Morphohistological features of pancreatic stump are the main determinant of pancreatic fistula after pancreatoduodenectomy.

Science.gov (United States)

Ridolfi, Cristina; Angiolini, Maria Rachele; Gavazzi, Francesca; Spaggiari, Paola; Tinti, Maria Carla; Uccelli, Fara; Madonini, Marco; Montorsi, Marco; Zerbi, Alessandro

2014-01-01

Pancreatic surgery is challenging and associated with high morbidity, mainly represented by postoperative pancreatic fistula (POPF) and its further consequences. Identification of risk factors for POPF is essential for proper postoperative management. Evaluation of the role of morphological and histological features of pancreatic stump, other than main pancreatic duct diameter and glandular texture, in POPF occurrence after pancreaticoduodenectomy. Between March 2011 and April 2013, we performed 145 consecutive pancreaticoduodenectomies. We intraoperatively recorded morphological features of pancreatic stump and collected data about postoperative morbidity. Our dedicated pathologist designed a score to quantify fibrosis and inflammation of pancreatic tissue. Overall morbidity was 59,3%. Mortality was 4,1%. POPF rate was 28,3%, while clinically significant POPF were 15,8%. Male sex (P = 0.009), BMI ≥ 25 (P = 0.002), prolonged surgery (P = 0.001), soft pancreatic texture (P < 0.001), small pancreatic duct (P < 0.001), pancreatic duct decentralization on stump anteroposterior axis, especially if close to the posterior margin (P = 0.031), large stump area (P = 0.001), and extended stump mobilization (P = 0.001) were related to higher POPF rate. Our fibrosis-and-inflammation score is strongly associated with POPF (P = 0.001). Pancreatic stump features evaluation, including histology, can help the surgeon in fitting postoperative management to patient individual risk after pancreaticoduodenectomy.

The Main Features of and Response to The Current Asian Security Situation

Institute of Scientific and Technical Information of China (English)

Hu; Shisheng

2015-01-01

I.The Main Features of the Current Asian Security Situation The strategic game between China and the United States becomes the most powerful driving force to change the Asian traditional security situation.The United States has tried its best to delay China’s rising pace or"to standardize"China’s rising path by using its huge military advantage and forward military deployment and strengthening its security network of alliance and friends,so as to maintain its dominant position in

Main features of licensing requirements for nuclear installations in several OECD member countries

International Nuclear Information System (INIS)

Reyners, P.

1977-01-01

The present paper contains a brief description of the main features of the above-mentioned six countries' licensing systems, namely the legal regime applicable, the appropriate licensing bodies, the general frame and scope of the respective national regimes, the involvement of the public and technical safety bodies as well as the inspection procedures. This description is supplemented by some introductory remarks. (orig.) [de

Main features of licensing requirements for nuclear installations in several OECD member countries

International Nuclear Information System (INIS)

Reyners, P.

1975-01-01

The present paper contains a brief description of the main features of the above-mentioned six countries' licensing systems, namely the legal regime applicable, the appropriate licensing bodies, the general frame and scope of the respective national regimes, the involvement of the public and technical safety bodies as well as the inspection procedures. This description is supplemented by some introductory remarks. (orig.) [de

Main injector synchronous timing system

International Nuclear Information System (INIS)

Blokland, W.; Steimel, J.

1998-01-01

The Synchronous Timing System is designed to provide sub-nanosecond timing to instrumentation during the acceleration of particles in the Main Injector. Increased energy of the beam particles leads to a small but significant increase in speed, reducing the time it takes to complete a full turn of the ring by 61 nanoseconds (or more than 3 rf buckets). In contrast, the reference signal, used to trigger instrumentation and transmitted over a cable, has a constant group delay. This difference leads to a phase slip during the ramp and prevents instrumentation such as dampers from properly operating without additional measures. The Synchronous Timing System corrects for this phase slip as well as signal propagation time changes due to temperature variations. A module at the LLRF system uses a 1.2 Gbit/s G-Link chip to transmit the rf clock and digital data (e.g. the current frequency) over a single mode fiber around the ring. Fiber optic couplers at service buildings split off part of this signal for a local module which reconstructs a synchronous beam reference signal. This paper describes the background, design and expected performance of the Synchronous Timing System. copyright 1998 American Institute of Physics

Main injector synchronous timing system

International Nuclear Information System (INIS)

Blokland, Willem; Steimel, James

1998-01-01

The Synchronous Timing System is designed to provide sub-nanosecond timing to instrumentation during the acceleration of particles in the Main Injector. Increased energy of the beam particles leads to a small but significant increase in speed, reducing the time it takes to complete a full turn of the ring by 61 nanoseconds (or more than 3 rf buckets). In contrast, the reference signal, used to trigger instrumentation and transmitted over a cable, has a constant group delay. This difference leads to a phase slip during the ramp and prevents instrumentation such as dampers from properly operating without additional measures. The Synchronous Timing System corrects for this phase slip as well as signal propagation time changes due to temperature variations. A module at the LLRF system uses a 1.2 Gbit/s G-Link chip to transmit the rf clock and digital data (e.g. the current frequency) over a single mode fiber around the ring. Fiber optic couplers at service buildings split off part of this signal for a local module which reconstructs a synchronous beam reference signal. This paper describes the background, design and expected performance of the Synchronous Timing System

Morphohistological Features of Pancreatic Stump Are the Main Determinant of Pancreatic Fistula after Pancreatoduodenectomy

Directory of Open Access Journals (Sweden)

Cristina Ridolfi

2014-01-01

Full Text Available Introduction. Pancreatic surgery is challenging and associated with high morbidity, mainly represented by postoperative pancreatic fistula (POPF and its further consequences. Identification of risk factors for POPF is essential for proper postoperative management. Aim of the Study. Evaluation of the role of morphological and histological features of pancreatic stump, other than main pancreatic duct diameter and glandular texture, in POPF occurrence after pancreaticoduodenectomy. Patients and Methods. Between March 2011 and April 2013, we performed 145 consecutive pancreaticoduodenectomies. We intraoperatively recorded morphological features of pancreatic stump and collected data about postoperative morbidity. Our dedicated pathologist designed a score to quantify fibrosis and inflammation of pancreatic tissue. Results. Overall morbidity was 59,3%. Mortality was 4,1%. POPF rate was 28,3%, while clinically significant POPF were 15,8%. Male sex (P=0.009, BMI≥25 (P=0.002, prolonged surgery (P=0.001, soft pancreatic texture (P<0.001, small pancreatic duct (P<0.001, pancreatic duct decentralization on stump anteroposterior axis, especially if close to the posterior margin (P=0.031, large stump area (P=0.001, and extended stump mobilization (P=0.001 were related to higher POPF rate. Our fibrosis-and-inflammation score is strongly associated with POPF (P=0.001. Discussion and Conclusions. Pancreatic stump features evaluation, including histology, can help the surgeon in fitting postoperative management to patient individual risk after pancreaticoduodenectomy.

A window-based time series feature extraction method.

Science.gov (United States)

Katircioglu-Öztürk, Deniz; Güvenir, H Altay; Ravens, Ursula; Baykal, Nazife

2017-10-01

This study proposes a robust similarity score-based time series feature extraction method that is termed as Window-based Time series Feature ExtraCtion (WTC). Specifically, WTC generates domain-interpretable results and involves significantly low computational complexity thereby rendering itself useful for densely sampled and populated time series datasets. In this study, WTC is applied to a proprietary action potential (AP) time series dataset on human cardiomyocytes and three precordial leads from a publicly available electrocardiogram (ECG) dataset. This is followed by comparing WTC in terms of predictive accuracy and computational complexity with shapelet transform and fast shapelet transform (which constitutes an accelerated variant of the shapelet transform). The results indicate that WTC achieves a slightly higher classification performance with significantly lower execution time when compared to its shapelet-based alternatives. With respect to its interpretable features, WTC has a potential to enable medical experts to explore definitive common trends in novel datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Domino effect in chemical accidents: main features and accident sequences.

Science.gov (United States)

Darbra, R M; Palacios, Adriana; Casal, Joaquim

2010-11-15

The main features of domino accidents in process/storage plants and in the transportation of hazardous materials were studied through an analysis of 225 accidents involving this effect. Data on these accidents, which occurred after 1961, were taken from several sources. Aspects analyzed included the accident scenario, the type of accident, the materials involved, the causes and consequences and the most common accident sequences. The analysis showed that the most frequent causes are external events (31%) and mechanical failure (29%). Storage areas (35%) and process plants (28%) are by far the most common settings for domino accidents. Eighty-nine per cent of the accidents involved flammable materials, the most frequent of which was LPG. The domino effect sequences were analyzed using relative probability event trees. The most frequent sequences were explosion→fire (27.6%), fire→explosion (27.5%) and fire→fire (17.8%). Copyright © 2010 Elsevier B.V. All rights reserved.

A Time-Frequency Approach to Feature Extraction for a Brain-Computer Interface with a Comparative Analysis of Performance Measures

Directory of Open Access Journals (Sweden)

T. M. McGinnity

2005-11-01

Full Text Available The paper presents an investigation into a time-frequency (TF method for extracting features from the electroencephalogram (EEG recorded from subjects performing imagination of left- and right-hand movements. The feature extraction procedure (FEP extracts frequency domain information to form features whilst time-frequency resolution is attained by localising the fast Fourier transformations (FFTs of the signals to specific windows localised in time. All features are extracted at the rate of the signal sampling interval from a main feature extraction (FE window through which all data passes. Subject-specific frequency bands are selected for optimal feature extraction and intraclass variations are reduced by smoothing the spectra for each signal by an interpolation (IP process. The TF features are classified using linear discriminant analysis (LDA. The FE window has potential advantages for the FEP to be applied in an online brain-computer interface (BCI. The approach achieves good performance when quantified by classification accuracy (CA rate, information transfer (IT rate, and mutual information (MI. The information that these performance measures provide about a BCI system is analysed and the importance of this is demonstrated through the results.

Effects of pilot injection parameters on low temperature combustion diesel engines equipped with solenoid injectors featuring conventional and rate-shaped main injection

International Nuclear Information System (INIS)

D’Ambrosio, S.; Ferrari, A.

2016-01-01

Highlights: • The influence of the principal pilot injection parameters is discussed for low-temperature combustion systems. • Swirl-sweep and dwell-time sweep results are combined to analyze soot emissions. • The pilot injection effects are investigated in injection profiles featuring rate-shaped main injections. - Abstract: The potential of pilot injection has been assessed on a low-temperature combustion diesel engine for automotive applications, which was characterized by a reduced compression-ratio, high EGR rates and postponed main injection timings. Dwell time sweeps have been carried out for pilot injections with distinct energizing times under different representative steady-state working conditions of the medium load and speed area of the New European Driving Cycle. The results of in-cylinder analyses of the pressure, heat-release rate, temperature and emissions are presented. Combustion noise has been shown to decrease significantly when the pilot injected mass increases, while it is scarcely affected by the dwell time between the pilot and main injections. The HC, CO and fuel consumption trends, with respect to both the pilot injection dwell time and mass, are in line with those of conventional combustion systems, and in particular decreasing trends occur as the pilot injection energizing time is increased. Furthermore, a reduced sensitivity of NO_x emissions to both dwell time and pilot injected mass has been found, compared to conventional combustion systems. Finally, it has been observed that soot emissions diminish as the energizing time is shortened, and their dependence on dwell time is influenced to a great extent by the presence of local zones with reduced air-to-fuel ratios within the cylinder. A combined analysis of the results of swirl sweeps and dwell time sweeps is here proposed as a methodology for the detection of any possible interference between pilot combustion burned gases and the main injected fuel. The effect of pilot

Ocean observing systems support operational forecasts for the timing of Maine's lobster fishery

Science.gov (United States)

Mills, K.; Hernandez, C.; Pershing, A. J.

2016-02-01

American lobster supports one of the most valuable fisheries in the United States, with a landed value in 2013 exceeding $460M. Although US lobstermen are free to fish throughout the year, the New England climate, lobster biology and fleet dynamics lead to a strong annual cycle with catch rates rising rapidly in early summer and landings peaking in late summer. When this annual cycle is disrupted, it can impact the supply and ultimately the price of lobsters. During the record warm conditions in 2012, the rise in catch rates occurred three weeks ahead of normal. Combined with higher than normal landings from the spring Canadian fishery, the early and high volume landings in 2012 led to a collapse in price that severely stressed the U. S. fishery, especially in Maine where over 85% of the landings occur. Based on this experience, we have been developing seasonal forecasts of the phenology of Maine lobster landings. Using temperatures at 50m from four NERACOOS buoys in the Gulf of Maine, we can reliably forecast the date when the Maine lobster fishery will `turn on' for the year, with prediction accuracy peaking in April. The high-landings period normally starts in July, and the 2-3 month lead-time provides some advance warning to dealers and processors of when their capacity needs to be ready and to fishermen of potential supply chain and market impacts such as we observed in 2012. We are currently working towards finer-scale regional forecasts along the Maine coast that may include other features that will provide information to help the lobster industry adapt to the rapid changes that are underway in the Gulf of Maine.

Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems

Directory of Open Access Journals (Sweden)

Baolei Xu

2014-01-01

Full Text Available We introduce a new motor parameter imagery paradigm using clench speed and clench force motor imagery. The time-frequency-phase features are extracted from mu rhythm and beta rhythms, and the features are optimized using three process methods: no-scaled feature using “MIFS” feature selection criterion, scaled feature using “MIFS” feature selection criterion, and scaled feature using “mRMR” feature selection criterion. Support vector machines (SVMs and extreme learning machines (ELMs are compared for classification between clench speed and clench force motor imagery using the optimized feature. Our results show that no significant difference in the classification rate between SVMs and ELMs is found. The scaled feature combinations can get higher classification accuracy than the no-scaled feature combinations at significant level of 0.01, and the “mRMR” feature selection criterion can get higher classification rate than the “MIFS” feature selection criterion at significant level of 0.01. The time-frequency-phase feature can improve the classification rate by about 20% more than the time-frequency feature, and the best classification rate between clench speed motor imagery and clench force motor imagery is 92%. In conclusion, the motor parameter imagery paradigm has the potential to increase the direct control commands for BCI control and the time-frequency-phase feature has the ability to improve BCI classification accuracy.

Multiple Time-Instances Features of Degraded Speech for Single Ended Quality Measurement

Directory of Open Access Journals (Sweden)

Rajesh Kumar Dubey

2017-01-01

Full Text Available The use of single time-instance features, where entire speech utterance is used for feature computation, is not accurate and adequate in capturing the time localized information of short-time transient distortions and their distinction from plosive sounds of speech, particularly degraded by impulsive noise. Hence, the importance of estimating features at multiple time-instances is sought. In this, only active speech segments of degraded speech are used for features computation at multiple time-instances on per frame basis. Here, active speech means both voiced and unvoiced frames except silence. The features of different combinations of multiple contiguous active speech segments are computed and called multiple time-instances features. The joint GMM training has been done using these features along with the subjective MOS of the corresponding speech utterance to obtain the parameters of GMM. These parameters of GMM and multiple time-instances features of test speech are used to compute the objective MOS values of different combinations of multiple contiguous active speech segments. The overall objective MOS of the test speech utterance is obtained by assigning equal weight to the objective MOS values of the different combinations of multiple contiguous active speech segments. This algorithm outperforms the Recommendation ITU-T P.563 and recently published algorithms.

