Training lay-people to use automatic external defibrillators: are all of their needs being met?

Science.gov (United States)

Harrison-Paul, Russell; Timmons, Stephen; van Schalkwyk, Wilna Dirkse

2006-10-01

We explored the experiences of lay people who have been trained to use automatic external defibrillators. The research questions were: (1) How can training courses help prepare people for dealing with real life situations? (2) Who is ultimately responsible for providing critical incident debriefing and how should this be organised? (3) What is the best process for providing feedback to those who have used an AED? Fifty-three semi-structured, qualitative interviews were conducted, some with those who had been trained and others with trainers. Locations included airports, railway stations, private companies and first responder schemes. Geographically, we covered Nottinghamshire, Lincolnshire, Yorkshire, Staffordshire, Essex and the West Midlands in the UK. Our analysis of the data indicates that most people believe scenarios based within their place of work were most useful in preparing for 'real life'. Many people had not received critical incident debriefing after using an AED. There were a variety of systems in place to provide support after an incident, many of which were informal. Training scenarios should be conducted outside the classroom. There should be more focus on critical incident debriefing during training and a clear identification of who should provide support after an incident. Other issues which were of interest included: (1) people's views on do not attempt resuscitation (DNAR); (2) perceived boundaries of responsibility when using an AED; (3) when is someone no longer 'qualified' to use an AED?

A training approach to improve stepping automaticity while dual-tasking in Parkinson's disease: A prospective pilot study.

Science.gov (United States)

Chomiak, Taylor; Watts, Alexander; Meyer, Nicole; Pereira, Fernando V; Hu, Bin

2017-02-01

Deficits in motor movement automaticity in Parkinson's disease (PD), especially during multitasking, are early and consistent hallmarks of cognitive function decline, which increases fall risk and reduces quality of life. This study aimed to test the feasibility and potential efficacy of a wearable sensor-enabled technological platform designed for an in-home music-contingent stepping-in-place (SIP) training program to improve step automaticity during dual-tasking (DT). This was a 4-week prospective intervention pilot study. The intervention uses a sensor system and algorithm that runs off the iPod Touch which calculates step height (SH) in real-time. These measurements were then used to trigger auditory (treatment group, music; control group, radio podcast) playback in real-time through wireless headphones upon maintenance of repeated large amplitude stepping. With small steps or shuffling, auditory playback stops, thus allowing participants to use anticipatory motor control to regain positive feedback. Eleven participants were recruited from an ongoing trial (Trial Number: ISRCTN06023392). Fear of falling (FES-I), general cognitive functioning (MoCA), self-reported freezing of gait (FOG-Q), and DT step automaticity were evaluated. While we found no significant effect of training on FES-I, MoCA, or FOG-Q, we did observe a significant group (music vs podcast) by training interaction in DT step automaticity (Ptraining to increase motor automaticity for people living with PD. The training approach described here can be implemented at home to meet the growing demand for self-management of symptoms by patients.

How to Park Freight Trains on Rail-Rail Transshipment Yards: The Train Location Problem

OpenAIRE

Michael Kellner; Nils Boysen; Malte Fliedner

2010-01-01

In modern rail-rail transshipment yards huge gantry cranes spanning all railway tracks allow for an efficent transshipment of containers between different freight trains. This way, multiple trains loaded with cargo for varying destinations can be consolidated to a reduced number of homogeneous trains, which is an essential requirement of hub-and-spoke railway systems. An important problem during the daily operations of such a transshipment yard is the train location problem, which assigns eac...

Training cows to approach the milking unit in response to acoustic signals in an automatic milking system during the grazing season

DEFF Research Database (Denmark)

Wredle, E.; Munksgaard, Lene; Sporndly, E.

2006-01-01

connected to the automatic milking system. The cows were trained indoors using an operant conditioning technique. All cows had 12 training sessions with 7–12 signals given at variable intervals. An evaluation period followed the training period. During evaluation, the trained cows received an individual...... cows housed in a barn with an automatic milking system. A small box emitting an acoustic signal was attached to the collar of the 10 cows. During the training period, the signal was induced manually from a distance and during the evaluation period, signals were activated automatically from a computer...... (with no signal) in early season was 9.7 ± 0.18 h (P 7 ± 0.56 h and 9.0 ± 0.20 h for the five cows trained in late season and a reference group (with no signal), respectively. During the evaluation in a full herd situation, the response ranged between 15 and 75...

An Automatic Identification Procedure to Promote the use of FES-Cycling Training for Hemiparetic Patients

Directory of Open Access Journals (Sweden)

Emilia Ambrosini

2014-01-01

Full Text Available Cycling induced by Functional Electrical Stimulation (FES training currently requires a manual setting of different parameters, which is a time-consuming and scarcely repeatable procedure. We proposed an automatic procedure for setting session-specific parameters optimized for hemiparetic patients. This procedure consisted of the identification of the stimulation strategy as the angular ranges during which FES drove the motion, the comparison between the identified strategy and the physiological muscular activation strategy, and the setting of the pulse amplitude and duration of each stimulated muscle. Preliminary trials on 10 healthy volunteers helped define the procedure. Feasibility tests on 8 hemiparetic patients (5 stroke, 3 traumatic brain injury were performed. The procedure maximized the motor output within the tolerance constraint, identified a biomimetic strategy in 6 patients, and always lasted less than 5 minutes. Its reasonable duration and automatic nature make the procedure usable at the beginning of every training session, potentially enhancing the performance of FES-cycling training.

Monitoring the Performance of the Pedestrian Transfer Function of Train Stations Using Automatic Fare Collection Data

NARCIS (Netherlands)

Van den Heuvel, J.P.A.; Hoogenraad, J.H.

2014-01-01

Over the last years all train stations in The Netherlands have been equipped with automatic fare collection gates and/or validators. All public transport passengers use a smart card to pay their fare. In this paper we present a monitor for the performance of the pedestrian function of train stations

Direct spondylolisthesis identification and measurement in MR/CT using detectors trained by articulated parameterized spine model

Science.gov (United States)

Cai, Yunliang; Leung, Stephanie; Warrington, James; Pandey, Sachin; Shmuilovich, Olga; Li, Shuo

2017-02-01

The identification of spondylolysis and spondylolisthesis is important in spinal diagnosis, rehabilitation, and surgery planning. Accurate and automatic detection of spinal portion with spondylolisthesis problem will significantly reduce the manual work of physician and provide a more robust evaluation for the spine condition. Most existing automatic identification methods adopted the indirect approach which used vertebrae locations to measure the spondylolisthesis. However, these methods relied heavily on automatic vertebra detection which often suffered from the pool spatial accuracy and the lack of validated pathological training samples. In this study, we present a novel spondylolisthesis detection method which can directly locate the irregular spine portion and output the corresponding grading. The detection is done by a set of learning-based detectors which are discriminatively trained by synthesized spondylolisthesis image samples. To provide sufficient pathological training samples, we used a parameterized spine model to synthesize different types of spondylolysis images from real MR/CT scans. The parameterized model can automatically locate the vertebrae in spine images and estimate their pose orientations, and can inversely alter the vertebrae locations and poses by changing the corresponding parameters. Various training samples can then be generated from only a few spine MR/CT images. The preliminary results suggest great potential for the fast and efficient spondylolisthesis identification and measurement in both MR and CT spine images.

Differential effects of spaced vs. massed training in long-term object-identity and object-location recognition memory.

Science.gov (United States)

Bello-Medina, Paola C; Sánchez-Carrasco, Livia; González-Ornelas, Nadia R; Jeffery, Kathryn J; Ramírez-Amaya, Víctor

2013-08-01

Here we tested whether the well-known superiority of spaced training over massed training is equally evident in both object identity and object location recognition memory. We trained animals with objects placed in a variable or in a fixed location to produce a location-independent object identity memory or a location-dependent object representation. The training consisted of 5 trials that occurred either on one day (Massed) or over the course of 5 consecutive days (Spaced). The memory test was done in independent groups of animals either 24h or 7 days after the last training trial. In each test the animals were exposed to either a novel object, when trained with the objects in variable locations, or to a familiar object in a novel location, when trained with objects in fixed locations. The difference in time spent exploring the changed versus the familiar objects was used as a measure of recognition memory. For the object-identity-trained animals, spaced training produced clear evidence of recognition memory after both 24h and 7 days, but massed-training animals showed it only after 24h. In contrast, for the object-location-trained animals, recognition memory was evident after both retention intervals and with both training procedures. When objects were placed in variable locations for the two types of training and the test was done with a brand-new location, only the spaced-training animals showed recognition at 24h, but surprisingly, after 7 days, animals trained using both procedures were able to recognize the change, suggesting a post-training consolidation process. We suggest that the two training procedures trigger different neural mechanisms that may differ in the two segregated streams that process object information and that may consolidate differently. Copyright © 2013 Elsevier B.V. All rights reserved.

Automatic Optimizer Generation Method Based on Location and Context Information to Improve Mobile Services

Directory of Open Access Journals (Sweden)

Yunsik Son

2017-01-01

Full Text Available Several location-based services (LBSs have been recently developed for smartphones. Among these are proactive LBSs, which provide services to smartphone users by periodically collecting background logs. However, because they consume considerable battery power, they are not widely used for various LBS-based services. Battery consumption, in particular, is a significant issue on account of the characteristics of mobile systems. This problem involves a greater service restriction when performing complex operations. Therefore, to successfully enable various services based on location, this problem must be solved. In this paper, we introduce a technique to automatically generate a customized service optimizer for each application, service type, and platform using location and situation information. By using the proposed technique, energy and computing resources can be more efficiently employed for each service. Thus, users should receive more effective LBSs on mobile devices, such as smartphones.

An Automatic User Grouping Model for a Group Recommender System in Location-Based Social Networks

Directory of Open Access Journals (Sweden)

Elahe Khazaei

2018-02-01

Full Text Available Spatial group recommendation refers to suggesting places to a given set of users. In a group recommender system, members of a group should have similar preferences in order to increase the level of satisfaction. Location-based social networks (LBSNs provide rich content, such as user interactions and location/event descriptions, which can be leveraged for group recommendations. In this paper, an automatic user grouping model is introduced that obtains information about users and their preferences through an LBSN. The preferences of the users, proximity of the places the users have visited in terms of spatial range, users’ free days, and the social relationships among users are extracted automatically from location histories and users’ profiles in the LBSN. These factors are combined to determine the similarities among users. The users are partitioned into groups based on these similarities. Group size is the key to coordinating group members and enhancing their satisfaction. Therefore, a modified k-medoids method is developed to cluster users into groups with specific sizes. To evaluate the efficiency of the proposed method, its mean intra-cluster distance and its distribution of cluster sizes are compared to those of general clustering algorithms. The results reveal that the proposed method compares favourably with general clustering approaches, such as k-medoids and spectral clustering, in separating users into groups of a specific size with a lower mean intra-cluster distance.

Automatic Attraction of Visual Attention by Supraletter Features of Former Target Strings

Directory of Open Access Journals (Sweden)

Søren eKyllingsbæk

2014-11-01

Full Text Available Observers were trained to search for a particular horizontal string of 3 capital letters presented among similar strings consisting of exactly the same letters in different orders. The training was followed by a test in which the observers searched for a new target that was identical to one of the former distractors. The new distractor set consisted of the remaining former distractors plus the former target. On each trial, three letter-strings were displayed, which included the target string with a probability of .5. In Experiment 1, the strings were centered at different locations on the circumference of an imaginary circle around the fixation point. The training phase of Experiment 2 was similar, but in the test phase of the experiment, the strings were located in a vertical array centered on fixation, and in target-present arrays, the target always appeared at fixation. In both experiments, performance (d’ degraded on trials in which former targets were present, suggesting that the former targets automatically drew processing resources away from the current targets. Apparently, the two experiments showed automatic attraction of visual attention by supraletter features of former target strings.

REALIZATION OF TRAINING PROGRAMME ON THE BASIS OF LINGUISTIC DATABASE FOR AUTOMATIC TEXTS PROCESSING SYSTEM

Directory of Open Access Journals (Sweden)

M. A. Makarych

2016-01-01

Full Text Available Due to the constant increasing of electronic textual information, modern society needs for the automatic processing of natural language (NL. The main purpose of NL automatic text processing systems is to analyze and create texts and represent their content. The purpose of the paper is the development of linguistic and software bases of an automatic system for processing English publicistic texts. This article discusses the examples of different approaches to the creation of linguistic databases for processing systems. The author gives a detailed description of basic building blocks for a new linguistic processor: lexical-semantic, syntactical and semantic-syntactical. The main advantage of the processor is using special semantic codes in the alphabetical dictionary. The semantic codes have been developed in accordance with a lexical-semantic classification. It helps to precisely define semantic functions of the keywords that are situated in parsing groups and allows the automatic system to avoid typical mistakes. The author also represents the realization of a developed linguistic database in the form of a training computer program.

Automatic Mitigation of Sensor Variations for Signal Strength Based Location Systems

DEFF Research Database (Denmark)

Kjærgaard, Mikkel Baun

2006-01-01

n the area of pervasive computing a key concept is context-awareness. One type of context information is location information of wireless network clients. Research in indoor localization of wireless network clients based on signal strength is receiving a lot of attention. However, not much...... of this research is directed towards handling the issue of adapting a signal strength based indoor localization system to the hardware and software of a specific wireless network client, be it a tag, PDA or laptop. Therefore current indoor localization systems need to be manually adapted to work optimally...... with specific hardware and software. A second problem is that for a specific hardware there will be more than one driver available and they will have different properties when used for localization. Therefore the contribution of this paper is twofold. First, an automatic system for evaluating the fitness...

Comparison Of Semi-Automatic And Automatic Slick Detection Algorithms For Jiyeh Power Station Oil Spill, Lebanon

Science.gov (United States)

Osmanoglu, B.; Ozkan, C.; Sunar, F.

2013-10-01

After air strikes on July 14 and 15, 2006 the Jiyeh Power Station started leaking oil into the eastern Mediterranean Sea. The power station is located about 30 km south of Beirut and the slick covered about 170 km of coastline threatening the neighboring countries Turkey and Cyprus. Due to the ongoing conflict between Israel and Lebanon, cleaning efforts could not start immediately resulting in 12 000 to 15 000 tons of fuel oil leaking into the sea. In this paper we compare results from automatic and semi-automatic slick detection algorithms. The automatic detection method combines the probabilities calculated for each pixel from each image to obtain a joint probability, minimizing the adverse effects of atmosphere on oil spill detection. The method can readily utilize X-, C- and L-band data where available. Furthermore wind and wave speed observations can be used for a more accurate analysis. For this study, we utilize Envisat ASAR ScanSAR data. A probability map is generated based on the radar backscatter, effect of wind and dampening value. The semi-automatic algorithm is based on supervised classification. As a classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) classifier is used since it is more flexible and efficient than conventional maximum likelihood classifier for multisource and multi-temporal data. The learning algorithm for ANN MLP is chosen as the Levenberg-Marquardt (LM). Training and test data for supervised classification are composed from the textural information created from SAR images. This approach is semiautomatic because tuning the parameters of classifier and composing training data need a human interaction. We point out the similarities and differences between the two methods and their results as well as underlining their advantages and disadvantages. Due to the lack of ground truth data, we compare obtained results to each other, as well as other published oil slick area assessments.

Automatically controlled training systems

International Nuclear Information System (INIS)

Milashenko, A.; Afanasiev, A.

1990-01-01

This paper reports that the computer system for NPP personnel training was developed for training centers in the Soviet Union. The system should be considered as the first step in training, taking into account that further steps are to be devoted to part-task and full scope simulator training. The training room consists of 8-12 IBM PC/AT personal computers combined into a network. A trainee accesses the system in a dialor manner. Software enables the instructor to determine the trainee's progress in different subjects of the program. The quality of any trainee preparedness may be evaluated by Knowledge Control operation. Simplified dynamic models are adopted for separate areas of the program. For example, the system of neutron flux monitoring has a dedicated model. Currently, training, requalification and support of professional qualifications of nuclear power plant operators is being emphasized. A significant number of emergency situations during work are occurring due to operator errors. Based on data from September-October 1989, more than half of all unplanned drops in power and stoppages of power plants were due to operator error. As a comparison, problems due to equipment malfunction accounted for no more than a third of the total. The role of personnel, especially of the operators, is significant during normal operations, since energy production costs as well as losses are influenced by the capability of the staff. These facts all point to the importance of quality training of personnel

Automatic training of lemmatization rules that handle morphological changes in pre-, in- and suffixes alike

DEFF Research Database (Denmark)

Jongejan, Bart; Dalianis, Hercules

2009-01-01

We propose a method to automatically train lemmatization rules that handle prefix, infix and suffix changes to generate the lemma from the full form of a word. We explain how the lemmatization rules are created and how the lemmatizer works. We trained this lemmatizer on Danish, Dutch, English......, German, Greek, Icelandic, Norwegian, Polish, Slovene and Swedish full form-lemma pairs respectively. We obtained significant improvements of 24 percent for Polish, 2.3 percent for Dutch, 1.5 percent for English, 1.2 percent for German and 1.0 percent for Swedish compared to plain suffix lemmatization...... using a suffix-only lemmatizer. Icelandic deteriorated with 1.9 percent. We also made an observation regarding the number of produced lemmatization rules as a function of the number of training pairs....

Improving automatic earthquake locations in subduction zones: a case study for GEOFON catalog of Tonga-Fiji region

Science.gov (United States)

Nooshiri, Nima; Heimann, Sebastian; Saul, Joachim; Tilmann, Frederik; Dahm, Torsten

2015-04-01

Automatic earthquake locations are sometimes associated with very large residuals up to 10 s even for clear arrivals, especially for regional stations in subduction zones because of their strongly heterogeneous velocity structure associated. Although these residuals are most likely not related to measurement errors but unmodelled velocity heterogeneity, these stations are usually removed from or down-weighted in the location procedure. While this is possible for large events, it may not be useful if the earthquake is weak. In this case, implementation of travel-time station corrections may significantly improve the automatic locations. Here, the shrinking box source-specific station term method (SSST) [Lin and Shearer, 2005] has been applied to improve relative location accuracy of 1678 events that occurred in the Tonga subduction zone between 2010 and mid-2014. Picks were obtained from the GEOFON earthquake bulletin for all available station networks. We calculated a set of timing corrections for each station which vary as a function of source position. A separate time correction was computed for each source-receiver path at the given station by smoothing the residual field over nearby events. We begin with a very large smoothing radius essentially encompassing the whole event set and iterate by progressively shrinking the smoothing radius. In this way, we attempted to correct for the systematic errors, that are introduced into the locations by the inaccuracies in the assumed velocity structure, without solving for a new velocity model itself. One of the advantages of the SSST technique is that the event location part of the calculation is separate from the station term calculation and can be performed using any single event location method. In this study, we applied a non-linear, probabilistic, global-search earthquake location method using the software package NonLinLoc [Lomax et al., 2000]. The non-linear location algorithm implemented in NonLinLoc is less

Automatic recognition of falls in gait-slip training: Harness load cell based criteria.

Science.gov (United States)

Yang, Feng; Pai, Yi-Chung

2011-08-11

Over-head-harness systems, equipped with load cell sensors, are essential to the participants' safety and to the outcome assessment in perturbation training. The purpose of this study was to first develop an automatic outcome recognition criterion among young adults for gait-slip training and then verify such criterion among older adults. Each of 39 young and 71 older subjects, all protected by safety harness, experienced 8 unannounced, repeated slips, while walking on a 7m walkway. Each trial was monitored with a motion capture system, bilateral ground reaction force (GRF), harness force, and video recording. The fall trials were first unambiguously indentified with careful visual inspection of all video records. The recoveries without balance loss (in which subjects' trailing foot landed anteriorly to the slipping foot) were also first fully recognized from motion and GRF analyses. These analyses then set the gold standard for the outcome recognition with load cell measurements. Logistic regression analyses based on young subjects' data revealed that the peak load cell force was the best predictor of falls (with 100% accuracy) at the threshold of 30% body weight. On the other hand, the peak moving average force of load cell across 1s period, was the best predictor (with 100% accuracy) separating recoveries with backward balance loss (in which the recovery step landed posterior to slipping foot) from harness assistance at the threshold of 4.5% body weight. These threshold values were fully verified using the data from older adults (100% accuracy in recognizing falls). Because of the increasing popularity in the perturbation training coupling with the protective over-head-harness system, this new criterion could have far reaching implications in automatic outcome recognition during the movement therapy. Copyright © 2011 Elsevier Ltd. All rights reserved.

Second-order sliding mode controller with model reference adaptation for automatic train operation

Science.gov (United States)

Ganesan, M.; Ezhilarasi, D.; Benni, Jijo

2017-11-01

In this paper, a new approach to model reference based adaptive second-order sliding mode control together with adaptive state feedback is presented to control the longitudinal dynamic motion of a high speed train for automatic train operation with the objective of minimal jerk travel by the passengers. The nonlinear dynamic model for the longitudinal motion of the train comprises of a locomotive and coach subsystems is constructed using multiple point-mass model by considering the forces acting on the vehicle. An adaptation scheme using Lyapunov criterion is derived to tune the controller gains by considering a linear, stable reference model that ensures the stability of the system in closed loop. The effectiveness of the controller tracking performance is tested under uncertain passenger load, coupler-draft gear parameters, propulsion resistance coefficients variations and environmental disturbances due to side wind and wet rail conditions. The results demonstrate improved tracking performance of the proposed control scheme with a least jerk under maximum parameter uncertainties when compared to constant gain second-order sliding mode control.

Railway automatic safety protection system based on GPS

Directory of Open Access Journals (Sweden)

Fu Hai Juan

2016-01-01

Full Text Available The automatic protection system of railway safety is designed for the railway construction workers to protect alarm, and the safety protection device by using GPS satellite positioning system to acquire location information of the operating point, through the CTC/TDCS system and computer monitoring system for the running of the train position and the arithmetic distance. Achieving timely and continuously forecasts about the distance of the train which is apart from the operating point to prompt the voice alarm of the approaching train. Using digital technology to realize the function of the traditional analog interphone, eliminates the quality problems of the call. With the GSM-R, mobile wireless transmission channel and terminal technology, it overcomes the restrictions of the analog interphone which influenced by communication distance and more problems of blind areas. Finally to achieve practical, convenient, applicable and adaptable design goals.

Automatic reconstruction of fault networks from seismicity catalogs including location uncertainty

International Nuclear Information System (INIS)

Wang, Y.

2013-01-01

Within the framework of plate tectonics, the deformation that arises from the relative movement of two plates occurs across discontinuities in the earth's crust, known as fault zones. Active fault zones are the causal locations of most earthquakes, which suddenly release tectonic stresses within a very short time. In return, fault zones slowly grow by accumulating slip due to such earthquakes by cumulated damage at their tips, and by branching or linking between pre-existing faults of various sizes. Over the last decades, a large amount of knowledge has been acquired concerning the overall phenomenology and mechanics of individual faults and earthquakes: A deep physical and mechanical understanding of the links and interactions between and among them is still missing, however. One of the main issues lies in our failure to always succeed in assigning an earthquake to its causative fault. Using approaches based in pattern-recognition theory, more insight into the relationship between earthquakes and fault structure can be gained by developing an automatic fault network reconstruction approach using high resolution earthquake data sets at largely different scales and by considering individual event uncertainties. This thesis introduces the Anisotropic Clustering of Location Uncertainty Distributions (ACLUD) method to reconstruct active fault networks on the basis of both earthquake locations and their estimated individual uncertainties. This method consists in fitting a given set of hypocenters with an increasing amount of finite planes until the residuals of the fit compare with location uncertainties. After a massive search through the large solution space of possible reconstructed fault networks, six different validation procedures are applied in order to select the corresponding best fault network. Two of the validation steps (cross-validation and Bayesian Information Criterion (BIC)) process the fit residuals, while the four others look for solutions that

Automatic reconstruction of fault networks from seismicity catalogs including location uncertainty

Energy Technology Data Exchange (ETDEWEB)

Wang, Y.

2013-07-01

Within the framework of plate tectonics, the deformation that arises from the relative movement of two plates occurs across discontinuities in the earth's crust, known as fault zones. Active fault zones are the causal locations of most earthquakes, which suddenly release tectonic stresses within a very short time. In return, fault zones slowly grow by accumulating slip due to such earthquakes by cumulated damage at their tips, and by branching or linking between pre-existing faults of various sizes. Over the last decades, a large amount of knowledge has been acquired concerning the overall phenomenology and mechanics of individual faults and earthquakes: A deep physical and mechanical understanding of the links and interactions between and among them is still missing, however. One of the main issues lies in our failure to always succeed in assigning an earthquake to its causative fault. Using approaches based in pattern-recognition theory, more insight into the relationship between earthquakes and fault structure can be gained by developing an automatic fault network reconstruction approach using high resolution earthquake data sets at largely different scales and by considering individual event uncertainties. This thesis introduces the Anisotropic Clustering of Location Uncertainty Distributions (ACLUD) method to reconstruct active fault networks on the basis of both earthquake locations and their estimated individual uncertainties. This method consists in fitting a given set of hypocenters with an increasing amount of finite planes until the residuals of the fit compare with location uncertainties. After a massive search through the large solution space of possible reconstructed fault networks, six different validation procedures are applied in order to select the corresponding best fault network. Two of the validation steps (cross-validation and Bayesian Information Criterion (BIC)) process the fit residuals, while the four others look for solutions that

Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods.

Science.gov (United States)

Kong, Xiangyi; Gong, Shun; Su, Lijuan; Howard, Newton; Kong, Yanguo

2018-01-01

Automatic early detection of acromegaly is theoretically possible from facial photographs, which can lessen the prevalence and increase the cure probability. In this study, several popular machine learning algorithms were used to train a retrospective development dataset consisting of 527 acromegaly patients and 596 normal subjects. We firstly used OpenCV to detect the face bounding rectangle box, and then cropped and resized it to the same pixel dimensions. From the detected faces, locations of facial landmarks which were the potential clinical indicators were extracted. Frontalization was then adopted to synthesize frontal facing views to improve the performance. Several popular machine learning methods including LM, KNN, SVM, RT, CNN, and EM were used to automatically identify acromegaly from the detected facial photographs, extracted facial landmarks, and synthesized frontal faces. The trained models were evaluated using a separate dataset, of which half were diagnosed as acromegaly by growth hormone suppression test. The best result of our proposed methods showed a PPV of 96%, a NPV of 95%, a sensitivity of 96% and a specificity of 96%. Artificial intelligence can automatically early detect acromegaly with a high sensitivity and specificity. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Validation of an automatic system (DoubleCage) for detecting the location of animals during preference tests.

Science.gov (United States)

Tsai, P P; Nagelschmidt, N; Kirchner, J; Stelzer, H D; Hackbarth, H

2012-01-01

Preference tests have often been performed for collecting information about animals' acceptance of environmental refinement objects. In numerous published studies animals were individually tested during preference experiments, as it is difficult to observe group-housed animals with an automatic system. Thus, videotaping is still the most favoured method for observing preferences of socially-housed animals. To reduce the observation workload and to be able to carry out preference testing of socially-housed animals, an automatic recording system (DoubleCage) was developed for determining the location of group-housed animals in a preference test set-up. This system is able to distinguish the transition of individual animals between two cages and to record up to 16 animals at the same time (four animals per cage). The present study evaluated the reliability of the DoubleCage system. The data recorded by the DoubleCage program and the data obtained by human observation were compared. The measurements of the DoubleCage system and manual observation of the videotapes are comparable and significantly correlated (P animals and a considerable reduction of animal observation time.

Automatic lung segmentation in functional SPECT images using active shape models trained on reference lung shapes from CT.

Science.gov (United States)

Cheimariotis, Grigorios-Aris; Al-Mashat, Mariam; Haris, Kostas; Aletras, Anthony H; Jögi, Jonas; Bajc, Marika; Maglaveras, Nicolaos; Heiberg, Einar

2018-02-01

Image segmentation is an essential step in quantifying the extent of reduced or absent lung function. The aim of this study is to develop and validate a new tool for automatic segmentation of lungs in ventilation and perfusion SPECT images and compare automatic and manual SPECT lung segmentations with reference computed tomography (CT) volumes. A total of 77 subjects (69 patients with obstructive lung disease, and 8 subjects without apparent perfusion of ventilation loss) performed low-dose CT followed by ventilation/perfusion (V/P) SPECT examination in a hybrid gamma camera system. In the training phase, lung shapes from the 57 anatomical low-dose CT images were used to construct two active shape models (right lung and left lung) which were then used for image segmentation. The algorithm was validated in 20 patients, comparing its results to reference delineation of corresponding CT images, and by comparing automatic segmentation to manual delineations in SPECT images. The Dice coefficient between automatic SPECT delineations and manual SPECT delineations were 0.83 ± 0.04% for the right and 0.82 ± 0.05% for the left lung. There was statistically significant difference between reference volumes from CT and automatic delineations for the right (R = 0.53, p = 0.02) and left lung (R = 0.69, p automatic quantification of wide range of measurements.

Chemical name extraction based on automatic training data generation and rich feature set.

Science.gov (United States)

Yan, Su; Spangler, W Scott; Chen, Ying

2013-01-01

The automation of extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Another difficulty is the selection of informative features of chemical names, since comprehensive domain knowledge on chemistry nomenclature is required. Leveraging random text generation techniques, we explore the idea of automatically creating training sets for the task of chemical name extraction. Assuming the availability of an incomplete list of chemical names, called a dictionary, we are able to generate well-controlled, random, yet realistic chemical-like training documents. We statistically analyze the construction of chemical names based on the incomplete dictionary, and propose a series of new features, without relying on any domain knowledge. Compared to state-of-the-art models learned from manually labeled data and domain knowledge, our solution shows better or comparable results in annotating real-world data with less human effort. Moreover, we report an interesting observation about the language for chemical names. That is, both the structural and semantic components of chemical names follow a Zipfian distribution, which resembles many natural languages.

49 CFR 37.91 - Wheelchair locations and food service on intercity rail trains.

Science.gov (United States)

2010-10-01

... 49 Transportation 1 2010-10-01 2010-10-01 false Wheelchair locations and food service on intercity rail trains. 37.91 Section 37.91 Transportation Office of the Secretary of Transportation TRANSPORTATION SERVICES FOR INDIVIDUALS WITH DISABILITIES (ADA) Acquisition of Accessible Vehicles By Public Entities § 37.91 Wheelchair locations and food...

Training shortest-path tractography: Automatic learning of spatial priors

DEFF Research Database (Denmark)

Kasenburg, Niklas; Liptrot, Matthew George; Reislev, Nina Linde

2016-01-01

Tractography is the standard tool for automatic delineation of white matter tracts from diffusion weighted images. However, the output of tractography often requires post-processing to remove false positives and ensure a robust delineation of the studied tract, and this demands expert prior...... knowledge. Here we demonstrate how such prior knowledge, or indeed any prior spatial information, can be automatically incorporated into a shortest-path tractography approach to produce more robust results. We describe how such a prior can be automatically generated (learned) from a population, and we...

Landslide susceptibility mapping using decision-tree based CHi-squared automatic interaction detection (CHAID) and Logistic regression (LR) integration

International Nuclear Information System (INIS)

Althuwaynee, Omar F; Pradhan, Biswajeet; Ahmad, Noordin

2014-01-01

This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. This new algorithm was developed to overcome the subjectivity of the manual categorization of scale data of landslide conditioning factors, and to predict rainfall-induced susceptibility map in Kuala Lumpur city and surrounding areas using geographic information system (GIS). The main objective of this article is to use CHi-squared automatic interaction detection (CHAID) method to perform the best classification fit for each conditioning factor, then, combining it with logistic regression (LR). LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. A cluster pattern of landslide locations was extracted in previous study using nearest neighbor index (NNI), which were then used to identify the clustered landslide locations range. Clustered locations were used as model training data with 14 landslide conditioning factors such as; topographic derived parameters, lithology, NDVI, land use and land cover maps. Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. Finally the relationship between conditioning factors were assessed and the landslide susceptibility map (LSM) was produced. An area under the curve (AUC) was used to test the model reliability and prediction capability with the training and validation landslide locations respectively. This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. Also it provided a valuable scientific basis for spatial decision making in planning and urban management studies

Landslide susceptibility mapping using decision-tree based CHi-squared automatic interaction detection (CHAID) and Logistic regression (LR) integration

Science.gov (United States)

Althuwaynee, Omar F.; Pradhan, Biswajeet; Ahmad, Noordin

2014-06-01

This article uses methodology based on chi-squared automatic interaction detection (CHAID), as a multivariate method that has an automatic classification capacity to analyse large numbers of landslide conditioning factors. This new algorithm was developed to overcome the subjectivity of the manual categorization of scale data of landslide conditioning factors, and to predict rainfall-induced susceptibility map in Kuala Lumpur city and surrounding areas using geographic information system (GIS). The main objective of this article is to use CHi-squared automatic interaction detection (CHAID) method to perform the best classification fit for each conditioning factor, then, combining it with logistic regression (LR). LR model was used to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. A cluster pattern of landslide locations was extracted in previous study using nearest neighbor index (NNI), which were then used to identify the clustered landslide locations range. Clustered locations were used as model training data with 14 landslide conditioning factors such as; topographic derived parameters, lithology, NDVI, land use and land cover maps. Pearson chi-squared value was used to find the best classification fit between the dependent variable and conditioning factors. Finally the relationship between conditioning factors were assessed and the landslide susceptibility map (LSM) was produced. An area under the curve (AUC) was used to test the model reliability and prediction capability with the training and validation landslide locations respectively. This study proved the efficiency and reliability of decision tree (DT) model in landslide susceptibility mapping. Also it provided a valuable scientific basis for spatial decision making in planning and urban management studies.

Improving Accuracy and Simplifying Training in Fingerprinting-Based Indoor Location Algorithms at Room Level

Directory of Open Access Journals (Sweden)

Mario Muñoz-Organero

2016-01-01

Full Text Available Fingerprinting-based algorithms are popular in indoor location systems based on mobile devices. Comparing the RSSI (Received Signal Strength Indicator from different radio wave transmitters, such as Wi-Fi access points, with prerecorded fingerprints from located points (using different artificial intelligence algorithms, fingerprinting-based systems can locate unknown points with a few meters resolution. However, training the system with already located fingerprints tends to be an expensive task both in time and in resources, especially if large areas are to be considered. Moreover, the decision algorithms tend to be of high memory and CPU consuming in such cases and so does the required time for obtaining the estimated location for a new fingerprint. In this paper, we study, propose, and validate a way to select the locations for the training fingerprints which reduces the amount of required points while improving the accuracy of the algorithms when locating points at room level resolution. We present a comparison of different artificial intelligence decision algorithms and select those with better results. We do a comparison with other systems in the literature and draw conclusions about the improvements obtained in our proposal. Moreover, some techniques such as filtering nonstable access points for improving accuracy are introduced, studied, and validated.

Variations in area-level disadvantage of Australian registered fitness trainers usual training locations

Directory of Open Access Journals (Sweden)

Jason A. Bennie

2016-07-01

Full Text Available Abstract Background Leisure-time physical activity and strength training participation levels are low and socioeconomically distributed. Fitness trainers (e.g. gym/group instructors may have a role in increasing these participation levels. However, it is not known whether the training location and characteristics of Australian fitness trainers vary between areas that differ in socioeconomic status. Methods In 2014, a sample of 1,189 Australian trainers completed an online survey with questions about personal and fitness industry-related characteristics (e.g. qualifications, setting, and experience and postcode of their usual training location. The Australian Bureau of Statistics ‘Index of Relative Socioeconomic Disadvantage’ (IRSD was matched to training location and used to assess where fitness professionals trained and whether their experience, qualification level and delivery methods differed by area-level disadvantage. Linear regression analysis was used to examine the relationship between IRSD score and selected characteristics adjusting for covariates (e.g. sex, age. Results Overall, 47 % of respondents worked in areas within the three least-disadvantaged deciles. In contrast, only 14.8 % worked in the three most-disadvantaged deciles. In adjusted regression models, fitness industry qualification was positively associated with a higher IRSD score (i.e. working in the least-disadvantaged areas (Cert III: ref; Cert IV β:13.44 [95 % CI 3.86-23.02]; Diploma β:15.77 [95 % CI: 2.17-29.37]; Undergraduate β:23.14 [95 % CI: 9.41-36.86]. Conclusions Fewer Australian fitness trainers work in areas with high levels of socioeconomic disadvantaged areas than in areas with low levels of disadvantage. A higher level of fitness industry qualifications was associated with working in areas with lower levels of disadvantage. Future research should explore the effectiveness of providing incentives that encourage more fitness trainers and those with

Variations in area-level disadvantage of Australian registered fitness trainers usual training locations.

Science.gov (United States)

Bennie, Jason A; Thornton, Lukar E; van Uffelen, Jannique G Z; Banting, Lauren K; Biddle, Stuart J H

2016-07-11

Leisure-time physical activity and strength training participation levels are low and socioeconomically distributed. Fitness trainers (e.g. gym/group instructors) may have a role in increasing these participation levels. However, it is not known whether the training location and characteristics of Australian fitness trainers vary between areas that differ in socioeconomic status. In 2014, a sample of 1,189 Australian trainers completed an online survey with questions about personal and fitness industry-related characteristics (e.g. qualifications, setting, and experience) and postcode of their usual training location. The Australian Bureau of Statistics 'Index of Relative Socioeconomic Disadvantage' (IRSD) was matched to training location and used to assess where fitness professionals trained and whether their experience, qualification level and delivery methods differed by area-level disadvantage. Linear regression analysis was used to examine the relationship between IRSD score and selected characteristics adjusting for covariates (e.g. sex, age). Overall, 47 % of respondents worked in areas within the three least-disadvantaged deciles. In contrast, only 14.8 % worked in the three most-disadvantaged deciles. In adjusted regression models, fitness industry qualification was positively associated with a higher IRSD score (i.e. working in the least-disadvantaged areas) (Cert III: ref; Cert IV β:13.44 [95 % CI 3.86-23.02]; Diploma β:15.77 [95 % CI: 2.17-29.37]; Undergraduate β:23.14 [95 % CI: 9.41-36.86]). Fewer Australian fitness trainers work in areas with high levels of socioeconomic disadvantaged areas than in areas with low levels of disadvantage. A higher level of fitness industry qualifications was associated with working in areas with lower levels of disadvantage. Future research should explore the effectiveness of providing incentives that encourage more fitness trainers and those with higher qualifications to work in more socioeconomically

Variations in area-level disadvantage of Australian registered fitness trainers usual training locations

OpenAIRE

Bennie, Jason A.; Thornton, Lukar E.; van Uffelen, Jannique G. Z.; Banting, Lauren K.; Biddle, Stuart J. H.

2016-01-01

Background Leisure-time physical activity and strength training participation levels are low and socioeconomically distributed. Fitness trainers (e.g. gym/group instructors) may have a role in increasing these participation levels. However, it is not known whether the training location and characteristics of Australian fitness trainers vary between areas that differ in socioeconomic status. Methods In 2014, a sample of 1,189 Australian trainers completed an online survey with questions about ...

INCREASING RELIABILITY OF STEPPED AUTOMATIC STARTING AND RHEOSTAT BREAKING SYSTEM OF ELECTRIC TRAINS ER9T AND EPL9T

Directory of Open Access Journals (Sweden)

N. H. Visin

2010-06-01

Full Text Available The article examines transitional processes in the power circuit of tractive motors and their influence on the work of stepped automatic starting of electric trains ER9T and EPL9T. The recommendations for increasing the reliability of operation of multiple-unit rolling stock are proposed.

Integrated training support system for PWR operator training simulator

International Nuclear Information System (INIS)

Sakaguchi, Junichi; Komatsu, Yasuki

1999-01-01

The importance of operator training using operator training simulator has been recognized intensively. Since 1986, we have been developing and providing many PWR simulators in Japan. We also have developed some training support systems connected with the simulator and the integrated training support system to improve training effect and to reduce instructor's workload. This paper describes the concept and the effect of the integrated training support system and of the following sub-systems. We have PES (Performance Enhancement System) that evaluates training performance automatically by analyzing many plant parameters and operation data. It can reduce the deviation of training performance evaluation between instructors. PEL (Parameter and Event data Logging system), that is the subset of PES, has some data-logging functions. And we also have TPES (Team Performance Enhancement System) that is used aiming to improve trainees' ability for communication between operators. Trainee can have conversation with virtual trainees that TPES plays automatically. After that, TPES automatically display some advice to be improved. RVD (Reactor coolant system Visual Display) displays the distributed hydraulic-thermal condition of the reactor coolant system in real-time graphically. It can make trainees understand the inside plant condition in more detail. These sub-systems have been used in a training center and have contributed the improvement of operator training and have gained in popularity. (author)

Automatic face morphing for transferring facial animation

NARCIS (Netherlands)

Bui Huu Trung, B.H.T.; Bui, T.D.; Poel, Mannes; Heylen, Dirk K.J.; Nijholt, Antinus; Hamza, H.M.

2003-01-01

In this paper, we introduce a novel method of automatically finding the training set of RBF networks for morphing a prototype face to represent a new face. This is done by automatically specifying and adjusting corresponding feature points on a target face. The RBF networks are then used to transfer

Cardiopulmonary resuscitation and automatic external defibrillator training in schools: "is anyone learning how to save a life?".

Science.gov (United States)

Hart, Devin; Flores-Medrano, Oscar; Brooks, Steve; Buick, Jason E; Morrison, Laurie J

2013-09-01

Bystander resuscitation efforts, such as cardiopulmonary resuscitation (CPR) and use of an automatic external defibrillator (AED), save lives in cardiac arrest cases. School training in CPR and AED use may increase the currently low community rates of bystander resuscitation. The study objective was to determine the rates of CPR and AED training in Toronto secondary schools and to identify barriers to training and training techniques. This prospective study consisted of telephone interviews conducted with key school staff knowledgeable about CPR and AED teaching. An encrypted Web-based tool with prespecified variables and built-in logic was employed to standardize data collection. Of 268 schools contacted, 93% were available for interview and 83% consented to participate. Students and staff were trained in CPR in 51% and 80% of schools, respectively. Private schools had the lowest training rate (39%). Six percent of schools provided AED training to students and 47% provided AED training to staff. Forty-eight percent of schools had at least one AED installed, but 25% were unaware if their AED was registered with emergency services dispatch. Cost (17%), perceived need (11%), and school population size (10%) were common barriers to student training. Frequently employed training techniques were interactive (32%), didactic instruction (30%) and printed material (16%). CPR training rates for staff and students were moderate overall and lowest in private schools, whereas training rates in AED use were poor in all schools. Identified barriers to training include cost and student population size (perceived to be too small to be cost-effective or too large to be implemented). Future studies should assess the application of convenient and cost-effective teaching alternatives not presently in use.

An Automated Motion Detection and Reward System for Animal Training.

Science.gov (United States)

Miller, Brad; Lim, Audrey N; Heidbreder, Arnold F; Black, Kevin J

2015-12-04

A variety of approaches has been used to minimize head movement during functional brain imaging studies in awake laboratory animals. Many laboratories expend substantial effort and time training animals to remain essentially motionless during such studies. We could not locate an "off-the-shelf" automated training system that suited our needs.  We developed a time- and labor-saving automated system to train animals to hold still for extended periods of time. The system uses a personal computer and modest external hardware to provide stimulus cues, monitor movement using commercial video surveillance components, and dispense rewards. A custom computer program automatically increases the motionless duration required for rewards based on performance during the training session but allows changes during sessions. This system was used to train cynomolgus monkeys (Macaca fascicularis) for awake neuroimaging studies using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). The automated system saved the trainer substantial time, presented stimuli and rewards in a highly consistent manner, and automatically documented training sessions. We have limited data to prove the training system's success, drawn from the automated records during training sessions, but we believe others may find it useful. The system can be adapted to a range of behavioral training/recording activities for research or commercial applications, and the software is freely available for non-commercial use.

Neural network based automated algorithm to identify joint locations on hand/wrist radiographs for arthritis assessment

International Nuclear Information System (INIS)

Duryea, J.; Zaim, S.; Wolfe, F.

2002-01-01

Arthritis is a significant and costly healthcare problem that requires objective and quantifiable methods to evaluate its progression. Here we describe software that can automatically determine the locations of seven joints in the proximal hand and wrist that demonstrate arthritic changes. These are the five carpometacarpal (CMC1, CMC2, CMC3, CMC4, CMC5), radiocarpal (RC), and the scaphocapitate (SC) joints. The algorithm was based on an artificial neural network (ANN) that was trained using independent sets of digitized hand radiographs and manually identified joint locations. The algorithm used landmarks determined automatically by software developed in our previous work as starting points. Other than requiring user input of the location of nonanatomical structures and the orientation of the hand on the film, the procedure was fully automated. The software was tested on two datasets: 50 digitized hand radiographs from patients participating in a large clinical study, and 60 from subjects participating in arthritis research studies and who had mild to moderate rheumatoid arthritis (RA). It was evaluated by a comparison to joint locations determined by a trained radiologist using manual tracing. The success rate for determining the CMC, RC, and SC joints was 87%-99%, for normal hands and 81%-99% for RA hands. This is a first step in performing an automated computer-aided assessment of wrist joints for arthritis progression. The software provides landmarks that will be used by subsequent image processing routines to analyze each joint individually for structural changes such as erosions and joint space narrowing

Automatic generation of a subject-specific model for accurate markerless motion capture and biomechanical applications.

Science.gov (United States)

Corazza, Stefano; Gambaretto, Emiliano; Mündermann, Lars; Andriacchi, Thomas P

2010-04-01

A novel approach for the automatic generation of a subject-specific model consisting of morphological and joint location information is described. The aim is to address the need for efficient and accurate model generation for markerless motion capture (MMC) and biomechanical studies. The algorithm applied and expanded on previous work on human shapes space by embedding location information for ten joint centers in a subject-specific free-form surface. The optimal locations of joint centers in the 3-D mesh were learned through linear regression over a set of nine subjects whose joint centers were known. The model was shown to be sufficiently accurate for both kinematic (joint centers) and morphological (shape of the body) information to allow accurate tracking with MMC systems. The automatic model generation algorithm was applied to 3-D meshes of different quality and resolution such as laser scans and visual hulls. The complete method was tested using nine subjects of different gender, body mass index (BMI), age, and ethnicity. Experimental training error and cross-validation errors were 19 and 25 mm, respectively, on average over the joints of the ten subjects analyzed in the study.

Manually locating physical and virtual reality objects.

Science.gov (United States)

Chen, Karen B; Kimmel, Ryan A; Bartholomew, Aaron; Ponto, Kevin; Gleicher, Michael L; Radwin, Robert G

2014-09-01

In this study, we compared how users locate physical and equivalent three-dimensional images of virtual objects in a cave automatic virtual environment (CAVE) using the hand to examine how human performance (accuracy, time, and approach) is affected by object size, location, and distance. Virtual reality (VR) offers the promise to flexibly simulate arbitrary environments for studying human performance. Previously, VR researchers primarily considered differences between virtual and physical distance estimation rather than reaching for close-up objects. Fourteen participants completed manual targeting tasks that involved reaching for corners on equivalent physical and virtual boxes of three different sizes. Predicted errors were calculated from a geometric model based on user interpupillary distance, eye location, distance from the eyes to the projector screen, and object. Users were 1.64 times less accurate (p virtual versus physical box corners using the hands. Predicted virtual targeting errors were on average 1.53 times (p virtual targets but not significantly different for close-up virtual targets. Target size, location, and distance, in addition to binocular disparity, affected virtual object targeting inaccuracy. Observed virtual box inaccuracy was less than predicted for farther locations, suggesting possible influence of cues other than binocular vision. Human physical interaction with objects in VR for simulation, training, and prototyping involving reaching and manually handling virtual objects in a CAVE are more accurate than predicted when locating farther objects.

Transfer after process-based object-location memory training in healthy older adults.

Science.gov (United States)

Zimmermann, Kathrin; von Bastian, Claudia C; Röcke, Christina; Martin, Mike; Eschen, Anne

2016-11-01

A substantial part of age-related episodic memory decline has been attributed to the decreasing ability of older adults to encode and retrieve associations among simultaneously processed information units from long-term memory. In addition, this ability seems to share unique variance with reasoning. In this study, we therefore examined whether process-based training of the ability to learn and remember associations has the potential to induce transfer effects to untrained episodic memory and reasoning tasks in healthy older adults (60-75 years). For this purpose, the experimental group (n = 36) completed 30 sessions of process-based object-location memory training, while the active control group (n = 31) practiced visual perception on the same material. Near (spatial episodic memory), intermediate (verbal episodic memory), and far transfer effects (reasoning) were each assessed with multiple tasks at four measurements (before, midway through, immediately after, and 4 months after training). Linear mixed-effects models revealed transfer effects on spatial episodic memory and reasoning that were still observed 4 months after training. These results provide first empirical evidence that process-based training can enhance healthy older adults' associative memory performance and positively affect untrained episodic memory and reasoning abilities. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

MRI-alone radiation therapy planning for prostate cancer: Automatic fiducial marker detection

International Nuclear Information System (INIS)

Ghose, Soumya; Mitra, Jhimli; Rivest-Hénault, David; Fazlollahi, Amir; Fripp, Jurgen; Dowling, Jason A.; Stanwell, Peter; Pichler, Peter; Sun, Jidi; Greer, Peter B.

2016-01-01

Purpose: The feasibility of radiation therapy treatment planning using substitute computed tomography (sCT) generated from magnetic resonance images (MRIs) has been demonstrated by a number of research groups. One challenge with an MRI-alone workflow is the accurate identification of intraprostatic gold fiducial markers, which are frequently used for prostate localization prior to each dose delivery fraction. This paper investigates a template-matching approach for the detection of these seeds in MRI. Methods: Two different gradient echo T1 and T2* weighted MRI sequences were acquired from fifteen prostate cancer patients and evaluated for seed detection. For training, seed templates from manual contours were selected in a spectral clustering manifold learning framework. This aids in clustering “similar” gold fiducial markers together. The marker with the minimum distance to a cluster centroid was selected as the representative template of that cluster during training. During testing, Gaussian mixture modeling followed by a Markovian model was used in automatic detection of the probable candidates. The probable candidates were rigidly registered to the templates identified from spectral clustering, and a similarity metric is computed for ranking and detection. Results: A fiducial detection accuracy of 95% was obtained compared to manual observations. Expert radiation therapist observers were able to correctly identify all three implanted seeds on 11 of the 15 scans (the proposed method correctly identified all seeds on 10 of the 15). Conclusions: An novel automatic framework for gold fiducial marker detection in MRI is proposed and evaluated with detection accuracies comparable to manual detection. When radiation therapists are unable to determine the seed location in MRI, they refer back to the planning CT (only available in the existing clinical framework); similarly, an automatic quality control is built into the automatic software to ensure that all gold

MRI-alone radiation therapy planning for prostate cancer: Automatic fiducial marker detection

Energy Technology Data Exchange (ETDEWEB)

Ghose, Soumya, E-mail: soumya.ghose@case.edu; Mitra, Jhimli [Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106 and CSIRO Health and Biosecurity, The Australian e-Health & Research Centre, Herston, QLD 4029 (Australia); Rivest-Hénault, David; Fazlollahi, Amir; Fripp, Jurgen; Dowling, Jason A. [CSIRO Health and Biosecurity, The Australian e-Health & Research Centre, Herston, QLD 4029 (Australia); Stanwell, Peter [School of health sciences, The University of Newcastle, Newcastle, NSW 2308 (Australia); Pichler, Peter [Department of Radiation Oncology, Cavalry Mater Newcastle Hospital, Newcastle, NSW 2298 (Australia); Sun, Jidi; Greer, Peter B. [School of Mathematical and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia and Department of Radiation Oncology, Cavalry Mater Newcastle Hospital, Newcastle, NSW 2298 (Australia)

2016-05-15

Purpose: The feasibility of radiation therapy treatment planning using substitute computed tomography (sCT) generated from magnetic resonance images (MRIs) has been demonstrated by a number of research groups. One challenge with an MRI-alone workflow is the accurate identification of intraprostatic gold fiducial markers, which are frequently used for prostate localization prior to each dose delivery fraction. This paper investigates a template-matching approach for the detection of these seeds in MRI. Methods: Two different gradient echo T1 and T2* weighted MRI sequences were acquired from fifteen prostate cancer patients and evaluated for seed detection. For training, seed templates from manual contours were selected in a spectral clustering manifold learning framework. This aids in clustering “similar” gold fiducial markers together. The marker with the minimum distance to a cluster centroid was selected as the representative template of that cluster during training. During testing, Gaussian mixture modeling followed by a Markovian model was used in automatic detection of the probable candidates. The probable candidates were rigidly registered to the templates identified from spectral clustering, and a similarity metric is computed for ranking and detection. Results: A fiducial detection accuracy of 95% was obtained compared to manual observations. Expert radiation therapist observers were able to correctly identify all three implanted seeds on 11 of the 15 scans (the proposed method correctly identified all seeds on 10 of the 15). Conclusions: An novel automatic framework for gold fiducial marker detection in MRI is proposed and evaluated with detection accuracies comparable to manual detection. When radiation therapists are unable to determine the seed location in MRI, they refer back to the planning CT (only available in the existing clinical framework); similarly, an automatic quality control is built into the automatic software to ensure that all gold

The research of automatic speed control algorithm based on Green CBTC

Science.gov (United States)

Lin, Ying; Xiong, Hui; Wang, Xiaoliang; Wu, Youyou; Zhang, Chuanqi

2017-06-01

Automatic speed control algorithm is one of the core technologies of train operation control system. It’s a typical multi-objective optimization control algorithm, which achieve the train speed control for timing, comfort, energy-saving and precise parking. At present, the train speed automatic control technology is widely used in metro and inter-city railways. It has been found that the automatic speed control technology can effectively reduce the driver’s intensity, and improve the operation quality. However, the current used algorithm is poor at energy-saving, even not as good as manual driving. In order to solve the problem of energy-saving, this paper proposes an automatic speed control algorithm based on Green CBTC system. Based on the Green CBTC system, the algorithm can adjust the operation status of the train to improve the efficient using rate of regenerative braking feedback energy while ensuring the timing, comfort and precise parking targets. Due to the reason, the energy-using of Green CBTC system is lower than traditional CBTC system. The simulation results show that the algorithm based on Green CBTC system can effectively reduce the energy-using due to the improvement of the using rate of regenerative braking feedback energy.

Object-location training elicits an overlapping but temporally distinct transcriptional profile from contextual fear conditioning.

