to the urbanisation of a tour operator EIS.

  7. Variable selection in multivariate calibration based on clustering of variable concept.

    Science.gov (United States)

    Farrokhnia, Maryam; Karimi, Sadegh

    2016-01-01

    Recently we have proposed a new variable selection algorithm, based on clustering of variable concept (CLoVA) in classification problem. With the same idea, this new concept has been applied to a regression problem and then the obtained results have been compared with conventional variable selection strategies for PLS. The basic idea behind the clustering of variable is that, the instrument channels are clustered into different clusters via clustering algorithms. Then, the spectral data of each cluster are subjected to PLS regression. Different real data sets (Cargill corn, Biscuit dough, ACE QSAR, Soy, and Tablet) have been used to evaluate the influence of the clustering of variables on the prediction performances of PLS. Almost in the all cases, the statistical parameter especially in prediction error shows the superiority of CLoVA-PLS respect to other variable selection strategies. Finally the synergy clustering of variable (sCLoVA-PLS), which is used the combination of cluster, has been proposed as an efficient and modification of CLoVA algorithm. The obtained statistical parameter indicates that variable clustering can split useful part from redundant ones, and then based on informative cluster; stable model can be reached. Copyright © 2015 Elsevier B.V. All rights reserved.

  8. IDENTIFICAÇÃO DE CLUSTERS INTERNACIONAIS COM BASE NAS DIMENSÕES CULTURAIS DE HOFSTEDE. / Identification of international clusters based on the hofstede’s cultural dimensions

    Directory of Open Access Journals (Sweden)

    Valderí de Castro Alcântara1

    2012-08-01

    Full Text Available Haja vista que a cultura de um país influencia a cultura organizacional das empresas nele presente e ainda é fator determinante no processo de internacionalização, torna-se relevante compreender e mensurar as características culturais de cada país. Os estudos de Hofstede (1984 apresentam uma metodologia útil para comparação entre culturas. Tal metodologia leva em consideração as características deuma cultura que possibilita diferenciar um país de outro. Dessa forma, é possível observar que determinados países compartilham certos traços culturais e, assim, é possível agrupá-los segundo critérios pré-estabelecidos. O presente trabalho objetiva utilizar-se de procedimentos estatísticos multivariados Clusters Analyses, K-Means Cluster Analysis e Análise Discriminante para determinar e validar agrupamentos de países, com base nas dimensões culturais de Hofstede (Distance Index, Individualism, Masculinity e Uncertainty Avoidance Index. Os resultados determinaram quatro clusters: Cluster 1 - países com cultura masculina e individualista; Cluster 2 - cultura coletivista e aversa à incerteza; Cluster 3 - cultura feminina e com baixa distância hierárquica; e Cluster 4 - cultura com elevada distância hierárquica e propensão à incerteza./ Considering that the culture of a country influences the organizational culture of this company and it is still a determining factor in the internationalization process becomes important to understand and measure the cultural characteristics of each country. The studies of Hofstede (1984 present a useful methodology for comparing cultures, this methodology takes into account the characteristics of a culturethat allows to differentiate one from another country. Thus one can observe that certain countries share certain cultural traits and so it is possible grouping them according to predetermined criteria. The present work aims to utilize multivariate statistical procedures Cluster Analyses

  9. Clustering by Partitioning around Medoids using Distance-Based ...

    African Journals Online (AJOL)

    OLUWASOGO

    outperforms both the Euclidean and Manhattan distance metrics in certain situations. KEYWORDS: PAM ... version of a dataset, compare the quality of clusters obtained from the Euclidean .... B. Theoretical Framework and Methodology.

  10. Trust-based hexagonal clustering for efficient certificate ...

    Indian Academy of Sciences (India)

    Clustering; certificate management; MANET; security; trust; Voronoi. ... terms of effectiveness of revocation scheme (with respect to revocation rate and time), security, ... Engineering, Thiagarajar College of Engineering, Madurai 625015, India ...

  11. Personalized Profile Based Search Interface With Ranked and Clustered Display

    National Research Council Canada - National Science Library

    Kumar, Sachin; Oztekin, B. U; Ertoz, Levent; Singhal, Saurabh; Han, Euihong; Kumar, Vipin

    2001-01-01

    We have developed an experimental meta-search engine, which takes the snippets from traditional search engines and presents them to the user either in the form of clusters, indices or re-ranked list...

  12. An AK-LDMeans algorithm based on image clustering

    Science.gov (United States)

    Chen, Huimin; Li, Xingwei; Zhang, Yongbin; Chen, Nan

    2018-03-01

    Clustering is an effective analytical technique for handling unmarked data for value mining. Its ultimate goal is to mark unclassified data quickly and correctly. We use the roadmap for the current image processing as the experimental background. In this paper, we propose an AK-LDMeans algorithm to automatically lock the K value by designing the Kcost fold line, and then use the long-distance high-density method to select the clustering centers to further replace the traditional initial clustering center selection method, which further improves the efficiency and accuracy of the traditional K-Means Algorithm. And the experimental results are compared with the current clustering algorithm and the results are obtained. The algorithm can provide effective reference value in the fields of image processing, machine vision and data mining.

  13. An intelligent clustering based methodology for confusable diseases ...

    African Journals Online (AJOL)

    Journal of Computer Science and Its Application ... In this paper, an intelligent system driven by fuzzy clustering algorithm and Adaptive Neuro-Fuzzy Inference System for ... Data on patients diagnosed and confirmed by laboratory tests of viral ...

  14. Improved Density Based Spatial Clustering of Applications of Noise Clustering Algorithm for Knowledge Discovery in Spatial Data

    Directory of Open Access Journals (Sweden)

    Arvind Sharma

    2016-01-01

    Full Text Available There are many techniques available in the field of data mining and its subfield spatial data mining is to understand relationships between data objects. Data objects related with spatial features are called spatial databases. These relationships can be used for prediction and trend detection between spatial and nonspatial objects for social and scientific reasons. A huge data set may be collected from different sources as satellite images, X-rays, medical images, traffic cameras, and GIS system. To handle this large amount of data and set relationship between them in a certain manner with certain results is our primary purpose of this paper. This paper gives a complete process to understand how spatial data is different from other kinds of data sets and how it is refined to apply to get useful results and set trends to predict geographic information system and spatial data mining process. In this paper a new improved algorithm for clustering is designed because role of clustering is very indispensable in spatial data mining process. Clustering methods are useful in various fields of human life such as GIS (Geographic Information System, GPS (Global Positioning System, weather forecasting, air traffic controller, water treatment, area selection, cost estimation, planning of rural and urban areas, remote sensing, and VLSI designing. This paper presents study of various clustering methods and algorithms and an improved algorithm of DBSCAN as IDBSCAN (Improved Density Based Spatial Clustering of Application of Noise. The algorithm is designed by addition of some important attributes which are responsible for generation of better clusters from existing data sets in comparison of other methods.

  15. Are Rich People Perceived as More Trustworthy? Perceived Socioeconomic Status Modulates Judgments of Trustworthiness and Trust Behavior Based on Facial Appearance

    Directory of Open Access Journals (Sweden)

    Yue Qi

    2018-04-01

    Full Text Available In the era of globalization, people meet strangers from different countries more often than ever. Previous research indicates that impressions of trustworthiness based on facial appearance play an important role in interpersonal cooperation behaviors. The current study examined whether additional information about socioeconomic status (SES, including national prosperity and individual monthly income, affects facial judgments and appearance-based trust decisions. Besides reproducing previous conclusions that trustworthy faces receive more money than untrustworthy faces, the present study showed that high-income individuals were judged as more trustworthy than low-income individuals, and also were given more money in a trust game. However, trust behaviors were not modulated by the nationality of the faces. The present research suggests that people are more likely to trust strangers with a high income, compared with individuals with a low income.

  16. Odor valence linearly modulates attractiveness, but not age assessment, of invariant facial features in a memory-based rating task.

    Science.gov (United States)

    Seubert, Janina; Gregory, Kristen M; Chamberland, Jessica; Dessirier, Jean-Marc; Lundström, Johan N

    2014-01-01

    Scented cosmetic products are used across cultures as a way to favorably influence one's appearance. While crossmodal effects of odor valence on perceived attractiveness of facial features have been demonstrated experimentally, it is unknown whether they represent a phenomenon specific to affective processing. In this experiment, we presented odors in the context of a face battery with systematic feature manipulations during a speeded response task. Modulatory effects of linear increases of odor valence were investigated by juxtaposing subsequent memory-based ratings tasks--one predominantly affective (attractiveness) and a second, cognitive (age). The linear modulation pattern observed for attractiveness was consistent with additive effects of face and odor appraisal. Effects of odor valence on age perception were not linearly modulated and may be the result of cognitive interference. Affective and cognitive processing of faces thus appear to differ in their susceptibility to modulation by odors, likely as a result of privileged access of olfactory stimuli to affective brain networks. These results are critically discussed with respect to potential biases introduced by the preceding speeded response task.

  17. A user credit assessment model based on clustering ensemble for broadband network new media service supervision

    Science.gov (United States)

    Liu, Fang; Cao, San-xing; Lu, Rui

    2012-04-01

    This paper proposes a user credit assessment model based on clustering ensemble aiming to solve the problem that users illegally spread pirated and pornographic media contents within the user self-service oriented broadband network new media platforms. Its idea is to do the new media user credit assessment by establishing indices system based on user credit behaviors, and the illegal users could be found according to the credit assessment results, thus to curb the bad videos and audios transmitted on the network. The user credit assessment model based on clustering ensemble proposed by this paper which integrates the advantages that swarm intelligence clustering is suitable for user credit behavior analysis and K-means clustering could eliminate the scattered users existed in the result of swarm intelligence clustering, thus to realize all the users' credit classification automatically. The model's effective verification experiments are accomplished which are based on standard credit application dataset in UCI machine learning repository, and the statistical results of a comparative experiment with a single model of swarm intelligence clustering indicates this clustering ensemble model has a stronger creditworthiness distinguishing ability, especially in the aspect of predicting to find user clusters with the best credit and worst credit, which will facilitate the operators to take incentive measures or punitive measures accurately. Besides, compared with the experimental results of Logistic regression based model under the same conditions, this clustering ensemble model is robustness and has better prediction accuracy.

  18. DCE: A Distributed Energy-Efficient Clustering Protocol for Wireless Sensor Network Based on Double-Phase Cluster-Head Election.

    Science.gov (United States)

    Han, Ruisong; Yang, Wei; Wang, Yipeng; You, Kaiming

    2017-05-01

    Clustering is an effective technique used to reduce energy consumption and extend the lifetime of wireless sensor network (WSN). The characteristic of energy heterogeneity of WSNs should be considered when designing clustering protocols. We propose and evaluate a novel distributed energy-efficient clustering protocol called DCE for heterogeneous wireless sensor networks, based on a Double-phase Cluster-head Election scheme. In DCE, the procedure of cluster head election is divided into two phases. In the first phase, tentative cluster heads are elected with the probabilities which are decided by the relative levels of initial and residual energy. Then, in the second phase, the tentative cluster heads are replaced by their cluster members to form the final set of cluster heads if any member in their cluster has more residual energy. Employing two phases for cluster-head election ensures that the nodes with more energy have a higher chance to be cluster heads. Energy consumption is well-distributed in the proposed protocol, and the simulation results show that DCE achieves longer stability periods than other typical clustering protocols in heterogeneous scenarios.

  19. Construction and application of Red5 cluster based on OpenStack

    Science.gov (United States)

    Wang, Jiaqing; Song, Jianxin

    2017-08-01

    With the application and development of cloud computing technology in various fields, the resource utilization rate of the data center has been improved obviously, and the system based on cloud computing platform has also improved the expansibility and stability. In the traditional way, Red5 cluster resource utilization is low and the system stability is poor. This paper uses cloud computing to efficiently calculate the resource allocation ability, and builds a Red5 server cluster based on OpenStack. Multimedia applications can be published to the Red5 cloud server cluster. The system achieves the flexible construction of computing resources, but also greatly improves the stability of the cluster and service efficiency.

  20. [Idiopathic facial paralysis in children].

    Science.gov (United States)

    Achour, I; Chakroun, A; Ayedi, S; Ben Rhaiem, Z; Mnejja, M; Charfeddine, I; Hammami, B; Ghorbel, A

    2015-05-01

    Idiopathic facial palsy is the most common cause of facial nerve palsy in children. Controversy exists regarding treatment options. The objectives of this study were to review the epidemiological and clinical characteristics as well as the outcome of idiopathic facial palsy in children to suggest appropriate treatment. A retrospective study was conducted on children with a diagnosis of idiopathic facial palsy from 2007 to 2012. A total of 37 cases (13 males, 24 females) with a mean age of 13.9 years were included in this analysis. The mean duration between onset of Bell's palsy and consultation was 3 days. Of these patients, 78.3% had moderately severe (grade IV) or severe paralysis (grade V on the House and Brackmann grading). Twenty-seven patients were treated in an outpatient context, three patients were hospitalized, and seven patients were treated as outpatients and subsequently hospitalized. All patients received corticosteroids. Eight of them also received antiviral treatment. The complete recovery rate was 94.6% (35/37). The duration of complete recovery was 7.4 weeks. Children with idiopathic facial palsy have a very good prognosis. The complete recovery rate exceeds 90%. However, controversy exists regarding treatment options. High-quality studies have been conducted on adult populations. Medical treatment based on corticosteroids alone or combined with antiviral treatment is certainly effective in improving facial function outcomes in adults. In children, the recommendation for prescription of steroids and antiviral drugs based on adult treatment appears to be justified. Randomized controlled trials in the pediatric population are recommended to define a strategy for management of idiopathic facial paralysis. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

  1. An Energy Centric Cluster-Based Routing Protocol for Wireless Sensor Networks.

    Science.gov (United States)

    Hosen, A S M Sanwar; Cho, Gi Hwan

    2018-05-11

    Clustering is an effective way to prolong the lifetime of a wireless sensor network (WSN). The common approach is to elect cluster heads to take routing and controlling duty, and to periodically rotate each cluster head's role to distribute energy consumption among nodes. However, a significant amount of energy dissipates due to control messages overhead, which results in a shorter network lifetime. This paper proposes an energy-centric cluster-based routing mechanism in WSNs. To begin with, cluster heads are elected based on the higher ranks of the nodes. The rank is defined by residual energy and average distance from the member nodes. With the role of data aggregation and data forwarding, a cluster head acts as a caretaker for cluster-head election in the next round, where the ranks' information are piggybacked along with the local data sending during intra-cluster communication. This reduces the number of control messages for the cluster-head election as well as the cluster formation in detail. Simulation results show that our proposed protocol saves the energy consumption among nodes and achieves a significant improvement in the network lifetime.

  2. Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks.

    Science.gov (United States)

    Aadil, Farhan; Raza, Ali; Khan, Muhammad Fahad; Maqsood, Muazzam; Mehmood, Irfan; Rho, Seungmin

    2018-05-03

    Flying ad-hoc networks (FANETs) are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs) represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR) and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption.

  3. Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks

    Directory of Open Access Journals (Sweden)

    Farhan Aadil

    2018-05-01

    Full Text Available Flying ad-hoc networks (FANETs are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption.

  4. Research on retailer data clustering algorithm based on Spark

    Science.gov (United States)

    Huang, Qiuman; Zhou, Feng

    2017-03-01

    Big data analysis is a hot topic in the IT field now. Spark is a high-reliability and high-performance distributed parallel computing framework for big data sets. K-means algorithm is one of the classical partition methods in clustering algorithm. In this paper, we study the k-means clustering algorithm on Spark. Firstly, the principle of the algorithm is analyzed, and then the clustering analysis is carried out on the supermarket customers through the experiment to find out the different shopping patterns. At the same time, this paper proposes the parallelization of k-means algorithm and the distributed computing framework of Spark, and gives the concrete design scheme and implementation scheme. This paper uses the two-year sales data of a supermarket to validate the proposed clustering algorithm and achieve the goal of subdividing customers, and then analyze the clustering results to help enterprises to take different marketing strategies for different customer groups to improve sales performance.

  5. Optimal colour quality of LED clusters based on memory colours.

    Science.gov (United States)

    Smet, Kevin; Ryckaert, Wouter R; Pointer, Michael R; Deconinck, Geert; Hanselaer, Peter

    2011-03-28

    The spectral power distributions of tri- and tetrachromatic clusters of Light-Emitting-Diodes, composed of simulated and commercially available LEDs, were optimized with a genetic algorithm to maximize the luminous efficacy of radiation and the colour quality as assessed by the memory colour quality metric developed by the authors. The trade-off of the colour quality as assessed by the memory colour metric and the luminous efficacy of radiation was investigated by calculating the Pareto optimal front using the NSGA-II genetic algorithm. Optimal peak wavelengths and spectral widths of the LEDs were derived, and over half of them were found to be close to Thornton's prime colours. The Pareto optimal fronts of real LED clusters were always found to be smaller than those of the simulated clusters. The effect of binning on designing a real LED cluster was investigated and was found to be quite large. Finally, a real LED cluster of commercially available AlGaInP, InGaN and phosphor white LEDs was optimized to obtain a higher score on memory colour quality scale than its corresponding CIE reference illuminant.

  6. An incremental DPMM-based method for trajectory clustering, modeling, and retrieval.

    Science.gov (United States)

    Hu, Weiming; Li, Xi; Tian, Guodong; Maybank, Stephen; Zhang, Zhongfei

    2013-05-01

    Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.

  7. The Prevalence of Cosmetic Facial Plastic Procedures among Facial Plastic Surgeons.

    Science.gov (United States)

    Moayer, Roxana; Sand, Jordan P; Han, Albert; Nabili, Vishad; Keller, Gregory S

    2018-04-01

    This is the first study to report on the prevalence of cosmetic facial plastic surgery use among facial plastic surgeons. The aim of this study is to determine the frequency with which facial plastic surgeons have cosmetic procedures themselves. A secondary aim is to determine whether trends in usage of cosmetic facial procedures among facial plastic surgeons are similar to that of nonsurgeons. The study design was an anonymous, five-question, Internet survey distributed via email set in a single academic institution. Board-certified members of the American Academy of Facial Plastic and Reconstructive Surgery (AAFPRS) were included in this study. Self-reported history of cosmetic facial plastic surgery or minimally invasive procedures were recorded. The survey also queried participants for demographic data. A total of 216 members of the AAFPRS responded to the questionnaire. Ninety percent of respondents were male ( n  = 192) and 10.3% were female ( n  = 22). Thirty-three percent of respondents were aged 31 to 40 years ( n  = 70), 25% were aged 41 to 50 years ( n  = 53), 21.4% were aged 51 to 60 years ( n  = 46), and 20.5% were older than 60 years ( n  = 44). Thirty-six percent of respondents had a surgical cosmetic facial procedure and 75% has at least one minimally invasive cosmetic facial procedure. Facial plastic surgeons are frequent users of cosmetic facial plastic surgery. This finding may be due to access, knowledge base, values, or attitudes. By better understanding surgeon attitudes toward facial plastic surgery, we can improve communication with patients and delivery of care. This study is a first step in understanding use of facial plastic procedures among facial plastic surgeons. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

  8. An improved initialization center k-means clustering algorithm based on distance and density

    Science.gov (United States)

    Duan, Yanling; Liu, Qun; Xia, Shuyin

    2018-04-01

    Aiming at the problem of the random initial clustering center of k means algorithm that the clustering results are influenced by outlier data sample and are unstable in multiple clustering, a method of central point initialization method based on larger distance and higher density is proposed. The reciprocal of the weighted average of distance is used to represent the sample density, and the data sample with the larger distance and the higher density are selected as the initial clustering centers to optimize the clustering results. Then, a clustering evaluation method based on distance and density is designed to verify the feasibility of the algorithm and the practicality, the experimental results on UCI data sets show that the algorithm has a certain stability and practicality.

  9. Beverages-Food Industry Cluster Development Based on Value Chain in Indonesia

    OpenAIRE

    Lasmono Tri Sunaryanto; Gatot Sasongko; Ira Yumastuti

    2014-01-01

    This study wants to develop the cluster-based food and beverage industry value chain that corresponds to the potential in the regions in Java Economic Corridor. Targeted research: a description of SME development strategies that have been implemented, composed, and can be applied to an SME cluster development strategy of food and beverage, as well as a proven implementation strategy of SME cluster development of food and beverage. To achieve these objectives, implemented descriptive methods, ...

  10. Trend analysis using non-stationary time series clustering based on the finite element method

    OpenAIRE

    Gorji Sefidmazgi, M.; Sayemuzzaman, M.; Homaifar, A.; Jha, M. K.; Liess, S.

    2014-01-01

    In order to analyze low-frequency variability of climate, it is useful to model the climatic time series with multiple linear trends and locate the times of significant changes. In this paper, we have used non-stationary time series clustering to find change points in the trends. Clustering in a multi-dimensional non-stationary time series is challenging, since the problem is mathematically ill-posed. Clustering based on the finite element method (FEM) is one of the methods ...

  11. Cluster-cluster clustering

    International Nuclear Information System (INIS)

    Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C.S.; Yale Univ., New Haven, CT; California Univ., Santa Barbara; Cambridge Univ., England; Sussex Univ., Brighton, England)

    1985-01-01

    The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales. 30 references

  12. Facial Sports Injuries

    Science.gov (United States)

    ... the patient has HIV or hepatitis. Facial Fractures Sports injuries can cause potentially serious broken bones or fractures of the face. Common symptoms of facial fractures include: swelling and bruising, ...

  13. Clustering-based approaches to SAGE data mining

    Directory of Open Access Journals (Sweden)

    Wang Haiying

    2008-07-01

    Full Text Available Abstract Serial analysis of gene expression (SAGE is one of the most powerful tools for global gene expression profiling. It has led to several biological discoveries and biomedical applications, such as the prediction of new gene functions and the identification of biomarkers in human cancer research. Clustering techniques have become fundamental approaches in these applications. This paper reviews relevant clustering techniques specifically designed for this type of data. It places an emphasis on current limitations and opportunities in this area for supporting biologically-meaningful data mining and visualisation.

  14. Kernel method for clustering based on optimal target vector

    International Nuclear Information System (INIS)

    Angelini, Leonardo; Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano

    2006-01-01

    We introduce Ising models, suitable for dichotomic clustering, with couplings that are (i) both ferro- and anti-ferromagnetic (ii) depending on the whole data-set and not only on pairs of samples. Couplings are determined exploiting the notion of optimal target vector, here introduced, a link between kernel supervised and unsupervised learning. The effectiveness of the method is shown in the case of the well-known iris data-set and in benchmarks of gene expression levels, where it works better than existing methods for dichotomic clustering

  15. Subspace-Based Holistic Registration for Low-Resolution Facial Images

    Directory of Open Access Journals (Sweden)

    Boom BJ

    2010-01-01

    Full Text Available Subspace-based holistic registration is introduced as an alternative to landmark-based face registration, which has a poor performance on low-resolution images, as obtained in camera surveillance applications. The proposed registration method finds the alignment by maximizing the similarity score between a probe and a gallery image. We use a novel probabilistic framework for both user-independent as well as user-specific face registration. The similarity is calculated using the probability that the face image is correctly aligned in a face subspace, but additionally we take the probability into account that the face is misaligned based on the residual error in the dimensions perpendicular to the face subspace. We perform extensive experiments on the FRGCv2 database to evaluate the impact that the face registration methods have on face recognition. Subspace-based holistic registration on low-resolution images can improve face recognition in comparison with landmark-based registration on high-resolution images. The performance of the tested face recognition methods after subspace-based holistic registration on a low-resolution version of the FRGC database is similar to that after manual registration.

  16. A brain-computer interface for potential non-verbal facial communication based on EEG signals related to specific emotions.

    Science.gov (United States)

    Kashihara, Koji

    2014-01-01

    Unlike assistive technology for verbal communication, the brain-machine or brain-computer interface (BMI/BCI) has not been established as a non-verbal communication tool for amyotrophic lateral sclerosis (ALS) patients. Face-to-face communication enables access to rich emotional information, but individuals suffering from neurological disorders, such as ALS and autism, may not express their emotions or communicate their negative feelings. Although emotions may be inferred by looking at facial expressions, emotional prediction for neutral faces necessitates advanced judgment. The process that underlies brain neuronal responses to neutral faces and causes emotional changes remains unknown. To address this problem, therefore, this study attempted to decode conditioned emotional reactions to neutral face stimuli. This direction was motivated by the assumption that if electroencephalogram (EEG) signals can be used to detect patients' emotional responses to specific inexpressive faces, the results could be incorporated into the design and development of BMI/BCI-based non-verbal communication tools. To these ends, this study investigated how a neutral face associated with a negative emotion modulates rapid central responses in face processing and then identified cortical activities. The conditioned neutral face-triggered event-related potentials that originated from the posterior temporal lobe statistically significantly changed during late face processing (600-700 ms) after stimulus, rather than in early face processing activities, such as P1 and N170 responses. Source localization revealed that the conditioned neutral faces increased activity in the right fusiform gyrus (FG). This study also developed an efficient method for detecting implicit negative emotional responses to specific faces by using EEG signals. A classification method based on a support vector machine enables the easy classification of neutral faces that trigger specific individual emotions. In

  17. Density-Based Clustering with Geographical Background Constraints Using a Semantic Expression Model

    Directory of Open Access Journals (Sweden)

    Qingyun Du

    2016-05-01

    Full Text Available A semantics-based method for density-based clustering with constraints imposed by geographical background knowledge is proposed. In this paper, we apply an ontological approach to the DBSCAN (Density-Based Geospatial Clustering of Applications with Noise algorithm in the form of knowledge representation for constraint clustering. When used in the process of clustering geographic information, semantic reasoning based on a defined ontology and its relationships is primarily intended to overcome the lack of knowledge of the relevant geospatial data. Better constraints on the geographical knowledge yield more reasonable clustering results. This article uses an ontology to describe the four types of semantic constraints for geographical backgrounds: “No Constraints”, “Constraints”, “Cannot-Link Constraints”, and “Must-Link Constraints”. This paper also reports the implementation of a prototype clustering program. Based on the proposed approach, DBSCAN can be applied with both obstacle and non-obstacle constraints as a semi-supervised clustering algorithm and the clustering results are displayed on a digital map.