Real-time hypothesis driven feature extraction on parallel processing architectures

DEFF Research Database (Denmark)

Granmo, O.-C.; Jensen, Finn Verner

2002-01-01

the problem of higher-order feature-content/feature-feature correlation, causally complexly interacting features are identified through Bayesian network d-separation analysis and combined into joint features. When used on a moderately complex object-tracking case, the technique is able to select...... extraction, which selectively extract relevant features one-by-one, have in some cases achieved real-time performance on single processing element architectures. In this paperwe propose a novel technique which combines the above two approaches. Features are selectively extracted in parallelizable sets...

Main features of Kola, Leningrad and Ignalina NPPs for emergency preparedness purposes

Energy Technology Data Exchange (ETDEWEB)

Holmstroem, H. [VTT Energy (Finland)

2001-12-01

Of the nuclear power plants situated in the Nordic and their neighbouring countries, the Ignalina, Lenigrad and Kola plants are considered to pose the largest risks to the public. The purpose of this report is to provide basic relevant information about these three plants for use in a case of a major nuclear accident or incident in any of them. The report could be used e.g. by authorities dealing with the resulting emergency measures to provide the public and the media with relevant information about the plant in question. The report can also be used for quick general familiarization With the plants in question. The total activity inventories for all the plants are listed at the end of the report, in Chapter 4. The release of noble gases is close to 100 % in most severe accidents, but the releases of other elements depend strongly on the plant features and the nature of the accident. This report has been compiled from several sources. The main source has been an earlier NKS-report: 'Design and Safety Features of Nuclear Reactors Neighbouring the Nordic Countries', TemaNord 1994:595, 1994. Only limited editing has been done. Sources of the figures are presented in parenthesis after the figure titles. (au)

Current Russian patriotism: matter, features, main directions

Directory of Open Access Journals (Sweden)

Lutovinov Vladimir Ilich

2013-11-01

Full Text Available The article considers understanding and the main point of patriotism as one of high cultural values. The main approaches that reveal different sides of this phenomenon, its role and importance in a history of Russia in the 21st century are inferred from the analysis of viewpoints of Russian thinkers and contemporary researchers. The patriotism formation problems in Russian society and their condition are defined, the need of patriotic level rise as one of the conditions for great Russia rebirth is substantiated.

Eye movement identification based on accumulated time feature

Science.gov (United States)

Guo, Baobao; Wu, Qiang; Sun, Jiande; Yan, Hua

2017-06-01

Eye movement is a new kind of feature for biometrical recognition, it has many advantages compared with other features such as fingerprint, face, and iris. It is not only a sort of static characteristics, but also a combination of brain activity and muscle behavior, which makes it effective to prevent spoofing attack. In addition, eye movements can be incorporated with faces, iris and other features recorded from the face region into multimode systems. In this paper, we do an exploring study on eye movement identification based on the eye movement datasets provided by Komogortsev et al. in 2011 with different classification methods. The time of saccade and fixation are extracted from the eye movement data as the eye movement features. Furthermore, the performance analysis was conducted on different classification methods such as the BP, RBF, ELMAN and SVM in order to provide a reference to the future research in this field.

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System.

Science.gov (United States)

Sheik, Sadique; Coath, Martin; Indiveri, Giacomo; Denham, Susan L; Wennekers, Thomas; Chicca, Elisabetta

2012-01-01

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems.

Emergent auditory feature tuning in a real-time neuromorphic VLSI system

Directory of Open Access Journals (Sweden)

Sadique eSheik

2012-02-01

Full Text Available Many sounds of ecological importance, such as communication calls, are characterised by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamocortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP, which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectrotemporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step towards the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems.

CONSIDERATIONS ON THE MAIN FEATURES OF THE AGRICULTURAL POPULATION IN THE EUROPEAN UNION

Directory of Open Access Journals (Sweden)

Agatha POPESCU

2013-12-01

Full Text Available The paper aimed to analyze the EU-27 population by means of the following indicators: population structure by origin, age, gender, training level, income in member states, emphasizing Romania’s position among other countries. In the EU-27 there large differences regarding rural and agricultural population.The main trend is the reduction of agricultural population, but there are countries where it is increasing and states where it is declining. Despite at EU level 5 % of its 504 million inhabitants are working in agriculture, there are states with a higher percentage of agricultural workforce and also with a lower labor percentage. Rural population aging and decreasing number, migration to cities, relatively low training level, gender discrepances from a country to another, low income per annual working income mainly in the 12 countries which joined the EU in 2004 and 2007 below the EU average income are the major features of the EU-27 agricultural labor. Romania has the highest agricultural population working in small sized farms and the lowest income per farmer below Euro 2,000. The gap beween the EU and Romania could be diminished by training, high technologies, implementation of associative forms in agriculture destined to grow up productivity and competitiveness.

Main features and potentialities of gas-blanket systems

International Nuclear Information System (INIS)

Lehnert, B.

1977-02-01

A review is given of the features and potentialities of cold-blanket systems, with respect to plasma equilibrium, stability, and reactor technology. The treatment is concentrated on quasi-steady magnetized plasmas confined at moderately high beta values. The cold-blanket concept has specific potentialities as a fusion reactor, e.g. in connection with the desired densities and dimensions of full-scale systems, refuelling, as well as ash and impurity removal, and stability. (author)

Bearing performance degradation assessment based on time-frequency code features and SOM network

International Nuclear Information System (INIS)

Zhang, Yan; Tang, Baoping; Han, Yan; Deng, Lei

2017-01-01

Bearing performance degradation assessment and prognostics are extremely important in supporting maintenance decision and guaranteeing the system’s reliability. To achieve this goal, this paper proposes a novel feature extraction method for the degradation assessment and prognostics of bearings. Features of time-frequency codes (TFCs) are extracted from the time-frequency distribution using a hybrid procedure based on short-time Fourier transform (STFT) and non-negative matrix factorization (NMF) theory. An alternative way to design the health indicator is investigated by quantifying the similarity between feature vectors using a self-organizing map (SOM) network. On the basis of this idea, a new health indicator called time-frequency code quantification error (TFCQE) is proposed to assess the performance degradation of the bearing. This indicator is constructed based on the bearing real-time behavior and the SOM model that is previously trained with only the TFC vectors under the normal condition. Vibration signals collected from the bearing run-to-failure tests are used to validate the developed method. The comparison results demonstrate the superiority of the proposed TFCQE indicator over many other traditional features in terms of feature quality metrics, incipient degradation identification and achieving accurate prediction. Highlights • Time-frequency codes are extracted to reflect the signals’ characteristics. • SOM network served as a tool to quantify the similarity between feature vectors. • A new health indicator is proposed to demonstrate the whole stage of degradation development. • The method is useful for extracting the degradation features and detecting the incipient degradation. • The superiority of the proposed method is verified using experimental data. (paper)

Establish and Evaluate Ada Runtime Features of Interest for Real-Time Systems

Science.gov (United States)

1989-02-15

Runtime Features of Interest for Real - Time Systems -,-. CLEARED POR :)E,4 pUEL tCATLON SEP 2 0 19E19 ,CETM ORP t ’R RE LOO O Nt-U~HM- ANDQ SECURITY...ESTABLISH AND EVALUATE py ADA RUNTIME FEATURES OF INTEREST FOR REAL - TIME SYSTEMS CONTRACT NUMBER: MDA 903-87-D-0056 IITRI PROJECT NUMBER: T06168 PREPARED...2 2.0 SELECTION PROCESS OVERVIEW .................................... 3 2.1 REAL - TIME SYSTEMS IDENTIFICATION ........................... 4 2.2

THEORETICAL QUESTIONS OF INVESTMENT RISK RESEARCH, ITS MAIN FEATURES AND CLASSIFICATION

Directory of Open Access Journals (Sweden)

I. A. Kadyrbaev

2016-01-01

Full Text Available The article examines framework methodology of investment risk. The subject of the study are the basic theoretical positions directly related to the economic category of "investment risk". The purpose of this writing is the improvement of the methodology of the study of investment risk in the investment activity. This objective is to define the tasks, which consist in the formulation of the concept of "investment risk", the development of investment risk classification to provide investors with effective protection against such risks. The implementation  of the tasks will create conditions for the growth of investments in Russia. Currently, effective investment strategy for such an increase, is among the priority directions of development of the Russian economy.The article deals with logically interrelated study of basic economic categories, influencing directly on the investment risk. Author provided classification of investments in accordance with the level of risk. Examine the matter of the financial-economic category of the concept of "investment risk" and the classification of investment risks. Specified main features of the investment risk, which allowed to reveal specifics, which consists in the redistribution of capital in various types of assets in order to maximize profits or to obtain significant social effect.

Hidden discriminative features extraction for supervised high-order time series modeling.

Science.gov (United States)

Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee

2016-11-01

In this paper, an orthogonal Tucker-decomposition-based extraction of high-order discriminative subspaces from a tensor-based time series data structure is presented, named as Tensor Discriminative Feature Extraction (TDFE). TDFE relies on the employment of category information for the maximization of the between-class scatter and the minimization of the within-class scatter to extract optimal hidden discriminative feature subspaces that are simultaneously spanned by every modality for supervised tensor modeling. In this context, the proposed tensor-decomposition method provides the following benefits: i) reduces dimensionality while robustly mining the underlying discriminative features, ii) results in effective interpretable features that lead to an improved classification and visualization, and iii) reduces the processing time during the training stage and the filtering of the projection by solving the generalized eigenvalue issue at each alternation step. Two real third-order tensor-structures of time series datasets (an epilepsy electroencephalogram (EEG) that is modeled as channel×frequency bin×time frame and a microarray data that is modeled as gene×sample×time) were used for the evaluation of the TDFE. The experiment results corroborate the advantages of the proposed method with averages of 98.26% and 89.63% for the classification accuracies of the epilepsy dataset and the microarray dataset, respectively. These performance averages represent an improvement on those of the matrix-based algorithms and recent tensor-based, discriminant-decomposition approaches; this is especially the case considering the small number of samples that are used in practice. Copyright © 2016 Elsevier Ltd. All rights reserved.

MECAR (Main Ring Excitation Controller and Regulator): A real time learning regulator for the Fermilab Main Ring or the Main Injector synchrotron

International Nuclear Information System (INIS)

Flora, R.; Martin, K.; Moibenko, A.; Pfeffer, H.; Wolff, D.; Prieto, P.; Hays, S.

1995-04-01

The real time computer for controlling and regulating the FNAL Main Ring power supplies has been upgraded with a new learning control system. The learning time of the system has been reduced by an order of magnitude, mostly through the implementation of a 95 tap FIR filter in the learning algorithm. The magnet system consists of three buses, which must track each other during a ramp from 100 to 1700 amps at a 2.4 second repetition rate. This paper will present the system configuration and the tools used during development and testing

Main modelling features of the ASTEC V2.1 major version

International Nuclear Information System (INIS)

Chatelard, P.; Belon, S.; Bosland, L.; Carénini, L.; Coindreau, O.; Cousin, F.; Marchetto, C.; Nowack, H.; Piar, L.; Chailan, L.

2016-01-01

Highlights: • Recent modelling improvements of the ASTEC European severe accident code are outlined. • Key new physical models now available in the ASTEC V2.1 major version are described. • ASTEC progress towards a multi-design reactor code is illustrated for BWR and PHWR. • ASTEC strong link with the on-going EC CESAM FP7 project is emphasized. • Main remaining modelling issues (on which IRSN efforts are now directing) are given. - Abstract: A new major version of the European severe accident integral code ASTEC, developed by IRSN with some GRS support, was delivered in November 2015 to the ASTEC worldwide community. Main modelling features of this V2.1 version are summarised in this paper. In particular, the in-vessel coupling technique between the reactor coolant system thermal-hydraulics module and the core degradation module has been strongly re-engineered to remove some well-known weaknesses of the former V2.0 series. The V2.1 version also includes new core degradation models specifically addressing BWR and PHWR reactor types, as well as several other physical modelling improvements, notably on reflooding of severely damaged cores, Zircaloy oxidation under air atmosphere, corium coolability during corium concrete interaction and source term evaluation. Moreover, this V2.1 version constitutes the back-bone of the CESAM FP7 project, which final objective is to further improve ASTEC for use in Severe Accident Management analysis of the Gen.II–III nuclear power plants presently under operation or foreseen in near future in Europe. As part of this European project, IRSN efforts to continuously improve both code numerical robustness and computing performances at plant scale as well as users’ tools are being intensified. Besides, ASTEC will continue capitalising the whole knowledge on severe accidents phenomenology by progressively keeping physical models at the state of the art through a regular feed-back from the interpretation of the current and

Forecasting the Seasonal Timing of Maine's Lobster Fishery

Directory of Open Access Journals (Sweden)

Katherine E. Mills

2017-11-01

Full Text Available The fishery for American lobster is currently the highest-valued commercial fishery in the United States, worth over US$620 million in dockside value in 2015. During a marine heat wave in 2012, the fishery was disrupted by the early warming of spring ocean temperatures and subsequent influx of lobster landings. This situation resulted in a price collapse, as the supply chain was not prepared for the early and abundant landings of lobsters. Motivated by this series of events, we have developed a forecast of when the Maine (USA lobster fishery will shift into its high volume summer landings period. The forecast uses a regression approach to relate spring ocean temperatures derived from four NERACOOS buoys along the coast of Maine to the start day of the high landings period of the fishery. Tested against conditions in past years, the forecast is able to predict the start day to within 1 week of the actual start, and the forecast can be issued 3–4 months prior to the onset of the high-landings period, providing valuable lead-time for the fishery and its associated supply chain to prepare for the upcoming season. Forecast results are conveyed in a probabilistic manner and are updated weekly over a 6-week forecasting period so that users can assess the certainty and consistency of the forecast and factor the uncertainty into their use of the information in a given year. By focusing on the timing of events, this type of seasonal forecast provides climate-relevant information to users at time scales that are meaningful for operational decisions. As climate change alters seasonal phenology and reduces the reliability of past experience as a guide for future expectations, this type of forecast can enable fishing industry participants to better adjust to and prepare for operating in the context of climate change.

Haar-like Features for Robust Real-Time Face Recognition

DEFF Research Database (Denmark)

Nasrollahi, Kamal; Moeslund, Thomas B.

2013-01-01

Face recognition is still a very challenging task when the input face image is noisy, occluded by some obstacles, of very low-resolution, not facing the camera, and not properly illuminated. These problems make the feature extraction and consequently the face recognition system unstable....... The proposed system in this paper introduces the novel idea of using Haar-like features, which have commonly been used for object detection, along with a probabilistic classifier for face recognition. The proposed system is simple, real-time, effective and robust against most of the mentioned problems....... Experimental results on public databases show that the proposed system indeed outperforms the state-of-the-art face recognition systems....

Detection of Coronal Mass Ejections Using Multiple Features and Space-Time Continuity

Science.gov (United States)

Zhang, Ling; Yin, Jian-qin; Lin, Jia-ben; Feng, Zhi-quan; Zhou, Jin

2017-07-01

Coronal Mass Ejections (CMEs) release tremendous amounts of energy in the solar system, which has an impact on satellites, power facilities and wireless transmission. To effectively detect a CME in Large Angle Spectrometric Coronagraph (LASCO) C2 images, we propose a novel algorithm to locate the suspected CME regions, using the Extreme Learning Machine (ELM) method and taking into account the features of the grayscale and the texture. Furthermore, space-time continuity is used in the detection algorithm to exclude the false CME regions. The algorithm includes three steps: i) define the feature vector which contains textural and grayscale features of a running difference image; ii) design the detection algorithm based on the ELM method according to the feature vector; iii) improve the detection accuracy rate by using the decision rule of the space-time continuum. Experimental results show the efficiency and the superiority of the proposed algorithm in the detection of CMEs compared with other traditional methods. In addition, our algorithm is insensitive to most noise.