Science.gov (United States)

Poplawski, Shane G; Schoch, Hannah; Wimmer, Mathieu; Hawk, Joshua D; Walsh, Jennifer L; Giese, Karl P; Abel, Ted

2014-12-01

Hippocampus-dependent learning is known to induce changes in gene expression, but information on gene expression differences between different learning paradigms that require the hippocampus is limited. The bulk of studies investigating RNA expression after learning use the contextual fear conditioning task, which couples a novel environment with a footshock. Although contextual fear conditioning has been useful in discovering gene targets, gene expression after spatial memory tasks has received less attention. In this study, we used the object-location memory task and studied gene expression at two time points after learning in a high-throughput manner using a microfluidic qPCR approach. We found that expression of the classic immediate-early genes changes after object-location training in a fashion similar to that observed after contextual fear conditioning. However, the temporal dynamics of gene expression are different between the two tasks, with object-location memory producing gene expression changes that last at least 2 hours. Our findings indicate that different training paradigms may give rise to distinct temporal dynamics of gene expression after learning. Copyright © 2014 Elsevier Inc. All rights reserved.

Object-Location Training Elicits an Overlapping but Temporally Distinct Transcriptional Profile from Contextual Fear Conditioning

Science.gov (United States)

Wimmer, Mathieu; Hawk, Joshua D.; Walsh, Jennifer L.; Giese, Karl P.; Abel, Ted

2014-01-01

Hippocampus-dependent learning is known to induce changes in gene expression, but information on gene expression differences between different learning paradigms that require the hippocampus is limited. The bulk of studies investigating RNA expression after learning use the contextual fear conditioning task, which couples a novel environment with a footshock. Although contextual fear conditioning has been useful in discovering gene targets, gene expression after spatial memory tasks has received less attention. In this study, we used the object-location memory task and studied gene expression at two time points after learning in a high-throughput manner using a microfluidic qPCR approach. We found that expression of the classic immediate-early genes changes after object-location training in a fashion similar to that observed after contextual fear conditioning. However, the temporal dynamics of gene expression are different between the two tasks, with object-location memory producing gene expression changes that last at least 2 hours. Our findings indicate that different training paradigms may give rise to distinct temporal dynamics of gene expression after learning. PMID:25242102

Teaching Health Center Graduate Medical Education Locations Predominantly Located in Federally Designated Underserved Areas.

Science.gov (United States)

Barclift, Songhai C; Brown, Elizabeth J; Finnegan, Sean C; Cohen, Elena R; Klink, Kathleen

2016-05-01

Background The Teaching Health Center Graduate Medical Education (THCGME) program is an Affordable Care Act funding initiative designed to expand primary care residency training in community-based ambulatory settings. Statute suggests, but does not require, training in underserved settings. Residents who train in underserved settings are more likely to go on to practice in similar settings, and graduates more often than not practice near where they have trained. Objective The objective of this study was to describe and quantify federally designated clinical continuity training sites of the THCGME program. Methods Geographic locations of the training sites were collected and characterized as Health Professional Shortage Area, Medically Underserved Area, Population, or rural areas, and were compared with the distribution of Centers for Medicare and Medicaid Services (CMS)-funded training positions. Results More than half of the teaching health centers (57%) are located in states that are in the 4 quintiles with the lowest CMS-funded resident-to-population ratio. Of the 109 training sites identified, more than 70% are located in federally designated high-need areas. Conclusions The THCGME program is a model that funds residency training in community-based ambulatory settings. Statute suggests, but does not explicitly require, that training take place in underserved settings. Because the majority of the 109 clinical training sites of the 60 funded programs in 2014-2015 are located in federally designated underserved locations, the THCGME program deserves further study as a model to improve primary care distribution into high-need communities.

THE AUTOMATIC LIGHTENING LOCATION SYSTEM AND ITS ...

African Journals Online (AJOL)

ES Obe

systems. The implications of the lightening location system for the Nigerian electric power system are also highlighted. ... system (the LLP type) is currently operating in may countries .... (iii) Real time lightning maps will aid service restoration.

Bus Travel Time Deviation Analysis Using Automatic Vehicle Location Data and Structural Equation Modeling

Directory of Open Access Journals (Sweden)

Xiaolin Gong

2015-01-01

Full Text Available To investigate the influences of causes of unreliability and bus schedule recovery phenomenon on microscopic segment-level travel time variance, this study adopts Structural Equation Modeling (SEM to specify, estimate, and measure the theoretical proposed models. The SEM model establishes and verifies hypotheses for interrelationships among travel time deviations, departure delays, segment lengths, dwell times, and number of traffic signals and access connections. The finally accepted model demonstrates excellent fitness. Most of the hypotheses are supported by the sample dataset from bus Automatic Vehicle Location system. The SEM model confirms the bus schedule recovery phenomenon. The departure delays at bus terminals and upstream travel time deviations indeed have negative impacts on travel time fluctuation of buses en route. Meanwhile, the segment length directly and negatively impacts travel time variability and inversely positively contributes to the schedule recovery process; this exogenous variable also indirectly and positively influences travel times through the existence of signalized intersections and access connections. This study offers a rational approach to analyzing travel time deviation feature. The SEM model structure and estimation results facilitate the understanding of bus service performance characteristics and provide several implications for bus service planning, management, and operation.

Automatic Thermal Infrared Panoramic Imaging Sensor

National Research Council Canada - National Science Library

Gutin, Mikhail; Tsui, Eddy K; Gutin, Olga; Wang, Xu-Ming; Gutin, Alexey

2006-01-01

.... Automatic detection, location, and tracking of targets outside protected area ensures maximum protection and at the same time reduces the workload on personnel, increases reliability and confidence...

A Linear Programming Approach for Determining Travel Cost Minimizing ECSS Training Locations

Science.gov (United States)

2010-03-01

can accommodate the simultaneous training of many personnel. 1. Gulfport CRTC, Mississippi 2. Savannah CRTC, Georgia 3. Alpena CRTC, Michigan 4...327 224 Savannah CRTC 266 182 Alpena CRTC 493 343 Volk Field CRTC 136 91 Hill ALC 210 147 Hanscom ALC 414 287 Tinker ALC 620 434 Robins ALC 1332...Location Frequency within Optimal Solutions across All Phases 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Gulfport CRTC Savannah CRTC Alpena CRTC Volk

A neurocomputational model of automatic sequence production.

Science.gov (United States)

Helie, Sebastien; Roeder, Jessica L; Vucovich, Lauren; Rünger, Dennis; Ashby, F Gregory

2015-07-01

Most behaviors unfold in time and include a sequence of submovements or cognitive activities. In addition, most behaviors are automatic and repeated daily throughout life. Yet, relatively little is known about the neurobiology of automatic sequence production. Past research suggests a gradual transfer from the associative striatum to the sensorimotor striatum, but a number of more recent studies challenge this role of the BG in automatic sequence production. In this article, we propose a new neurocomputational model of automatic sequence production in which the main role of the BG is to train cortical-cortical connections within the premotor areas that are responsible for automatic sequence production. The new model is used to simulate four different data sets from human and nonhuman animals, including (1) behavioral data (e.g., RTs), (2) electrophysiology data (e.g., single-neuron recordings), (3) macrostructure data (e.g., TMS), and (4) neurological circuit data (e.g., inactivation studies). We conclude with a comparison of the new model with existing models of automatic sequence production and discuss a possible new role for the BG in automaticity and its implication for Parkinson's disease.

Near-real time 3D probabilistic earthquakes locations at Mt. Etna volcano

Science.gov (United States)

Barberi, G.; D'Agostino, M.; Mostaccio, A.; Patane', D.; Tuve', T.

2012-04-01

Automatic procedure for locating earthquake in quasi-real time must provide a good estimation of earthquakes location within a few seconds after the event is first detected and is strongly needed for seismic warning system. The reliability of an automatic location algorithm is influenced by several factors such as errors in picking seismic phases, network geometry, and velocity model uncertainties. On Mt. Etna, the seismic network is managed by INGV and the quasi-real time earthquakes locations are performed by using an automatic-picking algorithm based on short-term-average to long-term-average ratios (STA/LTA) calculated from an approximate squared envelope function of the seismogram, which furnish a list of P-wave arrival times, and the location algorithm Hypoellipse, with a 1D velocity model. The main purpose of this work is to investigate the performances of a different automatic procedure to improve the quasi-real time earthquakes locations. In fact, as the automatic data processing may be affected by outliers (wrong picks), the use of a traditional earthquake location techniques based on a least-square misfit function (L2-norm) often yield unstable and unreliable solutions. Moreover, on Mt. Etna, the 1D model is often unable to represent the complex structure of the volcano (in particular the strong lateral heterogeneities), whereas the increasing accuracy in the 3D velocity models at Mt. Etna during recent years allows their use today in routine earthquake locations. Therefore, we selected, as reference locations, all the events occurred on Mt. Etna in the last year (2011) which was automatically detected and located by means of the Hypoellipse code. By using this dataset (more than 300 events), we applied a nonlinear probabilistic earthquake location algorithm using the Equal Differential Time (EDT) likelihood function, (Font et al., 2004; Lomax, 2005) which is much more robust in the presence of outliers in the data. Successively, by using a probabilistic

Assessment of Machine Learning Algorithms for Automatic Benthic Cover Monitoring and Mapping Using Towed Underwater Video Camera and High-Resolution Satellite Images

Directory of Open Access Journals (Sweden)

Hassan Mohamed

2018-05-01

Full Text Available Benthic habitat monitoring is essential for many applications involving biodiversity, marine resource management, and the estimation of variations over temporal and spatial scales. Nevertheless, both automatic and semi-automatic analytical methods for deriving ecologically significant information from towed camera images are still limited. This study proposes a methodology that enables a high-resolution towed camera with a Global Navigation Satellite System (GNSS to adaptively monitor and map benthic habitats. First, the towed camera finishes a pre-programmed initial survey to collect benthic habitat videos, which can then be converted to geo-located benthic habitat images. Second, an expert labels a number of benthic habitat images to class habitats manually. Third, attributes for categorizing these images are extracted automatically using the Bag of Features (BOF algorithm. Fourth, benthic cover categories are detected automatically using Weighted Majority Voting (WMV ensembles for Support Vector Machines (SVM, K-Nearest Neighbor (K-NN, and Bagging (BAG classifiers. Fifth, WMV-trained ensembles can be used for categorizing more benthic cover images automatically. Finally, correctly categorized geo-located images can provide ground truth samples for benthic cover mapping using high-resolution satellite imagery. The proposed methodology was tested over Shiraho, Ishigaki Island, Japan, a heterogeneous coastal area. The WMV ensemble exhibited 89% overall accuracy for categorizing corals, sediments, seagrass, and algae species. Furthermore, the same WMV ensemble produced a benthic cover map using a Quickbird satellite image with 92.7% overall accuracy.

Automatic crown cover mapping to improve forest inventory

Science.gov (United States)

Claude Vidal; Jean-Guy Boureau; Nicolas Robert; Nicolas Py; Josiane Zerubia; Xavier Descombes; Guillaume Perrin

2009-01-01

To automatically analyze near infrared aerial photographs, the French National Institute for Research in Computer Science and Control developed together with the French National Forest Inventory (NFI) a method for automatic crown cover mapping. This method uses a Reverse Jump Monte Carlo Markov Chain algorithm to locate the crowns and describe those using ellipses or...

Imaging different components of a tectonic tremor sequence in southwestern Japan using an automatic statistical detection and location method

Science.gov (United States)

Poiata, Natalia; Vilotte, Jean-Pierre; Bernard, Pascal; Satriano, Claudio; Obara, Kazushige

2018-06-01

In this study, we demonstrate the capability of an automatic network-based detection and location method to extract and analyse different components of tectonic tremor activity by analysing a 9-day energetic tectonic tremor sequence occurring at the downdip extension of the subducting slab in southwestern Japan. The applied method exploits the coherency of multiscale, frequency-selective characteristics of non-stationary signals recorded across the seismic network. Use of different characteristic functions, in the signal processing step of the method, allows to extract and locate the sources of short-duration impulsive signal transients associated with low-frequency earthquakes and of longer-duration energy transients during the tectonic tremor sequence. Frequency-dependent characteristic functions, based on higher-order statistics' properties of the seismic signals, are used for the detection and location of low-frequency earthquakes. This allows extracting a more complete (˜6.5 times more events) and time-resolved catalogue of low-frequency earthquakes than the routine catalogue provided by the Japan Meteorological Agency. As such, this catalogue allows resolving the space-time evolution of the low-frequency earthquakes activity in great detail, unravelling spatial and temporal clustering, modulation in response to tide, and different scales of space-time migration patterns. In the second part of the study, the detection and source location of longer-duration signal energy transients within the tectonic tremor sequence is performed using characteristic functions built from smoothed frequency-dependent energy envelopes. This leads to a catalogue of longer-duration energy sources during the tectonic tremor sequence, characterized by their durations and 3-D spatial likelihood maps of the energy-release source regions. The summary 3-D likelihood map for the 9-day tectonic tremor sequence, built from this catalogue, exhibits an along-strike spatial segmentation of

Imaging different components of a tectonic tremor sequence in southwestern Japan using an automatic statistical detection and location method

Science.gov (United States)

Poiata, Natalia; Vilotte, Jean-Pierre; Bernard, Pascal; Satriano, Claudio; Obara, Kazushige

2018-02-01

In this study, we demonstrate the capability of an automatic network-based detection and location method to extract and analyse different components of tectonic tremor activity by analysing a 9-day energetic tectonic tremor sequence occurring at the down-dip extension of the subducting slab in southwestern Japan. The applied method exploits the coherency of multi-scale, frequency-selective characteristics of non-stationary signals recorded across the seismic network. Use of different characteristic functions, in the signal processing step of the method, allows to extract and locate the sources of short-duration impulsive signal transients associated with low-frequency earthquakes and of longer-duration energy transients during the tectonic tremor sequence. Frequency-dependent characteristic functions, based on higher-order statistics' properties of the seismic signals, are used for the detection and location of low-frequency earthquakes. This allows extracting a more complete (˜6.5 times more events) and time-resolved catalogue of low-frequency earthquakes than the routine catalogue provided by the Japan Meteorological Agency. As such, this catalogue allows resolving the space-time evolution of the low-frequency earthquakes activity in great detail, unravelling spatial and temporal clustering, modulation in response to tide, and different scales of space-time migration patterns. In the second part of the study, the detection and source location of longer-duration signal energy transients within the tectonic tremor sequence is performed using characteristic functions built from smoothed frequency-dependent energy envelopes. This leads to a catalogue of longer-duration energy sources during the tectonic tremor sequence, characterized by their durations and 3-D spatial likelihood maps of the energy-release source regions. The summary 3-D likelihood map for the 9-day tectonic tremor sequence, built from this catalogue, exhibits an along-strike spatial segmentation of

The pedagogical effectiveness of ASR-based computer assisted pronunciation training

NARCIS (Netherlands)

Neri, A.

2007-01-01

Computer Assisted Pronunciation Training (CAPT) systems with Automatic Speech Recognition (ASR) technology have become increasingly popular to train pronunciation in the second language (L2). The advantage of these systems is the provision of a self-paced, stress-free type of training with automatic

Location selection in the visual domain

NARCIS (Netherlands)

van der Lubbe, Robert Henricus Johannes; Woestenburg, Jaap C.

2000-01-01

According to A.H.C. Van der Heijden (1992), attentional selection of visual stimuli can be considered as location selection. Depending on the type of task, location selection can be considered to be automatic )e.g., in case of abrupt onsets), directly controlled (e.g., in case of symbolic precues),

Full waveform approach for the automatic detection and location of acoustic emissions from hydraulic fracturing at Äspö (Sweden)

Science.gov (United States)

Ángel López Comino, José; Cesca, Simone; Heimann, Sebastian; Grigoli, Francesco; Milkereit, Claus; Dahm, Torsten; Zang, Arno

2017-04-01

A crucial issue to analyse the induced seismicity for hydraulic fracturing is the detection and location of massive microseismic or acoustic emissions (AE) activity, with robust and sufficiently accurate automatic algorithms. Waveform stacking and coherence analysis have been tested for local seismic monitoring and mining induced seismicity improving the classical detection and location methods (e.g. short-term-average/long-term-average and automatic picking of the P and S waves first arrivals). These techniques are here applied using a full waveform approach for a hydraulic fracturing experiment (Nova project 54-14-1) that took place 410 m below surface in the Äspö Hard Rock Laboratory (Sweden). Continuous waveform recording with a near field network composed by eleven AE sensors are processed. The piezoelectric sensors have their highest sensitive in the frequency range 1 to 100 kHz, but sampling rates were extended to 1 MHz. We present the results obtained during the conventional, continuous water-injection experiment HF2 (Hydraulic Fracture 2). The event detector is based on the stacking of characteristic functions. It follows a delay-and-stack approach, where the likelihood of the hypocenter location in a pre-selected seismogenic volume is mapped by assessing the coherence of the P onset times at different stations. A low detector threshold is chosen, in order not to loose weaker events. This approach also increases the number of false detections. Therefore, the dataset has been revised manually, and detected events classified in terms of true AE events related to the fracturing process, electronic noise related to 50 Hz overtones, long period and other signals. The location of the AE events is further refined using a more accurate waveform stacking method which uses both P and S phases. A 3D grid is generated around the hydraulic fracturing volume and we retrieve a multidimensional matrix, whose absolute maximum corresponds to the spatial coordinates of the

Automatically processed alpha-track radon monitor

International Nuclear Information System (INIS)

Langner, G.H. Jr.

1993-01-01

An automatically processed alpha-track radon monitor is provided which includes a housing having an aperture allowing radon entry, and a filter that excludes the entry of radon daughters into the housing. A flexible track registration material is located within the housing that records alpha-particle emissions from the decay of radon and radon daughters inside the housing. The flexible track registration material is capable of being spliced such that the registration material from a plurality of monitors can be spliced into a single strip to facilitate automatic processing of the registration material from the plurality of monitors. A process for the automatic counting of radon registered by a radon monitor is also provided

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.

Automatic computation of 2D cardiac measurements from B-mode echocardiography

Science.gov (United States)

Park, JinHyeong; Feng, Shaolei; Zhou, S. Kevin

2012-03-01

We propose a robust and fully automatic algorithm which computes the 2D echocardiography measurements recommended by America Society of Echocardiography. The algorithm employs knowledge-based imaging technologies which can learn the expert's knowledge from the training images and expert's annotation. Based on the models constructed from the learning stage, the algorithm searches initial location of the landmark points for the measurements by utilizing heart structure of left ventricle including mitral valve aortic valve. It employs the pseudo anatomic M-mode image generated by accumulating the line images in 2D parasternal long axis view along the time to refine the measurement landmark points. The experiment results with large volume of data show that the algorithm runs fast and is robust comparable to expert.

Automatically sweeping dual-channel boxcar integrator

International Nuclear Information System (INIS)

Keefe, D.J.; Patterson, D.R.

1978-01-01

An automatically sweeping dual-channel boxcar integrator has been developed to automate the search for a signal that repeatedly follows a trigger pulse by a constant or slowly varying time delay when that signal is completely hidden in random electrical noise and dc-offset drifts. The automatically sweeping dual-channel boxcar integrator improves the signal-to-noise ratio and eliminates dc-drift errors in the same way that a conventional dual-channel boxcar integrator does, but, in addition, automatically locates the hidden signal. When the signal is found, its time delay is displayed with 100-ns resolution, and its peak value is automatically measured and displayed. This relieves the operator of the tedious, time-consuming, and error-prone search for the signal whenever the time delay changes. The automatically sweeping boxcar integrator can also be used as a conventional dual-channel boxcar integrator. In either mode, it can repeatedly integrate a signal up to 990 times and thus make accurate measurements of the signal pulse height in the presence of random noise, dc offsets, and unsynchronized interfering signals

Automatic classification of blank substrate defects

Science.gov (United States)

Boettiger, Tom; Buck, Peter; Paninjath, Sankaranarayanan; Pereira, Mark; Ronald, Rob; Rost, Dan; Samir, Bhamidipati

2014-10-01

Mask preparation stages are crucial in mask manufacturing, since this mask is to later act as a template for considerable number of dies on wafer. Defects on the initial blank substrate, and subsequent cleaned and coated substrates, can have a profound impact on the usability of the finished mask. This emphasizes the need for early and accurate identification of blank substrate defects and the risk they pose to the patterned reticle. While Automatic Defect Classification (ADC) is a well-developed technology for inspection and analysis of defects on patterned wafers and masks in the semiconductors industry, ADC for mask blanks is still in the early stages of adoption and development. Calibre ADC is a powerful analysis tool for fast, accurate, consistent and automatic classification of defects on mask blanks. Accurate, automated classification of mask blanks leads to better usability of blanks by enabling defect avoidance technologies during mask writing. Detailed information on blank defects can help to select appropriate job-decks to be written on the mask by defect avoidance tools [1][4][5]. Smart algorithms separate critical defects from the potentially large number of non-critical defects or false defects detected at various stages during mask blank preparation. Mechanisms used by Calibre ADC to identify and characterize defects include defect location and size, signal polarity (dark, bright) in both transmitted and reflected review images, distinguishing defect signals from background noise in defect images. The Calibre ADC engine then uses a decision tree to translate this information into a defect classification code. Using this automated process improves classification accuracy, repeatability and speed, while avoiding the subjectivity of human judgment compared to the alternative of manual defect classification by trained personnel [2]. This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at MP Mask

Changes in default mode network as automaticity develops in a categorization task.

Science.gov (United States)

Shamloo, Farzin; Helie, Sebastien

2016-10-15

The default mode network (DMN) is a set of brain regions in which blood oxygen level dependent signal is suppressed during attentional focus on the external environment. Because automatic task processing requires less attention, development of automaticity in a rule-based categorization task may result in less deactivation and altered functional connectivity of the DMN when compared to the initial learning stage. We tested this hypothesis by re-analyzing functional magnetic resonance imaging data of participants trained in rule-based categorization for over 10,000 trials (Helie et al., 2010) [12,13]. The results show that some DMN regions are deactivated in initial training but not after automaticity has developed. There is also a significant decrease in DMN deactivation after extensive practice. Seed-based functional connectivity analyses with the precuneus, medial prefrontal cortex (two important DMN regions) and Brodmann area 6 (an important region in automatic categorization) were also performed. The results show increased functional connectivity with both DMN and non-DMN regions after the development of automaticity, and a decrease in functional connectivity between the medial prefrontal cortex and ventromedial orbitofrontal cortex. Together, these results further support the hypothesis of a strategy shift in automatic categorization and bridge the cognitive and neuroscientific conceptions of automaticity in showing that the reduced need for cognitive resources in automatic processing is accompanied by a disinhibition of the DMN and stronger functional connectivity between DMN and task-related brain regions. Copyright © 2016 Elsevier B.V. All rights reserved.

Automatic methods for processing track-detector data at the PAVICOM facility

International Nuclear Information System (INIS)

Aleksandrov, A.B.; Goncharova, L.A.; Polukhina, N.G.; Fejnberg, E.L.; Davydov, D.A.; Publichenko, P.A.; Roganova, T.M.

2007-01-01

New automatic methods essentially simplify and hasten the data treatment of tracking detectors. It allows handling big data files and appreciably improves their statistics; this fact predetermines an elaboration of new experiments, which suppose to use large volume targets, emulsive and solid-state large square tracking detectors. Thereupon the problem of training competent physicists able to work on modern automatic equipment is very relevant. About ten Moscow students working in LPI at PAVICOM facility master new methods every year. Most of the students working in high-energy physics take the print only about archaic hand methods of data handling from tracking detectors. In 2005 on the base of the PAVICOM facility and physics training of the MSU a new educational work for determination of the energy of neutrons passing through nuclear emulsion, which lets students acquire a base habit of data handling from tracking detectors using an automatic facility, was prepared; it can be included in the training process for students of any physical faculty. Specialists mastering methods of an automatic handling by the simple and obvious example of tracking detectors will be able to use their knowledge in various areas of science and techniques. The organization of upper division courses is a new additional aspect of using the PAVICOM facility described in an earlier paper [4

A study of the utility of heat collectors in reducing the response time of automatic fire sprinklers located in production modules of Building 707

International Nuclear Information System (INIS)

Shanley, J.H. Jr.; Budnick, E.K. Jr.

1990-01-01

Several of the ten production Modules in Building 707 at the Department of Energy Rocky Flats Plant recently underwent an alteration which can adversely affect the performance of the installed automatic fire sprinkler systems. The Modules have an approximate floor to ceiling height of 17.5 ft. The alterations involved removing the drop ceilings in the Modules which had been at a height of 12 ft above the floor. The sprinkler systems were originally installed with the sprinkler heads located below the drop ceiling in accordance with the nationally recognized NFPA 13, Standard for the Installation of Automatic Sprinkler Systems. The ceiling removal affects the sprinkler's response time and also violates NFPA 13. The scope of this study included evaluation of the feasibility of utilizing heat collectors to reduce the delays in sprinkler response created by the removal of the drop ceilings. The study also includes evaluation of substituting quick response sprinklers for the standard sprinklers currently in place, in combination with a heat collector

Automatic classification of MR scans in Alzheimer's disease

OpenAIRE

García, Fernando Pérez; uk, fernando perezgarcia ucl ac

2018-01-01

Presentation of the paper "Automatic classification of MR scans in Alzheimer's disease" by Klöppel et al. for the journal club of the Centre for Doctoral Training in Medical Image Computing at University College London.

Automatic Task Classification via Support Vector Machine and Crowdsourcing

Directory of Open Access Journals (Sweden)

Hyungsik Shin

2018-01-01

Full Text Available Automatic task classification is a core part of personal assistant systems that are widely used in mobile devices such as smartphones and tablets. Even though many industry leaders are providing their own personal assistant services, their proprietary internals and implementations are not well known to the public. In this work, we show through real implementation and evaluation that automatic task classification can be implemented for mobile devices by using the support vector machine algorithm and crowdsourcing. To train our task classifier, we collected our training data set via crowdsourcing using the Amazon Mechanical Turk platform. Our classifier can classify a short English sentence into one of the thirty-two predefined tasks that are frequently requested while using personal mobile devices. Evaluation results show high prediction accuracy of our classifier ranging from 82% to 99%. By using large amount of crowdsourced data, we also illustrate the relationship between training data size and the prediction accuracy of our task classifier.

On the automaticity of response inhibition in individuals with alcoholism.

Science.gov (United States)

Noël, Xavier; Brevers, Damien; Hanak, Catherine; Kornreich, Charles; Verbanck, Paul; Verbruggen, Frederick

2016-06-01

Response inhibition is usually considered a hallmark of executive control. However, recent work indicates that stop performance can become associatively mediated ('automatic') over practice. This study investigated automatic response inhibition in sober and recently detoxified individuals with alcoholism.. We administered to forty recently detoxified alcoholics and forty healthy participants a modified stop-signal task that consisted of a training phase in which a subset of the stimuli was consistently associated with stopping or going, and a test phase in which this mapping was reversed. In the training phase, stop performance improved for the consistent stop stimuli, compared with control stimuli that were not associated with going or stopping. In the test phase, go performance tended to be impaired for old stop stimuli. Combined, these findings support the automatic inhibition hypothesis. Importantly, performance was similar in both groups, which indicates that automatic inhibitory control develops normally in individuals with alcoholism.. This finding is specific to individuals with alcoholism without other psychiatric disorders, which is rather atypical and prevents generalization. Personalized stimuli with a stronger affective content should be used in future studies. These results advance our understanding of behavioral inhibition in individuals with alcoholism. Furthermore, intact automatic inhibitory control may be an important element of successful cognitive remediation of addictive behaviors.. Copyright © 2016 Elsevier Ltd. All rights reserved.

Automatic segmentation of vertebrae from radiographs

DEFF Research Database (Denmark)

Mysling, Peter; Petersen, Peter Kersten; Nielsen, Mads

2011-01-01

Segmentation of vertebral contours is an essential task in the design of automatic tools for vertebral fracture assessment. In this paper, we propose a novel segmentation technique which does not require operator interaction. The proposed technique solves the segmentation problem in a hierarchical...... is constrained by a conditional shape model, based on the variability of the coarse spine location estimates. The technique is evaluated on a data set of manually annotated lumbar radiographs. The results compare favorably to the previous work in automatic vertebra segmentation, in terms of both segmentation...

A comparison of automatic histogram constructions

NARCIS (Netherlands)

Davies, P.L.; Gather, U.; Nordman, D.J.; Weinert, H.

2009-01-01

Even for a well-trained statistician the construction of a histogram for a given real-valued data set is a difficult problem. It is even more difficult to construct a fully automatic procedure which specifies the number and widths of the bins in a satisfactory manner for a wide range of data sets.

Automatic generation of gene finders for eukaryotic species

DEFF Research Database (Denmark)

Terkelsen, Kasper Munch; Krogh, A.

2006-01-01

and quality of reliable gene annotation grows. Results We present a procedure, Agene, that automatically generates a species-specific gene predictor from a set of reliable mRNA sequences and a genome. We apply a Hidden Markov model (HMM) that implements explicit length distribution modelling for all gene......Background The number of sequenced eukaryotic genomes is rapidly increasing. This means that over time it will be hard to keep supplying customised gene finders for each genome. This calls for procedures to automatically generate species-specific gene finders and to re-train them as the quantity...... structure blocks using acyclic discrete phase type distributions. The state structure of the each HMM is generated dynamically from an array of sub-models to include only gene features represented in the training set. Conclusion Acyclic discrete phase type distributions are well suited to model sequence...

An automatic tsunami warning system: TREMORS application in Europe

Science.gov (United States)

Reymond, D.; Robert, S.; Thomas, Y.; Schindelé, F.

1996-03-01

An integrated system named TREMORS (Tsunami Risk Evaluation through seismic Moment of a Real-time System) has been installed in EVORA station, in Portugal which has been affected by historical tsunamis. The system is based on a three component long period seismic station linked to a compatible IBM_PC with a specific software. The goals of this system are the followings: detect earthquake, locate them, compute their seismic moment, give a seismic warning. The warnings are based on the seismic moment estimation and all the processing are made automatically. The finality of this study is to check the quality of estimation of the main parameters of interest in a goal of tsunami warning: the location which depends of azimuth and distance, and at last the seismic moment, M 0, which controls the earthquake size. The sine qua non condition for obtaining an automatic location is that the 3 main seismic phases P, S, R must be visible. This study gives satisfying results (automatic analysis): ± 5° errors in azimuth and epicentral distance, and a standard deviation of less than a factor 2 for the seismic moment M 0.

Distributed solar photovoltaic array location and extent dataset for remote sensing object identification

Science.gov (United States)

Bradbury, Kyle; Saboo, Raghav; L. Johnson, Timothy; Malof, Jordan M.; Devarajan, Arjun; Zhang, Wuming; M. Collins, Leslie; G. Newell, Richard

2016-12-01

Earth-observing remote sensing data, including aerial photography and satellite imagery, offer a snapshot of the world from which we can learn about the state of natural resources and the built environment. The components of energy systems that are visible from above can be automatically assessed with these remote sensing data when processed with machine learning methods. Here, we focus on the information gap in distributed solar photovoltaic (PV) arrays, of which there is limited public data on solar PV deployments at small geographic scales. We created a dataset of solar PV arrays to initiate and develop the process of automatically identifying solar PV locations using remote sensing imagery. This dataset contains the geospatial coordinates and border vertices for over 19,000 solar panels across 601 high-resolution images from four cities in California. Dataset applications include training object detection and other machine learning algorithms that use remote sensing imagery, developing specific algorithms for predictive detection of distributed PV systems, estimating installed PV capacity, and analysis of the socioeconomic correlates of PV deployment.

Remembering spatial locations: effects of material and intelligence.

Science.gov (United States)

Zucco, G M; Tessari, A; Soresi, S

1995-04-01

The aim of the present work was to test some of the criteria for automaticity of spatial-location coding claimed by Hasher and Zacks, particularly individual differences (as intelligence invariance) and effortful encoding strategies. Two groups of subjects, 15 with mental retardation (Down Syndrome, mean chronological age, 20.9 yr.; mean mental age, 11.6 yr.) and 15 normal children (mean age, 11.5 yr.), were administered four kinds of stimuli (pictures, concrete words, nonsense pictures, and abstract words) at one location on a card. Subsequently, subjects were presented the items on the card's centre and were required to place the items in their original locations. Analysis indicated that those with Down Syndrome scored lower than normal children on the four tasks and that stimuli were better or worse remembered according to their characteristics, e.g., their imaginability. Results do not support some of the conditions claimed to be necessary criteria for automaticity in the recall of spatial locations as stated by Hasher and Zacks.

Differential Arc expression in the hippocampus and striatum during the transition from attentive to automatic navigation on a plus maze

Science.gov (United States)

Gardner, Robert S.; Suarez, Daniel F.; Robinson-Burton, Nadira K.; Rudnicky, Christopher J.; Gulati, Asish; Ascoli, Giorgio A.; Dumas, Theodore C.

2016-01-01

The strategies utilized to effectively perform a given task change with practice and experience. During a spatial navigation task, with relatively little training, performance is typically attentive enabling an individual to locate the position of a goal by relying on spatial landmarks. These (place) strategies require an intact hippocampus. With task repetition, performance becomes automatic; the same goal is reached using a fixed response or sequence of actions. These (response) strategies require an intact striatum. The current work aims to understand the activation patterns across these neural structures during this experience-dependent strategy transition. This was accomplished by region-specific measurement of activity-dependent immediate early gene expression among rats trained to different degrees on a dual-solution task (i.e., a task that can be solved using either place or response navigation). As expected, rats increased their reliance on response navigation with extended task experience. In addition, dorsal hippocampal expression of the immediate early gene Arc was considerably reduced in rats that used a response strategy late in training (as compared with hippocampal expression in rats that used a place strategy early in training). In line with these data, vicarious trial and error, a behavior linked to hippocampal function, also decreased with task repetition. Although Arc mRNA expression in dorsal medial or lateral striatum alone did not correlate with training stage, the ratio of expression in the medial striatum to that in the lateral striatum was relatively high among rats that used a place strategy early in training as compared with the ratio among over-trained response rats. Altogether, these results identify specific changes in the activation of dissociated neural systems that may underlie the experience-dependent emergence of response-based automatic navigation. PMID:26976088

Fully automatic time-window selection using machine learning for global adjoint tomography

Science.gov (United States)

Chen, Y.; Hill, J.; Lei, W.; Lefebvre, M. P.; Bozdag, E.; Komatitsch, D.; Tromp, J.

2017-12-01

Selecting time windows from seismograms such that the synthetic measurements (from simulations) and measured observations are sufficiently close is indispensable in a global adjoint tomography framework. The increasing amount of seismic data collected everyday around the world demands "intelligent" algorithms for seismic window selection. While the traditional FLEXWIN algorithm can be "automatic" to some extent, it still requires both human input and human knowledge or experience, and thus is not deemed to be fully automatic. The goal of intelligent window selection is to automatically select windows based on a learnt engine that is built upon a huge number of existing windows generated through the adjoint tomography project. We have formulated the automatic window selection problem as a classification problem. All possible misfit calculation windows are classified as either usable or unusable. Given a large number of windows with a known selection mode (select or not select), we train a neural network to predict the selection mode of an arbitrary input window. Currently, the five features we extract from the windows are its cross-correlation value, cross-correlation time lag, amplitude ratio between observed and synthetic data, window length, and minimum STA/LTA value. More features can be included in the future. We use these features to characterize each window for training a multilayer perceptron neural network (MPNN). Training the MPNN is equivalent to solve a non-linear optimization problem. We use backward propagation to derive the gradient of the loss function with respect to the weighting matrices and bias vectors and use the mini-batch stochastic gradient method to iteratively optimize the MPNN. Numerical tests show that with a careful selection of the training data and a sufficient amount of training data, we are able to train a robust neural network that is capable of detecting the waveforms in an arbitrary earthquake data with negligible detection error

A swarm-trained k-nearest prototypes adaptive classifier with automatic feature selection for interval data.

Science.gov (United States)

Silva Filho, Telmo M; Souza, Renata M C R; Prudêncio, Ricardo B C

2016-08-01

Some complex data types are capable of modeling data variability and imprecision. These data types are studied in the symbolic data analysis field. One such data type is interval data, which represents ranges of values and is more versatile than classic point data for many domains. This paper proposes a new prototype-based classifier for interval data, trained by a swarm optimization method. Our work has two main contributions: a swarm method which is capable of performing both automatic selection of features and pruning of unused prototypes and a generalized weighted squared Euclidean distance for interval data. By discarding unnecessary features and prototypes, the proposed algorithm deals with typical limitations of prototype-based methods, such as the problem of prototype initialization. The proposed distance is useful for learning classes in interval datasets with different shapes, sizes and structures. When compared to other prototype-based methods, the proposed method achieves lower error rates in both synthetic and real interval datasets. Copyright © 2016 Elsevier Ltd. All rights reserved.

Support vector machine for automatic pain recognition

Science.gov (United States)

Monwar, Md Maruf; Rezaei, Siamak

2009-02-01

Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

No Effects of Non-invasive Brain Stimulation on Multiple Sessions of Object-Location-Memory Training in Healthy Older Adults.

Science.gov (United States)

Külzow, Nadine; Cavalcanti de Sousa, Angelica Vieira; Cesarz, Magda; Hanke, Julie-Marie; Günsberg, Alida; Harder, Solvejg; Koblitz, Swantje; Grittner, Ulrike; Flöel, Agnes

2017-01-01

Object-location memory (OLM) is known to decline with normal aging, a process accelerated in pathological conditions like mild cognitive impairment (MCI). In order to maintain cognitive health and to delay the transition from healthy to pathological conditions, novel strategies are being explored. Tentative evidence suggests that combining cognitive training and anodal transcranial direct current stimulation (atDCS), both reported to induce small and often inconsistent behavioral improvements, could generate larger or more consistent improvements or both, compared to each intervention alone. Here, we explored the combined efficacy of these techniques on OLM. In a subject-blind sham-controlled cross-over design 32 healthy older adults underwent a 3-day visuospatial training paired with either anodal (20 min) or sham (30 s) atDCS (1 mA, temporoparietal). Subjects were asked to learn the correct object-location pairings on a street map, shown over five learning blocks on each training day. Acquisition performance was assessed by accuracy on a given learning block in terms of percentage of correct responses. Training success (performance on last training day) and delayed memory after 1-month were analyzed by mixed model analysis and were controlled for gender, age, education, sequence of stimulation and baseline performance. Exploratory analysis of atDCS effects on within-session (online) and between-session (offline) memory performance were conducted. Moreover, transfer effects on similar trained (visuospatial) and less similar (visuo-constructive, verbal) untrained memory tasks were explored, both immediately after training, and on follow-up. We found that atDCS paired with OLM-training did not enhance success in training or performance in 1-month delayed memory or transfer tasks. In sum, this study did not support the notion that the combined atDCS-training approach improves immediate or delayed OLM in older adults. However, specifics of the experimental design, and

No Effects of Non-invasive Brain Stimulation on Multiple Sessions of Object-Location-Memory Training in Healthy Older Adults

Directory of Open Access Journals (Sweden)

Nadine Külzow

2018-01-01

Full Text Available Object-location memory (OLM is known to decline with normal aging, a process accelerated in pathological conditions like mild cognitive impairment (MCI. In order to maintain cognitive health and to delay the transition from healthy to pathological conditions, novel strategies are being explored. Tentative evidence suggests that combining cognitive training and anodal transcranial direct current stimulation (atDCS, both reported to induce small and often inconsistent behavioral improvements, could generate larger or more consistent improvements or both, compared to each intervention alone. Here, we explored the combined efficacy of these techniques on OLM. In a subject-blind sham-controlled cross-over design 32 healthy older adults underwent a 3-day visuospatial training paired with either anodal (20 min or sham (30 s atDCS (1 mA, temporoparietal. Subjects were asked to learn the correct object-location pairings on a street map, shown over five learning blocks on each training day. Acquisition performance was assessed by accuracy on a given learning block in terms of percentage of correct responses. Training success (performance on last training day and delayed memory after 1-month were analyzed by mixed model analysis and were controlled for gender, age, education, sequence of stimulation and baseline performance. Exploratory analysis of atDCS effects on within-session (online and between-session (offline memory performance were conducted. Moreover, transfer effects on similar trained (visuospatial and less similar (visuo-constructive, verbal untrained memory tasks were explored, both immediately after training, and on follow-up. We found that atDCS paired with OLM-training did not enhance success in training or performance in 1-month delayed memory or transfer tasks. In sum, this study did not support the notion that the combined atDCS-training approach improves immediate or delayed OLM in older adults. However, specifics of the experimental

Accuracy of working length determination with root ZX apex locator ...

African Journals Online (AJOL)

Jane

2011-07-18

Jul 18, 2011 ... generation of electronic apex locators (EALs), called root. ZX (J. Morita Co., Tustin, .... dentinocemento junction. Dent Items Interest, 50: 855-857. ... apex locator with an automatic compensation circuit. J. Endod. 28: 706-709.

Acquisition of automatic imitation is sensitive to sensorimotor contingency.

Science.gov (United States)

Cook, Richard; Press, Clare; Dickinson, Anthony; Heyes, Cecilia

2010-08-01

The associative sequence learning model proposes that the development of the mirror system depends on the same mechanisms of associative learning that mediate Pavlovian and instrumental conditioning. To test this model, two experiments used the reduction of automatic imitation through incompatible sensorimotor training to assess whether mirror system plasticity is sensitive to contingency (i.e., the extent to which activation of one representation predicts activation of another). In Experiment 1, residual automatic imitation was measured following incompatible training in which the action stimulus was a perfect predictor of the response (contingent) or not at all predictive of the response (noncontingent). A contingency effect was observed: There was less automatic imitation indicative of more learning in the contingent group. Experiment 2 replicated this contingency effect and showed that, as predicted by associative learning theory, it can be abolished by signaling trials in which the response occurs in the absence of an action stimulus. These findings support the view that mirror system development depends on associative learning and indicate that this learning is not purely Hebbian. If this is correct, associative learning theory could be used to explain, predict, and intervene in mirror system development.

A Machine Vision System for Automatically Grading Hardwood Lumber - (Proceedings)

Science.gov (United States)

Richard W. Conners; Tai-Hoon Cho; Chong T. Ng; Thomas H. Drayer; Joe G. Tront; Philip A. Araman; Robert L. Brisbon

1990-01-01

Any automatic system for grading hardwood lumber can conceptually be divided into two components. One of these is a machine vision system for locating and identifying grading defects. The other is an automatic grading program that accepts as input the output of the machine vision system and, based on these data, determines the grade of a board. The progress that has...

An Overview of Automaticity and Implications For Training the Thinking Process

National Research Council Canada - National Science Library

Holt, Brian

2002-01-01

...., visual search to battlefield thinking). The results of this examination suggest that automaticity can be developed using consistent rules and extensive practice that vary depending on the type of task...

Automatic detection and visualisation of MEG ripple oscillations in epilepsy

Directory of Open Access Journals (Sweden)

Nicole van Klink

2017-01-01

Full Text Available High frequency oscillations (HFOs, 80–500 Hz in invasive EEG are a biomarker for the epileptic focus. Ripples (80–250 Hz have also been identified in non-invasive MEG, yet detection is impeded by noise, their low occurrence rates, and the workload of visual analysis. We propose a method that identifies ripples in MEG through noise reduction, beamforming and automatic detection with minimal user effort. We analysed 15 min of presurgical resting-state interictal MEG data of 25 patients with epilepsy. The MEG signal-to-noise was improved by using a cross-validation signal space separation method, and by calculating ~2400 beamformer-based virtual sensors in the grey matter. Ripples in these sensors were automatically detected by an algorithm optimized for MEG. A small subset of the identified ripples was visually checked. Ripple locations were compared with MEG spike dipole locations and the resection area if available. Running the automatic detection algorithm resulted in on average 905 ripples per patient, of which on average 148 ripples were visually reviewed. Reviewing took approximately 5 min per patient, and identified ripples in 16 out of 25 patients. In 14 patients the ripple locations showed good or moderate concordance with the MEG spikes. For six out of eight patients who had surgery, the ripple locations showed concordance with the resection area: 4/5 with good outcome and 2/3 with poor outcome. Automatic ripple detection in beamformer-based virtual sensors is a feasible non-invasive tool for the identification of ripples in MEG. Our method requires minimal user effort and is easily applicable in a clinical setting.

System for automatic detection of lung nodules exhibiting growth

Science.gov (United States)

Novak, Carol L.; Shen, Hong; Odry, Benjamin L.; Ko, Jane P.; Naidich, David P.

2004-05-01

Lung nodules that exhibit growth over time are considered highly suspicious for malignancy. We present a completely automated system for detection of growing lung nodules, using initial and follow-up multi-slice CT studies. The system begins with automatic detection of lung nodules in the later CT study, generating a preliminary list of candidate nodules. Next an automatic system for registering locations in two studies matches each candidate in the later study to its corresponding position in the earlier study. Then a method for automatic segmentation of lung nodules is applied to each candidate and its matching location, and the computed volumes are compared. The output of the system is a list of nodule candidates that are new or have exhibited volumetric growth since the previous scan. In a preliminary test of 10 patients examined by two radiologists, the automatic system identified 18 candidates as growing nodules. 7 (39%) of these corresponded to validated nodules or other focal abnormalities that exhibited growth. 4 of the 7 true detections had not been identified by either of the radiologists during their initial examinations of the studies. This technique represents a powerful method of surveillance that may reduce the probability of missing subtle or early malignant disease.

Development and Comparative Study of Effects of Training Algorithms on Performance of Artificial Neural Network Based Analog and Digital Automatic Modulation Recognition

Directory of Open Access Journals (Sweden)

Jide Julius Popoola

2015-11-01

Full Text Available This paper proposes two new classifiers that automatically recognise twelve combined analog and digital modulated signals without any a priori knowledge of the modulation schemes and the modulation parameters. The classifiers are developed using pattern recognition approach. Feature keys extracted from the instantaneous amplitude, instantaneous phase and the spectrum symmetry of the simulated signals are used as inputs to the artificial neural network employed in developing the classifiers. The two developed classifiers are trained using scaled conjugate gradient (SCG and conjugate gradient (CONJGRAD training algorithms. Sample results of the two classifiers show good success recognition performance with an average overall recognition rate above 99.50% at signal-to-noise ratio (SNR value from 0 dB and above with the two training algorithms employed and an average overall recognition rate slightly above 99.00% and 96.40% respectively at - 5 dB SNR value for SCG and CONJGRAD training algorithms. The comparative performance evaluation of the two developed classifiers using the two training algorithms shows that the two training algorithms have different effects on both the response rate and efficiency of the two developed artificial neural networks classifiers. In addition, the result of the performance evaluation carried out on the overall success recognition rates between the two developed classifiers in this study using pattern recognition approach with the two training algorithms and one reported classifier in surveyed literature using decision-theoretic approach shows that the classifiers developed in this study perform favourably with regard to accuracy and performance probability as compared to classifier presented in previous study.

Automatic approach to deriving fuzzy slope positions

Science.gov (United States)

Zhu, Liang-Jun; Zhu, A.-Xing; Qin, Cheng-Zhi; Liu, Jun-Zhi

2018-03-01

Fuzzy characterization of slope positions is important for geographic modeling. Most of the existing fuzzy classification-based methods for fuzzy characterization require extensive user intervention in data preparation and parameter setting, which is tedious and time-consuming. This paper presents an automatic approach to overcoming these limitations in the prototype-based inference method for deriving fuzzy membership value (or similarity) to slope positions. The key contribution is a procedure for finding the typical locations and setting the fuzzy inference parameters for each slope position type. Instead of being determined totally by users in the prototype-based inference method, in the proposed approach the typical locations and fuzzy inference parameters for each slope position type are automatically determined by a rule set based on prior domain knowledge and the frequency distributions of topographic attributes. Furthermore, the preparation of topographic attributes (e.g., slope gradient, curvature, and relative position index) is automated, so the proposed automatic approach has only one necessary input, i.e., the gridded digital elevation model of the study area. All compute-intensive algorithms in the proposed approach were speeded up by parallel computing. Two study cases were provided to demonstrate that this approach can properly, conveniently and quickly derive the fuzzy slope positions.

Automatic cross-sectioning and monitoring system locates defects in electronic devices

Science.gov (United States)

Jacobs, G.; Slaughter, B.

1971-01-01

System consists of motorized grinding and lapping apparatus, sample holder, and electronic control circuit. Low power microscope examines device to pinpoint location of circuit defect, and monitor displays output signal when defect is located exactly.

The irace package: Iterated racing for automatic algorithm configuration

Directory of Open Access Journals (Sweden)

Manuel López-Ibáñez

2016-01-01

Full Text Available Modern optimization algorithms typically require the setting of a large number of parameters to optimize their performance. The immediate goal of automatic algorithm configuration is to find, automatically, the best parameter settings of an optimizer. Ultimately, automatic algorithm configuration has the potential to lead to new design paradigms for optimization software. The irace package is a software package that implements a number of automatic configuration procedures. In particular, it offers iterated racing procedures, which have been used successfully to automatically configure various state-of-the-art algorithms. The iterated racing procedures implemented in irace include the iterated F-race algorithm and several extensions and improvements over it. In this paper, we describe the rationale underlying the iterated racing procedures and introduce a number of recent extensions. Among these, we introduce a restart mechanism to avoid premature convergence, the use of truncated sampling distributions to handle correctly parameter bounds, and an elitist racing procedure for ensuring that the best configurations returned are also those evaluated in the highest number of training instances. We experimentally evaluate the most recent version of irace and demonstrate with a number of example applications the use and potential of irace, in particular, and automatic algorithm configuration, in general.

A Machine Vision System for Automatically Grading Hardwood Lumber - (Industrial Metrology)

Science.gov (United States)

Richard W. Conners; Tai-Hoon Cho; Chong T. Ng; Thomas T. Drayer; Philip A. Araman; Robert L. Brisbon

1992-01-01

Any automatic system for grading hardwood lumber can conceptually be divided into two components. One of these is a machine vision system for locating and identifying grading defects. The other is an automatic grading program that accepts as input the output of the machine vision system and, based on these data, determines the grade of a board. The progress that has...

Cost-benefit analysis of the ATM automatic deposit service

Directory of Open Access Journals (Sweden)

Ivica Županović

2015-03-01

Full Text Available Bankers and other financial experts have analyzed the value of automated teller machines (ATM in terms of growing consumer demand, rising costs of technology development, decreasing profitability and market share. This paper presents a step-by-step cost-benefit analysis of the ATM automatic deposit service. The first step is to determine user attitudes towards using ATM automatic deposit service by using the Technology Acceptance Model (TAM. The second step is to determine location priorities for ATMs that provide automatic deposit services using the Analytic Hierarchy Process (AHP model. The results of the previous steps enable a highly efficient application of cost-benefit analysis for evaluating costs and benefits of automatic deposit services. To understand fully the proposed procedure outside of theoretical terms, a real-world application of a case study is conducted.

49 CFR 236.401 - Automatic block signal system and interlocking standards applicable to traffic control systems.

Science.gov (United States)

2010-10-01

... TRAIN CONTROL SYSTEMS, DEVICES, AND APPLIANCES Traffic Control Systems Standards § 236.401 Automatic... 49 Transportation 4 2010-10-01 2010-10-01 false Automatic block signal system and interlocking standards applicable to traffic control systems. 236.401 Section 236.401 Transportation Other Regulations...

Acquisition of Automatic Imitation Is Sensitive to Sensorimotor Contingency

Science.gov (United States)

Cook, Richard; Press, Clare; Dickinson, Anthony; Heyes, Cecilia

2010-01-01

The associative sequence learning model proposes that the development of the mirror system depends on the same mechanisms of associative learning that mediate Pavlovian and instrumental conditioning. To test this model, two experiments used the reduction of automatic imitation through incompatible sensorimotor training to assess whether mirror…

Capacitive system detects and locates fluid leaks

Science.gov (United States)

1966-01-01

Electronic monitoring system automatically detects and locates minute leaks in seams of large fluid storage tanks and pipelines covered with thermal insulation. The system uses a capacitive tape-sensing element that is adhesively bonded over seams where fluid leaks are likely to occur.

Automatic measurement of cusps in 2.5D dental images

Science.gov (United States)

Wolf, Mattias; Paulus, Dietrich W.; Niemann, Heinrich

1996-01-01

Automatic reconstruction of occlusal surfaces of teeth is an application which might become more and more urgent due to the toxicity of amalgam. Modern dental chairside equipment is currently restricted to the production of inlays. The automatic reconstruction of the occlusal surface is presently not possible. For manufacturing an occlusal surface it is required to extract features from which it is possible to reconstruct destroyed teeth. In this paper, we demonstrate how intact upper molars can be automatically extracted in dental range and intensity images. After normalization of the 3D location, the sizes of the cusps are detected and the distances between them are calculated. In the presented approach, the detection of the upper molar is based on a knowledge-based segmentation which includes anatomic knowledge. After the segmentation of the interesting tooth the central fossa is calculated. The normalization of the spatial location is archieved by aligning the detected fossa with a reference axis. After searching the cusp tips in the range image the image is resized. The methods have been successfully tested on 60 images. The results have been compared with the results of a dentist's evaluation on a sample of 20 images. The results will be further used for automatic production of tooth inlays.

CLG for Automatic Image Segmentation

OpenAIRE

Christo Ananth; S.Santhana Priya; S.Manisha; T.Ezhil Jothi; M.S.Ramasubhaeswari

2017-01-01

This paper proposes an automatic segmentation method which effectively combines Active Contour Model, Live Wire method and Graph Cut approach (CLG). The aim of Live wire method is to provide control to the user on segmentation process during execution. Active Contour Model provides a statistical model of object shape and appearance to a new image which are built during a training phase. In the graph cut technique, each pixel is represented as a node and the distance between those nodes is rep...

Radiation-hygienic estimation of training reactors location

International Nuclear Information System (INIS)

Konstantinov, Yu.O.; Fedorin, Eh.V.

1978-01-01

The radiation exposure conditions are provided during the normal operation (excluding emergency situations) of four training pool type reactors. Radiation monitoring of the environment near the reactors do not show any increase in external irradiation or in radioactive contamination over what is considered normal radiation background in the locality. Therefore it is possible to judge the potential levels of additional exposure of the population to radiation from the reactors only by means of theoretic modeling of the radiation conditions. Tabular data on maximal levels of this additional radiation are presented, and it is concluded from these data that it is permissible to install training and research reactors up to 3000 kilowatts within large cities, including dwelling areas

Automatic Texture and Orthophoto Generation from Registered Panoramic Views

DEFF Research Database (Denmark)

Krispel, Ulrich; Evers, Henrik Leander; Tamke, Martin

2015-01-01

are automatically identified from the geometry and an image per view is created via projection. We combine methods of computer vision to train a classifier to detect the objects of interest from these orthographic views. Furthermore, these views can be used for automatic texturing of the proxy geometry....... from range data only. In order to detect these elements, we developed a method that utilizes range data and color information from high-resolution panoramic images of indoor scenes, taken at the scanners position. A proxy geometry is derived from the point clouds; orthographic views of the scene...