  18. A Fast Density-Based Clustering Algorithm for Real-Time Internet of Things Stream

    Science.gov (United States)

    Ying Wah, Teh

    2014-01-01

    Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets. PMID:25110753

  19. Beyond Apprenticeship: Knowledge Brokers and Sustainability of Apprentice-Based Clusters

    Directory of Open Access Journals (Sweden)

    Huasheng Zhu

    2016-12-01

    Full Text Available Knowledge learning and diffusion have long been discussed in the literature on the dynamics of industrial clusters, but recent literature provides little evidence for how different actors serve as knowledge brokers in the upgrading process of apprentice-based clusters, and does not dynamically consider how to preserve the sustainability of these clusters. This paper uses empirical evidence from an antique furniture manufacturing cluster in Xianyou, Fujian Province, in southeastern China, to examine the growth trajectory of the knowledge learning system of an antique furniture manufacturing cluster. It appears that the apprentice-based learning system is crucial during early stages of the cluster evolution, but later becomes complemented and relatively substituted by the role of both local governments and focal outsiders. This finding addresses the context of economic transformation and provides empirical insights into knowledge acquisition in apprentice-based clusters to question the rationality based on European and North American cases, and to provide a broader perspective for policy makers to trigger and sustain the development of apprentice-based clusters.

  20. A fast density-based clustering algorithm for real-time Internet of Things stream.

    Science.gov (United States)

    Amini, Amineh; Saboohi, Hadi; Wah, Teh Ying; Herawan, Tutut

    2014-01-01

    Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets.

  1. Interactive K-Means Clustering Method Based on User Behavior for Different Analysis Target in Medicine.

    Science.gov (United States)

    Lei, Yang; Yu, Dai; Bin, Zhang; Yang, Yang

    2017-01-01

    Clustering algorithm as a basis of data analysis is widely used in analysis systems. However, as for the high dimensions of the data, the clustering algorithm may overlook the business relation between these dimensions especially in the medical fields. As a result, usually the clustering result may not meet the business goals of the users. Then, in the clustering process, if it can combine the knowledge of the users, that is, the doctor's knowledge or the analysis intent, the clustering result can be more satisfied. In this paper, we propose an interactive K -means clustering method to improve the user's satisfactions towards the result. The core of this method is to get the user's feedback of the clustering result, to optimize the clustering result. Then, a particle swarm optimization algorithm is used in the method to optimize the parameters, especially the weight settings in the clustering algorithm to make it reflect the user's business preference as possible. After that, based on the parameter optimization and adjustment, the clustering result can be closer to the user's requirement. Finally, we take an example in the breast cancer, to testify our method. The experiments show the better performance of our algorithm.

  2. A ROBUST CLUSTER HEAD SELECTION BASED ON NEIGHBORHOOD CONTRIBUTION AND AVERAGE MINIMUM POWER FOR MANETs

    Directory of Open Access Journals (Sweden)

    S.Balaji

    2015-06-01

    Full Text Available Mobile Adhoc network is an instantaneous wireless network that is dynamic in nature. It supports single hop and multihop communication. In this infrastructure less network, clustering is a significant model to maintain the topology of the network. The clustering process includes different phases like cluster formation, cluster head selection, cluster maintenance. Choosing cluster head is important as the stability of the network depends on well-organized and resourceful cluster head. When the node has increased number of neighbors it can act as a link between the neighbor nodes which in further reduces the number of hops in multihop communication. Promisingly the node with more number of neighbors should also be available with enough energy to provide stability in the network. Hence these aspects demand the focus. In weight based cluster head selection, closeness and average minimum power required is considered for purging the ineligible nodes. The optimal set of nodes selected after purging will compete to become cluster head. The node with maximum weight selected as cluster head. Mathematical formulation is developed to show the proposed method provides optimum result. It is also suggested that weight factor in calculating the node weight should give precise importance to energy and node stability.

  3. Accelerated EM-based clustering of large data sets

    NARCIS (Netherlands)

    Verbeek, J.J.; Nunnink, J.R.J.; Vlassis, N.

    2006-01-01

    Motivated by the poor performance (linear complexity) of the EM algorithm in clustering large data sets, and inspired by the successful accelerated versions of related algorithms like k-means, we derive an accelerated variant of the EM algorithm for Gaussian mixtures that: (1) offers speedups that

  4. Cluster-based service discovery for heterogeneous wireless sensor networks

    NARCIS (Netherlands)

    Marin Perianu, Raluca; Scholten, Johan; Havinga, Paul J.M.; Hartel, Pieter H.

    2007-01-01

    We propose an energy-efficient service discovery protocol for heterogeneous wireless sensor networks. Our solution exploits a cluster overlay, where the clusterhead nodes form a distributed service registry. A service lookup results in visiting only the clusterhead nodes. We aim for minimizing the

  5. A fuzzy logic based clustering strategy for improving vehicular ad ...

    Indian Academy of Sciences (India)

    with safety and other information, and provide some services such as .... et al 2013) due to direction parameter taken into account (for two-way ... eters for decision making of cluster head in order to optimize CH selection process is the first time ...

  6. Facial Expression Recognition

    NARCIS (Netherlands)

    Pantic, Maja; Li, S.; Jain, A.

    2009-01-01

    Facial expression recognition is a process performed by humans or computers, which consists of: 1. Locating faces in the scene (e.g., in an image; this step is also referred to as face detection), 2. Extracting facial features from the detected face region (e.g., detecting the shape of facial

  7. Fault-tolerant measurement-based quantum computing with continuous-variable cluster states.

    Science.gov (United States)

    Menicucci, Nicolas C

    2014-03-28

    A long-standing open question about Gaussian continuous-variable cluster states is whether they enable fault-tolerant measurement-based quantum computation. The answer is yes. Initial squeezing in the cluster above a threshold value of 20.5 dB ensures that errors from finite squeezing acting on encoded qubits are below the fault-tolerance threshold of known qubit-based error-correcting codes. By concatenating with one of these codes and using ancilla-based error correction, fault-tolerant measurement-based quantum computation of theoretically indefinite length is possible with finitely squeezed cluster states.

  8. Energy Threshold-based Cluster Head Rotation for Routing Protocol in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Hadi Raheem Ali

    2018-05-01

    Full Text Available Energy efficiency represents a fundamental issue in WSNs, since the network lifetime period entirely depends on the energy of sensor nodes, which are usually battery-operated. In this article, an unequal clustering-based routing protocol has been suggested, where parameters of energy, distance, and density are involved in the cluster head election. Besides, the sizes of clusters are unequal according to distance, energy, and density. Furthermore, the cluster heads are not changed every round unless the residual energy reaches a specific threshold of energy. The outcomes of the conducted simulation confirmed that the performance of the suggested protocol achieves improvement in energy efficiency.

  9. Analyzing the factors affecting network lifetime cluster-based wireless sensor network

    International Nuclear Information System (INIS)

    Malik, A.S.; Qureshi, A.

    2010-01-01

    Cluster-based wireless sensor networks enable the efficient utilization of the limited energy resources of the deployed sensor nodes and hence prolong the node as well as network lifetime. Low Energy Adaptive Clustering Hierarchy (Leach) is one of the most promising clustering protocol proposed for wireless sensor networks. This paper provides the energy utilization and lifetime analysis for cluster-based wireless sensor networks based upon LEACH protocol. Simulation results identify some important factors that induce unbalanced energy utilization between the sensor nodes and hence affect the network lifetime in these types of networks. These results highlight the need for a standardized, adaptive and distributed clustering technique that can increase the network lifetime by further balancing the energy utilization among sensor nodes. (author)

  10. TRUSTWORTHY OPTIMIZED CLUSTERING BASED TARGET DETECTION AND TRACKING FOR WIRELESS SENSOR NETWORK

    Directory of Open Access Journals (Sweden)

    C. Jehan

    2016-06-01

    Full Text Available In this paper, an efficient approach is proposed to address the problem of target tracking in wireless sensor network (WSN. The problem being tackled here uses adaptive dynamic clustering scheme for tracking the target. It is a specific problem in object tracking. The proposed adaptive dynamic clustering target tracking scheme uses three steps for target tracking. The first step deals with the identification of clusters and cluster heads using OGSAFCM. Here, kernel fuzzy c-means (KFCM and gravitational search algorithm (GSA are combined to create clusters. At first, oppositional gravitational search algorithm (OGSA is used to optimize the initial clustering center and then the KFCM algorithm is availed to guide the classification and the cluster formation process. In the OGSA, the concept of the opposition based population initialization in the basic GSA to improve the convergence profile. The identified clusters are changed dynamically. The second step deals with the data transmission to the cluster heads. The third step deals with the transmission of aggregated data to the base station as well as the detection of target. From the experimental results, the proposed scheme efficiently and efficiently identifies the target. As a result the tracking error is minimized.

  11. Facial orientation and facial shape in extant great apes: a geometric morphometric analysis of covariation.

    Science.gov (United States)

    Neaux, Dimitri; Guy, Franck; Gilissen, Emmanuel; Coudyzer, Walter; Vignaud, Patrick; Ducrocq, Stéphane

    2013-01-01

    The organization of the bony face is complex, its morphology being influenced in part by the rest of the cranium. Characterizing the facial morphological variation and craniofacial covariation patterns in extant hominids is fundamental to the understanding of their evolutionary history. Numerous studies on hominid facial shape have proposed hypotheses concerning the relationship between the anterior facial shape, facial block orientation and basicranial flexion. In this study we test these hypotheses in a sample of adult specimens belonging to three extant hominid genera (Homo, Pan and Gorilla). Intraspecific variation and covariation patterns are analyzed using geometric morphometric methods and multivariate statistics, such as partial least squared on three-dimensional landmarks coordinates. Our results indicate significant intraspecific covariation between facial shape, facial block orientation and basicranial flexion. Hominids share similar characteristics in the relationship between anterior facial shape and facial block orientation. Modern humans exhibit a specific pattern in the covariation between anterior facial shape and basicranial flexion. This peculiar feature underscores the role of modern humans' highly-flexed basicranium in the overall integration of the cranium. Furthermore, our results are consistent with the hypothesis of a relationship between the reduction of the value of the cranial base angle and a downward rotation of the facial block in modern humans, and to a lesser extent in chimpanzees.

  12. The Effect of Cluster-Based Instruction on Mathematic Achievement in Inclusive Schools

    Science.gov (United States)

    Gunarhadi, Sunardi; Anwar, Mohammad; Andayani, Tri Rejeki; Shaari, Abdull Sukor

    2016-01-01

    The research aimed to investigate the effect of Cluster-Based Instruction (CBI) on the academic achievement of Mathematics in inclusive schools. The sample was 68 students in two intact classes, including those with learning disabilities, selected using a cluster random technique among 17 inclusive schools in the regency of Surakarta. The two…

  13. A Survey on the Taxonomy of Cluster-Based Routing Protocols for Homogeneous Wireless Sensor Networks

    Science.gov (United States)

    Naeimi, Soroush; Ghafghazi, Hamidreza; Chow, Chee-Onn; Ishii, Hiroshi

    2012-01-01

    The past few years have witnessed increased interest among researchers in cluster-based protocols for homogeneous networks because of their better scalability and higher energy efficiency than other routing protocols. Given the limited capabilities of sensor nodes in terms of energy resources, processing and communication range, the cluster-based protocols should be compatible with these constraints in either the setup state or steady data transmission state. With focus on these constraints, we classify routing protocols according to their objectives and methods towards addressing the shortcomings of clustering process on each stage of cluster head selection, cluster formation, data aggregation and data communication. We summarize the techniques and methods used in these categories, while the weakness and strength of each protocol is pointed out in details. Furthermore, taxonomy of the protocols in each phase is given to provide a deeper understanding of current clustering approaches. Ultimately based on the existing research, a summary of the issues and solutions of the attributes and characteristics of clustering approaches and some open research areas in cluster-based routing protocols that can be further pursued are provided. PMID:22969350

  14. Scalable Integrated Region-Based Image Retrieval Using IRM and Statistical Clustering.

    Science.gov (United States)

    Wang, James Z.; Du, Yanping

    Statistical clustering is critical in designing scalable image retrieval systems. This paper presents a scalable algorithm for indexing and retrieving images based on region segmentation. The method uses statistical clustering on region features and IRM (Integrated Region Matching), a measure developed to evaluate overall similarity between images…

  15. How clustering dynamics influence lumber utilization patterns in the Amish-based furniture industry in Ohio

    Science.gov (United States)

    Matthew S. Bumgardner; Gary W. Graham; P. Charles Goebel; Robert L. Romig

    2011-01-01

    Preliminary studies have suggested that the Amish-based furniture and related products manufacturing cluster located in and around Holmes County, Ohio, uses sizeable quantities of hardwood lumber. The number of firms within the cluster has grown even as the broader domestic furniture manufacturing sector has contracted. The present study was undertaken in 2008 (spring/...

  16. Profiling physical activity motivation based on self-determination theory: a cluster analysis approach.

    Science.gov (United States)

    Friederichs, Stijn Ah; Bolman, Catherine; Oenema, Anke; Lechner, Lilian

    2015-01-01

    In order to promote physical activity uptake and maintenance in individuals who do not comply with physical activity guidelines, it is important to increase our understanding of physical activity motivation among this group. The present study aimed to examine motivational profiles in a large sample of adults who do not comply with physical activity guidelines. The sample for this study consisted of 2473 individuals (31.4% male; age 44.6 ± 12.9). In order to generate motivational profiles based on motivational regulation, a cluster analysis was conducted. One-way analyses of variance were then used to compare the clusters in terms of demographics, physical activity level, motivation to be active and subjective experience while being active. Three motivational clusters were derived based on motivational regulation scores: a low motivation cluster, a controlled motivation cluster and an autonomous motivation cluster. These clusters differed significantly from each other with respect to physical activity behavior, motivation to be active and subjective experience while being active. Overall, the autonomous motivation cluster displayed more favorable characteristics compared to the other two clusters. The results of this study provide additional support for the importance of autonomous motivation in the context of physical activity behavior. The three derived clusters may be relevant in the context of physical activity interventions as individuals within the different clusters might benefit most from different intervention approaches. In addition, this study shows that cluster analysis is a useful method for differentiating between motivational profiles in large groups of individuals who do not comply with physical activity guidelines.

  17. Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Gulnaz Ahmed

    2017-02-01

    Full Text Available The longer network lifetime of Wireless Sensor Networks (WSNs is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is the best choice for these kind of issues. In this paper, we introduce a novel clustering protocol called Markov chain model-based optimal cluster heads (MOCHs selection for WSNs. In our proposed model, we introduce a simple strategy for the optimal number of cluster heads selection to overcome the problem of uneven energy distribution in the network. The attractiveness of our model is that the BS controls the number of cluster heads while the cluster heads control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured in each cluster. We perform an extensive range of simulation using five quality measures, namely: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of cluster heads in the network, and the transmission time of the network to analyze the proposed model. We compare MOCHs against Sleep-awake Energy Efficient Distributed (SEED clustering, Artificial Bee Colony (ABC, Zone Based Routing (ZBR, and Centralized Energy Efficient Clustering (CEEC using the above-discussed quality metrics and found that the lifetime of the proposed model is almost 1095, 2630, 3599, and 2045 rounds (time steps greater than SEED, ABC, ZBR, and CEEC, respectively. The obtained results demonstrate that the MOCHs is better than SEED, ABC, ZBR, and CEEC in terms of energy efficiency and the network throughput.

  18. Peripheral facial weakness (Bell's palsy).

    Science.gov (United States)

    Basić-Kes, Vanja; Dobrota, Vesna Dermanović; Cesarik, Marijan; Matovina, Lucija Zadro; Madzar, Zrinko; Zavoreo, Iris; Demarin, Vida

    2013-06-01

    Peripheral facial weakness is a facial nerve damage that results in muscle weakness on one side of the face. It may be idiopathic (Bell's palsy) or may have a detectable cause. Almost 80% of peripheral facial weakness cases are primary and the rest of them are secondary. The most frequent causes of secondary peripheral facial weakness are systemic viral infections, trauma, surgery, diabetes, local infections, tumor, immune disorders, drugs, degenerative diseases of the central nervous system, etc. The diagnosis relies upon the presence of typical signs and symptoms, blood chemistry tests, cerebrospinal fluid investigations, nerve conduction studies and neuroimaging methods (cerebral MRI, x-ray of the skull and mastoid). Treatment of secondary peripheral facial weakness is based on therapy for the underlying disorder, unlike the treatment of Bell's palsy that is controversial due to the lack of large, randomized, controlled, prospective studies. There are some indications that steroids or antiviral agents are beneficial but there are also studies that show no beneficial effect. Additional treatments include eye protection, physiotherapy, acupuncture, botulinum toxin, or surgery. Bell's palsy has a benign prognosis with complete recovery in about 80% of patients, 15% experience some mode of permanent nerve damage and severe consequences remain in 5% of patients.

  19. A Brain–Computer Interface for Potential Nonverbal Facial Communication Based on EEG Signals Related to Specific Emotions

    Directory of Open Access Journals (Sweden)

    Koji eKashihara

    2014-08-01

    Full Text Available Unlike assistive technology for verbal communication, the brain–machine or brain–computer interface (BMI/BCI has not been established as a nonverbal communication tool for amyotrophic lateral sclerosis (ALS patients. Face-to-face communication enables access to rich emotional information, but individuals suffering from neurological disorders, such as ALS and autism, may not express their emotions or communicate their negative feelings. Although emotions may be inferred by looking at facial expressions, emotional prediction for neutral faces necessitates advanced judgment. The process that underlies brain neuronal responses to neutral faces and causes emotional changes remains unknown. To address this problem, therefore, this study attempted to decode conditioned emotional reactions to neutral face stimuli. This direction was motivated by the assumption that if electroencephalogram (EEG signals can be used to detect patients’ emotional responses to specific inexpressive faces, the results could be incorporated into the design and development of BMI/BCI-based nonverbal communication tools. To these ends, this study investigated how a neutral face associated with a negative emotion modulates rapid central responses in face processing and then identified cortical activities. The conditioned neutral face-triggered event-related potentials that originated from the posterior temporal lobe statistically significantly changed during late face processing (600–700 ms after stimulus, rather than in early face processing activities, such as P1 and N170 responses. Source localization revealed that the conditioned neutral faces increased activity in the right fusiform gyrus. This study also developed an efficient method for detecting implicit negative emotional responses to specific faces by using EEG signals.

  20. Facial Asymmetry Evaluation in Juvenile Idiopathic Arthritis Patients Based On Cone-Beam Computed Tomography And 3D Photography

    DEFF Research Database (Denmark)

    Economou, Stalo; Stoustrup, Peter Bangsgaard; Kristensen, Kasper Dahl

    AIMS: The aim of the study was to assess the degree of and correlation between facial hard and soft tissue asymmetry in patients with juvenile idiopathic arthritis, identify valid soft tissue points for clinical examination and assess the smallest clinical detectable level of dentofacial asymmetr...

  1. Network based approaches reveal clustering in protein point patterns

    Science.gov (United States)

    Parker, Joshua; Barr, Valarie; Aldridge, Joshua; Samelson, Lawrence E.; Losert, Wolfgang

    2014-03-01

    Recent advances in super-resolution imaging have allowed for the sub-diffraction measurement of the spatial location of proteins on the surfaces of T-cells. The challenge is to connect these complex point patterns to the internal processes and interactions, both protein-protein and protein-membrane. We begin analyzing these patterns by forming a geometric network amongst the proteins and looking at network measures, such the degree distribution. This allows us to compare experimentally observed patterns to models. Specifically, we find that the experimental patterns differ from heterogeneous Poisson processes, highlighting an internal clustering structure. Further work will be to compare our results to simulated protein-protein interactions to determine clustering mechanisms.

  2. Brain tumor segmentation based on a hybrid clustering technique

    Directory of Open Access Journals (Sweden)

    Eman Abdel-Maksoud

    2015-03-01

    This paper presents an efficient image segmentation approach using K-means clustering technique integrated with Fuzzy C-means algorithm. It is followed by thresholding and level set segmentation stages to provide an accurate brain tumor detection. The proposed technique can get benefits of the K-means clustering for image segmentation in the aspects of minimal computation time. In addition, it can get advantages of the Fuzzy C-means in the aspects of accuracy. The performance of the proposed image segmentation approach was evaluated by comparing it with some state of the art segmentation algorithms in case of accuracy, processing time, and performance. The accuracy was evaluated by comparing the results with the ground truth of each processed image. The experimental results clarify the effectiveness of our proposed approach to deal with a higher number of segmentation problems via improving the segmentation quality and accuracy in minimal execution time.

  3. Map-based trigonometric parallaxes of open clusters - The Pleiades

    Science.gov (United States)

    Gatewood, George; Castelaz, Michael; Han, Inwoo; Persinger, Timothy; Stein, John

    1990-01-01

    The multichannel astrometric photometer and Thaw refractor of the University of Pittsburgh's Allegheny Observatory have been used to determine the trigonometric parallax of the Pleiades star cluster. The distance determined, 150 with a standard error of 18 parsecs, places the cluster slightly farther away than generally accepted. This suggests that the basis of many estimations of the cosmic distance scale is approximately 20 percent short. The accuracy of the determination is limited by the number and choice of reference stars. With careful attention to the selection of reference stars in several Pleiades regions, it should be possible to examine differences in the photometric and trigonometric modulus at a precision of 0.1 magnitudes.

  4. Flocking-based Document Clustering on the Graphics Processing Unit

    Energy Technology Data Exchange (ETDEWEB)

    Cui, Xiaohui [ORNL; Potok, Thomas E [ORNL; Patton, Robert M [ORNL; ST Charles, Jesse Lee [ORNL

    2008-01-01

    Abstract?Analyzing and grouping documents by content is a complex problem. One explored method of solving this problem borrows from nature, imitating the flocking behavior of birds. Each bird represents a single document and flies toward other documents that are similar to it. One limitation of this method of document clustering is its complexity O(n2). As the number of documents grows, it becomes increasingly difficult to receive results in a reasonable amount of time. However, flocking behavior, along with most naturally inspired algorithms such as ant colony optimization and particle swarm optimization, are highly parallel and have found increased performance on expensive cluster computers. In the last few years, the graphics processing unit (GPU) has received attention for its ability to solve highly-parallel and semi-parallel problems much faster than the traditional sequential processor. Some applications see a huge increase in performance on this new platform. The cost of these high-performance devices is also marginal when compared with the price of cluster machines. In this paper, we have conducted research to exploit this architecture and apply its strengths to the document flocking problem. Our results highlight the potential benefit the GPU brings to all naturally inspired algorithms. Using the CUDA platform from NIVIDA? we developed a document flocking implementation to be run on the NIVIDA?GEFORCE 8800. Additionally, we developed a similar but sequential implementation of the same algorithm to be run on a desktop CPU. We tested the performance of each on groups of news articles ranging in size from 200 to 3000 documents. The results of these tests were very significant. Performance gains ranged from three to nearly five times improvement of the GPU over the CPU implementation. This dramatic improvement in runtime makes the GPU a potentially revolutionary platform for document clustering algorithms.

  5. A Cluster-based Approach Towards Detecting and Modeling Network Dictionary Attacks

    Directory of Open Access Journals (Sweden)

    A. Tajari Siahmarzkooh

    2016-12-01

    Full Text Available In this paper, we provide an approach to detect network dictionary attacks using a data set collected as flows based on which a clustered graph is resulted. These flows provide an aggregated view of the network traffic in which the exchanged packets in the network are considered so that more internally connected nodes would be clustered. We show that dictionary attacks could be detected through some parameters namely the number and the weight of clusters in time series and their evolution over the time. Additionally, the Markov model based on the average weight of clusters,will be also created. Finally, by means of our suggested model, we demonstrate that artificial clusters of the flows are created for normal and malicious traffic. The results of the proposed approach on CAIDA 2007 data set suggest a high accuracy for the model and, therefore, it provides a proper method for detecting the dictionary attack.

  6. Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions.

    Science.gov (United States)

    Tokuda, Tomoki; Yoshimoto, Junichiro; Shimizu, Yu; Okada, Go; Takamura, Masahiro; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

    2017-01-01

    We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

  7. Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions.

    Directory of Open Access Journals (Sweden)

    Tomoki Tokuda

    Full Text Available We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data.