Description of the Main Features of the Series Production of the LHC Main Dipole Magnets

CERN Document Server

Savary, F; Chevret, P; de Rijk, G; Fessia, P; Liénard, P; Miles, J; Modena, M; Rossi, L; Tommasini, D; Vlogaert, J; Bresson, D; Grunblatt, G; Decoene, JF; Bressani, F; Drago, G; Gagliardi, P; Eysselein, F; Gärtner, W; Lublow, P

2008-01-01

The series production of the LHC main dipole magnets was completed in November 2006. This paper presents the organization implemented at CERN and the milestones fixed to fullfil the technical requirements and to respect the master schedule of the machine installation. The CERN organization for the production follow-up, the quality assurance and the magnet testing, as well as the organization of the three main contractors will be described. A description of the design work and procurement of most of the specific heavy tooling and key components will be given with emphasis on the advantages and drawbacks.

ReliefSeq: a gene-wise adaptive-K nearest-neighbor feature selection tool for finding gene-gene interactions and main effects in mRNA-Seq gene expression data.

Directory of Open Access Journals (Sweden)

Brett A McKinney

Full Text Available Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. While several tools have been developed for assessing differential expression in sequence-based transcriptomics, the detection of statistical interactions between transcripts has received less attention in the area of RNA-seq analysis. We describe a new extension and assessment of Relief-F for feature selection in RNA-seq data. The ReliefSeq implementation adapts the number of nearest neighbors (k for each gene to optimize the Relief-F test statistics (importance scores for finding both main effects and interactions. We compare this gene-wise adaptive-k (gwak Relief-F method with standard RNA-seq feature selection tools, such as DESeq and edgeR, and with the popular machine learning method Random Forests. We demonstrate performance on a panel of simulated data that have a range of distributional properties reflected in real mRNA-seq data including multiple transcripts with varying sizes of main effects and interaction effects. For simulated main effects, gwak-Relief-F feature selection performs comparably to standard tools DESeq and edgeR for ranking relevant transcripts. For gene-gene interactions, gwak-Relief-F outperforms all comparison methods at ranking relevant genes in all but the highest fold change/highest signal situations where it performs similarly. The gwak-Relief-F algorithm outperforms Random Forests for detecting relevant genes in all simulation experiments. In addition, Relief-F is comparable to the other methods based on computational time. We also apply ReliefSeq to an RNA-Seq study of smallpox vaccine to identify gene expression changes between vaccinia virus-stimulated and unstimulated samples. ReliefSeq is an attractive tool for inclusion in the suite of tools used for analysis of mRNA-Seq data; it has power to

Analysis of muscle fatigue conditions using time-frequency images and GLCM features

Directory of Open Access Journals (Sweden)

Karthick P.A.

2016-09-01

Full Text Available In this work, an attempt has been made to differentiate muscle non-fatigue and fatigue conditions using sEMG signals and texture representation of the time-frequency images. The sEMG signals are recorded from the biceps brachii muscle of 25 healthy adult volunteers during dynamic fatiguing contraction. The first and last curls of these signals are considered as the non-fatigue and fatigue zones, respectively. These signals are preprocessed and the time-frequency spectrum is computed using short time fourier transform (STFT. Gray-Level Co-occurrence Matrix (GLCM is extracted from low (15–45 Hz, medium (46–95 Hz and high (96–150 Hz frequency bands of the time-frequency images. Further, the features such as contrast, correlation, energy and homogeneity are calculated from the resultant matrices. The results show that the high frequency band based features are able to differentiate non-fatigue and fatigue conditions. The features such as correlation, contrast and homogeneity extracted at angles 0°, 45°, 90°, and 135° are found to be distinct with high statistical significance (p < 0.0001. Hence, this framework can be used for analysis of neuromuscular disorders.

Automated Feature Design for Time Series Classification by Genetic Programming

OpenAIRE

Harvey, Dustin Yewell

2014-01-01

Time series classification (TSC) methods discover and exploit patterns in time series and other one-dimensional signals. Although many accurate, robust classifiers exist for multivariate feature sets, general approaches are needed to extend machine learning techniques to make use of signal inputs. Numerous applications of TSC can be found in structural engineering, especially in the areas of structural health monitoring and non-destructive evaluation. Additionally, the fields of process contr...

Posterior fossa malformations: main features and limits in prenatal diagnosis

Energy Technology Data Exchange (ETDEWEB)

Garel, Catherine [Hopital d' Enfants Armand-Trousseau, Department of Radiology, Paris (France)

2010-06-15

Posterior fossa (PF) malformations are commonly observed during prenatal screening. Their understanding requires knowledge of the main steps of PF development and knowledge of normal patterns in US and MR imaging. The vast majority of PF malformations can be strongly suspected by acquiring a midline sagittal slice and a transverse slice and by systematically scrutinizing the elements of the PF: cerebellar vermis, hemispheres, brainstem, fourth ventricle, PF fluid spaces and tentorium. Analysis of cerebellar echogenicity and biometry is also useful. This review explains how to approach the diagnosis of the main PF malformations by performing these two slices and answering six key questions about the elements of the PF. The main imaging characteristics of PF malformations are also reviewed. (orig.)

A Study on Clinical and Pathologic Features in Lupus Nephritis with Mainly IgA Deposits and a Literature Review

Directory of Open Access Journals (Sweden)

Liu Hongyan

2013-01-01

Full Text Available Objective. To study the clinical and pathologic features of systemic lupus erythematosus (SLE that has atypical lupus nephritis (LN with mainly IgA deposits. Methods. We searched the SLE patients who had nephritis with mainly IgA deposits in our hospital and selected the information including clinical manifestations, laboratory tests, treatments, and prognosis. Results. From January 2009 to June 2012, 5 patients were definitely diagnosed as SLE according to both 1982 and 2009 ACR classification criteria. But renal biopsy showed that all cases had mainly IgA deposits and were free of IgG, C1q, and fibrinogen-related antigen deposits under immunofluorescent microscopy, which did not match with typical LN. There were 2 males and 3 females, aging from 31 to 64 years and with an average of years. The 5 cases had multiple-system involvements, mainly the renal system. Compared to primary IgAN, the atypical LN showed some differences: older than primary IgAN, more women than men, no previous infection history, lower incidence of serum IgA elevation, and ACL positive rate as high as 100%. Conclusion. Nephritis with mainly IgAN deposits, as an atypical LN, may be a special subtype of SLE.

Problems of Software Detection of Periodic Features in a Time ...

African Journals Online (AJOL)

Problems arise when attempts are made to extract automatically, visually obvious periodic features indicative of defects in a vibration time series for diagnosis using computers. Such problems may be interpretational in nature arising either from insufficient knowledge of the mechanism, or the convolution of the source signal ...

Main features of the pressurized component life management related R and D activity in Hungary

International Nuclear Information System (INIS)

Gillemot, F.; Oszwald, F.

1995-01-01

During the last one and half years the following main developments related to the life-time management of the NPP units took place in Hungary: 1. National project named AGNES (Advanced General New Evaluation of Safety) finished. 2. The first results of the extended surveillance program of NPP Paks has been measured. 3. The upgraded ultrasonic testing equipment used for RPV outside testing, and nozzle testing is regularly used in refuelling periods. 4. Radiation embrittlement and thermal ageing research started on RPV materials, mainly on cladding. 5. Participation in the development of the IAEA RPV ageing database. 6. Participation in IAEA pilot studies on ageing. 7. Operation of the Budapest Research Reactor, and building a new high capacity helium cooled irradiation rig. 8. Nondestructive evaluation of material ageing of a steam generator vessels. 9. A life-time calculation of Paks RPV-s. This report is a short survey of these developments. 19 refs, 4 figs

Matching-range-constrained real-time loop closure detection with CNNs features.

Science.gov (United States)

Bai, Dongdong; Wang, Chaoqun; Zhang, Bo; Yi, Xiaodong; Tang, Yuhua

2016-01-01

The loop closure detection (LCD) is an essential part of visual simultaneous localization and mapping systems (SLAM). LCD is capable of identifying and compensating the accumulation drift of localization algorithms to produce an consistent map if the loops are checked correctly. Deep convolutional neural networks (CNNs) have outperformed state-of-the-art solutions that use traditional hand-crafted features in many computer vision and pattern recognition applications. After the great success of CNNs, there has been much interest in applying CNNs features to robotic fields such as visual LCD. Some researchers focus on using a pre-trained CNNs model as a method of generating an image representation appropriate for visual loop closure detection in SLAM. However, there are many fundamental differences and challenges involved in character between simple computer vision applications and robotic applications. Firstly, the adjacent images in the dataset of loop closure detection might have more resemblance than the images that form the loop closure. Secondly, real-time performance is one of the most critical demands for robots. In this paper, we focus on making use of the feature generated by CNNs layers to implement LCD in real environment. In order to address the above challenges, we explicitly provide a value to limit the matching range of images to solve the first problem; meanwhile we get better results than state-of-the-art methods and improve the real-time performance using an efficient feature compression method.

Towards real-time detection and tracking of spatio-temporal features: Blob-filaments in fusion plasma

International Nuclear Information System (INIS)

Wu, Lingfei; Wu, Kesheng; Sim, Alex; Churchill, Michael; Choi, Jong Youl

2016-01-01

A novel algorithm and implementation of real-time identification and tracking of blob-filaments in fusion reactor data is presented. Similar spatio-temporal features are important in many other applications, for example, ignition kernels in combustion and tumor cells in a medical image. This work presents an approach for extracting these features by dividing the overall task into three steps: local identification of feature cells, grouping feature cells into extended feature, and tracking movement of feature through overlapping in space. Through our extensive work in parallelization, we demonstrate that this approach can effectively make use of a large number of compute nodes to detect and track blob-filaments in real time in fusion plasma. Here, on a set of 30GB fusion simulation data, we observed linear speedup on 1024 processes and completed blob detection in less than three milliseconds using Edison, a Cray XC30 system at NERSC.

hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction.

Science.gov (United States)

Fulcher, Ben D; Jones, Nick S

2017-11-22

Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis. Previous work addressed this problem by comparing implementations of thousands of diverse scientific time-series analysis methods in an approach termed highly comparative time-series analysis. Here, we introduce hctsa, a software tool for applying this methodological approach to data. hctsa includes an architecture for computing over 7,700 time-series features and a suite of analysis and visualization algorithms to automatically select useful and interpretable time-series features for a given application. Using exemplar applications to high-throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to quantify and understand informative structure in time-series data. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Features of calculation of reasonable time of the trial in civil cases in the context of the practice of the European court of human rights

Directory of Open Access Journals (Sweden)

Т. Цувіна

2015-11-01

Full Text Available Problem setting. European Convention of Human Rights (ECHR guarantees right to a fair trial within a reasonable time for everyone (par. 1 art. 6 ECHR. Reasonable time of the trial is an element of the right to a fair trial. One of the main directions for development of civil procedure in Ukraine is the implementation of international standards of fair trial, in particular standards of reasonable time of the trial. Recent research and publications analyses. Foreign and Ukrainian scientists such as Komarov V. V., Neshataeva T. M., Sakara N. U. and others in their works paid attention to different aspects of problems connected with the right to a fair trial within a reasonable time, but a comprehensive study devoted to a features of calculation of reasonable time of the trial taking into account the practice of the ECHR on this issue wasn’t conducted. Paper objective. Main objective of the article is to study decisions of the ECHR concerning the interpretation of Par. 1, Art. 6 ECHR and analyze features of calculation of reasonable time of the trial to make recommendations on implementation of such national level. Paper main body. As a rule, according to a practice of ECHR reasonable time of civil proceedings begins on the date on which the case is referred to a judicial authority. Thus ECHR can take as the starting point the date of a preliminary application to an administrative authority, especially when this is a prerequisite for commencement of proceedings. The end of reasonable time of the trial connected with the moment when the court decision become final or its execution. Conclusions of the research. Calculation of reasonable time of the trial in civil cases in circumstances when an application to the court was preceded by a seeking for protection from the authorities and public servants of executive power has features. In such situations a calculation of reasonable time of the trial doesn’t begin from the moment of seeking for

Relating interesting quantitative time series patterns with text events and text features

Science.gov (United States)

Wanner, Franz; Schreck, Tobias; Jentner, Wolfgang; Sharalieva, Lyubka; Keim, Daniel A.

2013-12-01

In many application areas, the key to successful data analysis is the integrated analysis of heterogeneous data. One example is the financial domain, where time-dependent and highly frequent quantitative data (e.g., trading volume and price information) and textual data (e.g., economic and political news reports) need to be considered jointly. Data analysis tools need to support an integrated analysis, which allows studying the relationships between textual news documents and quantitative properties of the stock market price series. In this paper, we describe a workflow and tool that allows a flexible formation of hypotheses about text features and their combinations, which reflect quantitative phenomena observed in stock data. To support such an analysis, we combine the analysis steps of frequent quantitative and text-oriented data using an existing a-priori method. First, based on heuristics we extract interesting intervals and patterns in large time series data. The visual analysis supports the analyst in exploring parameter combinations and their results. The identified time series patterns are then input for the second analysis step, in which all identified intervals of interest are analyzed for frequent patterns co-occurring with financial news. An a-priori method supports the discovery of such sequential temporal patterns. Then, various text features like the degree of sentence nesting, noun phrase complexity, the vocabulary richness, etc. are extracted from the news to obtain meta patterns. Meta patterns are defined by a specific combination of text features which significantly differ from the text features of the remaining news data. Our approach combines a portfolio of visualization and analysis techniques, including time-, cluster- and sequence visualization and analysis functionality. We provide two case studies, showing the effectiveness of our combined quantitative and textual analysis work flow. The workflow can also be generalized to other

Time-Trend in Epidemiological and Pathological Features of Schistosoma-Associated Bladder Cancer

International Nuclear Information System (INIS)

ZAGHLOUL, M.S.; EL-BARADIE, M.; NAZMY, M.; NOUH, A.; MONEER, M.; YOUNIS, A.

2008-01-01

To investigate the different emerging trends in the features of bladder cancer along 17 years. Patients and Methods: During a 17-year period (1988- 2004), 5071 epithelial bladder cancer patients underwent radical cystectomy at the National Cancer Institute (NCI), Cairo University, Egypt. The time was divided into 3 time periods to detect changes of the clinico pathologic features of patients in these periods. Results: There was a significant progressive increase in the patients' age with time and decrease in squamous/ transitional ratio, with transient increase in male predominance during the 2nd time period. Moreover, there was a decrease in the well differentiated (grade 1) tumor (p<0.001) and an increase in the frequency of pelvic nodal involvement (p<0.001). Transitional cell carcinoma (TCC) patients were significantly older than those with squamous cell carcinoma (SCC) (p<0.001). Progressive increase of age with time was evident in TCC, SCC and adenocarcinoma patients. Male to female ratio changed significantly in TCC and SCC. Conclusion: Time trend was confirmed with relative decrease in frequency of SCC and increase of TCC with changes in their pathological details. The differences between their characteristics and that of the Western countries are decreasing.

Haemorrhage - the main presenting feature of diverticular disease of ...