An automatic procedure for high-resolution earthquake locations: a case study from the TABOO near fault observatory (Northern Apennines, Italy)

Science.gov (United States)

Valoroso, Luisa; Chiaraluce, Lauro; Di Stefano, Raffaele; Latorre, Diana; Piccinini, Davide

2014-05-01

The characterization of the geometry, kinematics and rheology of fault zones by seismological data depends on our capability of accurately locate the largest number of low-magnitude seismic events. To this aim, we have been working for the past three years to develop an advanced modular earthquake location procedure able to automatically retrieve high-resolution earthquakes catalogues directly from continuous waveforms data. We use seismograms recorded at about 60 seismic stations located both at surface and at depth. The network covers an area of about 80x60 km with a mean inter-station distance of 6 km. These stations are part of a Near fault Observatory (TABOO; http://taboo.rm.ingv.it/), consisting of multi-sensor stations (seismic, geodetic, geochemical and electromagnetic). This permanent scientific infrastructure managed by the INGV is devoted to studying the earthquakes preparatory phase and the fast/slow (i.e., seismic/aseismic) deformation process active along the Alto Tiberina fault (ATF) located in the northern Apennines (Italy). The ATF is potentially one of the rare worldwide examples of active low-angle (picking procedure that provides consistently weighted P- and S-wave arrival times, P-wave first motion polarities and the maximum waveform amplitude for local magnitude calculation; iii) both linearized iterative and non-linear global-search earthquake location algorithms to compute accurate absolute locations of single-events in a 3D geological model (see Latorre et al. same session); iv) cross-correlation and double-difference location methods to compute high-resolution relative event locations. This procedure is now running off-line with a delay of 1 week to the real-time. We are now implementing this procedure to obtain high-resolution double-difference earthquake locations in real-time (DDRT). We show locations of ~30k low-magnitude earthquakes recorded during the past 4 years (2010-2013) of network operation, reaching a completeness magnitude of

Automatic Detection of Electric Power Troubles (ADEPT)

Science.gov (United States)

Wang, Caroline; Zeanah, Hugh; Anderson, Audie; Patrick, Clint; Brady, Mike; Ford, Donnie

1988-11-01

Automatic Detection of Electric Power Troubles (A DEPT) is an expert system that integrates knowledge from three different suppliers to offer an advanced fault-detection system. It is designed for two modes of operation: real time fault isolation and simulated modeling. Real time fault isolation of components is accomplished on a power system breadboard through the Fault Isolation Expert System (FIES II) interface with a rule system developed in-house. Faults are quickly detected and displayed and the rules and chain of reasoning optionally provided on a laser printer. This system consists of a simulated space station power module using direct-current power supplies for solar arrays on three power buses. For tests of the system's ablilty to locate faults inserted via switches, loads are configured by an INTEL microcomputer and the Symbolics artificial intelligence development system. As these loads are resistive in nature, Ohm's Law is used as the basis for rules by which faults are located. The three-bus system can correct faults automatically where there is a surplus of power available on any of the three buses. Techniques developed and used can be applied readily to other control systems requiring rapid intelligent decisions. Simulated modeling, used for theoretical studies, is implemented using a modified version of Kennedy Space Center's KATE (Knowledge-Based Automatic Test Equipment), FIES II windowing, and an ADEPT knowledge base.

Training approach-avoidance of smiling faces affects emotional vulnerability in socially anxious individuals

Science.gov (United States)

Rinck, Mike; Telli, Sibel; Kampmann, Isabel L.; Woud, Marcella L.; Kerstholt, Merel; te Velthuis, Sarai; Wittkowski, Matthias; Becker, Eni S.

2013-01-01

Previous research revealed an automatic behavioral bias in high socially anxious individuals (HSAs): although their explicit evaluations of smiling faces are positive, they show automatic avoidance of these faces. This is reflected by faster pushing than pulling of smiling faces in an Approach-Avoidance Task (AAT; Heuer et al., 2007). The current study addressed the causal role of this avoidance bias for social anxiety. To this end, we used the AAT to train HSAs, either to approach smiling faces or to avoid them. We examined whether such an AAT training could change HSAs' automatic avoidance tendencies, and if yes, whether AAT effects would generalize to a new approach task with new facial stimuli, and to mood and anxiety in a social threat situation (a video-recorded self-presentation). We found that HSAs trained to approach smiling faces did indeed approach female faces faster after the training than HSAs trained to avoid smiling faces. Moreover, approach-faces training reduced emotional vulnerability: it led to more positive mood and lower anxiety after the self-presentation than avoid-faces training. These results suggest that automatic approach-avoidance tendencies have a causal role in social anxiety, and that they can be modified by a simple computerized training. This may open new avenues in the therapy of social phobia. PMID:23970862

Training Approach-Avoidance of Smiling Faces Affects Emotional Vulnerability in Socially Anxious Individuals

Directory of Open Access Journals (Sweden)

Mike eRinck

2013-08-01

Full Text Available Previous research revealed an automatic behavioral bias in high socially anxious individuals (HSAs: Although their explicit evaluations of smiling faces are positive, they show automatic avoidance of these faces. This is reflected by faster pushing than pulling of smiling faces in an Approach-Avoidance Task (AAT; Heuer, Rinck, & Becker, 2007. The current study addressed the causal role of this avoidance bias for social anxiety. To this end, we used the AAT to train HSAs, either to approach smiling faces or to avoid them. We examined whether such an AAT training could change HSAs’ automatic avoidance tendencies, and if yes, whether AAT effects would generalize to a new approach task with new facial stimuli, and to mood and anxiety in a social threat situation (a video-recorded self-presentation. We found that HSAs trained to approach smiling faces did indeed approach female faces faster after the training than HSAs trained to avoid smiling faces. Moreover, approach-faces training reduced emotional vulnerability: It led to more positive mood and lower anxiety after the self-presentation than avoid-faces training. These results suggest that automatic approach-avoidance tendencies have a causal role in social anxiety, and that they can be modified by a simple computerized training. This may open new avenues in the therapy of social phobia.

Nonlinear Synchronization for Automatic Learning of 3D Pose Variability in Human Motion Sequences

Directory of Open Access Journals (Sweden)

Mozerov M

2010-01-01

Full Text Available A dense matching algorithm that solves the problem of synchronizing prerecorded human motion sequences, which show different speeds and accelerations, is proposed. The approach is based on minimization of MRF energy and solves the problem by using Dynamic Programming. Additionally, an optimal sequence is automatically selected from the input dataset to be a time-scale pattern for all other sequences. The paper utilizes an action specific model which automatically learns the variability of 3D human postures observed in a set of training sequences. The model is trained using the public CMU motion capture dataset for the walking action, and a mean walking performance is automatically learnt. Additionally, statistics about the observed variability of the postures and motion direction are also computed at each time step. The synchronized motion sequences are used to learn a model of human motion for action recognition and full-body tracking purposes.

Effects of Spatial Ability, Gender Differences, and Pictorial Training on Children Using 2-D and 3-D Environments to Recall Landmark Locations from Memory

Science.gov (United States)

Kopcha, Theodore J.; Otumfuor, Beryl A.; Wang, Lu

2015-01-01

This study examines the effects of spatial ability, gender differences, and pictorial training on fourth grade students' ability to recall landmark locations from memory. Ninety-six students used Google Earth over a 3-week period to locate landmarks (3-D) and mark their location on a 2-D topographical map. Analysis of covariance on posttest scores…

Towards Autonomous Agriculture: Automatic Ground Detection Using Trinocular Stereovision

Directory of Open Access Journals (Sweden)

Annalisa Milella

2012-09-01

Full Text Available Autonomous driving is a challenging problem, particularly when the domain is unstructured, as in an outdoor agricultural setting. Thus, advanced perception systems are primarily required to sense and understand the surrounding environment recognizing artificial and natural structures, topology, vegetation and paths. In this paper, a self-learning framework is proposed to automatically train a ground classifier for scene interpretation and autonomous navigation based on multi-baseline stereovision. The use of rich 3D data is emphasized where the sensor output includes range and color information of the surrounding environment. Two distinct classifiers are presented, one based on geometric data that can detect the broad class of ground and one based on color data that can further segment ground into subclasses. The geometry-based classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the system automatically learns to associate geometric appearance of 3D stereo-generated data with class labels. Then, it makes predictions based on past observations. It serves as well to provide training labels to the color-based classifier. Once trained, the color-based classifier is able to recognize similar terrain classes in stereo imagery. The system is continuously updated online using the latest stereo readings, thus making it feasible for long range and long duration navigation, over changing environments. Experimental results, obtained with a tractor test platform operating in a rural environment, are presented to validate this approach, showing an average classification precision and recall of 91.0% and 77.3%, respectively.

Study on a Footwork Training and Testing System

Directory of Open Access Journals (Sweden)

Qi Hu

2018-02-01

Full Text Available In the sport science fields, for a long time there are various attempts to explore more advanced technology in order to collect kinds of information concerned during athletes training and matches. In the paper, a footwork training and testing system has been developed by adopting the advanced technology of Wireless Sensor Network (WSN. The system is comprised of some wireless senor nodes and gateways, system control software and so on. By means of the system, the daily footwork training methods and modes will be simulated to automatically guide the training of the athletes, at the same time the training data concerned will be automatically recorded, including moving velocity, moving frequency and success average, moving exercise duration and so on, and it is facilitate to evaluate digitally the training and testing effects for coaches and athletes. The system will bring about an auxiliary means in sport science training and research, make coaches and researchers have more options for the technical and information forms, and provide the technology foundation for synchronizing and intermingling the training and testing smoothly.

Automatic classification of time-variable X-ray sources

Energy Technology Data Exchange (ETDEWEB)

Lo, Kitty K.; Farrell, Sean; Murphy, Tara; Gaensler, B. M. [Sydney Institute for Astronomy, School of Physics, The University of Sydney, Sydney, NSW 2006 (Australia)

2014-05-01

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

Automatic classification of time-variable X-ray sources

International Nuclear Information System (INIS)

Lo, Kitty K.; Farrell, Sean; Murphy, Tara; Gaensler, B. M.

2014-01-01

To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

System for the quality assurance of personnel training programs

International Nuclear Information System (INIS)

Rjona, Orison; Venegas, Maria del C.; Rodriguez, Lazaro; Lopez, Miguel A.; Armenteros, Ana L.

1999-01-01

In this work are described the fundamental possibilities and characteristics of a software that allows to carry out the management and automatic evaluation of all data gotten during jobs analysis and design, development, implementation and evaluation of personnel training programs of nuclear and radioactive installations and risk industries. The system that is introduced, GESAT, proportion a tool of centralized managerial control of training data and the obtaining of the quality objectives of each installation in the training of their personnel. GESAT includes all phases of SAT method (Systematic Approach to Training). It constitutes the necessary practical support for the elaboration, implementation and evaluation of training programs, allowing the establishment of restrictions and controls and avoiding inconsistencies in the process. It offers the possibility of automatic evaluation that identify fundamental deficiencies in the planning and implementation of training programs. This evaluation facilitates the systematic feed back and the continuous improvement of the training programs.(author)

ATIPS: Automatic Travel Itinerary Planning System for Domestic Areas

Science.gov (United States)

2016-01-01

Leisure travel has become a topic of great interest to Taiwanese residents in recent years. Most residents expect to be able to relax on a vacation during the holidays; however, the complicated procedure of travel itinerary planning is often discouraging and leads them to abandon the idea of traveling. In this paper, we design an automatic travel itinerary planning system for the domestic area (ATIPS) using an algorithm to automatically plan a domestic travel itinerary based on user intentions that allows users to minimize the process of trip planning. Simply by entering the travel time, the departure point, and the destination location, the system can automatically generate a travel itinerary. According to the results of the experiments, 70% of users were satisfied with the result of our system, and 82% of users were satisfied with the automatic user preference learning mechanism of ATIPS. Our algorithm also provides a framework for substituting modules or weights and offers a new method for travel planning. PMID:26839529

ATIPS: Automatic Travel Itinerary Planning System for Domestic Areas

Directory of Open Access Journals (Sweden)

Hsien-Tsung Chang

2016-01-01

Full Text Available Leisure travel has become a topic of great interest to Taiwanese residents in recent years. Most residents expect to be able to relax on a vacation during the holidays; however, the complicated procedure of travel itinerary planning is often discouraging and leads them to abandon the idea of traveling. In this paper, we design an automatic travel itinerary planning system for the domestic area (ATIPS using an algorithm to automatically plan a domestic travel itinerary based on user intentions that allows users to minimize the process of trip planning. Simply by entering the travel time, the departure point, and the destination location, the system can automatically generate a travel itinerary. According to the results of the experiments, 70% of users were satisfied with the result of our system, and 82% of users were satisfied with the automatic user preference learning mechanism of ATIPS. Our algorithm also provides a framework for substituting modules or weights and offers a new method for travel planning.

ATIPS: Automatic Travel Itinerary Planning System for Domestic Areas.

Science.gov (United States)

Chang, Hsien-Tsung; Chang, Yi-Ming; Tsai, Meng-Tze

2016-01-01

Leisure travel has become a topic of great interest to Taiwanese residents in recent years. Most residents expect to be able to relax on a vacation during the holidays; however, the complicated procedure of travel itinerary planning is often discouraging and leads them to abandon the idea of traveling. In this paper, we design an automatic travel itinerary planning system for the domestic area (ATIPS) using an algorithm to automatically plan a domestic travel itinerary based on user intentions that allows users to minimize the process of trip planning. Simply by entering the travel time, the departure point, and the destination location, the system can automatically generate a travel itinerary. According to the results of the experiments, 70% of users were satisfied with the result of our system, and 82% of users were satisfied with the automatic user preference learning mechanism of ATIPS. Our algorithm also provides a framework for substituting modules or weights and offers a new method for travel planning.

Automated Management of Exercise Intervention at the Point of Care: Application of a Web-Based Leg Training System.

Science.gov (United States)

Dedov, Vadim N; Dedova, Irina V

2015-11-23

Recent advances in information and communication technology have prompted development of Web-based health tools to promote physical activity, the key component of cardiac rehabilitation and chronic disease management. Mobile apps can facilitate behavioral changes and help in exercise monitoring, although actual training usually takes place away from the point of care in specialized gyms or outdoors. Daily participation in conventional physical activities is expensive, time consuming, and mostly relies on self-management abilities of patients who are typically aged, overweight, and unfit. Facilitation of sustained exercise training at the point of care might improve patient engagement in cardiac rehabilitation. In this study we aimed to test the feasibility of execution and automatic monitoring of several exercise regimens on-site using a Web-enabled leg training system. The MedExercise leg rehabilitation machine was equipped with wireless temperature sensors in order to monitor its usage by the rise of temperature in the resistance unit (Δt°). Personal electronic devices such as laptop computers were fitted with wireless gateways and relevant software was installed to monitor the usage of training machines. Cloud-based software allowed monitoring of participant training over the Internet. Seven healthy participants applied the system at various locations with training protocols typically used in cardiac rehabilitation. The heart rates were measured by fingertip pulse oximeters. Exercising in home chairs, in bed, and under an office desk was made feasible and resulted in an intensity-dependent increase of participants' heart rates and Δt° in training machine temperatures. Participants self-controlled their activities on smart devices, while a supervisor monitored them over the Internet. Individual Δt° reached during 30 minutes of moderate-intensity continuous training averaged 7.8°C (SD 1.6). These Δt° were used as personalized daily doses of exercise with

Expectation-Maximization Tensor Factorization for Practical Location Privacy Attacks

Directory of Open Access Journals (Sweden)

Murakami Takao

2017-10-01

Full Text Available Location privacy attacks based on a Markov chain model have been widely studied to de-anonymize or de-obfuscate mobility traces. An adversary can perform various kinds of location privacy attacks using a personalized transition matrix, which is trained for each target user. However, the amount of training data available to the adversary can be very small, since many users do not disclose much location information in their daily lives. In addition, many locations can be missing from the training traces, since many users do not disclose their locations continuously but rather sporadically. In this paper, we show that the Markov chain model can be a threat even in this realistic situation. Specifically, we focus on a training phase (i.e. mobility profile building phase and propose Expectation-Maximization Tensor Factorization (EMTF, which alternates between computing a distribution of missing locations (E-step and computing personalized transition matrices via tensor factorization (M-step. Since the time complexity of EMTF is exponential in the number of missing locations, we propose two approximate learning methods, one of which uses the Viterbi algorithm while the other uses the Forward Filtering Backward Sampling (FFBS algorithm. We apply our learning methods to a de-anonymization attack and a localization attack, and evaluate them using three real datasets. The results show that our learning methods significantly outperform a random guess, even when there is only one training trace composed of 10 locations per user, and each location is missing with probability 80% (i.e. even when users hardly disclose two temporally-continuous locations.

Using Face Recognition in the Automatic Door Access Control in a Secured Room

Directory of Open Access Journals (Sweden)

Gheorghe Gilca

2017-06-01

Full Text Available The aim of this paper is to help users improve the door security of sensitive locations by using face detection and recognition. This paper is comprised mainly of three subsystems: face detection, face recognition and automatic door access control. The door will open automatically for the known person due to the command of the microcontroller.

Retraining automatic action-tendencies to approach alcohol in hazardous drinkers

NARCIS (Netherlands)

Wiers, R.W.H.J.; Rinck, M.; Kordts, R.; Houben, K.; Strack, F.

2010-01-01

The main aim of this study was to test whether automatic action-tendencies to approach alcohol can be modified, and whether this affects drinking behaviour. Design and participants - Forty-two hazardous drinkers were assigned randomly to a condition in which they were implicitly trained to avoid or

Retraining automatic action-tendencies to approach alcohol in hazardous drinkers

NARCIS (Netherlands)

Wiers, R.W.; Rinck, M.; Kordts, R.; Houben, K.; Strack, F.

2010-01-01

Aims: The main aim of this study was to test whether automatic action-tendencies to approach alcohol can be modified, and whether this affects drinking behaviour. Design and participants: Forty-two hazardous drinkers were assigned randomly to a condition in which they were implicitly trained to

Automatic targeting of plasma spray gun

Science.gov (United States)

Abbatiello, Leonard A.; Neal, Richard E.

1978-01-01

A means for monitoring the material portion in the flame of a plasma spray gun during spraying operations is provided. A collimated detector, sensitive to certain wavelengths of light emission, is used to locate the centroid of the material with each pass of the gun. The response from the detector is then relayed to the gun controller to be used to automatically realign the gun.

Automatic targeting of plasma spray gun

International Nuclear Information System (INIS)

Abbatiello, L.A.; Neal, R.E.

1978-01-01

A means for monitoring the material portion in the flame of a plasma spray gun during spraying operations is described. A collimated detector, sensitive to certain wavelengths of light emission, is used to locate the centroid of the material with each pass of the gun. The response from the detector is then relayed to the gun controller to be used to automatically realign the gun

Automatic failure identification of the nuclear power plant pellet fuel

International Nuclear Information System (INIS)

Oliveira, Adriano Fortunato de

2010-01-01

This paper proposed the development of an automatic technique for evaluating defects to help in the stage of fabrication of fuel elements. Was produced an intelligent image analysis for automatic recognition of defects in uranium pellets. Therefore, an Artificial Neural Network (ANN) was trained using segments of histograms of pellets, containing examples of both normal (no fault) and of defectives pellets (with major defects normally found). The images of the pellets were segmented into 11 shares. Histograms were made of these segments and trained the ANN. Besides automating the process, the system was able to obtain this classification accuracy of 98.33%. Although this percentage represents a significant advance ever in the quality control process, the use of more advanced techniques of photography and lighting will reduce it to insignificant levels with low cost. Technologically, the method developed, should it ever be implemented, will add substantial value in terms of process quality control and production outages in relation to domestic manufacturing of nuclear fuel. (author)

Chocolate equals stop. Chocolate-specific inhibition training reduces chocolate intake and go associations with chocolate.

Science.gov (United States)

Houben, Katrijn; Jansen, Anita

2015-04-01

Earlier research has demonstrated that food-specific inhibition training wherein food cues are repeatedly and consistently mapped onto stop signals decreases food intake and bodyweight. The mechanisms underlying these training effects, however, remain unclear. It has been suggested that consistently pairing stimuli with stop signals induces automatic stop associations with those stimuli, thereby facilitating automatic, bottom-up inhibition. This study examined this hypothesis with respect to food-inhibition training. Participants performed a training that consistently paired chocolate with no go cues (chocolate/no-go) or with go cues (chocolate/go). Following training, we measured automatic associations between chocolate and stop versus go, as well as food intake and desire to eat. As expected, food that was consistently mapped onto stopping was indeed more associated with stopping versus going afterwards. In replication of previous results, participants in the no-go condition also showed less desire to eat and reduced food intake relative to the go condition. Together these findings support the idea that food-specific inhibition training prompts the development of automatic inhibition associations, which subsequently facilitate inhibitory control over unwanted food-related urges. Copyright © 2015 Elsevier Ltd. All rights reserved.

Estimating cost-effectiveness of mass cardiopulmonary resuscitation training strategies to improve survival from cardiac arrest in private locations.

Science.gov (United States)

Swor, Robert; Compton, Scott

2004-01-01

Most cardiopulmonary resuscitation (CPR) trainees are young, and most cardiac arrests occur in private residences witnessed by older individuals. To estimate the cost-effectiveness of a CPR training program targeted at citizens over the age of 50 years compared with that of current nontargeted public CPR training. A model was developed using cardiac arrest and known demographic data from a single suburban zip code (population 36,325) including: local data (1997-1999) regarding cardiac arrest locations (public vs. private); incremental survival with CPR (historical survival rate 7.8%, adjusted odds ratio for CPR 2.0); arrest bystander demographics obtained from bystander telephone interviews; zip code demographics regarding population age and distribution; and 12.50 dollars per student for the cost of CPR training. Published rates of CPR training programs by age were used to estimate the numbers typically trained. Several assumptions were made: 1) there would be one bystander per. arrest; 2) the bystander would always perform CPR if trained; 3) cardiac arrest would be evenly distributed in the population; and 4) CPR training for a proportion of the population would proportionally increase CPR provision. Rates of arrest, bystanders by age, number of CPR trainees needed to result in increased arrest survival, and training cost per life saved for a one-year study period were calculated. There were 24.3 cardiac arrests per year, with 21.9 (90%) occurring in homes. In 66.5% of the home arrests, the bystander was more than 50 years old. To yield one additional survivor using the current CPR training strategy, 12,306 people needed to be trained (3,510 bystanders aged 50 years), which resulted in CPR provision to 7.14 additional patients. The training cost per life saved for a bystander aged 50 years was 785,040 dollars. Using a strategy of training only those cost of 53,383 dollars per life saved. Using these assumptions, current CPR training strategy is not a cost

Evaluating automatic laughter segmentation in meetings using acoustic and acoustic-phonetic features

NARCIS (Netherlands)

Truong, K.P.; Leeuwen, D.A. van

2007-01-01

In this study, we investigated automatic laughter segmentation in meetings. We first performed laughterspeech discrimination experiments with traditional spectral features and subsequently used acousticphonetic features. In segmentation, we used Gaussian Mixture Models that were trained with

Detection of infarct lesions from single MRI modality using inconsistency between voxel intensity and spatial location--a 3-D automatic approach.

Science.gov (United States)

Shen, Shan; Szameitat, André J; Sterr, Annette

2008-07-01

Detection of infarct lesions using traditional segmentation methods is always problematic due to intensity similarity between lesions and normal tissues, so that multispectral MRI modalities were often employed for this purpose. However, the high costs of MRI scan and the severity of patient conditions restrict the collection of multiple images. Therefore, in this paper, a new 3-D automatic lesion detection approach was proposed, which required only a single type of anatomical MRI scan. It was developed on a theory that, when lesions were present, the voxel-intensity-based segmentation and the spatial-location-based tissue distribution should be inconsistent in the regions of lesions. The degree of this inconsistency was calculated, which indicated the likelihood of tissue abnormality. Lesions were identified when the inconsistency exceeded a defined threshold. In this approach, the intensity-based segmentation was implemented by the conventional fuzzy c-mean (FCM) algorithm, while the spatial location of tissues was provided by prior tissue probability maps. The use of simulated MRI lesions allowed us to quantitatively evaluate the performance of the proposed method, as the size and location of lesions were prespecified. The results showed that our method effectively detected lesions with 40-80% signal reduction compared to normal tissues (similarity index > 0.7). The capability of the proposed method in practice was also demonstrated on real infarct lesions from 15 stroke patients, where the lesions detected were in broad agreement with true lesions. Furthermore, a comparison to a statistical segmentation approach presented in the literature suggested that our 3-D lesion detection approach was more reliable. Future work will focus on adapting the current method to multiple sclerosis lesion detection.

Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

Science.gov (United States)

Wang, Jun Yi; Ngo, Michael M; Hessl, David; Hagerman, Randi J; Rivera, Susan M

2016-01-01

Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as

Automatic gamma radiation scanning device and feed mechanism for plural sample holders

International Nuclear Information System (INIS)

Byrd, W.J.

1976-01-01

Apparatus is disclosed for measuring the level of gamma radiation contained in a plurality of biological samples which are located on the fibrous sheet member carried by a sample holder. The apparatus is adapted to count the radiation level of the number of closely spaced samples located in rows and columns on the sheet by automatically sequencing through the individual samples within the rows and to advance the holder to bring successive rows into proximity with the detector. The detector is moved from sample to sample within the rows, although a number of detectors can be employed. A plurality of sample holders are automatically advanced to the detector. 25 claims, 5 drawing figures

Radiation dosimetry by automatic image analysis of dicentric chromosomes

International Nuclear Information System (INIS)

Bayley, R.; Carothers, A.; Farrow, S.; Gordon, J.; Ji, L.; Piper, J.; Rutovitz, D.; Stark, M.; Chen, X.; Wald, N.; Pittsburgh Univ., PA

1991-01-01

A system for scoring dicentric chromosomes by image analysis comprised fully automatic location of mitotic cells, automatic retrieval, focus and digitisation at high resolution, automatic rejection of nuclei and debris and detection and segmentation of chromosome clusters, automatic centromere location, and subsequent rapid interactive visual review of potential dicentric chromosomes to confirm positives and reject false positives. A calibration set of about 15000 cells was used to establish the quadratic dose response for 60 Co γ-irradiation. The dose-response function parameters were established by a maximum likelihood technique, and confidence limits in the dose response and in the corresponding inverse curve, of estimated dose for observed dicentric frequency, were established by Monte Carlo techniques. The system was validated in a blind trial by analysing a test comprising a total of about 8000 cells irradiated to 1 of 10 dose levels, and estimating the doses from the observed dicentric frequency. There was a close correspondence between the estimated and true doses. The overall sensitivity of the system in terms of the proportion of the total population of dicentrics present in the cells analysed that were detected by the system was measured to be about 40%. This implies that about 2.5 times more cells must be analysed by machine than by visual analysis. Taking this factor into account, the measured review time and false positive rates imply that analysis by the system of sufficient cells to provide the equivalent of a visual analysis of 500 cells would require about 1 h for operator review. (author). 20 refs.; 4 figs.; 5 tabs

Automatic categorization of diverse experimental information in the bioscience literature.

Science.gov (United States)

Fang, Ruihua; Schindelman, Gary; Van Auken, Kimberly; Fernandes, Jolene; Chen, Wen; Wang, Xiaodong; Davis, Paul; Tuli, Mary Ann; Marygold, Steven J; Millburn, Gillian; Matthews, Beverley; Zhang, Haiyan; Brown, Nick; Gelbart, William M; Sternberg, Paul W

2012-01-26

Curation of information from bioscience literature into biological knowledge databases is a crucial way of capturing experimental information in a computable form. During the biocuration process, a critical first step is to identify from all published literature the papers that contain results for a specific data type the curator is interested in annotating. This step normally requires curators to manually examine many papers to ascertain which few contain information of interest and thus, is usually time consuming. We developed an automatic method for identifying papers containing these curation data types among a large pool of published scientific papers based on the machine learning method Support Vector Machine (SVM). This classification system is completely automatic and can be readily applied to diverse experimental data types. It has been in use in production for automatic categorization of 10 different experimental datatypes in the biocuration process at WormBase for the past two years and it is in the process of being adopted in the biocuration process at FlyBase and the Saccharomyces Genome Database (SGD). We anticipate that this method can be readily adopted by various databases in the biocuration community and thereby greatly reducing time spent on an otherwise laborious and demanding task. We also developed a simple, readily automated procedure to utilize training papers of similar data types from different bodies of literature such as C. elegans and D. melanogaster to identify papers with any of these data types for a single database. This approach has great significance because for some data types, especially those of low occurrence, a single corpus often does not have enough training papers to achieve satisfactory performance. We successfully tested the method on ten data types from WormBase, fifteen data types from FlyBase and three data types from Mouse Genomics Informatics (MGI). It is being used in the curation work flow at WormBase for

Declarative Terrain Modeling for Military Training Games

Directory of Open Access Journals (Sweden)

Ruben M. Smelik

2010-01-01

Full Text Available Military training instructors increasingly often employ computer games to train soldiers in all sorts of skills and tactics. One of the difficulties instructors face when using games as a training tool is the creation of suitable content, including scenarios, entities, and corresponding terrain models. Terrain plays a key role in many military training games, as for example, in our case game Tactical Air Defense. However, current manual terrain editors are both too complex and too time-consuming to be useful for instructors; automatic terrain generation methods show a lot of potential, but still lack user control and intuitive editing capabilities. We present a novel way for instructors to model terrain for their training games: instead of constructing a terrain model using complex modeling tools, instructors can declare the required properties of their terrain using an advanced sketching interface. Our framework integrates terrain generation methods and manages dependencies between terrain features in order to automatically create a complete 3D terrain model that matches the sketch. With our framework, instructors can easily design a large variety of terrain models that meet their training requirements.

Small-Scale Helicopter Automatic Autorotation : Modeling, Guidance, and Control

NARCIS (Netherlands)

Taamallah, S.

2015-01-01

Our research objective consists in developing a, model-based, automatic safety recovery system, for a small-scale helicopter Unmanned Aerial Vehicle (UAV) in autorotation, i.e. an engine OFF flight condition, that safely flies and lands the helicopter to a pre-specified ground location. In pursuit

Automatic patient respiration failure detection system with wireless transmission

Science.gov (United States)

Dimeff, J.; Pope, J. M.

1968-01-01

Automatic respiration failure detection system detects respiration failure in patients with a surgically implanted tracheostomy tube, and actuates an audible and/or visual alarm. The system incorporates a miniature radio transmitter so that the patient is unencumbered by wires yet can be monitored from a remote location.

Auditory signal design for automatic number plate recognition system

NARCIS (Netherlands)

Heydra, C.G.; Jansen, R.J.; Van Egmond, R.

2014-01-01

This paper focuses on the design of an auditory signal for the Automatic Number Plate Recognition system of Dutch national police. The auditory signal is designed to alert police officers of suspicious cars in their proximity, communicating priority level and location of the suspicious car and

Techniques for Automatic Creation of Terrain Databases for Training and Mission Preparation

NARCIS (Netherlands)

Kuijper, F.; Son, R. van; Meurs, F. van; Smelik, R.M.; Kraker, J.K. de

2010-01-01

In the support of defense agencies and civil authorities TNO runs a research program that strives after automatic generation of terrain databases for a variety of simulation applications. Earlier papers by TNO at the IMAGE conference have reported in-depth on specific projects within this program.

Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode

Directory of Open Access Journals (Sweden)

Tao Ye

2018-06-01

Full Text Available Many accidents happen under shunting mode when the speed of a train is below 45 km/h. In this mode, train attendants observe the railway condition ahead using the traditional manual method and tell the observation results to the driver in order to avoid danger. To address this problem, an automatic object detection system based on convolutional neural network (CNN is proposed to detect objects ahead in shunting mode, which is called Feature Fusion Refine neural network (FR-Net. It consists of three connected modules, i.e., the depthwise-pointwise convolution, the coarse detection module, and the object detection module. Depth-wise-pointwise convolutions are used to improve the detection in real time. The coarse detection module coarsely refine the locations and sizes of prior anchors to provide better initialization for the subsequent module and also reduces search space for the classification, whereas the object detection module aims to regress accurate object locations and predict the class labels for the prior anchors. The experimental results on the railway traffic dataset show that FR-Net achieves 0.8953 mAP with 72.3 FPS performance on a machine with a GeForce GTX1080Ti with the input size of 320 × 320 pixels. The results imply that FR-Net takes a good tradeoff both on effectiveness and real time performance. The proposed method can meet the needs of practical application in shunting mode.

Signal Compression in Automatic Ultrasonic testing of Rails

Directory of Open Access Journals (Sweden)

Tomasz Ciszewski

2007-01-01

Full Text Available Full recording of the most important information carried by the ultrasonic signals allows realizing statistical analysis of measurement data. Statistical analysis of the results gathered during automatic ultrasonic tests gives data which lead, together with use of features of measuring method, differential lossy coding and traditional method of lossless data compression (Huffman’s coding, dictionary coding, to a comprehensive, efficient data compression algorithm. The subject of the article is to present the algorithm and the benefits got by using it in comparison to alternative compression methods. Storage of large amount  of data allows to create an electronic catalogue of ultrasonic defects. If it is created, the future qualification system training in the new solutions of the automat for test in rails will be possible.

Antares automatic beam alignment system

International Nuclear Information System (INIS)

Appert, Q.; Swann, T.; Sweatt, W.; Saxman, A.

1980-01-01

Antares is a 24-beam-line CO 2 laser system for controlled fusion research, under construction at Los Alamos Scientific Laboratory (LASL). Rapid automatic alignment of this system is required prior to each experiment shot. The alignment requirements, operational constraints, and a developed prototype system are discussed. A visible-wavelength alignment technique is employed that uses a telescope/TV system to view point light sources appropriately located down the beamline. Auto-alignment is accomplished by means of a video centroid tracker, which determines the off-axis error of the point sources. The error is nulled by computer-driven, movable mirrors in a closed-loop system. The light sources are fiber-optic terminations located at key points in the optics path, primarily at the center of large copper mirrors, and remotely illuminated to reduce heating effects

Automatic Imitation

Science.gov (United States)

Heyes, Cecilia

2011-01-01

"Automatic imitation" is a type of stimulus-response compatibility effect in which the topographical features of task-irrelevant action stimuli facilitate similar, and interfere with dissimilar, responses. This article reviews behavioral, neurophysiological, and neuroimaging research on automatic imitation, asking in what sense it is "automatic"…

Sample Selection for Training Cascade Detectors

OpenAIRE

V?llez, Noelia; Deniz, Oscar; Bueno, Gloria

2015-01-01

Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represent anything except the object of interest. In this respect, the negative set typically contains orders of magnitude more images than the positive set. However, imbalanced training databases lead to biased classifiers. In this paper, we focus our a...

An evaluation of automatic coronary artery calcium scoring methods with cardiac CT using the orCaScore framework.

Science.gov (United States)

Wolterink, Jelmer M; Leiner, Tim; de Vos, Bob D; Coatrieux, Jean-Louis; Kelm, B Michael; Kondo, Satoshi; Salgado, Rodrigo A; Shahzad, Rahil; Shu, Huazhong; Snoeren, Miranda; Takx, Richard A P; van Vliet, Lucas J; van Walsum, Theo; Willems, Tineke P; Yang, Guanyu; Zheng, Yefeng; Viergever, Max A; Išgum, Ivana

2016-05-01

The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD) events. In clinical practice, CAC is manually identified and automatically quantified in cardiac CT using commercially available software. This is a tedious and time-consuming process in large-scale studies. Therefore, a number of automatic methods that require no interaction and semiautomatic methods that require very limited interaction for the identification of CAC in cardiac CT have been proposed. Thus far, a comparison of their performance has been lacking. The objective of this study was to perform an independent evaluation of (semi)automatic methods for CAC scoring in cardiac CT using a publicly available standardized framework. Cardiac CT exams of 72 patients distributed over four CVD risk categories were provided for (semi)automatic CAC scoring. Each exam consisted of a noncontrast-enhanced calcium scoring CT (CSCT) and a corresponding coronary CT angiography (CCTA) scan. The exams were acquired in four different hospitals using state-of-the-art equipment from four major CT scanner vendors. The data were divided into 32 training exams and 40 test exams. A reference standard for CAC in CSCT was defined by consensus of two experts following a clinical protocol. The framework organizers evaluated the performance of (semi)automatic methods on test CSCT scans, per lesion, artery, and patient. Five (semi)automatic methods were evaluated. Four methods used both CSCT and CCTA to identify CAC, and one method used only CSCT. The evaluated methods correctly detected between 52% and 94% of CAC lesions with positive predictive values between 65% and 96%. Lesions in distal coronary arteries were most commonly missed and aortic calcifications close to the coronary ostia were the most common false positive errors. The majority (between 88% and 98%) of correctly identified CAC lesions were assigned to the correct artery. Linearly weighted Cohen's kappa

Child vocalization composition as discriminant information for automatic autism detection.

Science.gov (United States)

Xu, Dongxin; Gilkerson, Jill; Richards, Jeffrey; Yapanel, Umit; Gray, Sharmi

2009-01-01

Early identification is crucial for young children with autism to access early intervention. The existing screens require either a parent-report questionnaire and/or direct observation by a trained practitioner. Although an automatic tool would benefit parents, clinicians and children, there is no automatic screening tool in clinical use. This study reports a fully automatic mechanism for autism detection/screening for young children. This is a direct extension of the LENA (Language ENvironment Analysis) system, which utilizes speech signal processing technology to analyze and monitor a child's natural language environment and the vocalizations/speech of the child. It is discovered that child vocalization composition contains rich discriminant information for autism detection. By applying pattern recognition and machine learning approaches to child vocalization composition data, accuracy rates of 85% to 90% in cross-validation tests for autism detection have been achieved at the equal-error-rate (EER) point on a data set with 34 children with autism, 30 language delayed children and 76 typically developing children. Due to its easy and automatic procedure, it is believed that this new tool can serve a significant role in childhood autism screening, especially in regards to population-based or universal screening.

[Job-sharing in postgraduate medical training: not automatically a nice duet].

Science.gov (United States)

Levi, M

2004-02-14

Part-time work is an increasingly common phenomenon amongst medical professionals. Therefore many postgraduate training programmes for resident physicians also offer the opportunity of part-time work, which is usually in the form of an 80% full-time equivalent post. A new initiative has created the possibility of job-sharing, in which each of the participants fulfills 50% of one training position. Although the experience of the participants is mainly positive, it is unclear how this development will impact the quality of patient care and how it will affect the fulfillment of the training objectives. A more systematic evaluation of job-sharing in postgraduate medical training programmes is required to clarify these points.

Automatic/Control Processing Concepts and Their Implications for the Training of Skills.

Science.gov (United States)

1982-04-01

driving a car are examples of automatic processes. Controll p s is comparatively slow, serial, limited by short-term memory, and requires subject effort...development has convinced us that moivation a oftn more Jmportn nti mAn =other iJli velLJoa jjthpgy gI. njj Lautomatic U_2,LLjjk. Motivation Is much more

Accuracy of Automatic Cephalometric Software on Landmark Identification

Science.gov (United States)

Anuwongnukroh, N.; Dechkunakorn, S.; Damrongsri, S.; Nilwarat, C.; Pudpong, N.; Radomsutthisarn, W.; Kangern, S.

2017-11-01

This study was to assess the accuracy of an automatic cephalometric analysis software in the identification of cephalometric landmarks. Thirty randomly selected digital lateral cephalograms of patients undergoing orthodontic treatment were used in this study. Thirteen landmarks (S, N, Or, A-point, U1T, U1A, B-point, Gn, Pog, Me, Go, L1T, and L1A) were identified on the digital image by an automatic cephalometric software and on cephalometric tracing by manual method. Superimposition of printed image and manual tracing was done by registration at the soft tissue profiles. The accuracy of landmarks located by the automatic method was compared with that of the manually identified landmarks by measuring the mean differences of distances of each landmark on the Cartesian plane where X and Y coordination axes passed through the center of ear rod. One-Sample T test was used to evaluate the mean differences. Statistically significant mean differences (pmean differences in both horizontal and vertical directions. Small mean differences (mean differences were found for A-point (3.0 4mm) in vertical direction. Only 5 of 13 landmarks (38.46%; S, N, Gn, Pog, and Go) showed no significant mean difference between the automatic and manual landmarking methods. It is concluded that if this automatic cephalometric analysis software is used for orthodontic diagnosis, the orthodontist must correct or modify the position of landmarks in order to increase the accuracy of cephalometric analysis.

Improving labeling efficiency in automatic quality control of MRSI data.

Science.gov (United States)

Pedrosa de Barros, Nuno; McKinley, Richard; Wiest, Roland; Slotboom, Johannes

2017-12-01

To improve the efficiency of the labeling task in automatic quality control of MR spectroscopy imaging data. 28'432 short and long echo time (TE) spectra (1.5 tesla; point resolved spectroscopy (PRESS); repetition time (TR)= 1,500 ms) from 18 different brain tumor patients were labeled by two experts as either accept or reject, depending on their quality. For each spectrum, 47 signal features were extracted. The data was then used to run several simulations and test an active learning approach using uncertainty sampling. The performance of the classifiers was evaluated as a function of the number of patients in the training set, number of spectra in the training set, and a parameter α used to control the level of classification uncertainty required for a new spectrum to be selected for labeling. The results showed that the proposed strategy allows reductions of up to 72.97% for short TE and 62.09% for long TE in the amount of data that needs to be labeled, without significant impact in classification accuracy. Further reductions are possible with significant but minimal impact in performance. Active learning using uncertainty sampling is an effective way to increase the labeling efficiency for training automatic quality control classifiers. Magn Reson Med 78:2399-2405, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Automatic categorization of diverse experimental information in the bioscience literature

Directory of Open Access Journals (Sweden)

Fang Ruihua

2012-01-01

Full Text Available Abstract Background Curation of information from bioscience literature into biological knowledge databases is a crucial way of capturing experimental information in a computable form. During the biocuration process, a critical first step is to identify from all published literature the papers that contain results for a specific data type the curator is interested in annotating. This step normally requires curators to manually examine many papers to ascertain which few contain information of interest and thus, is usually time consuming. We developed an automatic method for identifying papers containing these curation data types among a large pool of published scientific papers based on the machine learning method Support Vector Machine (SVM. This classification system is completely automatic and can be readily applied to diverse experimental data types. It has been in use in production for automatic categorization of 10 different experimental datatypes in the biocuration process at WormBase for the past two years and it is in the process of being adopted in the biocuration process at FlyBase and the Saccharomyces Genome Database (SGD. We anticipate that this method can be readily adopted by various databases in the biocuration community and thereby greatly reducing time spent on an otherwise laborious and demanding task. We also developed a simple, readily automated procedure to utilize training papers of similar data types from different bodies of literature such as C. elegans and D. melanogaster to identify papers with any of these data types for a single database. This approach has great significance because for some data types, especially those of low occurrence, a single corpus often does not have enough training papers to achieve satisfactory performance. Results We successfully tested the method on ten data types from WormBase, fifteen data types from FlyBase and three data types from Mouse Genomics Informatics (MGI. It is being used in

Automatic categorization of diverse experimental information in the bioscience literature

Science.gov (United States)

2012-01-01

Background Curation of information from bioscience literature into biological knowledge databases is a crucial way of capturing experimental information in a computable form. During the biocuration process, a critical first step is to identify from all published literature the papers that contain results for a specific data type the curator is interested in annotating. This step normally requires curators to manually examine many papers to ascertain which few contain information of interest and thus, is usually time consuming. We developed an automatic method for identifying papers containing these curation data types among a large pool of published scientific papers based on the machine learning method Support Vector Machine (SVM). This classification system is completely automatic and can be readily applied to diverse experimental data types. It has been in use in production for automatic categorization of 10 different experimental datatypes in the biocuration process at WormBase for the past two years and it is in the process of being adopted in the biocuration process at FlyBase and the Saccharomyces Genome Database (SGD). We anticipate that this method can be readily adopted by various databases in the biocuration community and thereby greatly reducing time spent on an otherwise laborious and demanding task. We also developed a simple, readily automated procedure to utilize training papers of similar data types from different bodies of literature such as C. elegans and D. melanogaster to identify papers with any of these data types for a single database. This approach has great significance because for some data types, especially those of low occurrence, a single corpus often does not have enough training papers to achieve satisfactory performance. Results We successfully tested the method on ten data types from WormBase, fifteen data types from FlyBase and three data types from Mouse Genomics Informatics (MGI). It is being used in the curation work flow at

The design of an automatically-tuned beamline

International Nuclear Information System (INIS)

Ball, M.S.; Ellison, T.J.P.; Hamilton, B.J.; Jones, W.P.

1994-01-01

A new 30 m beamline (BL1C) is being assembled to connect the new High Intensity Polarized Ion Source (HIPIOS) to the IUCF cyclotrons. This line is being instrumented for complete automatic optimization of all transverse and longitudinal ion optical elements by providing a unique feedback signal for each controllable device. Transversely, steerers and 4-quadrant electrostatic pickups are located approximately 90 degree apart in betatron phase advance along the beamline. Each pickup is instrumented with a single-board, 4-layer op-amp circuit (BPM system) which measures the beam intensity, horizontal (H) and vertical (V) position, and H and V 10 Hz position modulation. The transverse beam ellipse parameters are first automatically determined at the entrance to the beamline by measuring the beam size using a wire scanner as a function of the strength of a quadrupole. The computer then programs the amplitude and phase of four 10 Hz modulators which vary the current in 4 steerers to move the beam centroid around this (reduced area) ellipse in 4-dimensional phase space. The BPM system then outputs voltages proportional to the beam intensity, centroid location, and envelope. Computer algorithms will then set the steerers and quadrupoles to correct the beam position, dispersion, and envelope. Longitudinally, hardware feedback loops, with a bandwidth adjustable from 10 Hz to 30 kHz, will phase-lock the beam to the two bunching systems; another hardware system will automatically vary the buncher amplitudes to compensate for the significant and varying space charge defocusing as the beam current fluctuates. The bunchers' quiescent phases and amplitudes will be optimized using software ''synchronous detectors.''

Automatic Keyframe Summarization of User-Generated Video

Science.gov (United States)

2014-06-01

over longer periods of space and time. Additionally, the storyline may be less crafted or coherent when compared to professional cinema . As such, shot...attention in videos, whether it be their presence, location, identity , actions, or relationships to other humans. In this regard, automatic human capture...among other things. A person AOC has an identity property. Properties of an AOC that a stakeholder considers important are called POCs. 3.1.3

Radar automatic target recognition (ATR) and non-cooperative target recognition (NCTR)

CERN Document Server

Blacknell, David

2013-01-01

The ability to detect and locate targets by day or night, over wide areas, regardless of weather conditions has long made radar a key sensor in many military and civil applications. However, the ability to automatically and reliably distinguish different targets represents a difficult challenge. Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR) captures material presented in the NATO SET-172 lecture series to provide an overview of the state-of-the-art and continuing challenges of radar target recognition. Topics covered include the problem as applied to th

Automatic operation of CSR Drayton coal stockyard. [Australia

Energy Technology Data Exchange (ETDEWEB)

Fauerbach, R

1985-12-01

The automatic remote control of the stackers and reclaimer at the coal stockyard of the CSR Drayton opencast coal mine in Australia is described. Each machine is controlled by an on-board programmable logic controller (PLC) which monitors the machine location in its working section in relation to the other machines and can halt operations should any danger of a collision be imminent.

Automatic construction of a recurrent neural network based classifier for vehicle passage detection

Science.gov (United States)

Burnaev, Evgeny; Koptelov, Ivan; Novikov, German; Khanipov, Timur

2017-03-01

Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

Automatic alignment of radionuclide images

International Nuclear Information System (INIS)

Barber, D.C.

1982-01-01

The variability of the position, dimensions and orientation of a radionuclide image within the field of view of a gamma camera hampers attempts to analyse the image numerically. This paper describes a method of using a set of training images of a particular type, in this case right lateral brain images, to define the likely variations in the position, dimensions and orientation for that type of image and to provide alignment data for a program that automatically aligns new images of the specified type to a standard position, size and orientation. Examples are given of the use of this method on three types of radionuclide image. (author)

Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

Directory of Open Access Journals (Sweden)

Jun Yi Wang

Full Text Available Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation to 0.978 (for SegAdapter-corrected segmentation for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large

Image simulation for automatic license plate recognition

Science.gov (United States)

Bala, Raja; Zhao, Yonghui; Burry, Aaron; Kozitsky, Vladimir; Fillion, Claude; Saunders, Craig; Rodríguez-Serrano, José

2012-01-01

Automatic license plate recognition (ALPR) is an important capability for traffic surveillance applications, including toll monitoring and detection of different types of traffic violations. ALPR is a multi-stage process comprising plate localization, character segmentation, optical character recognition (OCR), and identification of originating jurisdiction (i.e. state or province). Training of an ALPR system for a new jurisdiction typically involves gathering vast amounts of license plate images and associated ground truth data, followed by iterative tuning and optimization of the ALPR algorithms. The substantial time and effort required to train and optimize the ALPR system can result in excessive operational cost and overhead. In this paper we propose a framework to create an artificial set of license plate images for accelerated training and optimization of ALPR algorithms. The framework comprises two steps: the synthesis of license plate images according to the design and layout for a jurisdiction of interest; and the modeling of imaging transformations and distortions typically encountered in the image capture process. Distortion parameters are estimated by measurements of real plate images. The simulation methodology is successfully demonstrated for training of OCR.

Estimation of bladder wall location in ultrasound images.

Science.gov (United States)

Topper, A K; Jernigan, M E

1991-05-01

A method of automatically estimating the location of the bladder wall in ultrasound images is proposed. Obtaining this estimate is intended to be the first stage in the development of an automatic bladder volume calculation system. The first step in the bladder wall estimation scheme involves globally processing the images using standard image processing techniques to highlight the bladder wall. Separate processing sequences are required to highlight the anterior bladder wall and the posterior bladder wall. The sequence to highlight the anterior bladder wall involves Gaussian smoothing and second differencing followed by zero-crossing detection. Median filtering followed by thresholding and gradient detection is used to highlight as much of the rest of the bladder wall as was visible in the original images. Then a 'bladder wall follower'--a line follower with rules based on the characteristics of ultrasound imaging and the anatomy involved--is applied to the processed images to estimate the bladder wall location by following the portions of the bladder wall which are highlighted and filling in the missing segments. The results achieved using this scheme are presented.

Automated training site selection for large-area remote-sensing image analysis

Science.gov (United States)

McCaffrey, Thomas M.; Franklin, Steven E.

1993-11-01

A computer program is presented to select training sites automatically from remotely sensed digital imagery. The basic ideas are to guide the image analyst through the process of selecting typical and representative areas for large-area image classifications by minimizing bias, and to provide an initial list of potential classes for which training sites are required to develop a classification scheme or to verify classification accuracy. Reducing subjectivity in training site selection is achieved by using a purely statistical selection of homogeneous sites which then can be compared to field knowledge, aerial photography, or other remote-sensing imagery and ancillary data to arrive at a final selection of sites to be used to train the classification decision rules. The selection of the homogeneous sites uses simple tests based on the coefficient of variance, the F-statistic, and the Student's i-statistic. Comparisons of site means are conducted with a linear growing list of previously located homogeneous pixels. The program supports a common pixel-interleaved digital image format and has been tested on aerial and satellite optical imagery. The program is coded efficiently in the C programming language and was developed under AIX-Unix on an IBM RISC 6000 24-bit color workstation.

Influence of Japan's 2004 postgraduate training on ophthalmologist location choice, supply and distribution.

Science.gov (United States)

Sakai-Bizmark, Rie; Goto, Rei; Hiragi, Shusuke; Tamura, Hiroshi

2018-03-27

Highly-competent patient care is paramount to medicine. Quality training and patient accessibility to physicians with a wide range of specializations is essential. Yet, poor quality of life for physicians cannot be ignored, being detrimental to patient care and leading to personnel leaving the medical profession. In 2004, the Japanese government reformed postgraduate training for medical graduates, adding a 2-year, hands-on rotation through different specialties before the specialization residency was begun. Residents could now choose practice location, but it sparked concerns that physician distribution disparities had been created. Japanese media reported that residents were choosing specialties deemed to offer a higher quality of life, like Ophthalmology or Dermatology, over underserved areas like Obstetrics or Cardiology. To explore the consequences of Japan's policy efforts, through the residency reform in 2004, to improve physician training, analyzing ophthalmologist supply and distribution in the context of providing the best possible patient care and access while maintaining physician quality of life. Using secondary data, we analyzed changes in ophthalmologist supply at the secondary tier of medical care (STM). We applied ordinary least-squares regression models to ophthalmologist density to reflect community factors such as residential quality and access to further professional development, to serve as predictors of ophthalmologist supply. Coefficient equality tests examined predictor differences before and after 2004. Similar analyses were conducted for all physicians excluding ophthalmologists (other physicians). Ophthalmologist coverage in top and bottom 10% of STMs revealed supply inequalities. Change in ophthalmologist supply was inversely associated with baseline ophthalmologist density before (P supply were not associated with baseline other physician density before 2004 (P = 0.5), but positively associated after 2004 (P supply in STMs were

An automatic fault management model for distribution networks

Energy Technology Data Exchange (ETDEWEB)

Lehtonen, M; Haenninen, S [VTT Energy, Espoo (Finland); Seppaenen, M [North-Carelian Power Co (Finland); Antila, E; Markkila, E [ABB Transmit Oy (Finland)

1998-08-01

An automatic computer model, called the FI/FL-model, for fault location, fault isolation and supply restoration is presented. The model works as an integrated part of the substation SCADA, the AM/FM/GIS system and the medium voltage distribution network automation systems. In the model, three different techniques are used for fault location. First, by comparing the measured fault current to the computed one, an estimate for the fault distance is obtained. This information is then combined, in order to find the actual fault point, with the data obtained from the fault indicators in the line branching points. As a third technique, in the absence of better fault location data, statistical information of line section fault frequencies can also be used. For combining the different fault location information, fuzzy logic is used. As a result, the probability weights for the fault being located in different line sections, are obtained. Once the faulty section is identified, it is automatically isolated by remote control of line switches. Then the supply is restored to the remaining parts of the network. If needed, reserve connections from other adjacent feeders can also be used. During the restoration process, the technical constraints of the network are checked. Among these are the load carrying capacity of line sections, voltage drop and the settings of relay protection. If there are several possible network topologies, the model selects the technically best alternative. The FI/IL-model has been in trial use at two substations of the North-Carelian Power Company since November 1996. This chapter lists the practical experiences during the test use period. Also the benefits of this kind of automation are assessed and future developments are outlined

Segmenting articular cartilage automatically using a voxel classification approach

DEFF Research Database (Denmark)

Folkesson, Jenny; Dam, Erik B; Olsen, Ole F

2007-01-01

We present a fully automatic method for articular cartilage segmentation from magnetic resonance imaging (MRI) which we use as the foundation of a quantitative cartilage assessment. We evaluate our method by comparisons to manual segmentations by a radiologist and by examining the interscan...... reproducibility of the volume and area estimates. Training and evaluation of the method is performed on a data set consisting of 139 scans of knees with a status ranging from healthy to severely osteoarthritic. This is, to our knowledge, the only fully automatic cartilage segmentation method that has good...... agreement with manual segmentations, an interscan reproducibility as good as that of a human expert, and enables the separation between healthy and osteoarthritic populations. While high-field scanners offer high-quality imaging from which the articular cartilage have been evaluated extensively using manual...