  8. Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions

    Science.gov (United States)

    Yoshimoto, Junichiro; Shimizu, Yu; Okada, Go; Takamura, Masahiro; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

    2017-01-01

    We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data. PMID:29049392

  9. Gender in facial representations: a contrast-based study of adaptation within and between the sexes.

    Science.gov (United States)

    Oruç, Ipek; Guo, Xiaoyue M; Barton, Jason J S

    2011-01-18

    Face aftereffects are proving to be an effective means of examining the properties of face-specific processes in the human visual system. We examined the role of gender in the neural representation of faces using a contrast-based adaptation method. If faces of different genders share the same representational face space, then adaptation to a face of one gender should affect both same- and different-gender faces. Further, if these aftereffects differ in magnitude, this may indicate distinct gender-related factors in the organization of this face space. To control for a potential confound between physical similarity and gender, we used a Bayesian ideal observer and human discrimination data to construct a stimulus set in which pairs of different-gender faces were equally dissimilar as same-gender pairs. We found that the recognition of both same-gender and different-gender faces was suppressed following a brief exposure of 100 ms. Moreover, recognition was more suppressed for test faces of a different-gender than those of the same-gender as the adaptor, despite the equivalence in physical and psychophysical similarity. Our results suggest that male and female faces likely occupy the same face space, allowing transfer of aftereffects between the genders, but that there are special properties that emerge along gender-defining dimensions of this space.

  10. Reconstruction of a digital core containing clay minerals based on a clustering algorithm

    Science.gov (United States)

    He, Yanlong; Pu, Chunsheng; Jing, Cheng; Gu, Xiaoyu; Chen, Qingdong; Liu, Hongzhi; Khan, Nasir; Dong, Qiaoling

    2017-10-01

    It is difficult to obtain a core sample and information for digital core reconstruction of mature sandstone reservoirs around the world, especially for an unconsolidated sandstone reservoir. Meanwhile, reconstruction and division of clay minerals play a vital role in the reconstruction of the digital cores, although the two-dimensional data-based reconstruction methods are specifically applicable as the microstructure reservoir simulation methods for the sandstone reservoir. However, reconstruction of clay minerals is still challenging from a research viewpoint for the better reconstruction of various clay minerals in the digital cores. In the present work, the content of clay minerals was considered on the basis of two-dimensional information about the reservoir. After application of the hybrid method, and compared with the model reconstructed by the process-based method, the digital core containing clay clusters without the labels of the clusters' number, size, and texture were the output. The statistics and geometry of the reconstruction model were similar to the reference model. In addition, the Hoshen-Kopelman algorithm was used to label various connected unclassified clay clusters in the initial model and then the number and size of clay clusters were recorded. At the same time, the K -means clustering algorithm was applied to divide the labeled, large connecting clusters into smaller clusters on the basis of difference in the clusters' characteristics. According to the clay minerals' characteristics, such as types, textures, and distributions, the digital core containing clay minerals was reconstructed by means of the clustering algorithm and the clay clusters' structure judgment. The distributions and textures of the clay minerals of the digital core were reasonable. The clustering algorithm improved the digital core reconstruction and provided an alternative method for the simulation of different clay minerals in the digital cores.

  11. Reconstruction of a digital core containing clay minerals based on a clustering algorithm.

    Science.gov (United States)

    He, Yanlong; Pu, Chunsheng; Jing, Cheng; Gu, Xiaoyu; Chen, Qingdong; Liu, Hongzhi; Khan, Nasir; Dong, Qiaoling

    2017-10-01

    It is difficult to obtain a core sample and information for digital core reconstruction of mature sandstone reservoirs around the world, especially for an unconsolidated sandstone reservoir. Meanwhile, reconstruction and division of clay minerals play a vital role in the reconstruction of the digital cores, although the two-dimensional data-based reconstruction methods are specifically applicable as the microstructure reservoir simulation methods for the sandstone reservoir. However, reconstruction of clay minerals is still challenging from a research viewpoint for the better reconstruction of various clay minerals in the digital cores. In the present work, the content of clay minerals was considered on the basis of two-dimensional information about the reservoir. After application of the hybrid method, and compared with the model reconstructed by the process-based method, the digital core containing clay clusters without the labels of the clusters' number, size, and texture were the output. The statistics and geometry of the reconstruction model were similar to the reference model. In addition, the Hoshen-Kopelman algorithm was used to label various connected unclassified clay clusters in the initial model and then the number and size of clay clusters were recorded. At the same time, the K-means clustering algorithm was applied to divide the labeled, large connecting clusters into smaller clusters on the basis of difference in the clusters' characteristics. According to the clay minerals' characteristics, such as types, textures, and distributions, the digital core containing clay minerals was reconstructed by means of the clustering algorithm and the clay clusters' structure judgment. The distributions and textures of the clay minerals of the digital core were reasonable. The clustering algorithm improved the digital core reconstruction and provided an alternative method for the simulation of different clay minerals in the digital cores.

  12. Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis.

    Science.gov (United States)

    Li, Chaoshun; Zhou, Jianzhong

    2014-09-01

    Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  13. A small-world network model of facial emotion recognition.

    Science.gov (United States)

    Takehara, Takuma; Ochiai, Fumio; Suzuki, Naoto

    2016-01-01

    Various models have been proposed to increase understanding of the cognitive basis of facial emotions. Despite those efforts, interactions between facial emotions have received minimal attention. If collective behaviours relating to each facial emotion in the comprehensive cognitive system could be assumed, specific facial emotion relationship patterns might emerge. In this study, we demonstrate that the frameworks of complex networks can effectively capture those patterns. We generate 81 facial emotion images (6 prototypes and 75 morphs) and then ask participants to rate degrees of similarity in 3240 facial emotion pairs in a paired comparison task. A facial emotion network constructed on the basis of similarity clearly forms a small-world network, which features an extremely short average network distance and close connectivity. Further, even if two facial emotions have opposing valences, they are connected within only two steps. In addition, we show that intermediary morphs are crucial for maintaining full network integration, whereas prototypes are not at all important. These results suggest the existence of collective behaviours in the cognitive systems of facial emotions and also describe why people can efficiently recognize facial emotions in terms of information transmission and propagation. For comparison, we construct three simulated networks--one based on the categorical model, one based on the dimensional model, and one random network. The results reveal that small-world connectivity in facial emotion networks is apparently different from those networks, suggesting that a small-world network is the most suitable model for capturing the cognitive basis of facial emotions.

  14. Substructures in DAFT/FADA survey clusters based on XMM and optical data

    Science.gov (United States)

    Durret, F.; DAFT/FADA Team

    2014-07-01

    The DAFT/FADA survey was initiated to perform weak lensing tomography on a sample of 90 massive clusters in the redshift range [0.4,0.9] with HST imaging available. The complementary deep multiband imaging constitutes a high quality imaging data base for these clusters. In X-rays, we have analysed the XMM-Newton and/or Chandra data available for 32 clusters, and for 23 clusters we fit the X-ray emissivity with a beta-model and subtract it to search for substructures in the X-ray gas. This study was coupled with a dynamical analysis for the 18 clusters with at least 15 spectroscopic galaxy redshifts in the cluster range, based on a Serna & Gerbal (SG) analysis. We detected ten substructures in eight clusters by both methods (X-rays and SG). The percentage of mass included in substructures is found to be roughly constant with redshift, with values of 5-15%. Most of the substructures detected both in X-rays and with the SG method are found to be relatively recent infalls, probably at their first cluster pericenter approach.

  15. KM-FCM: A fuzzy clustering optimization algorithm based on Mahalanobis distance

    Directory of Open Access Journals (Sweden)

    Zhiwen ZU

    2018-04-01

    Full Text Available The traditional fuzzy clustering algorithm uses Euclidean distance as the similarity criterion, which is disadvantageous to the multidimensional data processing. In order to solve this situation, Mahalanobis distance is used instead of the traditional Euclidean distance, and the optimization of fuzzy clustering algorithm based on Mahalanobis distance is studied to enhance the clustering effect and ability. With making the initialization means by Heuristic search algorithm combined with k-means algorithm, and in terms of the validity function which could automatically adjust the optimal clustering number, an optimization algorithm KM-FCM is proposed. The new algorithm is compared with FCM algorithm, FCM-M algorithm and M-FCM algorithm in three standard data sets. The experimental results show that the KM-FCM algorithm is effective. It has higher clustering accuracy than FCM, FCM-M and M-FCM, recognizing high-dimensional data clustering well. It has global optimization effect, and the clustering number has no need for setting in advance. The new algorithm provides a reference for the optimization of fuzzy clustering algorithm based on Mahalanobis distance.

  16. Distributed Similarity based Clustering and Compressed Forwarding for wireless sensor networks.

    Science.gov (United States)

    Arunraja, Muruganantham; Malathi, Veluchamy; Sakthivel, Erulappan

    2015-11-01

    Wireless sensor networks are engaged in various data gathering applications. The major bottleneck in wireless data gathering systems is the finite energy of sensor nodes. By conserving the on board energy, the life span of wireless sensor network can be well extended. Data communication being the dominant energy consuming activity of wireless sensor network, data reduction can serve better in conserving the nodal energy. Spatial and temporal correlation among the sensor data is exploited to reduce the data communications. Data similar cluster formation is an effective way to exploit spatial correlation among the neighboring sensors. By sending only a subset of data and estimate the rest using this subset is the contemporary way of exploiting temporal correlation. In Distributed Similarity based Clustering and Compressed Forwarding for wireless sensor networks, we construct data similar iso-clusters with minimal communication overhead. The intra-cluster communication is reduced using adaptive-normalized least mean squares based dual prediction framework. The cluster head reduces the inter-cluster data payload using a lossless compressive forwarding technique. The proposed work achieves significant data reduction in both the intra-cluster and the inter-cluster communications, with the optimal data accuracy of collected data. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  17. Genetic determinants of facial clefting

    DEFF Research Database (Denmark)

    Jugessur, Astanand; Shi, Min; Gjessing, Håkon Kristian

    2009-01-01

    BACKGROUND: Facial clefts are common birth defects with a strong genetic component. To identify fetal genetic risk factors for clefting, 1536 SNPs in 357 candidate genes were genotyped in two population-based samples from Scandinavia (Norway: 562 case-parent and 592 control-parent triads; Denmark...

  18. SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis

    International Nuclear Information System (INIS)

    Harmon, S; Wendelberger, B; Jeraj, R

    2014-01-01

    Purpose: Radiogenomics aims to establish relationships between patient genotypes and imaging phenotypes. An open question remains on how best to integrate information from these distinct datasets. This work investigates if similarities in genetic features across patients correspond to similarities in PET-imaging features, assessed with various clustering algorithms. Methods: [ 18 F]FDG PET data was obtained for 26 NSCLC patients from a public database (TCIA). Tumors were contoured using an in-house segmentation algorithm combining gradient and region-growing techniques; resulting ROIs were used to extract 54 PET-based features. Corresponding genetic microarray data containing 48,778 elements were also obtained for each tumor. Given mismatch in feature sizes, two dimension reduction techniques were also applied to the genetic data: principle component analysis (PCA) and selective filtering of 25 NSCLC-associated genes-ofinterest (GOI). Gene datasets (full, PCA, and GOI) and PET feature datasets were independently clustered using K-means and hierarchical clustering using variable number of clusters (K). Jaccard Index (JI) was used to score similarity of cluster assignments across different datasets. Results: Patient clusters from imaging data showed poor similarity to clusters from gene datasets, regardless of clustering algorithms or number of clusters (JI mean = 0.3429±0.1623). Notably, we found clustering algorithms had different sensitivities to data reduction techniques. Using hierarchical clustering, the PCA dataset showed perfect cluster agreement to the full-gene set (JI =1) for all values of K, and the agreement between the GOI set and the full-gene set decreased as number of clusters increased (JI=0.9231 and 0.5769 for K=2 and 5, respectively). K-means clustering assignments were highly sensitive to data reduction and showed poor stability for different values of K (JI range : 0.2301–1). Conclusion: Using commonly-used clustering algorithms, we found

  19. SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Harmon, S; Wendelberger, B [University of Wisconsin-Madison, Madison, WI (United States); Jeraj, R [University of Wisconsin-Madison, Madison, WI (United States); University of Ljubljana (Slovenia)

    2014-06-01

    Purpose: Radiogenomics aims to establish relationships between patient genotypes and imaging phenotypes. An open question remains on how best to integrate information from these distinct datasets. This work investigates if similarities in genetic features across patients correspond to similarities in PET-imaging features, assessed with various clustering algorithms. Methods: [{sup 18}F]FDG PET data was obtained for 26 NSCLC patients from a public database (TCIA). Tumors were contoured using an in-house segmentation algorithm combining gradient and region-growing techniques; resulting ROIs were used to extract 54 PET-based features. Corresponding genetic microarray data containing 48,778 elements were also obtained for each tumor. Given mismatch in feature sizes, two dimension reduction techniques were also applied to the genetic data: principle component analysis (PCA) and selective filtering of 25 NSCLC-associated genes-ofinterest (GOI). Gene datasets (full, PCA, and GOI) and PET feature datasets were independently clustered using K-means and hierarchical clustering using variable number of clusters (K). Jaccard Index (JI) was used to score similarity of cluster assignments across different datasets. Results: Patient clusters from imaging data showed poor similarity to clusters from gene datasets, regardless of clustering algorithms or number of clusters (JI{sub mean}= 0.3429±0.1623). Notably, we found clustering algorithms had different sensitivities to data reduction techniques. Using hierarchical clustering, the PCA dataset showed perfect cluster agreement to the full-gene set (JI =1) for all values of K, and the agreement between the GOI set and the full-gene set decreased as number of clusters increased (JI=0.9231 and 0.5769 for K=2 and 5, respectively). K-means clustering assignments were highly sensitive to data reduction and showed poor stability for different values of K (JI{sub range}: 0.2301–1). Conclusion: Using commonly-used clustering algorithms

  20. Variant facial artery in the submandibular region.

    Science.gov (United States)

    Vadgaonkar, Rajanigandha; Rai, Rajalakshmi; Prabhu, Latha V; Bv, Murlimanju; Samapriya, Neha

    2012-07-01

    Facial artery has been considered to be the most important vascular pedicle in facial rejuvenation procedures and submandibular gland (SMG) resection. It usually arises from the external carotid artery and passes from the carotid to digastric triangle, deep to the posterior belly of digastric muscle, and lodges in a groove at the posterior end of the SMG. It then passes between SMG and the mandible to reach the face after winding around the base of the mandible. During a routine dissection, in a 62-year-old female cadaver, in Kasturba Medical College Mangalore, an unusual pattern in the cervical course of facial artery was revealed. The right facial artery was found to pierce the whole substance of the SMG before winding around the lower border of the mandible to enter the facial region. Awareness of existence of such a variant and its comparison to the normal anatomy will be useful to oral and maxillofacial surgeons.

  1. Unsupervised Performance Evaluation Strategy for Bridge Superstructure Based on Fuzzy Clustering and Field Data

    Directory of Open Access Journals (Sweden)

    Yubo Jiao

    2013-01-01

    Full Text Available Performance evaluation of a bridge is critical for determining the optimal maintenance strategy. An unsupervised bridge superstructure state assessment method is proposed in this paper based on fuzzy clustering and bridge field measured data. Firstly, the evaluation index system of bridge is constructed. Secondly, a certain number of bridge health monitoring data are selected as clustering samples to obtain the fuzzy similarity matrix and fuzzy equivalent matrix. Finally, different thresholds are selected to form dynamic clustering maps and determine the best classification based on statistic analysis. The clustering result is regarded as a sample base, and the bridge state can be evaluated by calculating the fuzzy nearness between the unknown bridge state data and the sample base. Nanping Bridge in Jilin Province is selected as the engineering project to verify the effectiveness of the proposed method.

  2. An integrated approach to fingerprint indexing using spectral clustering based on minutiae points

    CSIR Research Space (South Africa)

    Mngenge, NA

    2015-07-01

    Full Text Available this problem by constructing a rotational, scale and translation (RST) invariant fingerprint descriptor based on minutiae points. The proposed RST invariant descriptor dimensions are then reduced and passed to a spectral clustering algorithm which automatically...

  3. A time-series approach for clustering farms based on slaughterhouse health aberration data.

    Science.gov (United States)

    Hulsegge, B; de Greef, K H

    2018-05-01

    A large amount of data is collected routinely in meat inspection in pig slaughterhouses. A time series clustering approach is presented and applied that groups farms based on similar statistical characteristics of meat inspection data over time. A three step characteristic-based clustering approach was used from the idea that the data contain more info than the incidence figures. A stratified subset containing 511,645 pigs was derived as a study set from 3.5 years of meat inspection data. The monthly averages of incidence of pleuritis and of pneumonia of 44 Dutch farms (delivering 5149 batches to 2 pig slaughterhouses) were subjected to 1) derivation of farm level data characteristics 2) factor analysis and 3) clustering into groups of farms. The characteristic-based clustering was able to cluster farms for both lung aberrations. Three groups of data characteristics were informative, describing incidence, time pattern and degree of autocorrelation. The consistency of clustering similar farms was confirmed by repetition of the analysis in a larger dataset. The robustness of the clustering was tested on a substantially extended dataset. This confirmed the earlier results, three data distribution aspects make up the majority of distinction between groups of farms and in these groups (clusters) the majority of the farms was allocated comparable to the earlier allocation (75% and 62% for pleuritis and pneumonia, respectively). The difference between pleuritis and pneumonia in their seasonal dependency was confirmed, supporting the biological relevance of the clustering. Comparison of the identified clusters of statistically comparable farms can be used to detect farm level risk factors causing the health aberrations beyond comparison on disease incidence and trend alone. Copyright © 2018 Elsevier B.V. All rights reserved.

  4. Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

    Science.gov (United States)

    Hallac, David; Vare, Sagar; Boyd, Stephen; Leskovec, Jure

    2018-01-01

    Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios. PMID:29770257

  5. Recognition of genetically modified product based on affinity propagation clustering and terahertz spectroscopy

    Science.gov (United States)

    Liu, Jianjun; Kan, Jianquan

    2018-04-01

    In this paper, based on the terahertz spectrum, a new identification method of genetically modified material by support vector machine (SVM) based on affinity propagation clustering is proposed. This algorithm mainly uses affinity propagation clustering algorithm to make cluster analysis and labeling on unlabeled training samples, and in the iterative process, the existing SVM training data are continuously updated, when establishing the identification model, it does not need to manually label the training samples, thus, the error caused by the human labeled samples is reduced, and the identification accuracy of the model is greatly improved.

  6. Novel Clustering Method Based on K-Medoids and Mobility Metric

    Directory of Open Access Journals (Sweden)

    Y. Hamzaoui

    2018-06-01

    Full Text Available The structure and constraint of MANETS influence negatively the performance of QoS, moreover the main routing protocols proposed generally operate in flat routing. Hence, this structure gives the bad results of QoS when the network becomes larger and denser. To solve this problem we use one of the most popular methods named clustering. The present paper comes within the frameworks of research to improve the QoS in MANETs. In this paper we propose a new algorithm of clustering based on the new mobility metric and K-Medoid to distribute the nodes into several clusters. Intuitively our algorithm can give good results in terms of stability of the cluster, and can also extend life time of cluster head.

  7. Collaborative Filtering Based on Sequential Extraction of User-Item Clusters

    Science.gov (United States)

    Honda, Katsuhiro; Notsu, Akira; Ichihashi, Hidetomo

    Collaborative filtering is a computational realization of “word-of-mouth” in network community, in which the items prefered by “neighbors” are recommended. This paper proposes a new item-selection model for extracting user-item clusters from rectangular relation matrices, in which mutual relations between users and items are denoted in an alternative process of “liking or not”. A technique for sequential co-cluster extraction from rectangular relational data is given by combining the structural balancing-based user-item clustering method with sequential fuzzy cluster extraction appraoch. Then, the tecunique is applied to the collaborative filtering problem, in which some items may be shared by several user clusters.

  8. Neural network based cluster creation in the ATLAS silicon Pixel Detector

    CERN Document Server

    Andreazza, A; The ATLAS collaboration

    2013-01-01

    The read-out from individual pixels on planar semi-conductor sensors are grouped into clusters to reconstruct the location where a charged particle passed through the sensor. The resolution given by individual pixel sizes is significantly improved by using the information from the charge sharing between pixels. Such analog cluster creation techniques have been used by the ATLAS experiment for many years to obtain an excellent performance. However, in dense environments, such as those inside high-energy jets, clusters have an increased probability of merging the charge deposited by multiple particles. Recently, a neural network based algorithm which estimates both the cluster position and whether a cluster should be split has been developed for the ATLAS Pixel Detector. The algorithm significantly reduces ambiguities in the assignment of pixel detector measurement to tracks within jets and improves the position accuracy with respect to standard interpolation techniques by taking into account the 2-dimensional ...

  9. Internet2-based 3D PET image reconstruction using a PC cluster

    International Nuclear Information System (INIS)

    Shattuck, D.W.; Rapela, J.; Asma, E.; Leahy, R.M.; Chatzioannou, A.; Qi, J.

    2002-01-01

    We describe an approach to fast iterative reconstruction from fully three-dimensional (3D) PET data using a network of PentiumIII PCs configured as a Beowulf cluster. To facilitate the use of this system, we have developed a browser-based interface using Java. The system compresses PET data on the user's machine, sends these data over a network, and instructs the PC cluster to reconstruct the image. The cluster implements a parallelized version of our preconditioned conjugate gradient method for fully 3D MAP image reconstruction. We report on the speed-up factors using the Beowulf approach and the impacts of communication latencies in the local cluster network and the network connection between the user's machine and our PC cluster. (author)

  10. Neural network based cluster creation in the ATLAS silicon pixel detector

    CERN Document Server

    Selbach, K E; The ATLAS collaboration

    2012-01-01

    The read-out from individual pixels on planar semi-conductor sensors are grouped into clusters to reconstruct the location where a charged particle passed through the sensor. The resolution given by individual pixel sizes is significantly improved by using the information from the charge sharing between pixels. Such analog cluster creation techniques have been used by the ATLAS experiment for many years to obtain an excellent performance. However, in dense environments, such as those inside high-energy jets, clusters have an increased probability of merging the charge deposited by multiple particles. Recently, a neural network based algorithm which estimates both the cluster position and whether a cluster should be split has been developed for the ATLAS pixel detector. The algorithm significantly reduces ambiguities in the assignment of pixel detector measurement to tracks within jets and improves the position accuracy with respect to standard interpolation techniques by taking into account the 2-dimensional ...

  11. An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

    Directory of Open Access Journals (Sweden)

    Qingming Zhan

    2017-08-01

    Full Text Available An adaptive spatial clustering (ASC algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation, or Voronoi diagram. It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need for prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.

  12. A Cluster-Based Fuzzy Fusion Algorithm for Event Detection in Heterogeneous Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    ZiQi Hao

    2015-01-01

    Full Text Available As limited energy is one of the tough challenges in wireless sensor networks (WSN, energy saving becomes important in increasing the lifecycle of the network. Data fusion enables combining information from several sources thus to provide a unified scenario, which can significantly save sensor energy and enhance sensing data accuracy. In this paper, we propose a cluster-based data fusion algorithm for event detection. We use k-means algorithm to form the nodes into clusters, which can significantly reduce the energy consumption of intracluster communication. Distances between cluster heads and event and energy of clusters are fuzzified, thus to use a fuzzy logic to select the clusters that will participate in data uploading and fusion. Fuzzy logic method is also used by cluster heads for local decision, and then the local decision results are sent to the base station. Decision-level fusion for final decision of event is performed by base station according to the uploaded local decisions and fusion support degree of clusters calculated by fuzzy logic method. The effectiveness of this algorithm is demonstrated by simulation results.

  13. Fatigue Feature Extraction Analysis based on a K-Means Clustering Approach

    Directory of Open Access Journals (Sweden)

    M.F.M. Yunoh

    2015-06-01

    Full Text Available This paper focuses on clustering analysis using a K-means approach for fatigue feature dataset extraction. The aim of this study is to group the dataset as closely as possible (homogeneity for the scattered dataset. Kurtosis, the wavelet-based energy coefficient and fatigue damage are calculated for all segments after the extraction process using wavelet transform. Kurtosis, the wavelet-based energy coefficient and fatigue damage are used as input data for the K-means clustering approach. K-means clustering calculates the average distance of each group from the centroid and gives the objective function values. Based on the results, maximum values of the objective function can be seen in the two centroid clusters, with a value of 11.58. The minimum objective function value is found at 8.06 for five centroid clusters. It can be seen that the objective function with the lowest value for the number of clusters is equal to five; which is therefore the best cluster for the dataset.

  14. Analyzing Dynamic Probabilistic Risk Assessment Data through Topology-Based Clustering

    Energy Technology Data Exchange (ETDEWEB)

    Diego Mandelli; Dan Maljovec; BeiWang; Valerio Pascucci; Peer-Timo Bremer

    2013-09-01

    We investigate the use of a topology-based clustering technique on the data generated by dynamic event tree methodologies. The clustering technique we utilizes focuses on a domain-partitioning algorithm based on topological structures known as the Morse-Smale complex, which partitions the data points into clusters based on their uniform gradient flow behavior. We perform both end state analysis and transient analysis to classify the set of nuclear scenarios. We demonstrate our methodology on a dataset generated for a sodium-cooled fast reactor during an aircraft crash scenario. The simulation tracks the temperature of the reactor as well as the time for a recovery team to fix the passive cooling system. Combined with clustering results obtained previously through mean shift methodology, we present the user with complementary views of the data that help illuminate key features that may be otherwise hidden using a single methodology. By clustering the data, the number of relevant test cases to be selected for further analysis can be drastically reduced by selecting a representative from each cluster. Identifying the similarities of simulations within a cluster can also aid in the drawing of important conclusions with respect to safety analysis.