African Journals Online (AJOL)

Haemqrrhage is one of the less common presentations of diverticular disease. This retrospective 5 year study of 23 patients has identified it as the main presentation (74%) among South African blacks in whom the disease is uncommon, but emerging as a clinical problem. Women constituted a statistically significant ...

Online feature selection with streaming features.

Science.gov (United States)

Wu, Xindong; Yu, Kui; Ding, Wei; Wang, Hao; Zhu, Xingquan

2013-05-01

We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online Streaming Feature Selection (OSFS) include 1) the continuous growth of feature volumes over time, 2) a large feature space, possibly of unknown or infinite size, and 3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly. An efficient Fast-OSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on high-dimensional datasets and also with a real-world case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.

How does the predicted geomagnetic main field variation alter the thermosphere-ionosphere storm-time response?

Science.gov (United States)

Maute, A. I.; Lu, G.; Richmond, A. D.

2017-12-01

Earth's magnetic main field plays an important role in the thermosphere-ionosphere (TI) system, as well as its coupling to Earth's magnetosphere. The ionosphere consists of a weakly ionized plasma strongly influenced by the main field and embedded in the thermosphere. Therefore, ion-neutral coupling and ionospheric electrodynamics can influence the plasma distribution and neutral dynamics. There are strong longitude variations of the TI storm response. At high latitude magnetosphere-ionosphere coupling is organized by the geomagnetic main field, leading in general to stronger northern middle latitude storm time response in the American sector due to the geomagnetic dipole location. In addition, the weak geomagnetic main field in the American sector leads to larger local ExB drift and can alter the plasma densities. During geomagnetic storms the intense energy input into the high latitude region is redistributed globally, leading to thermospheric heating, wind circulation changes and alterations of the ionospheric electrodynamics. The storm time changes are measurable in the plasma density, ion drift, temperature, neutral composition, and other parameters. All these changes depend, to some degree, on the geomagnetic main field which changes on decadal time scales. In this study, we employ a forecast model of the geomagnetic main field based on data assimilation and geodynamo modeling [Aubert et al., 2015]. The main field model predicts that in 50 years the South Atlantic Anomaly is further weakened by 2 mT and drifts westward by approximately 10o. The dipole axis moves northward and westward by 2o and 6o, respectively. Simulating the March 2015 geomagnetic storm with the Thermosphere-Ionosphere Electrodynamics General Circulation Model (TIE-GCM) driven by the Assimilative Mapping of Ionospheric Electrodynamics (AMIE), we evaluate the thermosphere-ionosphere response using the geomagnetic main field of 2015, 2065, and 2115. We compare the TI response for 2015 with

Feature Space Dimensionality Reduction for Real-Time Vision-Based Food Inspection

Directory of Open Access Journals (Sweden)

Mai Moussa CHETIMA

2009-03-01

Full Text Available Machine vision solutions are becoming a standard for quality inspection in several manufacturing industries. In the processed-food industry where the appearance attributes of the product are essential to customer’s satisfaction, visual inspection can be reliably achieved with machine vision. But such systems often involve the extraction of a larger number of features than those actually needed to ensure proper quality control, making the process less efficient and difficult to tune. This work experiments with several feature selection techniques in order to reduce the number of attributes analyzed by a real-time vision-based food inspection system. Identifying and removing as much irrelevant and redundant information as possible reduces the dimensionality of the data and allows classification algorithms to operate faster. In some cases, accuracy on classification can even be improved. Filter-based and wrapper-based feature selectors are experimentally evaluated on different bakery products to identify the best performing approaches.

The influence of dose on the kinetic parameters and dosimetric features of the main thermoluminescence glow peak in α-Al{sub 2}O{sub 3}:C,Mg

Energy Technology Data Exchange (ETDEWEB)

Kalita, J.M.; Chithambo, M.L., E-mail: m.chithambo@ru.ac.za

2017-03-01

Highlights: • Influence of dose on thermoluminescence features of α-Al{sub 2}O{sub 3}:C,Mg have been studied. • Kinetic parameters of the main peak are independent of dose (0.1–100 Gy). • Dose response of the main peak: 0.1–30 Gy, superlinear; 30–100 Gy, sublinear. • Fading of the main peak: ∼22% within 2400 s. • Reproducibility: coefficient of variation in the results of 10 re-cycles: >0.071%. - Abstract: The influence of dose (0.1–100 Gy) on the kinetic parameters and the dosimetric features of the main glow peak of α-Al{sub 2}O{sub 3}:C,Mg have been investigated. Thermoluminescence (TL) measured at 1 °C/s shows a very high intensity glow peak at 161 °C and six secondary peaks at 42, 72, 193, 279, 330, 370 °C respectively. Analysis shows that the main peak follows first order kinetics irrespective of the irradiation dose. The activation energy is found to be consistent at 1.37 eV and the frequency factor is of the order of 10{sup 14} s{sup −1} for any dose between 0.1 and 100 Gy. Further, the analysis for thermal quenching of the main peak of 0.1 Gy irradiated sample shows that the activation energy for thermal quenching is (0.94 ± 0.04) eV. Regarding the dosimetric features of α-Al{sub 2}O{sub 3}:C,Mg, the dose response of the main peak is superlinear within 0.1 to 30 Gy of beta dose and then it becomes sublinear up to 100 Gy. Fading analysis shows that the intensity of the main peak drops to ∼22% of its initial value within 2400 s after irradiation and thereafter to ∼14% within 64,800 s. Analysis of the reproducibility shows that the coefficient of variation in the results for 10 identical TL measurements show that reproducibility improves with increase in dose.

Main features of anthropogenic inner-urban soils in Szeged, Hungary

Science.gov (United States)

Puskás, Irén.; Farsang, Andrea

2010-05-01

At the beginning of the 21st century, due to the intensive urbanization it is necessary to gather more and more information on altered physical, chemical and biological parameters of urban soils in order to ensure their suitable management and protection for appropriate living conditions. Nowadays, these measures are very relevant since negative environmental effects can modify the soil forming factors in cities. Szeged, the 4th largest city of Hungary, proved to be an ideal sampling area for the research of urban soils since its original surface has been altered by intensive anthropogenic activities. The main objectives of my research are the investigation, description and evaluation of the altered soils in Szeged. For the physical and chemical analysis (humus, nitrogen, carbonate content, heavy metals, pH, artefacts etc.) of soils 124 samples were taken from the horizons of 25 profiles in Szeged and its peripherals (as control samples). The profiles were sampled at sites affected by different extent of artificial infill according to infill maps (1. profiles fully made up of infill; 2. so-called mixed profiles consisting of considerable amount of infill material and buried soil horizons; 3. natural profiles located in the peripherals of the city). With the help of the above-mentioned parameters, the studied soils of Szeged were assigned into the classification system of WRB(2006), which classifies the soils of urban and industrial areas as an individual soil group (under the term Technosols) for the first time. In accordance with the WRB(2006) nomenclature three main soil types can be identified in Szeged with respect to the degree of human influence: profiles slightly influenced, strongly modified, completely altered by human activities. During this poster, we present the peculiarities of typical urban profiles strongly and completely altered by human influence. Most profiles were placed into the group of Technosols due to the considerable transformation of their

Fault diagnosis for analog circuits utilizing time-frequency features and improved VVRKFA

Science.gov (United States)

He, Wei; He, Yigang; Luo, Qiwu; Zhang, Chaolong

2018-04-01

This paper proposes a novel scheme for analog circuit fault diagnosis utilizing features extracted from the time-frequency representations of signals and an improved vector-valued regularized kernel function approximation (VVRKFA). First, the cross-wavelet transform is employed to yield the energy-phase distribution of the fault signals over the time and frequency domain. Since the distribution is high-dimensional, a supervised dimensionality reduction technique—the bilateral 2D linear discriminant analysis—is applied to build a concise feature set from the distributions. Finally, VVRKFA is utilized to locate the fault. In order to improve the classification performance, the quantum-behaved particle swarm optimization technique is employed to gradually tune the learning parameter of the VVRKFA classifier. The experimental results for the analog circuit faults classification have demonstrated that the proposed diagnosis scheme has an advantage over other approaches.

Main physics features driving design concept and physics design constraints

International Nuclear Information System (INIS)

Fujisawa, Noboru; Sugihara, Masayoshi; Yamamoto, Shin

1987-07-01

Major physics design philosophies are described, which are essential bases for a plasma design and may have significant impacts on a reactor design concept. Those design philosophies are classified into two groups, physics design drivers and physics design constraints. The design drivers are featured by the fact that a designer is free to choose and the choice may be guided by his opinion, such as ignition, a pulse length, an operation scenario, etc.. The design constraints may follow a physical law, such as plasma confinement, β-limit, density limit, and so on. (author)

Fault Diagnosis of Rotating Machinery Based on Multisensor Information Fusion Using SVM and Time-Domain Features

Directory of Open Access Journals (Sweden)

Ling-li Jiang

2014-01-01

Full Text Available Multisensor information fusion, when applied to fault diagnosis, the time-space scope, and the quantity of information are expanded compared to what could be acquired by a single sensor, so the diagnostic object can be described more comprehensively. This paper presents a methodology of fault diagnosis in rotating machinery using multisensor information fusion that all the features are calculated using vibration data in time domain to constitute fusional vector and the support vector machine (SVM is used for classification. The effectiveness of the presented methodology is tested by three case studies: diagnostic of faulty gear, rolling bearing, and identification of rotor crack. For each case study, the sensibilities of the features are analyzed. The results indicate that the peak factor is the most sensitive feature in the twelve time-domain features for identifying gear defect, and the mean, amplitude square, root mean square, root amplitude, and standard deviation are all sensitive for identifying gear, rolling bearing, and rotor crack defect comparatively.

State-of-the-art of wind turbine design codes: main features overview for cost-effective generation

Energy Technology Data Exchange (ETDEWEB)

Molenaar, D-P.; Dijkstra, S. [Delft University of Technology (Netherlands). Mechanical Engineering Systems and Control Group

1999-07-01

For successful large-scale application of wind energy, the price of electricity generated by wind turbines should decrease. Model-based control can be important since it has the potential to reduce fatigue loads, while simultaneously maintaining a desired amount of energy production. The controller synthesis, however, requires a mathematical model describing the most important dynamics of the complete wind turbine. In the wind energy community there is a wide variety in codes used to model a wind turbine's dynamic behaviour or to carry out design calculations. In this paper, the main features of the state-of-the-art wind turbine design codes have been investigated in order to judge the appropriateness of using one of these for the modeling, identification and control of flexible, variable speed wind turbines. It can be concluded that, although the sophistication of the design codes has increased enormously over the last two decades, they are, in general, not suitable for the design, and easy implementation of optimal operating strategies.

The main features of control and operation of steam turbines at nuclear power plants

International Nuclear Information System (INIS)

Czinkoczky, B.

1981-01-01

The output and speed control of steam turbines at nuclear power plants as well as the combination of both controls are reviewed and evaluated. At the same time the tasks of unit control at nuclear power plants, the control of steady main steam pressure and medium pressure of primary circuit, further the connection of reactor and turbine controls and the self-controlling properties of pressurized water reactor are dealt with. Hydraulic and electro-hydraulic speed control, the connection of cach-up dampers and speed control and the application of electro-hydraulic signal converters are discussed. The accomplishment of protection is also described. (author)

A Dynamic Approach to School Improvement : Main Features and Impact

NARCIS (Netherlands)

Creemers, Bert; Kyriakides, L.; Antoniou, P.

2013-01-01

This paper refers to the dynamic approach to school improvement (DASI) which attempts to contribute to the merging of educational effectiveness research and school improvement. The main underlying assumptions and the implementation phases of DASI are discussed. Moreover, a study aiming to compare

De-stoning technology for improving olive oil nutritional and sensory features: The right idea at the wrong time.

Science.gov (United States)

Restuccia, Donatella; Clodoveo, Maria Lisa; Corbo, Filomena; Loizzo, Monica Rosa

2018-04-01

De-stoning technology has been introduced in the olive oil sector more than twenty years ago. It has not gained momentum because, sometimes, innovative ideas are not accepted since they are suggested at the wrong time or under the wrong circumstances. Virgin olive oil (VOO) is one of the most popular functional foods, mainly due to its antioxidant properties. These features, as well as other nutritional characteristics are generally enhanced by the de-stoning process. However, despite the improvement of the nutritional value, in the past the de-stoned oil didn't achieve marketing success mainly in relation to technological limitations (i.e. low oil yield). Only in recent years healthy properties became an element able to influence consumers' behavior, overcoming the limit of low oil yields and attracting the attention of olive oil producers. An analysis of the advantages, in terms of product quality and process sustainability, is given in this review. Here, for the first time, the fragmented results reported in literature are critically analyzed underlining the contradictions reported by different authors showing the main reasons for the unlucky fate of this technology in the industrial sector. In the final section the challenges, that future research must focus on, are presented, including emerging technologies in VOO processing. Literature data, for the first time discussed here exhaustively, show that de-stoning technology is a mechanical strategy useful to increase the nutritional and the sensory quality of the product. Moreover, it reduces the depletion of natural resources obtaining a selective crushing of the drupe by removing the stones from the olive paste so increasing the sustainability and efficiency of VOO extraction plants. Copyright © 2018 Elsevier Ltd. All rights reserved.

Main engineering features driving design concept and engineering design constraints

International Nuclear Information System (INIS)

Saito, Ryusei; Kobayashi, Takeshi; Yamada, Masao

1987-09-01

Major engineering design philosophies are described, which are essential bases for an engineering design and may have significant impacts on a reactor design concept. Those design philosophies are classified into two groups, engineering design drivers and engineering design constraints. The design drivers are featured by the fact that a designer is free to choose and the choice may be guided by his opinion, such as coil system, a mechanical configuration, a tritium breeding scenario, etc.. The design constraints may follow a natural law or engineering limit, such as material strength, coil current density, and so on. (author)

Analytical Features: A Knowledge-Based Approach to Audio Feature Generation

Directory of Open Access Journals (Sweden)

Pachet François

2009-01-01

Full Text Available We present a feature generation system designed to create audio features for supervised classification tasks. The main contribution to feature generation studies is the notion of analytical features (AFs, a construct designed to support the representation of knowledge about audio signal processing. We describe the most important aspects of AFs, in particular their dimensional type system, on which are based pattern-based random generators, heuristics, and rewriting rules. We show how AFs generalize or improve previous approaches used in feature generation. We report on several projects using AFs for difficult audio classification tasks, demonstrating their advantage over standard audio features. More generally, we propose analytical features as a paradigm to bring raw signals into the world of symbolic computation.

Time-Contrastive Learning Based DNN Bottleneck Features for Text-Dependent Speaker Verification

DEFF Research Database (Denmark)

Sarkar, Achintya Kumar; Tan, Zheng-Hua

2017-01-01

In this paper, we present a time-contrastive learning (TCL) based bottleneck (BN) feature extraction method for speech signals with an application to text-dependent (TD) speaker verification (SV). It is well-known that speech signals exhibit quasi-stationary behavior in and only in a short interval......, and the TCL method aims to exploit this temporal structure. More specifically, it trains deep neural networks (DNNs) to discriminate temporal events obtained by uniformly segmenting speech signals, in contrast to existing DNN based BN feature extraction methods that train DNNs using labeled data...... to discriminate speakers or pass-phrases or phones or a combination of them. In the context of speaker verification, speech data of fixed pass-phrases are used for TCL-BN training, while the pass-phrases used for TCL-BN training are excluded from being used for SV, so that the learned features can be considered...