AUTOMATIC RECOGNITION OF FALLS IN GAIT-SLIP: A HARNESS LOAD CELL BASED CRITERION

OpenAIRE

Yang, Feng; Pai, Yi-Chung

2011-01-01

Over-head-harness systems, equipped with load cell sensors, are essential to the participants’ safety and to the outcome assessment in perturbation training. The purpose of this study was to first develop an automatic outcome recognition criterion among young adults for gait-slip training and then verify such criterion among older adults. Each of 39 young and 71 older subjects, all protected by safety harness, experienced 8 unannounced, repeated slips, while walking on a 7-m walkway. Each tri...

Field training

International Nuclear Information System (INIS)

Mumford, G.E.; Hadaway, E.H.

1991-01-01

Individualized, personal training can be used to increase an employee's awareness of the HSE program. Such training can stimulate personal commitment and provide personal skills that can be utilized for the benefit of the overall HSE effort. But, providing such training within our industry can be a difficult task due to the scheduling, travel arrangements, and cost associated with bringing employees from isolated, remote locations to centrally located training facilities. One method of overcoming these obstacles involves the use of field instructors to provide the training at the many, and varied number of individuals can be reached with minimal disruption to their work scheduling or to their time off. In fact, this type of on-site training is already used by some oil companies and drilling contractors with encouraging results. This paper describes one drilling contractor's experiences with such a training program. The results after eight years how that this program not only can provide and efficient, economical means of employee training, but also can have a direct application to employee motivation regarding a company's HSE effort

Automatic anatomy partitioning of the torso region on CT images by using multiple organ localizations with a group-wise calibration technique

Science.gov (United States)

Zhou, Xiangrong; Morita, Syoichi; Zhou, Xinxin; Chen, Huayue; Hara, Takeshi; Yokoyama, Ryujiro; Kanematsu, Masayuki; Hoshi, Hiroaki; Fujita, Hiroshi

2015-03-01

This paper describes an automatic approach for anatomy partitioning on three-dimensional (3D) computedtomography (CT) images that divide the human torso into several volume-of-interesting (VOI) images based on anatomical definition. The proposed approach combines several individual detections of organ-location with a groupwise organ-location calibration and correction to achieve an automatic and robust multiple-organ localization task. The essence of the proposed method is to jointly detect the 3D minimum bounding box for each type of organ shown on CT images based on intra-organ-image-textures and inter-organ-spatial-relationship in the anatomy. Machine-learning-based template matching and generalized Hough transform-based point-distribution estimation are used in the detection and calibration processes. We apply this approach to the automatic partitioning of a torso region on CT images, which are divided into 35 VOIs presenting major organ regions and tissues required by routine diagnosis in clinical medicine. A database containing 4,300 patient cases of high-resolution 3D torso CT images is used for training and performance evaluations. We confirmed that the proposed method was successful in target organ localization on more than 95% of CT cases. Only two organs (gallbladder and pancreas) showed a lower success rate: 71 and 78% respectively. In addition, we applied this approach to another database that included 287 patient cases of whole-body CT images scanned for positron emission tomography (PET) studies and used for additional performance evaluation. The experimental results showed that no significant difference between the anatomy partitioning results from those two databases except regarding the spleen. All experimental results showed that the proposed approach was efficient and useful in accomplishing localization tasks for major organs and tissues on CT images scanned using different protocols.

Automated testing and reverification for training simulators using SATAR

International Nuclear Information System (INIS)

Charles, R.D.; Gaddy, C.D.; Nargarkar, A.; Colley, R.

1990-01-01

This paper reports that simulators used to train nuclear power plant operators must be recertified periodically to ensure fidelity for training (10 CFR 55.45). The objective of the Simulator Automated Testing and Reverification (SATAR) project was to develop software to reverify dynamic simulator performance automatically. The software resides in a standard configuration personal computer and in the simulator computer; the two computers are linked via serial ports. SATAR will automatically run performance tests, collect and analyze data, and compare data with baseline performance data. With SATAR, support from operations and simulator support personnel can be reduced greatly, and the repeatability of performance tests can be improved

Mitosis Counting in Breast Cancer: Object-Level Interobserver Agreement and Comparison to an Automatic Method.

Science.gov (United States)

Veta, Mitko; van Diest, Paul J; Jiwa, Mehdi; Al-Janabi, Shaimaa; Pluim, Josien P W

2016-01-01

Tumor proliferation speed, most commonly assessed by counting of mitotic figures in histological slide preparations, is an important biomarker for breast cancer. Although mitosis counting is routinely performed by pathologists, it is a tedious and subjective task with poor reproducibility, particularly among non-experts. Inter- and intraobserver reproducibility of mitosis counting can be improved when a strict protocol is defined and followed. Previous studies have examined only the agreement in terms of the mitotic count or the mitotic activity score. Studies of the observer agreement at the level of individual objects, which can provide more insight into the procedure, have not been performed thus far. The development of automatic mitosis detection methods has received large interest in recent years. Automatic image analysis is viewed as a solution for the problem of subjectivity of mitosis counting by pathologists. In this paper we describe the results from an interobserver agreement study between three human observers and an automatic method, and make two unique contributions. For the first time, we present an analysis of the object-level interobserver agreement on mitosis counting. Furthermore, we train an automatic mitosis detection method that is robust with respect to staining appearance variability and compare it with the performance of expert observers on an "external" dataset, i.e. on histopathology images that originate from pathology labs other than the pathology lab that provided the training data for the automatic method. The object-level interobserver study revealed that pathologists often do not agree on individual objects, even if this is not reflected in the mitotic count. The disagreement is larger for objects from smaller size, which suggests that adding a size constraint in the mitosis counting protocol can improve reproducibility. The automatic mitosis detection method can perform mitosis counting in an unbiased way, with substantial

Automatic segmentation of the lateral geniculate nucleus: Application to control and glaucoma patients.

Science.gov (United States)

Wang, Jieqiong; Miao, Wen; Li, Jing; Li, Meng; Zhen, Zonglei; Sabel, Bernhard; Xian, Junfang; He, Huiguang

2015-11-30

The lateral geniculate nucleus (LGN) is a key relay center of the visual system. Because the LGN morphology is affected by different diseases, it is of interest to analyze its morphology by segmentation. However, existing LGN segmentation methods are non-automatic, inefficient and prone to experimenters' bias. To address these problems, we proposed an automatic LGN segmentation algorithm based on T1-weighted imaging. First, the prior information of LGN was used to create a prior mask. Then region growing was applied to delineate LGN. We evaluated this automatic LGN segmentation method by (1) comparison with manually segmented LGN, (2) anatomically locating LGN in the visual system via LGN-based tractography, (3) application to control and glaucoma patients. The similarity coefficients of automatic segmented LGN and manually segmented one are 0.72 (0.06) for the left LGN and 0.77 (0.07) for the right LGN. LGN-based tractography shows the subcortical pathway seeding from LGN passes the optic tract and also reaches V1 through the optic radiation, which is consistent with the LGN location in the visual system. In addition, LGN asymmetry as well as LGN atrophy along with age is observed in normal controls. The investigation of glaucoma effects on LGN volumes demonstrates that the bilateral LGN volumes shrink in patients. The automatic LGN segmentation is objective, efficient, valid and applicable. Experiment results proved the validity and applicability of the algorithm. Our method will speed up the research on visual system and greatly enhance studies of different vision-related diseases. Copyright © 2015 Elsevier B.V. All rights reserved.

THE POSSIBILITIES OF PRACTICAL IMPLEMENTATION OF REGRESSION ANALYSIS IN LOCATION CHOICE

Directory of Open Access Journals (Sweden)

Marija Andjelković PEŠIĆ

2008-06-01

Full Text Available “Location, location, location” is a well worn catch phrase of the real estate industry. Invest in a property in the right location and watch your investment soar in value. The same catch cry may or may not hold true for the restaurant (hotel industry, open a restaurant in the right location and are you on the way to become a winner? The question is: Did the best locations for a restaurant (hotel automatically mean that the restaurant (hotel is profitable? The intention of this paper is to show if there is positive correlation between the best location and restaurant’s (hotel’s profitability.

Automatic methods of the processing of data from track detectors on the basis of the PAVICOM facility

Science.gov (United States)

Aleksandrov, A. B.; Goncharova, L. A.; Davydov, D. A.; Publichenko, P. A.; Roganova, T. M.; Polukhina, N. G.; Feinberg, E. L.

2007-02-01

New automatic methods essentially simplify and increase the rate of the processing of data from track detectors. This provides a possibility of processing large data arrays and considerably improves their statistical significance. This fact predetermines the development of new experiments which plan to use large-volume targets, large-area emulsion, and solid-state track detectors [1]. In this regard, the problem of training qualified physicists who are capable of operating modern automatic equipment is very important. Annually, about ten Moscow students master the new methods, working at the Lebedev Physical Institute at the PAVICOM facility [2 4]. Most students specializing in high-energy physics are only given an idea of archaic manual methods of the processing of data from track detectors. In 2005, on the basis of the PAVICOM facility and the physicstraining course of Moscow State University, a new training work was prepared. This work is devoted to the determination of the energy of neutrons passing through a nuclear emulsion. It provides the possibility of acquiring basic practical skills of the processing of data from track detectors using automatic equipment and can be included in the educational process of students of any physical faculty. Those who have mastered the methods of automatic data processing in a simple and pictorial example of track detectors will be able to apply their knowledge in various fields of science and technique. Formulation of training works for pregraduate and graduate students is a new additional aspect of application of the PAVICOM facility described earlier in [4].

Automatic Tortuosity-Based Retinopathy of Prematurity Screening System

Science.gov (United States)

Sukkaew, Lassada; Uyyanonvara, Bunyarit; Makhanov, Stanislav S.; Barman, Sarah; Pangputhipong, Pannet

Retinopathy of Prematurity (ROP) is an infant disease characterized by increased dilation and tortuosity of the retinal blood vessels. Automatic tortuosity evaluation from retinal digital images is very useful to facilitate an ophthalmologist in the ROP screening and to prevent childhood blindness. This paper proposes a method to automatically classify the image into tortuous and non-tortuous. The process imitates expert ophthalmologists' screening by searching for clearly tortuous vessel segments. First, a skeleton of the retinal blood vessels is extracted from the original infant retinal image using a series of morphological operators. Next, we propose to partition the blood vessels recursively using an adaptive linear interpolation scheme. Finally, the tortuosity is calculated based on the curvature of the resulting vessel segments. The retinal images are then classified into two classes using segments characterized by the highest tortuosity. For an optimal set of training parameters the prediction is as high as 100%.

Automatic Differentiation and Deep Learning

CERN Multimedia

CERN. Geneva

2018-01-01

Statistical learning has been getting more and more interest from the particle-physics community in recent times, with neural networks and gradient-based optimization being a focus. In this talk we shall discuss three things: automatic differention tools: tools to quickly build DAGs of computation that are fully differentiable. We shall focus on one such tool "PyTorch".  Easy deployment of trained neural networks into large systems with many constraints: for example, deploying a model at the reconstruction phase where the neural network has to be integrated into CERN's bulk data-processing C++-only environment Some recent models in deep learning for segmentation and generation that might be useful for particle physics problems.

Semi-automatic ultrasonic inspection of PWR upper internal immersed components

International Nuclear Information System (INIS)

Dombret, P.; Coquette, A.; Cermak, J.; Verspeelt, D.

1985-01-01

The present paper describes the characteristics of a semi-automatic ultrasonic inspection system. Components inspected are the so-called flexures, small pins located at the upper part of control rod tube-guide, some of which happened to broke in a few Westinghouse type PWR's. Inspection results and other system capabilities are also mentioned

Automatic fog detection for public safety by using camera images

Science.gov (United States)

Pagani, Giuliano Andrea; Roth, Martin; Wauben, Wiel

2017-04-01

Fog and reduced visibility have considerable impact on the performance of road, maritime, and aeronautical transportation networks. The impact ranges from minor delays to more serious congestions or unavailability of the infrastructure and can even lead to damage or loss of lives. Visibility is traditionally measured manually by meteorological observers using landmarks at known distances in the vicinity of the observation site. Nowadays, distributed cameras facilitate inspection of more locations from one remote monitoring center. The main idea is, however, still deriving the visibility or presence of fog by an operator judging the scenery and the presence of landmarks. Visibility sensors are also used, but they are rather costly and require regular maintenance. Moreover, observers, and in particular sensors, give only visibility information that is representative for a limited area. Hence the current density of visibility observations is insufficient to give detailed information on the presence of fog. Cameras are more and more deployed for surveillance and security reasons in cities and for monitoring traffic along main transportation ways. In addition to this primary use of cameras, we consider cameras as potential sensors to automatically identify low visibility conditions. The approach that we follow is to use machine learning techniques to determine the presence of fog and/or to make an estimation of the visibility. For that purpose a set of features are extracted from the camera images such as the number of edges, brightness, transmission of the image dark channel, fractal dimension. In addition to these image features, we also consider meteorological variables such as wind speed, temperature, relative humidity, and dew point as additional features to feed the machine learning model. The results obtained with a training and evaluation set consisting of 10-minute sampled images for two KNMI locations over a period of 1.5 years by using decision trees methods

Performance analysis of a self-locating mobile sensor

DEFF Research Database (Denmark)

Bøgsted, Martin; Rasmussen, Jakob Gulddahl; Lundbye-Christensen, Søren

to an autoregressive model. Measurement uncertainty is assumed to follow a Gaussian distribution and the probability for detecting a distance to a given sensor is assumed to fall off exponentially with squared distance. The combined model is formulated as a nonlinear state space model and Bayesian inference......We consider the ability of a mobile sensor to locate its own geographical location, the so-called self-localization problem. The need to locate people and objects has inspired the development of many systems for automatic localization. Most systems are based on location information and measured...... the performance of localization algorithms in mobile and critical situations. This is done by exploring the performance of various filtering techniques for self-localization of a mobile sensor in a field of sensors. More specifically, we model the mobility of the sensor such that the velocity varies according...

Automatic personnel contamination monitor

International Nuclear Information System (INIS)

Lattin, Kenneth R.

1978-01-01

United Nuclear Industries, Inc. (UNI) has developed an automatic personnel contamination monitor (APCM), which uniquely combines the design features of both portal and hand and shoe monitors. In addition, this prototype system also has a number of new features, including: micro computer control and readout, nineteen large area gas flow detectors, real-time background compensation, self-checking for system failures, and card reader identification and control. UNI's experience in operating the Hanford N Reactor, located in Richland, Washington, has shown the necessity of automatically monitoring plant personnel for contamination after they have passed through the procedurally controlled radiation zones. This final check ensures that each radiation zone worker has been properly checked before leaving company controlled boundaries. Investigation of the commercially available portal and hand and shoe monitors indicated that they did not have the sensitivity or sophistication required for UNI's application, therefore, a development program was initiated, resulting in the subject monitor. Field testing shows good sensitivity to personnel contamination with the majority of alarms showing contaminants on clothing, face and head areas. In general, the APCM has sensitivity comparable to portal survey instrumentation. The inherit stand-in, walk-on feature of the APCM not only makes it easy to use, but makes it difficult to bypass. (author)

What does visual suffix interference tell us about spatial location in working memory?

Science.gov (United States)

Allen, Richard J; Castellà, Judit; Ueno, Taiji; Hitch, Graham J; Baddeley, Alan D

2015-01-01

A visual object can be conceived of as comprising a number of features bound together by their joint spatial location. We investigate the question of whether the spatial location is automatically bound to the features or whether the two are separable, using a previously developed paradigm whereby memory is disrupted by a visual suffix. Participants were shown a sample array of four colored shapes, followed by a postcue indicating the target for recall. On randomly intermixed trials, a to-be-ignored suffix array consisting of two different colored shapes was presented between the sample and the postcue. In a random half of suffix trials, one of the suffix items overlaid the location of the target. If location was automatically encoded, one might expect the colocation of target and suffix to differentially impair performance. We carried out three experiments, cuing for recall by spatial location (Experiment 1), color or shape (Experiment 2), or both randomly intermixed (Experiment 3). All three studies showed clear suffix effects, but the colocation of target and suffix was differentially disruptive only when a spatial cue was used. The results suggest that purely visual shape-color binding can be retained and accessed without requiring information about spatial location, even when task demands encourage the encoding of location, consistent with the idea of an abstract and flexible visual working memory system.

Towards an Automatic Framework for Urban Settlement Mapping from Satellite Images: Applications of Geo-referenced Social Media and One Class Classification

Science.gov (United States)

Miao, Zelang

2017-04-01

Currently, urban dwellers comprise more than half of the world's population and this percentage is still dramatically increasing. The explosive urban growth over the next two decades poses long-term profound impact on people as well as the environment. Accurate and up-to-date delineation of urban settlements plays a fundamental role in defining planning strategies and in supporting sustainable development of urban settlements. In order to provide adequate data about urban extents and land covers, classifying satellite data has become a common practice, usually with accurate enough results. Indeed, a number of supervised learning methods have proven effective in urban area classification, but they usually depend on a large amount of training samples, whose collection is a time and labor expensive task. This issue becomes particularly serious when classifying large areas at the regional/global level. As an alternative to manual ground truth collection, in this work we use geo-referenced social media data. Cities and densely populated areas are an extremely fertile land for the production of individual geo-referenced data (such as GPS and social network data). Training samples derived from geo-referenced social media have several advantages: they are easy to collect, usually they are freely exploitable; and, finally, data from social media are spatially available in many locations, and with no doubt in most urban areas around the world. Despite these advantages, the selection of training samples from social media meets two challenges: 1) there are many duplicated points; 2) method is required to automatically label them as "urban/non-urban". The objective of this research is to validate automatic sample selection from geo-referenced social media and its applicability in one class classification for urban extent mapping from satellite images. The findings in this study shed new light on social media applications in the field of remote sensing.

Automatic detection of anatomical regions in frontal x-ray images: comparing convolutional neural networks to random forest

Science.gov (United States)

Olory Agomma, R.; Vázquez, C.; Cresson, T.; De Guise, J.

2018-02-01

Most algorithms to detect and identify anatomical structures in medical images require either to be initialized close to the target structure, or to know that the structure is present in the image, or to be trained on a homogeneous database (e.g. all full body or all lower limbs). Detecting these structures when there is no guarantee that the structure is present in the image, or when the image database is heterogeneous (mixed configurations), is a challenge for automatic algorithms. In this work we compared two state-of-the-art machine learning techniques in order to determine which one is the most appropriate for predicting targets locations based on image patches. By knowing the position of thirteen landmarks points, labelled by an expert in EOS frontal radiography, we learn the displacement between salient points detected in the image and these thirteen landmarks. The learning step is carried out with a machine learning approach by exploring two methods: Convolutional Neural Network (CNN) and Random Forest (RF). The automatic detection of the thirteen landmarks points in a new image is then obtained by averaging the positions of each one of these thirteen landmarks estimated from all the salient points in the new image. We respectively obtain for CNN and RF, an average prediction error (both mean and standard deviation in mm) of 29 +/-18 and 30 +/- 21 for the thirteen landmarks points, indicating the approximate location of anatomical regions. On the other hand, the learning time is 9 days for CNN versus 80 minutes for RF. We provide a comparison of the results between the two machine learning approaches.

Location Privacy in RFID Applications

Science.gov (United States)

Sadeghi, Ahmad-Reza; Visconti, Ivan; Wachsmann, Christian

RFID-enabled systems allow fully automatic wireless identification of objects and are rapidly becoming a pervasive technology with various applications. However, despite their benefits, RFID-based systems also pose challenging risks, in particular concerning user privacy. Indeed, improvident use of RFID can disclose sensitive information about users and their locations allowing detailed user profiles. Hence, it is crucial to identify and to enforce appropriate security and privacy requirements of RFID applications (that are also compliant to legislation). This chapter first discusses security and privacy requirements for RFID-enabled systems, focusing in particular on location privacy issues. Then it explores the advances in RFID applications, stressing the security and privacy shortcomings of existing proposals. Finally, it presents new promising directions for privacy-preserving RFID systems, where as a case study we focus electronic tickets (e-tickets) for public transportation.

The automaticity of vantage point shifts within a synaesthetes' spatial calendar.

Science.gov (United States)

Jarick, Michelle; Jensen, Candice; Dixon, Michael J; Smilek, Daniel

2011-09-01

Time-space synaesthetes report that time units (e.g., months, days, hours) occupy idiosyncratic spatial locations. For the synaesthete (L), the months of the year are projected out in external space in the shape of a 'scoreboard 7', where January to July extend across the top from left to right and August to December make up the vertical segment from top to bottom. Interestingly, L can change the mental vantage point (MVP) from where she views her month-space depending on whether she sees or hears the month name. We used a spatial cueing task to demonstrate that L's attention could be directed to locations within her time-space and change vantage points automatically - from trial to trial. We also sought to eliminate any influence of strategy on L's performance by shortening the interval between the cue and target onset to only 150 ms, and have the targets fall in synaesthetically cued locations on only 15% of trials. If L's performance was attributable to intentionally using the cue to predict target location, these manipulations should eliminate any cueing effects. In two separate experiments, we found that L still showed an attentional bias consistent with her synaesthesia. Thus, we attribute L's rapid and resilient cueing effects to the automaticity of her spatial forms. ©2011 The British Psychological Society.

Automatic Angular alignment of LHC Collimators

CERN Document Server

Azzopardi, Gabriella; Salvachua Ferrando, Belen Maria; Mereghetti, Alessio; Bruce, Roderik; Redaelli, Stefano; CERN. Geneva. ATS Department

2017-01-01

The LHC is equipped with a complex collimation system to protect sensitive equipment from unavoidable beam losses. Collimators are positioned close to the beam using an alignment procedure. Until now they have always been aligned assuming no tilt between the collimator and the beam, however, tank misalignments or beam envelope angles at large-divergence locations could introduce a tilt limiting the collimation performance. Three different algorithms were implemented to automatically align a chosen collimator at various angles. The implementation was tested on a number of collimators during this MD and no human intervention was required.

Integrated automatic non-destructive testing in industrial production and in the operation of technical plant

International Nuclear Information System (INIS)

Hoeller, P.

1989-01-01

The article deals with non-destructive testing (NDT) in automated manufacture and in the automated operation of industrial plant. In both areas of application, the tests are coupled to the process (real time operation) and the results are used for the control of manufacture or of the course of the process. The control process can be coupled to the process in open loop or closed loop. The subject is explained by the following examples: 1) Automated testing of sheets in a steelworks. 2) Automatic NDT on machine parts in tempering and machining by the 3MA system (3MA: micro-magnetic, multi-parameter, micro-structure and stress analysis). 3) Automated ultrasonic testing in manufacture and in the operation of plants with the ALOK data collection and processing system (ALOK: amplitude, running time, location curves). 4) Automated wheel running surface test on Intercity experimental train, and 5) automated level measurement on BWR pressure vessels. (orig./MM) [de

Automatic, time-interval traffic counts for recreation area management planning

Science.gov (United States)

D. L. Erickson; C. J. Liu; H. K. Cordell

1980-01-01

Automatic, time-interval recorders were used to count directional vehicular traffic on a multiple entry/exit road network in the Red River Gorge Geological Area, Daniel Boone National Forest. Hourly counts of entering and exiting traffic differed according to recorder location, but an aggregated distribution showed a delayed peak in exiting traffic thought to be...

Automatic differentiation bibliography

Energy Technology Data Exchange (ETDEWEB)

Corliss, G.F. [comp.

1992-07-01

This is a bibliography of work related to automatic differentiation. Automatic differentiation is a technique for the fast, accurate propagation of derivative values using the chain rule. It is neither symbolic nor numeric. Automatic differentiation is a fundamental tool for scientific computation, with applications in optimization, nonlinear equations, nonlinear least squares approximation, stiff ordinary differential equation, partial differential equations, continuation methods, and sensitivity analysis. This report is an updated version of the bibliography which originally appeared in Automatic Differentiation of Algorithms: Theory, Implementation, and Application.

Man-system interface based on automatic speech recognition: integration to a virtual control desk

Energy Technology Data Exchange (ETDEWEB)

Jorge, Carlos Alexandre F.; Mol, Antonio Carlos A.; Pereira, Claudio M.N.A.; Aghina, Mauricio Alves C., E-mail: calexandre@ien.gov.b, E-mail: mol@ien.gov.b, E-mail: cmnap@ien.gov.b, E-mail: mag@ien.gov.b [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil); Nomiya, Diogo V., E-mail: diogonomiya@gmail.co [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil)

2009-07-01

This work reports the implementation of a man-system interface based on automatic speech recognition, and its integration to a virtual nuclear power plant control desk. The later is aimed to reproduce a real control desk using virtual reality technology, for operator training and ergonomic evaluation purpose. An automatic speech recognition system was developed to serve as a new interface with users, substituting computer keyboard and mouse. They can operate this virtual control desk in front of a computer monitor or a projection screen through spoken commands. The automatic speech recognition interface developed is based on a well-known signal processing technique named cepstral analysis, and on artificial neural networks. The speech recognition interface is described, along with its integration with the virtual control desk, and results are presented. (author)

Man-system interface based on automatic speech recognition: integration to a virtual control desk

International Nuclear Information System (INIS)

Jorge, Carlos Alexandre F.; Mol, Antonio Carlos A.; Pereira, Claudio M.N.A.; Aghina, Mauricio Alves C.; Nomiya, Diogo V.

2009-01-01

This work reports the implementation of a man-system interface based on automatic speech recognition, and its integration to a virtual nuclear power plant control desk. The later is aimed to reproduce a real control desk using virtual reality technology, for operator training and ergonomic evaluation purpose. An automatic speech recognition system was developed to serve as a new interface with users, substituting computer keyboard and mouse. They can operate this virtual control desk in front of a computer monitor or a projection screen through spoken commands. The automatic speech recognition interface developed is based on a well-known signal processing technique named cepstral analysis, and on artificial neural networks. The speech recognition interface is described, along with its integration with the virtual control desk, and results are presented. (author)

Automatic Chessboard Detection for Intrinsic and Extrinsic Camera Parameter Calibration

Directory of Open Access Journals (Sweden)

Jose María Armingol

2010-03-01

Full Text Available There are increasing applications that require precise calibration of cameras to perform accurate measurements on objects located within images, and an automatic algorithm would reduce this time consuming calibration procedure. The method proposed in this article uses a pattern similar to that of a chess board, which is found automatically in each image, when no information regarding the number of rows or columns is supplied to aid its detection. This is carried out by means of a combined analysis of two Hough transforms, image corners and invariant properties of the perspective transformation. Comparative analysis with more commonly used algorithms demonstrate the viability of the algorithm proposed, as a valuable tool for camera calibration.

Microseismic event location using global optimization algorithms: An integrated and automated workflow

Science.gov (United States)

Lagos, Soledad R.; Velis, Danilo R.

2018-02-01

We perform the location of microseismic events generated in hydraulic fracturing monitoring scenarios using two global optimization techniques: Very Fast Simulated Annealing (VFSA) and Particle Swarm Optimization (PSO), and compare them against the classical grid search (GS). To this end, we present an integrated and optimized workflow that concatenates into an automated bash script the different steps that lead to the microseismic events location from raw 3C data. First, we carry out the automatic detection, denoising and identification of the P- and S-waves. Secondly, we estimate their corresponding backazimuths using polarization information, and propose a simple energy-based criterion to automatically decide which is the most reliable estimate. Finally, after taking proper care of the size of the search space using the backazimuth information, we perform the location using the aforementioned algorithms for 2D and 3D usual scenarios of hydraulic fracturing processes. We assess the impact of restricting the search space and show the advantages of using either VFSA or PSO over GS to attain significant speed-ups.

Automatic welding detection by an intelligent tool pipe inspection

Science.gov (United States)

Arizmendi, C. J.; Garcia, W. L.; Quintero, M. A.

2015-07-01

This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called “smart pig” in Oil and Gas pipelines. The model uses a signal noise reduction phase by means of pre-processing algorithms and attribute-selection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent.

Trend analysis of nuclear reactor automatic trip events subjected to operator's human error at United States nuclear power plants

International Nuclear Information System (INIS)

Takagawa, Kenichi

2009-01-01

Trends in nuclear reactor automatic trip events due to human errors during plant operating mode have been analyzed by extracting 20 events which took place in the United States during the period of seven years from 2002 to 2008, cited in the LERs (Licensee Event Reports) submitted to the US Nuclear Regulatory Commission (NRC). It was shown that the yearly number of events was relatively large before 2005, and thereafter the number decreased. A period of stable operation, in which the yearly number was kept very small, continued for about three years, and then the yearly number turned to increase again. Before 2005, automatic trip events occurred more frequently during periodic inspections or start-up/shut-down operations. The recent trends, however, indicate that trip events became more frequent due to human errors during daily operations. Human errors were mostly caused by the self-conceit and carelessness of operators through the whole period. The before mentioned trends in the yearly number of events might be explained as follows. The decrease in the automatic trip events is attributed to sharing trouble information, leading as a consequence to improvement of the manual and training for the operations which have a higher potential risk of automatic trip. Then, while the period of stable operation continued, some operators came to pay less attention to preventing human errors and not interest in the training, leading to automatic trip events in reality due to miss-operation. From these analyses on trouble experiences in the US, we learnt the followings to prevent the occurrence similar troubles in Japan: Operators should be thoroughly skilled in basic actions to prevent human errors as persons concerned. And it should be further emphasized that they should elaborate by imaging actual plant operations even though the simulator training gives them successful experiences. (author)

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography.

Science.gov (United States)

Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

2016-07-07

Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

Pervasive Sound Sensing: A Weakly Supervised Training Approach.

Science.gov (United States)

Kelly, Daniel; Caulfield, Brian

2016-01-01

Modern smartphones present an ideal device for pervasive sensing of human behavior. Microphones have the potential to reveal key information about a person's behavior. However, they have been utilized to a significantly lesser extent than other smartphone sensors in the context of human behavior sensing. We postulate that, in order for microphones to be useful in behavior sensing applications, the analysis techniques must be flexible and allow easy modification of the types of sounds to be sensed. A simplification of the training data collection process could allow a more flexible sound classification framework. We hypothesize that detailed training, a prerequisite for the majority of sound sensing techniques, is not necessary and that a significantly less detailed and time consuming data collection process can be carried out, allowing even a nonexpert to conduct the collection, labeling, and training process. To test this hypothesis, we implement a diverse density-based multiple instance learning framework, to identify a target sound, and a bag trimming algorithm, which, using the target sound, automatically segments weakly labeled sound clips to construct an accurate training set. Experiments reveal that our hypothesis is a valid one and results show that classifiers, trained using the automatically segmented training sets, were able to accurately classify unseen sound samples with accuracies comparable to supervised classifiers, achieving an average F -measure of 0.969 and 0.87 for two weakly supervised datasets.

The same-location cost is unrelated to attentional settings: an object-updating account.

Science.gov (United States)

Carmel, Tomer; Lamy, Dominique

2014-08-01

What mechanisms allow us to ignore salient yet irrelevant visual information has been a matter of intense debate. According to the contingent-capture hypothesis, such information is filtered out, whereas according to the salience-based account, it captures attention automatically. Several recent studies have reported a same-location cost that appears to fit neither of these accounts. These showed that responses may actually be slower when the target appears at the location just occupied by an irrelevant singleton distractor. Here, we investigated the mechanisms underlying this same-location cost. Our findings show that the same-location cost is unrelated to automatic attentional capture or strategic setting of attentional priorities, and therefore invalidate the feature-based inhibition and fast attentional disengagement accounts of this effect. In addition, we show that the cost is wiped out when the cue and target are not perceived as parts of the same object. We interpret these findings as indicating that the same-location cost has been previously misinterpreted by both bottom-up and top-down theories of attentional capture. We propose that it is better understood as a consequence of object updating, namely, as the cost of updating the information stored about an object when this object changes across time.

X-train: teaching professionals remotely.

Science.gov (United States)

Santerre, Charles R

2005-05-01

Increased popularity of the Internet, along with the development of new software applications have dramatically improved our ability to create and deliver online continuing education trainings to professionals in the areas of nutrition and food safety. In addition, these technological advances permit effective and affordable measurement of training outcomes, i.e., changes in knowledge, attitude, and behavior, that result from these educational efforts. Impact assessment of engagement programs is becoming increasing important for demonstrating the value of training activities to stakeholders. A novel software program, called X-Train, takes advantage of technological advances (databases, computer graphics, Web-based interfaces, and network speed) for delivering high-quality trainings to teachers and health care professionals. X-Train automatically collects outcome data, and generates and sends certificates of completion and communicates with participants through electronic messages. X-Train can be used as a collaborative tool whereby experts from various academic institutions are brought together to develop Web-based trainings. Finally, X-Train uses a unique approach that encourages cooperative extension specialists and educators to promote these educational opportunities within their state or county.

DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG.

Science.gov (United States)

Supratak, Akara; Dong, Hao; Wu, Chao; Guo, Yike

2017-11-01

This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-engineered features, which require prior knowledge of sleep analysis. Only a few of them encode the temporal information, such as transition rules, which is important for identifying the next sleep stages, into the extracted features. In the proposed model, we utilize convolutional neural networks to extract time-invariant features, and bidirectional-long short-term memory to learn transition rules among sleep stages automatically from EEG epochs. We implement a two-step training algorithm to train our model efficiently. We evaluated our model using different single-channel EEGs (F4-EOG (left), Fpz-Cz, and Pz-Oz) from two public sleep data sets, that have different properties (e.g., sampling rate) and scoring standards (AASM and R&K). The results showed that our model achieved similar overall accuracy and macro F1-score (MASS: 86.2%-81.7, Sleep-EDF: 82.0%-76.9) compared with the state-of-the-art methods (MASS: 85.9%-80.5, Sleep-EDF: 78.9%-73.7) on both data sets. This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different data sets without utilizing any hand-engineered features.

Automatic recognition of conceptualization zones in scientific articles and two life science applications.

Science.gov (United States)

Liakata, Maria; Saha, Shyamasree; Dobnik, Simon; Batchelor, Colin; Rebholz-Schuhmann, Dietrich

2012-04-01

Scholarly biomedical publications report on the findings of a research investigation. Scientists use a well-established discourse structure to relate their work to the state of the art, express their own motivation and hypotheses and report on their methods, results and conclusions. In previous work, we have proposed ways to explicitly annotate the structure of scientific investigations in scholarly publications. Here we present the means to facilitate automatic access to the scientific discourse of articles by automating the recognition of 11 categories at the sentence level, which we call Core Scientific Concepts (CoreSCs). These include: Hypothesis, Motivation, Goal, Object, Background, Method, Experiment, Model, Observation, Result and Conclusion. CoreSCs provide the structure and context to all statements and relations within an article and their automatic recognition can greatly facilitate biomedical information extraction by characterizing the different types of facts, hypotheses and evidence available in a scientific publication. We have trained and compared machine learning classifiers (support vector machines and conditional random fields) on a corpus of 265 full articles in biochemistry and chemistry to automatically recognize CoreSCs. We have evaluated our automatic classifications against a manually annotated gold standard, and have achieved promising accuracies with 'Experiment', 'Background' and 'Model' being the categories with the highest F1-scores (76%, 62% and 53%, respectively). We have analysed the task of CoreSC annotation both from a sentence classification as well as sequence labelling perspective and we present a detailed feature evaluation. The most discriminative features are local sentence features such as unigrams, bigrams and grammatical dependencies while features encoding the document structure, such as section headings, also play an important role for some of the categories. We discuss the usefulness of automatically generated Core

Automatic creation of simulation configuration

International Nuclear Information System (INIS)

Oudot, G.; Poizat, F.

1993-01-01

SIPA, which stands for 'Simulator for Post Accident', includes: 1) a sophisticated software oriented workshop SWORD (which stands for 'Software Workshop Oriented towards Research and Development') designed in the ADA language including integrated CAD system and software tools for automatic generation of simulation software and man-machine interface in order to operate run-time simulation; 2) a 'simulator structure' based on hardware equipment and software for supervision and communications; 3) simulation configuration generated by SWORD, operated under the control of the 'simulator structure' and run on a target computer. SWORD has already been used to generate two simulation configurations (French 900 MW and 1300 MW nuclear power plants), which are now fully operational on the SIPA training simulator. (Z.S.) 1 ref

Automatic identification in mining

Energy Technology Data Exchange (ETDEWEB)

Puckett, D; Patrick, C [Mine Computers and Electronics Inc., Morehead, KY (United States)

1998-06-01

The feasibility of monitoring the locations and vital statistics of equipment and personnel in surface and underground mining operations has increased with advancements in radio frequency identification (RFID) technology. This paper addresses the use of RFID technology, which is relatively new to the mining industry, to track surface equipment in mine pits, loading points and processing facilities. Specific applications are discussed, including both simplified and complex truck tracking systems and an automatic pit ticket system. This paper concludes with a discussion of the future possibilities of using RFID technology in mining including monitoring heart and respiration rates, body temperatures and exertion levels; monitoring repetitious movements for the study of work habits; and logging air quality via personnel sensors. 10 refs., 5 figs.

AUTOMATIC BUILDING OUTLINING FROM MULTI-VIEW OBLIQUE IMAGES

Directory of Open Access Journals (Sweden)

J. Xiao

2012-07-01

Full Text Available Automatic building detection plays an important role in many applications. Multiple overlapped airborne images as well as lidar point clouds are among the most popular data sources used for this purpose. Multi-view overlapped oblique images bear both height and colour information, and additionally we explicitly have access to the vertical extent of objects, therefore we explore the usability of this data source solely to detect and outline buildings in this paper. The outline can then be used for further 3D modelling. In the previous work, building hypotheses are generated using a box model based on detected façades from four directions. In each viewing direction, façade edges extracted from images and height information by stereo matching from an image pair is used for the façade detection. Given that many façades were missing due to occlusion or lack of texture whilst building roofs can be viewed in most images, this work mainly focuses on improve the building box outline by adding roof information. Stereo matched point cloud generated from oblique images are combined with the features from images. Initial roof patches are located in the point cloud. Then AdaBoost is used to integrate geometric and radiometric attributes extracted from oblique image on grid pixel level with the aim to refine the roof area. Generalized contours of the roof pixels are taken as building outlines. The preliminary test has been done by training with five buildings and testing around sixty building clusters. The proposed method performs well concerning covering the irregular roofs as well as improve the sides location of slope roof buildings. Outline result comparing with cadastral map shows almost all above 70% completeness and correctness in an area-based assessment, as well as 20% to 40% improvement in correctness with respect to our previous work.

P2-13: Location word Cues' Effect on Location Discrimination Task: Cross-Modal Study

Directory of Open Access Journals (Sweden)

Satoko Ohtsuka

2012-10-01

Full Text Available As is well known, participants are slower and make more errors in responding to the display color of an incongruent color word than a congruent one. This traditional stroop effect is often accounted for with relatively automatic and dominant word processing. Although the word dominance account has been widely supported, it is not clear in what extent of perceptual tasks it is valid. Here we aimed to examine whether the word dominance effect is observed in location stroop tasks and in audio-visual situations. The participants were required to press a key according to the location of visual (Experiment 1 and audio (Experiment 2 targets, left or right, as soon as possible. A cue of written (Experiments 1a and 2a or spoken (Experiments 1b and 2b location words, “left” or “right”, was presented on the left or right side of the fixation with cue lead times (CLT of 200 ms and 1200 ms. Reaction time from target presentation to key press was recorded as a dependent variable. The results were that the location validity effect was marked in within-modality but less so in cross-modality trials. The word validity effect was strong in within- but not in cross-modality trials. The CLT gave some effect of inhibition of return. So the word dominance could be less effective in location tasks and in cross-modal situations. The spatial correspondence seems to overcome the word effect.

Automatic delineation and 3D visualization of the human ventricular system using probabilistic neural networks

Science.gov (United States)

Hatfield, Fraser N.; Dehmeshki, Jamshid

1998-09-01

Neurosurgery is an extremely specialized area of medical practice, requiring many years of training. It has been suggested that virtual reality models of the complex structures within the brain may aid in the training of neurosurgeons as well as playing an important role in the preparation for surgery. This paper focuses on the application of a probabilistic neural network to the automatic segmentation of the ventricles from magnetic resonance images of the brain, and their three dimensional visualization.

Individual Differences in Automatic Emotion Regulation Interact with Primed Emotion Regulation during an Anger Provocation

Directory of Open Access Journals (Sweden)

Ping Hu

2017-04-01

Full Text Available The current study investigated the interactive effects of individual differences in automatic emotion regulation (AER and primed emotion regulation strategy on skin conductance level (SCL and heart rate during provoked anger. The study was a 2 × 2 [AER tendency (expression vs. control × priming (expression vs. control] between subject design. Participants were assigned to two groups according to their performance on an emotion regulation-IAT (differentiating automatic emotion control tendency and automatic emotion expression tendency. Then participants of the two groups were randomly assigned to two emotion regulation priming conditions (emotion control priming or emotion expression priming. Anger was provoked by blaming participants for slow performance during a subsequent backward subtraction task. In anger provocation, SCL of individuals with automatic emotion control tendencies in the control priming condition was lower than of those with automatic emotion control tendencies in the expression priming condition. However, SCL of individuals with automatic emotion expression tendencies did no differ in the automatic emotion control priming or the automatic emotion expression priming condition. Heart rate during anger provocation was higher in individuals with automatic emotion expression tendencies than in individuals with automatic emotion control tendencies regardless of priming condition. This pattern indicates an interactive effect of individual differences in AER and emotion regulation priming on SCL, which is an index of emotional arousal. Heart rate was only sensitive to the individual differences in AER, and did not reflect this interaction. This finding has implications for clinical studies of the use of emotion regulation strategy training suggesting that different practices are optimal for individuals who differ in AER tendencies.

Automatic physical inference with information maximizing neural networks

Science.gov (United States)

Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.

2018-04-01

Compressing large data sets to a manageable number of summaries that are informative about the underlying parameters vastly simplifies both frequentist and Bayesian inference. When only simulations are available, these summaries are typically chosen heuristically, so they may inadvertently miss important information. We introduce a simulation-based machine learning technique that trains artificial neural networks to find nonlinear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). In test cases where the posterior can be derived exactly, likelihood-free inference based on automatically derived IMNN summaries produces nearly exact posteriors, showing that these summaries are good approximations to sufficient statistics. In a series of numerical examples of increasing complexity and astrophysical relevance we show that IMNNs are robustly capable of automatically finding optimal, nonlinear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima. We anticipate that the automatic physical inference method described in this paper will be essential to obtain both accurate and precise cosmological parameter estimates from complex and large astronomical data sets, including those from LSST and Euclid.

Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning.

Science.gov (United States)

Wang, Guan; Sun, Yu; Wang, Jianxin

2017-01-01

Automatic and accurate estimation of disease severity is essential for food security, disease management, and yield loss prediction. Deep learning, the latest breakthrough in computer vision, is promising for fine-grained disease severity classification, as the method avoids the labor-intensive feature engineering and threshold-based segmentation. Using the apple black rot images in the PlantVillage dataset, which are further annotated by botanists with four severity stages as ground truth, a series of deep convolutional neural networks are trained to diagnose the severity of the disease. The performances of shallow networks trained from scratch and deep models fine-tuned by transfer learning are evaluated systemically in this paper. The best model is the deep VGG16 model trained with transfer learning, which yields an overall accuracy of 90.4% on the hold-out test set. The proposed deep learning model may have great potential in disease control for modern agriculture.

Automatic construction of 3D-ASM intensity models by simulating image acquisition: application to myocardial gated SPECT studies.

Science.gov (United States)

Tobon-Gomez, Catalina; Butakoff, Constantine; Aguade, Santiago; Sukno, Federico; Moragas, Gloria; Frangi, Alejandro F

2008-11-01

Active shape models bear a great promise for model-based medical image analysis. Their practical use, though, is undermined due to the need to train such models on large image databases. Automatic building of point distribution models (PDMs) has been successfully addressed and a number of autolandmarking techniques are currently available. However, the need for strategies to automatically build intensity models around each landmark has been largely overlooked in the literature. This work demonstrates the potential of creating intensity models automatically by simulating image generation. We show that it is possible to reuse a 3D PDM built from computed tomography (CT) to segment gated single photon emission computed tomography (gSPECT) studies. Training is performed on a realistic virtual population where image acquisition and formation have been modeled using the SIMIND Monte Carlo simulator and ASPIRE image reconstruction software, respectively. The dataset comprised 208 digital phantoms (4D-NCAT) and 20 clinical studies. The evaluation is accomplished by comparing point-to-surface and volume errors against a proper gold standard. Results show that gSPECT studies can be successfully segmented by models trained under this scheme with subvoxel accuracy. The accuracy in estimated LV function parameters, such as end diastolic volume, end systolic volume, and ejection fraction, ranged from 90.0% to 94.5% for the virtual population and from 87.0% to 89.5% for the clinical population.

An Automatic Assembling System for Sealing Rings Based on Machine Vision

Directory of Open Access Journals (Sweden)

Mingyu Gao

2017-01-01

Full Text Available In order to grab and place the sealing rings of battery lid quickly and accurately, an automatic assembling system for sealing rings based on machine vision is developed in this paper. The whole system is composed of the light sources, cameras, industrial control units, and a 4-degree-of-freedom industrial robot. Specifically, the sealing rings are recognized and located automatically with the machine vision module. Then industrial robot is controlled for grabbing the sealing rings dynamically under the joint work of multiple control units and visual feedback. Furthermore, the coordinates of the fast-moving battery lid are tracked by the machine vision module. Finally the sealing rings are placed on the sealing ports of battery lid accurately and automatically. Experimental results demonstrate that the proposed system can grab the sealing rings and place them on the sealing port of the fast-moving battery lid successfully. More importantly, the proposed system can improve the efficiency of the battery production line obviously.

Artificial intelligence in sports on the example of weight training.

Science.gov (United States)

Novatchkov, Hristo; Baca, Arnold

2013-01-01

The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 10-12 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice. Key pointsArtificial intelligence is a promising field for sport-related analysis.Implementations integrating pattern recognition techniques enable the automatic evaluation of data

Modular training as technology of professional skills development of mechanical engineers

OpenAIRE

Shamshina Irina

2016-01-01

There are main provisions of modular training program by “Theory of Automatic Control” for students of technical universities is treating. Analyze of advantages and disadvantages of modular training system in comparison with the traditional system in the formation of future engineers’ professional skills. Detection of changes in the level of learning, basic skills and motivational sphere of students en-rolled in the modular training program.

GLOBAL SOLAR RADIATION INTERCEPTION BY GRAPEVINES TRAINED TO A VERTICAL TRELLIS SYSTEM

Directory of Open Access Journals (Sweden)

CLAUDIA GUIMARÃES CAMARGO CAMPOS

2016-01-01

Full Text Available ABSTRACT In this paper we assess the utilization of radiant energy in the growing of grapevines (Cabernet Sauvignon trained to a vertical trellis system, and estimate the global solar radiation interception taking into account the physical characteristics of the training system at different phenological stages. The experiment was based on daily measurements of global solar radiation made by an automatic weather station placed at the vineyard of a winery located in the municipality of São Joaquim, in the southern Brazilian State of Santa Catarina (Villa Francioni winery, 28º 15’ 14” S, 49º 57’ 02” W, 1294m a.s.l.. Growth and phenological development of the shoots were evaluated. The global solar radiation is intercepted by the canopy (trained to a vertical trellis system in different orientations and the accumulated total is slightly greater on the east than on the west face of the canopy, especially after flowering. The daily variability of global solar radiation intercepted by the canopy is greater after flowering. The accumulated solar energy incident on the canopy increases until the onset of ripening. From the results, vineyards trained to a vertical trellis system in the north-south direction provide favorable sunlight exposure to leaves and fruits and are promising in quality and productivity.

Automatic Fiscal Stabilizers

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Narcis Eduard Mitu

2013-11-01

Full Text Available Policies or institutions (built into an economic system that automatically tend to dampen economic cycle fluctuations in income, employment, etc., without direct government intervention. For example, in boom times, progressive income tax automatically reduces money supply as incomes and spendings rise. Similarly, in recessionary times, payment of unemployment benefits injects more money in the system and stimulates demand. Also called automatic stabilizers or built-in stabilizers.

'H-Bahn' - Dortmund demonstration system. Automatic vehicle protection system

Energy Technology Data Exchange (ETDEWEB)

Rosenkranz

1984-01-01

The automatic vehicle protection system of the H-Bahn at the Universtiy of Dortmund is responsible for fail-safe operating of the automatic vehicles. Its functions are protection of vehicle operation and protection of passengers boarding and leaving the vehicles. These functions are managed decentrally by two fail-safe operating controllers. Besides the well-known relay-techniques of railway-fail-safe systems, electronics are applied which are based on safe operating URTL-microcontrollers. These are controlled by software stored in EPROMs. A connection link using glass-fibres serves for safe data-exchange between the two fail-safe operating controllers. The experts' favourable reports on 'train protection and safety during passenger processing' were completed in March 84; thus, transportation of passengers could start in April 84.

Automatic bladder segmentation on CBCT for multiple plan ART of bladder cancer using a patient-specific bladder model

Energy Technology Data Exchange (ETDEWEB)

Xiangfei, Chai; Hulshof, Maarten; Bel, Arjan [Department of Radiotherapy, Academic medical Center, University of Amsterdam, 1105 AZ, Amsterdam (Netherlands); Van Herk, Marcel; Betgen, Anja [Department of Radiotherapy, The Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, 1066 CX, Amsterdam (Netherlands)

2012-06-21

In multiple plan adaptive radiotherapy (ART) strategies of bladder cancer, a library of plans corresponding to different bladder volumes is created based on images acquired in early treatment sessions. Subsequently, the plan for the smallest PTV safely covering the bladder on cone-beam CT (CBCT) is selected as the plan of the day. The aim of this study is to develop an automatic bladder segmentation approach suitable for CBCT scans and test its ability to select the appropriate plan from the library of plans for such an ART procedure. Twenty-three bladder cancer patients with a planning CT and on average 11.6 CBCT scans were included in our study. For each patient, all CBCT scans were matched to the planning CT on bony anatomy. Bladder contours were manually delineated for each planning CT (for model building) and CBCT (for model building and validation). The automatic segmentation method consisted of two steps. A patient-specific bladder deformation model was built from the training data set of each patient (the planning CT and the first five CBCT scans). Then, the model was applied to automatically segment bladders in the validation data of the same patient (the remaining CBCT scans). Principal component analysis (PCA) was applied to the training data to model patient-specific bladder deformation patterns. The number of PCA modes for each patient was chosen such that the bladder shapes in the training set could be represented by such number of PCA modes with less than 0.1 cm mean residual error. The automatic segmentation started from the bladder shape of a reference CBCT, which was adjusted by changing the weight of each PCA mode. As a result, the segmentation contour was deformed consistently with the training set to fit the bladder in the validation image. A cost function was defined by the absolute difference between the directional gradient field of reference CBCT sampled on the corresponding bladder contour and the directional gradient field of validation

Automatic bladder segmentation on CBCT for multiple plan ART of bladder cancer using a patient-specific bladder model

International Nuclear Information System (INIS)

Chai Xiangfei; Hulshof, Maarten; Bel, Arjan; Van Herk, Marcel; Betgen, Anja

2012-01-01

In multiple plan adaptive radiotherapy (ART) strategies of bladder cancer, a library of plans corresponding to different bladder volumes is created based on images acquired in early treatment sessions. Subsequently, the plan for the smallest PTV safely covering the bladder on cone-beam CT (CBCT) is selected as the plan of the day. The aim of this study is to develop an automatic bladder segmentation approach suitable for CBCT scans and test its ability to select the appropriate plan from the library of plans for such an ART procedure. Twenty-three bladder cancer patients with a planning CT and on average 11.6 CBCT scans were included in our study. For each patient, all CBCT scans were matched to the planning CT on bony anatomy. Bladder contours were manually delineated for each planning CT (for model building) and CBCT (for model building and validation). The automatic segmentation method consisted of two steps. A patient-specific bladder deformation model was built from the training data set of each patient (the planning CT and the first five CBCT scans). Then, the model was applied to automatically segment bladders in the validation data of the same patient (the remaining CBCT scans). Principal component analysis (PCA) was applied to the training data to model patient-specific bladder deformation patterns. The number of PCA modes for each patient was chosen such that the bladder shapes in the training set could be represented by such number of PCA modes with less than 0.1 cm mean residual error. The automatic segmentation started from the bladder shape of a reference CBCT, which was adjusted by changing the weight of each PCA mode. As a result, the segmentation contour was deformed consistently with the training set to fit the bladder in the validation image. A cost function was defined by the absolute difference between the directional gradient field of reference CBCT sampled on the corresponding bladder contour and the directional gradient field of validation

The Origins of Belief Representation: Monkeys Fail to Automatically Represent Others’ Beliefs

Science.gov (United States)

Martin, Alia; Santos, Laurie R.

2014-01-01

Young infants’ successful performance on false belief tasks has led several researchers to argue that there may be a core knowledge system for representing the beliefs of other agents, emerging early in human development and constraining automatic belief processing into adulthood. One way to investigate this purported core belief representation system is to examine whether non-human primates share such a system. Although non-human primates have historically performed poorly on false belief tasks that require executive function capacities, little work has explored how primates perform on more automatic measures of belief processing. To get at this issue, we modified Kovács et al. (2010)’s test of automatic belief representation to examine whether one non-human primate species—the rhesus macaque (Macaca mulatta)—is automatically influenced by another agent’s beliefs when tracking an object’s location. Monkeys saw an event in which a human agent watched an apple move back and forth between two boxes and an outcome in which one box was revealed to be empty. By occluding segments of the apple’s movement from either the monkey or the agent, we manipulated both the monkeys’ belief (true or false) and agent’s belief (true or false) about the final location of the apple. We found that monkeys looked longer at events that violated their own beliefs than at events that were consistent with their beliefs. In contrast to human infants, however, monkeys’ expectations were not influenced by another agent’s beliefs, suggesting that belief representation may be an aspect of core knowledge unique to humans. PMID:24374209

Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.

Science.gov (United States)

Zeng, Xueqiang; Luo, Gang

2017-12-01

Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.

Automatic determination of L/H transition times in DIII-D through a collaborative distributed environment

International Nuclear Information System (INIS)

Farias, G.; Vega, J.; González, S.; Pereira, A.; Lee, X.; Schissel, D.; Gohil, P.

2012-01-01

Highlights: ► An automatic predictor of L/H transition times has been implemented for the DIII-D tokamak. ► The system predicts the transition combining two techniques: a morphological pattern recognition algorithm and a support vector machines multi-layer model. ► The predictor is employed within a collaborative distributed computing environment. The system is trained remotely in the Ciemat computer cluster and operated on the DIII-D site. - Abstract: An automatic predictor of L/H transition times has been implemented for the DIII-D tokamak. The system predicts the transition combining two techniques: A morphological pattern recognition algorithm, which estimates the transition based on the waveform of a Dα emission signal, and a support vector machines multi-layer model, which predicts the L/H transition using a non-parametric model. The predictor is employed within a collaborative distributed computing environment. The system is trained remotely in the Ciemat computer cluster and operated on the DIII-D site.