  15. Fuzzy Weight Cluster-Based Routing Algorithm for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Teng Gao

    2015-01-01

    Full Text Available Cluster-based protocol is a kind of important routing in wireless sensor networks. However, due to the uneven distribution of cluster heads in classical clustering algorithm, some nodes may run out of energy too early, which is not suitable for large-scale wireless sensor networks. In this paper, a distributed clustering algorithm based on fuzzy weighted attributes is put forward to ensure both energy efficiency and extensibility. On the premise of a comprehensive consideration of all attributes, the corresponding weight of each parameter is assigned by using the direct method of fuzzy engineering theory. Then, each node works out property value. These property values will be mapped to the time axis and be triggered by a timer to broadcast cluster headers. At the same time, the radio coverage method is adopted, in order to avoid collisions and to ensure the symmetrical distribution of cluster heads. The aggregated data are forwarded to the sink node in the form of multihop. The simulation results demonstrate that clustering algorithm based on fuzzy weighted attributes has a longer life expectancy and better extensibility than LEACH-like algorithms.

  16. Facial paresis in patients with mesial temporal sclerosis: clinical and quantitative MRI-based evidence of widespread disease.

    Science.gov (United States)

    Lin, Katia; Carrete, Henrique; Lin, Jaime; de Oliveira, Pedro Alessandro Leite; Caboclo, Luis Otávio Sales Ferreira; Sakamoto, Américo Ceiki; Yacubian, Elza Márcia Targas

    2007-08-01

    To assess the frequency and significance of facial paresis (FP) in a well-defined cohort of mesial temporal lobe epilepsy (MTLE) patients. One hundred consecutive patients with MRI findings consistent with mesial temporal sclerosis (MTS) and concordant electroclinical data underwent facial motor examination at rest, with voluntary expression, and with spontaneous smiling. Hippocampal, amygdaloid, and temporopolar (TP) volumetric measures were acquired. Thirty healthy subjects, matched according to age and sex, were taken as controls. Central-type FP was found in 46 patients. In 41 (89%) of 46, it was visualized at rest, with voluntary and emotional expression characterizing true facial motor paresis. In 33 (72%) of 46 patients, FP was contralateral to the side of MTS. By using a 2-SD cutoff from the mean of normal controls, we found reduction in TP volume ipsilateral to MTS in 61% of patients with FP and in 33% of those without (p = 0.01). Febrile seizures as initial precipitating injury (IPI) were observed in 34% of the patients and were classified as complex in 12 (26%) of 46 of those with FP and in five (9%) of 54 of those without (p = 0.02). The presence of FP was significantly associated with a shorter latent period and younger age at onset of habitual seizures, in particular, with secondarily generalized tonic-clonic seizures. Facial paresis is a reliable lateralizing sign in MTLE and was associated with history of complex febrile seizures as IPI, younger age at onset of disease, and atrophy of temporal pole ipsilateral to MTS, indicating more widespread disease.

  17. A clustering based method to evaluate soil corrosivity for pipeline external integrity management

    International Nuclear Information System (INIS)

    Yajima, Ayako; Wang, Hui; Liang, Robert Y.; Castaneda, Homero

    2015-01-01

    One important category of transportation infrastructure is underground pipelines. Corrosion of these buried pipeline systems may cause pipeline failures with the attendant hazards of property loss and fatalities. Therefore, developing the capability to estimate the soil corrosivity is important for designing and preserving materials and for risk assessment. The deterioration rate of metal is highly influenced by the physicochemical characteristics of a material and the environment of its surroundings. In this study, the field data obtained from the southeast region of Mexico was examined using various data mining techniques to determine the usefulness of these techniques for clustering soil corrosivity level. Specifically, the soil was classified into different corrosivity level clusters by k-means and Gaussian mixture model (GMM). In terms of physical space, GMM shows better separability; therefore, the distributions of the material loss of the buried petroleum pipeline walls were estimated via the empirical density within GMM clusters. The soil corrosivity levels of the clusters were determined based on the medians of metal loss. The proposed clustering method was demonstrated to be capable of classifying the soil into different levels of corrosivity severity. - Highlights: • The clustering approach is applied to the data extracted from a real-life pipeline system. • Soil properties in the right-of-way are analyzed via clustering techniques to assess corrosivity. • GMM is selected as the preferred method for detecting the hidden pattern of in-situ data. • K–W test is performed for significant difference of corrosivity level between clusters

  18. Fuzzy clustering-based segmented attenuation correction in whole-body PET

    CERN Document Server

    Zaidi, H; Boudraa, A; Slosman, DO

    2001-01-01

    Segmented-based attenuation correction is now a widely accepted technique to reduce noise contribution of measured attenuation correction. In this paper, we present a new method for segmenting transmission images in positron emission tomography. This reduces the noise on the correction maps while still correcting for differing attenuation coefficients of specific tissues. Based on the Fuzzy C-Means (FCM) algorithm, the method segments the PET transmission images into a given number of clusters to extract specific areas of differing attenuation such as air, the lungs and soft tissue, preceded by a median filtering procedure. The reconstructed transmission image voxels are therefore segmented into populations of uniform attenuation based on the human anatomy. The clustering procedure starts with an over-specified number of clusters followed by a merging process to group clusters with similar properties and remove some undesired substructures using anatomical knowledge. The method is unsupervised, adaptive and a...

  19. A Model-Based Cluster Analysis of Maternal Emotion Regulation and Relations to Parenting Behavior.

    Science.gov (United States)

    Shaffer, Anne; Whitehead, Monica; Davis, Molly; Morelen, Diana; Suveg, Cynthia

    2017-10-15

    In a diverse community sample of mothers (N = 108) and their preschool-aged children (M age  = 3.50 years), this study conducted person-oriented analyses of maternal emotion regulation (ER) based on a multimethod assessment incorporating physiological, observational, and self-report indicators. A model-based cluster analysis was applied to five indicators of maternal ER: maternal self-report, observed negative affect in a parent-child interaction, baseline respiratory sinus arrhythmia (RSA), and RSA suppression across two laboratory tasks. Model-based cluster analyses revealed four maternal ER profiles, including a group of mothers with average ER functioning, characterized by socioeconomic advantage and more positive parenting behavior. A dysregulated cluster demonstrated the greatest challenges with parenting and dyadic interactions. Two clusters of intermediate dysregulation were also identified. Implications for assessment and applications to parenting interventions are discussed. © 2017 Family Process Institute.

  20. On Two Mixture-Based Clustering Approaches Used in Modeling an Insurance Portfolio

    Directory of Open Access Journals (Sweden)

    Tatjana Miljkovic

    2018-05-01

    Full Text Available We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM and mixture-based clustering for an ordered stereotype model (OSM. The latter is for modeling of ordinal variables, and the former is for modeling losses as a function of mixed-type of covariates. The article extends the idea of mixture modeling to a multivariate classification for the purpose of testing unobserved heterogeneity in an insurance portfolio. The application of both methods is illustrated on a well-known French automobile portfolio, in which the model fitting is performed using the expectation-maximization (EM algorithm. Our findings show that these mixture-based clustering methods can be used to further test unobserved heterogeneity in an insurance portfolio and as such may be considered in insurance pricing, underwriting, and risk management.

  1. Constraints on Ωm and σ8 from the potential-based cluster temperature function

    Science.gov (United States)

    Angrick, Christian; Pace, Francesco; Bartelmann, Matthias; Roncarelli, Mauro

    2015-12-01

    The abundance of galaxy clusters is in principle a powerful tool to constrain cosmological parameters, especially Ωm and σ8, due to the exponential dependence in the high-mass regime. While the best observables are the X-ray temperature and luminosity, the abundance of galaxy clusters, however, is conventionally predicted as a function of mass. Hence, the intrinsic scatter and the uncertainties in the scaling relations between mass and either temperature or luminosity lower the reliability of galaxy clusters to constrain cosmological parameters. In this article, we further refine the X-ray temperature function for galaxy clusters by Angrick et al., which is based on the statistics of perturbations in the cosmic gravitational potential and proposed to replace the classical mass-based temperature function, by including a refined analytic merger model and compare the theoretical prediction to results from a cosmological hydrodynamical simulation. Although we find already a good agreement if we compare with a cluster temperature function based on the mass-weighted temperature, including a redshift-dependent scaling between mass-based and spectroscopic temperature yields even better agreement between theoretical model and numerical results. As a proof of concept, incorporating this additional scaling in our model, we constrain the cosmological parameters Ωm and σ8 from an X-ray sample of galaxy clusters and tentatively find agreement with the recent cosmic microwave background based results from the Planck mission at 1σ-level.

  2. A robust approach based on Weibull distribution for clustering gene expression data

    Directory of Open Access Journals (Sweden)

    Gong Binsheng

    2011-05-01

    Full Text Available Abstract Background Clustering is a widely used technique for analysis of gene expression data. Most clustering methods group genes based on the distances, while few methods group genes according to the similarities of the distributions of the gene expression levels. Furthermore, as the biological annotation resources accumulated, an increasing number of genes have been annotated into functional categories. As a result, evaluating the performance of clustering methods in terms of the functional consistency of the resulting clusters is of great interest. Results In this paper, we proposed the WDCM (Weibull Distribution-based Clustering Method, a robust approach for clustering gene expression data, in which the gene expressions of individual genes are considered as the random variables following unique Weibull distributions. Our WDCM is based on the concept that the genes with similar expression profiles have similar distribution parameters, and thus the genes are clustered via the Weibull distribution parameters. We used the WDCM to cluster three cancer gene expression data sets from the lung cancer, B-cell follicular lymphoma and bladder carcinoma and obtained well-clustered results. We compared the performance of WDCM with k-means and Self Organizing Map (SOM using functional annotation information given by the Gene Ontology (GO. The results showed that the functional annotation ratios of WDCM are higher than those of the other methods. We also utilized the external measure Adjusted Rand Index to validate the performance of the WDCM. The comparative results demonstrate that the WDCM provides the better clustering performance compared to k-means and SOM algorithms. The merit of the proposed WDCM is that it can be applied to cluster incomplete gene expression data without imputing the missing values. Moreover, the robustness of WDCM is also evaluated on the incomplete data sets. Conclusions The results demonstrate that our WDCM produces clusters

  3. Alerts Visualization and Clustering in Network-based Intrusion Detection

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Dr. Li [University of Tennessee; Gasior, Wade C [ORNL; Dasireddy, Swetha [University of Tennessee

    2010-04-01

    Today's Intrusion detection systems when deployed on a busy network overload the network with huge number of alerts. This behavior of producing too much raw information makes it less effective. We propose a system which takes both raw data and Snort alerts to visualize and analyze possible intrusions in a network. Then we present with two models for the visualization of clustered alerts. Our first model gives the network administrator with the logical topology of the network and detailed information of each node that involves its associated alerts and connections. In the second model, flocking model, presents the network administrator with the visual representation of IDS data in which each alert is represented in different color and the alerts with maximum similarity move together. This gives network administrator with the idea of detecting various of intrusions through visualizing the alert patterns.

  4. Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading.

    Science.gov (United States)

    Inano, Rika; Oishi, Naoya; Kunieda, Takeharu; Arakawa, Yoshiki; Yamao, Yukihiro; Shibata, Sumiya; Kikuchi, Takayuki; Fukuyama, Hidenao; Miyamoto, Susumu

    2014-01-01

    Gliomas are the most common intra-axial primary brain tumour; therefore, predicting glioma grade would influence therapeutic strategies. Although several methods based on single or multiple parameters from diagnostic images exist, a definitive method for pre-operatively determining glioma grade remains unknown. We aimed to develop an unsupervised method using multiple parameters from pre-operative diffusion tensor images for obtaining a clustered image that could enable visual grading of gliomas. Fourteen patients with low-grade gliomas and 19 with high-grade gliomas underwent diffusion tensor imaging and three-dimensional T1-weighted magnetic resonance imaging before tumour resection. Seven features including diffusion-weighted imaging, fractional anisotropy, first eigenvalue, second eigenvalue, third eigenvalue, mean diffusivity and raw T2 signal with no diffusion weighting, were extracted as multiple parameters from diffusion tensor imaging. We developed a two-level clustering approach for a self-organizing map followed by the K-means algorithm to enable unsupervised clustering of a large number of input vectors with the seven features for the whole brain. The vectors were grouped by the self-organizing map as protoclusters, which were classified into the smaller number of clusters by K-means to make a voxel-based diffusion tensor-based clustered image. Furthermore, we also determined if the diffusion tensor-based clustered image was really helpful for predicting pre-operative glioma grade in a supervised manner. The ratio of each class in the diffusion tensor-based clustered images was calculated from the regions of interest manually traced on the diffusion tensor imaging space, and the common logarithmic ratio scales were calculated. We then applied support vector machine as a classifier for distinguishing between low- and high-grade gliomas. Consequently, the sensitivity, specificity, accuracy and area under the curve of receiver operating characteristic

  5. Direct Reconstruction of CT-based Attenuation Correction Images for PET with Cluster-Based Penalties

    Science.gov (United States)

    Kim, Soo Mee; Alessio, Adam M.; De Man, Bruno; Asma, Evren; Kinahan, Paul E.

    2015-01-01

    Extremely low-dose CT acquisitions for the purpose of PET attenuation correction will have a high level of noise and biasing artifacts due to factors such as photon starvation. This work explores a priori knowledge appropriate for CT iterative image reconstruction for PET attenuation correction. We investigate the maximum a posteriori (MAP) framework with cluster-based, multinomial priors for the direct reconstruction of the PET attenuation map. The objective function for direct iterative attenuation map reconstruction was modeled as a Poisson log-likelihood with prior terms consisting of quadratic (Q) and mixture (M) distributions. The attenuation map is assumed to have values in 4 clusters: air+background, lung, soft tissue, and bone. Under this assumption, the MP was a mixture probability density function consisting of one exponential and three Gaussian distributions. The relative proportion of each cluster was jointly estimated during each voxel update of direct iterative coordinate decent (dICD) method. Noise-free data were generated from NCAT phantom and Poisson noise was added. Reconstruction with FBP (ramp filter) was performed on the noise-free (ground truth) and noisy data. For the noisy data, dICD reconstruction was performed with the combination of different prior strength parameters (β and γ) of Q- and M-penalties. The combined quadratic and mixture penalties reduces the RMSE by 18.7% compared to post-smoothed iterative reconstruction and only 0.7% compared to quadratic alone. For direct PET attenuation map reconstruction from ultra-low dose CT acquisitions, the combination of quadratic and mixture priors offers regularization of both variance and bias and is a potential method to derive attenuation maps with negligible patient dose. However, the small improvement in quantitative accuracy relative to the substantial increase in algorithm complexity does not currently justify the use of mixture-based PET attenuation priors for reconstruction of CT

  6. Weighted similarity-based clustering of chemical structures and bioactivity data in early drug discovery.

    Science.gov (United States)

    Perualila-Tan, Nolen Joy; Shkedy, Ziv; Talloen, Willem; Göhlmann, Hinrich W H; Moerbeke, Marijke Van; Kasim, Adetayo

    2016-08-01

    The modern process of discovering candidate molecules in early drug discovery phase includes a wide range of approaches to extract vital information from the intersection of biology and chemistry. A typical strategy in compound selection involves compound clustering based on chemical similarity to obtain representative chemically diverse compounds (not incorporating potency information). In this paper, we propose an integrative clustering approach that makes use of both biological (compound efficacy) and chemical (structural features) data sources for the purpose of discovering a subset of compounds with aligned structural and biological properties. The datasets are integrated at the similarity level by assigning complementary weights to produce a weighted similarity matrix, serving as a generic input in any clustering algorithm. This new analysis work flow is semi-supervised method since, after the determination of clusters, a secondary analysis is performed wherein it finds differentially expressed genes associated to the derived integrated cluster(s) to further explain the compound-induced biological effects inside the cell. In this paper, datasets from two drug development oncology projects are used to illustrate the usefulness of the weighted similarity-based clustering approach to integrate multi-source high-dimensional information to aid drug discovery. Compounds that are structurally and biologically similar to the reference compounds are discovered using this proposed integrative approach.

  7. A Clustering-Based Automatic Transfer Function Design for Volume Visualization

    Directory of Open Access Journals (Sweden)

    Tianjin Zhang

    2016-01-01

    Full Text Available The two-dimensional transfer functions (TFs designed based on intensity-gradient magnitude (IGM histogram are effective tools for the visualization and exploration of 3D volume data. However, traditional design methods usually depend on multiple times of trial-and-error. We propose a novel method for the automatic generation of transfer functions by performing the affinity propagation (AP clustering algorithm on the IGM histogram. Compared with previous clustering algorithms that were employed in volume visualization, the AP clustering algorithm has much faster convergence speed and can achieve more accurate clustering results. In order to obtain meaningful clustering results, we introduce two similarity measurements: IGM similarity and spatial similarity. These two similarity measurements can effectively bring the voxels of the same tissue together and differentiate the voxels of different tissues so that the generated TFs can assign different optical properties to different tissues. Before performing the clustering algorithm on the IGM histogram, we propose to remove noisy voxels based on the spatial information of voxels. Our method does not require users to input the number of clusters, and the classification and visualization process is automatic and efficient. Experiments on various datasets demonstrate the effectiveness of the proposed method.

  8. Facial Transplantation Surgery Introduction

    OpenAIRE

    Eun, Seok-Chan

    2015-01-01

    Severely disfiguring facial injuries can have a devastating impact on the patient's quality of life. During the past decade, vascularized facial allotransplantation has progressed from an experimental possibility to a clinical reality in the fields of disease, trauma, and congenital malformations. This technique may now be considered a viable option for repairing complex craniofacial defects for which the results of autologous reconstruction remain suboptimal. Vascularized facial allotranspla...

  9. Marker optimization for facial motion acquisition and deformation.

    Science.gov (United States)

    Le, Binh H; Zhu, Mingyang; Deng, Zhigang

    2013-11-01

    A long-standing problem in marker-based facial motion capture is what are the optimal facial mocap marker layouts. Despite its wide range of potential applications, this problem has not yet been systematically explored to date. This paper describes an approach to compute optimized marker layouts for facial motion acquisition as optimization of characteristic control points from a set of high-resolution, ground-truth facial mesh sequences. Specifically, the thin-shell linear deformation model is imposed onto the example pose reconstruction process via optional hard constraints such as symmetry and multiresolution constraints. Through our experiments and comparisons, we validate the effectiveness, robustness, and accuracy of our approach. Besides guiding minimal yet effective placement of facial mocap markers, we also describe and demonstrate its two selected applications: marker-based facial mesh skinning and multiresolution facial performance capture.

  10. Cluster Validity Classification Approaches Based on Geometric Probability and Application in the Classification of Remotely Sensed Images

    Directory of Open Access Journals (Sweden)

    LI Jian-Wei

    2014-08-01

    Full Text Available On the basis of the cluster validity function based on geometric probability in literature [1, 2], propose a cluster analysis method based on geometric probability to process large amount of data in rectangular area. The basic idea is top-down stepwise refinement, firstly categories then subcategories. On all clustering levels, use the cluster validity function based on geometric probability firstly, determine clusters and the gathering direction, then determine the center of clustering and the border of clusters. Through TM remote sensing image classification examples, compare with the supervision and unsupervised classification in ERDAS and the cluster analysis method based on geometric probability in two-dimensional square which is proposed in literature 2. Results show that the proposed method can significantly improve the classification accuracy.

  11. [Facial tics and spasms].

    Science.gov (United States)

    Potgieser, Adriaan R E; van Dijk, J Marc C; Elting, Jan Willem J; de Koning-Tijssen, Marina A J

    2014-01-01

    Facial tics and spasms are socially incapacitating, but effective treatment is often available. The clinical picture is sufficient for distinguishing between the different diseases that cause this affliction.We describe three cases of patients with facial tics or spasms: one case of tics, which are familiar to many physicians; one case of blepharospasms; and one case of hemifacial spasms. We discuss the differential diagnosis and the treatment possibilities for facial tics and spasms. Early diagnosis and treatment is important, because of the associated social incapacitation. Botulin toxin should be considered as a treatment option for facial tics and a curative neurosurgical intervention should be considered for hemifacial spasms.

  12. Effective Social Relationship Measurement and Cluster Based Routing in Mobile Opportunistic Networks †

    Science.gov (United States)

    Zeng, Feng; Zhao, Nan; Li, Wenjia

    2017-01-01

    In mobile opportunistic networks, the social relationship among nodes has an important impact on data transmission efficiency. Motivated by the strong share ability of “circles of friends” in communication networks such as Facebook, Twitter, Wechat and so on, we take a real-life example to show that social relationships among nodes consist of explicit and implicit parts. The explicit part comes from direct contact among nodes, and the implicit part can be measured through the “circles of friends”. We present the definitions of explicit and implicit social relationships between two nodes, adaptive weights of explicit and implicit parts are given according to the contact feature of nodes, and the distributed mechanism is designed to construct the “circles of friends” of nodes, which is used for the calculation of the implicit part of social relationship between nodes. Based on effective measurement of social relationships, we propose a social-based clustering and routing scheme, in which each node selects the nodes with close social relationships to form a local cluster, and the self-control method is used to keep all cluster members always having close relationships with each other. A cluster-based message forwarding mechanism is designed for opportunistic routing, in which each node only forwards the copy of the message to nodes with the destination node as a member of the local cluster. Simulation results show that the proposed social-based clustering and routing outperforms the other classic routing algorithms. PMID:28498309

  13. Effective Social Relationship Measurement and Cluster Based Routing in Mobile Opportunistic Networks.

    Science.gov (United States)

    Zeng, Feng; Zhao, Nan; Li, Wenjia

    2017-05-12

    In mobile opportunistic networks, the social relationship among nodes has an important impact on data transmission efficiency. Motivated by the strong share ability of "circles of friends" in communication networks such as Facebook, Twitter, Wechat and so on, we take a real-life example to show that social relationships among nodes consist of explicit and implicit parts. The explicit part comes from direct contact among nodes, and the implicit part can be measured through the "circles of friends". We present the definitions of explicit and implicit social relationships between two nodes, adaptive weights of explicit and implicit parts are given according to the contact feature of nodes, and the distributed mechanism is designed to construct the "circles of friends" of nodes, which is used for the calculation of the implicit part of social relationship between nodes. Based on effective measurement of social relationships, we propose a social-based clustering and routing scheme, in which each node selects the nodes with close social relationships to form a local cluster, and the self-control method is used to keep all cluster members always having close relationships with each other. A cluster-based message forwarding mechanism is designed for opportunistic routing, in which each node only forwards the copy of the message to nodes with the destination node as a member of the local cluster. Simulation results show that the proposed social-based clustering and routing outperforms the other classic routing algorithms.

  14. Cluster management.

    Science.gov (United States)

    Katz, R

    1992-11-01

    Cluster management is a management model that fosters decentralization of management, develops leadership potential of staff, and creates ownership of unit-based goals. Unlike shared governance models, there is no formal structure created by committees and it is less threatening for managers. There are two parts to the cluster management model. One is the formation of cluster groups, consisting of all staff and facilitated by a cluster leader. The cluster groups function for communication and problem-solving. The second part of the cluster management model is the creation of task forces. These task forces are designed to work on short-term goals, usually in response to solving one of the unit's goals. Sometimes the task forces are used for quality improvement or system problems. Clusters are groups of not more than five or six staff members, facilitated by a cluster leader. A cluster is made up of individuals who work the same shift. For example, people with job titles who work days would be in a cluster. There would be registered nurses, licensed practical nurses, nursing assistants, and unit clerks in the cluster. The cluster leader is chosen by the manager based on certain criteria and is trained for this specialized role. The concept of cluster management, criteria for choosing leaders, training for leaders, using cluster groups to solve quality improvement issues, and the learning process necessary for manager support are described.