Drifter Observations of the Gulf of Maine Coastal Current.

Science.gov (United States)

Manning, J P; McGillicuddy, D J; Pettigrew, N R; Churchill, J H; Incze, L S

2009-04-15

Two-hundred and twenty seven satellite-tracked drifters were deployed in the Gulf of Maine (GoM) from 1988 to 2007, primarily during spring and summer. The archive of tracks includes over 100,000 kilometers logged thus far. Statistics such as transit times, mean velocities, response to wind events, and preferred pathways are compiled for various areas of the coastal GoM. We compare Lagrangian flow with Eulerian estimates from near-by moorings and evaluate drifter trajectories using Ekman theory and 3-D ocean circulation models. Results indicate that the Gulf of Maine Coastal Current is a strong and persistent feature centered on the 94 ± 23 meter isobath, but that particles: a) deviate from the seasonal-mean core fairly regularly, and are often re-entrained; b) follow a slower (9 cm/s), less-constrained path in the western portion off the coast of Maine relative to the eastern (16 cm/s) section; and c) can be affected by wind events and small scale baroclinic structures. Residence times calculated for each ½ degree grid cell throughout the GoM depict some regions (Eastern Maine and Western Nova Scotia) as being relatively steady, flow-through systems, while others (Penobscot, Great South Channel) have more variable, branching pathways. Travel times for drifters that are retained within the coastal current along the entire western side of the Gulf of Maine are typically less than two months (55 days).

Ageing and feature binding in visual working memory: The role of presentation time.

Science.gov (United States)

Rhodes, Stephen; Parra, Mario A; Logie, Robert H

2016-01-01

A large body of research has clearly demonstrated that healthy ageing is accompanied by an associative memory deficit. Older adults exhibit disproportionately poor performance on memory tasks requiring the retention of associations between items (e.g., pairs of unrelated words). In contrast to this robust deficit, older adults' ability to form and temporarily hold bound representations of an object's surface features, such as colour and shape, appears to be relatively well preserved. However, the findings of one set of experiments suggest that older adults may struggle to form temporary bound representations in visual working memory when given more time to study objects. However, these findings were based on between-participant comparisons across experimental paradigms. The present study directly assesses the role of presentation time in the ability of younger and older adults to bind shape and colour in visual working memory using a within-participant design. We report new evidence that giving older adults longer to study memory objects does not differentially affect their immediate memory for feature combinations relative to individual features. This is in line with a growing body of research suggesting that there is no age-related impairment in immediate memory for colour-shape binding.

Robust and Reversible Audio Watermarking by Modifying Statistical Features in Time Domain

Directory of Open Access Journals (Sweden)

Shijun Xiang

2017-01-01

Full Text Available Robust and reversible watermarking is a potential technique in many sensitive applications, such as lossless audio or medical image systems. This paper presents a novel robust reversible audio watermarking method by modifying the statistic features in time domain in the way that the histogram of these statistical values is shifted for data hiding. Firstly, the original audio is divided into nonoverlapped equal-sized frames. In each frame, the use of three samples as a group generates a prediction error and a statistical feature value is calculated as the sum of all the prediction errors in the frame. The watermark bits are embedded into the frames by shifting the histogram of the statistical features. The watermark is reversible and robust to common signal processing operations. Experimental results have shown that the proposed method not only is reversible but also achieves satisfactory robustness to MP3 compression of 64 kbps and additive Gaussian noise of 35 dB.

Real-time ultrasound image classification for spine anesthesia using local directional Hadamard features.

Science.gov (United States)

Pesteie, Mehran; Abolmaesumi, Purang; Ashab, Hussam Al-Deen; Lessoway, Victoria A; Massey, Simon; Gunka, Vit; Rohling, Robert N

2015-06-01

Injection therapy is a commonly used solution for back pain management. This procedure typically involves percutaneous insertion of a needle between or around the vertebrae, to deliver anesthetics near nerve bundles. Most frequently, spinal injections are performed either blindly using palpation or under the guidance of fluoroscopy or computed tomography. Recently, due to the drawbacks of the ionizing radiation of such imaging modalities, there has been a growing interest in using ultrasound imaging as an alternative. However, the complex spinal anatomy with different wave-like structures, affected by speckle noise, makes the accurate identification of the appropriate injection plane difficult. The aim of this study was to propose an automated system that can identify the optimal plane for epidural steroid injections and facet joint injections. A multi-scale and multi-directional feature extraction system to provide automated identification of the appropriate plane is proposed. Local Hadamard coefficients are obtained using the sequency-ordered Hadamard transform at multiple scales. Directional features are extracted from local coefficients which correspond to different regions in the ultrasound images. An artificial neural network is trained based on the local directional Hadamard features for classification. The proposed method yields distinctive features for classification which successfully classified 1032 images out of 1090 for epidural steroid injection and 990 images out of 1052 for facet joint injection. In order to validate the proposed method, a leave-one-out cross-validation was performed. The average classification accuracy for leave-one-out validation was 94 % for epidural and 90 % for facet joint targets. Also, the feature extraction time for the proposed method was 20 ms for a native 2D ultrasound image. A real-time machine learning system based on the local directional Hadamard features extracted by the sequency-ordered Hadamard transform for

Post-Soviet transformation of bureaucracy in Lithuania: main features and trends

OpenAIRE

Pivoras, Saulius

2008-01-01

The purpose of this article is to analyze the reforms and development of public administration and public bureaucracy in Lithuania from the prism of the post-Soviet transformation concept. In other words, the effort is to establish a continuation of the features of the Soviet bureaucratic - administrative system, to the extent these can be discussed, and their influence on the public bureaucracy of the independent Republic of Lithuania. It is being ascertained that the purpose of the reforms ...

Gearbox fault diagnosis based on time-frequency domain synchronous averaging and feature extraction technique

Science.gov (United States)

Zhang, Shengli; Tang, Jiong

2016-04-01

Gearbox is one of the most vulnerable subsystems in wind turbines. Its healthy status significantly affects the efficiency and function of the entire system. Vibration based fault diagnosis methods are prevalently applied nowadays. However, vibration signals are always contaminated by noise that comes from data acquisition errors, structure geometric errors, operation errors, etc. As a result, it is difficult to identify potential gear failures directly from vibration signals, especially for the early stage faults. This paper utilizes synchronous averaging technique in time-frequency domain to remove the non-synchronous noise and enhance the fault related time-frequency features. The enhanced time-frequency information is further employed in gear fault classification and identification through feature extraction algorithms including Kernel Principal Component Analysis (KPCA), Multilinear Principal Component Analysis (MPCA), and Locally Linear Embedding (LLE). Results show that the LLE approach is the most effective to classify and identify different gear faults.

Resonance-Based Time-Frequency Manifold for Feature Extraction of Ship-Radiated Noise

Science.gov (United States)

Yan, Jiaquan; Sun, Haixin; Chen, Hailan; Junejo, Naveed Ur Rehman; Cheng, En

2018-01-01

In this paper, a novel time-frequency signature using resonance-based sparse signal decomposition (RSSD), phase space reconstruction (PSR), time-frequency distribution (TFD) and manifold learning is proposed for feature extraction of ship-radiated noise, which is called resonance-based time-frequency manifold (RTFM). This is suitable for analyzing signals with oscillatory, non-stationary and non-linear characteristics in a situation of serious noise pollution. Unlike the traditional methods which are sensitive to noise and just consider one side of oscillatory, non-stationary and non-linear characteristics, the proposed RTFM can provide the intact feature signature of all these characteristics in the form of a time-frequency signature by the following steps: first, RSSD is employed on the raw signal to extract the high-oscillatory component and abandon the low-oscillatory component. Second, PSR is performed on the high-oscillatory component to map the one-dimensional signal to the high-dimensional phase space. Third, TFD is employed to reveal non-stationary information in the phase space. Finally, manifold learning is applied to the TFDs to fetch the intrinsic non-linear manifold. A proportional addition of the top two RTFMs is adopted to produce the improved RTFM signature. All of the case studies are validated on real audio recordings of ship-radiated noise. Case studies of ship-radiated noise on different datasets and various degrees of noise pollution manifest the effectiveness and robustness of the proposed method. PMID:29565288

A Two-Dimensional Solar Tracking Stationary Guidance Method Based on Feature-Based Time Series

Directory of Open Access Journals (Sweden)

Keke Zhang

2018-01-01

Full Text Available The amount of satellite energy acquired has a direct impact on operational capacities of the satellite. As for practical high functional density microsatellites, solar tracking guidance design of solar panels plays an extremely important role. Targeted at stationary tracking problems incurred in a new system that utilizes panels mounted in the two-dimensional turntable to acquire energies to the greatest extent, a two-dimensional solar tracking stationary guidance method based on feature-based time series was proposed under the constraint of limited satellite attitude coupling control capability. By analyzing solar vector variation characteristics within an orbit period and solar vector changes within the whole life cycle, such a method could be adopted to establish a two-dimensional solar tracking guidance model based on the feature-based time series to realize automatic switching of feature-based time series and stationary guidance under the circumstance of different β angles and the maximum angular velocity control, which was applicable to near-earth orbits of all orbital inclination. It was employed to design a two-dimensional solar tracking stationary guidance system, and a mathematical simulation for guidance performance was carried out in diverse conditions under the background of in-orbit application. The simulation results show that the solar tracking accuracy of two-dimensional stationary guidance reaches 10∘ and below under the integrated constraints, which meet engineering application requirements.

Dhruva: Main design features, operational experience and utilization

Energy Technology Data Exchange (ETDEWEB)

Agarwal, S.K. [Reactor Group, BARC, Trombay, Mumbai 400085 (India)]. E-mail: swarajagarwal2000@yahoo.com; Karhadkar, C.G. [Reactor Group, BARC, Trombay, Mumbai 400085 (India); Zope, A.K. [Reactor Group, BARC, Trombay, Mumbai 400085 (India); Singh, Kanchhi [Reactor Group, BARC, Trombay, Mumbai 400085 (India)

2006-04-15

Dhruva is a product of technological initiatives taken during mid seventies when a need was felt for another research reactor having a high neutron flux to meet the growing demands of research and development in the frontier areas of science and engineering. In addition production of radioisotopes of high specific activity and the diverse requirements of a broad based user community had to be synthesized into a viable system, which could be engineered within the limited means available in the country. This high neutron flux reactor was designed, constructed and commissioned entirely by Indian scientists and engineers and it reflects the country's resolve to achieve self-reliance in the nuclear reactor technology. Dhruva is a 100 MW (thermal) research reactor with metallic natural uranium as fuel, heavy water as moderator, coolant and reflector, giving a maximum thermal neutron flux of 1.8 x 10{sup 14} n/cm{sup 2}/s. Since its first criticality on 8th August 1985, a number of experimental facilities have been added which have proven to be highly attractive for universities and industrial researchers for their scientific merits in various fields. One of the major utilization areas has been the neutron beam research using several neutron spectrometers, all of which were built in-house. A guide tube facility comprising of two neutron guides and another experimental set-up with a multi-instrument beam line have enabled further enhancement of the utilization of this National Facility by the academic institutions in the country. Production of radioisotopes of high specific activity and in increased quantity has fulfilled growing demands for many applications. The write-up provides an overview of the reactor covering its design; layout, safety features, utilization and operating experience along with description of some of the specific experimental facilities.

Time-frequency feature analysis and recognition of fission neutrons signal based on support vector machine

International Nuclear Information System (INIS)

Jin Jing; Wei Biao; Feng Peng; Tang Yuelin; Zhou Mi

2010-01-01

Based on the interdependent relationship between fission neutrons ( 252 Cf) and fission chain ( 235 U system), the paper presents the time-frequency feature analysis and recognition in fission neutron signal based on support vector machine (SVM) through the analysis on signal characteristics and the measuring principle of the 252 Cf fission neutron signal. The time-frequency characteristics and energy features of the fission neutron signal are extracted by using wavelet decomposition and de-noising wavelet packet decomposition, and then applied to training and classification by means of support vector machine based on statistical learning theory. The results show that, it is effective to obtain features of nuclear signal via wavelet decomposition and de-noising wavelet packet decomposition, and the latter can reflect the internal characteristics of the fission neutron system better. With the training accomplished, the SVM classifier achieves an accuracy rate above 70%, overcoming the lack of training samples, and verifying the effectiveness of the algorithm. (authors)

Main issues of the licensing of the Creys-Malville's LMFBR

International Nuclear Information System (INIS)

Natta, M.; Dufresne, J.; Jaffres, R.; Meyer-Heine, A.; Orzoni, G.

1986-06-01

This report presents the main features of general interest concerning the licensing procedures, procedures which are still in progress. The design studies and the construction of the Creys-Malville power plant were submitted to several assessments which allowed to verify the correct realisation of the plant and to intervene in due time on important issues. All related aspects of the start-up tests are followed by the safety authorities in satisfactory conditions without increasing significantly the applicant own duties

Mesoblastic nephroma: Pathological features

African Journals Online (AJOL)

N.M. El-Badawy

determined mainly by its histologic type, we found it worthwhile to elaborate more on the gross and microscopic features of ... behavior of mesoblastic nephroma is determined mainly by its his- .... However, it exhibits a nodular growth pattern at.

Testing the structure of earthquake networks from multivariate time series of successive main shocks in Greece

Science.gov (United States)

Chorozoglou, D.; Kugiumtzis, D.; Papadimitriou, E.

2018-06-01

The seismic hazard assessment in the area of Greece is attempted by studying the earthquake network structure, such as small-world and random. In this network, a node represents a seismic zone in the study area and a connection between two nodes is given by the correlation of the seismic activity of two zones. To investigate the network structure, and particularly the small-world property, the earthquake correlation network is compared with randomized ones. Simulations on multivariate time series of different length and number of variables show that for the construction of randomized networks the method randomizing the time series performs better than methods randomizing directly the original network connections. Based on the appropriate randomization method, the network approach is applied to time series of earthquakes that occurred between main shocks in the territory of Greece spanning the period 1999-2015. The characterization of networks on sliding time windows revealed that small-world structure emerges in the last time interval, shortly before the main shock.

Automatic sleep staging using empirical mode decomposition, discrete wavelet transform, time-domain, and nonlinear dynamics features of heart rate variability signals.

Science.gov (United States)

Ebrahimi, Farideh; Setarehdan, Seyed-Kamaledin; Ayala-Moyeda, Jose; Nazeran, Homer

2013-10-01

The conventional method for sleep staging is to analyze polysomnograms (PSGs) recorded in a sleep lab. The electroencephalogram (EEG) is one of the most important signals in PSGs but recording and analysis of this signal presents a number of technical challenges, especially at home. Instead, electrocardiograms (ECGs) are much easier to record and may offer an attractive alternative for home sleep monitoring. The heart rate variability (HRV) signal proves suitable for automatic sleep staging. Thirty PSGs from the Sleep Heart Health Study (SHHS) database were used. Three feature sets were extracted from 5- and 0.5-min HRV segments: time-domain features, nonlinear-dynamics features and time-frequency features. The latter was achieved by using empirical mode decomposition (EMD) and discrete wavelet transform (DWT) methods. Normalized energies in important frequency bands of HRV signals were computed using time-frequency methods. ANOVA and t-test were used for statistical evaluations. Automatic sleep staging was based on HRV signal features. The ANOVA followed by a post hoc Bonferroni was used for individual feature assessment. Most features were beneficial for sleep staging. A t-test was used to compare the means of extracted features in 5- and 0.5-min HRV segments. The results showed that the extracted features means were statistically similar for a small number of features. A separability measure showed that time-frequency features, especially EMD features, had larger separation than others. There was not a sizable difference in separability of linear features between 5- and 0.5-min HRV segments but separability of nonlinear features, especially EMD features, decreased in 0.5-min HRV segments. HRV signal features were classified by linear discriminant (LD) and quadratic discriminant (QD) methods. Classification results based on features from 5-min segments surpassed those obtained from 0.5-min segments. The best result was obtained from features using 5-min HRV

Cost reduction of LWRs - The main features

International Nuclear Information System (INIS)

Board, J.A.; Norman, D.