Mobile Security Using Android: Locate Your Droid

OpenAIRE

Mr. Amandeep Singh Arora; Ms. Jasvinder Kumar

2015-01-01

For the past several decades, designers have processed security for a wide variety of applications from mobiles to automatic machines. Losing a smart phone can be an especially bad thing, since they are so expensive, and carry so much information. This application needs to be installed before. With this application user has the choice to launch phone tracking and finding cell phone by sending SMS requests to their lost phone and even getting the locations of the phones that are lo...

Estimation of break location and size for loss of coolant accidents using neural networks

International Nuclear Information System (INIS)

Na, Man Gyun; Shin, Sun Ho; Jung, Dong Won; Kim, Soong Pyung; Jeong, Ji Hwan; Lee, Byung Chul

2004-01-01

In this work, a probabilistic neural network (PNN) that has been applied well to the classification problems is used in order to identify the break locations of loss of coolant accidents (LOCA) such as hot-leg, cold-leg and steam generator tubes. Also, a fuzzy neural network (FNN) is designed to estimate the break size. The inputs to PNN and FNN are time-integrated values obtained by integrating measurement signals during a short time interval after reactor scram. An automatic structure constructor for the fuzzy neural network automatically selects the input variables from the time-integrated values of many measured signals, and optimizes the number of rules and its related parameters. It is verified that the proposed algorithm identifies very well the break locations of LOCAs and also, estimate their break size accurately

Automatic and controlled components of judgment and decision making.

Science.gov (United States)

Ferreira, Mario B; Garcia-Marques, Leonel; Sherman, Steven J; Sherman, Jeffrey W

2006-11-01

The categorization of inductive reasoning into largely automatic processes (heuristic reasoning) and controlled analytical processes (rule-based reasoning) put forward by dual-process approaches of judgment under uncertainty (e.g., K. E. Stanovich & R. F. West, 2000) has been primarily a matter of assumption with a scarcity of direct empirical findings supporting it. The present authors use the process dissociation procedure (L. L. Jacoby, 1991) to provide convergent evidence validating a dual-process perspective to judgment under uncertainty based on the independent contributions of heuristic and rule-based reasoning. Process dissociations based on experimental manipulation of variables were derived from the most relevant theoretical properties typically used to contrast the two forms of reasoning. These include processing goals (Experiment 1), cognitive resources (Experiment 2), priming (Experiment 3), and formal training (Experiment 4); the results consistently support the author's perspective. They conclude that judgment under uncertainty is neither an automatic nor a controlled process but that it reflects both processes, with each making independent contributions.

LEARNING VECTOR QUANTIZATION FOR ADAPTED GAUSSIAN MIXTURE MODELS IN AUTOMATIC SPEAKER IDENTIFICATION

Directory of Open Access Journals (Sweden)

IMEN TRABELSI

2017-05-01

Full Text Available Speaker Identification (SI aims at automatically identifying an individual by extracting and processing information from his/her voice. Speaker voice is a robust a biometric modality that has a strong impact in several application areas. In this study, a new combination learning scheme has been proposed based on Gaussian mixture model-universal background model (GMM-UBM and Learning vector quantization (LVQ for automatic text-independent speaker identification. Features vectors, constituted by the Mel Frequency Cepstral Coefficients (MFCC extracted from the speech signal are used to train the New England subset of the TIMIT database. The best results obtained (90% for gender- independent speaker identification, 97 % for male speakers and 93% for female speakers for test data using 36 MFCC features.

Automatic titrator for high precision plutonium assay

International Nuclear Information System (INIS)

Jackson, D.D.; Hollen, R.M.

1986-01-01

Highly precise assay of plutonium metal is required for accountability measurements. We have developed an automatic titrator for this determination which eliminates analyst bias and requires much less analyst time. The analyst is only required to enter sample data and start the titration. The automated instrument titrates the sample, locates the end point, and outputs the results as a paper tape printout. Precision of the titration is less than 0.03% relative standard deviation for a single determination at the 250-mg plutonium level. The titration time is less than 5 min

The location but not the attributes of visual cues are automatically encoded into working memory.

Science.gov (United States)

Chen, Hui; Wyble, Brad

2015-02-01

Although it has been well known that visual cues affect the perception of subsequent visual stimuli, relatively little is known about how the cues themselves are processed. The present study attempted to characterize the processing of a visual cue by investigating what information about the cue is stored in terms of both location ("where" is the cue) and attributes ("what" are the attributes of the cue). In 11 experiments subjects performed several trials of reporting a target letter and then answered an unexpected question about the cue (e.g., the location, color, or identity of the cue). This surprise question revealed that participants could report the location of the cue even when the cue never indicated the target location and they were explicitly told to ignore it. Furthermore, the memory trace of this location information endured during encoding of the subsequent target. In contrast to location, attributes of the cue (e.g., color) were poorly reported, even for attributes that were used by subjects to perform the task. These results shed new light on the mechanisms underlying cueing effects and suggest also that the visual system may create empty object files in response to visual cues. Copyright © 2014 Elsevier Ltd. All rights reserved.

Age effects on voluntary and automatic adjustments in anti-pointing tasks

NARCIS (Netherlands)

Verneau, M.; Kamp, J. van der; Looze, M.P. de; Savelsbergh, G.J.P.

2016-01-01

We examined the effects of age on automatic and voluntary motor adjustments in pointing tasks. To this end, young (20–25 years) and middle-aged adults (48–62 years) were instructed to point at a target that could unexpectedly change its location (to the left or right) or its color (to green or red)

Semi-supervised learning based probabilistic latent semantic analysis for automatic image annotation

Institute of Scientific and Technical Information of China (English)

Tian Dongping

2017-01-01

In recent years, multimedia annotation problem has been attracting significant research attention in multimedia and computer vision areas, especially for automatic image annotation, whose purpose is to provide an efficient and effective searching environment for users to query their images more easily.In this paper, a semi-supervised learning based probabilistic latent semantic analysis ( PL-SA) model for automatic image annotation is presenred.Since it' s often hard to obtain or create la-beled images in large quantities while unlabeled ones are easier to collect, a transductive support vector machine ( TSVM) is exploited to enhance the quality of the training image data.Then, differ-ent image features with different magnitudes will result in different performance for automatic image annotation.To this end, a Gaussian normalization method is utilized to normalize different features extracted from effective image regions segmented by the normalized cuts algorithm so as to reserve the intrinsic content of images as complete as possible.Finally, a PLSA model with asymmetric mo-dalities is constructed based on the expectation maximization( EM) algorithm to predict a candidate set of annotations with confidence scores.Extensive experiments on the general-purpose Corel5k dataset demonstrate that the proposed model can significantly improve performance of traditional PL-SA for the task of automatic image annotation.

Automated location detection of injection site for preclinical stereotactic neurosurgery procedure

Science.gov (United States)

Abbaszadeh, Shiva; Wu, Hemmings C. H.

2017-03-01

Currently, during stereotactic neurosurgery procedures, the manual task of locating the proper area for needle insertion or implantation of electrode/cannula/optic fiber can be time consuming. The requirement of the task is to quickly and accurately find the location for insertion. In this study we investigate an automated method to locate the entry point of region of interest. This method leverages a digital image capture system, pattern recognition, and motorized stages. Template matching of known anatomical identifiable regions is used to find regions of interest (e.g. Bregma) in rodents. For our initial study, we tackle the problem of automatically detecting the entry point.

AUTOMATIC ARCHITECTURAL STYLE RECOGNITION

Directory of Open Access Journals (Sweden)

M. Mathias

2012-09-01

Full Text Available Procedural modeling has proven to be a very valuable tool in the field of architecture. In the last few years, research has soared to automatically create procedural models from images. However, current algorithms for this process of inverse procedural modeling rely on the assumption that the building style is known. So far, the determination of the building style has remained a manual task. In this paper, we propose an algorithm which automates this process through classification of architectural styles from facade images. Our classifier first identifies the images containing buildings, then separates individual facades within an image and determines the building style. This information could then be used to initialize the building reconstruction process. We have trained our classifier to distinguish between several distinct architectural styles, namely Flemish Renaissance, Haussmannian and Neoclassical. Finally, we demonstrate our approach on various street-side images.

Automatic annotation of protein motif function with Gene Ontology terms

Directory of Open Access Journals (Sweden)

Gopalakrishnan Vanathi

2004-09-01

Full Text Available Abstract Background Conserved protein sequence motifs are short stretches of amino acid sequence patterns that potentially encode the function of proteins. Several sequence pattern searching algorithms and programs exist foridentifying candidate protein motifs at the whole genome level. However, amuch needed and importanttask is to determine the functions of the newly identified protein motifs. The Gene Ontology (GO project is an endeavor to annotate the function of genes or protein sequences with terms from a dynamic, controlled vocabulary and these annotations serve well as a knowledge base. Results This paperpresents methods to mine the GO knowledge base and use the association between the GO terms assigned to a sequence and the motifs matched by the same sequence as evidence for predicting the functions of novel protein motifs automatically. The task of assigning GO terms to protein motifsis viewed as both a binary classification and information retrieval problem, where PROSITE motifs are used as samples for mode training and functional prediction. The mutual information of a motif and aGO term association isfound to be a very useful feature. We take advantageof the known motifs to train a logistic regression classifier, which allows us to combine mutual information with other frequency-based features and obtain a probability of correctassociation. The trained logistic regression model has intuitively meaningful and logically plausible parameter values, and performs very well empirically according to our evaluation criteria. Conclusions In this research, different methods for automatic annotation of protein motifs have been investigated. Empirical result demonstrated that the methods have a great potential for detecting and augmenting information about thefunctions of newly discovered candidate protein motifs.

Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning

Directory of Open Access Journals (Sweden)

Guan Wang

2017-01-01

Full Text Available Automatic and accurate estimation of disease severity is essential for food security, disease management, and yield loss prediction. Deep learning, the latest breakthrough in computer vision, is promising for fine-grained disease severity classification, as the method avoids the labor-intensive feature engineering and threshold-based segmentation. Using the apple black rot images in the PlantVillage dataset, which are further annotated by botanists with four severity stages as ground truth, a series of deep convolutional neural networks are trained to diagnose the severity of the disease. The performances of shallow networks trained from scratch and deep models fine-tuned by transfer learning are evaluated systemically in this paper. The best model is the deep VGG16 model trained with transfer learning, which yields an overall accuracy of 90.4% on the hold-out test set. The proposed deep learning model may have great potential in disease control for modern agriculture.

Applying machine-learning techniques to Twitter data for automatic hazard-event classification.

Science.gov (United States)

Filgueira, R.; Bee, E. J.; Diaz-Doce, D.; Poole, J., Sr.; Singh, A.

2017-12-01

The constant flow of information offered by tweets provides valuable information about all sorts of events at a high temporal and spatial resolution. Over the past year we have been analyzing in real-time geological hazards/phenomenon, such as earthquakes, volcanic eruptions, landslides, floods or the aurora, as part of the GeoSocial project, by geo-locating tweets filtered by keywords in a web-map. However, not all the filtered tweets are related with hazard/phenomenon events. This work explores two classification techniques for automatic hazard-event categorization based on tweets about the "Aurora". First, tweets were filtered using aurora-related keywords, removing stop words and selecting the ones written in English. For classifying the remaining between "aurora-event" or "no-aurora-event" categories, we compared two state-of-art techniques: Support Vector Machine (SVM) and Deep Convolutional Neural Networks (CNN) algorithms. Both approaches belong to the family of supervised learning algorithms, which make predictions based on labelled training dataset. Therefore, we created a training dataset by tagging 1200 tweets between both categories. The general form of SVM is used to separate two classes by a function (kernel). We compared the performance of four different kernels (Linear Regression, Logistic Regression, Multinomial Naïve Bayesian and Stochastic Gradient Descent) provided by Scikit-Learn library using our training dataset to build the SVM classifier. The results shown that the Logistic Regression (LR) gets the best accuracy (87%). So, we selected the SVM-LR classifier to categorise a large collection of tweets using the "dispel4py" framework.Later, we developed a CNN classifier, where the first layer embeds words into low-dimensional vectors. The next layer performs convolutions over the embedded word vectors. Results from the convolutional layer are max-pooled into a long feature vector, which is classified using a softmax layer. The CNN's accuracy

Automatic meter reading and PowerPlus services: Concept to implementation

Energy Technology Data Exchange (ETDEWEB)

Perks, D.R. [Alberta Power Ltd., Edmonton, AB (Canada)

1995-12-31

The Distribution Control System Inc.`s Two Way Automatic Communication System (TWACS) was implemented with GE Canada`s 170S automatic meter reader (AMR) at Alberta Power Ltd. Core and extended features are being marketed as PowerPlus{sup TM.} The technology used in the systems, design philosophy, systems components, outbound communication, inbound communication, throughput, and AMR were described. Objectives for the pilot project were to test reliability, accuracy and cost of implementation. Scope of the pilot, and project results were presented. Business aspects of PowerPlus{sup TM }marketing were described. Implementation schedule, constraints, technical problems, training, communication plan, strategy and 1994 year end status of the project were reviewed. Plans for continued development were described. It was predicted that the versatility of the TWACS system, and hard work of every department of Alberta Power will ensure that the implementation program will complete success. 5 figs.

Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework

Science.gov (United States)

2014-01-01

Motivation Knowing the location of a protein within the cell is important for understanding its function, role in biological processes, and potential use as a drug target. Much progress has been made in developing computational methods that predict single locations for proteins. Most such methods are based on the over-simplifying assumption that proteins localize to a single location. However, it has been shown that proteins localize to multiple locations. While a few recent systems attempt to predict multiple locations of proteins, their performance leaves much room for improvement. Moreover, they typically treat locations as independent and do not attempt to utilize possible inter-dependencies among locations. Our hypothesis is that directly incorporating inter-dependencies among locations into both the classifier-learning and the prediction process can improve location prediction performance. Results We present a new method and a preliminary system we have developed that directly incorporates inter-dependencies among locations into the location-prediction process of multiply-localized proteins. Our method is based on a collection of Bayesian network classifiers, where each classifier is used to predict a single location. Learning the structure of each Bayesian network classifier takes into account inter-dependencies among locations, and the prediction process uses estimates involving multiple locations. We evaluate our system on a dataset of single- and multi-localized proteins (the most comprehensive protein multi-localization dataset currently available, derived from the DBMLoc dataset). Our results, obtained by incorporating inter-dependencies, are significantly higher than those obtained by classifiers that do not use inter-dependencies. The performance of our system on multi-localized proteins is comparable to a top performing system (YLoc+), without being restricted only to location-combinations present in the training set. PMID:24646119

Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework.

Science.gov (United States)

Simha, Ramanuja; Shatkay, Hagit

2014-03-19

Knowing the location of a protein within the cell is important for understanding its function, role in biological processes, and potential use as a drug target. Much progress has been made in developing computational methods that predict single locations for proteins. Most such methods are based on the over-simplifying assumption that proteins localize to a single location. However, it has been shown that proteins localize to multiple locations. While a few recent systems attempt to predict multiple locations of proteins, their performance leaves much room for improvement. Moreover, they typically treat locations as independent and do not attempt to utilize possible inter-dependencies among locations. Our hypothesis is that directly incorporating inter-dependencies among locations into both the classifier-learning and the prediction process can improve location prediction performance. We present a new method and a preliminary system we have developed that directly incorporates inter-dependencies among locations into the location-prediction process of multiply-localized proteins. Our method is based on a collection of Bayesian network classifiers, where each classifier is used to predict a single location. Learning the structure of each Bayesian network classifier takes into account inter-dependencies among locations, and the prediction process uses estimates involving multiple locations. We evaluate our system on a dataset of single- and multi-localized proteins (the most comprehensive protein multi-localization dataset currently available, derived from the DBMLoc dataset). Our results, obtained by incorporating inter-dependencies, are significantly higher than those obtained by classifiers that do not use inter-dependencies. The performance of our system on multi-localized proteins is comparable to a top performing system (YLoc+), without being restricted only to location-combinations present in the training set.

Automation of controller's management by train work on the byelorussian ferrous road

Directory of Open Access Journals (Sweden)

A.A. Erofeev

2012-04-01

Full Text Available The description of the automated system of working out of the look-ahead train schedule is resulted. Appointment, structure and system structure are considered. Requirements to the entrance information are established. Procedures of automatic construction and dispatching updatings of the look-ahead train schedule are regulated

Perceptual learning of basic visual features remains task specific with Training-Plus-Exposure (TPE) training.

Science.gov (United States)

Cong, Lin-Juan; Wang, Ru-Jie; Yu, Cong; Zhang, Jun-Yun

2016-01-01

Visual perceptual learning is known to be specific to the trained retinal location, feature, and task. However, location and feature specificity can be eliminated by double-training or TPE training protocols, in which observers receive additional exposure to the transfer location or feature dimension via an irrelevant task besides the primary learning task Here we tested whether these new training protocols could even make learning transfer across different tasks involving discrimination of basic visual features (e.g., orientation and contrast). Observers practiced a near-threshold orientation (or contrast) discrimination task. Following a TPE training protocol, they also received exposure to the transfer task via performing suprathreshold contrast (or orientation) discrimination in alternating blocks of trials in the same sessions. The results showed no evidence for significant learning transfer to the untrained near-threshold contrast (or orientation) discrimination task after discounting the pretest effects and the suprathreshold practice effects. These results thus do not support a hypothetical task-independent component in perceptual learning of basic visual features. They also set the boundary of the new training protocols in their capability to enable learning transfer.

An automatic taxonomy of galaxy morphology using unsupervised machine learning

Science.gov (United States)

Hocking, Alex; Geach, James E.; Sun, Yi; Davey, Neil

2018-01-01

We present an unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous unsupervised machine learning approaches used in astronomy we use no pre-selection or pre-filtering of target galaxy type to identify galaxies that are similar. We demonstrate the technique on the Hubble Space Telescope (HST) Frontier Fields. By training the algorithm using galaxies from one field (Abell 2744) and applying the result to another (MACS 0416.1-2403), we show how the algorithm can cleanly separate early and late type galaxies without any form of pre-directed training for what an 'early' or 'late' type galaxy is. We then apply the technique to the HST Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) fields, creating a catalogue of approximately 60 000 classifications. We show how the automatic classification groups galaxies of similar morphological (and photometric) type and make the classifications public via a catalogue, a visual catalogue and galaxy similarity search. We compare the CANDELS machine-based classifications to human-classifications from the Galaxy Zoo: CANDELS project. Although there is not a direct mapping between Galaxy Zoo and our hierarchical labelling, we demonstrate a good level of concordance between human and machine classifications. Finally, we show how the technique can be used to identify rarer objects and present lensed galaxy candidates from the CANDELS imaging.

CERPI and CEREL, two computer codes for the automatic identification and determination of gamma emitters in thermal-neutron-activated samples

International Nuclear Information System (INIS)

Giannini, M.; Oliva, P.R.; Ramorino, M.C.

1979-01-01

A computer code that automatically analyzes gamma-ray spectra obtained with Ge(Li) detectors is described. The program contains such features as automatic peak location and fitting, determination of peak energies and intensities, nuclide identification, and calculation of masses and errors. Finally, the results obtained with this computer code for a lunar sample are reported and briefly discussed

Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.

Science.gov (United States)

Nguyen, Thanh; Bui, Vy; Lam, Van; Raub, Christopher B; Chang, Lin-Ching; Nehmetallah, George

2017-06-26

We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells' motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

CAALYX: a new generation of location-based services in healthcare

Directory of Open Access Journals (Sweden)

Sdogati Claudio

2007-03-01

Full Text Available Abstract Recent advances in mobile positioning systems and telecommunications are providing the technology needed for the development of location-aware tele-care applications. This paper introduces CAALYX – Complete Ambient Assisted Living Experiment, an EU-funded project that aims at increasing older people's autonomy and self-confidence by developing a wearable light device capable of measuring specific vital signs of the elderly, detecting falls and location, and communicating automatically in real-time with his/her care provider in case of an emergency, wherever the older person happens to be, at home or outside.

Peer-led training in basic life support and resuscitation using an automatic external defibrillator

DEFF Research Database (Denmark)

Løfgren, Bo; Petersen, Christina Børlum; Mikkelsen, Ronni

2009-01-01

Peer-led training has been identified as a useful tool for delivering undergraduate healthcare training. In this paper we describe the implementation of the European Resuscitation Council BLS/AED Course as a peer-led training program for medical students....

Smart intimation and location of faults in distribution system

Science.gov (United States)

Hari Krishna, K.; Srinivasa Rao, B.

2018-04-01

Location of faults in the distribution system is one of the most complicated problems that we are facing today. Identification of fault location and severity of fault within a short time is required to provide continuous power supply but fault identification and information transfer to the operator is the biggest challenge in the distribution network. This paper proposes a fault location method in the distribution system based on Arduino nano and GSM module with flame sensor. The main idea is to locate the fault in the distribution transformer by sensing the arc coming out from the fuse element. The biggest challenge in the distribution network is to identify the location and the severity of faults under different conditions. Well operated transmission and distribution systems will play a key role for uninterrupted power supply. Whenever fault occurs in the distribution system the time taken to locate and eliminate the fault has to be reduced. The proposed design was achieved with flame sensor and GSM module. Under faulty condition, the system will automatically send an alert message to the operator in the distribution system, about the abnormal conditions near the transformer, site code and its exact location for possible power restoration.

Finding weak points automatically

International Nuclear Information System (INIS)

Archinger, P.; Wassenberg, M.

1999-01-01

Operators of nuclear power stations have to carry out material tests at selected components by regular intervalls. Therefore a full automaticated test, which achieves a clearly higher reproducibility, compared to part automaticated variations, would provide a solution. In addition the full automaticated test reduces the dose of radiation for the test person. (orig.) [de

Development of an automatic scanning system for nuclear emulsion analysis in the OPERA experiment and study of neutrino interactions location

International Nuclear Information System (INIS)

Arrabito, L.

2007-10-01

Following Super Kamiokande and K2K experiments, Opera (Oscillation Project with Emulsion tracking Apparatus), aims to confirm neutrino oscillation in the atmospheric sector. Taking advantage of a technique already employed in Chorus and in Donut, the Emulsion Cloud Chamber (ECC), Opera will be able to observe the ν μ → ν τ oscillation, through the ν τ appearance in a pure ν μ beam. The Opera experiment, with its ∼ 100000 m 2 of nuclear emulsions, needs a very fast automatic scanning system. Optical and mechanics components have been customized in order to achieve a speed of about 20 cm 2 /hour per emulsion layer (44 μm thick), while keeping a sub-micro-metric resolution. The first part of this thesis was dedicated to the optimization of 4 scanning systems at the French scanning station, based in Lyon. An experimental study on a dry objective scanning system has also been realized. The obtained results show that the performances of dry scanning are similar with respect to the traditional oil scanning, so that it can be successfully used for Opera. The second part of this work was devoted to the study of the neutrino interaction location and reconstruction strategy actually used in Opera. A dedicated test beam was performed at CERN in order to simulate Opera conditions. The obtained results definitely confirm that the proposed strategy is well adapted for tau search. (author)

Blocking of Goal-Location Learning Based on Shape

Science.gov (United States)

Alexander, Tim; Wilson, Stuart P.; Wilson, Paul N.

2009-01-01

Using desktop, computer-simulated virtual environments (VEs), the authors conducted 5 experiments to investigate blocking of learning about a goal location based on Shape B as a consequence of preliminary training to locate that goal using Shape A. The shapes were large 2-dimensional horizontal figures on the ground. Blocking of spatial learning…

The automatic component of habit in health behavior: habit as cue-contingent automaticity.

Science.gov (United States)

Orbell, Sheina; Verplanken, Bas

2010-07-01

Habit might be usefully characterized as a form of automaticity that involves the association of a cue and a response. Three studies examined habitual automaticity in regard to different aspects of the cue-response relationship characteristic of unhealthy and healthy habits. In each study, habitual automaticity was assessed by the Self-Report Habit Index (SRHI). In Study 1 SRHI scores correlated with attentional bias to smoking cues in a Stroop task. Study 2 examined the ability of a habit cue to elicit an unwanted habit response. In a prospective field study, habitual automaticity in relation to smoking when drinking alcohol in a licensed public house (pub) predicted the likelihood of cigarette-related action slips 2 months later after smoking in pubs had become illegal. In Study 3 experimental group participants formed an implementation intention to floss in response to a specified situational cue. Habitual automaticity of dental flossing was rapidly enhanced compared to controls. The studies provided three different demonstrations of the importance of cues in the automatic operation of habits. Habitual automaticity assessed by the SRHI captured aspects of a habit that go beyond mere frequency or consistency of the behavior. PsycINFO Database Record (c) 2010 APA, all rights reserved.

Experience of automation failures in training: effects on trust, automation bias, complacency and performance.

Science.gov (United States)

Sauer, Juergen; Chavaillaz, Alain; Wastell, David

2016-06-01

This work examined the effects of operators' exposure to various types of automation failures in training. Forty-five participants were trained for 3.5 h on a simulated process control environment. During training, participants either experienced a fully reliable, automatic fault repair facility (i.e. faults detected and correctly diagnosed), a misdiagnosis-prone one (i.e. faults detected but not correctly diagnosed) or a miss-prone one (i.e. faults not detected). One week after training, participants were tested for 3 h, experiencing two types of automation failures (misdiagnosis, miss). The results showed that automation bias was very high when operators trained on miss-prone automation encountered a failure of the diagnostic system. Operator errors resulting from automation bias were much higher when automation misdiagnosed a fault than when it missed one. Differences in trust levels that were instilled by the different training experiences disappeared during the testing session. Practitioner Summary: The experience of automation failures during training has some consequences. A greater potential for operator errors may be expected when an automatic system failed to diagnose a fault than when it failed to detect one.

Fully automatic characterization and data collection from crystals of biological macromolecules

International Nuclear Information System (INIS)

Svensson, Olof; Malbet-Monaco, Stéphanie; Popov, Alexander; Nurizzo, Didier; Bowler, Matthew W.

2015-01-01

A fully automatic system has been developed that performs X-ray centring and characterization of, and data collection from, large numbers of cryocooled crystals without human intervention. Considerable effort is dedicated to evaluating macromolecular crystals at synchrotron sources, even for well established and robust systems. Much of this work is repetitive, and the time spent could be better invested in the interpretation of the results. In order to decrease the need for manual intervention in the most repetitive steps of structural biology projects, initial screening and data collection, a fully automatic system has been developed to mount, locate, centre to the optimal diffraction volume, characterize and, if possible, collect data from multiple cryocooled crystals. Using the capabilities of pixel-array detectors, the system is as fast as a human operator, taking an average of 6 min per sample depending on the sample size and the level of characterization required. Using a fast X-ray-based routine, samples are located and centred systematically at the position of highest diffraction signal and important parameters for sample characterization, such as flux, beam size and crystal volume, are automatically taken into account, ensuring the calculation of optimal data-collection strategies. The system is now in operation at the new ESRF beamline MASSIF-1 and has been used by both industrial and academic users for many different sample types, including crystals of less than 20 µm in the smallest dimension. To date, over 8000 samples have been evaluated on MASSIF-1 without any human intervention

Fully automatic characterization and data collection from crystals of biological macromolecules

Energy Technology Data Exchange (ETDEWEB)

Svensson, Olof; Malbet-Monaco, Stéphanie; Popov, Alexander; Nurizzo, Didier, E-mail: nurizzo@esrf.fr [European Synchrotron Radiation Facility, 71 Avenue des Martyrs, CS 40220, 38043 Grenoble (France); Bowler, Matthew W., E-mail: nurizzo@esrf.fr [European Molecular Biology Laboratory, Grenoble Outstation, 71 Avenue des Martyrs, CS 90181, 38042 Grenoble (France); Université Grenoble Alpes–EMBL–CNRS, Grenoble Outstation, 71 Avenue des Martyrs, CS 90181, 38042 Grenoble (France); European Synchrotron Radiation Facility, 71 Avenue des Martyrs, CS 40220, 38043 Grenoble (France)

2015-07-31

A fully automatic system has been developed that performs X-ray centring and characterization of, and data collection from, large numbers of cryocooled crystals without human intervention. Considerable effort is dedicated to evaluating macromolecular crystals at synchrotron sources, even for well established and robust systems. Much of this work is repetitive, and the time spent could be better invested in the interpretation of the results. In order to decrease the need for manual intervention in the most repetitive steps of structural biology projects, initial screening and data collection, a fully automatic system has been developed to mount, locate, centre to the optimal diffraction volume, characterize and, if possible, collect data from multiple cryocooled crystals. Using the capabilities of pixel-array detectors, the system is as fast as a human operator, taking an average of 6 min per sample depending on the sample size and the level of characterization required. Using a fast X-ray-based routine, samples are located and centred systematically at the position of highest diffraction signal and important parameters for sample characterization, such as flux, beam size and crystal volume, are automatically taken into account, ensuring the calculation of optimal data-collection strategies. The system is now in operation at the new ESRF beamline MASSIF-1 and has been used by both industrial and academic users for many different sample types, including crystals of less than 20 µm in the smallest dimension. To date, over 8000 samples have been evaluated on MASSIF-1 without any human intervention.

CERPI and CEREL, two computer codes for the automatic identification and determination of gamma emitters in thermal neutron activated samples

International Nuclear Information System (INIS)

Giannini, M.; Oliva, P.R.; Ramorino, C.

1978-01-01

A description is given of a computer code which automatically analyses gamma-ray spectra obtained with Ge(Li) detectors. The program contains features as automatic peak location and fitting, determination of peak energies and intensities, nuclide identification and calculation of masses and errors. Finally the results obtained with our computer code for a lunar sample are reported and briefly discussed

Automatic characterization of loose parts impact damage risk parameters

International Nuclear Information System (INIS)

Glass, S.W.; Phillips, J.M.

1985-01-01

Loose parts caught in the high-velocity flows of the reactor coolant fluid strike against nuclear steam supply system (NSSS) components and can cause significant damage. Loose parts monitor systems (LPMS) have been available for years to detect metal-to-metal impacts. Once detected, however, an assessment of the damage risk potential for leaving the part in the system versus shutting it down and removing the part must be made. The principal parameters used in the damage risk assessment are time delays between the first and subsequent sensor indications (used to assess the impact location) and a correlation between the waveform and the impact energy of the part (how hard the part impacted). These parameters are not well suited to simple automatic techniques. The task has historically been performed by loose parts diagnostic experts who base much of their evaluation on experience and subjective interpretation of impact data waveforms. Three of the principal goals in developing the Babcock and Wilcox (B and W) LPMS-III were (a) to develop an accurate automatic assessment for the time delays, (b) to develop an automatic estimate of the impact energy, and (c) to present the data in a meaningful manner to the operator

Designing simulator-based training: An approach integrating cognitive task analysis and four-component instructional design

NARCIS (Netherlands)

Tjiam, I.M.; Schout, B.M.; Hendrikx, A.J.M.; Scherpbier, A.J.J.A.; Witjes, J.A.; Van Merrienboer, J.J.

2012-01-01

Most studies of simulator-based surgical skills training have focused on the acquisition of psychomotor skills, but surgical procedures are complex tasks requiring both psychomotor and cognitive skills. As skills training is modelled on expert performance consisting partly of unconscious automatic

Training IBM Watson using Automatically Generated Question-Answer Pairs

OpenAIRE

Lee, Jangho; Kim, Gyuwan; Yoo, Jaeyoon; Jung, Changwoo; Kim, Minseok; Yoon, Sungroh

2016-01-01

IBM Watson is a cognitive computing system capable of question answering in natural languages. It is believed that IBM Watson can understand large corpora and answer relevant questions more effectively than any other question-answering system currently available. To unleash the full power of Watson, however, we need to train its instance with a large number of well-prepared question-answer pairs. Obviously, manually generating such pairs in a large quantity is prohibitively time consuming and...

Method for Automatic Selection of Parameters in Normal Tissue Complication Probability Modeling.

Science.gov (United States)

Christophides, Damianos; Appelt, Ane L; Gusnanto, Arief; Lilley, John; Sebag-Montefiore, David

2018-07-01

To present a fully automatic method to generate multiparameter normal tissue complication probability (NTCP) models and compare its results with those of a published model, using the same patient cohort. Data were analyzed from 345 rectal cancer patients treated with external radiation therapy to predict the risk of patients developing grade 1 or ≥2 cystitis. In total, 23 clinical factors were included in the analysis as candidate predictors of cystitis. Principal component analysis was used to decompose the bladder dose-volume histogram into 8 principal components, explaining more than 95% of the variance. The data set of clinical factors and principal components was divided into training (70%) and test (30%) data sets, with the training data set used by the algorithm to compute an NTCP model. The first step of the algorithm was to obtain a bootstrap sample, followed by multicollinearity reduction using the variance inflation factor and genetic algorithm optimization to determine an ordinal logistic regression model that minimizes the Bayesian information criterion. The process was repeated 100 times, and the model with the minimum Bayesian information criterion was recorded on each iteration. The most frequent model was selected as the final "automatically generated model" (AGM). The published model and AGM were fitted on the training data sets, and the risk of cystitis was calculated. The 2 models had no significant differences in predictive performance, both for the training and test data sets (P value > .05) and found similar clinical and dosimetric factors as predictors. Both models exhibited good explanatory performance on the training data set (P values > .44), which was reduced on the test data sets (P values < .05). The predictive value of the AGM is equivalent to that of the expert-derived published model. It demonstrates potential in saving time, tackling problems with a large number of parameters, and standardizing variable selection in NTCP

Microprocessor controlled system for automatic and semi-automatic syntheses of radiopharmaceuticals

International Nuclear Information System (INIS)

Ruth, T.J.; Adam, M.J.; Morris, D.; Jivan, S.

1986-01-01

A computer based system has been constructed to control the automatic synthesis of 2-deoxy-2-( 18 F)fluoro-D-glucose and is also being used in the development of an automatic synthesis of L-6-( 18 F)fluorodopa. (author)

Automatic Earthquake Detection by Active Learning

Science.gov (United States)

Bergen, K.; Beroza, G. C.

2017-12-01

In recent years, advances in machine learning have transformed fields such as image recognition, natural language processing and recommender systems. Many of these performance gains have relied on the availability of large, labeled data sets to train high-accuracy models; labeled data sets are those for which each sample includes a target class label, such as waveforms tagged as either earthquakes or noise. Earthquake seismologists are increasingly leveraging machine learning and data mining techniques to detect and analyze weak earthquake signals in large seismic data sets. One of the challenges in applying machine learning to seismic data sets is the limited labeled data problem; learning algorithms need to be given examples of earthquake waveforms, but the number of known events, taken from earthquake catalogs, may be insufficient to build an accurate detector. Furthermore, earthquake catalogs are known to be incomplete, resulting in training data that may be biased towards larger events and contain inaccurate labels. This challenge is compounded by the class imbalance problem; the events of interest, earthquakes, are infrequent relative to noise in continuous data sets, and many learning algorithms perform poorly on rare classes. In this work, we investigate the use of active learning for automatic earthquake detection. Active learning is a type of semi-supervised machine learning that uses a human-in-the-loop approach to strategically supplement a small initial training set. The learning algorithm incorporates domain expertise through interaction between a human expert and the algorithm, with the algorithm actively posing queries to the user to improve detection performance. We demonstrate the potential of active machine learning to improve earthquake detection performance with limited available training data.

Automatic feature design for optical character recognition using an evolutionary search procedure.

Science.gov (United States)

Stentiford, F W

1985-03-01

An automatic evolutionary search is applied to the problem of feature extraction in an OCR application. A performance measure based on feature independence is used to generate features which do not appear to suffer from peaking effects [17]. Features are extracted from a training set of 30 600 machine printed 34 class alphanumeric characters derived from British mail. Classification results on the training set and a test set of 10 200 characters are reported for an increasing number of features. A 1.01 percent forced decision error rate is obtained on the test data using 316 features. The hardware implementation should be cheap and fast to operate. The performance compares favorably with current low cost OCR page readers.

Automatic Camera Control

DEFF Research Database (Denmark)

Burelli, Paolo; Preuss, Mike

2014-01-01

Automatically generating computer animations is a challenging and complex problem with applications in games and film production. In this paper, we investigate howto translate a shot list for a virtual scene into a series of virtual camera configurations — i.e automatically controlling the virtual...

Automatic Quantification of Radiographic Wrist Joint Space Width of Patients With Rheumatoid Arthritis.

Science.gov (United States)

Huo, Yinghe; Vincken, Koen L; van der Heijde, Desiree; de Hair, Maria J H; Lafeber, Floris P; Viergever, Max A

2017-11-01

Objective: Wrist joint space narrowing is a main radiographic outcome of rheumatoid arthritis (RA). Yet, automatic radiographic wrist joint space width (JSW) quantification for RA patients has not been widely investigated. The aim of this paper is to present an automatic method to quantify the JSW of three wrist joints that are least affected by bone overlapping and are frequently involved in RA. These joints are located around the scaphoid bone, viz. the multangular-navicular, capitate-navicular-lunate, and radiocarpal joints. Methods: The joint space around the scaphoid bone is detected by using consecutive searches of separate path segments, where each segment location aids in constraining the subsequent one. For joint margin delineation, first the boundary not affected by X-ray projection is extracted, followed by a backtrace process to obtain the actual joint margin. The accuracy of the quantified JSW is evaluated by comparison with the manually obtained ground truth. Results: Two of the 50 radiographs used for evaluation of the method did not yield a correct path through all three wrist joints. The delineated joint margins of the remaining 48 radiographs were used for JSW quantification. It was found that 90% of the joints had a JSW deviating less than 20% from the mean JSW of manual indications, with the mean JSW error less than 10%. Conclusion: The proposed method is able to automatically quantify the JSW of radiographic wrist joints reliably. The proposed method may aid clinical researchers to study the progression of wrist joint damage in RA studies. Objective: Wrist joint space narrowing is a main radiographic outcome of rheumatoid arthritis (RA). Yet, automatic radiographic wrist joint space width (JSW) quantification for RA patients has not been widely investigated. The aim of this paper is to present an automatic method to quantify the JSW of three wrist joints that are least affected by bone overlapping and are frequently involved in RA. These joints

A pattern recognition approach based on DTW for automatic transient identification in nuclear power plants

International Nuclear Information System (INIS)

Galbally, Javier; Galbally, David

2015-01-01

Highlights: • Novel transient identification method for NPPs. • Low-complexity. • Low training data requirements. • High accuracy. • Fully reproducible protocol carried out on a real benchmark. - Abstract: Automatic identification of transients in nuclear power plants (NPPs) allows monitoring the fatigue damage accumulated by critical components during plant operation, and is therefore of great importance for ensuring that usage factors remain within the original design bases postulated by the plant designer. Although several schemes to address this important issue have been explored in the literature, there is still no definitive solution available. In the present work, a new method for automatic transient identification is proposed, based on the Dynamic Time Warping (DTW) algorithm, largely used in other related areas such as signature or speech recognition. The novel transient identification system is evaluated on real operational data following a rigorous pattern recognition protocol. Results show the high accuracy of the proposed approach, which is combined with other interesting features such as its low complexity and its very limited requirements of training data

Sample Selection for Training Cascade Detectors.

Science.gov (United States)

Vállez, Noelia; Deniz, Oscar; Bueno, Gloria

2015-01-01

Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represent anything except the object of interest. In this respect, the negative set typically contains orders of magnitude more images than the positive set. However, imbalanced training databases lead to biased classifiers. In this paper, we focus our attention on a negative sample selection method to properly balance the training data for cascade detectors. The method is based on the selection of the most informative false positive samples generated in one stage to feed the next stage. The results show that the proposed cascade detector with sample selection obtains on average better partial AUC and smaller standard deviation than the other compared cascade detectors.

Situated cognitive engineering : the requirements and design of automatically directed scenario-based training

NARCIS (Netherlands)

Peeters, M.M.M.; Meyer, J.J.C.; Bosch, K. van den; Neerincx, M.A.

2012-01-01

Serious games enable trainees to practice independently of school, staff, and fellow students. This is important as amount of practice directly relates to training efficacy. It is also known that personalized guidance elevates the benefits of training. How to achieve automated guidance, for example

[The Internet:an alternative to face-to-face training for teachers in remote locations?].

Science.gov (United States)

Gagnon, Suzanne; Minguet, Cassian

2008-08-01

For some family medicine supervisors working in rural and remote areas, access to face-to-face training is problematic. They need distance training programs designed specifically for them. To study the advantages, disadvantages, and feasibility of a training program for these supervisors that is delivered over the Internet. This was a pilot project for international on-line training consisting of a platform of courses and a collaborative type of Web conferencing that ran for 2 hours each week for 5 weeks. The training focused on the acquisition of teaching skills and the use of information and communications technology, and included discussions on topics related to practising and teaching in rural areas. Such a program is feasible and economical. The main difficulties are recruiting participants, keeping them in the program, and the amount of time spent on development and supervision. Participants who persevered reported high levels of satisfaction. The content of this type of training, barriers to participation, and the role of distance education in rural supervisor training programs remain to be explored.

Motor automaticity in Parkinson’s disease

Science.gov (United States)

Wu, Tao; Hallett, Mark; Chan, Piu

2017-01-01

Bradykinesia is the most important feature contributing to motor difficulties in Parkinson’s disease (PD). However, the pathophysiology underlying bradykinesia is not fully understood. One important aspect is that PD patients have difficulty in performing learned motor skills automatically, but this problem has been generally overlooked. Here we review motor automaticity associated motor deficits in PD, such as reduced arm swing, decreased stride length, freezing of gait, micrographia and reduced facial expression. Recent neuroimaging studies have revealed some neural mechanisms underlying impaired motor automaticity in PD, including less efficient neural coding of movement, failure to shift automated motor skills to the sensorimotor striatum, instability of the automatic mode within the striatum, and use of attentional control and/or compensatory efforts to execute movements usually performed automatically in healthy people. PD patients lose previously acquired automatic skills due to their impaired sensorimotor striatum, and have difficulty in acquiring new automatic skills or restoring lost motor skills. More investigations on the pathophysiology of motor automaticity, the effect of L-dopa or surgical treatments on automaticity, and the potential role of using measures of automaticity in early diagnosis of PD would be valuable. PMID:26102020

Reward processing in the value-driven attention network: reward signals tracking cue identity and location.

Science.gov (United States)

Anderson, Brian A

2017-03-01

Through associative reward learning, arbitrary cues acquire the ability to automatically capture visual attention. Previous studies have examined the neural correlates of value-driven attentional orienting, revealing elevated activity within a network of brain regions encompassing the visual corticostriatal loop [caudate tail, lateral occipital complex (LOC) and early visual cortex] and intraparietal sulcus (IPS). Such attentional priority signals raise a broader question concerning how visual signals are combined with reward signals during learning to create a representation that is sensitive to the confluence of the two. This study examines reward signals during the cued reward training phase commonly used to generate value-driven attentional biases. High, compared with low, reward feedback preferentially activated the value-driven attention network, in addition to regions typically implicated in reward processing. Further examination of these reward signals within the visual system revealed information about the identity of the preceding cue in the caudate tail and LOC, and information about the location of the preceding cue in IPS, while early visual cortex represented both location and identity. The results reveal teaching signals within the value-driven attention network during associative reward learning, and further suggest functional specialization within different regions of this network during the acquisition of an integrated representation of stimulus value. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

A new preprocessing parameter estimation based on geodesic active contour model for automatic vestibular neuritis diagnosis.

Science.gov (United States)

Ben Slama, Amine; Mouelhi, Aymen; Sahli, Hanene; Manoubi, Sondes; Mbarek, Chiraz; Trabelsi, Hedi; Fnaiech, Farhat; Sayadi, Mounir

2017-07-01

The diagnostic of the vestibular neuritis (VN) presents many difficulties to traditional assessment methods This paper deals with a fully automatic VN diagnostic system based on nystagmus parameter estimation using a pupil detection algorithm. A geodesic active contour model is implemented to find an accurate segmentation region of the pupil. Hence, the novelty of the proposed algorithm is to speed up the standard segmentation by using a specific mask located on the region of interest. This allows a drastically computing time reduction and a great performance and accuracy of the obtained results. After using this fast segmentation algorithm, the obtained estimated parameters are represented in temporal and frequency settings. A useful principal component analysis (PCA) selection procedure is then applied to obtain a reduced number of estimated parameters which are used to train a multi neural network (MNN). Experimental results on 90 eye movement videos show the effectiveness and the accuracy of the proposed estimation algorithm versus previous work. Copyright © 2017 Elsevier B.V. All rights reserved.

Testing continuous earthquake detection and location in Alentejo (South Portugal) by waveform coherency analysis

Science.gov (United States)

Matos, Catarina; Grigoli, Francesco; Cesca, Simone; Custódio, Susana

2015-04-01

In the last decade a permanent seismic network of 30 broadband stations, complemented by dense temporary deployments, covered Portugal. This extraordinary network coverage enables now the computation of a high-resolution image of the seismicity of Portugal, which in turn will shed light on the seismotectonics of Portugal. The large data volumes available cannot be analyzed by traditional time-consuming manual location procedures. In this presentation we show first results on the automatic detection and location of earthquakes occurred in a selected region in the south of Portugal Our main goal is to implement an automatic earthquake detection and location routine in order to have a tool to quickly process large data sets, while at the same time detecting low magnitude earthquakes (i.e., lowering the detection threshold). We present a modified version of the automatic seismic event location by waveform coherency analysis developed by Grigoli et al. (2013, 2014), designed to perform earthquake detections and locations in continuous data. The event detection is performed by continuously computing the short-term-average/long-term-average of two different characteristic functions (CFs). For the P phases we used a CF based on the vertical energy trace, while for S phases we used a CF based on the maximum eigenvalue of the instantaneous covariance matrix (Vidale 1991). Seismic event detection and location is obtained by performing waveform coherence analysis scanning different hypocentral coordinates. We apply this technique to earthquakes in the Alentejo region (South Portugal), taking advantage from a small aperture seismic network installed in the south of Portugal for two years (2010 - 2011) during the DOCTAR experiment. In addition to the good network coverage, the Alentejo region was chosen for its simple tectonic setting and also because the relationship between seismicity, tectonics and local lithospheric structure is intriguing and still poorly understood. Inside

Objective assessment of the aesthetic outcomes of breast cancer treatment: toward automatic localization of fiducial points on digital photographs

Science.gov (United States)

Udpa, Nitin; Sampat, Mehul P.; Kim, Min Soon; Reece, Gregory P.; Markey, Mia K.

2007-03-01

The contemporary goals of breast cancer treatment are not limited to cure but include maximizing quality of life. All breast cancer treatment can adversely affect breast appearance. Developing objective, quantifiable methods to assess breast appearance is important to understand the impact of deformity on patient quality of life, guide selection of current treatments, and make rational treatment advances. A few measures of aesthetic properties such as symmetry have been developed. They are computed from the distances between manually identified fiducial points on digital photographs. However, this is time-consuming and subject to intra- and inter-observer variability. The purpose of this study is to investigate methods for automatic localization of fiducial points on anterior-posterior digital photographs taken to document the outcomes of breast reconstruction. Particular emphasis is placed on automatic localization of the nipple complex since the most widely used aesthetic measure, the Breast Retraction Assessment, quantifies the symmetry of nipple locations. The nipple complexes are automatically localized using normalized cross-correlation with a template bank of variants of Gaussian and Laplacian of Gaussian filters. A probability map of likely nipple locations determined from the image database is used to reduce the number of false positive detections from the matched filter operation. The accuracy of the nipple detection was evaluated relative to markings made by three human observers. The impact of using the fiducial point locations as identified by the automatic method, as opposed to the manual method, on the calculation of the Breast Retraction Assessment was also evaluated.

Situated cognitive engineering: the requirements and design of automatically directed scenario-based training

NARCIS (Netherlands)

Peeters, M.M.M.; van den Bosch, K.; Meyer, J-J.Ch.; Neerincx, M.A.

2012-01-01

Serious games enable trainees to practice independently of school, staff, and fellow students. This is important as amount of practice directly relates to training efficacy. It is also known that personalized guidance elevates the benefits of training. How to achieve automated guidance, for example to

A fast and automatic mosaic method for high-resolution satellite images

Science.gov (United States)

Chen, Hongshun; He, Hui; Xiao, Hongyu; Huang, Jing

2015-12-01

We proposed a fast and fully automatic mosaic method for high-resolution satellite images. First, the overlapped rectangle is computed according to geographical locations of the reference and mosaic images and feature points on both the reference and mosaic images are extracted by a scale-invariant feature transform (SIFT) algorithm only from the overlapped region. Then, the RANSAC method is used to match feature points of both images. Finally, the two images are fused into a seamlessly panoramic image by the simple linear weighted fusion method or other method. The proposed method is implemented in C++ language based on OpenCV and GDAL, and tested by Worldview-2 multispectral images with a spatial resolution of 2 meters. Results show that the proposed method can detect feature points efficiently and mosaic images automatically.

Development of sensor system for indoor location based service implementation

Energy Technology Data Exchange (ETDEWEB)

Cha, Joo Heon; Lee, Kyung Ho [Kookmin Univ., Seoul (Korea, Republic of)

2012-11-15

This paper introduces a sensor system based on indoor locations in order to implement the Building Energy Management System. This system consists of a thermopile sensor and an ultrasonic sensor. The sensor module is rotated by 360 .deg. and yawed up and down by two electric motors. Therefore, it can simultaneously detect the number and location of the inhabitants in the room. It uses wireless technology to communicate with the building manager or the smart home server, and it can save electric energy by controlling the lighting system or heating/air conditioning equipment automatically. We also demonstrate the usefulness of the proposed system by applying it to a real environment.

Development of sensor system for indoor location based service implementation

International Nuclear Information System (INIS)

Cha, Joo Heon; Lee, Kyung Ho

2012-01-01

This paper introduces a sensor system based on indoor locations in order to implement the Building Energy Management System. This system consists of a thermopile sensor and an ultrasonic sensor. The sensor module is rotated by 360 .deg. and yawed up and down by two electric motors. Therefore, it can simultaneously detect the number and location of the inhabitants in the room. It uses wireless technology to communicate with the building manager or the smart home server, and it can save electric energy by controlling the lighting system or heating/air conditioning equipment automatically. We also demonstrate the usefulness of the proposed system by applying it to a real environment

Automatic lip reading by using multimodal visual features

Science.gov (United States)

Takahashi, Shohei; Ohya, Jun

2013-12-01

Since long time ago, speech recognition has been researched, though it does not work well in noisy places such as in the car or in the train. In addition, people with hearing-impaired or difficulties in hearing cannot receive benefits from speech recognition. To recognize the speech automatically, visual information is also important. People understand speeches from not only audio information, but also visual information such as temporal changes in the lip shape. A vision based speech recognition method could work well in noisy places, and could be useful also for people with hearing disabilities. In this paper, we propose an automatic lip-reading method for recognizing the speech by using multimodal visual information without using any audio information such as speech recognition. First, the ASM (Active Shape Model) is used to track and detect the face and lip in a video sequence. Second, the shape, optical flow and spatial frequencies of the lip features are extracted from the lip detected by ASM. Next, the extracted multimodal features are ordered chronologically so that Support Vector Machine is performed in order to learn and classify the spoken words. Experiments for classifying several words show promising results of this proposed method.

An intelligent system for real time automatic defect inspection on specular coated surfaces

Science.gov (United States)

Li, Jinhua; Parker, Johné M.; Hou, Zhen

2005-07-01

Product visual inspection is still performed manually or semi automatically in most industries from simple ceramic tile grading to complex automotive body panel paint defect and surface quality inspection. Moreover, specular surfaces present additional challenge to conventional vision systems due to specular reflections, which may mask the true location of objects and lead to incorrect measurements. There are some sophisticated visual inspection methods developed in recent years. Unfortunately, most of them are highly computational. Systems built on those methods are either inapplicable or very costly to achieve real time inspection. In this paper, we describe an integrated low-cost intelligent system developed to automatically capture, extract, and segment defects on specular surfaces with uniform color coatings. The system inspects and locates regular surface defects with lateral dimensions as small as a millimeter. The proposed system is implemented on a group of smart cameras using its on-board processing ability to achieve real time inspection. The experimental results on real test panels demonstrate the effectiveness and robustness of proposed system.

Automatic leak location in a pipe; Localizador automatico de fugas en un ducto

Energy Technology Data Exchange (ETDEWEB)

Carrera Mendez, Rolando; Verde Rodarte, Cristina [Universidad Nacional Autonoma de Mexico (Mexico)

2001-06-01

A real time method for detecting and locating leaks in pipes is analysed. By monitoring pressure gradients of a pipe in a steady state operation, expressions that indicate the existence of a leak and its location are derived. For small leaks it is possible to deduce alternative expressions based on in- and outflow deviations from their normal values. These deviations are estimated by means of a nonlinear dynamic flow observer. It is an easy task to carry out this system and the detection and location algorithm require low resource. As a result of this analysis it was found that the identifier is very sensitive to variations of the D'Arcy-Weissbach friction. [Spanish] Se describe y analiza un metodo que permite detectar y ubicar fugas de liquidos en un ducto sin tomas laterales en tiempo real. A partir de una operacion en estado permanente y del monitoreo de los gradientes de presion en los extremos de la tuberia, se derivan expresiones que permiten detectar la existencia de una fuga y su localizacion. Tambien se muestra que para fugas pequenas se pueden obtener expresiones alternativas para la deteccion y localizacion, basadas en las desviaciones de los flujos con respecto a sus valores nominales. Dicha desviacion se estima con un observador dinamico no lineal del comportamiento de flujo. La realizacion es de facil implantacion y el algoritmo se deteccion y localizacion requiere de pocos recursos de computacion. Del analisis presentado se desprende que el localizador es muy sensible a las variaciones en la estimacion de la friccion de D'Arcy-Weissbach del ducto.

Automatic Photoelectric Telescope Service

International Nuclear Information System (INIS)

Genet, R.M.; Boyd, L.J.; Kissell, K.E.; Crawford, D.L.; Hall, D.S.; BDM Corp., McLean, VA; Kitt Peak National Observatory, Tucson, AZ; Dyer Observatory, Nashville, TN)

1987-01-01

Automatic observatories have the potential of gathering sizable amounts of high-quality astronomical data at low cost. The Automatic Photoelectric Telescope Service (APT Service) has realized this potential and is routinely making photometric observations of a large number of variable stars. However, without observers to provide on-site monitoring, it was necessary to incorporate special quality checks into the operation of the APT Service at its multiple automatic telescope installation on Mount Hopkins. 18 references

Automatic imitation: A meta-analysis.