  15. Perceived functional impact of abnormal facial appearance.

    Science.gov (United States)

    Rankin, Marlene; Borah, Gregory L

    2003-06-01

    Functional facial deformities are usually described as those that impair respiration, eating, hearing, or speech. Yet facial scars and cutaneous deformities have a significant negative effect on social functionality that has been poorly documented in the scientific literature. Insurance companies are declining payments for reconstructive surgical procedures for facial deformities caused by congenital disabilities and after cancer or trauma operations that do not affect mechanical facial activity. The purpose of this study was to establish a large, sample-based evaluation of the perceived social functioning, interpersonal characteristics, and employability indices for a range of facial appearances (normal and abnormal). Adult volunteer evaluators (n = 210) provided their subjective perceptions based on facial physical appearance, and an analysis of the consequences of facial deformity on parameters of preferential treatment was performed. A two-group comparative research design rated the differences among 10 examples of digitally altered facial photographs of actual patients among various age and ethnic groups with "normal" and "abnormal" congenital deformities or posttrauma scars. Photographs of adult patients with observable congenital and posttraumatic deformities (abnormal) were digitally retouched to eliminate the stigmatic defects (normal). The normal and abnormal photographs of identical patients were evaluated by the large sample study group on nine parameters of social functioning, such as honesty, employability, attractiveness, and effectiveness, using a visual analogue rating scale. Patients with abnormal facial characteristics were rated as significantly less honest (p = 0.007), less employable (p = 0.001), less trustworthy (p = 0.01), less optimistic (p = 0.001), less effective (p = 0.02), less capable (p = 0.002), less intelligent (p = 0.03), less popular (p = 0.001), and less attractive (p = 0.001) than were the same patients with normal facial

  16. Dynamic Characteristics Analysis and Stabilization of PV-Based Multiple Microgrid Clusters

    DEFF Research Database (Denmark)

    Zhao, Zhuoli; Yang, Ping; Wang, Yuewu

    2018-01-01

    -based multiple microgrid clusters. A detailed small-signal model for PV-based microgrid clusters considering local adaptive dynamic droop control mechanism of the voltage-source PV system is developed. The complete dynamic model is then used to access and compare the dynamic characteristics of the single...... microgrid and interconnected microgrids. In order to enhance system stability of the PV microgrid clusters, a tie-line flow and stabilization strategy is proposed to suppress the introduced interarea and local oscillations. Robustly selecting of the key control parameters is transformed to a multiobjective......As the penetration of PV generation increases, there is a growing operational demand on PV systems to participate in microgrid frequency regulation. It is expected that future distribution systems will consist of multiple microgrid clusters. However, interconnecting PV microgrids may lead to system...

  17. K-means-clustering-based fiber nonlinearity equalization techniques for 64-QAM coherent optical communication system.

    Science.gov (United States)

    Zhang, Junfeng; Chen, Wei; Gao, Mingyi; Shen, Gangxiang

    2017-10-30

    In this work, we proposed two k-means-clustering-based algorithms to mitigate the fiber nonlinearity for 64-quadrature amplitude modulation (64-QAM) signal, the training-sequence assisted k-means algorithm and the blind k-means algorithm. We experimentally demonstrated the proposed k-means-clustering-based fiber nonlinearity mitigation techniques in 75-Gb/s 64-QAM coherent optical communication system. The proposed algorithms have reduced clustering complexity and low data redundancy and they are able to quickly find appropriate initial centroids and select correctly the centroids of the clusters to obtain the global optimal solutions for large k value. We measured the bit-error-ratio (BER) performance of 64-QAM signal with different launched powers into the 50-km single mode fiber and the proposed techniques can greatly mitigate the signal impairments caused by the amplified spontaneous emission noise and the fiber Kerr nonlinearity and improve the BER performance.

  18. A new collaborative recommendation approach based on users clustering using artificial bee colony algorithm.

    Science.gov (United States)

    Ju, Chunhua; Xu, Chonghuan

    2013-01-01

    Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.

  19. A novel artificial bee colony based clustering algorithm for categorical data.

    Science.gov (United States)

    Ji, Jinchao; Pang, Wei; Zheng, Yanlin; Wang, Zhe; Ma, Zhiqiang

    2015-01-01

    Data with categorical attributes are ubiquitous in the real world. However, existing partitional clustering algorithms for categorical data are prone to fall into local optima. To address this issue, in this paper we propose a novel clustering algorithm, ABC-K-Modes (Artificial Bee Colony clustering based on K-Modes), based on the traditional k-modes clustering algorithm and the artificial bee colony approach. In our approach, we first introduce a one-step k-modes procedure, and then integrate this procedure with the artificial bee colony approach to deal with categorical data. In the search process performed by scout bees, we adopt the multi-source search inspired by the idea of batch processing to accelerate the convergence of ABC-K-Modes. The performance of ABC-K-Modes is evaluated by a series of experiments in comparison with that of the other popular algorithms for categorical data.

  20. A New Collaborative Recommendation Approach Based on Users Clustering Using Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Chunhua Ju

    2013-01-01

    Full Text Available Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users’ preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.

  1. Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments

    Directory of Open Access Journals (Sweden)

    Wen Liu

    2016-12-01

    Full Text Available Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS. Due to the absence of satellite signal in Global Navigation Satellite System (GNSS, various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has been attracting much interest from researchers because of its pervasive deployment, flexibility and robustness to dense cluttered indoor environments. One challenge, however, is the deployment of Access Points (AP, which would bring a significant influence on the system positioning accuracy. This paper concentrates on WLAN based fingerprinting indoor location by analyzing the AP deployment influence, and studying the advantages of coordinate-based clustering compared to traditional RSS-based clustering. A coordinate-based clustering method for indoor fingerprinting location, named Smallest-Enclosing-Circle-based (SEC, is then proposed aiming at reducing the positioning error lying in the AP deployment and improving robustness to dense cluttered environments. All measurements are conducted in indoor public areas, such as the National Center For the Performing Arts (as Test-bed 1 and the XiDan Joy City (Floors 1 and 2, as Test-bed 2, and results show that SEC clustering algorithm can improve system positioning accuracy by about 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1 and 73.7% for Test-bed 2 Floor 2 compared with traditional RSS-based clustering algorithms such as K-means.

  2. Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments.

    Science.gov (United States)

    Liu, Wen; Fu, Xiao; Deng, Zhongliang

    2016-12-02

    Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS). Due to the absence of satellite signal in Global Navigation Satellite System (GNSS), various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has been attracting much interest from researchers because of its pervasive deployment, flexibility and robustness to dense cluttered indoor environments. One challenge, however, is the deployment of Access Points (AP), which would bring a significant influence on the system positioning accuracy. This paper concentrates on WLAN based fingerprinting indoor location by analyzing the AP deployment influence, and studying the advantages of coordinate-based clustering compared to traditional RSS-based clustering. A coordinate-based clustering method for indoor fingerprinting location, named Smallest-Enclosing-Circle-based (SEC), is then proposed aiming at reducing the positioning error lying in the AP deployment and improving robustness to dense cluttered environments. All measurements are conducted in indoor public areas, such as the National Center For the Performing Arts (as Test-bed 1) and the XiDan Joy City (Floors 1 and 2, as Test-bed 2), and results show that SEC clustering algorithm can improve system positioning accuracy by about 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1 and 73.7% for Test-bed 2 Floor 2 compared with traditional RSS-based clustering algorithms such as K-means.

  3. A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

    OpenAIRE

    Li, Guohui; Zhang, Songling; Yang, Hong

    2017-01-01

    Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed ...

  4. DIRECTIONAL OPPORTUNISTIC MECHANISM IN CLUSTER MESSAGE CRITICALITY LEVEL BASED ZIGBEE ROUTING

    OpenAIRE

    B.Rajeshkanna *1, Dr.M.Anitha 2

    2018-01-01

    The cluster message criticality level based zigbee routing(CMCLZOR) has been proposed for routing the cluster messages in wireless smart energy home area networks. It employs zigbee opportunistic shortcut tree routing(ZOSTR) and AODV individually for routing normal messages and highly critical messages respectively. ZOSTR allows the receiving nodes to compete for forwarding a packet with the priority of left-over hops rather than stating single next hop node like unicast protocols. Since it h...

  5. Positive and negative symptom scores are correlated with activation in different brain regions during facial emotion perception in schizophrenia patients: a voxel-based sLORETA source activity study.

    Science.gov (United States)

    Kim, Do-Won; Kim, Han-Sung; Lee, Seung-Hwan; Im, Chang-Hwan

    2013-12-01

    Schizophrenia is one of the most devastating of all mental illnesses, and has dimensional characteristics that include both positive and negative symptoms. One problem reported in schizophrenia patients is that they tend to show deficits in face emotion processing, on which negative symptoms are thought to have stronger influence. In this study, four event-related potential (ERP) components (P100, N170, N250, and P300) and their source activities were analyzed using EEG data acquired from 23 schizophrenia patients while they were presented with facial emotion picture stimuli. Correlations between positive and negative syndrome scale (PANSS) scores and source activations during facial emotion processing were calculated to identify the brain areas affected by symptom scores. Our analysis demonstrates that PANSS positive scores are negatively correlated with major areas of the left temporal lobule for early ERP components (P100, N170) and with the right middle frontal lobule for a later component (N250), which indicates that positive symptoms affect both early face processing and facial emotion processing. On the other hand, PANSS negative scores are negatively correlated with several clustered regions, including the left fusiform gyrus (at P100), most of which are not overlapped with regions showing correlations with PANSS positive scores. Our results suggest that positive and negative symptoms affect independent brain regions during facial emotion processing, which may help to explain the heterogeneous characteristics of schizophrenia. © 2013 Elsevier B.V. All rights reserved.

  6. Normalized mutual information based PET-MR registration using K-Means clustering and shading correction

    NARCIS (Netherlands)

    Knops, Z.F.; Maintz, J.B.A.; Viergever, M.A.; Pluim, J.P.W.; Gee, J.C.; Maintz, J.B.A.; Vannier, M.W.

    2003-01-01

    A method for the efficient re-binning and shading based correction of intensity distributions of the images prior to normalized mutual information based registration is presented. Our intensity distribution re-binning method is based on the K-means clustering algorithm as opposed to the generally

  7. Clustering-based Feature Learning on Variable Stars

    Science.gov (United States)

    Mackenzie, Cristóbal; Pichara, Karim; Protopapas, Pavlos

    2016-04-01

    The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline.

  8. Depth data research of GIS based on clustering analysis algorithm

    Science.gov (United States)

    Xiong, Yan; Xu, Wenli

    2018-03-01

    The data of GIS have spatial distribution. Geographic data has both spatial characteristics and attribute characteristics, and also changes with time. Therefore, the amount of data is very large. Nowadays, many industries and departments in the society are using GIS. However, without proper data analysis and mining scheme, GIS will not exert its maximum effectiveness and will waste a lot of data. In this paper, we use the geographic information demand of a national security department as the experimental object, combining the characteristics of GIS data, taking into account the characteristics of time, space, attributes and so on, and using cluster analysis algorithm. We further study the mining scheme for depth data, and get the algorithm model. This algorithm can automatically classify sample data, and then carry out exploratory analysis. The research shows that the algorithm model and the information mining scheme can quickly find hidden depth information from the surface data of GIS, thus improving the efficiency of the security department. This algorithm can also be extended to other fields.

  9. CLUSTERING-BASED FEATURE LEARNING ON VARIABLE STARS

    International Nuclear Information System (INIS)

    Mackenzie, Cristóbal; Pichara, Karim; Protopapas, Pavlos

    2016-01-01

    The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline

  10. CLUSTERING-BASED FEATURE LEARNING ON VARIABLE STARS

    Energy Technology Data Exchange (ETDEWEB)

    Mackenzie, Cristóbal; Pichara, Karim [Computer Science Department, Pontificia Universidad Católica de Chile, Santiago (Chile); Protopapas, Pavlos [Institute for Applied Computational Science, Harvard University, Cambridge, MA (United States)

    2016-04-01

    The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline.

  11. Particle Swarm Optimization and harmony search based clustering and routing in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Veena Anand

    2017-01-01

    Full Text Available Wireless Sensor Networks (WSN has the disadvantage of limited and non-rechargeable energy resource in WSN creates a challenge and led to development of various clustering and routing algorithms. The paper proposes an approach for improving network lifetime by using Particle swarm optimization based clustering and Harmony Search based routing in WSN. So in this paper, global optimal cluster head are selected and Gateway nodes are introduced to decrease the energy consumption of the CH while sending aggregated data to the Base station (BS. Next, the harmony search algorithm based Local Search strategy finds best routing path for gateway nodes to the Base Station. Finally, the proposed algorithm is presented.

  12. Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods.

    Science.gov (United States)

    Šubelj, Lovro; van Eck, Nees Jan; Waltman, Ludo

    2016-01-01

    Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community.

  13. Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods

    Science.gov (United States)

    Šubelj, Lovro; van Eck, Nees Jan; Waltman, Ludo

    2016-01-01

    Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community. PMID:27124610

  14. An ant colony based resilience approach to cascading failures in cluster supply network

    Science.gov (United States)

    Wang, Yingcong; Xiao, Renbin

    2016-11-01

    Cluster supply chain network is a typical complex network and easily suffers cascading failures under disruption events, which is caused by the under-load of enterprises. Improving network resilience can increase the ability of recovery from cascading failures. Social resilience is found in ant colony and comes from ant's spatial fidelity zones (SFZ). Starting from the under-load failures, this paper proposes a resilience method to cascading failures in cluster supply chain network by leveraging on social resilience of ant colony. First, the mapping between ant colony SFZ and cluster supply chain network SFZ is presented. Second, a new cascading model for cluster supply chain network is constructed based on under-load failures. Then, the SFZ-based resilience method and index to cascading failures are developed according to ant colony's social resilience. Finally, a numerical simulation and a case study are used to verify the validity of the cascading model and the resilience method. Experimental results show that, the cluster supply chain network becomes resilient to cascading failures under the SFZ-based resilience method, and the cluster supply chain network resilience can be enhanced by improving the ability of enterprises to recover and adjust.

  15. Regional SAR Image Segmentation Based on Fuzzy Clustering with Gamma Mixture Model

    Science.gov (United States)

    Li, X. L.; Zhao, Q. H.; Li, Y.

    2017-09-01

    Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.

  16. MCBT: Multi-Hop Cluster Based Stable Backbone Trees for Data Collection and Dissemination in WSNs

    Directory of Open Access Journals (Sweden)

    Tae-Jin Lee

    2009-07-01

    Full Text Available We propose a stable backbone tree construction algorithm using multi-hop clusters for wireless sensor networks (WSNs. The hierarchical cluster structure has advantages in data fusion and aggregation. Energy consumption can be decreased by managing nodes with cluster heads. Backbone nodes, which are responsible for performing and managing multi-hop communication, can reduce the communication overhead such as control traffic and minimize the number of active nodes. Previous backbone construction algorithms, such as Hierarchical Cluster-based Data Dissemination (HCDD and Multicluster, Mobile, Multimedia radio network (MMM, consume energy quickly. They are designed without regard to appropriate factors such as residual energy and degree (the number of connections or edges to other nodes of a node for WSNs. Thus, the network is quickly disconnected or has to reconstruct a backbone. We propose a distributed algorithm to create a stable backbone by selecting the nodes with higher energy or degree as the cluster heads. This increases the overall network lifetime. Moreover, the proposed method balances energy consumption by distributing the traffic load among nodes around the cluster head. In the simulation, the proposed scheme outperforms previous clustering schemes in terms of the average and the standard deviation of residual energy or degree of backbone nodes, the average residual energy of backbone nodes after disseminating the sensed data, and the network lifetime.

  17. Heartbeat Rate Measurement from Facial Video

    DEFF Research Database (Denmark)

    Haque, Mohammad Ahsanul; Irani, Ramin; Nasrollahi, Kamal

    2016-01-01

    Heartbeat Rate (HR) reveals a person’s health condition. This paper presents an effective system for measuring HR from facial videos acquired in a more realistic environment than the testing environment of current systems. The proposed method utilizes a facial feature point tracking method...... by combining a ‘Good feature to track’ and a ‘Supervised descent method’ in order to overcome the limitations of currently available facial video based HR measuring systems. Such limitations include, e.g., unrealistic restriction of the subject’s movement and artificial lighting during data capture. A face...

  18. Fusing Facial Features for Face Recognition

    Directory of Open Access Journals (Sweden)

    Jamal Ahmad Dargham

    2012-06-01

    Full Text Available Face recognition is an important biometric method because of its potential applications in many fields, such as access control, surveillance, and human-computer interaction. In this paper, a face recognition system that fuses the outputs of three face recognition systems based on Gabor jets is presented. The first system uses the magnitude, the second uses the phase, and the third uses the phase-weighted magnitude of the jets. The jets are generated from facial landmarks selected using three selection methods. It was found out that fusing the facial features gives better recognition rate than either facial feature used individually regardless of the landmark selection method.

  19. Interference among the Processing of Facial Emotion, Face Race, and Face Gender

    OpenAIRE

    Li, Yongna; Tse, Chi-Shing

    2016-01-01

    People are able to simultaneously process multiple dimensions of facial properties. Facial processing models are based on the processing of facial properties. This paper examined the processing of facial emotion, face race and face gender using categorization tasks. The same set of Chinese, White and Black faces, each posing a neutral, happy or angry expression, was used in three experiments. Facial emotion interfered with face race in all the tasks. The interaction of face race and face gend...

  20. A nonparametric Bayesian approach for clustering bisulfate-based DNA methylation profiles.

    Science.gov (United States)

    Zhang, Lin; Meng, Jia; Liu, Hui; Huang, Yufei

    2012-01-01

    DNA methylation occurs in the context of a CpG dinucleotide. It is an important epigenetic modification, which can be inherited through cell division. The two major types of methylation include hypomethylation and hypermethylation. Unique methylation patterns have been shown to exist in diseases including various types of cancer. DNA methylation analysis promises to become a powerful tool in cancer diagnosis, treatment and prognostication. Large-scale methylation arrays are now available for studying methylation genome-wide. The Illumina methylation platform simultaneously measures cytosine methylation at more than 1500 CpG sites associated with over 800 cancer-related genes. Cluster analysis is often used to identify DNA methylation subgroups for prognosis and diagnosis. However, due to the unique non-Gaussian characteristics, traditional clustering methods may not be appropriate for DNA and methylation data, and the determination of optimal cluster number is still problematic. A Dirichlet process beta mixture model (DPBMM) is proposed that models the DNA methylation expressions as an infinite number of beta mixture distribution. The model allows automatic learning of the relevant parameters such as the cluster mixing proportion, the parameters of beta distribution for each cluster, and especially the number of potential clusters. Since the model is high dimensional and analytically intractable, we proposed a Gibbs sampling "no-gaps" solution for computing the posterior distributions, hence the estimates of the parameters. The proposed algorithm was tested on simulated data as well as methylation data from 55 Glioblastoma multiform (GBM) brain tissue samples. To reduce the computational burden due to the high data dimensionality, a dimension reduction method is adopted. The two GBM clusters yielded by DPBMM are based on data of different number of loci (P-value < 0.1), while hierarchical clustering cannot yield statistically significant clusters.

  1. FRCA: A Fuzzy Relevance-Based Cluster Head Selection Algorithm for Wireless Mobile Ad-Hoc Sensor Networks

    Directory of Open Access Journals (Sweden)

    Taegwon Jeong

    2011-05-01

    Full Text Available Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP, the Weighted-based Adaptive Clustering Algorithm (WACA, and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM. The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.

  2. FRCA: a fuzzy relevance-based cluster head selection algorithm for wireless mobile ad-hoc sensor networks.

    Science.gov (United States)

    Lee, Chongdeuk; Jeong, Taegwon

    2011-01-01

    Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA) to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP), the Weighted-based Adaptive Clustering Algorithm (WACA), and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM). The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.

  3. Cross-layer cluster-based energy-efficient protocol for wireless sensor networks.

    Science.gov (United States)

    Mammu, Aboobeker Sidhik Koyamparambil; Hernandez-Jayo, Unai; Sainz, Nekane; de la Iglesia, Idoia

    2015-04-09

    Recent developments in electronics and wireless communications have enabled the improvement of low-power and low-cost wireless sensors networks (WSNs). One of the most important challenges in WSNs is to increase the network lifetime due to the limited energy capacity of the network nodes. Another major challenge in WSNs is the hot spots that emerge as locations under heavy traffic load. Nodes in such areas quickly drain energy resources, leading to disconnection in network services. In such an environment, cross-layer cluster-based energy-efficient algorithms (CCBE) can prolong the network lifetime and energy efficiency. CCBE is based on clustering the nodes to different hexagonal structures. A hexagonal cluster consists of cluster members (CMs) and a cluster head (CH). The CHs are selected from the CMs based on nodes near the optimal CH distance and the residual energy of the nodes. Additionally, the optimal CH distance that links to optimal energy consumption is derived. To balance the energy consumption and the traffic load in the network, the CHs are rotated among all CMs. In WSNs, energy is mostly consumed during transmission and reception. Transmission collisions can further decrease the energy efficiency. These collisions can be avoided by using a contention-free protocol during the transmission period. Additionally, the CH allocates slots to the CMs based on their residual energy to increase sleep time. Furthermore, the energy consumption of CH can be further reduced by data aggregation. In this paper, we propose a data aggregation level based on the residual energy of CH and a cost-aware decision scheme for the fusion of data. Performance results show that the CCBE scheme performs better in terms of network lifetime, energy consumption and throughput compared to low-energy adaptive clustering hierarchy (LEACH) and hybrid energy-efficient distributed clustering (HEED).

  4. Management of Energy Consumption on Cluster Based Routing Protocol for MANET

    Science.gov (United States)

    Hosseini-Seno, Seyed-Amin; Wan, Tat-Chee; Budiarto, Rahmat; Yamada, Masashi

    The usage of light-weight mobile devices is increasing rapidly, leading to demand for more telecommunication services. Consequently, mobile ad hoc networks and their applications have become feasible with the proliferation of light-weight mobile devices. Many protocols have been developed to handle service discovery and routing in ad hoc networks. However, the majority of them did not consider one critical aspect of this type of network, which is the limited of available energy in each node. Cluster Based Routing Protocol (CBRP) is a robust/scalable routing protocol for Mobile Ad hoc Networks (MANETs) and superior to existing protocols such as Ad hoc On-demand Distance Vector (AODV) in terms of throughput and overhead. Therefore, based on this strength, methods to increase the efficiency of energy usage are incorporated into CBRP in this work. In order to increase the stability (in term of life-time) of the network and to decrease the energy consumption of inter-cluster gateway nodes, an Enhanced Gateway Cluster Based Routing Protocol (EGCBRP) is proposed. Three methods have been introduced by EGCBRP as enhancements to the CBRP: improving the election of cluster Heads (CHs) in CBRP which is based on the maximum available energy level, implementing load balancing for inter-cluster traffic using multiple gateways, and implementing sleep state for gateway nodes to further save the energy. Furthermore, we propose an Energy Efficient Cluster Based Routing Protocol (EECBRP) which extends the EGCBRP sleep state concept into all idle member nodes, excluding the active nodes in all clusters. The experiment results show that the EGCBRP decreases the overall energy consumption of the gateway nodes up to 10% and the EECBRP reduces the energy consumption of the member nodes up to 60%, both of which in turn contribute to stabilizing the network.

  5. Cross-Layer Cluster-Based Energy-Efficient Protocol for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Aboobeker Sidhik Koyamparambil Mammu

    2015-04-01

    Full Text Available Recent developments in electronics and wireless communications have enabled the improvement of low-power and low-cost wireless sensors networks (WSNs. One of the most important challenges in WSNs is to increase the network lifetime due to the limited energy capacity of the network nodes. Another major challenge in WSNs is the hot spots that emerge as locations under heavy traffic load. Nodes in such areas quickly drain energy resources, leading to disconnection in network services. In such an environment, cross-layer cluster-based energy-efficient algorithms (CCBE can prolong the network lifetime and energy efficiency. CCBE is based on clustering the nodes to different hexagonal structures. A hexagonal cluster consists of cluster members (CMs and a cluster head (CH. The CHs are selected from the CMs based on nodes near the optimal CH distance and the residual energy of the nodes. Additionally, the optimal CH distance that links to optimal energy consumption is derived. To balance the energy consumption and the traffic load in the network, the CHs are rotated among all CMs. In WSNs, energy is mostly consumed during transmission and reception. Transmission collisions can further decrease the energy efficiency. These collisions can be avoided by using a contention-free protocol during the transmission period. Additionally, the CH allocates slots to the CMs based on their residual energy to increase sleep time. Furthermore, the energy consumption of CH can be further reduced by data aggregation. In this paper, we propose a data aggregation level based on the residual energy of CH and a cost-aware decision scheme for the fusion of data. Performance results show that the CCBE scheme performs better in terms of network lifetime, energy consumption and throughput compared to low-energy adaptive clustering hierarchy (LEACH and hybrid energy-efficient distributed clustering (HEED.

  6. DEMARCATE: Density-based magnetic resonance image clustering for assessing tumor heterogeneity in cancer.

    Science.gov (United States)

    Saha, Abhijoy; Banerjee, Sayantan; Kurtek, Sebastian; Narang, Shivali; Lee, Joonsang; Rao, Ganesh; Martinez, Juan; Bharath, Karthik; Rao, Arvind U K; Baladandayuthapani, Veerabhadran

    2016-01-01

    Tumor heterogeneity is a crucial area of cancer research wherein inter- and intra-tumor differences are investigated to assess and monitor disease development and progression, especially in cancer. The proliferation of imaging and linked genomic data has enabled us to evaluate tumor heterogeneity on multiple levels. In this work, we examine magnetic resonance imaging (MRI) in patients with brain cancer to assess image-based tumor heterogeneity. Standard approaches to this problem use scalar summary measures (e.g., intensity-based histogram statistics) that do not adequately capture the complete and finer scale information in the voxel-level data. In this paper, we introduce a novel technique, DEMARCATE (DEnsity-based MAgnetic Resonance image Clustering for Assessing Tumor hEterogeneity) to explore the entire tumor heterogeneity density profiles (THDPs) obtained from the full tumor voxel space. THDPs are smoothed representations of the probability density function of the tumor images. We develop tools for analyzing such objects under the Fisher-Rao Riemannian framework that allows us to construct metrics for THDP comparisons across patients, which can be used in conjunction with standard clustering approaches. Our analyses of The Cancer Genome Atlas (TCGA) based Glioblastoma dataset reveal two significant clusters of patients with marked differences in tumor morphology, genomic characteristics and prognostic clinical outcomes. In addition, we see enrichment of image-based clusters with known molecular subtypes of glioblastoma multiforme, which further validates our representation of tumor heterogeneity and subsequent clustering techniques.