1991-01-01

For effective generation cost reductions to be achieved while maintaining safety levels, the impact of each of the above element and their interactions must be fully assessed, together with the effects of uncertainty on commercial risk. the amount of interest during construction which must be paid can be minimised by adopting designs, output ratings (unit size), and construction methods which minimise construction time. However this must be done without unduly increasing specific capital cost. Total capital costs can be reduced by sharing design and project launching costs and front-end design and licensing costs, across a series of identical plants. The paper is reviewing costs and performance factors such as those above with the aim of identifying the strategies which might be necessary within and between countries in order to create an environment which would enable cost reductions on LWRs to be made. (author)

An algorithm for automatic detection of chromosome aberrations induced by radiation using features of gray level profile across the main axis of chromosome image

International Nuclear Information System (INIS)

Kawashima, Hironao; Imai, Katsuhiro; Fukuoka, Hideya; Yamamoto, Mikio; Hayata, Isamu.

1990-01-01

A simple algorithm for detecting chromosome aberrations induced by radiation is developed. Microscopic images of conventional Giemsa stained chromosomes of rearranged chromosomes (abnormal chromosomes) including dicentric chromosomes, ordinary acentric fragments, small acentric fragments, and acentric rings are used as samples. Variation of width along the main axis and gray level profile across the main axis of the chromosome image are used as features for classification. In 7 microscopic images which include 257 single chromosomes, 90.0% (231 chromosomes) are correctly classified into 6 categories and 23 of 26 abnormal chromosomes are correctly identified. As a result of discrimination between a normal and an abnormal chromosome, 95.3% of abnormal chromosomes are detected. (author)

SU-F-R-20: Image Texture Features Correlate with Time to Local Failure in Lung SBRT Patients

Energy Technology Data Exchange (ETDEWEB)

Andrews, M; Abazeed, M; Woody, N; Stephans, K; Videtic, G; Xia, P; Zhuang, T [The Cleveland Clinic Foundation, Cleveland, OH (United States)

2016-06-15

Purpose: To explore possible correlation between CT image-based texture and histogram features and time-to-local-failure in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT).Methods and Materials: From an IRB-approved lung SBRT registry for patients treated between 2009–2013 we selected 48 (20 male, 28 female) patients with local failure. Median patient age was 72.3±10.3 years. Mean time to local failure was 15 ± 7.1 months. Physician-contoured gross tumor volumes (GTV) on the planning CT images were processed and 3D gray-level co-occurrence matrix (GLCM) based texture and histogram features were calculated in Matlab. Data were exported to R and a multiple linear regression model was used to examine the relationship between texture features and time-to-local-failure. Results: Multiple linear regression revealed that entropy (p=0.0233, multiple R2=0.60) from GLCM-based texture analysis and the standard deviation (p=0.0194, multiple R2=0.60) from the histogram-based features were statistically significantly correlated with the time-to-local-failure. Conclusion: Image-based texture analysis can be used to predict certain aspects of treatment outcomes of NSCLC patients treated with SBRT. We found entropy and standard deviation calculated for the GTV on the CT images displayed a statistically significant correlation with and time-to-local-failure in lung SBRT patients.

SU-F-R-20: Image Texture Features Correlate with Time to Local Failure in Lung SBRT Patients

International Nuclear Information System (INIS)

Andrews, M; Abazeed, M; Woody, N; Stephans, K; Videtic, G; Xia, P; Zhuang, T

2016-01-01

Purpose: To explore possible correlation between CT image-based texture and histogram features and time-to-local-failure in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT).Methods and Materials: From an IRB-approved lung SBRT registry for patients treated between 2009–2013 we selected 48 (20 male, 28 female) patients with local failure. Median patient age was 72.3±10.3 years. Mean time to local failure was 15 ± 7.1 months. Physician-contoured gross tumor volumes (GTV) on the planning CT images were processed and 3D gray-level co-occurrence matrix (GLCM) based texture and histogram features were calculated in Matlab. Data were exported to R and a multiple linear regression model was used to examine the relationship between texture features and time-to-local-failure. Results: Multiple linear regression revealed that entropy (p=0.0233, multiple R2=0.60) from GLCM-based texture analysis and the standard deviation (p=0.0194, multiple R2=0.60) from the histogram-based features were statistically significantly correlated with the time-to-local-failure. Conclusion: Image-based texture analysis can be used to predict certain aspects of treatment outcomes of NSCLC patients treated with SBRT. We found entropy and standard deviation calculated for the GTV on the CT images displayed a statistically significant correlation with and time-to-local-failure in lung SBRT patients.

Collins' bypass for the main ring

International Nuclear Information System (INIS)

Ohnuma, S.

1982-01-01

Design of the bypass for the main ring at Fermilab is discussed. Specific design features discussed include space, path length, geometric closure, matching of betatron functions, and external dispersion. Bypass parameters are given

Time Domain Feature Extraction Technique for earth's electric field signal prior to the Earthquake

International Nuclear Information System (INIS)

Astuti, W; Sediono, W; Akmeliawati, R; Salami, M J E

2013-01-01

Earthquake is one of the most destructive of natural disasters that killed many people and destroyed a lot of properties. By considering these catastrophic effects, it is highly important of knowing ahead of earthquakes in order to reduce the number of victims and material losses. Earth's electric field is one of the features that can be used to predict earthquakes (EQs), since it has significant changes in the amplitude of the signal prior to the earthquake. This paper presents a detailed analysis of the earth's electric field due to earthquakes which occurred in Greece, between January 1, 2008 and June 30, 2008. In that period of time, 13 earthquakes had occurred. 6 of them were recorded with magnitudes greater than Ms=5R (5R), while 7 of them were recorded with magnitudes greater than Ms=6R (6R). Time domain feature extraction technique is applied to analyze the 1st significant changes in the earth's electric field prior to the earthquake. Two different time domain feature extraction techniques are applied in this work, namely Simple Square Integral (SSI) and Root Mean Square (RMS). The 1st significant change of the earth's electric field signal in each of monitoring sites is extracted using those two techniques. The feature extraction result can be used as input parameter for an earthquake prediction system

A frequency and pulse-width co-modulation strategy for transcutaneous neuromuscular electrical stimulation based on sEMG time-domain features

Science.gov (United States)

Zhou, Yu-Xuan; Wang, Hai-Peng; Bao, Xue-Liang; Lü, Xiao-Ying; Wang, Zhi-Gong

2016-02-01

Objective. Surface electromyography (sEMG) is often used as a control signal in neuromuscular electrical stimulation (NMES) systems to enhance the voluntary control and proprioceptive sensory feedback of paralyzed patients. Most sEMG-controlled NMES systems use the envelope of the sEMG signal to modulate the stimulation intensity (current amplitude or pulse width) with a constant frequency. The aims of this study were to develop a strategy that co-modulates frequency and pulse width based on features of the sEMG signal and to investigate the torque-reproduction performance and the level of fatigue resistance achieved with our strategy. Approach. We examined the relationships between wrist torque and two stimulation parameters (frequency and pulse width) and between wrist torque and two sEMG time-domain features (mean absolute value (MAV) and number of slope sign changes (NSS)) in eight healthy volunteers. By using wrist torque as an intermediate variable, customized and generalized transfer functions were constructed to convert the two features of the sEMG signal into the two stimulation parameters, thereby establishing a MAV/NSS dual-coding (MNDC) algorithm. Wrist torque reproduction performance was assessed by comparing the torque generated by the algorithms with that originally recorded during voluntary contractions. Muscle fatigue was assessed by measuring the decline percentage of the peak torque and by comparing the torque time integral of the response to test stimulation trains before and after fatigue sessions. Main Results. The MNDC approach could produce a wrist torque that closely matched the voluntary wrist torque. In addition, a smaller decay in the wrist torque was observed after the MNDC-coded fatigue stimulation was applied than after stimulation using pulse-width modulation alone. Significance. Compared with pulse-width modulation stimulation strategies that are based on sEMG detection, the MNDC strategy is more effective for both voluntary muscle

Mutual Information Based Dynamic Integration of Multiple Feature Streams for Robust Real-Time LVCSR

Science.gov (United States)

Sato, Shoei; Kobayashi, Akio; Onoe, Kazuo; Homma, Shinichi; Imai, Toru; Takagi, Tohru; Kobayashi, Tetsunori

We present a novel method of integrating the likelihoods of multiple feature streams, representing different acoustic aspects, for robust speech recognition. The integration algorithm dynamically calculates a frame-wise stream weight so that a higher weight is given to a stream that is robust to a variety of noisy environments or speaking styles. Such a robust stream is expected to show discriminative ability. A conventional method proposed for the recognition of spoken digits calculates the weights front the entropy of the whole set of HMM states. This paper extends the dynamic weighting to a real-time large-vocabulary continuous speech recognition (LVCSR) system. The proposed weight is calculated in real-time from mutual information between an input stream and active HMM states in a searchs pace without an additional likelihood calculation. Furthermore, the mutual information takes the width of the search space into account by calculating the marginal entropy from the number of active states. In this paper, we integrate three features that are extracted through auditory filters by taking into account the human auditory system's ability to extract amplitude and frequency modulations. Due to this, features representing energy, amplitude drift, and resonant frequency drifts, are integrated. These features are expected to provide complementary clues for speech recognition. Speech recognition experiments on field reports and spontaneous commentary from Japanese broadcast news showed that the proposed method reduced error words by 9.2% in field reports and 4.7% in spontaneous commentaries relative to the best result obtained from a single stream.

Long-Range Untethered Real-Time Live Gas Main Robotic Inspection System

Energy Technology Data Exchange (ETDEWEB)

Hagen Schempf; Daphne D' Zurko

2004-10-31

Under funding from the Department of Energy (DOE) and the Northeast Gas Association (NGA), Carnegie Mellon University (CMU) developed an untethered, wireless remote controlled inspection robot dubbed Explorer. The project entailed the design and prototyping of a wireless self-powered video-inspection robot capable of accessing live 6- and 8-inch diameter cast-iron and steel mains, while traversing turns and Ts and elbows under real-time control with live video feedback to an operator. The design is that of a segmented actively articulated and wheel-leg powered robot design, with fisheye imaging capability and self-powered battery storage and wireless real-time communication link. The prototype was functionally tested in an above ground pipe-network, in order to debug all mechanical, electrical and software subsystems, and develop the necessary deployment and retrieval, as well as obstacle-handling scripts. A pressurized natural gas test-section was used to certify it for operation in natural gas at up to 60 psig. Two subsequent live-main field-trials in both cast-iron and steel pipe, demonstrated its ability to be safely launched, operated and retrieved under real-world conditions. The system's ability to safely and repeatably exidrecover from angled and vertical launchers, traverse multi-thousand foot long pipe-sections, make T and varied-angle elbow-turns while wirelessly sending live video and handling command and control messages, was clearly demonstrated. Video-inspection was clearly shown to be a viable tool to understand the state of this critical buried infrastructure, irrespective of low- (cast-iron) or high-pressure (steel) conditions. This report covers the different aspects of specifications, requirements, design, prototyping, integration and testing and field-trialing of the Explorer platform.

Real-time implementation of optimized maximum noise fraction transform for feature extraction of hyperspectral images

Science.gov (United States)

Wu, Yuanfeng; Gao, Lianru; Zhang, Bing; Zhao, Haina; Li, Jun

2014-01-01

We present a parallel implementation of the optimized maximum noise fraction (G-OMNF) transform algorithm for feature extraction of hyperspectral images on commodity graphics processing units (GPUs). The proposed approach explored the algorithm data-level concurrency and optimized the computing flow. We first defined a three-dimensional grid, in which each thread calculates a sub-block data to easily facilitate the spatial and spectral neighborhood data searches in noise estimation, which is one of the most important steps involved in OMNF. Then, we optimized the processing flow and computed the noise covariance matrix before computing the image covariance matrix to reduce the original hyperspectral image data transmission. These optimization strategies can greatly improve the computing efficiency and can be applied to other feature extraction algorithms. The proposed parallel feature extraction algorithm was implemented on an Nvidia Tesla GPU using the compute unified device architecture and basic linear algebra subroutines library. Through the experiments on several real hyperspectral images, our GPU parallel implementation provides a significant speedup of the algorithm compared with the CPU implementation, especially for highly data parallelizable and arithmetically intensive algorithm parts, such as noise estimation. In order to further evaluate the effectiveness of G-OMNF, we used two different applications: spectral unmixing and classification for evaluation. Considering the sensor scanning rate and the data acquisition time, the proposed parallel implementation met the on-board real-time feature extraction.

An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis

Directory of Open Access Journals (Sweden)

Jian-Hua Zhong

2016-01-01

Full Text Available Fault diagnosis is very important to maintain the operation of a gas turbine generator system (GTGS in power plants, where any abnormal situations will interrupt the electricity supply. The fault diagnosis of the GTGS faces the main challenge that the acquired data, vibration or sound signals, contain a great deal of redundant information which extends the fault identification time and degrades the diagnostic accuracy. To improve the diagnostic performance in the GTGS, an effective fault feature extraction framework is proposed to solve the problem of the signal disorder and redundant information in the acquired signal. The proposed framework combines feature extraction with a general machine learning method, support vector machine (SVM, to implement an intelligent fault diagnosis. The feature extraction method adopts wavelet packet transform and time-domain statistical features to extract the features of faults from the vibration signal. To further reduce the redundant information in extracted features, kernel principal component analysis is applied in this study. Experimental results indicate that the proposed feature extracted technique is an effective method to extract the useful features of faults, resulting in improvement of the performance of fault diagnosis for the GTGS.

Phase information of time-frequency transforms as a key feature for classification of atrial fibrillation episodes

International Nuclear Information System (INIS)

Ortigosa, Nuria; Fernández, Carmen; Galbis, Antonio; Cano, Óscar

2015-01-01

Patients suffering from atrial fibrillation can be classified into different subtypes, according to the temporal pattern of the arrhythmia and its recurrence. Nowadays, clinicians cannot differentiate a priori between the different subtypes, and patient classification is done afterwards, when its clinical course is available. In this paper we present a comparison of classification performances when differentiating paroxysmal and persistent atrial fibrillation episodes by means of support vector machines. We analyze short surface electrocardiogram recordings by extracting modulus and phase features from several time-frequency transforms: short-time Fourier transform, Wigner–Ville, Choi–Williams, Stockwell transform, and general Fourier-family transform. Overall, accuracy higher than 81% is obtained when classifying phase information features of real test ECGs from a heterogeneous cohort of patients (in terms of progression of the arrhythmia and antiarrhythmic treatment) recorded in a tertiary center. Therefore, phase features can facilitate the clinicians’ choice of the most appropriate treatment for each patient by means of a non-invasive technique (the surface ECG). (paper)

Real-time object tracking based on scale-invariant features employing bio-inspired hardware.