Science.gov (United States)

Cracco, Emiel; Bardi, Lara; Desmet, Charlotte; Genschow, Oliver; Rigoni, Davide; De Coster, Lize; Radkova, Ina; Deschrijver, Eliane; Brass, Marcel

2018-05-01

Automatic imitation is the finding that movement execution is facilitated by compatible and impeded by incompatible observed movements. In the past 15 years, automatic imitation has been studied to understand the relation between perception and action in social interaction. Although research on this topic started in cognitive science, interest quickly spread to related disciplines such as social psychology, clinical psychology, and neuroscience. However, important theoretical questions have remained unanswered. Therefore, in the present meta-analysis, we evaluated seven key questions on automatic imitation. The results, based on 161 studies containing 226 experiments, revealed an overall effect size of g z = 0.95, 95% CI [0.88, 1.02]. Moderator analyses identified automatic imitation as a flexible, largely automatic process that is driven by movement and effector compatibility, but is also influenced by spatial compatibility. Automatic imitation was found to be stronger for forced choice tasks than for simple response tasks, for human agents than for nonhuman agents, and for goalless actions than for goal-directed actions. However, it was not modulated by more subtle factors such as animacy beliefs, motion profiles, or visual perspective. Finally, there was no evidence for a relation between automatic imitation and either empathy or autism. Among other things, these findings point toward actor-imitator similarity as a crucial modulator of automatic imitation and challenge the view that imitative tendencies are an indicator of social functioning. The current meta-analysis has important theoretical implications and sheds light on longstanding controversies in the literature on automatic imitation and related domains. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The Effects of Background Noise on the Performance of an Automatic Speech Recogniser

Science.gov (United States)

Littlefield, Jason; HashemiSakhtsari, Ahmad

2002-11-01

Ambient or environmental noise is a major factor that affects the performance of an automatic speech recognizer. Large vocabulary, speaker-dependent, continuous speech recognizers are commercially available. Speech recognizers, perform well in a quiet environment, but poorly in a noisy environment. Speaker-dependent speech recognizers require training prior to them being tested, where the level of background noise in both phases affects the performance of the recognizer. This study aims to determine whether the best performance of a speech recognizer occurs when the levels of background noise during the training and test phases are the same, and how the performance is affected when the levels of background noise during the training and test phases are different. The relationship between the performance of the speech recognizer and upgrading the computer speed and amount of memory as well as software version was also investigated.

49 CFR 236.206 - Battery or power supply with respect to relay; location.

Science.gov (United States)

2010-10-01

... 49 Transportation 4 2010-10-01 2010-10-01 false Battery or power supply with respect to relay..., AND APPLIANCES Automatic Block Signal Systems Standards § 236.206 Battery or power supply with respect to relay; location. The battery or power supply for each signal control relay circuit, where an open...

Medical specialists' choice of location: the role of geographical attachment in Norway.

Science.gov (United States)

Kristiansen, I S; Førde, O H

1992-01-01

The relation between current place of work (area of the country) and factors that might possibly represent doctors geographical attachments was studied in a sample of 322 Norwegian medical specialists. Location of hospital residency, age and geographical origin of spouse were associated with current location. Geographical attachment seems to influence doctors' locational choices from start of medical school until the end of their residency. The probability that a doctor shall locate in peripheral areas may increase from less than 10% to more than 50% if the doctor has the residency training in the periphery. Hence, favoring entrance to medical schools of students from the underserved areas, and location of graduate and postgraduate medical training in the underserved areas, as far as it is feasible while still maintaining medical standards, is suggested by the study.

Automatic seismic support design of piping system by an object oriented expert system

International Nuclear Information System (INIS)

Nakatogawa, T.; Takayama, Y.; Hayashi, Y.; Fukuda, T.; Yamamoto, Y.; Haruna, T.

1990-01-01

The seismic support design of piping systems of nuclear power plants requires many experienced engineers and plenty of man-hours, because the seismic design conditions are very severe, the bulk volume of the piping systems is hyge and the design procedures are very complicated. Therefore we have developed a piping seismic design expert system, which utilizes the piping design data base of a 3 dimensional CAD system and automatically determines the piping support locations and support styles. The data base of this system contains the maximum allowable seismic support span lengths for straight piping and the span length reduction factors for bends, branches, concentrated masses in the piping, and so forth. The system automatically produces the support design according to the design knowledge extracted and collected from expert design engineers, and using design information such as piping specifications which give diameters and thickness and piping geometric configurations. The automatic seismic support design provided by this expert system achieves in the reduction of design man-hours, improvement of design quality, verification of design result, optimization of support locations and prevention of input duplication. In the development of this system, we had to derive the design logic from expert design engineers and this could not be simply expressed descriptively. Also we had to make programs for different kinds of design knowledge. For these reasons we adopted the object oriented programming paradigm (Smalltalk-80) which is suitable for combining programs and carrying out the design work

Adaptive learning compressive tracking based on Markov location prediction

Science.gov (United States)

Zhou, Xingyu; Fu, Dongmei; Yang, Tao; Shi, Yanan

2017-03-01

Object tracking is an interdisciplinary research topic in image processing, pattern recognition, and computer vision which has theoretical and practical application value in video surveillance, virtual reality, and automatic navigation. Compressive tracking (CT) has many advantages, such as efficiency and accuracy. However, when there are object occlusion, abrupt motion and blur, similar objects, and scale changing, the CT has the problem of tracking drift. We propose the Markov object location prediction to get the initial position of the object. Then CT is used to locate the object accurately, and the classifier parameter adaptive updating strategy is given based on the confidence map. At the same time according to the object location, extract the scale features, which is able to deal with object scale variations effectively. Experimental results show that the proposed algorithm has better tracking accuracy and robustness than current advanced algorithms and achieves real-time performance.

Is gaze following purely reflexive or goal-directed instead? Revisiting the automaticity of orienting attention by gaze cues.

Science.gov (United States)

Ricciardelli, Paola; Carcagno, Samuele; Vallar, Giuseppe; Bricolo, Emanuela

2013-01-01

Distracting gaze has been shown to elicit automatic gaze following. However, it is still debated whether the effects of perceived gaze are a simple automatic spatial orienting response or are instead sensitive to the context (i.e. goals and task demands). In three experiments, we investigated the conditions under which gaze following occurs. Participants were instructed to saccade towards one of two lateral targets. A face distracter, always present in the background, could gaze towards: (a) a task-relevant target--("matching" goal-directed gaze shift)--congruent or incongruent with the instructed direction, (b) a task-irrelevant target, orthogonal to the one instructed ("non-matching" goal-directed gaze shift), or (c) an empty spatial location (no-goal-directed gaze shift). Eye movement recordings showed faster saccadic latencies in correct trials in congruent conditions especially when the distracting gaze shift occurred before the instruction to make a saccade. Interestingly, while participants made a higher proportion of gaze-following errors (i.e. errors in the direction of the distracting gaze) in the incongruent conditions when the distracter's gaze shift preceded the instruction onset indicating an automatic gaze following, they never followed the distracting gaze when it was directed towards an empty location or a stimulus that was never the target. Taken together, these findings suggest that gaze following is likely to be a product of both automatic and goal-driven orienting mechanisms.

Automatic and Intentional Number Processing Both Rely on Intact Right Parietal Cortex: A Combined fMRI and Neuronavigated TMS Study

Science.gov (United States)

Cohen Kadosh, Roi; Bien, Nina; Sack, Alexander T.

2012-01-01

Practice and training usually lead to performance increase in a given task. In addition, a shift from intentional toward more automatic processing mechanisms is often observed. It is currently debated whether automatic and intentional processing is subserved by the same or by different mechanism(s), and whether the same or different regions in the brain are recruited. Previous correlational evidence provided by behavioral, neuroimaging, modeling, and neuropsychological studies addressing this question yielded conflicting results. Here we used transcranial magnetic stimulation (TMS) to compare the causal influence of disrupting either left or right parietal cortex during automatic and intentional numerical processing, as reflected by the size congruity effect and the numerical distance effect, respectively. We found a functional hemispheric asymmetry within parietal cortex with only the TMS-induced right parietal disruption impairing both automatic and intentional numerical processing. In contrast, disrupting the left parietal lobe with TMS, or applying sham stimulation, did not affect performance during automatic or intentional numerical processing. The current results provide causal evidence for the functional relevance of right, but not left, parietal cortex for intentional, and automatic numerical processing, implying that at least within the parietal cortices, automatic, and intentional numerical processing rely on the same underlying hemispheric lateralization. PMID:22347175

Automatic indexing, compiling and classification

International Nuclear Information System (INIS)

Andreewsky, Alexandre; Fluhr, Christian.

1975-06-01

A review of the principles of automatic indexing, is followed by a comparison and summing-up of work by the authors and by a Soviet staff from the Moscou INFORM-ELECTRO Institute. The mathematical and linguistic problems of the automatic building of thesaurus and automatic classification are examined [fr

Sample Selection for Training Cascade Detectors.

Directory of Open Access Journals (Sweden)

Noelia Vállez

Full Text Available Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represent anything except the object of interest. In this respect, the negative set typically contains orders of magnitude more images than the positive set. However, imbalanced training databases lead to biased classifiers. In this paper, we focus our attention on a negative sample selection method to properly balance the training data for cascade detectors. The method is based on the selection of the most informative false positive samples generated in one stage to feed the next stage. The results show that the proposed cascade detector with sample selection obtains on average better partial AUC and smaller standard deviation than the other compared cascade detectors.

WE-AB-BRA-05: Fully Automatic Segmentation of Male Pelvic Organs On CT Without Manual Intervention

International Nuclear Information System (INIS)

Gao, Y; Lian, J; Chen, R; Wang, A; Shen, D

2015-01-01

Purpose: We aim to develop a fully automatic tool for accurate contouring of major male pelvic organs in CT images for radiotherapy without any manual initialization, yet still achieving superior performance than the existing tools. Methods: A learning-based 3D deformable shape model was developed for automatic contouring. Specifically, we utilized a recent machine learning method, random forest, to jointly learn both image regressor and classifier for each organ. In particular, the image regressor is trained to predict the 3D displacement from each vertex of the 3D shape model towards the organ boundary based on the local image appearance around the location of this vertex. The predicted 3D displacements are then used to drive the 3D shape model towards the target organ. Once the shape model is deformed close to the target organ, it is further refined by an organ likelihood map estimated by the learned classifier. As the organ likelihood map provides good guideline for the organ boundary, the precise contouring Result could be achieved, by deforming the 3D shape model locally to fit boundaries in the organ likelihood map. Results: We applied our method to 29 previously-treated prostate cancer patients, each with one planning CT scan. Compared with manually delineated pelvic organs, our method obtains overlap ratios of 85.2%±3.74% for the prostate, 94.9%±1.62% for the bladder, and 84.7%±1.97% for the rectum, respectively. Conclusion: This work demonstrated feasibility of a novel machine-learning based approach for accurate and automatic contouring of major male pelvic organs. It shows the potential to replace the time-consuming and inconsistent manual contouring in the clinic. Also, compared with the existing works, our method is more accurate and also efficient since it does not require any manual intervention, such as manual landmark placement. Moreover, our method obtained very similar contouring results as the clinical experts. Project is partially support

WE-AB-BRA-05: Fully Automatic Segmentation of Male Pelvic Organs On CT Without Manual Intervention

Energy Technology Data Exchange (ETDEWEB)

Gao, Y; Lian, J; Chen, R; Wang, A; Shen, D [Univ North Carolina, Chapel Hill, NC (United States)

2015-06-15

Purpose: We aim to develop a fully automatic tool for accurate contouring of major male pelvic organs in CT images for radiotherapy without any manual initialization, yet still achieving superior performance than the existing tools. Methods: A learning-based 3D deformable shape model was developed for automatic contouring. Specifically, we utilized a recent machine learning method, random forest, to jointly learn both image regressor and classifier for each organ. In particular, the image regressor is trained to predict the 3D displacement from each vertex of the 3D shape model towards the organ boundary based on the local image appearance around the location of this vertex. The predicted 3D displacements are then used to drive the 3D shape model towards the target organ. Once the shape model is deformed close to the target organ, it is further refined by an organ likelihood map estimated by the learned classifier. As the organ likelihood map provides good guideline for the organ boundary, the precise contouring Result could be achieved, by deforming the 3D shape model locally to fit boundaries in the organ likelihood map. Results: We applied our method to 29 previously-treated prostate cancer patients, each with one planning CT scan. Compared with manually delineated pelvic organs, our method obtains overlap ratios of 85.2%±3.74% for the prostate, 94.9%±1.62% for the bladder, and 84.7%±1.97% for the rectum, respectively. Conclusion: This work demonstrated feasibility of a novel machine-learning based approach for accurate and automatic contouring of major male pelvic organs. It shows the potential to replace the time-consuming and inconsistent manual contouring in the clinic. Also, compared with the existing works, our method is more accurate and also efficient since it does not require any manual intervention, such as manual landmark placement. Moreover, our method obtained very similar contouring results as the clinical experts. Project is partially support

Auditing hierarchical cycles to locate other inconsistencies in the UMLS.

Science.gov (United States)

Halper, Michael; Morrey, C Paul; Chen, Yan; Elhanan, Gai; Hripcsak, George; Perl, Yehoshua

2011-01-01

A cycle in the parent relationship hierarchy of the UMLS is a configuration that effectively makes some concept(s) an ancestor of itself. Such a structural inconsistency can easily be found automatically. A previous strategy for disconnecting cycles is to break them with the deletion of one or more parent relationships-irrespective of the correctness of the deleted relationships. A methodology is introduced for auditing of cycles that seeks to discover and delete erroneous relationships only. Cycles involving three concepts are the primary consideration. Hypotheses about the high probability of locating an erroneous parent relationship in a cycle are proposed and confirmed with statistical confidence and lend credence to the auditing approach. A cycle may serve as an indicator of other non-structural inconsistencies that are otherwise difficult to detect automatically. An extensive auditing example shows how a cycle can indicate further inconsistencies.

Observer Based Traction/Braking Control Design for High Speed Trains Considering Adhesion Nonlinearity

OpenAIRE

Cai, Wenchuan; Liao, Wenhao; Li, Danyong; Song, Yongduan

2014-01-01

Train traction/braking control, one of the key enabling technologies for automatic train operation, literally takes its action through adhesion force. However, adhesion coefficient of high speed train (HST) is uncertain in general because it varies with wheel-rail surface condition and running speed; thus, it is extremely difficult to be measured, which makes traction/braking control design and implementation of HSTs greatly challenging. In this work, force observers are applied to estimate t...

49 CFR 232.407 - Operations requiring use of two-way end-of-train devices; prohibition on purchase of...

Science.gov (United States)

2010-10-01

... a secondary, fully independent braking system capable of safely stopping the train in the event of... either by using the manual toggle switch or through automatic activation, whenever it becomes necessary... automatic brake valve or the conductor's emergency brake valve. (g) En route failure of device on a freight...

The effects of short-lasting anti-saccade training in homonymous hemianopia with and without saccadic adaptation

Directory of Open Access Journals (Sweden)

Delphine eLévy-Bencheton

2016-01-01

Full Text Available Homonymous Visual Field Defects (HVFD are common following stroke and can be highly debilitating for visual perception and higher level cognitive functions such as exploring visual scene or reading a text. Rehabilitation using oculomotor compensatory methods with automatic training over a short duration (~15 days have been shown as efficient as longer voluntary training methods (>1 month. Here, we propose to evaluate and compare the effect of an original HVFD rehabilitation method based on a single 15 min voluntary anti-saccades task (AS toward the blind hemifield, with automatic sensorimotor adaptation to increase AS amplitude. In order to distinguish between adaptation and training effect, fourteen left- or right-HVFD patients were exposed, one month apart, to three training, two isolated AS task (Delayed-shift & No-shift paradigm and one combined with AS adaptation (Adaptation paradigm. A quality of life questionnaire (NEI-VFQ 25 and functional measurements (reading speed, visual exploration time in pop-out and serial tasks as well as oculomotor measurements were assessed before and after each training. We could not demonstrate significant adaptation at the group level, but we identified a group of 9 adapted patients. While AS training itself proved to demonstrate significant functional improvements in the overall patient group , we could also demonstrate in the sub-group of adapted patients and specifically following the adaptation training, an increase of saccade amplitude during the reading task (left-HVFD patients and the Serial exploration task, and improvement of the visual quality of life. We conclude that short-lasting AS training combined with adaptation could be implemented in rehabilitation methods of cognitive dysfunctions following HVFD. Indeed, both voluntary and automatic processes have shown interesting effects on the control of visually guided saccades in different cognitive tasks.

Design and development of an automated D.C. ground fault detection and location system for Cirus

International Nuclear Information System (INIS)

Marik, S.K.; Ramesh, N.; Jain, J.K.; Srivastava, A.P.

2002-01-01

Full text: The original design of Cirus safety system provided for automatic detection of ground fault in class I D.C. power supply system and its annunciation followed by delayed reactor trip. Identification of a faulty section was required to be done manually by switching off various sections one at a time thus requiring a lot of shutdown time to identify the faulty section. Since class I power supply is provided for safety control system, quick detection and location of ground faults in this supply is necessary as these faults have potential to bypass safety interlocks and hence the need for a new system for automatic location of a faulty section. Since such systems are not readily available in the market, in-house efforts were made to design and develop a plant-specific system, which has been installed and commissioned

ACIR: automatic cochlea image registration

Science.gov (United States)

Al-Dhamari, Ibraheem; Bauer, Sabine; Paulus, Dietrich; Lissek, Friedrich; Jacob, Roland

2017-02-01

Efficient Cochlear Implant (CI) surgery requires prior knowledge of the cochlea's size and its characteristics. This information helps to select suitable implants for different patients. To get these measurements, a segmentation method of cochlea medical images is needed. An important pre-processing step for good cochlea segmentation involves efficient image registration. The cochlea's small size and complex structure, in addition to the different resolutions and head positions during imaging, reveals a big challenge for the automated registration of the different image modalities. In this paper, an Automatic Cochlea Image Registration (ACIR) method for multi- modal human cochlea images is proposed. This method is based on using small areas that have clear structures from both input images instead of registering the complete image. It uses the Adaptive Stochastic Gradient Descent Optimizer (ASGD) and Mattes's Mutual Information metric (MMI) to estimate 3D rigid transform parameters. The use of state of the art medical image registration optimizers published over the last two years are studied and compared quantitatively using the standard Dice Similarity Coefficient (DSC). ACIR requires only 4.86 seconds on average to align cochlea images automatically and to put all the modalities in the same spatial locations without human interference. The source code is based on the tool elastix and is provided for free as a 3D Slicer plugin. Another contribution of this work is a proposed public cochlea standard dataset which can be downloaded for free from a public XNAT server.

Using WordNet to Complement Training Information in Text Categorization

OpenAIRE

Rodriguez, Manuel de Buenaga; Hidalgo, Jose Maria Gomez; Agudo, Belen Diaz

1997-01-01

Automatic Text Categorization (TC) is a complex and useful task for many natural language applications, and is usually performed through the use of a set of manually classified documents, a training collection. We suggest the utilization of additional resources like lexical databases to increase the amount of information that TC systems make use of, and thus, to improve their performance. Our approach integrates WordNet information with two training approaches through the Vector Space Model. ...

Automatic Detection of Storm Damages Using High-Altitude Photogrammetric Imaging

Science.gov (United States)

Litkey, P.; Nurminen, K.; Honkavaara, E.

2013-05-01

The risks of storms that cause damage in forests are increasing due to climate change. Quickly detecting fallen trees, assessing the amount of fallen trees and efficiently collecting them are of great importance for economic and environmental reasons. Visually detecting and delineating storm damage is a laborious and error-prone process; thus, it is important to develop cost-efficient and highly automated methods. Objective of our research project is to investigate and develop a reliable and efficient method for automatic storm damage detection, which is based on airborne imagery that is collected after a storm. The requirements for the method are the before-storm and after-storm surface models. A difference surface is calculated using two DSMs and the locations where significant changes have appeared are automatically detected. In our previous research we used four-year old airborne laser scanning surface model as the before-storm surface. The after-storm DSM was provided from the photogrammetric images using the Next Generation Automatic Terrain Extraction (NGATE) algorithm of Socet Set software. We obtained 100% accuracy in detection of major storm damages. In this investigation we will further evaluate the sensitivity of the storm-damage detection process. We will investigate the potential of national airborne photography, that is collected at no-leaf season, to automatically produce a before-storm DSM using image matching. We will also compare impact of the terrain extraction algorithm to the results. Our results will also promote the potential of national open source data sets in the management of natural disasters.

Development of a Deep Learning Algorithm for Automatic Diagnosis of Diabetic Retinopathy.

Science.gov (United States)

Raju, Manoj; Pagidimarri, Venkatesh; Barreto, Ryan; Kadam, Amrit; Kasivajjala, Vamsichandra; Aswath, Arun

2017-01-01

This paper mainly focuses on the deep learning application in classifying the stage of diabetic retinopathy and detecting the laterality of the eye using funduscopic images. Diabetic retinopathy is a chronic, progressive, sight-threatening disease of the retinal blood vessels. Ophthalmologists diagnose diabetic retinopathy through early funduscopic screening. Normally, there is a time delay in reporting and intervention, apart from the financial cost and risk of blindness associated with it. Using a convolutional neural network based approach for automatic diagnosis of diabetic retinopathy, we trained the prediction network on the publicly available Kaggle dataset. Approximately 35,000 images were used to train the network, which observed a sensitivity of 80.28% and a specificity of 92.29% on the validation dataset of ~53,000 images. Using 8,810 images, the network was trained for detecting the laterality of the eye and observed an accuracy of 93.28% on the validation set of 8,816 images.

Brain tumor locating in 3D MR volume using symmetry

Science.gov (United States)

Dvorak, Pavel; Bartusek, Karel

2014-03-01

This work deals with the automatic determination of a brain tumor location in 3D magnetic resonance volumes. The aim of this work is not the precise segmentation of the tumor and its parts but only the detection of its location. This work is the first step in the tumor segmentation process, an important topic in neuro-image processing. The algorithm expects 3D magnetic resonance volumes of brain containing a tumor. The detection is based on locating the area that breaks the left-right symmetry of the brain. This is done by multi-resolution comparing of corresponding regions in left and right hemisphere. The output of the computation is the probabilistic map of the tumor location. The created algorithm was tested on 80 volumes from publicly available BRATS databases containing 3D brain volumes afflicted by a brain tumor. These pathological structures had various sizes and shapes and were located in various parts of the brain. The locating performance of the algorithm was 85% for T1-weighted volumes, 91% for T1-weighted contrast enhanced volumes, 96% for FLAIR and T2-wieghted volumes and 95% for their combinations.

Distribution network fault section identification and fault location using artificial neural network

DEFF Research Database (Denmark)

Dashtdar, Masoud; Dashti, Rahman; Shaker, Hamid Reza

2018-01-01

In this paper, a method for fault location in power distribution network is presented. The proposed method uses artificial neural network. In order to train the neural network, a series of specific characteristic are extracted from the recorded fault signals in relay. These characteristics...... components of the sequences as well as three-phase signals could be obtained using statistics to extract the hidden features inside them and present them separately to train the neural network. Also, since the obtained inputs for the training of the neural network strongly depend on the fault angle, fault...... resistance, and fault location, the training data should be selected such that these differences are properly presented so that the neural network does not face any issues for identification. Therefore, selecting the signal processing function, data spectrum and subsequently, statistical parameters...

Training simulator for advanced gas-cooled reactor (AGR) shutdown sequence equipment

International Nuclear Information System (INIS)

Shankland, J.P.; Nixon, G.L.

1978-01-01

Successful shutdown of nuclear plant is of prime importance for both safety and economic reasons and large sums of money are spent on equipment to make shutdowns fully automatic, thus removing the possibility of operator errors. While this aim can largely be realized, one must consider the possibility of automatic equipment or plant failures when operators are required to take manual action, and off-line training facilities should be available to operating staff to minimize the risk of incorrect actions being taken. This paper presents the practice adopted at Hunterston 'B' Nuclear Power Station to solve this problem and concerns the computer-based training simulator for the Reactor Shutdown Sequence Equipment (RSSE) which was commissioned in January 1977. The plant associated with shutdown is briefly described and the reasoning which shows the need for a simulator is outlined. The paper also gives details of the comprehensive facilities available on the simulator and goes on to describe the form that shutdown training takes and the experience gained at this time. (author)

Assessment of automatic segmentation of teeth using a watershed-based method.

Science.gov (United States)

Galibourg, Antoine; Dumoncel, Jean; Telmon, Norbert; Calvet, Adèle; Michetti, Jérôme; Maret, Delphine

2018-01-01

Tooth 3D automatic segmentation (AS) is being actively developed in research and clinical fields. Here, we assess the effect of automatic segmentation using a watershed-based method on the accuracy and reproducibility of 3D reconstructions in volumetric measurements by comparing it with a semi-automatic segmentation(SAS) method that has already been validated. The study sample comprised 52 teeth, scanned with micro-CT (41 µm voxel size) and CBCT (76; 200 and 300 µm voxel size). Each tooth was segmented by AS based on a watershed method and by SAS. For all surface reconstructions, volumetric measurements were obtained and analysed statistically. Surfaces were then aligned using the SAS surfaces as the reference. The topography of the geometric discrepancies was displayed by using a colour map allowing the maximum differences to be located. AS reconstructions showed similar tooth volumes when compared with SAS for the 41 µm voxel size. A difference in volumes was observed, and increased with the voxel size for CBCT data. The maximum differences were mainly found at the cervical margins and incisal edges but the general form was preserved. Micro-CT, a modality used in dental research, provides data that can be segmented automatically, which is timesaving. AS with CBCT data enables the general form of the region of interest to be displayed. However, our AS method can still be used for metrically reliable measurements in the field of clinical dentistry if some manual refinements are applied.

Channel selection for automatic seizure detection

DEFF Research Database (Denmark)

Duun-Henriksen, Jonas; Kjaer, Troels Wesenberg; Madsen, Rasmus Elsborg

2012-01-01

Objective: To investigate the performance of epileptic seizure detection using only a few of the recorded EEG channels and the ability of software to select these channels compared with a neurophysiologist. Methods: Fifty-nine seizures and 1419 h of interictal EEG are used for training and testing...... of an automatic channel selection method. The characteristics of the seizures are extracted by the use of a wavelet analysis and classified by a support vector machine. The best channel selection method is based upon maximum variance during the seizure. Results: Using only three channels, a seizure detection...... sensitivity of 96% and a false detection rate of 0.14/h were obtained. This corresponds to the performance obtained when channels are selected through visual inspection by a clinical neurophysiologist, and constitutes a 4% improvement in sensitivity compared to seizure detection using channels recorded...

Model-based vision system for automatic recognition of structures in dental radiographs

Science.gov (United States)

Acharya, Raj S.; Samarabandu, Jagath K.; Hausmann, E.; Allen, K. A.

1991-07-01

X-ray diagnosis of destructive periodontal disease requires assessing serial radiographs by an expert to determine the change in the distance between cemento-enamel junction (CEJ) and the bone crest. To achieve this without the subjectivity of a human expert, a knowledge based system is proposed to automatically locate the two landmarks which are the CEJ and the level of alveolar crest at its junction with the periodontal ligament space. This work is a part of an ongoing project to automatically measure the distance between CEJ and the bone crest along a line parallel to the axis of the tooth. The approach presented in this paper is based on identifying a prominent feature such as the tooth boundary using local edge detection and edge thresholding to establish a reference and then using model knowledge to process sub-regions in locating the landmarks. Segmentation techniques invoked around these regions consists of a neural-network like hierarchical refinement scheme together with local gradient extraction, multilevel thresholding and ridge tracking. Recognition accuracy is further improved by first locating the easily identifiable parts of the bone surface and the interface between the enamel and the dentine and then extending these boundaries towards the periodontal ligament space and the tooth boundary respectively. The system is realized as a collection of tools (or knowledge sources) for pre-processing, segmentation, primary and secondary feature detection and a control structure based on the blackboard model to coordinate the activities of these tools.

Automatic control of negative emotions: evidence that structured practice increases the efficiency of emotion regulation.

Science.gov (United States)

Christou-Champi, Spyros; Farrow, Tom F D; Webb, Thomas L

2015-01-01

Emotion regulation (ER) is vital to everyday functioning. However, the effortful nature of many forms of ER may lead to regulation being inefficient and potentially ineffective. The present research examined whether structured practice could increase the efficiency of ER. During three training sessions, comprising a total of 150 training trials, participants were presented with negatively valenced images and asked either to "attend" (control condition) or "reappraise" (ER condition). A further group of participants did not participate in training but only completed follow-up measures. Practice increased the efficiency of ER as indexed by decreased time required to regulate emotions and increased heart rate variability (HRV). Furthermore, participants in the ER condition spontaneously regulated their negative emotions two weeks later and reported being more habitual in their use of ER. These findings indicate that structured practice can facilitate the automatic control of negative emotions and that these effects persist beyond training.

Neural-network classifiers for automatic real-world aerial image recognition

Science.gov (United States)

Greenberg, Shlomo; Guterman, Hugo

1996-08-01

We describe the application of the multilayer perceptron (MLP) network and a version of the adaptive resonance theory version 2-A (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images, independent of their positions and orientations, is required for automatic tracking and target recognition. Invariance is achieved by the use of different invariant feature spaces in combination with supervised and unsupervised neural networks. The performance of neural-network-based classifiers in conjunction with several types of invariant AAIR global features, such as the Fourier-transform space, Zernike moments, central moments, and polar transforms, are examined. The advantages of this approach are discussed. The performance of the MLP network is compared with that of a classical correlator. The MLP neural-network correlator outperformed the binary phase-only filter (BPOF) correlator. It was found that the ART 2-A distinguished itself with its speed and its low number of required training vectors. However, only the MLP classifier was able to deal with a combination of shift and rotation geometric distortions.

Automatic learning-based beam angle selection for thoracic IMRT

International Nuclear Information System (INIS)

Amit, Guy; Marshall, Andrea; Purdie, Thomas G.; Jaffray, David A.; Levinshtein, Alex; Hope, Andrew J.; Lindsay, Patricia; Pekar, Vladimir

2015-01-01

Purpose: The treatment of thoracic cancer using external beam radiation requires an optimal selection of the radiation beam directions to ensure effective coverage of the target volume and to avoid unnecessary treatment of normal healthy tissues. Intensity modulated radiation therapy (IMRT) planning is a lengthy process, which requires the planner to iterate between choosing beam angles, specifying dose–volume objectives and executing IMRT optimization. In thorax treatment planning, where there are no class solutions for beam placement, beam angle selection is performed manually, based on the planner’s clinical experience. The purpose of this work is to propose and study a computationally efficient framework that utilizes machine learning to automatically select treatment beam angles. Such a framework may be helpful for reducing the overall planning workload. Methods: The authors introduce an automated beam selection method, based on learning the relationships between beam angles and anatomical features. Using a large set of clinically approved IMRT plans, a random forest regression algorithm is trained to map a multitude of anatomical features into an individual beam score. An optimization scheme is then built to select and adjust the beam angles, considering the learned interbeam dependencies. The validity and quality of the automatically selected beams evaluated using the manually selected beams from the corresponding clinical plans as the ground truth. Results: The analysis included 149 clinically approved thoracic IMRT plans. For a randomly selected test subset of 27 plans, IMRT plans were generated using automatically selected beams and compared to the clinical plans. The comparison of the predicted and the clinical beam angles demonstrated a good average correspondence between the two (angular distance 16.8° ± 10°, correlation 0.75 ± 0.2). The dose distributions of the semiautomatic and clinical plans were equivalent in terms of primary target volume

Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning

Science.gov (United States)

Nguyen, Tan H.; Sridharan, Shamira; Macias, Virgilia; Kajdacsy-Balla, Andre; Melamed, Jonathan; Do, Minh N.; Popescu, Gabriel

2017-03-01

We present an approach for automatic diagnosis of tissue biopsies. Our methodology consists of a quantitative phase imaging tissue scanner and machine learning algorithms to process these data. We illustrate the performance by automatic Gleason grading of prostate specimens. The imaging system operates on the principle of interferometry and, as a result, reports on the nanoscale architecture of the unlabeled specimen. We use these data to train a random forest classifier to learn textural behaviors of prostate samples and classify each pixel in the image into different classes. Automatic diagnosis results were computed from the segmented regions. By combining morphological features with quantitative information from the glands and stroma, logistic regression was used to discriminate regions with Gleason grade 3 versus grade 4 cancer in prostatectomy tissue. The overall accuracy of this classification derived from a receiver operating curve was 82%, which is in the range of human error when interobserver variability is considered. We anticipate that our approach will provide a clinically objective and quantitative metric for Gleason grading, allowing us to corroborate results across instruments and laboratories and feed the computer algorithms for improved accuracy.

Automatic sets and Delone sets

International Nuclear Information System (INIS)

Barbe, A; Haeseler, F von

2004-01-01

Automatic sets D part of Z m are characterized by having a finite number of decimations. They are equivalently generated by fixed points of certain substitution systems, or by certain finite automata. As examples, two-dimensional versions of the Thue-Morse, Baum-Sweet, Rudin-Shapiro and paperfolding sequences are presented. We give a necessary and sufficient condition for an automatic set D part of Z m to be a Delone set in R m . The result is then extended to automatic sets that are defined as fixed points of certain substitutions. The morphology of automatic sets is discussed by means of examples

Design and implementation of a general and automatic test platform base on NI PXI system

Science.gov (United States)

Shi, Long

2018-05-01

Aiming at some difficulties of test equipment such as the short product life, poor generality and high development cost, a general and automatic test platform base on NI PXI system is designed in this paper, which is able to meet most test requirements of circuit boards. The test platform is devided into 5 layers, every layer is introduced in detail except for the "Equipment Under Test" layer. An output board of a track-side equipment, which is an important part of high speed train control system, is taken as an example to make the functional circuit test by the test platform. The results show that the test platform is easy to realize add-on functions development, automatic test, wide compatibility and strong generality.

General collaboration offer of Johnson Controls regarding the performance of air conditioning automatic control systems and other buildings` automatic control systems

Energy Technology Data Exchange (ETDEWEB)

Gniazdowski, J.

1995-12-31

JOHNSON CONTROLS manufactures measuring and control equipment (800 types) and is as well a {open_quotes}turn-key{close_quotes} supplier of complete automatic controls systems for heating, air conditioning, ventilation and refrigerating engineering branches. The Company also supplies Buildings` Computer-Based Supervision and Monitoring Systems that may be applied in both small and large structures. Since 1990 the company has been performing full-range trade and contracting activities on the Polish market. We have our own well-trained technical staff and we collaborate with a series of designing and contracting enterprises that enable us to have our projects carried out all over Poland. The prices of our supplies and services correspond with the level of the Polish market.

Mindfulness-Based Parent Training: Strategies to Lessen the Grip of Automaticity in Families with Disruptive Children

Science.gov (United States)

Dumas, Jean E.

2005-01-01

Disagreements and conflicts in families with disruptive children often reflect rigid patterns of behavior that have become overlearned and automatized with repeated practice. These patterns are mindless: They are performed with little or no awareness and are highly resistant to change. This article introduces a new, mindfulness-based model of…

Wild rufous hummingbirds use local landmarks to return to rewarded locations.

Science.gov (United States)

Pritchard, David J; Scott, Renee D; Healy, Susan D; Hurly, Andrew T

2016-01-01

Animals may remember an important location with reference to one or more visual landmarks. In the laboratory, birds and mammals often preferentially use landmarks near a goal ("local landmarks") to return to that location at a later date. Although we know very little about how animals in the wild use landmarks to remember locations, mammals in the wild appear to prefer to use distant landmarks to return to rewarded locations. To examine what cues wild birds use when returning to a goal, we trained free-living hummingbirds to search for a reward at a location that was specified by three nearby visual landmarks. Following training we expanded the landmark array to test the extent that the birds relied on the local landmarks to return to the reward. During the test the hummingbirds' search was best explained by the birds having used the experimental landmarks to remember the reward location. How the birds used the landmarks was not clear and seemed to change over the course of each test. These wild hummingbirds, then, can learn locations in reference to nearby visual landmarks. Copyright © 2015 Elsevier B.V. All rights reserved.

CT assessment of muscle hypertrophy utilizing automatic contouring techniques

International Nuclear Information System (INIS)

Steinbach, L.S.; Block, J.; Steiger, P.W.; Ellis, W.; Morris, J.; Genant, H.K.

1986-01-01

Quantitative CT was one method used to assess changes in density and area of thigh muscles in paraplegics before and after aerobic leg training. Muscle density and area were measured from the CT image by an automatic contouring algorithm. In the first three patients, total muscle density increased from 11.5% to 18.3% and area increased from 18.3% to 31.3%. In one patient who did not comply with the exercise regimen, only a 10% increase in muscle density and area was detected. This CT program is valuable in the assessment of composition and alteration of limb musculature in the treatment and follow-up of muscular disorders

Automatic Reverse Engineering of Private Flight Control Protocols of UAVs

Directory of Open Access Journals (Sweden)

Ran Ji

2017-01-01

Full Text Available The increasing use of civil unmanned aerial vehicles (UAVs has the potential to threaten public safety and privacy. Therefore, airspace administrators urgently need an effective method to regulate UAVs. Understanding the meaning and format of UAV flight control commands by automatic protocol reverse-engineering techniques is highly beneficial to UAV regulation. To improve our understanding of the meaning and format of UAV flight control commands, this paper proposes a method to automatically analyze the private flight control protocols of UAVs. First, we classify flight control commands collected from a binary network trace into clusters; then, we analyze the meaning of flight control commands by the accumulated error of each cluster; next, we extract the binary format of commands and infer field semantics in these commands; and finally, we infer the location of the check field in command and the generator polynomial matrix. The proposed approach is validated via experiments on a widely used consumer UAV.

Automatic quality control in clinical (1)H MRSI of brain cancer.

Science.gov (United States)

Pedrosa de Barros, Nuno; McKinley, Richard; Knecht, Urspeter; Wiest, Roland; Slotboom, Johannes

2016-05-01

MRSI grids frequently show spectra with poor quality, mainly because of the high sensitivity of MRS to field inhomogeneities. These poor quality spectra are prone to quantification and/or interpretation errors that can have a significant impact on the clinical use of spectroscopic data. Therefore, quality control of the spectra should always precede their clinical use. When performed manually, quality assessment of MRSI spectra is not only a tedious and time-consuming task, but is also affected by human subjectivity. Consequently, automatic, fast and reliable methods for spectral quality assessment are of utmost interest. In this article, we present a new random forest-based method for automatic quality assessment of (1)H MRSI brain spectra, which uses a new set of MRS signal features. The random forest classifier was trained on spectra from 40 MRSI grids that were classified as acceptable or non-acceptable by two expert spectroscopists. To account for the effects of intra-rater reliability, each spectrum was rated for quality three times by each rater. The automatic method classified these spectra with an area under the curve (AUC) of 0.976. Furthermore, in the subset of spectra containing only the cases that were classified every time in the same way by the spectroscopists, an AUC of 0.998 was obtained. Feature importance for the classification was also evaluated. Frequency domain skewness and kurtosis, as well as time domain signal-to-noise ratios (SNRs) in the ranges 50-75 ms and 75-100 ms, were the most important features. Given that the method is able to assess a whole MRSI grid faster than a spectroscopist (approximately 3 s versus approximately 3 min), and without loss of accuracy (agreement between classifier trained with just one session and any of the other labelling sessions, 89.88%; agreement between any two labelling sessions, 89.03%), the authors suggest its implementation in the clinical routine. The method presented in this article was implemented

Location of microseismic swarms induced by salt solution mining

Science.gov (United States)

Kinscher, J.; Bernard, P.; Contrucci, I.; Mangeney, A.; Piguet, J. P.; Bigarre, P.

2015-01-01

Ground failures, caving processes and collapses of large natural or man-made underground cavities can produce significant socio-economic damages and represent a serious risk envisaged by the mine managements and municipalities. In order to improve our understanding of the mechanisms governing such a geohazard and to test the potential of geophysical methods to prevent them, the development and collapse of a salt solution mining cavity was monitored in the Lorraine basin in northeastern France. During the experiment, a huge microseismic data set (˜50 000 event files) was recorded by a local microseismic network. 80 per cent of the data comprised unusual swarming sequences with complex clusters of superimposed microseismic events which could not be processed through standard automatic detection and location routines. Here, we present two probabilistic methods which provide a powerful tool to assess the spatio-temporal characteristics of these swarming sequences in an automatic manner. Both methods take advantage of strong attenuation effects and significantly polarized P-wave energies at higher frequencies (>100 Hz). The first location approach uses simple signal amplitude estimates for different frequency bands, and an attenuation model to constrain the hypocentre locations. The second approach was designed to identify significantly polarized P-wave energies and the associated polarization angles which provide very valuable information on the hypocentre location. Both methods are applied to a microseismic data set recorded during an important step of the development of the cavity, that is, before its collapse. From our results, systematic spatio-temporal epicentre migration trends are observed in the order of seconds to minutes and several tens of meters which are partially associated with cyclic behaviours. In addition, from spatio-temporal distribution of epicentre clusters we observed similar epicentre migration in the order of hours and days. All together, we

Low-cost asset tracking using location-aware camera phones

Science.gov (United States)

Chen, David; Tsai, Sam; Kim, Kyu-Han; Hsu, Cheng-Hsin; Singh, Jatinder Pal; Girod, Bernd

2010-08-01

Maintaining an accurate and up-to-date inventory of one's assets is a labor-intensive, tedious, and costly operation. To ease this difficult but important task, we design and implement a mobile asset tracking system for automatically generating an inventory by snapping photos of the assets with a smartphone. Since smartphones are becoming ubiquitous, construction and deployment of our inventory management solution is simple and costeffective. Automatic asset recognition is achieved by first segmenting individual assets out of the query photo and then performing bag-of-visual-features (BoVF) image matching on the segmented regions. The smartphone's sensor readings, such as digital compass and accelerometer measurements, can be used to determine the location of each asset, and this location information is stored in the inventory for each recognized asset. As a special case study, we demonstrate a mobile book tracking system, where users snap photos of books stacked on bookshelves to generate a location-aware book inventory. It is shown that segmenting the book spines is very important for accurate feature-based image matching into a database of book spines. Segmentation also provides the exact orientation of each book spine, so more discriminative upright local features can be employed for improved recognition. This system's mobile client has been implemented for smartphones running the Symbian or Android operating systems. The client enables a user to snap a picture of a bookshelf and to subsequently view the recognized spines in the smartphone's viewfinder. Two different pose estimates, one from BoVF geometric matching and the other from segmentation boundaries, are both utilized to accurately draw the boundary of each spine in the viewfinder for easy visualization. The BoVF representation also allows matching each photo of a bookshelf rack against a photo of the entire bookshelf, and the resulting feature matches are used in conjunction with the smartphone

A review of computer-based simulators for ultrasound training.

Science.gov (United States)

Blum, Tobias; Rieger, Andreas; Navab, Nassir; Friess, Helmut; Martignoni, Marc

2013-04-01

Computer-based simulators for ultrasound training are a topic of recent interest. During the last 15 years, many different systems and methods have been proposed. This article provides an overview and classification of systems in this domain and a discussion of their advantages. Systems are classified and discussed according to the image simulation method, user interactions and medical applications. Computer simulation of ultrasound has one key advantage over traditional training. It enables novel training concepts, for example, through advanced visualization, case databases, and automatically generated feedback. Qualitative evaluations have mainly shown positive learning effects. However, few quantitative evaluations have been performed and long-term effects have to be examined.

Automatic Content Creation for Games to Train Students Distinguishing Similar Chinese Characters

Science.gov (United States)

Lai, Kwong-Hung; Leung, Howard; Tang, Jeff K. T.

In learning Chinese, many students often have the problem of mixing up similar characters. This can cause misunderstanding and miscommunication in daily life. It is thus important for students learning the Chinese language to be able to distinguish similar characters and understand their proper usage. In this paper, we propose a game style framework in which the game content in identifying similar Chinese characters in idioms and words is created automatically. Our prior work on analyzing students’ Chinese handwriting can be applied in the similarity measure of Chinese characters. We extend this work by adding the component of radical extraction to speed up the search process. Experimental results show that the proposed method is more accurate and faster in finding more similar Chinese characters compared with the baseline method without considering the radical information.

Automatisms: bridging clinical neurology with criminal law.

Science.gov (United States)

Rolnick, Joshua; Parvizi, Josef

2011-03-01

The law, like neurology, grapples with the relationship between disease states and behavior. Sometimes, the two disciplines share the same terminology, such as automatism. In law, the "automatism defense" is a claim that action was involuntary or performed while unconscious. Someone charged with a serious crime can acknowledge committing the act and yet may go free if, relying on the expert testimony of clinicians, the court determines that the act of crime was committed in a state of automatism. In this review, we explore the relationship between the use of automatism in the legal and clinical literature. We close by addressing several issues raised by the automatism defense: semantic ambiguity surrounding the term automatism, the presence or absence of consciousness during automatisms, and the methodological obstacles that have hindered the study of cognition during automatisms. Copyright © 2010 Elsevier Inc. All rights reserved.

Low-Cost Impact Detection and Location for Automated Inspections of 3D Metallic Based Structures

Directory of Open Access Journals (Sweden)

Carlos Morón

2015-05-01

Full Text Available This paper describes a new low-cost means to detect and locate mechanical impacts (collisions on a 3D metal-based structure. We employ the simple and reasonably hypothesis that the use of a homogeneous material will allow certain details of the impact to be automatically determined by measuring the time delays of acoustic wave propagation throughout the 3D structure. The location of strategic piezoelectric sensors on the structure and an electronic-computerized system has allowed us to determine the instant and position at which the impact is produced. The proposed automatic system allows us to fully integrate impact point detection and the task of inspecting the point or zone at which this impact occurs. What is more, the proposed method can be easily integrated into a robot-based inspection system capable of moving over 3D metallic structures, thus avoiding (or minimizing the need for direct human intervention. Experimental results are provided to show the effectiveness of the proposed approach.

The Accuracy of Locating Lumbar Vertebrae When Using Palpation Versus Ultrasonography

DEFF Research Database (Denmark)

Mieritz, Rune Mygind; Kawchuk, Gregory Neil

2016-01-01

. The target was then located by 16 undergraduate chiropractic students using clinical palpation techniques learned in their academic program (with participant seated and prone) and ultrasonic imaging learned through a 5-minute training video. Presumed target locations identified by students were recorded...

Auditing Hierarchical Cycles to Locate Other Inconsistencies in the UMLS

Science.gov (United States)

Halper, Michael; Morrey, C. Paul; Chen, Yan; Elhanan, Gai; Hripcsak, George; Perl, Yehoshua

2011-01-01

A cycle in the parent relationship hierarchy of the UMLS is a configuration that effectively makes some concept(s) an ancestor of itself. Such a structural inconsistency can easily be found automatically. A previous strategy for disconnecting cycles is to break them with the deletion of one or more parent relationships—irrespective of the correctness of the deleted relationships. A methodology is introduced for auditing of cycles that seeks to discover and delete erroneous relationships only. Cycles involving three concepts are the primary consideration. Hypotheses about the high probability of locating an erroneous parent relationship in a cycle are proposed and confirmed with statistical confidence and lend credence to the auditing approach. A cycle may serve as an indicator of other non-structural inconsistencies that are otherwise difficult to detect automatically. An extensive auditing example shows how a cycle can indicate further inconsistencies. PMID:22195107

Object-Location-Aware Hashing for Multi-Label Image Retrieval via Automatic Mask Learning.

Science.gov (United States)

Huang, Chang-Qin; Yang, Shang-Ming; Pan, Yan; Lai, Han-Jiang

2018-09-01

Learning-based hashing is a leading approach of approximate nearest neighbor search for large-scale image retrieval. In this paper, we develop a deep supervised hashing method for multi-label image retrieval, in which we propose to learn a binary "mask" map that can identify the approximate locations of objects in an image, so that we use this binary "mask" map to obtain length-limited hash codes which mainly focus on an image's objects but ignore the background. The proposed deep architecture consists of four parts: 1) a convolutional sub-network to generate effective image features; 2) a binary "mask" sub-network to identify image objects' approximate locations; 3) a weighted average pooling operation based on the binary "mask" to obtain feature representations and hash codes that pay most attention to foreground objects but ignore the background; and 4) the combination of a triplet ranking loss designed to preserve relative similarities among images and a cross entropy loss defined on image labels. We conduct comprehensive evaluations on four multi-label image data sets. The results indicate that the proposed hashing method achieves superior performance gains over the state-of-the-art supervised or unsupervised hashing baselines.

Automatic frame-centered object representation and integration revealed by iconic memory, visual priming, and backward masking.

Science.gov (United States)

Lin, Zhicheng; He, Sheng

2012-10-25

Object identities ("what") and their spatial locations ("where") are processed in distinct pathways in the visual system, raising the question of how the what and where information is integrated. Because of object motions and eye movements, the retina-based representations are unstable, necessitating nonretinotopic representation and integration. A potential mechanism is to code and update objects according to their reference frames (i.e., frame-centered representation and integration). To isolate frame-centered processes, in a frame-to-frame apparent motion configuration, we (a) presented two preceding or trailing objects on the same frame, equidistant from the target on the other frame, to control for object-based (frame-based) effect and space-based effect, and (b) manipulated the target's relative location within its frame to probe frame-centered effect. We show that iconic memory, visual priming, and backward masking depend on objects' relative frame locations, orthogonal of the retinotopic coordinate. These findings not only reveal that iconic memory, visual priming, and backward masking can be nonretinotopic but also demonstrate that these processes are automatically constrained by contextual frames through a frame-centered mechanism. Thus, object representation is robustly and automatically coupled to its reference frame and continuously being updated through a frame-centered, location-specific mechanism. These findings lead to an object cabinet framework, in which objects ("files") within the reference frame ("cabinet") are orderly coded relative to the frame.

Artificial neural network controller for automatic ship berthing using head-up coordinate system

Directory of Open Access Journals (Sweden)

Nam-Kyun Im

2018-05-01

Full Text Available The Artificial Neural Network (ANN model has been known as one of the most effective theories for automatic ship berthing, as it has learning ability and mimics the actions of the human brain when performing the stages of ship berthing. However, existing ANN controllers can only bring a ship into a berth in a certain port, where the inputs of the ANN are the same as those of the teaching data. This means that those ANN controllers must be retrained when the ship arrives to a new port, which is time-consuming and costly. In this research, by using the head-up coordinate system, which includes the relative bearing and distance from the ship to the berth, a novel ANN controller is proposed to automatically control the ship into the berth in different ports without retraining the ANN structure. Numerical simulations were performed to verify the effectiveness of the proposed controller. First, teaching data were created in the original port to train the neural network; then, the controller was tested for automatic berthing in other ports, where the initial conditions of the inputs in the head-up coordinate system were similar to those of the teaching data in the original port. The results showed that the proposed controller has good performance for ship berthing in ports. Keywords: Automatic ship berthing, ANN controller, Head-up coordinate system, Low speed, Relative bearing

Locating single-point sources from arrival times containing large picking errors (LPEs): the virtual field optimization method (VFOM)

Science.gov (United States)

Li, Xi-Bing; Wang, Ze-Wei; Dong, Long-Jun

2016-01-01

Microseismic monitoring systems using local location techniques tend to be timely, automatic and stable. One basic requirement of these systems is the automatic picking of arrival times. However, arrival times generated by automated techniques always contain large picking errors (LPEs), which may make the location solution unreliable and cause the integrated system to be unstable. To overcome the LPE issue, we propose the virtual field optimization method (VFOM) for locating single-point sources. In contrast to existing approaches, the VFOM optimizes a continuous and virtually established objective function to search the space for the common intersection of the hyperboloids, which is determined by sensor pairs other than the least residual between the model-calculated and measured arrivals. The results of numerical examples and in-site blasts show that the VFOM can obtain more precise and stable solutions than traditional methods when the input data contain LPEs. Furthermore, we discuss the impact of LPEs on objective functions to determine the LPE-tolerant mechanism, velocity sensitivity and stopping criteria of the VFOM. The proposed method is also capable of locating acoustic sources using passive techniques such as passive sonar detection and acoustic emission.

Automatic alignment device for focal spot measurements in the center of the field for mammography

International Nuclear Information System (INIS)

Vieira, Marcelo A.C.; Watanabe, Alex O.; Oliveira Junior, Paulo D.; Schiabel, Homero

2010-01-01

Some quality control procedures used for mammography, such as focal spot evaluation, requires previous alignment of the measurement equipment with the X-ray central beam. However, alignment procedures are, in general, the most difficult task and the one that needs more time to be performed. Moreover, the operator sometimes is exposed to radiation during this procedure. This work presents an automatic alignment system for mammographic equipment that allows locating the central ray of the radiation beam and, immediately, aligns with it by dislocating itself automatically along the field. The system consists on a bidirectional moving device, connected to a CCD sensor for digital radiographic image acquisition. A computational analysis of a radiographic image, acquired at any position on the field, is performed in order to determine its positioning under the X-ray beam. Finally, a mechanical system for two moving directions, electronically controlled by a microcontroller under USB communication, makes the system to align automatically with the radiation beam central ray. The alignment process is fully automatic, fast and accurate, with no operator exposure to radiation, which allows a considerable time saving for quality control procedures achievement for mammography. (author)

Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques

Science.gov (United States)

Sun, Ziheng; Fang, Hui; Di, Liping; Yue, Peng

2016-09-01

It was an untouchable dream for remote sensing experts to realize total automatic image classification without inputting any parameter values. Experts usually spend hours and hours on tuning the input parameters of classification algorithms in order to obtain the best results. With the rapid development of knowledge engineering and cyberinfrastructure, a lot of data processing and knowledge reasoning capabilities become online accessible, shareable and interoperable. Based on these recent improvements, this paper presents an idea of parameterless automatic classification which only requires an image and automatically outputs a labeled vector. No parameters and operations are needed from endpoint consumers. An approach is proposed to realize the idea. It adopts an ontology database to store the experiences of tuning values for classifiers. A sample database is used to record training samples of image segments. Geoprocessing Web services are used as functionality blocks to finish basic classification steps. Workflow technology is involved to turn the overall image classification into a total automatic process. A Web-based prototypical system named PACS (Parameterless Automatic Classification System) is implemented. A number of images are fed into the system for evaluation purposes. The results show that the approach could automatically classify remote sensing images and have a fairly good average accuracy. It is indicated that the classified results will be more accurate if the two databases have higher quality. Once the experiences and samples in the databases are accumulated as many as an expert has, the approach should be able to get the results with similar quality to that a human expert can get. Since the approach is total automatic and parameterless, it can not only relieve remote sensing workers from the heavy and time-consuming parameter tuning work, but also significantly shorten the waiting time for consumers and facilitate them to engage in image

Optimizing Preseason Training Loads in Australian Football.