  7. Facial talon cusps.

    LENUS (Irish Health Repository)

    McNamara, T

    1997-12-01

    This is a report of two patients with isolated facial talon cusps. One occurred on a permanent mandibular central incisor; the other on a permanent maxillary canine. The locations of these talon cusps suggests that the definition of a talon cusp include teeth in addition to the incisor group and be extended to include the facial aspect of teeth.

  8. An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network.

    Science.gov (United States)

    Vimalarani, C; Subramanian, R; Sivanandam, S N

    2016-01-01

    Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

  9. An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    C. Vimalarani

    2016-01-01

    Full Text Available Wireless Sensor Network (WSN is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

  10. An Improved Semisupervised Outlier Detection Algorithm Based on Adaptive Feature Weighted Clustering

    Directory of Open Access Journals (Sweden)

    Tingquan Deng

    2016-01-01

    Full Text Available There exist already various approaches to outlier detection, in which semisupervised methods achieve encouraging superiority due to the introduction of prior knowledge. In this paper, an adaptive feature weighted clustering-based semisupervised outlier detection strategy is proposed. This method maximizes the membership degree of a labeled normal object to the cluster it belongs to and minimizes the membership degrees of a labeled outlier to all clusters. In consideration of distinct significance of features or components in a dataset in determining an object being an inlier or outlier, each feature is adaptively assigned different weights according to the deviation degrees between this feature of all objects and that of a certain cluster prototype. A series of experiments on a synthetic dataset and several real-world datasets are implemented to verify the effectiveness and efficiency of the proposal.

  11. Digital Signal Processing Based on a Clustering Algorithm for Ir/Au TES Microcalorimeter

    Science.gov (United States)

    Zen, N.; Kunieda, Y.; Takahashi, H.; Hiramoto, K.; Nakazawa, M.; Fukuda, D.; Ukibe, M.; Ohkubo, M.

    2006-02-01

    In recent years, cryogenic microcalorimeters using their superconducting transition edge have been under development for possible application to the research for astronomical X-ray observations. To improve the energy resolution of superconducting transition edge sensors (TES), several correction methods have been developed. Among them, a clustering method based on digital signal processing has recently been proposed. In this paper, we applied the clustering method to Ir/Au bilayer TES. This method resulted in almost a 10% improvement in the energy resolution. Conversely, from the point of view of imaging X-ray spectroscopy, we applied the clustering method to pixellated Ir/Au-TES devices. We will thus show how a clustering method which sorts signals by their shapes is also useful for position identification

  12. Dynamic Load Balanced Clustering using Elitism based Random Immigrant Genetic Approach for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    K. Mohaideen Pitchai

    2017-07-01

    Full Text Available Wireless Sensor Network (WSN consists of a large number of small sensors with restricted energy. Prolonged network lifespan, scalability, node mobility and load balancing are important needs for several WSN applications. Clustering the sensor nodes is an efficient technique to reach these goals. WSN have the characteristics of topology dynamics because of factors like energy conservation and node movement that leads to Dynamic Load Balanced Clustering Problem (DLBCP. In this paper, Elitism based Random Immigrant Genetic Approach (ERIGA is proposed to solve DLBCP which adapts to topology dynamics. ERIGA uses the dynamic Genetic Algorithm (GA components for solving the DLBCP. The performance of load balanced clustering process is enhanced with the help of this dynamic GA. As a result, the ERIGA achieves to elect suitable cluster heads which balances the network load and increases the lifespan of the network.

  13. Ionized-cluster source based on high-pressure corona discharge

    International Nuclear Information System (INIS)

    Lokuliyanage, K.; Huber, D.; Zappa, F.; Scheier, P.

    2006-01-01

    Full text: It has been demonstrated that energetic beams of large clusters, with thousands of atoms, can be a powerful tool for surface modification. Normally ionized cluster beams are obtained by electron impact on neutral beams produced in a supersonic expansion. At the University of Innsbruck we are pursuing the realization of a high current cluster ion source based on the corona discharge.The idea in the present case is that the ionization should occur prior to the supersonic expansion, thus supersede the need of subsequent electron impact. In this contribution we present the project of our source in its initial stage. The intensity distribution of cluster sizes as a function of the source parameters, such as input pressure, temperature and gap voltage, are investigated with the aid of a custom-built time of flight mass spectrometer. (author)

  14. Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis

    Directory of Open Access Journals (Sweden)

    Sen Zhang

    2015-01-01

    Full Text Available One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO, inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm.

  15. Innovative Development of Building Materials Industry of the Region Based on the Cluster Approach

    Directory of Open Access Journals (Sweden)

    Mottaeva Asiiat

    2016-01-01

    Full Text Available The article discusses issues of innovative development of building materials industry of the region based on the cluster approach. Determined the significance of regional cluster development of the industry of construction materials as the effective implementation of the innovative breakthrough of the region as an important part of strategies for strengthening innovation activities may be to support the formation and development of cluster structures. Analyses the current situation with innovation in the building materials industry of the region based on the cluster approach. In the course of the study revealed a direct correlation between involvement in innovative activities on a cluster basis, and the level of development of industry of construction materials. The conducted research allowed identifying the factors that determine the innovation process, systematization and classification which determine the sustainable functioning of the building materials industry in the period of active innovation. The proposed grouping of innovations for the construction industry taking into account industry-specific characteristics that reflect modern trends of scientific and technological progress in construction. Significance of the study lies in the fact that the proposals and practical recommendations can be used in the formation mechanism of innovative development of building materials industry and the overall regional construction complex of Russian regions by creating clusters of construction.

  16. A semantics-based method for clustering of Chinese web search results

    Science.gov (United States)

    Zhang, Hui; Wang, Deqing; Wang, Li; Bi, Zhuming; Chen, Yong

    2014-01-01

    Information explosion is a critical challenge to the development of modern information systems. In particular, when the application of an information system is over the Internet, the amount of information over the web has been increasing exponentially and rapidly. Search engines, such as Google and Baidu, are essential tools for people to find the information from the Internet. Valuable information, however, is still likely submerged in the ocean of search results from those tools. By clustering the results into different groups based on subjects automatically, a search engine with the clustering feature allows users to select most relevant results quickly. In this paper, we propose an online semantics-based method to cluster Chinese web search results. First, we employ the generalised suffix tree to extract the longest common substrings (LCSs) from search snippets. Second, we use the HowNet to calculate the similarities of the words derived from the LCSs, and extract the most representative features by constructing the vocabulary chain. Third, we construct a vector of text features and calculate snippets' semantic similarities. Finally, we improve the Chameleon algorithm to cluster snippets. Extensive experimental results have shown that the proposed algorithm has outperformed over the suffix tree clustering method and other traditional clustering methods.

  17. Trend analysis using non-stationary time series clustering based on the finite element method

    Science.gov (United States)

    Gorji Sefidmazgi, M.; Sayemuzzaman, M.; Homaifar, A.; Jha, M. K.; Liess, S.

    2014-05-01

    In order to analyze low-frequency variability of climate, it is useful to model the climatic time series with multiple linear trends and locate the times of significant changes. In this paper, we have used non-stationary time series clustering to find change points in the trends. Clustering in a multi-dimensional non-stationary time series is challenging, since the problem is mathematically ill-posed. Clustering based on the finite element method (FEM) is one of the methods that can analyze multidimensional time series. One important attribute of this method is that it is not dependent on any statistical assumption and does not need local stationarity in the time series. In this paper, it is shown how the FEM-clustering method can be used to locate change points in the trend of temperature time series from in situ observations. This method is applied to the temperature time series of North Carolina (NC) and the results represent region-specific climate variability despite higher frequency harmonics in climatic time series. Next, we investigated the relationship between the climatic indices with the clusters/trends detected based on this clustering method. It appears that the natural variability of climate change in NC during 1950-2009 can be explained mostly by AMO and solar activity.

  18. Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance.

    Science.gov (United States)

    Du, Xiangjun; Shao, Fengjing; Wu, Shunyao; Zhang, Hanlin; Xu, Si

    2017-07-01

    Water quality assessment is crucial for assessment of marine eutrophication, prediction of harmful algal blooms, and environment protection. Previous studies have developed many numeric modeling methods and data driven approaches for water quality assessment. The cluster analysis, an approach widely used for grouping data, has also been employed. However, there are complex correlations between water quality variables, which play important roles in water quality assessment but have always been overlooked. In this paper, we analyze correlations between water quality variables and propose an alternative method for water quality assessment with hierarchical cluster analysis based on Mahalanobis distance. Further, we cluster water quality data collected form coastal water of Bohai Sea and North Yellow Sea of China, and apply clustering results to evaluate its water quality. To evaluate the validity, we also cluster the water quality data with cluster analysis based on Euclidean distance, which are widely adopted by previous studies. The results show that our method is more suitable for water quality assessment with many correlated water quality variables. To our knowledge, it is the first attempt to apply Mahalanobis distance for coastal water quality assessment.

  19. Progressive Amalgamation of Building Clusters for Map Generalization Based on Scaling Subgroups

    Directory of Open Access Journals (Sweden)

    Xianjin He

    2018-03-01

    Full Text Available Map generalization utilizes transformation operations to derive smaller-scale maps from larger-scale maps, and is a key procedure for the modelling and understanding of geographic space. Studies to date have largely applied a fixed tolerance to aggregate clustered buildings into a single object, resulting in the loss of details that meet cartographic constraints and may be of importance for users. This study aims to develop a method that amalgamates clustered buildings gradually without significant modification of geometry, while preserving the map details as much as possible under cartographic constraints. The amalgamation process consists of three key steps. First, individual buildings are grouped into distinct clusters by using the graph-based spatial clustering application with random forest (GSCARF method. Second, building clusters are decomposed into scaling subgroups according to homogeneity with regard to the mean distance of subgroups. Thus, hierarchies of building clusters can be derived based on scaling subgroups. Finally, an amalgamation operation is progressively performed from the bottom-level subgroups to the top-level subgroups using the maximum distance of each subgroup as the amalgamating tolerance instead of using a fixed tolerance. As a consequence of this step, generalized intermediate scaling results are available, which can form the multi-scale representation of buildings. The experimental results show that the proposed method can generate amalgams with correct details, statistical area balance and orthogonal shape while satisfying cartographic constraints (e.g., minimum distance and minimum area.

  20. Investigating role stress in frontline bank employees: A cluster based approach

    Directory of Open Access Journals (Sweden)

    Arti Devi

    2013-09-01

    Full Text Available An effective role stress management programme would benefit from a segmentation of employees based on their experience of role stressors. This study explores role stressor based segments of frontline bank employees towards providing a framework for designing such a programme. Cluster analysis on a random sample of 501 frontline employees of commercial banks in Jammu and Kashmir (India revealed three distinct segments – “overloaded employees”, “unclear employees”, and “underutilised employees”, based on their experience of role stressors. The findings suggest a customised approach to role stress management, with the role stress management programme designed to address cluster specific needs.

  1. DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data.

    Science.gov (United States)

    Sun, Zhe; Wang, Ting; Deng, Ke; Wang, Xiao-Feng; Lafyatis, Robert; Ding, Ying; Hu, Ming; Chen, Wei

    2018-01-01

    Single cell transcriptome sequencing (scRNA-Seq) has become a revolutionary tool to study cellular and molecular processes at single cell resolution. Among existing technologies, the recently developed droplet-based platform enables efficient parallel processing of thousands of single cells with direct counting of transcript copies using Unique Molecular Identifier (UMI). Despite the technology advances, statistical methods and computational tools are still lacking for analyzing droplet-based scRNA-Seq data. Particularly, model-based approaches for clustering large-scale single cell transcriptomic data are still under-explored. We developed DIMM-SC, a Dirichlet Mixture Model for clustering droplet-based Single Cell transcriptomic data. This approach explicitly models UMI count data from scRNA-Seq experiments and characterizes variations across different cell clusters via a Dirichlet mixture prior. We performed comprehensive simulations to evaluate DIMM-SC and compared it with existing clustering methods such as K-means, CellTree and Seurat. In addition, we analyzed public scRNA-Seq datasets with known cluster labels and in-house scRNA-Seq datasets from a study of systemic sclerosis with prior biological knowledge to benchmark and validate DIMM-SC. Both simulation studies and real data applications demonstrated that overall, DIMM-SC achieves substantially improved clustering accuracy and much lower clustering variability compared to other existing clustering methods. More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods. DIMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/∼wec47/singlecell.html. wei.chen@chp.edu or hum@ccf.org. Supplementary data are available at Bioinformatics online. © The Author

  2. A facial marker in facial wasting rehabilitation.

    Science.gov (United States)

    Rauso, Raffaele; Tartaro, Gianpaolo; Freda, Nicola; Rusciani, Antonio; Curinga, Giuseppe

    2012-02-01

    Facial lipoatrophy is one of the most distressing manifestation for HIV patients. It can be stigmatizing, severely affecting quality of life and self-esteem, and it may result in reduced antiretroviral adherence. Several filling techniques have been proposed in facial wasting restoration, with different outcomes. The aim of this study is to present a triangular area that is useful to fill in facial wasting rehabilitation. Twenty-eight HIV patients rehabilitated for facial wasting were enrolled in this study. Sixteen were rehabilitated with a non-resorbable filler and twelve with structural fat graft harvested from lipohypertrophied areas. A photographic pre-operative and post-operative evaluation was performed by the patients and by two plastic surgeons who were "blinded." The filled area, in both patients rehabilitated with structural fat grafts or non-resorbable filler, was a triangular area of depression identified between the nasolabial fold, the malar arch, and the line that connects these two anatomical landmarks. The cosmetic result was evaluated after three months after the last filling procedure in the non-resorbable filler group and after three months post-surgery in the structural fat graft group. The mean patient satisfaction score was 8.7 as assessed with a visual analogue scale. The mean score for blinded evaluators was 7.6. In this study the authors describe a triangular area of the face, between the nasolabial fold, the malar arch, and the line that connects these two anatomical landmarks, where a good aesthetic facial restoration in HIV patients with facial wasting may be achieved regardless of which filling technique is used.

  3. Accurate landmarking of three-dimensional facial data in the presence of facial expressions and occlusions using a three-dimensional statistical facial feature model.

    Science.gov (United States)

    Zhao, Xi; Dellandréa, Emmanuel; Chen, Liming; Kakadiaris, Ioannis A

    2011-10-01

    Three-dimensional face landmarking aims at automatically localizing facial landmarks and has a wide range of applications (e.g., face recognition, face tracking, and facial expression analysis). Existing methods assume neutral facial expressions and unoccluded faces. In this paper, we propose a general learning-based framework for reliable landmark localization on 3-D facial data under challenging conditions (i.e., facial expressions and occlusions). Our approach relies on a statistical model, called 3-D statistical facial feature model, which learns both the global variations in configurational relationships between landmarks and the local variations of texture and geometry around each landmark. Based on this model, we further propose an occlusion classifier and a fitting algorithm. Results from experiments on three publicly available 3-D face databases (FRGC, BU-3-DFE, and Bosphorus) demonstrate the effectiveness of our approach, in terms of landmarking accuracy and robustness, in the presence of expressions and occlusions.

  4. Advances in facial reanimation.

    Science.gov (United States)

    Tate, James R; Tollefson, Travis T

    2006-08-01

    Facial paralysis often has a significant emotional impact on patients. Along with the myriad of new surgical techniques in managing facial paralysis comes the challenge of selecting the most effective procedure for the patient. This review delineates common surgical techniques and reviews state-of-the-art techniques. The options for dynamic reanimation of the paralyzed face must be examined in the context of several patient factors, including age, overall health, and patient desires. The best functional results are obtained with direct facial nerve anastomosis and interpositional nerve grafts. In long-standing facial paralysis, temporalis muscle transfer gives a dependable and quick result. Microvascular free tissue transfer is a reliable technique with reanimation potential whose results continue to improve as microsurgical expertise increases. Postoperative results can be improved with ancillary soft tissue procedures, as well as botulinum toxin. The paper provides an overview of recent advances in facial reanimation, including preoperative assessment, surgical reconstruction options, and postoperative management.

  5. a Web-Based Interactive Platform for Co-Clustering Spatio-Temporal Data

    Science.gov (United States)

    Wu, X.; Poorthuis, A.; Zurita-Milla, R.; Kraak, M.-J.

    2017-09-01

    Since current studies on clustering analysis mainly focus on exploring spatial or temporal patterns separately, a co-clustering algorithm is utilized in this study to enable the concurrent analysis of spatio-temporal patterns. To allow users to adopt and adapt the algorithm for their own analysis, it is integrated within the server side of an interactive web-based platform. The client side of the platform, running within any modern browser, is a graphical user interface (GUI) with multiple linked visualizations that facilitates the understanding, exploration and interpretation of the raw dataset and co-clustering results. Users can also upload their own datasets and adjust clustering parameters within the platform. To illustrate the use of this platform, an annual temperature dataset from 28 weather stations over 20 years in the Netherlands is used. After the dataset is loaded, it is visualized in a set of linked visualizations: a geographical map, a timeline and a heatmap. This aids the user in understanding the nature of their dataset and the appropriate selection of co-clustering parameters. Once the dataset is processed by the co-clustering algorithm, the results are visualized in the small multiples, a heatmap and a timeline to provide various views for better understanding and also further interpretation. Since the visualization and analysis are integrated in a seamless platform, the user can explore different sets of co-clustering parameters and instantly view the results in order to do iterative, exploratory data analysis. As such, this interactive web-based platform allows users to analyze spatio-temporal data using the co-clustering method and also helps the understanding of the results using multiple linked visualizations.

  6. A clustering approach to segmenting users of internet-based risk calculators.

    Science.gov (United States)

    Harle, C A; Downs, J S; Padman, R

    2011-01-01

    Risk calculators are widely available Internet applications that deliver quantitative health risk estimates to consumers. Although these tools are known to have varying effects on risk perceptions, little is known about who will be more likely to accept objective risk estimates. To identify clusters of online health consumers that help explain variation in individual improvement in risk perceptions from web-based quantitative disease risk information. A secondary analysis was performed on data collected in a field experiment that measured people's pre-diabetes risk perceptions before and after visiting a realistic health promotion website that provided quantitative risk information. K-means clustering was performed on numerous candidate variable sets, and the different segmentations were evaluated based on between-cluster variation in risk perception improvement. Variation in responses to risk information was best explained by clustering on pre-intervention absolute pre-diabetes risk perceptions and an objective estimate of personal risk. Members of a high-risk overestimater cluster showed large improvements in their risk perceptions, but clusters of both moderate-risk and high-risk underestimaters were much more muted in improving their optimistically biased perceptions. Cluster analysis provided a unique approach for segmenting health consumers and predicting their acceptance of quantitative disease risk information. These clusters suggest that health consumers were very responsive to good news, but tended not to incorporate bad news into their self-perceptions much. These findings help to quantify variation among online health consumers and may inform the targeted marketing of and improvements to risk communication tools on the Internet.

  7. Development and validation of a facial expression database based on the dimensional and categorical model of emotions.

    Science.gov (United States)

    Fujimura, Tomomi; Umemura, Hiroyuki

    2018-01-15

    The present study describes the development and validation of a facial expression database comprising five different horizontal face angles in dynamic and static presentations. The database includes twelve expression types portrayed by eight Japanese models. This database was inspired by the dimensional and categorical model of emotions: surprise, fear, sadness, anger with open mouth, anger with closed mouth, disgust with open mouth, disgust with closed mouth, excitement, happiness, relaxation, sleepiness, and neutral (static only). The expressions were validated using emotion classification and Affect Grid rating tasks [Russell, Weiss, & Mendelsohn, 1989. Affect Grid: A single-item scale of pleasure and arousal. Journal of Personality and Social Psychology, 57(3), 493-502]. The results indicate that most of the expressions were recognised as the intended emotions and could systematically represent affective valence and arousal. Furthermore, face angle and facial motion information influenced emotion classification and valence and arousal ratings. Our database will be available online at the following URL. https://www.dh.aist.go.jp/database/face2017/ .

  8. Advances in Bayesian Model Based Clustering Using Particle Learning

    Energy Technology Data Exchange (ETDEWEB)

    Merl, D M

    2009-11-19

    implementation of Carvalho et al that allow us to retain the computational advantages of particle learning while improving the suitability of the methodology to the analysis of streaming data and simultaneously facilitating the real time discovery of latent cluster structures. Section 4 demonstrates our methodological enhancements in the context of several simulated and classical data sets, showcasing the use of particle learning methods for online anomaly detection, label generation, drift detection, and semi-supervised classification, none of which would be achievable through a standard MCMC approach. Section 5 concludes with a discussion of future directions for research.

  9. Study of cluster headache: A hospital-based study

    Directory of Open Access Journals (Sweden)

    Amita Bhargava

    2014-01-01

    Full Text Available Introduction: Cluster headache (CH is uncommon and most painful of all primary headaches, and continues to be managed suboptimally because of wrong diagnosis. It needs to be diagnosed correctly and specifically treated. There are few studies and none from this region on CH. Materials and Methods: To study the detailed clinical profile of CH patients and to compare them among both the genders. Study was conducted at Mahatma Gandhi hospital, Jodhpur (from January 2011to December 2013. Study comprises 30 CH patients diagnosed according to International Headache Society guidelines (ICHD-II. Routine investigations and MRI brain was done in all patients. All measurements were reported as mean ± SD. Categorical variables were compared using the Chi-square test, and continuous variables were compared using Student′s t-test. SPSS for Windows, Version 16.0, was used for statistical analyses with the significance level set at P = 0.05. Results: M: F ratio was 9:1. Age at presentation was from 22-60 years (mean - 38 years. Latency before diagnosis was 3 months-12 years (mean - 3.5 years. All suffered from episodic CH and aura was found in none. Pain was strictly unilateral (right-19, left-11, predominantly over temporal region-18 (60%. Pain intensity was severe in 27 (90% and moderate in 3 (10%. Pain quality was throbbing in 12 (40%. Peak intensity was reached in 5 minutes-30 minutes and attack duration varied from 30 minutes to 3 hours (mean - 2.45 hours. Among autonomic features, conjunctival injection-23 (76.6% and lacrimation-25 (83.3% were most common. Restlessness during episode was found in 80%. CH duration varied from 10 days to 12 weeks. Circadian periodicity for attacks was noted in 24 (80%. Conclusion: Results are consistent with other studies on many accounts, but is different from Western studies with respect to low frequency of family history, chronic CH, restlessness and aura preceeding the attack. Detailed elicitation of history is

  10. Clustering and firm performance in project-based industries : the case of the global video game industry, 1972-2007

    NARCIS (Netherlands)

    Vaan, de M.; Boschma, R.A.; Frenken, K.

    2013-01-01

    Explanations of spatial clustering based on localization externalities are being questioned by recent empirical evidence showing that firms in clusters do not outperform firms outside clusters. We propose that these findings may be driven by the particularities of the industrial settings chosen in

  11. Insight into acid-base nucleation experiments by comparison of the chemical composition of positive, negative, and neutral clusters.

    Science.gov (United States)

    Bianchi, Federico; Praplan, Arnaud P; Sarnela, Nina; Dommen, Josef; Kürten, Andreas; Ortega, Ismael K; Schobesberger, Siegfried; Junninen, Heikki; Simon, Mario; Tröstl, Jasmin; Jokinen, Tuija; Sipilä, Mikko; Adamov, Alexey; Amorim, Antonio; Almeida, Joao; Breitenlechner, Martin; Duplissy, Jonathan; Ehrhart, Sebastian; Flagan, Richard C; Franchin, Alessandro; Hakala, Jani; Hansel, Armin; Heinritzi, Martin; Kangasluoma, Juha; Keskinen, Helmi; Kim, Jaeseok; Kirkby, Jasper; Laaksonen, Ari; Lawler, Michael J; Lehtipalo, Katrianne; Leiminger, Markus; Makhmutov, Vladimir; Mathot, Serge; Onnela, Antti; Petäjä, Tuukka; Riccobono, Francesco; Rissanen, Matti P; Rondo, Linda; Tomé, António; Virtanen, Annele; Viisanen, Yrjö; Williamson, Christina; Wimmer, Daniela; Winkler, Paul M; Ye, Penglin; Curtius, Joachim; Kulmala, Markku; Worsnop, Douglas R; Donahue, Neil M; Baltensperger, Urs

    2014-12-02

    We investigated the nucleation of sulfuric acid together with two bases (ammonia and dimethylamine), at the CLOUD chamber at CERN. The chemical composition of positive, negative, and neutral clusters was studied using three Atmospheric Pressure interface-Time Of Flight (APi-TOF) mass spectrometers: two were operated in positive and negative mode to detect the chamber ions, while the third was equipped with a nitrate ion chemical ionization source allowing detection of neutral clusters. Taking into account the possible fragmentation that can happen during the charging of the ions or within the first stage of the mass spectrometer, the cluster formation proceeded via essentially one-to-one acid-base addition for all of the clusters, independent of the type of the base. For the positive clusters, the charge is carried by one excess protonated base, while for the negative clusters it is carried by a deprotonated acid; the same is true for the neutral clusters after these have been ionized. During the experiments involving sulfuric acid and dimethylamine, it was possible to study the appearance time for all the clusters (positive, negative, and neutral). It appeared that, after the formation of the clusters containing three molecules of sulfuric acid, the clusters grow at a similar speed, independent of their charge. The growth rate is then probably limited by the arrival rate of sulfuric acid or cluster-cluster collision.