Science.gov (United States)

Yasukawa, Shinsuke; Okuno, Hirotsugu; Ishii, Kazuo; Yagi, Tetsuya

2016-09-01

We developed a vision sensor system that performs a scale-invariant feature transform (SIFT) in real time. To apply the SIFT algorithm efficiently, we focus on a two-fold process performed by the visual system: whole-image parallel filtering and frequency-band parallel processing. The vision sensor system comprises an active pixel sensor, a metal-oxide semiconductor (MOS)-based resistive network, a field-programmable gate array (FPGA), and a digital computer. We employed the MOS-based resistive network for instantaneous spatial filtering and a configurable filter size. The FPGA is used to pipeline process the frequency-band signals. The proposed system was evaluated by tracking the feature points detected on an object in a video. Copyright © 2016 Elsevier Ltd. All rights reserved.

Longitudinal Study of Sensory Features in Children with Autism Spectrum Disorder

Directory of Open Access Journals (Sweden)

Lucia Perez Repetto

2017-01-01

Full Text Available Background. Between 45 and 95% of children with Autism Spectrum Disorder (ASD present sensory features that affect their daily functioning. However, the data in the scientific literature are not conclusive regarding the evolution of sensory features in children with ASD. The main objective of this study was to analyze the sensory features of children within the age of 3-4 (T1 when they received their ASD diagnosis and two years later (T2 when they started school. Methods. We conducted a prospective cohort study to assess sensory features in 34 children with ASD over time. The data were collected using a standardized assessment tool, the Sensory Profile. Results. Our analyses show that sensory features in children with ASD are stable from the age of three to six years. The stability of sensory scores is independent of correction by covariates, such as cognitive level and autism severity scores. Conclusions. Children with ASD have sensory features that persist from the time of diagnosis at the age of 3 to 4 years to school age. This persistence of sensory features from an early age underscores the need to support these children and their parents. Sensory features should be detected early and managed to improve functional and psychosocial outcomes.

Adversarial Feature Selection Against Evasion Attacks.

Science.gov (United States)

Zhang, Fei; Chan, Patrick P K; Biggio, Battista; Yeung, Daniel S; Roli, Fabio

2016-03-01

Pattern recognition and machine learning techniques have been increasingly adopted in adversarial settings such as spam, intrusion, and malware detection, although their security against well-crafted attacks that aim to evade detection by manipulating data at test time has not yet been thoroughly assessed. While previous work has been mainly focused on devising adversary-aware classification algorithms to counter evasion attempts, only few authors have considered the impact of using reduced feature sets on classifier security against the same attacks. An interesting, preliminary result is that classifier security to evasion may be even worsened by the application of feature selection. In this paper, we provide a more detailed investigation of this aspect, shedding some light on the security properties of feature selection against evasion attacks. Inspired by previous work on adversary-aware classifiers, we propose a novel adversary-aware feature selection model that can improve classifier security against evasion attacks, by incorporating specific assumptions on the adversary's data manipulation strategy. We focus on an efficient, wrapper-based implementation of our approach, and experimentally validate its soundness on different application examples, including spam and malware detection.

Acoustic Features Influence Musical Choices Across Multiple Genres.

Science.gov (United States)

Barone, Michael D; Bansal, Jotthi; Woolhouse, Matthew H

2017-01-01

Based on a large behavioral dataset of music downloads, two analyses investigate whether the acoustic features of listeners' preferred musical genres influence their choice of tracks within non-preferred, secondary musical styles. Analysis 1 identifies feature distributions for pairs of genre-defined subgroups that are distinct. Using correlation analysis, these distributions are used to test the degree of similarity between subgroups' main genres and the other music within their download collections. Analysis 2 explores the issue of main-to-secondary genre influence through the production of 10 feature-influence matrices, one per acoustic feature, in which cell values indicate the percentage change in features for genres and subgroups compared to overall population averages. In total, 10 acoustic features and 10 genre-defined subgroups are explored within the two analyses. Results strongly indicate that the acoustic features of people's main genres influence the tracks they download within non-preferred, secondary musical styles. The nature of this influence and its possible actuating mechanisms are discussed with respect to research on musical preference, personality, and statistical learning.

NVL-C: Static Analysis Techniques for Efficient, Correct Programming of Non-Volatile Main Memory Systems

Energy Technology Data Exchange (ETDEWEB)

Lee, Seyong [ORNL; Vetter, Jeffrey S [ORNL

2016-01-01

Computer architecture experts expect that non-volatile memory (NVM) hierarchies will play a more significant role in future systems including mobile, enterprise, and HPC architectures. With this expectation in mind, we present NVL-C: a novel programming system that facilitates the efficient and correct programming of NVM main memory systems. The NVL-C programming abstraction extends C with a small set of intuitive language features that target NVM main memory, and can be combined directly with traditional C memory model features for DRAM. We have designed these new features to enable compiler analyses and run-time checks that can improve performance and guard against a number of subtle programming errors, which, when left uncorrected, can corrupt NVM-stored data. Moreover, to enable recovery of data across application or system failures, these NVL-C features include a flexible directive for specifying NVM transactions. So that our implementation might be extended to other compiler front ends and languages, the majority of our compiler analyses are implemented in an extended version of LLVM's intermediate representation (LLVM IR). We evaluate NVL-C on a number of applications to show its flexibility, performance, and correctness.

Exploring spatial-temporal dynamics of fire regime features in mainland Spain

Science.gov (United States)

Jiménez-Ruano, Adrián; Rodrigues Mimbrero, Marcos; de la Riva Fernández, Juan

2017-10-01

This paper explores spatial-temporal dynamics in fire regime features, such as fire frequency, burnt area, large fires and natural- and human-caused fires, as an essential part of fire regime characterization. Changes in fire features are analysed at different spatial - regional and provincial/NUTS3 - levels, together with summer and winter temporal scales, using historical fire data from Spain for the period 1974-2013. Temporal shifts in fire features are investigated by means of change point detection procedures - Pettitt test, AMOC (at most one change), PELT (pruned exact linear time) and BinSeg (binary segmentation) - at a regional level to identify changes in the time series of the features. A trend analysis was conducted using the Mann-Kendall and Sen's slope tests at both the regional and NUTS3 level. Finally, we applied a principal component analysis (PCA) and varimax rotation to trend outputs - mainly Sen's slope values - to summarize overall temporal behaviour and to explore potential links in the evolution of fire features. Our results suggest that most fire features show remarkable shifts between the late 1980s and the first half of the 1990s. Mann-Kendall outputs revealed negative trends in the Mediterranean region. Results from Sen's slope suggest high spatial and intra-annual variability across the study area. Fire activity related to human sources seems to be experiencing an overall decrease in the northwestern provinces, particularly pronounced during summer. Similarly, the Hinterland and the Mediterranean coast are gradually becoming less fire affected. Finally, PCA enabled trends to be synthesized into four main components: winter fire frequency (PC1), summer burnt area (PC2), large fires (PC3) and natural fires (PC4).

Effect of main injection timing for controlling the combustion phasing of a homogeneous charge compression ignition engine using a new dual injection strategy

International Nuclear Information System (INIS)

Das, Pranab; Subbarao, P.M.V.; Subrahmanyam, J.P.

2015-01-01

Highlights: • A new dual injection concept is developed by minimum geometry modification. • The occurrence of combustion parameters strongly depend on main injection timing. • At higher load, premixed equivalence ratio dominates over main injection timing. • Retarded of main injection timing tends to retard combustion phasing. • Slightly retarded main injection timing is recommended to avoid intense knocking. - Abstract: Homogeneous charge compression ignition combustion of diesel fuel is implemented using a novel dual injection strategy. A new experimental technique is developed to modify a single cylinder direct injection diesel engine to run on homogeneous combustion mode. Effect of main injection timing is investigated covering a range from 26 to 8 crank angle degrees before top dead center with an interval of 3°. Retarded main injection timing is identified as a control strategy for delaying combustion phasing and a means of controlled combustion phasing of direct injection homogeneous charge compression ignition combustion. Two load conditions were investigated and it was observed that at higher load, start of combustion depends more on fuel air equivalence ratio than main injection timing, whereas at low load, it significantly varies with varying main injection timing. Significant improvements in smoke and oxides of nitrogen emissions are observed when compared with the baseline conventional combustion. By studying different combustion parameters, it is observed that there is an improvement in performance and emissions with marginal loss in thermal efficiency when the main injection timing is 20° before top dead center. This is identified as the optimum main injection timing for such homogeneous combustion under the same operating condition

Optimal filtering of dynamics in short-time features for music organization

DEFF Research Database (Denmark)

Arenas-García, Jerónimo; Larsen, Jan; Hansen, Lars Kai

2006-01-01

There is an increasing interest in customizable methods for organizing music collections. Relevant music characterization can be obtained from short-time features, but it is not obvious how to combine them to get useful information. In this work, a novel method, denoted as the Positive Constrained...... Orthonormalized Partial Least Squares (POPLS), is proposed. Working on the periodograms of MFCCs time series, this supervised method finds optimal filters which pick up the most discriminative temporal information for any music organization task. Two examples are presented in the paper, the first being a simple...... proof-of-concept, where an altosax with and without vibrato is modelled. A more complex \\$11\\$ music genre classification setup is also investigated to illustrate the robustness and validity of the proposed method on larger datasets. Both experiments showed the good properties of our method, as well...

Using thermodynamic data to reproduce main seismic features of transition zone

Science.gov (United States)

Fomin, Ilya; Saukko, Anna; Edwards, Paul; Schiffer, Christian

2016-04-01

Most of the seismic tomography studies nowadays are based on comprehensive models with optimization of lots of parameters. These models are able to resolve very subtle features of the Earth's mantle, but the influence of each specific parameter is not seen directly. In our research we try to minimize the number of processed parameters to produce simple synthetic cases. The main goals of our model are to see how water content influences the depth of the transition zone, and if melting at the transition zone is plausible. We also attempt to see how water content and the presence of melts influence the signal strength of the transition zone in receiver functions. Our MATLAB-code calculates phase assemblage according to specific temperature and pressure within 2D numerical domain (e.g. 300x700 km). Phase properties are calculated with database of Stixrude and Lithgow-Bertelloni [2011], with corrections for water impact on elastic constants according to Liu et al., [2012]. We use the mantle phase composition 55% garnet and 45% olivine-polymorph, soliduses by Ohtani et al. [2004] and melt properties by Sakamaki et al. [2006]. These data are used to calculate seismic velocities and, furthermore, receiver functions with standard routines (e.g.[Schiffer et al., 2012]). Model predicts Vs within 5 to 5.5 km/s and Vp around 9.5-10 km/s within transition zone (Vp/Vs = 1.84-1.87), which is close to standard values. The presence of water enlarges the wadsleyite region, but also dampens the peak of receiver functions down to background level. Increase in water content causes melting at much shallower depths. Using a normal thermal gradient, we can get up to 10% of melt at depths around 390 km with 80% of water saturation, shown by a negative anomaly on receiver functions. This result is similar to data obtained for Afar Plateau [Thompson et al., 2015]. With cratonic thermal gradient, the olivine-wadsleyite transition and corresponding melt layer appear at depths around 350 km

A prototype feature system for feature retrieval using relationships

Science.gov (United States)

Choi, J.; Usery, E.L.

2009-01-01

Using a feature data model, geographic phenomena can be represented effectively by integrating space, theme, and time. This paper extends and implements a feature data model that supports query and visualization of geographic features using their non-spatial and temporal relationships. A prototype feature-oriented geographic information system (FOGIS) is then developed and storage of features named Feature Database is designed. Buildings from the U.S. Marine Corps Base, Camp Lejeune, North Carolina and subways in Chicago, Illinois are used to test the developed system. The results of the applications show the strength of the feature data model and the developed system 'FOGIS' when they utilize non-spatial and temporal relationships in order to retrieve and visualize individual features.

Recent Development of the Two-Stroke Engine. II - Design Features. 2; Design Features

Science.gov (United States)

Zeman, J.

1945-01-01

Completing the first paper dealing with charging methods and arrangements, the present paper discusses the design forms of two-stroke engines. Features which largely influence piston running are: (a) The shape and surface condition of the sliding parts. (b) The cylinder and piston materials. (c) Heat conditions in the piston, and lubrication. There is little essential difference between four-stroke and two-stroke engines with ordinary pistons. In large engines, for example, are always found separately cast or welded frames in which the stresses are taken up by tie rods. Twin piston and timing piston engines often differ from this design. Examples can be found in many engines of German or foreign make. Their methods of operation will be dealt with in the third part of the present paper, which also includes the bibliography. The development of two-stroke engine design is, of course, mainly concerned with such features as are inherently difficult to master; that is, the piston barrel and the design of the gudgeon pin bearing. Designers of four-stroke engines now-a-days experience approximately the same difficulties, since heat stresses have increased to the point of influencing conditions in the piston barrel. Features which notably affect this are: (a) The material. (b) Prevailing heat conditions.

Autonomous learning by simple dynamical systems with a discrete-time formulation

Science.gov (United States)

Bilen, Agustín M.; Kaluza, Pablo

2017-05-01

We present a discrete-time formulation for the autonomous learning conjecture. The main feature of this formulation is the possibility to apply the autonomous learning scheme to systems in which the errors with respect to target functions are not well-defined for all times. This restriction for the evaluation of functionality is a typical feature in systems that need a finite time interval to process a unit piece of information. We illustrate its application on an artificial neural network with feed-forward architecture for classification and a phase oscillator system with synchronization properties. The main characteristics of the discrete-time formulation are shown by constructing these systems with predefined functions.

Oahu Sewer Main Lines, Oahu County HI, 2016, Honolulu GIS

Data.gov (United States)

U.S. Environmental Protection Agency — Linear features representing sewer main lines as maintained by Honolulu ENV Department of Environmental Services. Includes an inventory of sewer mains used for...

Action Recognition by Joint Spatial-Temporal Motion Feature

Directory of Open Access Journals (Sweden)

Weihua Zhang

2013-01-01

Full Text Available This paper introduces a method for human action recognition based on optical flow motion features extraction. Automatic spatial and temporal alignments are combined together in order to encourage the temporal consistence on each action by an enhanced dynamic time warping (DTW algorithm. At the same time, a fast method based on coarse-to-fine DTW constraint to improve computational performance without reducing accuracy is induced. The main contributions of this study include (1 a joint spatial-temporal multiresolution optical flow computation method which can keep encoding more informative motion information than recent proposed methods, (2 an enhanced DTW method to improve temporal consistence of motion in action recognition, and (3 coarse-to-fine DTW constraint on motion features pyramids to speed up recognition performance. Using this method, high recognition accuracy is achieved on different action databases like Weizmann database and KTH database.

Main Features of a 3d GIS for a Monumental Complex with AN Historical-Cultural Relevance

Science.gov (United States)

Scianna, A.; La Guardia, M.