Science.gov (United States)

Carey, David L; Crow, Justin; Ong, Kok-Leong; Blanch, Peter; Morris, Meg E; Dascombe, Ben J; Crossley, Kay M

2018-02-01

To investigate whether preseason training plans for Australian football can be computer generated using current training-load guidelines to optimize injury-risk reduction and performance improvement. A constrained optimization problem was defined for daily total and sprint distance, using the preseason schedule of an elite Australian football team as a template. Maximizing total training volume and maximizing Banister-model-projected performance were both considered optimization objectives. Cumulative workload and acute:chronic workload-ratio constraints were placed on training programs to reflect current guidelines on relative and absolute training loads for injury-risk reduction. Optimization software was then used to generate preseason training plans. The optimization framework was able to generate training plans that satisfied relative and absolute workload constraints. Increasing the off-season chronic training loads enabled the optimization algorithm to prescribe higher amounts of "safe" training and attain higher projected performance levels. Simulations showed that using a Banister-model objective led to plans that included a taper in training load prior to competition to minimize fatigue and maximize projected performance. In contrast, when the objective was to maximize total training volume, more frequent training was prescribed to accumulate as much load as possible. Feasible training plans that maximize projected performance and satisfy injury-risk constraints can be automatically generated by an optimization problem for Australian football. The optimization methods allow for individualized training-plan design and the ability to adapt to changing training objectives and different training-load metrics.

Locating cloud-to-ground lightning return strokes by a neural network algorithm

International Nuclear Information System (INIS)

2001-01-01

A neuro-based approach is proposed for locating cloud-to-ground lightning strokes. Due to insufficient experimental data, we have use the results of an electromagnetic simulator for training the developed artificial neural network. The simulator utilizes the well-known transmission line and is capable of predicting the electromagnetic field due to a return stroke channel for various parameters associated with the shape of the channel base-current. The training process has been successfully done using the Levenberg-Marquard technique. The simulation results demonstrate that the return stroke channel locations can be predicted with an absolute error not greater than 1 km for return stroke channels located within 80 km of a lightning detection station

Locating Longitudinal Respondents After a 50-Year Hiatus

Directory of Open Access Journals (Sweden)

Stone Celeste

2014-06-01

Full Text Available Many longitudinal and follow-up studies face a common challenge: locating study participants. This study examines the extent to which a geographically dispersed subsample of participants can be relocated after 37 to 51 years of noncontact. Relying mostly on commercially available databases and administrative records, the 2011-12 Project Talent Follow-up Pilot Study (PTPS12 located nearly 85 percent of the original sample members, many of whom had not participated in the study since 1960. This study uses data collected in the base year to examine which subpopulations were the hardest to find after this extended hiatus. The results indicate that females were located at significantly lower rates than males. As expected, sample members with lower cognitive abilities were among the hardest-to-reach subpopulations. We next evaluate the extent to which biases introduced during the tracking phase can be minimized by using the multivariate chi-square automatic interaction detection (CHAID technique to calculate tracking loss adjustments. Unlike a 1995 study that found that these adjustments reduced statistical biases among its sample of located females, our results suggest that statistical adjustments were not as effective in PTPS12, where many participants had not been contacted in nearly 50 years and the tracking rates varied so greatly across subgroups.

Automatic seed picking for brachytherapy postimplant validation with 3D CT images.

Science.gov (United States)

Zhang, Guobin; Sun, Qiyuan; Jiang, Shan; Yang, Zhiyong; Ma, Xiaodong; Jiang, Haisong

2017-11-01

Postimplant validation is an indispensable part in the brachytherapy technique. It provides the necessary feedback to ensure the quality of operation. The ability to pick implanted seed relates directly to the accuracy of validation. To address it, an automatic approach is proposed for picking implanted brachytherapy seeds in 3D CT images. In order to pick seed configuration (location and orientation) efficiently, the approach starts with the segmentation of seed from CT images using a thresholding filter which based on gray-level histogram. Through the process of filtering and denoising, the touching seed and single seed are classified. The true novelty of this approach is found in the application of the canny edge detection and improved concave points matching algorithm to separate touching seeds. Through the computation of image moments, the seed configuration can be determined efficiently. Finally, two different experiments are designed to verify the performance of the proposed approach: (1) physical phantom with 60 model seeds, and (2) patient data with 16 cases. Through assessment of validated results by a medical physicist, the proposed method exhibited promising results. Experiment on phantom demonstrates that the error of seed location and orientation is within ([Formula: see text]) mm and ([Formula: see text])[Formula: see text], respectively. In addition, the most seed location and orientation error is controlled within 0.8 mm and 3.5[Formula: see text] in all cases, respectively. The average process time of seed picking is 8.7 s per 100 seeds. In this paper, an automatic, efficient and robust approach, performed on CT images, is proposed to determine the implanted seed location as well as orientation in a 3D workspace. Through the experiments with phantom and patient data, this approach also successfully exhibits good performance.

Trip optimization system and method for a train

Energy Technology Data Exchange (ETDEWEB)

Kumar, Ajith Kuttannair; Shaffer, Glenn Robert; Houpt, Paul Kenneth; Movsichoff, Bernardo Adrian; Chan, David So Keung

2017-08-15

A system for operating a train having one or more locomotive consists with each locomotive consist comprising one or more locomotives, the system including a locator element to determine a location of the train, a track characterization element to provide information about a track, a sensor for measuring an operating condition of the locomotive consist, a processor operable to receive information from the locator element, the track characterizing element, and the sensor, and an algorithm embodied within the processor having access to the information to create a trip plan that optimizes performance of the locomotive consist in accordance with one or more operational criteria for the train.

Fully automatic characterization and data collection from crystals of biological macromolecules.

Science.gov (United States)

Svensson, Olof; Malbet-Monaco, Stéphanie; Popov, Alexander; Nurizzo, Didier; Bowler, Matthew W

2015-08-01

Considerable effort is dedicated to evaluating macromolecular crystals at synchrotron sources, even for well established and robust systems. Much of this work is repetitive, and the time spent could be better invested in the interpretation of the results. In order to decrease the need for manual intervention in the most repetitive steps of structural biology projects, initial screening and data collection, a fully automatic system has been developed to mount, locate, centre to the optimal diffraction volume, characterize and, if possible, collect data from multiple cryocooled crystals. Using the capabilities of pixel-array detectors, the system is as fast as a human operator, taking an average of 6 min per sample depending on the sample size and the level of characterization required. Using a fast X-ray-based routine, samples are located and centred systematically at the position of highest diffraction signal and important parameters for sample characterization, such as flux, beam size and crystal volume, are automatically taken into account, ensuring the calculation of optimal data-collection strategies. The system is now in operation at the new ESRF beamline MASSIF-1 and has been used by both industrial and academic users for many different sample types, including crystals of less than 20 µm in the smallest dimension. To date, over 8000 samples have been evaluated on MASSIF-1 without any human intervention.

Content-aware automatic cropping for consumer photos

Science.gov (United States)

Tang, Hao; Tretter, Daniel; Lin, Qian

2013-03-01

Consumer photos are typically authored once, but need to be retargeted for reuse in various situations. These include printing a photo on different size paper, changing the size and aspect ratio of an embedded photo to accommodate the dynamic content layout of web pages or documents, adapting a large photo for browsing on small displays such as mobile phone screens, and improving the aesthetic quality of a photo that was badly composed at the capture time. In this paper, we propose a novel, effective, and comprehensive content-aware automatic cropping (hereafter referred to as "autocrop") method for consumer photos to achieve the above purposes. Our autocrop method combines the state-of-the-art context-aware saliency detection algorithm, which aims to infer the likely intent of the photographer, and the "branch-and-bound" efficient subwindow search optimization technique, which seeks to locate the globally optimal cropping rectangle in a fast manner. Unlike most current autocrop methods, which can only crop a photo into an arbitrary rectangle, our autocrop method can automatically crop a photo into either a rectangle of arbitrary dimensions or a rectangle of the desired aspect ratio specified by the user. The aggressiveness of the cropping operation may be either automatically determined by the method or manually indicated by the user with ease. In addition, our autocrop method is extended to support the cropping of a photo into non-rectangular shapes such as polygons of any number of sides. It may also be potentially extended to return multiple cropping suggestions, which will enable the creation of new photos to enrich the original photo collections. Our experimental results show that the proposed autocrop method in this paper can generate high-quality crops for consumer photos of various types.

Automatic gamma spectrometry analytical apparatus

International Nuclear Information System (INIS)

Lamargot, J.-P.; Wanin, Maurice.

1980-01-01

This invention falls within the area of quantitative or semi-quantitative analysis by gamma spectrometry and particularly refers to a device for bringing the samples into the counting position. The purpose of this invention is precisely to provide an automatic apparatus specifically adapted to the analysis of hard gamma radiations. To this effect, the invention relates to a gamma spectrometry analytical device comprising a lead containment, a detector of which the sensitive part is located inside the containment and additionally comprising a transfer system for bringing the analyzed samples in succession to a counting position inside the containment above the detector. A feed compartment enables the samples to be brought in turn one by one on to the transfer system through a duct connecting the compartment to the transfer system. Sequential systems for the coordinated forward feed of the samples in the compartment and the transfer system complete this device [fr

Neural Bases of Automaticity

Science.gov (United States)

Servant, Mathieu; Cassey, Peter; Woodman, Geoffrey F.; Logan, Gordon D.

2018-01-01

Automaticity allows us to perform tasks in a fast, efficient, and effortless manner after sufficient practice. Theories of automaticity propose that across practice processing transitions from being controlled by working memory to being controlled by long-term memory retrieval. Recent event-related potential (ERP) studies have sought to test this…

Online Workplace Training in Libraries

Directory of Open Access Journals (Sweden)

Connie K. Haley

2008-03-01

Full Text Available This study was designed to explore and describe the relationships between preference for online training and traditional face-to-face training. Included were variables of race, gender, age, education, experience of library employees, training providers, training locations, and institutional professional development policies, etc. in the library context. The author used a bivariate test, KruskalWallis test and Mann-Whitney U test to examine the relationship between preference for online training and related variables.

Brand and automaticity

OpenAIRE

Liu, J.

2008-01-01

A presumption of most consumer research is that consumers endeavor to maximize the utility of their choices and are in complete control of their purchasing and consumption behavior. However, everyday life experience suggests that many of our choices are not all that reasoned or conscious. Indeed, automaticity, one facet of behavior, is indispensable to complete the portrait of consumers. Despite its importance, little attention is paid to how the automatic side of behavior can be captured and...

Automatic Retrieval of Newly Instructed Cue-Task Associations Seen in Task-Conflict Effects in the First Trial after Cue-Task Instructions.

Science.gov (United States)

Meiran, Nachshon; Pereg, Maayan

2017-01-01

Novel stimulus-response associations are retrieved automatically even without prior practice. Is this true for novel cue-task associations? The experiment involved miniblocks comprising three phases and task switching. In the INSTRUCTION phase, two new stimuli (or familiar cues) were arbitrarily assigned as cues for up-down/right-left tasks performed on placeholder locations. In the UNIVALENT phase, there was no task cue since placeholder's location afforded one task but the placeholders were the stimuli that we assigned as task cues for the following BIVALENT phase (involving target locations affording both tasks). Thus, participants held the novel cue-task associations in memory while executing the UNIVALENT phase. Results show poorer performance in the first univalent trial when the placeholder was associated with the opposite task (incompatible) than when it was compatible, an effect that was numerically larger with newly instructed cues than with familiar cues. These results indicate automatic retrieval of newly instructed cue-task associations.

Automatic Program Development

DEFF Research Database (Denmark)

Automatic Program Development is a tribute to Robert Paige (1947-1999), our accomplished and respected colleague, and moreover our good friend, whose untimely passing was a loss to our academic and research community. We have collected the revised, updated versions of the papers published in his...... honor in the Higher-Order and Symbolic Computation Journal in the years 2003 and 2005. Among them there are two papers by Bob: (i) a retrospective view of his research lines, and (ii) a proposal for future studies in the area of the automatic program derivation. The book also includes some papers...... by members of the IFIP Working Group 2.1 of which Bob was an active member. All papers are related to some of the research interests of Bob and, in particular, to the transformational development of programs and their algorithmic derivation from formal specifications. Automatic Program Development offers...

Automatic determination of the size of elliptical nanoparticles from AFM images

International Nuclear Information System (INIS)

Sedlář, Jiří; Zitová, Barbara; Kopeček, Jaromír; Flusser, Jan; Todorciuc, Tatiana; Kratochvílová, Irena

2013-01-01

The objective of this work was to develop an accurate method for automatic determination of the size of elliptical nanoparticles from atomic force microscopy (AFM) images that would yield results consistent with results of manual measurements by experts. The proposed method was applied on phenylpyridyldiketopyrrolopyrrole (PPDP), a granular organic material with a wide scale of application and highly sensitive particle-size properties. A PPDP layer consists of similarly sized elliptical particles (c. 100 nm × 50 nm) and its properties can be estimated from the average length and width of the particles. The developed method is based on segmentation of salient particles by the watershed transform and approximation of their shapes by ellipses computed by image moments; it estimates the lengths and widths of the particles by the major and minor axes, respectively, of the corresponding ellipses. Its results proved to be consistent with results of manual measurements by a trained expert. The comparison showed that the developed method could be used in practice for precise automatic measurement of PPDP particles in AFM images

Development of advanced automatic operation system for nuclear ship. 1. Perfect automatic normal operation

International Nuclear Information System (INIS)

Nakazawa, Toshio; Yabuuti, Noriaki; Takahashi, Hiroki; Shimazaki, Junya

1999-02-01

Development of operation support system such as automatic operating system and anomaly diagnosis systems of nuclear reactor is very important in practical nuclear ship because of a limited number of operators and severe conditions in which receiving support from others in a case of accident is very difficult. The goal of development of the operation support systems is to realize the perfect automatic control system in a series of normal operation from the reactor start-up to the shutdown. The automatic control system for the normal operation has been developed based on operating experiences of the first Japanese nuclear ship 'Mutsu'. Automation technique was verified by 'Mutsu' plant data at manual operation. Fully automatic control of start-up and shutdown operations was achieved by setting the desired value of operation and the limiting value of parameter fluctuation, and by making the operation program of the principal equipment such as the main coolant pump and the heaters. This report presents the automatic operation system developed for the start-up and the shutdown of reactor and the verification of the system using the Nuclear Ship Engineering Simulator System. (author)

Effects of Word Recognition Training in a Picture-Word Interference Task: Automaticity vs. Speed.

Science.gov (United States)

Ehri, Linnea C.

First and second graders were taught to recognize a set of written words either more accurately or more rapidly. Both before and after word training, they named pictures printed with and without these words as distractors. Of interest was whether training would enhance or diminish the interference created by these words in the picture naming task.…

14 CFR 23.1329 - Automatic pilot system.

Science.gov (United States)

2010-01-01

... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false Automatic pilot system. 23.1329 Section 23...: Installation § 23.1329 Automatic pilot system. If an automatic pilot system is installed, it must meet the following: (a) Each system must be designed so that the automatic pilot can— (1) Be quickly and positively...

46 CFR 52.01-10 - Automatic controls.

Science.gov (United States)

2010-10-01

... 46 Shipping 2 2010-10-01 2010-10-01 false Automatic controls. 52.01-10 Section 52.01-10 Shipping... Requirements § 52.01-10 Automatic controls. (a) Each main boiler must meet the special requirements for automatic safety controls in § 62.35-20(a)(1) of this chapter. (b) Each automatically controlled auxiliary...

Augmented Reality Training for Assembly and Maintenance Skills

Directory of Open Access Journals (Sweden)

Preusche Carsten

2011-12-01

Full Text Available Augmented Reality (AR points out to be a good technology for training in the field of maintenance and assembly, as instructions or rather location-dependent information can be directly linked and/or attached to physical objects. Since objects to maintain usually contain a large number of similar components (e.g. screws, plugs, etc. the provision of location-dependent information is vitally important. Another advantage is that AR-based training takes place with the real physical devices of the training scenario. Thus, the trainee also practices the real use of the tools whereby the corresponding sensorimotor skills are trained.

Automatic welding and cladding in heavy fabrication

International Nuclear Information System (INIS)

Altamer, A. de

1980-01-01

A description is given of the automatic welding processes used by an Italian fabricator of pressure vessels for petrochemical and nuclear plant. The automatic submerged arc welding, submerged arc strip cladding, pulsed TIG, hot wire TIG and MIG welding processes have proved satisfactory in terms of process reliability, metal deposition rate, and cost effectiveness for low alloy and carbon steels. An example shows sequences required during automatic butt welding, including heat treatments. Factors which govern satisfactory automatic welding include automatic anti-drift rotator device, electrode guidance and bead programming system, the capability of single and dual head operation, flux recovery and slag removal systems, operator environment and controls, maintaining continuity of welding and automatic reverse side grinding. Automatic welding is used for: joining vessel sections; joining tubes to tubeplate; cladding of vessel rings and tubes, dished ends and extruded nozzles; nozzle to shell and butt welds, including narrow gap welding. (author)

Beyond left and right: Automaticity and flexibility of number-space associations.

Science.gov (United States)

Antoine, Sophie; Gevers, Wim

2016-02-01

Close links exist between the processing of numbers and the processing of space: relatively small numbers are preferentially associated with a left-sided response while relatively large numbers are associated with a right-sided response (the SNARC effect). Previous work demonstrated that the SNARC effect is triggered in an automatic manner and is highly flexible. Besides the left-right dimension, numbers associate with other spatial response mappings such as close/far responses, where small numbers are associated with a close response and large numbers with a far response. In two experiments we investigate the nature of this association. Associations between magnitude and close/far responses were observed using a magnitude-irrelevant task (Experiment 1: automaticity) and using a variable referent task (Experiment 2: flexibility). While drawing a strong parallel between both response mappings, the present results are also informative with regard to the question about what type of processing mechanism underlies both the SNARC effect and the association between numerical magnitude and close/far response locations.

Automatic system for detecting pornographic images

Science.gov (United States)

Ho, Kevin I. C.; Chen, Tung-Shou; Ho, Jun-Der

2002-09-01

Due to the dramatic growth of network and multimedia technology, people can more easily get variant information by using Internet. Unfortunately, it also makes the diffusion of illegal and harmful content much easier. So, it becomes an important topic for the Internet society to protect and safeguard Internet users from these content that may be encountered while surfing on the Net, especially children. Among these content, porno graphs cause more serious harm. Therefore, in this study, we propose an automatic system to detect still colour porno graphs. Starting from this result, we plan to develop an automatic system to search porno graphs or to filter porno graphs. Almost all the porno graphs possess one common characteristic that is the ratio of the size of skin region and non-skin region is high. Based on this characteristic, our system first converts the colour space from RGB colour space to HSV colour space so as to segment all the possible skin-colour regions from scene background. We also apply the texture analysis on the selected skin-colour regions to separate the skin regions from non-skin regions. Then, we try to group the adjacent pixels located in skin regions. If the ratio is over a given threshold, we can tell if the given image is a possible porno graph. Based on our experiment, less than 10% of non-porno graphs are classified as pornography, and over 80% of the most harmful porno graphs are classified correctly.

Automatic NMR-based identification of chemical reaction types in mixtures of co-occurring reactions.

Science.gov (United States)

Latino, Diogo A R S; Aires-de-Sousa, João

2014-01-01

The combination of chemoinformatics approaches with NMR techniques and the increasing availability of data allow the resolution of problems far beyond the original application of NMR in structure elucidation/verification. The diversity of applications can range from process monitoring, metabolic profiling, authentication of products, to quality control. An application related to the automatic analysis of complex mixtures concerns mixtures of chemical reactions. We encoded mixtures of chemical reactions with the difference between the (1)H NMR spectra of the products and the reactants. All the signals arising from all the reactants of the co-occurring reactions were taken together (a simulated spectrum of the mixture of reactants) and the same was done for products. The difference spectrum is taken as the representation of the mixture of chemical reactions. A data set of 181 chemical reactions was used, each reaction manually assigned to one of 6 types. From this dataset, we simulated mixtures where two reactions of different types would occur simultaneously. Automatic learning methods were trained to classify the reactions occurring in a mixture from the (1)H NMR-based descriptor of the mixture. Unsupervised learning methods (self-organizing maps) produced a reasonable clustering of the mixtures by reaction type, and allowed the correct classification of 80% and 63% of the mixtures in two independent test sets of different similarity to the training set. With random forests (RF), the percentage of correct classifications was increased to 99% and 80% for the same test sets. The RF probability associated to the predictions yielded a robust indication of their reliability. This study demonstrates the possibility of applying machine learning methods to automatically identify types of co-occurring chemical reactions from NMR data. Using no explicit structural information about the reactions participants, reaction elucidation is performed without structure elucidation of

Automatic NMR-based identification of chemical reaction types in mixtures of co-occurring reactions.

Directory of Open Access Journals (Sweden)

Diogo A R S Latino

Full Text Available The combination of chemoinformatics approaches with NMR techniques and the increasing availability of data allow the resolution of problems far beyond the original application of NMR in structure elucidation/verification. The diversity of applications can range from process monitoring, metabolic profiling, authentication of products, to quality control. An application related to the automatic analysis of complex mixtures concerns mixtures of chemical reactions. We encoded mixtures of chemical reactions with the difference between the (1H NMR spectra of the products and the reactants. All the signals arising from all the reactants of the co-occurring reactions were taken together (a simulated spectrum of the mixture of reactants and the same was done for products. The difference spectrum is taken as the representation of the mixture of chemical reactions. A data set of 181 chemical reactions was used, each reaction manually assigned to one of 6 types. From this dataset, we simulated mixtures where two reactions of different types would occur simultaneously. Automatic learning methods were trained to classify the reactions occurring in a mixture from the (1H NMR-based descriptor of the mixture. Unsupervised learning methods (self-organizing maps produced a reasonable clustering of the mixtures by reaction type, and allowed the correct classification of 80% and 63% of the mixtures in two independent test sets of different similarity to the training set. With random forests (RF, the percentage of correct classifications was increased to 99% and 80% for the same test sets. The RF probability associated to the predictions yielded a robust indication of their reliability. This study demonstrates the possibility of applying machine learning methods to automatically identify types of co-occurring chemical reactions from NMR data. Using no explicit structural information about the reactions participants, reaction elucidation is performed without structure

Using RFID Positioning Technology to Construct an Automatic Rehabilitation Scheduling Mechanism.

Science.gov (United States)

Wang, Ching-Sheng; Hung, Lun-Ping; Yen, Neil Y

2016-01-01

Accurately and efficiently identifying the location of patients during the course of rehabilitation is an important issue. Wireless transmission technology can reach this goal. Tracking technologies such as RFID (Radio frequency identification) can support process improvement and improve efficiencies of rehabilitation. There are few published models or methods to solve the problem of positioning and apply this technology in the rehabilitation center. We propose a mechanism to enhance the accuracy of positioning technology and provide information about turns and obstacles on the path; and user-centered services based on location-aware to enhanced quality care in rehabilitation environment. This paper outlines the requirements and the role of RFID in assisting rehabilitation environment. A prototype RFID hospital support tool is established. It is designed to provide assistance for monitoring rehabilitation patients. It can simultaneously calculate the rehabilitant's location and the duration of treatment, and automatically record the rehabilitation course of the rehabilitant, so as to improve the management efficiency of the rehabilitation program.

Computer vision for automatic inspection of agricultural produce

Science.gov (United States)

Molto, Enrique; Blasco, Jose; Benlloch, Jose V.

1999-01-01

Fruit and vegetables suffer different manipulations from the field to the final consumer. These are basically oriented towards the cleaning and selection of the product in homogeneous categories. For this reason, several research projects, aimed at fast, adequate produce sorting and quality control are currently under development around the world. Moreover, it is possible to find manual and semi- automatic commercial system capable of reasonably performing these tasks.However, in many cases, their accuracy is incompatible with current European market demands, which are constantly increasing. IVIA, the Valencian Research Institute of Agriculture, located in Spain, has been involved in several European projects related with machine vision for real-time inspection of various agricultural produces. This paper will focus on the work related with two products that have different requirements: fruit and olives. In the case of fruit, the Institute has developed a vision system capable of providing assessment of the external quality of single fruit to a robot that also receives information from other senors. The system use four different views of each fruit and has been tested on peaches, apples and citrus. Processing time of each image is under 500 ms using a conventional PC. The system provides information about primary and secondary color, blemishes and their extension, and stem presence and position, which allows further automatic orientation of the fruit in the final box using a robotic manipulator. Work carried out in olives was devoted to fast sorting of olives for consumption at table. A prototype has been developed to demonstrate the feasibility of a machine vision system capable of automatically sorting 2500 kg/h olives using low-cost conventional hardware.

Training Requirements in OSHA Standards. Revised.

Science.gov (United States)

Occupational Safety and Health Administration, Washington, DC.

This booklet contains excerpts of the training-related requirements of the standards promulgated by the Occupational Safety and Health Administration (OSHA). It is designed as an aid for employers, safety and health professionals, and others who need to know training requirements. (References to training may be difficult to locate in the long and…

Law enforcement attitudes towards naloxone following opioid overdose training.

Science.gov (United States)

Purviance, Donna; Ray, Bradley; Tracy, Abigail; Southard, Erik

2017-01-01

Opioid intoxication and overdoses are life-threatening emergencies requiring rapid treatment. One response to this has been to train law enforcement to detect the signs of an opioid overdose and train them to administer naloxone to reverse the effects. Although not a new concept, few studies have attempted to examine this policy. At 4 different locations in Indiana, law enforcement personnel were trained to detect the signs of an opioid-related overdose and how to administer naloxone to reverse the effects of the overdose. Pre and post surveys were administered at each location (N = 97). To examine changes in attitudes following training, the authors included items from the Opioid Overdose Attitudes Scale (OOAS), which measures respondents' competency, concerns, and readiness to administer naloxone. Among the full sample, naloxone training resulted in significant increases in competency, concerns, and readiness. Examining changes in attitudes by each location revealed that the training had the greatest effect on competency to administer naloxone and in easing concerns that law enforcement personal might have in administering naloxone. This study adds to others in showing that law enforcement personnel are receptive to naloxone training and that the OOAS is able to capture these attitudes. This study advances this literature by examining pre-post changes across multiple locations. As the distribution of naloxone continues to proliferate, this study and the OOAS may be valuable towards the development of an evidence-based training model for law enforcement.

Opto-mechanical devices for the Antares automatic beam alignment system

International Nuclear Information System (INIS)

Swann, T.; Combs, C.; Witt, J.

1981-01-01

Antares is a 24-beam CO 2 laser system for controlled fusion research, under construction at Los Alamos National Laboratory. Rapid automatic alignment of this system is required prior to each experimental shot. Unique opto-mechanical alignment devices, which have been developed specifically for this automatic alignment system, are discussed. A variable focus alignment telescope views point light sources. A beam expander/spatial filter processes both a visible Krypton Ion and a 10.6 μm CO 2 alignment laser. The periscope/carousel device provides the means by which the alignment telescope can sequentially view each of twelve optical trains in each power amplifier. The polyhedron alignment device projects a point-light source for both centering and pointing alignment at the polyhedron mirror. The rotating wedge alignment device provides a sequencing point-light source and also compensates for dispersion between visible and 10.6 μm radiation. The back reflector flip in remotely positions point-light sources at the back reflector mirrors. A light source box illuminates optic fibers with high intensity white light which is distributed to the various point-light sources in the system

Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics

Directory of Open Access Journals (Sweden)

Wei Chen

2018-01-01

Full Text Available Gliomas are the most common primary brain tumors, and the objective grading is of great importance for treatment. This paper presents an automatic computer-aided diagnosis of gliomas that combines automatic segmentation and radiomics, which can improve the diagnostic ability. The MRI data containing 220 high-grade gliomas and 54 low-grade gliomas are used to evaluate our system. A multiscale 3D convolutional neural network is trained to segment whole tumor regions. A wide range of radiomic features including first-order features, shape features, and texture features is extracted. By using support vector machines with recursive feature elimination for feature selection, a CAD system that has an extreme gradient boosting classifier with a 5-fold cross-validation is constructed for the grading of gliomas. Our CAD system is highly effective for the grading of gliomas with an accuracy of 91.27%, a weighted macroprecision of 91.27%, a weighted macrorecall of 91.27%, and a weighted macro-F1 score of 90.64%. This demonstrates that the proposed CAD system can assist radiologists for high accurate grading of gliomas and has the potential for clinical applications.

Automatic differentiation of functions

International Nuclear Information System (INIS)

Douglas, S.R.

1990-06-01

Automatic differentiation is a method of computing derivatives of functions to any order in any number of variables. The functions must be expressible as combinations of elementary functions. When evaluated at specific numerical points, the derivatives have no truncation error and are automatically found. The method is illustrated by simple examples. Source code in FORTRAN is provided

Automatic bad channel detection in intracranial electroencephalographic recordings using ensemble machine learning.

Science.gov (United States)

Tuyisenge, Viateur; Trebaul, Lena; Bhattacharjee, Manik; Chanteloup-Forêt, Blandine; Saubat-Guigui, Carole; Mîndruţă, Ioana; Rheims, Sylvain; Maillard, Louis; Kahane, Philippe; Taussig, Delphine; David, Olivier

2018-03-01

Intracranial electroencephalographic (iEEG) recordings contain "bad channels", which show non-neuronal signals. Here, we developed a new method that automatically detects iEEG bad channels using machine learning of seven signal features. The features quantified signals' variance, spatial-temporal correlation and nonlinear properties. Because the number of bad channels is usually much lower than the number of good channels, we implemented an ensemble bagging classifier known to be optimal in terms of stability and predictive accuracy for datasets with imbalanced class distributions. This method was applied on stereo-electroencephalographic (SEEG) signals recording during low frequency stimulations performed in 206 patients from 5 clinical centers. We found that the classification accuracy was extremely good: It increased with the number of subjects used to train the classifier and reached a plateau at 99.77% for 110 subjects. The classification performance was thus not impacted by the multicentric nature of data. The proposed method to automatically detect bad channels demonstrated convincing results and can be envisaged to be used on larger datasets for automatic quality control of iEEG data. This is the first method proposed to classify bad channels in iEEG and should allow to improve the data selection when reviewing iEEG signals. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Solar Powered Automatic Shrimp Feeding System

Directory of Open Access Journals (Sweden)

Dindo T. Ani

2015-12-01

Full Text Available - Automatic system has brought many revolutions in the existing technologies. One among the technologies, which has greater developments, is the solar powered automatic shrimp feeding system. For instance, the solar power which is a renewable energy can be an alternative solution to energy crisis and basically reducing man power by using it in an automatic manner. The researchers believe an automatic shrimp feeding system may help solve problems on manual feeding operations. The project study aimed to design and develop a solar powered automatic shrimp feeding system. It specifically sought to prepare the design specifications of the project, to determine the methods of fabrication and assembly, and to test the response time of the automatic shrimp feeding system. The researchers designed and developed an automatic system which utilizes a 10 hour timer to be set in intervals preferred by the user and will undergo a continuous process. The magnetic contactor acts as a switch connected to the 10 hour timer which controls the activation or termination of electrical loads and powered by means of a solar panel outputting electrical power, and a rechargeable battery in electrical communication with the solar panel for storing the power. By undergoing through series of testing, the components of the modified system were proven functional and were operating within the desired output. It was recommended that the timer to be used should be tested to avoid malfunction and achieve the fully automatic system and that the system may be improved to handle changes in scope of the project.

Simulation Training at a Medical Institute: An integral Part of the Educational Process

Directory of Open Access Journals (Sweden)

P. V. Ligatyuk

2015-01-01

Full Text Available Objective: to master and practically execute cardiopulmonary resuscitation (CPR procedural techniques, to acquire skills to use state-of-art equipment, and to teach work in the team. Subjects and methods. Forty-six interns and residents took a simulation course of training in basic CPR and automatic external defibrillation. Three-four days before the course, its participants received the certified translation of the European Resuscitation Council (ERC information material and studied it. The course education program encompasses lectures, lessons on a medical care algorithm in sudden cardiac arrest, and practical works using models, including chest compression, ventilation, and automatic external defibrillator (AED training. The duration of the course is 6—7 hours. Results. All the interns and residents were motivated to learn: to acquire first aid skills to manage sudden cardiac arrest. The ERC algorithm and a 4-stepped model to have practical skills were used. The taken course met expectations in 100% of the participants; all the interns and residents adequately acquired practical CPR skills and successfully completed their training. A questionnaire survey at the end of the course showed the high efficiency of the course. The training enhanced motivation in 29 interns and residents; they obtained an ERC provider degree; 10 interns and residents continue to take a course of training as an ERC instructor. 

Contaminants and nutrients in variable sea areas (Canvas). Application of automatic monitoring stations in the German marine environment

International Nuclear Information System (INIS)

Nies, H.; Bruegge, B.; Sterzenbach, D.; Knauth, H.D.; Schroeder, F.

1999-01-01

Permanent observation of parameters at sea stations can only be obtained by automatic sampling. The MERMAID technique developed in former projects provides a possibility to run automatic stations within the German MARNET measuring stations to obtain data on nutrients concentration on line and to collect organic micropollutants and the radionuclide 137 Cs by solid phase extraction from seawater and subsequent analysis in the laboratory. The BSH MARNET consists of ten stations located in the German Bight sector of the North Sea and the western Baltic. First results from the time series of nutrient and organic micropollutant concentrations has been presented

Advanced correlation grid: Analysis and visualisation of functional connectivity among multiple spike trains.

Science.gov (United States)

Masud, Mohammad Shahed; Borisyuk, Roman; Stuart, Liz

2017-07-15

This study analyses multiple spike trains (MST) data, defines its functional connectivity and subsequently visualises an accurate diagram of connections. This is a challenging problem. For example, it is difficult to distinguish the common input and the direct functional connection of two spike trains. The new method presented in this paper is based on the traditional pairwise cross-correlation function (CCF) and a new combination of statistical techniques. First, the CCF is used to create the Advanced Correlation Grid (ACG) correlation where both the significant peak of the CCF and the corresponding time delay are used for detailed analysis of connectivity. Second, these two features of functional connectivity are used to classify connections. Finally, the visualization technique is used to represent the topology of functional connections. Examples are presented in the paper to demonstrate the new Advanced Correlation Grid method and to show how it enables discrimination between (i) influence from one spike train to another through an intermediate spike train and (ii) influence from one common spike train to another pair of analysed spike trains. The ACG method enables scientists to automatically distinguish between direct connections from spurious connections such as common source connection and indirect connection whereas existing methods require in-depth analysis to identify such connections. The ACG is a new and effective method for studying functional connectivity of multiple spike trains. This method can identify accurately all the direct connections and can distinguish common source and indirect connections automatically. Copyright © 2017 Elsevier B.V. All rights reserved.

Intelligent trainee behavior assessment system for medical training employing video analysis

NARCIS (Netherlands)

Han, Jungong; With, de P.H.N.; Merién, A.E.R.; Oei, S.G.

2012-01-01

This paper addresses the problem of assessing a trainee’s performance during a simulated delivery training by employing automatic analysis of a video camera signal. We aim at providing objective statistics reflecting the trainee’s behavior, so that the instructor is able to give valuable suggestions

AUTOMATIC RECOGNITION OF INDOOR NAVIGATION ELEMENTS FROM KINECT POINT CLOUDS

Directory of Open Access Journals (Sweden)

L. Zeng

2017-09-01

Full Text Available This paper realizes automatically the navigating elements defined by indoorGML data standard – door, stairway and wall. The data used is indoor 3D point cloud collected by Kinect v2 launched in 2011 through the means of ORB-SLAM. By contrast, it is cheaper and more convenient than lidar, but the point clouds also have the problem of noise, registration error and large data volume. Hence, we adopt a shape descriptor – histogram of distances between two randomly chosen points, proposed by Osada and merges with other descriptor – in conjunction with random forest classifier to recognize the navigation elements (door, stairway and wall from Kinect point clouds. This research acquires navigation elements and their 3-d location information from each single data frame through segmentation of point clouds, boundary extraction, feature calculation and classification. Finally, this paper utilizes the acquired navigation elements and their information to generate the state data of the indoor navigation module automatically. The experimental results demonstrate a high recognition accuracy of the proposed method.

Automatic Recognition of Indoor Navigation Elements from Kinect Point Clouds

Science.gov (United States)

Zeng, L.; Kang, Z.

2017-09-01

This paper realizes automatically the navigating elements defined by indoorGML data standard - door, stairway and wall. The data used is indoor 3D point cloud collected by Kinect v2 launched in 2011 through the means of ORB-SLAM. By contrast, it is cheaper and more convenient than lidar, but the point clouds also have the problem of noise, registration error and large data volume. Hence, we adopt a shape descriptor - histogram of distances between two randomly chosen points, proposed by Osada and merges with other descriptor - in conjunction with random forest classifier to recognize the navigation elements (door, stairway and wall) from Kinect point clouds. This research acquires navigation elements and their 3-d location information from each single data frame through segmentation of point clouds, boundary extraction, feature calculation and classification. Finally, this paper utilizes the acquired navigation elements and their information to generate the state data of the indoor navigation module automatically. The experimental results demonstrate a high recognition accuracy of the proposed method.

Real-time Automatic Detectors of P and S Waves Using Singular Values Decomposition

Science.gov (United States)

Kurzon, I.; Vernon, F.; Rosenberger, A.; Ben-Zion, Y.

2013-12-01

We implement a new method for the automatic detection of the primary P and S phases using Singular Value Decomposition (SVD) analysis. The method is based on a real-time iteration algorithm of Rosenberger (2010) for the SVD of three component seismograms. Rosenberger's algorithm identifies the incidence angle by applying SVD and separates the waveforms into their P and S components. We have been using the same algorithm with the modification that we filter the waveforms prior to the SVD, and then apply SNR (Signal-to-Noise Ratio) detectors for picking the P and S arrivals, on the new filtered+SVD-separated channels. A recent deployment in San Jacinto Fault Zone area provides a very dense seismic network that allows us to test the detection algorithm in diverse setting, such as: events with different source mechanisms, stations with different site characteristics, and ray paths that diverge from the SVD approximation used in the algorithm, (e.g., rays propagating within the fault and recorded on linear arrays, crossing the fault). We have found that a Butterworth band-pass filter of 2-30Hz, with four poles at each of the corner frequencies, shows the best performance in a large variety of events and stations within the SJFZ. Using the SVD detectors we obtain a similar number of P and S picks, which is a rare thing to see in ordinary SNR detectors. Also for the actual real-time operation of the ANZA and SJFZ real-time seismic networks, the above filter (2-30Hz) shows a very impressive performance, tested on many events and several aftershock sequences in the region from the MW 5.2 of June 2005, through the MW 5.4 of July 2010, to MW 4.7 of March 2013. Here we show the results of testing the detectors on the most complex and intense aftershock sequence, the MW 5.2 of June 2005, in which in the very first hour there were ~4 events a minute. This aftershock sequence was thoroughly reviewed by several analysts, identifying 294 events in the first hour, located in a

From traditional locomotive engineers to automated train control; Vom triebfahrzeugfuehrergefuehrten Zug zum Fahrautomaten

Energy Technology Data Exchange (ETDEWEB)

Hohnecker, E. [Karlsruhe Univ. (T.H.) (Germany). Inst. fuer Strassenbau und Eisenbahnwesen

2000-07-01

Profitability and capacity in public guided transport must be increased. One option is automatic, driverless train control. There are many questions surrounding the legal, technical and operational aspects of automated control which need to be answered. These intrinsic aspects of the system will be presented and discussed. The various options leading to automatic train control as well as the necessary technical measures will also be presented. (orig.) [German] Die Wirtschaftlichkeit und die Leistungsfaehigkeit im oeffentlichen spurgefuehrten Verkehr muessen erhoeht werden. Als Loesung bietet sich zukuenftig auch der automatische und fahrerlose Betrieb an. Das Fahren ohne Triebfahrzeugfuehrer wirft jedoch eine Vielzahl von Fragestellungen auf, die sowohl in juristischer, technischer und betrieblicher Hinsicht beantwortet werden muessen. Diese systemimmanenten Aspekte werden dargestellt und diskutiert. Anschliessend werden die Moeglichkeiten auf dem Weg zum Fahrautomaten aufgezeigt und die technischen Massnahmen zur Umsetzung des automatischen Fahrens erlaeutert. (orig.)

Weighting training images by maximizing distribution similarity for supervised segmentation across scanners

DEFF Research Database (Denmark)

van Opbroek, Annegreet; Vernooij, Meike W; Ikram, M.Arfan

2015-01-01

Many automatic segmentation methods are based on supervised machine learning. Such methods have proven to perform well, on the condition that they are trained on a sufficiently large manually labeled training set that is representative of the images to segment. However, due to differences between...... scanners, scanning parameters, and patients such a training set may be difficult to obtain. We present a transfer-learning approach to segmentation by multi-feature voxelwise classification. The presented method can be trained using a heterogeneous set of training images that may be obtained with different...... scanners than the target image. In our approach each training image is given a weight based on the distribution of its voxels in the feature space. These image weights are chosen as to minimize the difference between the weighted probability density function (PDF) of the voxels of the training images...

The Soviet Soldier - Premilitary and Political Training.

Science.gov (United States)

1982-01-01

instruction relates to weapons training, including the care and maintenance of the light machine gun, the automatic rifle and the anti -tank grenade. Where...practice in firing these weapons. Finally, he acquires kowledge and skill in a particular military-technical specialty - as a motor vehicle driver... Power and Performnce, Hamden, Conn.: Shoe String Press, Inc., 1979 Flyagin, A. P., "Patriotic Indoctrination Is The Center of Attention", Sovetskiy

AUTOMATIC RECOGNITION OF FALLS IN GAIT-SLIP: A HARNESS LOAD CELL BASED CRITERION

Science.gov (United States)

Yang, Feng; Pai, Yi-Chung

2012-01-01

Over-head-harness systems, equipped with load cell sensors, are essential to the participants’ safety and to the outcome assessment in perturbation training. The purpose of this study was to first develop an automatic outcome recognition criterion among young adults for gait-slip training and then verify such criterion among older adults. Each of 39 young and 71 older subjects, all protected by safety harness, experienced 8 unannounced, repeated slips, while walking on a 7-m walkway. Each trial was monitored with a motion capture system, bilateral ground reaction force (GRF), harness force and video recording. The fall trials were first unambiguously indentified with careful visual inspection of all video records. The recoveries without balance loss (in which subjects’ trailing foot landed anteriorly to the slipping foot) were also first fully recognized from motion and GRF analyses. These analyses then set the gold standard for the outcome recognition with load cell measurements. Logistic regression analyses based on young subjects’ data revealed that peak load cell force was the best predictor of falls (with 100% accuracy) at the threshold of 30% body weight. On the other hand, the peak moving average force of load cell across 1-s period, was the best predictor (with 100% accuracy) separating recoveries with backward balance loss (in which the recovery step landed posterior to slipping foot) from harness assistance at the threshold of 4.5% body weight. These threshold values were fully verified using the data from older adults (100% accuracy in recognizing falls). Because of the increasing popularity in the perturbation training coupling with the protective over-head-harness system, this new criterion could have far reaching implications in automatic outcome recognition during the movement therapy. PMID:21696744

Training Standardization

International Nuclear Information System (INIS)

Agnihotri, Newal

2003-01-01

The article describes the benefits of and required process and recommendations for implementing the standardization of training in the nuclear power industry in the United States and abroad. Current Information and Communication Technologies (ICT) enable training standardization in the nuclear power industry. The delivery of training through the Internet, Intranet and video over IP will facilitate this standardization and bring multiple benefits to the nuclear power industry worldwide. As the amount of available qualified and experienced professionals decreases because of retirements and fewer nuclear engineering institutions, standardized training will help increase the number of available professionals in the industry. Technology will make it possible to use the experience of retired professionals who may be interested in working part-time from a remote location. Well-planned standardized training will prevent a fragmented approach among utilities, and it will save the industry considerable resources in the long run. It will also ensure cost-effective and safe nuclear power plant operation

Automatic detection of measurement points for non-contact vibrometer-based diagnosis of cardiac arrhythmias

Science.gov (United States)

Metzler, Jürgen; Kroschel, Kristian; Willersinn, Dieter

2017-03-01

Monitoring of the heart rhythm is the cornerstone of the diagnosis of cardiac arrhythmias. It is done by means of electrocardiography which relies on electrodes attached to the skin of the patient. We present a new system approach based on the so-called vibrocardiogram that allows an automatic non-contact registration of the heart rhythm. Because of the contactless principle, the technique offers potential application advantages in medical fields like emergency medicine (burn patient) or premature baby care where adhesive electrodes are not easily applicable. A laser-based, mobile, contactless vibrometer for on-site diagnostics that works with the principle of laser Doppler vibrometry allows the acquisition of vital functions in form of a vibrocardiogram. Preliminary clinical studies at the Klinikum Karlsruhe have shown that the region around the carotid artery and the chest region are appropriate therefore. However, the challenge is to find a suitable measurement point in these parts of the body that differs from person to person due to e. g. physiological properties of the skin. Therefore, we propose a new Microsoft Kinect-based approach. When a suitable measurement area on the appropriate parts of the body are detected by processing the Kinect data, the vibrometer is automatically aligned on an initial location within this area. Then, vibrocardiograms on different locations within this area are successively acquired until a sufficient measuring quality is achieved. This optimal location is found by exploiting the autocorrelation function.

A novel algorithm for automatic localization of human eyes

Institute of Scientific and Technical Information of China (English)

Liang Tao (陶亮); Juanjuan Gu (顾涓涓); Zhenquan Zhuang (庄镇泉)

2003-01-01

Based on geometrical facial features and image segmentation, we present a novel algorithm for automatic localization of human eyes in grayscale or color still images with complex background. Firstly, a determination criterion of eye location is established by the prior knowledge of geometrical facial features. Secondly,a range of threshold values that would separate eye blocks from others in a segmented face image (I.e.,a binary image) are estimated. Thirdly, with the progressive increase of the threshold by an appropriate step in that range, once two eye blocks appear from the segmented image, they will be detected by the determination criterion of eye location. Finally, the 2D correlation coefficient is used as a symmetry similarity measure to check the factuality of the two detected eyes. To avoid the background interference, skin color segmentation can be applied in order to enhance the accuracy of eye detection. The experimental results demonstrate the high efficiency of the algorithm and correct localization rate.

Automatic text summarization

CERN Document Server

Torres Moreno, Juan Manuel

2014-01-01

This new textbook examines the motivations and the different algorithms for automatic document summarization (ADS). We performed a recent state of the art. The book shows the main problems of ADS, difficulties and the solutions provided by the community. It presents recent advances in ADS, as well as current applications and trends. The approaches are statistical, linguistic and symbolic. Several exemples are included in order to clarify the theoretical concepts.  The books currently available in the area of Automatic Document Summarization are not recent. Powerful algorithms have been develop

Automatic Ultrasound Scanning

DEFF Research Database (Denmark)

Moshavegh, Ramin

on the user adjustments on the scanner interface to optimize the scan settings. This explains the huge interest in the subject of this PhD project entitled “AUTOMATIC ULTRASOUND SCANNING”. The key goals of the project have been to develop automated techniques to minimize the unnecessary settings...... on the scanners, and to improve the computer-aided diagnosis (CAD) in ultrasound by introducing new quantitative measures. Thus, four major issues concerning automation of the medical ultrasound are addressed in this PhD project. They touch upon gain adjustments in ultrasound, automatic synthetic aperture image...

Locational Pricing to Mitigate Voltage Problems Caused by High PV Penetration

Directory of Open Access Journals (Sweden)

Sam Weckx

2015-05-01

Full Text Available In this paper, a locational marginal pricing algorithm is proposed to control the voltage in unbalanced distribution grids. The increasing amount of photovoltaic (PV generation installed in the grid may cause the voltage to rise to unacceptable levels during periods of low consumption. With locational prices, the distribution system operator can steer the reactive power consumption and active power curtailment of PV panels to guarantee a safe network operation. Flexible loads also respond to these prices. A distributed gradient algorithm automatically defines the locational prices that avoid voltage problems. Using these locational prices results in a minimum cost for the distribution operator to control the voltage. Locational prices can differ between the three phases in unbalanced grids. This is caused by a higher consumption or production in one of the phases compared to the other phases and provides the opportunity for arbitrage, where power is transferred from a phase with a low price to a phase with a high price. The effect of arbitrage is analyzed. The proposed algorithm is applied to an existing three-phase four-wire radial grid. Several simulations with realistic data are performed.

Wide-Field Imaging Telescope-0 (WIT0) with automatic observing system

Science.gov (United States)

Ji, Tae-Geun; Byeon, Seoyeon; Lee, Hye-In; Park, Woojin; Lee, Sang-Yun; Hwang, Sungyong; Choi, Changsu; Gibson, Coyne Andrew; Kuehne, John W.; Prochaska, Travis; Marshall, Jennifer L.; Im, Myungshin; Pak, Soojong

2018-01-01

We introduce Wide-Field Imaging Telescope-0 (WIT0), with an automatic observing system. It is developed for monitoring the variabilities of many sources at a time, e.g. young stellar objects and active galactic nuclei. It can also find the locations of transient sources such as a supernova or gamma-ray bursts. In 2017 February, we installed the wide-field 10-inch telescope (Takahashi CCA-250) as a piggyback system on the 30-inch telescope at the McDonald Observatory in Texas, US. The 10-inch telescope has a 2.35 × 2.35 deg field-of-view with a 4k × 4k CCD Camera (FLI ML16803). To improve the observational efficiency of the system, we developed a new automatic observing software, KAOS30 (KHU Automatic Observing Software for McDonald 30-inch telescope), which was developed by Visual C++ on the basis of a windows operating system. The software consists of four control packages: the Telescope Control Package (TCP), the Data Acquisition Package (DAP), the Auto Focus Package (AFP), and the Script Mode Package (SMP). Since it also supports the instruments that are using the ASCOM driver, the additional hardware installations become quite simplified. We commissioned KAOS30 in 2017 August and are in the process of testing. Based on the WIT0 experiences, we will extend KAOS30 to control multiple telescopes in future projects.

Automatic characterization of sleep need dissipation dynamics using a single EEG signal.

Science.gov (United States)

Garcia-Molina, Gary; Bellesi, Michele; Riedner, Brady; Pastoor, Sander; Pfundtner, Stefan; Tononi, Giulio

2015-01-01

In the two-process model of sleep regulation, slow-wave activity (SWA, i.e. the EEG power in the 0.5-4 Hz frequency band) is considered a direct indicator of sleep need. SWA builds up during non-rapid eye movement (NREM) sleep, declines before the onset of rapid-eye-movement (REM) sleep, remains low during REM and the level of increase in successive NREM episodes gets progressively lower. Sleep need dissipates with a speed that is proportional to SWA and can be characterized in terms of the initial sleep need, and the decay rate. The goal in this paper is to automatically characterize sleep need from a single EEG signal acquired at a frontal location. To achieve this, a highly specific and reasonably sensitive NREM detection algorithm is proposed that leverages the concept of a single-class Kernel-based classifier. Using automatic NREM detection, we propose a method to estimate the decay rate and the initial sleep need. This method was tested on experimental data from 8 subjects who recorded EEG during three nights at home. We found that on average the estimates of the decay rate and the initial sleep need have higher values when automatic NREM detection was used as compared to manual NREM annotation. However, the average variability of these estimates across multiple nights of the same subject was lower when the automatic NREM detection classifier was used. While this method slightly over estimates the sleep need parameters, the reduced variability across subjects makes it more effective for within subject statistical comparisons of a given sleep intervention.

30 CFR 77.314 - Automatic temperature control instruments.

Science.gov (United States)

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Automatic temperature control instruments. 77... UNDERGROUND COAL MINES Thermal Dryers § 77.314 Automatic temperature control instruments. (a) Automatic temperature control instruments for thermal dryer system shall be of the recording type. (b) Automatic...

Automatic control systems engineering

International Nuclear Information System (INIS)

Shin, Yun Gi

2004-01-01

This book gives descriptions of automatic control for electrical electronics, which indicates history of automatic control, Laplace transform, block diagram and signal flow diagram, electrometer, linearization of system, space of situation, state space analysis of electric system, sensor, hydro controlling system, stability, time response of linear dynamic system, conception of root locus, procedure to draw root locus, frequency response, and design of control system.

Designing train-speed trajectory with energy efficiency and service quality

Science.gov (United States)

Jia, Jiannan; Yang, Kai; Yang, Lixing; Gao, Yuan; Li, Shukai

2018-05-01

With the development of automatic train operations, optimal trajectory design is significant to the performance of train operations in railway transportation systems. Considering energy efficiency and service quality, this article formulates a bi-objective train-speed trajectory optimization model to minimize simultaneously the energy consumption and travel time in an inter-station section. This article is distinct from previous studies in that more sophisticated train driving strategies characterized by the acceleration/deceleration gear, the cruising speed, and the speed-shift site are specifically considered. For obtaining an optimal train-speed trajectory which has equal satisfactory degree on both objectives, a fuzzy linear programming approach is applied to reformulate the objectives. In addition, a genetic algorithm is developed to solve the proposed train-speed trajectory optimization problem. Finally, a series of numerical experiments based on a real-world instance of Beijing-Tianjin Intercity Railway are implemented to illustrate the practicability of the proposed model as well as the effectiveness of the solution methodology.

Computer based training simulator for Hunterston Nuclear Power Station

International Nuclear Information System (INIS)

Bowden, R.S.M.; Hacking, D.

1978-01-01

For reasons which are stated, the Hunterston-B nuclear power station automatic control system includes a manual over-ride facility. It is therefore essential for the station engineers to be trained to recognise and control all feasible modes of plant and logic malfunction. A training simulator has been built which consists of a replica of the shutdown monitoring panel in the Central Control Room and is controlled by a mini-computer. This paper highlights the computer aspects of the simulator and relevant derived experience, under the following headings: engineering background; shutdown sequence equipment; simulator equipment; features; software; testing; maintenance. (U.K.)

Computer-aided training exam creation and personnel records management

International Nuclear Information System (INIS)

Lawton, R.K.; Louche, K.A.