  12. Clustering and firm performance in project-based industries: the case of the global video game industry, 1972-2007

    NARCIS (Netherlands)

    Vaan, M. de; Boschma, R.; Frenken, K.

    2013-01-01

    Explanations of spatial clustering based on localization externalities are being questioned by recent empirical evidence showing that firms in clusters do not outperform firms outside clusters. We propose that these findings may be driven by the particularities of the industrial settings chosen

  13. [Facial nerve neurinomas].

    Science.gov (United States)

    Sokołowski, Jacek; Bartoszewicz, Robert; Morawski, Krzysztof; Jamróz, Barbara; Niemczyk, Kazimierz

    2013-01-01

    Evaluation of diagnostic, surgical technique, treatment results facial nerve neurinomas and its comparison with literature was the main purpose of this study. Seven cases of patients (2005-2011) with facial nerve schwannomas were included to retrospective analysis in the Department of Otolaryngology, Medical University of Warsaw. All patients were assessed with history of the disease, physical examination, hearing tests, computed tomography and/or magnetic resonance imaging, electronystagmography. Cases were observed in the direction of potential complications and recurrences. Neurinoma of the facial nerve occurred in the vertical segment (n=2), facial nerve geniculum (n=1) and the internal auditory canal (n=4). The symptoms observed in patients were analyzed: facial nerve paresis (n=3), hearing loss (n=2), dizziness (n=1). Magnetic resonance imaging and computed tomography allowed to confirm the presence of the tumor and to assess its staging. Schwannoma of the facial nerve has been surgically removed using the middle fossa approach (n=5) and by antromastoidectomy (n=2). Anatomical continuity of the facial nerve was achieved in 3 cases. In the twelve months after surgery, facial nerve paresis was rated at level II-III° HB. There was no recurrence of the tumor in radiological observation. Facial nerve neurinoma is a rare tumor. Currently surgical techniques allow in most cases, the radical removing of the lesion and reconstruction of the VII nerve function. The rate of recurrence is low. A tumor of the facial nerve should be considered in the differential diagnosis of nerve VII paresis. Copyright © 2013 Polish Otorhinolaryngology - Head and Neck Surgery Society. Published by Elsevier Urban & Partner Sp. z.o.o. All rights reserved.

  14. Biological consequences of potential repair intermediates of clustered base damage site in Escherichia coli

    Energy Technology Data Exchange (ETDEWEB)

    Shikazono, Naoya, E-mail: shikazono.naoya@jaea.go.jp [Japan Atomic Energy Agency, Advanced Research Science Center, 2-4 Shirakata-Shirane, Tokai-mura, Naka-gun, Ibaraki 319-1195 (Japan); O' Neill, Peter [Gray Institute for Radiation Oncology and Biology, University of Oxford, Roosevelt Drive, Oxford OX3 7DQ (United Kingdom)

    2009-10-02

    Clustered DNA damage induced by a single radiation track is a unique feature of ionizing radiation. Using a plasmid-based assay in Escherichia coli, we previously found significantly higher mutation frequencies for bistranded clusters containing 7,8-dihydro-8-oxoguanine (8-oxoG) and 5,6-dihydrothymine (DHT) than for either a single 8-oxoG or a single DHT in wild type and in glycosylase-deficient strains of E. coli. This indicates that the removal of an 8-oxoG from a clustered damage site is most likely retarded compared to the removal of a single 8-oxoG. To gain further insights into the processing of bistranded base lesions, several potential repair intermediates following 8-oxoG removal were assessed. Clusters, such as DHT + apurinic/apyrimidinic (AP) and DHT + GAP have relatively low mutation frequencies, whereas clusters, such as AP + AP or GAP + AP, significantly reduce the number of transformed colonies, most probably through formation of a lethal double strand break (DSB). Bistranded AP sites placed 3' to each other with various interlesion distances also blocked replication. These results suggest that bistranded base lesions, i.e., single base lesions on each strand, but not clusters containing only AP sites and strand breaks, are repaired in a coordinated manner so that the formation of DSBs is avoided. We propose that, when either base lesion is initially excised from a bistranded base damage site, the remaining base lesion will only rarely be converted into an AP site or a single strand break in vivo.

  15. Biological consequences of potential repair intermediates of clustered base damage site in Escherichia coli

    International Nuclear Information System (INIS)

    Shikazono, Naoya; O'Neill, Peter

    2009-01-01

    Clustered DNA damage induced by a single radiation track is a unique feature of ionizing radiation. Using a plasmid-based assay in Escherichia coli, we previously found significantly higher mutation frequencies for bistranded clusters containing 7,8-dihydro-8-oxoguanine (8-oxoG) and 5,6-dihydrothymine (DHT) than for either a single 8-oxoG or a single DHT in wild type and in glycosylase-deficient strains of E. coli. This indicates that the removal of an 8-oxoG from a clustered damage site is most likely retarded compared to the removal of a single 8-oxoG. To gain further insights into the processing of bistranded base lesions, several potential repair intermediates following 8-oxoG removal were assessed. Clusters, such as DHT + apurinic/apyrimidinic (AP) and DHT + GAP have relatively low mutation frequencies, whereas clusters, such as AP + AP or GAP + AP, significantly reduce the number of transformed colonies, most probably through formation of a lethal double strand break (DSB). Bistranded AP sites placed 3' to each other with various interlesion distances also blocked replication. These results suggest that bistranded base lesions, i.e., single base lesions on each strand, but not clusters containing only AP sites and strand breaks, are repaired in a coordinated manner so that the formation of DSBs is avoided. We propose that, when either base lesion is initially excised from a bistranded base damage site, the remaining base lesion will only rarely be converted into an AP site or a single strand break in vivo.

  16. Hierarchical clustering of RGB surface water images based on MIA ...

    African Journals Online (AJOL)

    2009-11-25

    Nov 25, 2009 ... similar water-related images within a testing database of 126 RGB images. .... consequently treated by SVD-based PCA and the PCA outputs partitioned into .... green. Other colours, mostly brown and grey, dominate in.

  17. Outcome of a graduated minimally invasive facial reanimation in patients with facial paralysis.

    Science.gov (United States)

    Holtmann, Laura C; Eckstein, Anja; Stähr, Kerstin; Xing, Minzhi; Lang, Stephan; Mattheis, Stefan

    2017-08-01

    Peripheral paralysis of the facial nerve is the most frequent of all cranial nerve disorders. Despite advances in facial surgery, the functional and aesthetic reconstruction of a paralyzed face remains a challenge. Graduated minimally invasive facial reanimation is based on a modular principle. According to the patients' needs, precondition, and expectations, the following modules can be performed: temporalis muscle transposition and facelift, nasal valve suspension, endoscopic brow lift, and eyelid reconstruction. Applying a concept of a graduated minimally invasive facial reanimation may help minimize surgical trauma and reduce morbidity. Twenty patients underwent a graduated minimally invasive facial reanimation. A retrospective chart review was performed with a follow-up examination between 1 and 8 months after surgery. The FACEgram software was used to calculate pre- and postoperative eyelid closure, the level of brows, nasal, and philtral symmetry as well as oral commissure position at rest and oral commissure excursion with smile. As a patient-oriented outcome parameter, the Glasgow Benefit Inventory questionnaire was applied. There was a statistically significant improvement in the postoperative score of eyelid closure, brow asymmetry, nasal asymmetry, philtral asymmetry as well as oral commissure symmetry at rest (p facial nerve repair or microneurovascular tissue transfer cannot be applied, graduated minimally invasive facial reanimation is a promising option to restore facial function and symmetry at rest.

  18. Hedgehog bases for A{sub n} cluster polylogarithms and an application to six-point amplitudes

    Energy Technology Data Exchange (ETDEWEB)

    Parker, Daniel E.; Scherlis, Adam; Spradlin, Marcus; Volovich, Anastasia [Department of Physics, Brown University, Providence RI 02912 (United States)

    2015-11-20

    Multi-loop scattering amplitudes in N=4 Yang-Mills theory possess cluster algebra structure. In order to develop a computational framework which exploits this connection, we show how to construct bases of Goncharov polylogarithm functions, at any weight, whose symbol alphabet consists of cluster coordinates on the A{sub n} cluster algebra. Using such a basis we present a new expression for the 2-loop 6-particle NMHV amplitude which makes some of its cluster structure manifest.

  19. Sound-induced facial synkinesis following facial nerve paralysis

    NARCIS (Netherlands)

    Ma, Ming-San; van der Hoeven, Johannes H.; Nicolai, Jean-Philippe A.; Meek, Marcel F.

    Facial synkinesis (or synkinesia) (FS) occurs frequently after paresis or paralysis of the facial nerve and is in most cases due to aberrant regeneration of (branches of) the facial nerve. Patients suffer from inappropriate and involuntary synchronous facial muscle contractions. Here we describe two

  20. Ecosystem health pattern analysis of urban clusters based on emergy synthesis: Results and implication for management

    International Nuclear Information System (INIS)

    Su, Meirong; Fath, Brian D.; Yang, Zhifeng; Chen, Bin; Liu, Gengyuan

    2013-01-01

    The evaluation of ecosystem health in urban clusters will help establish effective management that promotes sustainable regional development. To standardize the application of emergy synthesis and set pair analysis (EM–SPA) in ecosystem health assessment, a procedure for using EM–SPA models was established in this paper by combining the ability of emergy synthesis to reflect health status from a biophysical perspective with the ability of set pair analysis to describe extensive relationships among different variables. Based on the EM–SPA model, the relative health levels of selected urban clusters and their related ecosystem health patterns were characterized. The health states of three typical Chinese urban clusters – Jing-Jin-Tang, Yangtze River Delta, and Pearl River Delta – were investigated using the model. The results showed that the health status of the Pearl River Delta was relatively good; the health for the Yangtze River Delta was poor. As for the specific health characteristics, the Pearl River Delta and Yangtze River Delta urban clusters were relatively strong in Vigor, Resilience, and Urban ecosystem service function maintenance, while the Jing-Jin-Tang was relatively strong in organizational structure and environmental impact. Guidelines for managing these different urban clusters were put forward based on the analysis of the results of this study. - Highlights: • The use of integrated emergy synthesis and set pair analysis model was standardized. • The integrated model was applied on the scale of an urban cluster. • Health patterns of different urban clusters were compared. • Policy suggestions were provided based on the health pattern analysis

  1. INTERSECTION DETECTION BASED ON QUALITATIVE SPATIAL REASONING ON STOPPING POINT CLUSTERS

    Directory of Open Access Journals (Sweden)

    S. Zourlidou

    2016-06-01

    Full Text Available The purpose of this research is to propose and test a method for detecting intersections by analysing collectively acquired trajectories of moving vehicles. Instead of solely relying on the geometric features of the trajectories, such as heading changes, which may indicate turning points and consequently intersections, we extract semantic features of the trajectories in form of sequences of stops and moves. Under this spatiotemporal prism, the extracted semantic information which indicates where vehicles stop can reveal important locations, such as junctions. The advantage of the proposed approach in comparison with existing turning-points oriented approaches is that it can detect intersections even when not all the crossing road segments are sampled and therefore no turning points are observed in the trajectories. The challenge with this approach is that first of all, not all vehicles stop at the same location – thus, the stop-location is blurred along the direction of the road; this, secondly, leads to the effect that nearby junctions can induce similar stop-locations. As a first step, a density-based clustering is applied on the layer of stop observations and clusters of stop events are found. Representative points of the clusters are determined (one per cluster and in a last step the existence of an intersection is clarified based on spatial relational cluster reasoning, with which less informative geospatial clusters, in terms of whether a junction exists and where its centre lies, are transformed in more informative ones. Relational reasoning criteria, based on the relative orientation of the clusters with their adjacent ones are discussed for making sense of the relation that connects them, and finally for forming groups of stop events that belong to the same junction.

  2. Distribution-based fuzzy clustering of electrical resistivity tomography images for interface detection

    Science.gov (United States)

    Ward, W. O. C.; Wilkinson, P. B.; Chambers, J. E.; Oxby, L. S.; Bai, L.

    2014-04-01

    A novel method for the effective identification of bedrock subsurface elevation from electrical resistivity tomography images is described. Identifying subsurface boundaries in the topographic data can be difficult due to smoothness constraints used in inversion, so a statistical population-based approach is used that extends previous work in calculating isoresistivity surfaces. The analysis framework involves a procedure for guiding a clustering approach based on the fuzzy c-means algorithm. An approximation of resistivity distributions, found using kernel density estimation, was utilized as a means of guiding the cluster centroids used to classify data. A fuzzy method was chosen over hard clustering due to uncertainty in hard edges in the topography data, and a measure of clustering uncertainty was identified based on the reciprocal of cluster membership. The algorithm was validated using a direct comparison of known observed bedrock depths at two 3-D survey sites, using real-time GPS information of exposed bedrock by quarrying on one site, and borehole logs at the other. Results show similarly accurate detection as a leading isosurface estimation method, and the proposed algorithm requires significantly less user input and prior site knowledge. Furthermore, the method is effectively dimension-independent and will scale to data of increased spatial dimensions without a significant effect on the runtime. A discussion on the results by automated versus supervised analysis is also presented.

  3. Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon

    Directory of Open Access Journals (Sweden)

    Moumita Saha

    2016-01-01

    Full Text Available Forecasting the Indian summer monsoon is a challenging task due to its complex and nonlinear behavior. A large number of global climatic variables with varying interaction patterns over years influence monsoon. Various statistical and neural prediction models have been proposed for forecasting monsoon, but many of them fail to capture variability over years. The skill of predictor variables of monsoon also evolves over time. In this article, we propose a joint-clustering of monsoon years and predictors for understanding and predicting the monsoon. This is achieved by subspace clustering algorithm. It groups the years based on prevailing global climatic condition using statistical clustering technique and subsequently for each such group it identifies significant climatic predictor variables which assist in better prediction. Prediction model is designed to frame individual cluster using random forest of regression tree. Prediction of aggregate and regional monsoon is attempted. Mean absolute error of 5.2% is obtained for forecasting aggregate Indian summer monsoon. Errors in predicting the regional monsoons are also comparable in comparison to the high variation of regional precipitation. Proposed joint-clustering based ensemble model is observed to be superior to existing monsoon prediction models and it also surpasses general nonclustering based prediction models.

  4. Model-based Clustering of Categorical Time Series with Multinomial Logit Classification

    Science.gov (United States)

    Frühwirth-Schnatter, Sylvia; Pamminger, Christoph; Winter-Ebmer, Rudolf; Weber, Andrea

    2010-09-01

    A common problem in many areas of applied statistics is to identify groups of similar time series in a panel of time series. However, distance-based clustering methods cannot easily be extended to time series data, where an appropriate distance-measure is rather difficult to define, particularly for discrete-valued time series. Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter [6], is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This model-based clustering method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to further explain group membership we present an extension to the approach of Pamminger and Frühwirth-Schnatter [6] by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule by using a multinomial logit model. The parameters are estimated for a fixed number of clusters within a Bayesian framework using an Markov chain Monte Carlo (MCMC) sampling scheme representing a (full) Gibbs-type sampler which involves only draws from standard distributions. Finally, an application to a panel of Austrian wage mobility data is presented which leads to an interesting segmentation of the Austrian labour market.

  5. An effective trust-based recommendation method using a novel graph clustering algorithm

    Science.gov (United States)

    Moradi, Parham; Ahmadian, Sajad; Akhlaghian, Fardin

    2015-10-01

    Recommender systems are programs that aim to provide personalized recommendations to users for specific items (e.g. music, books) in online sharing communities or on e-commerce sites. Collaborative filtering methods are important and widely accepted types of recommender systems that generate recommendations based on the ratings of like-minded users. On the other hand, these systems confront several inherent issues such as data sparsity and cold start problems, caused by fewer ratings against the unknowns that need to be predicted. Incorporating trust information into the collaborative filtering systems is an attractive approach to resolve these problems. In this paper, we present a model-based collaborative filtering method by applying a novel graph clustering algorithm and also considering trust statements. In the proposed method first of all, the problem space is represented as a graph and then a sparsest subgraph finding algorithm is applied on the graph to find the initial cluster centers. Then, the proposed graph clustering algorithm is performed to obtain the appropriate users/items clusters. Finally, the identified clusters are used as a set of neighbors to recommend unseen items to the current active user. Experimental results based on three real-world datasets demonstrate that the proposed method outperforms several state-of-the-art recommender system methods.

  6. Clustering Batik Images using Fuzzy C-Means Algorithm Based on Log-Average Luminance

    Directory of Open Access Journals (Sweden)

    Ahmad Sanmorino

    2012-06-01

    Full Text Available Batik is a fabric or clothes that are made ​​with a special staining technique called wax-resist dyeing and is one of the cultural heritage which has high artistic value. In order to improve the efficiency and give better semantic to the image, some researchers apply clustering algorithm for managing images before they can be retrieved. Image clustering is a process of grouping images based on their similarity. In this paper we attempt to provide an alternative method of grouping batik image using fuzzy c-means (FCM algorithm based on log-average luminance of the batik. FCM clustering algorithm is an algorithm that works using fuzzy models that allow all data from all cluster members are formed with different degrees of membership between 0 and 1. Log-average luminance (LAL is the average value of the lighting in an image. We can compare different image lighting from one image to another using LAL. From the experiments that have been made, it can be concluded that fuzzy c-means algorithm can be used for batik image clustering based on log-average luminance of each image possessed.

  7. Energy-Efficient Cluster Based Routing Protocol in Mobile Ad Hoc Networks Using Network Coding

    Directory of Open Access Journals (Sweden)

    Srinivas Kanakala

    2014-01-01

    Full Text Available In mobile ad hoc networks, all nodes are energy constrained. In such situations, it is important to reduce energy consumption. In this paper, we consider the issues of energy efficient communication in MANETs using network coding. Network coding is an effective method to improve the performance of wireless networks. COPE protocol implements network coding concept to reduce number of transmissions by mixing the packets at intermediate nodes. We incorporate COPE into cluster based routing protocol to further reduce the energy consumption. The proposed energy-efficient coding-aware cluster based routing protocol (ECCRP scheme applies network coding at cluster heads to reduce number of transmissions. We also modify the queue management procedure of COPE protocol to further improve coding opportunities. We also use an energy efficient scheme while selecting the cluster head. It helps to increase the life time of the network. We evaluate the performance of proposed energy efficient cluster based protocol using simulation. Simulation results show that the proposed ECCRP algorithm reduces energy consumption and increases life time of the network.

  8. Design and implementation of streaming media server cluster based on FFMpeg.

    Science.gov (United States)

    Zhao, Hong; Zhou, Chun-long; Jin, Bao-zhao

    2015-01-01

    Poor performance and network congestion are commonly observed in the streaming media single server system. This paper proposes a scheme to construct a streaming media server cluster system based on FFMpeg. In this scheme, different users are distributed to different servers according to their locations and the balance among servers is maintained by the dynamic load-balancing algorithm based on active feedback. Furthermore, a service redirection algorithm is proposed to improve the transmission efficiency of streaming media data. The experiment results show that the server cluster system has significantly alleviated the network congestion and improved the performance in comparison with the single server system.

  9. Design and Implementation of Streaming Media Server Cluster Based on FFMpeg

    Science.gov (United States)

    Zhao, Hong; Zhou, Chun-long; Jin, Bao-zhao

    2015-01-01

    Poor performance and network congestion are commonly observed in the streaming media single server system. This paper proposes a scheme to construct a streaming media server cluster system based on FFMpeg. In this scheme, different users are distributed to different servers according to their locations and the balance among servers is maintained by the dynamic load-balancing algorithm based on active feedback. Furthermore, a service redirection algorithm is proposed to improve the transmission efficiency of streaming media data. The experiment results show that the server cluster system has significantly alleviated the network congestion and improved the performance in comparison with the single server system. PMID:25734187

  10. Facial Scar Revision: Understanding Facial Scar Treatment

    Science.gov (United States)

    ... keep the head elevated when lying down, to use cold compresses to reduce swelling, and to avoid any activity that places undue stress on the area of the incision. Depending on the surgery performed and the site of the scar, the facial plastic surgeon will explain the types of activities to ...

  11. Formal And Informal Macro-Regional Transport Clusters As A Primary Step In The Design And Implementation Of Cluster-Based Strategies

    Directory of Open Access Journals (Sweden)

    Nežerenko Olga

    2015-09-01

    Full Text Available The aim of the study is the identification of a formal macro-regional transport and logistics cluster and its development trends on a macro-regional level in 2007-2011 by means of the hierarchical cluster analysis. The central approach of the study is based on two concepts: 1 the concept of formal and informal macro-regions, and 2 the concept of clustering which is based on the similarities shared by the countries of a macro-region and tightly related to the concept of macro-region. The authors seek to answer the question whether the formation of a formal transport cluster could provide the BSR a stable competitive position in the global transportation and logistics market.

  12. An anatomical study for localisation of zygomatic branch of facial nerve and masseteric nerve – an aid to nerve coaptation for facial reanimation surgery: A cadaver based study in Eastern India

    Directory of Open Access Journals (Sweden)

    Ratnadeep Poddar

    2017-01-01

    Full Text Available Context: In cases of chronic facial palsy, where direct neurotisation is possible, ipsilateral masseteric nerve is a very suitable motor donor. We have tried to specifically locate the masseteric nerve for this purpose. Aims: Describing an approach of localisation and exposure of both the zygomatic branch of Facial nerve and the nerve to masseter, with respect to a soft tissue reference point over face. Settings and Design: Observational cross sectional study, conducted on 12 fresh cadavers. Subjects and Methods: A curved incision was given, passing about 0.5cms in front of the tragal cartilage. A reference point “R” was pointed out. The zygomatic branch of facial nerve and masseteric nerve were dissected out and their specific locations were recorded from fixed reference points with help of copper wire and slide callipers. Statistical Analysis Used: Central Tendency measurements and Unpaired “t” test. Results: Zygomatic branch of the Facial nerve was located within a small circular area of radius 1 cm, the centre of which lies at a distance of 1.1 cms (±0.4cm in males and 0.2cm (±0.1cm in females from the point, 'R', in a vertical (coronal plane. The nerve to masseter was noted to lie within a circular area of 1 cm radius, the centre of which was at a distance of 2.5cms (±0.4cm and 1.7cms (±0.2cm from R, in male and female cadavers, respectively. Finally, Masseteric nerve's depth, from the masseteric surface was found to be 1cm (±0.1cm; male and 0.8cm (±0.1cm; female. Conclusions: This novel approach can reduce the post operative cosmetic morbidity and per-operative complications of facial reanimation surgery.

  13. A WEB-BASED SOLUTION TO VISUALIZE OPERATIONAL MONITORING LINUX CLUSTER FOR THE PROTODUNE DATA QUALITY MONITORING CLUSTER

    CERN Document Server

    Mosesane, Badisa

    2017-01-01

    The Neutrino computing cluster made of 300 Dell PowerEdge 1950 U1 nodes serves an integral role to the CERN Neutrino Platform (CENF). It represents an effort to foster fundamental research in the field of Neutrino physics as it provides data processing facility. We cannot begin to over emphasize the need for data quality monitoring coupled with automating system configurations and remote monitoring of the cluster. To achieve these, a software stack has been chosen to implement automatic propagation of configurations across all the nodes in the cluster. The bulk of these discusses and delves more into the automated configuration management system on this cluster to enable the fast online data processing and Data Quality (DQM) process for the Neutrino Platform cluster (npcmp.cern.ch).

  14. Graph-based Geospatial Prediction and Clustering for Situation Recognition

    OpenAIRE

    Tang, Mengfan

    2017-01-01

    Big data continues to grow and diversify at an increasing pace. To understand constantly evolving situations, data is collected from various location-based sensors as well as people using effective participatory sensing. Static sensors are placed at particular locations, monitoring and measuring important variables from the environment. Additionally, people contribute data in the form of mobile streams through participatory sensing. To process such disparate data for situation recognition, we...

  15. A study on facial expressions recognition

    Science.gov (United States)

    Xu, Jingjing

    2017-09-01

    In terms of communication, postures and facial expressions of such feelings like happiness, anger and sadness play important roles in conveying information. With the development of the technology, recently a number of algorithms dealing with face alignment, face landmark detection, classification, facial landmark localization and pose estimation have been put forward. However, there are a lot of challenges and problems need to be fixed. In this paper, a few technologies have been concluded and analyzed, and they all relate to handling facial expressions recognition and poses like pose-indexed based multi-view method for face alignment, robust facial landmark detection under significant head pose and occlusion, partitioning the input domain for classification, robust statistics face formalization.

  16. Comparison of Skin Moisturizer: Consumer-Based Brand Equity (CBBE Factors in Clusters Based on Consumer Ethnocentrism

    Directory of Open Access Journals (Sweden)

    Yossy Hanna Garlina

    2014-09-01

    Full Text Available This research aims to analyze relevant factors contributing to the four dimensions of consumer-based brand equity in skin moisturizer industry. It is then followed by the clustering of female consumers of skin moisturizer based on ethnocentrism and differentiating each cluster’s consumer-based brand equity dimensions towards a domestic skin moisturizer brand Mustika Ratu, skin moisturizer. Research used descriptive survey method analysis. Primary data was obtained through questionnaire distribution to 70 female respondents for factor analysis and 120 female respondents for cluster analysis and one way analysis of variance (ANOVA. This research employed factor analysis to obtain relevant factors contributing to the five dimensions of consumer-based brand equity in skin moisturizer industry. Cluster analysis and one way analysis of variance (ANOVA were to see the difference of consumer-based brand equity between highly ethnocentric consumer and low ethnocentric consumer towards the same skin moisturizer domestic brand, Mustika Ratu skin moisturizer. Research found in all individual dimension analysis, all variable means and individual means show distinct difference between the high ethnocentric consumer and the low ethnocentric consumer. The low ethnocentric consumer cluster tends to be lower in mean score of Brand Loyalty, Perceived Quality, Brand Awareness, Brand Association, and Overall Brand Equity than the high ethnocentric consumer cluster. Research concludes consumer ethnocentrism is positively correlated with preferences towards domestic products and negatively correlated with foreign-made product preference. It is, then, highly ethnocentric consumers have positive perception towards domestic product.

  17. Analytical network process based optimum cluster head selection in wireless sensor network.

    Science.gov (United States)

    Farman, Haleem; Javed, Huma; Jan, Bilal; Ahmad, Jamil; Ali, Shaukat; Khalil, Falak Naz; Khan, Murad

    2017-01-01

    Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of

  18. Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

    Science.gov (United States)

    Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

    2017-11-01

    In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

  19. Risk Assessment for Bridges Safety Management during Operation Based on Fuzzy Clustering Algorithm

    Directory of Open Access Journals (Sweden)

    Xia Hanyu

    2016-01-01

    Full Text Available In recent years, large span and large sea-crossing bridges are built, bridges accidents caused by improper operational management occur frequently. In order to explore the better methods for risk assessment of the bridges operation departments, the method based on fuzzy clustering algorithm is selected. Then, the implementation steps of fuzzy clustering algorithm are described, the risk evaluation system is built, and Taizhou Bridge is selected as an example, the quantitation of risk factors is described. After that, the clustering algorithm based on fuzzy equivalence is calculated on MATLAB 2010a. In the last, Taizhou Bridge operation management departments are classified and sorted according to the degree of risk, and the safety situation of operation departments is analyzed.

  20. K-Nearest Neighbor Intervals Based AP Clustering Algorithm for Large Incomplete Data

    Directory of Open Access Journals (Sweden)

    Cheng Lu

    2015-01-01

    Full Text Available The Affinity Propagation (AP algorithm is an effective algorithm for clustering analysis, but it can not be directly applicable to the case of incomplete data. In view of the prevalence of missing data and the uncertainty of missing attributes, we put forward a modified AP clustering algorithm based on K-nearest neighbor intervals (KNNI for incomplete data. Based on an Improved Partial Data Strategy, the proposed algorithm estimates the KNNI representation of missing attributes by using the attribute distribution information of the available data. The similarity function can be changed by dealing with the interval data. Then the improved AP algorithm can be applicable to the case of incomplete data. Experiments on several UCI datasets show that the proposed algorithm achieves impressive clustering results.

  1. Cluster Matters

    DEFF Research Database (Denmark)

    Gulati, Mukesh; Lund-Thomsen, Peter; Suresh, Sangeetha

    2018-01-01

    sell their products successfully in international markets, but there is also an increasingly large consumer base within India. Indeed, Indian industrial clusters have contributed to a substantial part of this growth process, and there are several hundred registered clusters within the country...... of this handbook, which focuses on the role of CSR in MSMEs. Hence we contribute to the literature on CSR in industrial clusters and specifically CSR in Indian industrial clusters by investigating the drivers of CSR in India’s industrial clusters....

  2. The effectiveness of ayurvedic oil-based nasal instillation (Nasya) medicines in the treatment of facial paralysis (Ardita): a systematic review.

    Science.gov (United States)

    Vivera, Manuel Joseph; Gomersall, Judith Streak

    2016-04-01

    did not include any studies examining effectiveness of Nasya compared to conventional treatment for Ardita. This review presents extremely limited evidence from only two small experimental studies that administration of Nasya oil alone may provide some relief from Ardita symptoms of facial distortion, speech disorder, inability to shut eyelids/upward eye rolling and dribbling of saliva in adult patients. No strong conclusions may be drawn from the evidence included in the review due to the limited number of studies, limited number of participants and poor quality of studies. Practitioners should advice Ardita patients that there is extremely limited evidence suggesting the potential effectiveness of Nasya oils alone or Nasya in conjunction with other Ayurvedic treatments in managing symptoms. However, given the absence of a strong evidence base, practitioners should be guided by clinical wisdom and patient preference. Well controlled clinical trials comparing standalone Nasya therapy to other Ayurvedic treatments and/or conventional medicine for Ardita symptoms need to be conducted to examine the relative effectiveness of different Nasya oils in treating Ardita.

  3. Virtual screening by a new Clustering-based Weighted Similarity Extreme Learning Machine approach.

    Science.gov (United States)

    Pasupa, Kitsuchart; Kudisthalert, Wasu

    2018-01-01

    Machine learning techniques are becoming popular in virtual screening tasks. One of the powerful machine learning algorithms is Extreme Learning Machine (ELM) which has been applied to many applications and has recently been applied to virtual screening. We propose the Weighted Similarity ELM (WS-ELM) which is based on a single layer feed-forward neural network in a conjunction of 16 different similarity coefficients as activation function in the hidden layer. It is known that the performance of conventional ELM is not robust due to random weight selection in the hidden layer. Thus, we propose a Clustering-based WS-ELM (CWS-ELM) that deterministically assigns weights by utilising clustering algorithms i.e. k-means clustering and support vector clustering. The experiments were conducted on one of the most challenging datasets-Maximum Unbiased Validation Dataset-which contains 17 activity classes carefully selected from PubChem. The proposed algorithms were then compared with other machine learning techniques such as support vector machine, random forest, and similarity searching. The results show that CWS-ELM in conjunction with support vector clustering yields the best performance when utilised together with Sokal/Sneath(1) coefficient. Furthermore, ECFP_6 fingerprint presents the best results in our framework compared to the other types of fingerprints, namely ECFP_4, FCFP_4, and FCFP_6.

  4. Group analyses of connectivity-based cortical parcellation using repeated k-means clustering.

    Science.gov (United States)

    Nanetti, Luca; Cerliani, Leonardo; Gazzola, Valeria; Renken, Remco; Keysers, Christian

    2009-10-01

    K-means clustering has become a popular tool for connectivity-based cortical segmentation using Diffusion Weighted Imaging (DWI) data. A sometimes ignored issue is, however, that the output of the algorithm depends on the initial placement of starting points, and that different sets of starting points therefore could lead to different solutions. In this study we explore this issue. We apply k-means clustering a thousand times to the same DWI dataset collected in 10 individuals to segment two brain regions: the SMA-preSMA on the medial wall, and the insula. At the level of single subjects, we found that in both brain regions, repeatedly applying k-means indeed often leads to a variety of rather different cortical based parcellations. By assessing the similarity and frequency of these different solutions, we show that approximately 256 k-means repetitions are needed to accurately estimate the distribution of possible solutions. Using nonparametric group statistics, we then propose a method to employ the variability of clustering solutions to assess the reliability with which certain voxels can be attributed to a particular cluster. In addition, we show that the proportion of voxels that can be attributed significantly to either cluster in the SMA and preSMA is relatively higher than in the insula and discuss how this difference may relate to differences in the anatomy of these regions.

  5. Cluster-based Dynamic Energy Management for Collaborative Target Tracking in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Dao-Wei Bi

    2007-07-01

    Full Text Available A primary criterion of wireless sensor network is energy efficiency. Focused onthe energy problem of target tracking in wireless sensor networks, this paper proposes acluster-based dynamic energy management mechanism. Target tracking problem isformulated by the multi-sensor detection model as well as energy consumption model. Adistributed adaptive clustering approach is investigated to form a reasonable routingframework which has uniform cluster head distribution. Dijkstra’s algorithm is utilized toobtain optimal intra-cluster routing. Target position is predicted by particle filter. Thepredicted target position is adopted to estimate the idle interval of sensor nodes. Hence,dynamic awakening approach is exploited to prolong sleep time of sensor nodes so that theoperation energy consumption of wireless sensor network can be reduced. The sensornodes around the target wake up on time and act as sensing candidates. With the candidatesensor nodes and predicted target position, the optimal sensor node selection is considered.Binary particle swarm optimization is proposed to minimize the total energy consumptionduring collaborative sensing and data reporting. Experimental results verify that theproposed clustering approach establishes a low-energy communication structure while theenergy efficiency of wireless sensor networks is enhanced by cluster-based dynamic energymanagement.

  6. The MPI facial expression database--a validated database of emotional and conversational facial expressions.

    Directory of Open Access Journals (Sweden)

    Kathrin Kaulard

    Full Text Available The ability to communicate is one of the core aspects of human life. For this, we use not only verbal but also nonverbal signals of remarkable complexity. Among the latter, facial expressions belong to the most important information channels. Despite the large variety of facial expressions we use in daily life, research on facial expressions has so far mostly focused on the emotional aspect. Consequently, most databases of facial expressions available to the research community also include only emotional expressions, neglecting the largely unexplored aspect of conversational expressions. To fill this gap, we present the MPI facial expression database, which contains a large variety of natural emotional and conversational expressions. The database contains 55 different facial expressions performed by 19 German participants. Expressions were elicited with the help of a method-acting protocol, which guarantees both well-defined and natural facial expressions. The method-acting protocol was based on every-day scenarios, which are used to define the necessary context information for each expression. All facial expressions are available in three repetitions, in two intensities, as well as from three different camera angles. A detailed frame annotation is provided, from which a dynamic and a static version of the database have been created. In addition to describing the database in detail, we also present the results of an experiment with two conditions that serve to validate the context scenarios as well as the naturalness and recognizability of the video sequences. Our results provide clear evidence that conversational expressions can be recognized surprisingly well from visual information alone. The MPI facial expression database will enable researchers from different research fields (including the perceptual and cognitive sciences, but also affective computing, as well as computer vision to investigate the processing of a wider range of natural

  7. The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions

    Science.gov (United States)

    Kaulard, Kathrin; Cunningham, Douglas W.; Bülthoff, Heinrich H.; Wallraven, Christian

    2012-01-01

    The ability to communicate is one of the core aspects of human life. For this, we use not only verbal but also nonverbal signals of remarkable complexity. Among the latter, facial expressions belong to the most important information channels. Despite the large variety of facial expressions we use in daily life, research on facial expressions has so far mostly focused on the emotional aspect. Consequently, most databases of facial expressions available to the research community also include only emotional expressions, neglecting the largely unexplored aspect of conversational expressions. To fill this gap, we present the MPI facial expression database, which contains a large variety of natural emotional and conversational expressions. The database contains 55 different facial expressions performed by 19 German participants. Expressions were elicited with the help of a method-acting protocol, which guarantees both well-defined and natural facial expressions. The method-acting protocol was based on every-day scenarios, which are used to define the necessary context information for each expression. All facial expressions are available in three repetitions, in two intensities, as well as from three different camera angles. A detailed frame annotation is provided, from which a dynamic and a static version of the database have been created. In addition to describing the database in detail, we also present the results of an experiment with two conditions that serve to validate the context scenarios as well as the naturalness and recognizability of the video sequences. Our results provide clear evidence that conversational expressions can be recognized surprisingly well from visual information alone. The MPI facial expression database will enable researchers from different research fields (including the perceptual and cognitive sciences, but also affective computing, as well as computer vision) to investigate the processing of a wider range of natural facial expressions

  8. Automated facial acne assessment from smartphone images

    Science.gov (United States)

    Amini, Mohammad; Vasefi, Fartash; Valdebran, Manuel; Huang, Kevin; Zhang, Haomiao; Kemp, William; MacKinnon, Nicholas

    2018-02-01

    A smartphone mobile medical application is presented, that provides analysis of the health of skin on the face using a smartphone image and cloud-based image processing techniques. The mobile application employs the use of the camera to capture a front face image of a subject, after which the captured image is spatially calibrated based on fiducial points such as position of the iris of the eye. A facial recognition algorithm is used to identify features of the human face image, to normalize the image, and to define facial regions of interest (ROI) for acne assessment. We identify acne lesions and classify them into two categories: those that are papules and those that are pustules. Automated facial acne assessment was validated by performing tests on images of 60 digital human models and 10 real human face images. The application was able to identify 92% of acne lesions within five facial ROIs. The classification accuracy for separating papules from pustules was 98%. Combined with in-app documentation of treatment, lifestyle factors, and automated facial acne assessment, the app can be used in both cosmetic and clinical dermatology. It allows users to quantitatively self-measure acne severity and treatment efficacy on an ongoing basis to help them manage their chronic facial acne.

  9. A Coupled Hidden Markov Random Field Model for Simultaneous Face Clustering and Tracking in Videos

    KAUST Repository

    Wu, Baoyuan

    2016-10-25

    Face clustering and face tracking are two areas of active research in automatic facial video processing. They, however, have long been studied separately, despite the inherent link between them. In this paper, we propose to perform simultaneous face clustering and face tracking from real world videos. The motivation for the proposed research is that face clustering and face tracking can provide useful information and constraints to each other, thus can bootstrap and improve the performances of each other. To this end, we introduce a Coupled Hidden Markov Random Field (CHMRF) to simultaneously model face clustering, face tracking, and their interactions. We provide an effective algorithm based on constrained clustering and optimal tracking for the joint optimization of cluster labels and face tracking. We demonstrate significant improvements over state-of-the-art results in face clustering and tracking on several videos.

  10. Mindfulness-based prevention for eating disorders: A school-based cluster randomized controlled study.

    Science.gov (United States)

    Atkinson, Melissa J; Wade, Tracey D

    2015-11-01

    Successful prevention of eating disorders represents an important goal due to damaging long-term impacts on health and well-being, modest treatment outcomes, and low treatment seeking among individuals at risk. Mindfulness-based approaches have received early support in the treatment of eating disorders, but have not been evaluated as a prevention strategy. This study aimed to assess the feasibility, acceptability, and efficacy of a novel mindfulness-based intervention for reducing the risk of eating disorders among adolescent females, under both optimal (trained facilitator) and task-shifted (non-expert facilitator) conditions. A school-based cluster randomized controlled trial was conducted in which 19 classes of adolescent girls (N = 347) were allocated to a three-session mindfulness-based intervention, dissonance-based intervention, or classes as usual control. A subset of classes (N = 156) receiving expert facilitation were analyzed separately as a proxy for delivery under optimal conditions. Task-shifted facilitation showed no significant intervention effects across outcomes. Under optimal facilitation, students receiving mindfulness demonstrated significant reductions in weight and shape concern, dietary restraint, thin-ideal internalization, eating disorder symptoms, and psychosocial impairment relative to control by 6-month follow-up. Students receiving dissonance showed significant reductions in socio-cultural pressures. There were no statistically significant differences between the two interventions. Moderate intervention acceptability was reported by both students and teaching staff. Findings show promise for the application of mindfulness in the prevention of eating disorders; however, further work is required to increase both impact and acceptability, and to enable successful outcomes when delivered by less expert providers. © 2015 Wiley Periodicals, Inc.

  11. Pediatric facial injuries: It's management

    OpenAIRE

    Singh, Geeta; Mohammad, Shadab; Pal, U. S.; Hariram,; Malkunje, Laxman R.; Singh, Nimisha

    2011-01-01

    Background: Facial injuries in children always present a challenge in respect of their diagnosis and management. Since these children are of a growing age every care should be taken so that later the overall growth pattern of the facial skeleton in these children is not jeopardized. Purpose: To access the most feasible method for the management of facial injuries in children without hampering the facial growth. Materials and Methods: Sixty child patients with facial trauma were selected rando...

  12. Possible world based consistency learning model for clustering and classifying uncertain data.

    Science.gov (United States)

    Liu, Han; Zhang, Xianchao; Zhang, Xiaotong

    2018-06-01

    Possible world has shown to be effective for handling various types of data uncertainty in uncertain data management. However, few uncertain data clustering and classification algorithms are proposed based on possible world. Moreover, existing possible world based algorithms suffer from the following issues: (1) they deal with each possible world independently and ignore the consistency principle across different possible worlds; (2) they require the extra post-processing procedure to obtain the final result, which causes that the effectiveness highly relies on the post-processing method and the efficiency is also not very good. In this paper, we propose a novel possible world based consistency learning model for uncertain data, which can be extended both for clustering and classifying uncertain data. This model utilizes the consistency principle to learn a consensus affinity matrix for uncertain data, which can make full use of the information across different possible worlds and then improve the clustering and classification performance. Meanwhile, this model imposes a new rank constraint on the Laplacian matrix of the consensus affinity matrix, thereby ensuring that the number of connected components in the consensus affinity matrix is exactly equal to the number of classes. This also means that the clustering and classification results can be directly obtained without any post-processing procedure. Furthermore, for the clustering and classification tasks, we respectively derive the efficient optimization methods to solve the proposed model. Experimental results on real benchmark datasets and real world uncertain datasets show that the proposed model outperforms the state-of-the-art uncertain data clustering and classification algorithms in effectiveness and performs competitively in efficiency. Copyright © 2018 Elsevier Ltd. All rights reserved.

  13. PCA based clustering for brain tumor segmentation of T1w MRI images.

    Science.gov (United States)

    Kaya, Irem Ersöz; Pehlivanlı, Ayça Çakmak; Sekizkardeş, Emine Gezmez; Ibrikci, Turgay

    2017-03-01

    Medical images are huge collections of information that are difficult to store and process consuming extensive computing time. Therefore, the reduction techniques are commonly used as a data pre-processing step to make the image data less complex so that a high-dimensional data can be identified by an appropriate low-dimensional representation. PCA is one of the most popular multivariate methods for data reduction. This paper is focused on T1-weighted MRI images clustering for brain tumor segmentation with dimension reduction by different common Principle Component Analysis (PCA) algorithms. Our primary aim is to present a comparison between different variations of PCA algorithms on MRIs for two cluster methods. Five most common PCA algorithms; namely the conventional PCA, Probabilistic Principal Component Analysis (PPCA), Expectation Maximization Based Principal Component Analysis (EM-PCA), Generalize Hebbian Algorithm (GHA), and Adaptive Principal Component Extraction (APEX) were applied to reduce dimensionality in advance of two clustering algorithms, K-Means and Fuzzy C-Means. In the study, the T1-weighted MRI images of the human brain with brain tumor were used for clustering. In addition to the original size of 512 lines and 512 pixels per line, three more different sizes, 256 × 256, 128 × 128 and 64 × 64, were included in the study to examine their effect on the methods. The obtained results were compared in terms of both the reconstruction errors and the Euclidean distance errors among the clustered images containing the same number of principle components. According to the findings, the PPCA obtained the best results among all others. Furthermore, the EM-PCA and the PPCA assisted K-Means algorithm to accomplish the best clustering performance in the majority as well as achieving significant results with both clustering algorithms for all size of T1w MRI images. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  14. Environment-based selection effects of Planck clusters

    Energy Technology Data Exchange (ETDEWEB)

    Kosyra, R.; Gruen, D.; Seitz, S.; Mana, A.; Rozo, E.; Rykoff, E.; Sanchez, A.; Bender, R.

    2015-07-24

    We investigate whether the large-scale structure environment of galaxy clusters imprints a selection bias on Sunyaev–Zel'dovich (SZ) catalogues. Such a selection effect might be caused by line of sight (LoS) structures that add to the SZ signal or contain point sources that disturb the signal extraction in the SZ survey. We use the Planck PSZ1 union catalogue in the Sloan Digital Sky Survey (SDSS) region as our sample of SZ-selected clusters. We calculate the angular two-point correlation function (2pcf) for physically correlated, foreground and background structure in the RedMaPPer SDSS DR8 catalogue with respect to each cluster. We compare our results with an optically selected comparison cluster sample and with theoretical predictions. In contrast to the hypothesis of no environment-based selection, we find a mean 2pcf for background structures of -0.049 on scales of ≲40 arcmin, significantly non-zero at ~4σ, which means that Planck clusters are more likely to be detected in regions of low background density. We hypothesize this effect arises either from background estimation in the SZ survey or from radio sources in the background. We estimate the defect in SZ signal caused by this effect to be negligibly small, of the order of ~10-4 of the signal of a typical Planck detection. Analogously, there are no implications on X-ray mass measurements. However, the environmental dependence has important consequences for weak lensing follow up of Planck galaxy clusters: we predict that projection effects account for half of the mass contained within a 15 arcmin radius of Planck galaxy clusters. We did not detect a background underdensity of CMASS LRGs, which also leaves a spatially varying redshift dependence of the Planck SZ selection function as a possible cause for our findings.

  15. Morphologic evaluation and classification of facial asymmetry using 3-dimensional computed tomography.

    Science.gov (United States)

    Baek, Chaehwan; Paeng, Jun-Young; Lee, Janice S; Hong, Jongrak

    2012-05-01

    A systematic classification is needed for the diagnosis and surgical treatment of facial asymmetry. The purposes of this study were to analyze the skeletal structures of patients with facial asymmetry and to objectively classify these patients into groups according to these structural characteristics. Patients with facial asymmetry and recent computed tomographic images from 2005 through 2009 were included in this study, which was approved by the institutional review board. Linear measurements, angles, and reference planes on 3-dimensional computed tomograms were obtained, including maxillary (upper midline deviation, maxilla canting, and arch form discrepancy) and mandibular (menton deviation, gonion to midsagittal plane, ramus height, and frontal ramus inclination) measurements. All measurements were analyzed using paired t tests with Bonferroni correction followed by K-means cluster analysis using SPSS 13.0 to determine an objective classification of facial asymmetry in the enrolled patients. Kruskal-Wallis test was performed to verify differences among clustered groups. P < .05 was considered statistically significant. Forty-three patients (18 male, 25 female) were included in the study. They were classified into 4 groups based on cluster analysis. Their mean age was 24.3 ± 4.4 years. Group 1 included subjects (44% of patients) with asymmetry caused by a shift or lateralization of the mandibular body. Group 2 included subjects (39%) with a significant difference between the left and right ramus height with menton deviation to the short side. Group 3 included subjects (12%) with atypical asymmetry, including deviation of the menton to the short side, prominence of the angle/gonion on the larger side, and reverse maxillary canting. Group 4 included subjects (5%) with severe maxillary canting, ramus height differences, and menton deviation to the short side. In this study, patients with asymmetry were classified into 4 statistically distinct groups according to

  16. Persistent facial pain conditions

    DEFF Research Database (Denmark)

    Forssell, Heli; Alstergren, Per; Bakke, Merete

    2016-01-01

    Persistent facial pains, especially temporomandibular disorders (TMD), are common conditions. As dentists are responsible for the treatment of most of these disorders, up-to date knowledge on the latest advances in the field is essential for successful diagnosis and management. The review covers...... TMD, and different neuropathic or putative neuropathic facial pains such as persistent idiopathic facial pain and atypical odontalgia, trigeminal neuralgia and painful posttraumatic trigeminal neuropathy. The article presents an overview of TMD pain as a biopsychosocial condition, its prevalence......, clinical features, consequences, central and peripheral mechanisms, diagnostic criteria (DC/TMD), and principles of management. For each of the neuropathic facial pain entities, the definitions, prevalence, clinical features, and diagnostics are described. The current understanding of the pathophysiology...

  17. Earthquakes clustering based on the magnitude and the depths in Molluca Province

    Energy Technology Data Exchange (ETDEWEB)

    Wattimanela, H. J., E-mail: hwattimaela@yahoo.com [Pattimura University, Ambon (Indonesia); Institute of Technology Bandung, Bandung (Indonesia); Pasaribu, U. S.; Indratno, S. W.; Puspito, A. N. T. [Institute of Technology Bandung, Bandung (Indonesia)

    2015-12-22

    In this paper, we present a model to classify the earthquakes occurred in Molluca Province. We use K-Means clustering method to classify the earthquake based on the magnitude and the depth of the earthquake. The result can be used for disaster mitigation and for designing evacuation route in Molluca Province.

  18. Earthquakes clustering based on the magnitude and the depths in Molluca Province

    International Nuclear Information System (INIS)

    Wattimanela, H. J.; Pasaribu, U. S.; Indratno, S. W.; Puspito, A. N. T.

    2015-01-01

    In this paper, we present a model to classify the earthquakes occurred in Molluca Province. We use K-Means clustering method to classify the earthquake based on the magnitude and the depth of the earthquake. The result can be used for disaster mitigation and for designing evacuation route in Molluca Province

  19. Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification

    Directory of Open Access Journals (Sweden)

    Yajie Zou

    2017-01-01

    Full Text Available Hotspot identification (HSID is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections similar to the target site from which safety performance functions (SPF used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, K-means, and hierarchical clustering to select “similar” sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.

  20. Refined tropical curve counts and canonical bases for quantum cluster algebras

    DEFF Research Database (Denmark)

    Mandel, Travis

    We express the (quantizations of the) Gross-Hacking-Keel-Kontsevich canonical bases for cluster algebras in terms of certain (Block-Göttsche) weighted counts of tropical curves. In the process, we obtain via scattering diagram techniques a new invariance result for these Block-Göttsche counts....