1985-01-01

A problem has existed in nuclear power plant training departments about how to choose questions for examinations without instructor bias, how to permanently store this exam so that it can be reconstructed, how to statistically analyze class, instructor, and student performance, and how to keep accurate, easily accessible records of all training. The design of the software package discussed in the paper is such that a complete record of classes, quizzes, exams, instructors, and analysis is available for each trainee. The need for classes is automatically available from the computer with randomly created exams available on request

Development of advanced automatic control system for nuclear ship. 2. Perfect automatic operation after reactor scram events

International Nuclear Information System (INIS)

Yabuuchi, Noriaki; Nakazawa, Toshio; Takahashi, Hiroki; Shimazaki, Junya; Hoshi, Tsutao

1997-11-01

An automatic operation system has been developed for the purpose of realizing a perfect automatic plant operation after reactor scram events. The goal of the automatic operation after a reactor scram event is to bring the reactor hot stand-by condition automatically. The basic functions of this system are as follows; to monitor actions of the equipments of safety actions after a reactor scram, to control necessary control equipments to bring a reactor to a hot stand-by condition automatically, and to energize a decay heat removal system. The performance evaluation on this system was carried out by comparing the results using to Nuclear Ship Engineering Simulation System (NESSY) and the those measured in the scram test of the nuclear ship 'Mutsu'. As the result, it was showed that this system had the sufficient performance to bring a reactor to a hot syand-by condition quickly and safety. (author)

Development of advanced automatic control system for nuclear ship. 2. Perfect automatic operation after reactor scram events

Energy Technology Data Exchange (ETDEWEB)

Yabuuchi, Noriaki; Nakazawa, Toshio; Takahashi, Hiroki; Shimazaki, Junya; Hoshi, Tsutao [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

1997-11-01

An automatic operation system has been developed for the purpose of realizing a perfect automatic plant operation after reactor scram events. The goal of the automatic operation after a reactor scram event is to bring the reactor hot stand-by condition automatically. The basic functions of this system are as follows; to monitor actions of the equipments of safety actions after a reactor scram, to control necessary control equipments to bring a reactor to a hot stand-by condition automatically, and to energize a decay heat removal system. The performance evaluation on this system was carried out by comparing the results using to Nuclear Ship Engineering Simulation System (NESSY) and the those measured in the scram test of the nuclear ship `Mutsu`. As the result, it was showed that this system had the sufficient performance to bring a reactor to a hot syand-by condition quickly and safety. (author)

Automatic Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets

Energy Technology Data Exchange (ETDEWEB)

Giancardo, Luca [ORNL; Meriaudeau, Fabrice [ORNL; Karnowski, Thomas Paul [ORNL; Li, Yaquin [University of Tennessee, Knoxville (UTK); Garg, Seema [University of North Carolina; Tobin Jr, Kenneth William [ORNL; Chaum, Edward [University of Tennessee, Knoxville (UTK)

2011-01-01

Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, we introduce a new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation. These features are employed to train a classifier able to automatically diagnose DME. We present a new publicly available dataset with ground-truth data containing 169 patients from various ethnic groups and levels of DME. This and other two publicly available datasets are employed to evaluate our algorithm. We are able to achieve diagnosis performance comparable to retina experts on the MESSIDOR (an independently labelled dataset with 1200 images) with cross-dataset testing. Our algorithm is robust to segmentation uncertainties, does not need ground truth at lesion level, and is very fast, generating a diagnosis on an average of 4.4 seconds per image on an 2.6 GHz platform with an unoptimised Matlab implementation.

Adaptive neuro-fuzzy inference system based automatic generation control

Energy Technology Data Exchange (ETDEWEB)

Hosseini, S.H.; Etemadi, A.H. [Department of Electrical Engineering, Sharif University of Technology, Tehran (Iran)

2008-07-15

Fixed gain controllers for automatic generation control are designed at nominal operating conditions and fail to provide best control performance over a wide range of operating conditions. So, to keep system performance near its optimum, it is desirable to track the operating conditions and use updated parameters to compute control gains. A control scheme based on artificial neuro-fuzzy inference system (ANFIS), which is trained by the results of off-line studies obtained using particle swarm optimization, is proposed in this paper to optimize and update control gains in real-time according to load variations. Also, frequency relaxation is implemented using ANFIS. The efficiency of the proposed method is demonstrated via simulations. Compliance of the proposed method with NERC control performance standard is verified. (author)

Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approach.

Science.gov (United States)

Avendi, Michael R; Kheradvar, Arash; Jafarkhani, Hamid

2017-12-01

This study aims to accurately segment the right ventricle (RV) from cardiac MRI using a fully automatic learning-based method. The proposed method uses deep learning algorithms, i.e., convolutional neural networks and stacked autoencoders, for automatic detection and initial segmentation of the RV chamber. The initial segmentation is then combined with the deformable models to improve the accuracy and robustness of the process. We trained our algorithm using 16 cardiac MRI datasets of the MICCAI 2012 RV Segmentation Challenge database and validated our technique using the rest of the dataset (32 subjects). An average Dice metric of 82.5% along with an average Hausdorff distance of 7.85 mm were achieved for all the studied subjects. Furthermore, a high correlation and level of agreement with the ground truth contours for end-diastolic volume (0.98), end-systolic volume (0.99), and ejection fraction (0.93) were observed. Our results show that deep learning algorithms can be effectively used for automatic segmentation of the RV. Computed quantitative metrics of our method outperformed that of the existing techniques participated in the MICCAI 2012 challenge, as reported by the challenge organizers. Magn Reson Med 78:2439-2448, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Automatic exchange unit for control rod drive device

International Nuclear Information System (INIS)

Nasu, Seiji; Sasaki, Masayoshi.

1982-01-01

Purpose: To enable automatic reoperation and continuation without external power interruption remedy device at the time of recovering the interrupted power soruce during automatic positioning operation. Constitution: In case of an automatic exchange unit for a control rod drive device of the control type for setting the deviation between the positioning target position and the present position of the device to zero, the position data of the drive device of the positioning target value of the device is automatically read, and an interlock of operation inhibit is applied to a control system until the data reading is completed and automatic operation start or restart conditions are sequentially confirmed. After the confirmation, the interlock is released to start the automatic operation or reoperation. Accordingly, the automatic operation can be safely restarted and continued. (Yoshihara, H.)

Validity of your safety awareness training

CERN Multimedia

DG Unit

2010-01-01

AIS is setting up an automatic e-mail reminder system for safety training. You are invited to forward this message to everyone concerned. Reminder: Please check the validity of your Safety courses Since April 2009 the compulsory basic Safety awareness courses (levels 1, 2 and 3) have been accessible on a "self-service" basis on the web (see CERN Bulletin). Participants are required to pass a test at the end of each course. The test is valid for 3 years so courses must be repeated on a regular basis. A system of automatic e-mail reminders already exists for level 4 courses on SIR and will be extended to the other levels shortly. The number of levels you are required to complete depends on your professional category. Activity Personnel concerned Level 1 Level 2 Level 3 Level 4     Basic safety Basic Safety ...

Multichannel display system with automatic sequential output of analog data

International Nuclear Information System (INIS)

Bykovskii, Yu.A.; Gruzinov, A.E.; Lagoda, V.B.

1989-01-01

The authors describe a device that, with maximum simplicity and autonomy, permits parallel data display from 16 measuring channels with automatic output to the screen of a storage oscilloscope in ∼ 50 μsec. The described device can be used to study the divergence characteristics of the ion component of plasma sources and in optical and x-ray spectroscopy of pulsed processes. Owing to its compactness and autonomy, the device can be located in the immediate vicinity of the detectors (for example, inside a vacuum chamber), which allows the number of vacuum electrical lead-ins and the induction level to be reduced

Automatic NMR field-frequency lock-pulsed phase locked loop approach.

Science.gov (United States)

Kan, S; Gonord, P; Fan, M; Sauzade, M; Courtieu, J

1978-06-01

A self-contained deuterium frequency-field lock scheme for a high-resolution NMR spectrometer is described. It is based on phase locked loop techniques in which the free induction decay signal behaves as a voltage-controlled oscillator. By pulsing the spins at an offset frequency of a few hundred hertz and using a digital phase-frequency discriminator this method not only eliminates the usual phase, rf power, offset adjustments needed in conventional lock systems but also possesses the automatic pull-in characteristics that dispense with the use of field sweeps to locate the NMR line prior to closure of the lock loop.

Automatic ultrasonic testing and the LOFT in-service inspection program

International Nuclear Information System (INIS)

Hunter, J.A.

1980-01-01

An automatic ultrasonic testing system has been developed which significantly improves the flaw indication detection and characterization capability over the capability of conventional volumetric examination techniques. The system utilizes an accurately located ultrasonic sensor to generate the examination data. A small computer performs and integrates control and data input/output functions. Computer software has been developed to provide a rigorous method for data analysis and ultrasonic image interpretation. The system has been used as part of an in-service inspection program to examine welds in thich austenitic stainless steel pipes in a small experimental nuclear reactor

Automatic segmentation of MR brain images of preterm infants using supervised classification.

Science.gov (United States)

Moeskops, Pim; Benders, Manon J N L; Chiţ, Sabina M; Kersbergen, Karina J; Groenendaal, Floris; de Vries, Linda S; Viergever, Max A; Išgum, Ivana

2015-09-01

Preterm birth is often associated with impaired brain development. The state and expected progression of preterm brain development can be evaluated using quantitative assessment of MR images. Such measurements require accurate segmentation of different tissue types in those images. This paper presents an algorithm for the automatic segmentation of unmyelinated white matter (WM), cortical grey matter (GM), and cerebrospinal fluid in the extracerebral space (CSF). The algorithm uses supervised voxel classification in three subsequent stages. In the first stage, voxels that can easily be assigned to one of the three tissue types are labelled. In the second stage, dedicated analysis of the remaining voxels is performed. The first and the second stages both use two-class classification for each tissue type separately. Possible inconsistencies that could result from these tissue-specific segmentation stages are resolved in the third stage, which performs multi-class classification. A set of T1- and T2-weighted images was analysed, but the optimised system performs automatic segmentation using a T2-weighted image only. We have investigated the performance of the algorithm when using training data randomly selected from completely annotated images as well as when using training data from only partially annotated images. The method was evaluated on images of preterm infants acquired at 30 and 40weeks postmenstrual age (PMA). When the method was trained using random selection from the completely annotated images, the average Dice coefficients were 0.95 for WM, 0.81 for GM, and 0.89 for CSF on an independent set of images acquired at 30weeks PMA. When the method was trained using only the partially annotated images, the average Dice coefficients were 0.95 for WM, 0.78 for GM and 0.87 for CSF for the images acquired at 30weeks PMA, and 0.92 for WM, 0.80 for GM and 0.85 for CSF for the images acquired at 40weeks PMA. Even though the segmentations obtained using training data

Facilitated orienting underlies fearful face-enhanced gaze cueing of spatial location

Directory of Open Access Journals (Sweden)

Joshua M. Carlson

2016-12-01

Full Text Available Faces provide a platform for non-verbal communication through emotional expression and eye gaze. Fearful facial expressions are salient indicators of potential threat within the environment, which automatically capture observers’ attention. However, the degree to which fearful facial expressions facilitate attention to others’ gaze is unresolved. Given that fearful gaze indicates the location of potential threat, it was hypothesized that fearful gaze facilitates location processing. To test this hypothesis, a gaze cueing study with fearful and neutral faces assessing target localization was conducted. The task consisted of leftward, rightward, and forward/straight gaze trials. The inclusion of forward gaze trials allowed for the isolation of orienting and disengagement components of gaze-directed attention. The results suggest that both neutral and fearful gaze modulates attention through orienting and disengagement components. Fearful gaze, however, resulted in quicker orienting than neutral gaze. Thus, fearful faces enhance gaze cueing of spatial location through facilitated orienting.

Position automatic determination technology

International Nuclear Information System (INIS)

1985-10-01

This book tells of method of position determination and characteristic, control method of position determination and point of design, point of sensor choice for position detector, position determination of digital control system, application of clutch break in high frequency position determination, automation technique of position determination, position determination by electromagnetic clutch and break, air cylinder, cam and solenoid, stop position control of automatic guide vehicle, stacker crane and automatic transfer control.

A new method for automatic tracking of facial landmarks in 3D motion captured images (4D).

Science.gov (United States)

Al-Anezi, T; Khambay, B; Peng, M J; O'Leary, E; Ju, X; Ayoub, A

2013-01-01

The aim of this study was to validate the automatic tracking of facial landmarks in 3D image sequences. 32 subjects (16 males and 16 females) aged 18-35 years were recruited. 23 anthropometric landmarks were marked on the face of each subject with non-permanent ink using a 0.5mm pen. The subjects were asked to perform three facial animations (maximal smile, lip purse and cheek puff) from rest position. Each animation was captured by the 3D imaging system. A single operator manually digitised the landmarks on the 3D facial models and their locations were compared with those of the automatically tracked ones. To investigate the accuracy of manual digitisation, the operator re-digitised the same set of 3D images of 10 subjects (5 male and 5 female) at 1 month interval. The discrepancies in x, y and z coordinates between the 3D position of the manual digitised landmarks and that of the automatic tracked facial landmarks were within 0.17mm. The mean distance between the manually digitised and the automatically tracked landmarks using the tracking software was within 0.55 mm. The automatic tracking of facial landmarks demonstrated satisfactory accuracy which would facilitate the analysis of the dynamic motion during facial animations. Copyright © 2012 International Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

Digital intelligent booster for DCC miniature train networks

Science.gov (United States)

Ursu, M. P.; Condruz, D. A.

2017-08-01

Modern miniature trains are now driven by means of the DCC (Digital Command and Control) system, which allows the human operator or a personal computer to launch commands to each individual train or even to control different features of the same train. The digital command station encodes these commands and sends them to the trains by means of electrical pulses via the rails of the railway network. Due to the development of the miniature railway network, it may happen that the power requirement of the increasing number of digital locomotives, carriages and accessories exceeds the nominal output power of the digital command station. This digital intelligent booster relieves the digital command station from powering the entire railway network all by itself, and it automatically handles the multiple powered sections of the network. This electronic device is also able to detect and process short-circuits and overload conditions, without the intervention of the digital command station.

Automatic ultrasound image enhancement for 2D semi-automatic breast-lesion segmentation

Science.gov (United States)

Lu, Kongkuo; Hall, Christopher S.

2014-03-01

Breast cancer is the fastest growing cancer, accounting for 29%, of new cases in 2012, and second leading cause of cancer death among women in the United States and worldwide. Ultrasound (US) has been used as an indispensable tool for breast cancer detection/diagnosis and treatment. In computer-aided assistance, lesion segmentation is a preliminary but vital step, but the task is quite challenging in US images, due to imaging artifacts that complicate detection and measurement of the suspect lesions. The lesions usually present with poor boundary features and vary significantly in size, shape, and intensity distribution between cases. Automatic methods are highly application dependent while manual tracing methods are extremely time consuming and have a great deal of intra- and inter- observer variability. Semi-automatic approaches are designed to counterbalance the advantage and drawbacks of the automatic and manual methods. However, considerable user interaction might be necessary to ensure reasonable segmentation for a wide range of lesions. This work proposes an automatic enhancement approach to improve the boundary searching ability of the live wire method to reduce necessary user interaction while keeping the segmentation performance. Based on the results of segmentation of 50 2D breast lesions in US images, less user interaction is required to achieve desired accuracy, i.e. < 80%, when auto-enhancement is applied for live-wire segmentation.

Deep generative learning of location-invariant visual word recognition

Science.gov (United States)

Di Bono, Maria Grazia; Zorzi, Marco

2013-01-01

It is widely believed that orthographic processing implies an approximate, flexible coding of letter position, as shown by relative-position and transposition priming effects in visual word recognition. These findings have inspired alternative proposals about the representation of letter position, ranging from noisy coding across the ordinal positions to relative position coding based on open bigrams. This debate can be cast within the broader problem of learning location-invariant representations of written words, that is, a coding scheme abstracting the identity and position of letters (and combinations of letters) from their eye-centered (i.e., retinal) locations. We asked whether location-invariance would emerge from deep unsupervised learning on letter strings and what type of intermediate coding would emerge in the resulting hierarchical generative model. We trained a deep network with three hidden layers on an artificial dataset of letter strings presented at five possible retinal locations. Though word-level information (i.e., word identity) was never provided to the network during training, linear decoding from the activity of the deepest hidden layer yielded near-perfect accuracy in location-invariant word recognition. Conversely, decoding from lower layers yielded a large number of transposition errors. Analyses of emergent internal representations showed that word selectivity and location invariance increased as a function of layer depth. Word-tuning and location-invariance were found at the level of single neurons, but there was no evidence for bigram coding. Finally, the distributed internal representation of words at the deepest layer showed higher similarity to the representation elicited by the two exterior letters than by other combinations of two contiguous letters, in agreement with the hypothesis that word edges have special status. These results reveal that the efficient coding of written words—which was the model's learning objective�

Deep generative learning of location-invariant visual word recognition.

Science.gov (United States)

Di Bono, Maria Grazia; Zorzi, Marco

2013-01-01

It is widely believed that orthographic processing implies an approximate, flexible coding of letter position, as shown by relative-position and transposition priming effects in visual word recognition. These findings have inspired alternative proposals about the representation of letter position, ranging from noisy coding across the ordinal positions to relative position coding based on open bigrams. This debate can be cast within the broader problem of learning location-invariant representations of written words, that is, a coding scheme abstracting the identity and position of letters (and combinations of letters) from their eye-centered (i.e., retinal) locations. We asked whether location-invariance would emerge from deep unsupervised learning on letter strings and what type of intermediate coding would emerge in the resulting hierarchical generative model. We trained a deep network with three hidden layers on an artificial dataset of letter strings presented at five possible retinal locations. Though word-level information (i.e., word identity) was never provided to the network during training, linear decoding from the activity of the deepest hidden layer yielded near-perfect accuracy in location-invariant word recognition. Conversely, decoding from lower layers yielded a large number of transposition errors. Analyses of emergent internal representations showed that word selectivity and location invariance increased as a function of layer depth. Word-tuning and location-invariance were found at the level of single neurons, but there was no evidence for bigram coding. Finally, the distributed internal representation of words at the deepest layer showed higher similarity to the representation elicited by the two exterior letters than by other combinations of two contiguous letters, in agreement with the hypothesis that word edges have special status. These results reveal that the efficient coding of written words-which was the model's learning objective

The ‘Continuing Misfortune’ of Automatism in Early Surrealism

Directory of Open Access Journals (Sweden)

Tessel M. Bauduin

2015-09-01

Full Text Available In the 1924 Manifesto of Surrealism surrealist leader André Breton (1896-1966 defined Surrealism as ‘psychic automatism in its pure state,’ positioning ‘psychic automatism’ as both a concept and a technique. This definition followed upon an intense period of experimentation with various forms of automatism among the proto-surrealist group; predominantly automatic writing, but also induced dream states. This article explores how surrealist ‘psychic automatism’ functioned as a mechanism for communication, or the expression of thought as directly as possible through the unconscious, in the first two decades of Surrealism. It touches upon automatic writing, hysteria as an automatic bodily performance of the unconscious, dreaming and the experimentation with induced dream states, and automatic drawing and other visual arts-techniques that could be executed more or less automatically as well. For all that the surrealists reinvented automatism for their own poetic, artistic and revolutionary aims, the automatic techniques were primarily drawn from contemporary Spiritualism, psychical research and experimentation with mediums, and the article teases out the connections to mediumistic automatism. It is demonstrated how the surrealists effectively and successfully divested automatism of all things spiritual. It furthermore becomes clear that despite various mishaps, automatism in many forms was a very successful creative technique within Surrealism.

Detection and segmentation of virus plaque using HOG and SVM: toward automatic plaque assay.

Science.gov (United States)

Mao, Yihao; Liu, Hong; Ye, Rong; Shi, Yonghong; Song, Zhijian

2014-01-01

Plaque assaying, measurement of the number, diameter, and area of plaques in a Petri dish image, is a standard procedure gauging the concentration of phage in biology. This paper presented a novel and effective method for implementing automatic plaque assaying. The method was mainly comprised of the following steps: In the training stage, after pre-processing the images for noise suppression, an initial training set was readied by sampling positive (with a plaque at the center) and negative (plaque-free) patches from the training images, and extracting the HOG features from each patch. The linear SVM classifier was trained in a self-learnt supervised learning strategy to avoid possible missing detection. Specifically, the training set which contained positive and negative patches sampled manually from training images was used to train the preliminary classifier which exhaustively searched the training images to predict the label for the unlabeled patches. The mislabeled patches were evaluated by experts and relabeled. And all the newly labeled patches and their corresponding HOG features were added to the initial training set to train the final classifier. In the testing stage, a sliding-window technique was first applied to the unseen image for obtaining HOG features, which were inputted into the classifier to predict whether the patch was positive. Second, a locally adaptive Otsu method was performed on the positive patches to segment the plaques. Finally, after removing the outliers, the parameters of the plaques were measured in the segmented plaques. The experimental results demonstrated that the accuracy of the proposed method was similar to the one measured manually by experts, but it took less than 30 seconds.

Quality assurance using outlier detection on an automatic segmentation method for the cerebellar peduncles

Science.gov (United States)

Li, Ke; Ye, Chuyang; Yang, Zhen; Carass, Aaron; Ying, Sarah H.; Prince, Jerry L.

2016-03-01

Cerebellar peduncles (CPs) are white matter tracts connecting the cerebellum to other brain regions. Automatic segmentation methods of the CPs have been proposed for studying their structure and function. Usually the performance of these methods is evaluated by comparing segmentation results with manual delineations (ground truth). However, when a segmentation method is run on new data (for which no ground truth exists) it is highly desirable to efficiently detect and assess algorithm failures so that these cases can be excluded from scientific analysis. In this work, two outlier detection methods aimed to assess the performance of an automatic CP segmentation algorithm are presented. The first one is a univariate non-parametric method using a box-whisker plot. We first categorize automatic segmentation results of a dataset of diffusion tensor imaging (DTI) scans from 48 subjects as either a success or a failure. We then design three groups of features from the image data of nine categorized failures for failure detection. Results show that most of these features can efficiently detect the true failures. The second method—supervised classification—was employed on a larger DTI dataset of 249 manually categorized subjects. Four classifiers—linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), and random forest classification (RFC)—were trained using the designed features and evaluated using a leave-one-out cross validation. Results show that the LR performs worst among the four classifiers and the other three perform comparably, which demonstrates the feasibility of automatically detecting segmentation failures using classification methods.

Automatic detection of ECG electrode misplacement: a tale of two algorithms

International Nuclear Information System (INIS)

Xia, Henian; Garcia, Gabriel A; Zhao, Xiaopeng

2012-01-01

Artifacts in an electrocardiogram (ECG) due to electrode misplacement can lead to wrong diagnoses. Various computer methods have been developed for automatic detection of electrode misplacement. Here we reviewed and compared the performance of two algorithms with the highest accuracies on several databases from PhysioNet. These algorithms were implemented into four models. For clean ECG records with clearly distinguishable waves, the best model produced excellent accuracies (> = 98.4%) for all misplacements except the LA/LL interchange (87.4%). However, the accuracies were significantly lower for records with noise and arrhythmias. Moreover, when the algorithms were tested on a database that was independent from the training database, the accuracies may be poor. For the worst scenario, the best accuracies for different types of misplacements ranged from 36.1% to 78.4%. A large number of ECGs of various qualities and pathological conditions are collected every day. To improve the quality of health care, the results of this paper call for more robust and accurate algorithms for automatic detection of electrode misplacement, which should be developed and tested using a database of extensive ECG records. (paper)

Automatically classifying sentences in full-text biomedical articles into Introduction, Methods, Results and Discussion.

Science.gov (United States)

Agarwal, Shashank; Yu, Hong

2009-12-01

Biomedical texts can be typically represented by four rhetorical categories: Introduction, Methods, Results and Discussion (IMRAD). Classifying sentences into these categories can benefit many other text-mining tasks. Although many studies have applied different approaches for automatically classifying sentences in MEDLINE abstracts into the IMRAD categories, few have explored the classification of sentences that appear in full-text biomedical articles. We first evaluated whether sentences in full-text biomedical articles could be reliably annotated into the IMRAD format and then explored different approaches for automatically classifying these sentences into the IMRAD categories. Our results show an overall annotation agreement of 82.14% with a Kappa score of 0.756. The best classification system is a multinomial naïve Bayes classifier trained on manually annotated data that achieved 91.95% accuracy and an average F-score of 91.55%, which is significantly higher than baseline systems. A web version of this system is available online at-http://wood.ims.uwm.edu/full_text_classifier/.

Programmable automatic alpha--beta air sample counter

International Nuclear Information System (INIS)

Howell, W.P.

1978-01-01

A programmable automatic alpha-beta air sample counter was developed for routine sample counting by operational health physics personnel. The system is composed of an automatic sample changer utilizing a large silicon diode detector, an electronic counting system with energy analysis capability, an automatic data acquisition controller, an interface module, and a teletypewriter with paper tape punch and paper tape reader. The system is operated through the teletypewriter keyboard and the paper tape reader, which are used to instruct the automatic data acquisition controller. Paper tape programs are provided for background counting, Chi 2 test, and sample counting. Output data are printed by the teletypewriter on standard continuous roll or multifold paper. Data are automatically corrected for background and counter efficiency

Automatic discrimination of fine roots in minirhizotron images.

Science.gov (United States)

Zeng, Guang; Birchfield, Stanley T; Wells, Christina E

2008-01-01

Minirhizotrons provide detailed information on the production, life history and mortality of fine roots. However, manual processing of minirhizotron images is time-consuming, limiting the number and size of experiments that can reasonably be analysed. Previously, an algorithm was developed to automatically detect and measure individual roots in minirhizotron images. Here, species-specific root classifiers were developed to discriminate detected roots from bright background artifacts. Classifiers were developed from training images of peach (Prunus persica), freeman maple (Acer x freemanii) and sweetbay magnolia (Magnolia virginiana) using the Adaboost algorithm. True- and false-positive rates for classifiers were estimated using receiver operating characteristic curves. Classifiers gave true positive rates of 89-94% and false positive rates of 3-7% when applied to nontraining images of the species for which they were developed. The application of a classifier trained on one species to images from another species resulted in little or no reduction in accuracy. These results suggest that a single root classifier can be used to distinguish roots from background objects across multiple minirhizotron experiments. By incorporating root detection and discrimination algorithms into an open-source minirhizotron image analysis application, many analysis tasks that are currently performed by hand can be automated.

Fast and Automatic Ultrasound Simulation from CT Images

Directory of Open Access Journals (Sweden)

Weijian Cong

2013-01-01

Full Text Available Ultrasound is currently widely used in clinical diagnosis because of its fast and safe imaging principles. As the anatomical structures present in an ultrasound image are not as clear as CT or MRI. Physicians usually need advance clinical knowledge and experience to distinguish diseased tissues. Fast simulation of ultrasound provides a cost-effective way for the training and correlation of ultrasound and the anatomic structures. In this paper, a novel method is proposed for fast simulation of ultrasound from a CT image. A multiscale method is developed to enhance tubular structures so as to simulate the blood flow. The acoustic response of common tissues is generated by weighted integration of adjacent regions on the ultrasound propagation path in the CT image, from which parameters, including attenuation, reflection, scattering, and noise, are estimated simultaneously. The thin-plate spline interpolation method is employed to transform the simulation image between polar and rectangular coordinate systems. The Kaiser window function is utilized to produce integration and radial blurring effects of multiple transducer elements. Experimental results show that the developed method is very fast and effective, allowing realistic ultrasound to be fast generated. Given that the developed method is fully automatic, it can be utilized for ultrasound guided navigation in clinical practice and for training purpose.

Robot-assisted automatic ultrasound calibration.

Science.gov (United States)

Aalamifar, Fereshteh; Cheng, Alexis; Kim, Younsu; Hu, Xiao; Zhang, Haichong K; Guo, Xiaoyu; Boctor, Emad M

2016-10-01

Ultrasound (US) calibration is the process of determining the unknown transformation from a coordinate frame such as the robot's tooltip to the US image frame and is a necessary task for any robotic or tracked US system. US calibration requires submillimeter-range accuracy for most applications, but it is a time-consuming and repetitive task. We provide a new framework for automatic US calibration with robot assistance and without the need for temporal calibration. US calibration based on active echo (AE) phantom was previously proposed, and its superiority over conventional cross-wire phantom-based calibration was shown. In this work, we use AE to guide the robotic arm motion through the process of data collection; we combine the capability of the AE point to localize itself in the frame of the US image with the automatic motion of the robotic arm to provide a framework for calibrating the arm to the US image automatically. We demonstrated the efficacy of the automated method compared to the manual method through experiments. To highlight the necessity of frequent ultrasound calibration, it is demonstrated that the calibration precision changed from 1.67 to 3.20 mm if the data collection is not repeated after a dismounting/mounting of the probe holder. In a large data set experiment, similar reconstruction precision of automatic and manual data collection was observed, while the time was reduced by 58 %. In addition, we compared ten automatic calibrations with ten manual ones, each performed in 15 min, and showed that all the automatic ones could converge in the case of setting the initial matrix as identity, while this was not achieved by manual data sets. Given the same initial matrix, the repeatability of the automatic was [0.46, 0.34, 0.80, 0.47] versus [0.42, 0.51, 0.98, 1.15] mm in the manual case for the US image four corners. The submillimeter accuracy requirement of US calibration makes frequent data collections unavoidable. We proposed an automated

77 FR 37072 - Filing Location for Foreign Labor Certification Program Temporary Program Applications; Change of...

Science.gov (United States)

2012-06-20

... DEPARTMENT OF LABOR Employment and Training Administration Filing Location for Foreign Labor... Administration, Department of Labor. ACTION: Notice. SUMMARY: This Notice announces a change in the location... date of this Notice. On that date, the Chicago NPC should be fully functional in the new location. For...

Location, location, location: Extracting location value from house prices

OpenAIRE

Kolbe, Jens; Schulz, Rainer; Wersing, Martin; Werwatz, Axel

2012-01-01

The price for a single-family house depends both on the characteristics of the building and on its location. We propose a novel semiparametric method to extract location values from house prices. After splitting house prices into building and land components, location values are estimated with adaptive weight smoothing. The adaptive estimator requires neither strong smoothness assumptions nor local symmetry. We apply the method to house transactions from Berlin, Germany. The estimated surface...

Sensitivity analysis and design optimization through automatic differentiation

International Nuclear Information System (INIS)

Hovland, Paul D; Norris, Boyana; Strout, Michelle Mills; Bhowmick, Sanjukta; Utke, Jean

2005-01-01

Automatic differentiation is a technique for transforming a program or subprogram that computes a function, including arbitrarily complex simulation codes, into one that computes the derivatives of that function. We describe the implementation and application of automatic differentiation tools. We highlight recent advances in the combinatorial algorithms and compiler technology that underlie successful implementation of automatic differentiation tools. We discuss applications of automatic differentiation in design optimization and sensitivity analysis. We also describe ongoing research in the design of language-independent source transformation infrastructures for automatic differentiation algorithms

Automatic, Global and Dynamic Student Modeling in a Ubiquitous Learning Environment

Directory of Open Access Journals (Sweden)

Sabine Graf

2009-03-01

Full Text Available Ubiquitous learning allows students to learn at any time and any place. Adaptivity plays an important role in ubiquitous learning, aiming at providing students with adaptive and personalized learning material, activities, and information at the right place and the right time. However, for providing rich adaptivity, the student model needs to be able to gather a variety of information about the students. In this paper, an automatic, global, and dynamic student modeling approach is introduced, which aims at identifying and frequently updating information about students’ progress, learning styles, interests and knowledge level, problem solving abilities, preferences for using the system, social connectivity, and current location. This information is gathered in an automatic way, using students’ behavior and actions in different learning situations provided by different components/services of the ubiquitous learning environment. By providing a comprehensive student model, students can be supported by rich adaptivity in every component/service of the learning environment. Furthermore, the information in the student model can help in giving teachers a better understanding about the students’ learning process.

Academic Training: Real Time Process Control - Lecture series

CERN Multimedia

Françoise Benz

2004-01-01

ACADEMIC TRAINING LECTURE REGULAR PROGRAMME 7, 8 and 9 June From 11:00 hrs to 12:00 hrs - Main Auditorium bldg. 500 Real Time Process Control T. Riesco / CERN-TS What exactly is meant by Real-time? There are several definitions of real-time, most of them contradictory. Unfortunately the topic is controversial, and there does not seem to be 100% agreement over the terminology. Real-time applications are becoming increasingly important in our daily lives and can be found in diverse environments such as the automatic braking system on an automobile, a lottery ticket system, or robotic environmental samplers on a space station. These lectures will introduce concepts and theory like basic concepts timing constraints, task scheduling, periodic server mechanisms, hard and soft real-time.ENSEIGNEMENT ACADEMIQUE ACADEMIC TRAINING Françoise Benz 73127 academic.training@cern.ch

A method for automatically constructing the initial contour of the common carotid artery

Directory of Open Access Journals (Sweden)

Yara Omran

2013-10-01

Full Text Available In this article we propose a novel method to automatically set the initial contour that is used by the Active contours algorithm.The proposed method exploits the accumulative intensity profiles to locate the points on the arterial wall. The intensity profiles of sections that intersect the artery show distinguishable characterstics that make it possible to recognize them from the profiles of sections that do not intersect the artery walls. The proposed method is applied on ultrasound images of the transverse section of the common carotid artery, but it can be extended to be used on the images of the longitudinal section. The intensity profiles are classified using Support vector machine algorithm, and the results of different kernels are compared. The extracted features used for the classification are basically statistical features of the intensity profiles. The echogenicity of the arterial lumen, and gives the profiles that intersect the artery a special shape that helps recognizing these profiles from other general profiles.The outlining of the arterial walls may seem a classic task in image processing. However, most of the methods used to outline the artery start from a manual, or semi-automatic, initial contour.The proposed method is highly appreciated in automating the entire process of automatic artery detection and segmentation.

Multi-Level Interval Estimation for Locating damage in Structures by Using Artificial Neural Networks

International Nuclear Information System (INIS)

Pan Danguang; Gao Yanhua; Song Junlei

2010-01-01

A new analysis technique, called multi-level interval estimation method, is developed for locating damage in structures. In this method, the artificial neural networks (ANN) analysis method is combined with the statistics theory to estimate the range of damage location. The ANN is multilayer perceptron trained by back-propagation. Natural frequencies and modal shape at a few selected points are used as input to identify the location and severity of damage. Considering the large-scale structures which have lots of elements, multi-level interval estimation method is developed to reduce the estimation range of damage location step-by-step. Every step, estimation range of damage location is obtained from the output of ANN by using the method of interval estimation. The next ANN training cases are selected from the estimation range after linear transform, and the output of new ANN estimation range of damage location will gained a reduced estimation range. Two numerical example analyses on 10-bar truss and 100-bar truss are presented to demonstrate the effectiveness of the proposed method.

Determination of the Number of Fixture Locating Points for Sheet Metal By Grey Model

Directory of Open Access Journals (Sweden)

Yang Bo

2017-01-01

Full Text Available In the process of the traditional fixture design for sheet metal part based on the "N-2-1" locating principle, the number of fixture locating points is determined by trial and error or the experience of the designer. To that end, a new design method based on grey theory is proposed to determine the number of sheet metal fixture locating points in this paper. Firstly, the training sample set is generated by Latin hypercube sampling (LHS and finite element analysis (FEA. Secondly, the GM(1, 1 grey model is constructed based on the established training sample set to approximate the mapping relationship between the number of fixture locating points and the concerned sheet metal maximum deformation. Thirdly, the final number of fixture locating points for sheet metal can be inversely calculated under the allowable maximum deformation. Finally, a sheet metal case is conducted and the results indicate that the proposed approach is effective and efficient in determining the number of fixture locating points for sheet metal.

Automatic localization of bifurcations and vessel crossings in digital fundus photographs using location regression

Science.gov (United States)

Niemeijer, Meindert; Dumitrescu, Alina V.; van Ginneken, Bram; Abrámoff, Michael D.

2011-03-01

Parameters extracted from the vasculature on the retina are correlated with various conditions such as diabetic retinopathy and cardiovascular diseases such as stroke. Segmentation of the vasculature on the retina has been a topic that has received much attention in the literature over the past decade. Analysis of the segmentation result, however, has only received limited attention with most works describing methods to accurately measure the width of the vessels. Analyzing the connectedness of the vascular network is an important step towards the characterization of the complete vascular tree. The retinal vascular tree, from an image interpretation point of view, originates at the optic disc and spreads out over the retina. The tree bifurcates and the vessels also cross each other. The points where this happens form the key to determining the connectedness of the complete tree. We present a supervised method to detect the bifurcations and crossing points of the vasculature of the retina. The method uses features extracted from the vasculature as well as the image in a location regression approach to find those locations of the segmented vascular tree where the bifurcation or crossing occurs (from here, POI, points of interest). We evaluate the method on the publicly available DRIVE database in which an ophthalmologist has marked the POI.

What Automaticity Deficit? Activation of Lexical Information by Readers with Dyslexia in a Rapid Automatized Naming Stroop-Switch Task

Science.gov (United States)

Jones, Manon W.; Snowling, Margaret J.; Moll, Kristina

2016-01-01

Reading fluency is often predicted by rapid automatized naming (RAN) speed, which as the name implies, measures the automaticity with which familiar stimuli (e.g., letters) can be retrieved and named. Readers with dyslexia are considered to have less "automatized" access to lexical information, reflected in longer RAN times compared with…

Final Scientific Report, Integrated Seismic Event Detection and Location by Advanced Array Processing

Energy Technology Data Exchange (ETDEWEB)

Kvaerna, T.; Gibbons. S.J.; Ringdal, F; Harris, D.B.

2007-01-30

In the field of nuclear explosion monitoring, it has become a priority to detect, locate, and identify seismic events down to increasingly small magnitudes. The consideration of smaller seismic events has implications for a reliable monitoring regime. Firstly, the number of events to be considered increases greatly; an exponential increase in naturally occurring seismicity is compounded by large numbers of seismic signals generated by human activity. Secondly, the signals from smaller events become more difficult to detect above the background noise and estimates of parameters required for locating the events may be subject to greater errors. Thirdly, events are likely to be observed by a far smaller number of seismic stations, and the reliability of event detection and location using a very limited set of observations needs to be quantified. For many key seismic stations, detection lists may be dominated by signals from routine industrial explosions which should be ascribed, automatically and with a high level of confidence, to known sources. This means that expensive analyst time is not spent locating routine events from repeating seismic sources and that events from unknown sources, which could be of concern in an explosion monitoring context, are more easily identified and can be examined with due care. We have obtained extensive lists of confirmed seismic events from mining and other artificial sources which have provided an excellent opportunity to assess the quality of existing fully-automatic event bulletins and to guide the development of new techniques for online seismic processing. Comparing the times and locations of confirmed events from sources in Fennoscandia and NW Russia with the corresponding time and location estimates reported in existing automatic bulletins has revealed substantial mislocation errors which preclude a confident association of detected signals with known industrial sources. The causes of the errors are well understood and are

Final Scientific Report, Integrated Seismic Event Detection and Location by Advanced Array Processing

International Nuclear Information System (INIS)

Kvaerna, T.; Gibbons. S.J.; Ringdal, F; Harris, D.B.

2007-01-01

In the field of nuclear explosion monitoring, it has become a priority to detect, locate, and identify seismic events down to increasingly small magnitudes. The consideration of smaller seismic events has implications for a reliable monitoring regime. Firstly, the number of events to be considered increases greatly; an exponential increase in naturally occurring seismicity is compounded by large numbers of seismic signals generated by human activity. Secondly, the signals from smaller events become more difficult to detect above the background noise and estimates of parameters required for locating the events may be subject to greater errors. Thirdly, events are likely to be observed by a far smaller number of seismic stations, and the reliability of event detection and location using a very limited set of observations needs to be quantified. For many key seismic stations, detection lists may be dominated by signals from routine industrial explosions which should be ascribed, automatically and with a high level of confidence, to known sources. This means that expensive analyst time is not spent locating routine events from repeating seismic sources and that events from unknown sources, which could be of concern in an explosion monitoring context, are more easily identified and can be examined with due care. We have obtained extensive lists of confirmed seismic events from mining and other artificial sources which have provided an excellent opportunity to assess the quality of existing fully-automatic event bulletins and to guide the development of new techniques for online seismic processing. Comparing the times and locations of confirmed events from sources in Fennoscandia and NW Russia with the corresponding time and location estimates reported in existing automatic bulletins has revealed substantial mislocation errors which preclude a confident association of detected signals with known industrial sources. The causes of the errors are well understood and are

14 CFR 29.1329 - Automatic pilot system.

Science.gov (United States)

2010-01-01

... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false Automatic pilot system. 29.1329 Section 29... pilot system. (a) Each automatic pilot system must be designed so that the automatic pilot can— (1) Be sufficiently overpowered by one pilot to allow control of the rotorcraft; and (2) Be readily and positively...

14 CFR 27.1329 - Automatic pilot system.

Science.gov (United States)

2010-01-01

... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false Automatic pilot system. 27.1329 Section 27... pilot system. (a) Each automatic pilot system must be designed so that the automatic pilot can— (1) Be sufficiently overpowered by one pilot to allow control of the rotorcraft; and (2) Be readily and positively...

Flavour-flavour learning occurs automatically and only in hungry participants.

Science.gov (United States)

Brunstrom, Jeffrey M; Fletcher, Hollie Z

2008-01-28

A novel flavour may become liked if it is presented repeatedly and in combination with a second flavour that is already liked. Conceptually, this 'flavour-flavour learning' is important, because it can account for many of our everyday food and flavour preferences. However, relatively little is known about the underlying process because learning paradigms have lacked reliability. Based on previous research we explored whether learning is determined by three variables; i) hunger state, ii) demand and contingency awareness, and iii) dietary restraint. Participants (male n=15/female n=15) consumed three different and novel-tasting fruit teas. One of the teas had a non-caloric sweetener added (CS+) and two were unsweetened (CS-). Before and after this training the participants ranked their preference for unsweetened versions of the three teas. We found that the training increased preference for the CS+ relative to the CS- teas. However, this effect was only found in hungry participants. We also found little evidence that learning was related to whether the participants could identify (recognition test) the specific tea that had been sweetened during training, suggesting that the underlying process is automatic and it operates outside conscious awareness. Learning was not predicted by dietary restraint (measured using the DEBQ-R scale). Together, these findings provide further evidence for a linkage between flavour-flavour learning and flavour-nutrient learning.

Automatic non-proliferative diabetic retinopathy screening system based on color fundus image.

Science.gov (United States)

Xiao, Zhitao; Zhang, Xinpeng; Geng, Lei; Zhang, Fang; Wu, Jun; Tong, Jun; Ogunbona, Philip O; Shan, Chunyan

2017-10-26

Non-proliferative diabetic retinopathy is the early stage of diabetic retinopathy. Automatic detection of non-proliferative diabetic retinopathy is significant for clinical diagnosis, early screening and course progression of patients. This paper introduces the design and implementation of an automatic system for screening non-proliferative diabetic retinopathy based on color fundus images. Firstly, the fundus structures, including blood vessels, optic disc and macula, are extracted and located, respectively. In particular, a new optic disc localization method using parabolic fitting is proposed based on the physiological structure characteristics of optic disc and blood vessels. Then, early lesions, such as microaneurysms, hemorrhages and hard exudates, are detected based on their respective characteristics. An equivalent optical model simulating human eyes is designed based on the anatomical structure of retina. Main structures and early lesions are reconstructed in the 3D space for better visualization. Finally, the severity of each image is evaluated based on the international criteria of diabetic retinopathy. The system has been tested on public databases and images from hospitals. Experimental results demonstrate that the proposed system achieves high accuracy for main structures and early lesions detection. The results of severity classification for non-proliferative diabetic retinopathy are also accurate and suitable. Our system can assist ophthalmologists for clinical diagnosis, automatic screening and course progression of patients.

New control method of on-board ATP system of Shinkansen trains

Energy Technology Data Exchange (ETDEWEB)

Fukuda, N.; Watanabe, T. [Railway Technical Research Inst. (Japan)

2000-07-01

We studied a new control method of the on-board automatic train protection (ATP) system for Shinkansen trains to shorten the operation time and not to degrade ride comfort at changes in deceleration of the train, while maintaining the safety and reliability of the present ATP signal system. We propose a new on-board pattern brake control system based on the present ATP data without changing the wayside equipment. By simulating the ATP braking of the proposed control method, we succeeded in shortening the operation time by 48 seconds per one station in comparison with the present ATP brake control system. This paper reports the concept of the system and simulation results of the on-board pattern. (orig.)

Automatic differentiation algorithms in model analysis

NARCIS (Netherlands)

Huiskes, M.J.

2002-01-01

Title: Automatic differentiation algorithms in model analysis
Author: M.J. Huiskes
Date: 19 March, 2002

In this thesis automatic differentiation algorithms and derivative-based methods

Automatic Transformation of MPI Programs to Asynchronous, Graph-Driven Form

Energy Technology Data Exchange (ETDEWEB)

Baden, Scott B [University of California, San Diego; Weare, John H [University of California, San Diego; Bylaska, Eric J [Pacific Northwest National Laboratory

2013-04-30

The goals of this project are to develop new, scalable, high-fidelity algorithms for atomic-level simulations and program transformations that automatically restructure existing applications, enabling them to scale forward to Petascale systems and beyond. The techniques enable legacy MPI application code to exploit greater parallelism though increased latency hiding and improved workload assignment. The techniques were successfully demonstrated on high-end scalable systems located at DOE laboratories. Besides the automatic MPI program transformations efforts, the project also developed several new scalable algorithms for ab-initio molecular dynamics, including new massively parallel algorithms for hybrid DFT and new parallel in time algorithms for molecular dynamics and ab-initio molecular dynamics. These algorithms were shown to scale to very large number of cores, and they were designed to work in the latency hiding framework developed in this project. The effectiveness of the developments was enhanced by the direct application to real grand challenge simulation problems covering a wide range of technologically important applications, time scales and accuracies. These included the simulation of the electronic structure of mineral/fluid interfaces, the very accurate simulation of chemical reactions in microsolvated environments, and the simulation of chemical behavior in very large enzyme reactions.

STS-47 MS Jemison trains in SLJ module at MSFC Payload Crew Training Complex

Science.gov (United States)

1992-01-01

STS-47 Endeavour, Orbiter Vehicle (OV) 105, Mission Specialist (MS) Mae C. Jemison, wearing Autogenic Feedback Training System 2 suit, works with the Frog Embryology Experiment in a General Purpose Workstation (GPWS) in the Spacelab Japan (SLJ) module mockup at the Payload Crew Training Complex. The experiment will study the effects of weightlessness on the development of frog eggs fertilized in space. The Payload Crew Training Complex is located at the Marshall Space Flight Center (MSFC) in Huntsville, Alabama. View provided with alternate number 92P-139.

Fully automatic oil spill detection from COSMO-SkyMed imagery using a neural network approach

Science.gov (United States)

Avezzano, Ruggero G.; Del Frate, Fabio; Latini, Daniele

2012-09-01

The increased amount of available Synthetic Aperture Radar (SAR) images acquired over the ocean represents an extraordinary potential for improving oil spill detection activities. On the other side this involves a growing workload on the operators at analysis centers. In addition, even if the operators go through extensive training to learn manual oil spill detection, they can provide different and subjective responses. Hence, the upgrade and improvements of algorithms for automatic detection that can help in screening the images and prioritizing the alarms are of great benefit. In the framework of an ASI Announcement of Opportunity for the exploitation of COSMO-SkyMed data, a research activity (ASI contract L/020/09/0) aiming at studying the possibility to use neural networks architectures to set up fully automatic processing chains using COSMO-SkyMed imagery has been carried out and results are presented in this paper. The automatic identification of an oil spill is seen as a three step process based on segmentation, feature extraction and classification. We observed that a PCNN (Pulse Coupled Neural Network) was capable of providing a satisfactory performance in the different dark spots extraction, close to what it would be produced by manual editing. For the classification task a Multi-Layer Perceptron (MLP) Neural Network was employed.

Using Highlighting to Train Attentional Expertise.

Science.gov (United States)

Roads, Brett; Mozer, Michael C; Busey, Thomas A

2016-01-01

Acquiring expertise in complex visual tasks is time consuming. To facilitate the efficient training of novices on where to look in these tasks, we propose an attentional highlighting paradigm. Highlighting involves dynamically modulating the saliency of a visual image to guide attention along the fixation path of a domain expert who had previously viewed the same image. In Experiment 1, we trained naive subjects via attentional highlighting on a fingerprint-matching task. Before and after training, we asked subjects to freely inspect images containing pairs of prints and determine whether the prints matched. Fixation sequences were automatically scored for the degree of expertise exhibited using a Bayesian discriminative model of novice and expert gaze behavior. Highlighted training causes gaze behavior to become more expert-like not only on the trained images but also on transfer images, indicating generalization of learning. In Experiment 2, to control for the possibility that the increase in expertise is due to mere exposure, we trained subjects via highlighting of fixation sequences from novices, not experts, and observed no transition toward expertise. In Experiment 3, to determine the specificity of the training effect, we trained subjects with expert fixation sequences from images other than the one being viewed, which preserves coarse-scale statistics of expert gaze but provides no information about fine-grain features. Observing at least a partial transition toward expertise, we obtain only weak evidence that the highlighting procedure facilitates the learning of critical local features. We discuss possible improvements to the highlighting procedure.

Using Highlighting to Train Attentional Expertise.

Directory of Open Access Journals (Sweden)

Brett Roads

Full Text Available Acquiring expertise in complex visual tasks is time consuming. To facilitate the efficient training of novices on where to look in these tasks, we propose an attentional highlighting paradigm. Highlighting involves dynamically modulating the saliency of a visual image to guide attention along the fixation path of a domain expert who had previously viewed the same image. In Experiment 1, we trained naive subjects via attentional highlighting on a fingerprint-matching task. Before and after training, we asked subjects to freely inspect images containing pairs of prints and determine whether the prints matched. Fixation sequences were automatically scored for the degree of expertise exhibited using a Bayesian discriminative model of novice and expert gaze behavior. Highlighted training causes gaze behavior to become more expert-like not only on the trained images but also on transfer images, indicating generalization of learning. In Experiment 2, to control for the possibility that the increase in expertise is due to mere exposure, we trained subjects via highlighting of fixation sequences from novices, not experts, and observed no transition toward expertise. In Experiment 3, to determine the specificity of the training effect, we trained subjects with expert fixation sequences from images other than the one being viewed, which preserves coarse-scale statistics of expert gaze but provides no information about fine-grain features. Observing at least a partial transition toward expertise, we obtain only weak evidence that the highlighting procedure facilitates the learning of critical local features. We discuss possible improvements to the highlighting procedure.

Spike Pattern Recognition for Automatic Collimation Alignment

CERN Document Server

Azzopardi, Gabriella; Salvachua Ferrando, Belen Maria; Mereghetti, Alessio; Redaelli, Stefano; CERN. Geneva. ATS Department

2017-01-01

The LHC makes use of a collimation system to protect its sensitive equipment by intercepting potentially dangerous beam halo particles. The appropriate collimator settings to protect the machine against beam losses relies on a very precise alignment of all the collimators with respect to the beam. The beam center at each collimator is then found by touching the beam halo using an alignment procedure. Until now, in order to determine whether a collimator is aligned with the beam or not, a user is required to follow the collimator’s BLM loss data and detect spikes. A machine learning (ML) model was trained in order to automatically recognize spikes when a collimator is aligned. The model was loosely integrated with the alignment implementation to determine the classification performance and reliability, without effecting the alignment process itself. The model was tested on a number of collimators during this MD and the machine learning was able to output the classifications in real-time.

Automatic analysis of the 2015 Gorkha earthquake aftershock sequence.

Science.gov (United States)

Baillard, C.; Lyon-Caen, H.; Bollinger, L.; Rietbrock, A.; Letort, J.; Adhikari, L. B.

2016-12-01

The Mw 7.8 Gorkha earthquake, that partially ruptured the Main Himalayan Thrust North of Kathmandu on the 25th April 2015, was the largest and most catastrophic earthquake striking Nepal since the great M8.4 1934 earthquake. This mainshock was followed by multiple aftershocks, among them, two notable events that occurred on the 12th May with magnitudes of 7.3 Mw and 6.3 Mw. Due to these recent events it became essential for the authorities and for the scientific community to better evaluate the seismic risk in the region through a detailed analysis of the earthquake catalog, amongst others, the spatio-temporal distribution of the Gorkha aftershock sequence. Here we complement this first study by doing a microseismic study using seismic data coming from the eastern part of the Nepalese Seismological Center network associated to one broadband station in Everest. Our primary goal is to deliver an accurate catalog of the aftershock sequence. Due to the exceptional number of events detected we performed an automatic picking/locating procedure which can be splitted in 4 steps: 1) Coarse picking of the onsets using a classical STA/LTA picker, 2) phase association of picked onsets to detect and declare seismic events, 3) Kurtosis pick refinement around theoretical arrival times to increase picking and location accuracy and, 4) local magnitude calculation based amplitude of waveforms. This procedure is time efficient ( 1 sec/event), reduces considerably the location uncertainties ( 2 to 5 km errors) and increases the number of events detected compared to manual processing. Indeed, the automatic detection rate is 10 times higher than the manual detection rate. By comparing to the USGS catalog we were able to give a new attenuation law to compute local magnitudes in the region. A detailed analysis of the seismicity shows a clear migration toward the east of the region and a sudden decrease of seismicity 100 km east of Kathmandu which may reveal the presence of a tectonic

Automatic intelligent cruise control

OpenAIRE

Stanton, NA; Young, MS

2006-01-01

This paper reports a study on the evaluation of automatic intelligent cruise control (AICC) from a psychological perspective. It was anticipated that AICC would have an effect upon the psychology of driving—namely, make the driver feel like they have less control, reduce the level of trust in the vehicle, make drivers less situationally aware, but might reduce the workload and make driving might less stressful. Drivers were asked to drive in a driving simulator under manual and automatic inte...

TU-C-17A-04: BEST IN PHYSICS (THERAPY) - A Supervised Framework for Automatic Contour Assessment for Radiotherapy Planning of Head- Neck Cancer

Energy Technology Data Exchange (ETDEWEB)

Chen, H; Kavanaugh, J; Tan, J; Dolly, S; Gay, H; Thorstad, W; Anastasio, M; Altman, M; Mutic, S; Li, H [Washington University School of Medicine, Saint Louis, MO (United States)

2014-06-15

Purpose: Precise contour delineation of tumor targets and critical structures from CT simulations is essential for accurate radiotherapy (RT) treatment planning. However, manual and automatic delineation processes can be error prone due to limitations in imaging techniques and individual anatomic variability. Tedious and laborious manual verification is hence needed. This study develops a general framework for automatically assessing RT contours for head-neck cancer patients using geometric attribute distribution models (GADMs). Methods: Geometric attributes (centroid and volume) were computed from physician-approved RT contours of 29 head-neck patients. Considering anatomical correlation between neighboring structures, the GADM for each attribute was trained to characterize intra- and interpatient structure variations using principal component analysis. Each trained GADM was scalable and deformable, but constrained by the principal attribute variations of the training contours. A new hierarchical model adaptation algorithm was utilized to assess the RT contour correctness for a given patient. Receiver operating characteristic (ROC) curves were employed to evaluate and tune system parameters for the training models. Results: Experiments utilizing training and non-training data sets with simulated contouring errors were conducted to validate the framework performance. Promising assessment results of contour normality/abnormality for the training contour-based data were achieved with excellent accuracy (0.99), precision (0.99), recall (0.83), and F-score (0.97), while corresponding values of 0.84, 0.96, 0.83, and 0.9 were achieved for the non-training data. Furthermore, the areas under the ROC curves were above 0.9, validating the accuracy of this test. Conclusion: The proposed framework can reliably identify contour normality/abnormality based upon intra- and inter-structure constraints derived from clinically-approved contours. It also allows physicians to

Neural Network Based Recognition of Signal Patterns in Application to Automatic Testing of Rails

Directory of Open Access Journals (Sweden)