2017-05-01

The last achievements of technologies in geomatics especially in survey and restitution of 3D models (UAV/drones and laser scanner technologies) generated new procedures and higher standards of quality in representation of archaeological sites. Together with Geomatics, the recent development of Information and Communication Technologies (ICT) strongly contribute to document and the Cultural Heritage (CH). The representation and documentation of CH using these new technologies has became necessary in order to satisfy different needs: - for restorers in order to acquire a deep knowledge of the cultural good and to define possible strategies of restoration; - for the conservation of information, allowing to preserve the 3D geometry of the monumental complex with the integration of descriptions about architectural elements; - for touristic aims, giving the opportunity of sharing CH information on web, allowing users to visit and explore, in a virtual way, monumental complexes, acquiring information details about architectural elements or the history of monumental complex. Looking through these new scenarios, the development of a 3D Geographic Information System (GIS) applied to a cultural good could be, today, an added value of fundamental importance for full description and data management of monumental complexes. In this work, the main features necessary for the correct construction of a 3D GIS of a monumental complex will be analyzed, with a particular focus on the possibilities for creating a standardized procedure to follow.

Assigning Main Orientation to an EOH Descriptor on Multispectral Images.

Science.gov (United States)

Li, Yong; Shi, Xiang; Wei, Lijun; Zou, Junwei; Chen, Fang

2015-07-01

This paper proposes an approach to compute an EOH (edge-oriented histogram) descriptor with main orientation. EOH has a better matching ability than SIFT (scale-invariant feature transform) on multispectral images, but does not assign a main orientation to keypoints. Alternatively, it tends to assign the same main orientation to every keypoint, e.g., zero degrees. This limits EOH to matching keypoints between images of translation misalignment only. Observing this limitation, we propose assigning to keypoints the main orientation that is computed with PIIFD (partial intensity invariant feature descriptor). In the proposed method, SIFT keypoints are detected from images as the extrema of difference of Gaussians, and every keypoint is assigned to the main orientation computed with PIIFD. Then, EOH is computed for every keypoint with respect to its main orientation. In addition, an implementation variant is proposed for fast computation of the EOH descriptor. Experimental results show that the proposed approach performs more robustly than the original EOH on image pairs that have a rotation misalignment.

Exploring spatial–temporal dynamics of fire regime features in mainland Spain

Directory of Open Access Journals (Sweden)

A. Jiménez-Ruano

2017-10-01

Full Text Available This paper explores spatial–temporal dynamics in fire regime features, such as fire frequency, burnt area, large fires and natural- and human-caused fires, as an essential part of fire regime characterization. Changes in fire features are analysed at different spatial – regional and provincial/NUTS3 – levels, together with summer and winter temporal scales, using historical fire data from Spain for the period 1974–2013. Temporal shifts in fire features are investigated by means of change point detection procedures – Pettitt test, AMOC (at most one change, PELT (pruned exact linear time and BinSeg (binary segmentation – at a regional level to identify changes in the time series of the features. A trend analysis was conducted using the Mann–Kendall and Sen's slope tests at both the regional and NUTS3 level. Finally, we applied a principal component analysis (PCA and varimax rotation to trend outputs – mainly Sen's slope values – to summarize overall temporal behaviour and to explore potential links in the evolution of fire features. Our results suggest that most fire features show remarkable shifts between the late 1980s and the first half of the 1990s. Mann–Kendall outputs revealed negative trends in the Mediterranean region. Results from Sen's slope suggest high spatial and intra-annual variability across the study area. Fire activity related to human sources seems to be experiencing an overall decrease in the northwestern provinces, particularly pronounced during summer. Similarly, the Hinterland and the Mediterranean coast are gradually becoming less fire affected. Finally, PCA enabled trends to be synthesized into four main components: winter fire frequency (PC1, summer burnt area (PC2, large fires (PC3 and natural fires (PC4.

Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features

KAUST Repository

Wang, Bing; Zhang, Jun; Chen, Peng; Ji, Zhiwei; Deng, Shuping; Li, Chi

2013-01-01

Background: Ion mobility-mass spectrometry (IMMS), an analytical technique which combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS), can rapidly separates ions on a millisecond time-scale. IMMS becomes a powerful tool to analyzing complex mixtures, especially for the analysis of peptides in proteomics. The high-throughput nature of this technique provides a challenge for the identification of peptides in complex biological samples. As an important parameter, peptide drift time can be used for enhancing downstream data analysis in IMMS-based proteomics.Results: In this paper, a model is presented based on least square support vectors regression (LS-SVR) method to predict peptide ion drift time in IMMS from the sequence-based features of peptide. Four descriptors were extracted from peptide sequence to represent peptide ions by a 34-component vector. The parameters of LS-SVR were selected by a grid searching strategy, and a 10-fold cross-validation approach was employed for the model training and testing. Our proposed method was tested on three datasets with different charge states. The high prediction performance achieve demonstrate the effectiveness and efficiency of the prediction model.Conclusions: Our proposed LS-SVR model can predict peptide drift time from sequence information in relative high prediction accuracy by a test on a dataset of 595 peptides. This work can enhance the confidence of protein identification by combining with current protein searching techniques. 2013 Wang et al.; licensee BioMed Central Ltd.

Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features

KAUST Repository

Wang, Bing

2013-05-09

Background: Ion mobility-mass spectrometry (IMMS), an analytical technique which combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS), can rapidly separates ions on a millisecond time-scale. IMMS becomes a powerful tool to analyzing complex mixtures, especially for the analysis of peptides in proteomics. The high-throughput nature of this technique provides a challenge for the identification of peptides in complex biological samples. As an important parameter, peptide drift time can be used for enhancing downstream data analysis in IMMS-based proteomics.Results: In this paper, a model is presented based on least square support vectors regression (LS-SVR) method to predict peptide ion drift time in IMMS from the sequence-based features of peptide. Four descriptors were extracted from peptide sequence to represent peptide ions by a 34-component vector. The parameters of LS-SVR were selected by a grid searching strategy, and a 10-fold cross-validation approach was employed for the model training and testing. Our proposed method was tested on three datasets with different charge states. The high prediction performance achieve demonstrate the effectiveness and efficiency of the prediction model.Conclusions: Our proposed LS-SVR model can predict peptide drift time from sequence information in relative high prediction accuracy by a test on a dataset of 595 peptides. This work can enhance the confidence of protein identification by combining with current protein searching techniques. 2013 Wang et al.; licensee BioMed Central Ltd.

Functional conjugated pyridines via main-group element tuning.

Science.gov (United States)

Stolar, Monika; Baumgartner, Thomas

2018-03-29

Pyridine-based materials have seen widespread attention for the development of n-type organic materials. In recent years, the incorporation of main-group elements has also explored significant advantages for the development and tunability of organic conjugated materials. The unique chemical and electronic structure of main-group elements has led to several enhancements in conventional organic materials. This Feature article highlights recent main-group based pyridine materials by discussing property enhancements and application in organic electronics.

An Evaluation of optional timing/synchronization features to support selection of an optimum design for the DCS digital communication network

Science.gov (United States)

Bradley, D. B.; Cain, J. B., III; Williard, M. W.

1978-01-01

The task was to evaluate the ability of a set of timing/synchronization subsystem features to provide a set of desirable characteristics for the evolving Defense Communications System digital communications network. The set of features related to the approaches by which timing/synchronization information could be disseminated throughout the network and the manner in which this information could be utilized to provide a synchronized network. These features, which could be utilized in a large number of different combinations, included mutual control, directed control, double ended reference links, independence of clock error measurement and correction, phase reference combining, and self organizing.

A hybrid feature selection and health indicator construction scheme for delay-time-based degradation modelling of rolling element bearings

Science.gov (United States)

Zhang, Bin; Deng, Congying; Zhang, Yi

2018-03-01

Rolling element bearings are mechanical components used frequently in most rotating machinery and they are also vulnerable links representing the main source of failures in such systems. Thus, health condition monitoring and fault diagnosis of rolling element bearings have long been studied to improve operational reliability and maintenance efficiency of rotatory machines. Over the past decade, prognosis that enables forewarning of failure and estimation of residual life attracted increasing attention. To accurately and efficiently predict failure of the rolling element bearing, the degradation requires to be well represented and modelled. For this purpose, degradation of the rolling element bearing is analysed with the delay-time-based model in this paper. Also, a hybrid feature selection and health indicator construction scheme is proposed for extraction of the bearing health relevant information from condition monitoring sensor data. Effectiveness of the presented approach is validated through case studies on rolling element bearing run-to-failure experiments.

Sleep Spindle Detection and Prediction Using a Mixture of Time Series and Chaotic Features

Directory of Open Access Journals (Sweden)

Amin Hekmatmanesh

2017-01-01

Full Text Available It is well established that sleep spindles (bursts of oscillatory brain electrical activity are significant indicators of learning, memory and some disease states. Therefore, many attempts have been made to detect these hallmark patterns automatically. In this pilot investigation, we paid special attention to nonlinear chaotic features of EEG signals (in combination with linear features to investigate the detection and prediction of sleep spindles. These nonlinear features included: Higuchi's, Katz's and Sevcik's Fractal Dimensions, as well as the Largest Lyapunov Exponent and Kolmogorov's Entropy. It was shown that the intensity map of various nonlinear features derived from the constructive interference of spindle signals could improve the detection of the sleep spindles. It was also observed that the prediction of sleep spindles could be facilitated by means of the analysis of these maps. Two well-known classifiers, namely the Multi-Layer Perceptron (MLP and the K-Nearest Neighbor (KNN were used to distinguish between spindle and non-spindle patterns. The MLP classifier produced a~high discriminative capacity (accuracy = 94.93%, sensitivity = 94.31% and specificity = 95.28% with significant robustness (accuracy ranging from 91.33% to 94.93%, sensitivity varying from 91.20% to 94.31%, and specificity extending from 89.79% to 95.28% in separating spindles from non-spindles. This classifier also generated the best results in predicting sleep spindles based on chaotic features. In addition, the MLP was used to find out the best time window for predicting the sleep spindles, with the experimental results reaching 97.96% accuracy.

Borderline personality features in childhood: the role of subtype, developmental timing, and chronicity of child maltreatment.

Science.gov (United States)

Hecht, Kathryn F; Cicchetti, Dante; Rogosch, Fred A; Crick, Nicki R

2014-08-01

Child maltreatment has been established as a risk factor for borderline personality disorder (BPD), yet few studies consider how maltreatment influences the development of BPD features through childhood and adolescence. Subtype, developmental timing, and chronicity of child maltreatment were examined as factors in the development of borderline personality features in childhood. Children (M age = 11.30, SD = 0.94), including 314 maltreated and 285 nonmaltreated children from comparable low socioeconomic backgrounds, provided self-reports of developmentally salient borderline personality traits. Maltreated children had higher overall borderline feature scores, had higher scores on each individual subscale, and were more likely to be identified as at high risk for development of BPD through raised scores on all four subscales. Chronicity of maltreatment predicted higher overall borderline feature scores, and patterns of onset and recency of maltreatment significantly predicted whether a participant would meet criteria for the high-risk group. Implications of findings and recommendations for intervention are discussed.

Borderline Personality Features in Childhood: The Role of Subtype, Developmental Timing and Chronicity of Child Maltreatment

Science.gov (United States)

Hecht, Kathryn F.; Cicchetti, Dante; Rogosch, Fred A.; Crick, Nicki

2014-01-01

Child maltreatment has been established as a risk factor for borderline personality disorder (BPD), yet few studies consider how maltreatment influences the development of BPD features through childhood and adolescence. Subtype, developmental timing and chronicity of child maltreatment were examined as factors in the development of borderline personality features in childhood. Children (M age = 11.30, SD = 0.94), including 314 maltreated and 285 nonmaltreated children from comparable low socioeconomic backgrounds, provided self-reports of developmentally salient borderline personality traits. Maltreated children had higher overall borderline feature scores, higher scores on each individual subscale and were more likely to be identified as at high risk for development of BPD through raised scores on all 4 subscales. Chronicity of maltreatment predicted higher overall borderline feature scores and patterns of onset and recency of maltreatment significantly predicted whether a participant would meet criteria for the high-risk group. Implications of findings and recommendations for intervention are discussed. PMID:25047300

Nearly Zero Energy Buildings: An Overview of the Main Construction Features across Europe

Directory of Open Access Journals (Sweden)

Giulia Paoletti

2017-05-01

Full Text Available Nearly Zero Energy Buildings (nZEBs represent the backbone to achieve ambitious European goals in terms of energy efficiency and CO2 emissions reduction. As defined in the EPBD, by 31 December 2020, all of the new buildings will have to reach a target of nearly zero energy. This target encourages the adoption of innovative business models as well as the technology development in the building sector, aimed at reducing energy demand and exploiting local renewable energy sources (RES. Assessing the share of implementation and the performance of technologies in new or renovated nZEBs is strategic to identify the market trends and to define design guidelines with the most effective solutions according to the context. In this regard, this paper analyses the construction features of a set of nZEBs, collected in 17 European countries within the EU IEE ZEBRA2020 project, with a special focus on the influence of the boundary conditions on the technologies adopted. The results show a general high insulation level of the envelope and recurrent specific technologies in the Heating Ventilation Air Conditioning (HVAC system (i.e., heat pumps and mechanical ventilation, while the climatic conditions do not drive significantly the design approach and the nZEB features.

The Main Shear Zone in Sør Rondane: A key feature for reconstructing the geodynamic evolution of East Antarctica

Science.gov (United States)

Ruppel, Antonia; Läufer, Andreas; Lisker, Frank; Jacobs, Joachim; Elburg, Marlina; Damaske, Detlef; Lucka, Nicole

2013-04-01

Structural investigations were carried out along the Main Shear Zone (MSZ) of western Sør Rondane (22°-25°E, 71.5°-72.5°S) to gain new information about the position of the East-/West-Gondwana suture and the ancient plate tectonic configuration during Gondwana amalgamation. The WSW-ENE striking MSZ divides south-western Sør Rondane in a northern amphibolite-facies terrane and a southern tonalite-trondhjemite-granodiorite (TTG) terrane. The structure can be traced over a distance of ca. 100 km and reaches several hundred meters in width. It is characterized by a right-lateral sense of movement and marked by a transpressional and also transtensional regime. Ductilely deformed granitoids (ca. 560 Ma: SHRIMP U-Pb of zircon) and ductile - brittle structures, which evolved in a transitional ductile to brittle regime in an undeformed syenite (ca. 499-459 Ma, Ar-Ar mica), provide a late Proterozoic/ early Paleozoic time limit for the activity of the shear zone (Shiraishi et al., 2008; Shiraishi et al., 1997). Documentation of ductile and brittle deformation allows reconstructing up to eight deformation stages. Cross-cutting relationships of structural features mapped in the field complemented by published kinematic data reveal the following relative age succession: [i] Dn+1 - formation of the main foliation during peak metamorphism, [ii] Dn+2 - isoclinal, intrafolial folding of the main foliation, mostly foliation-parallel mylonitic shear zones (1-2 meter thick), [iii] Dn+3 - formation of tight to closed folds, [iv] Dn+4 - formation of relatively upright, large-scale open folds, [v] Dn+5 - granitoid intrusion (e.g. Vengen granite), [vi] Dn+6 - dextral shearing between amphibolite and TTG terranes, formation of the MSZ, [vii] Dn+7 - intrusion of late- to post-tectonic granitoids, first stage of brittle deformation (late shearing along MSZ), intrusion of post-kinematic mafic dykes, [viii] Dn+8 - second stage of brittle deformation including formation of conjugate fault

A 181 GOPS AKAZE Accelerator Employing Discrete-Time Cellular Neural Networks for Real-Time Feature Extraction.

Science.gov (United States)

Jiang, Guangli; Liu, Leibo; Zhu, Wenping; Yin, Shouyi; Wei, Shaojun

2015-09-04

This paper proposes a real-time feature extraction VLSI architecture for high-resolution images based on the accelerated KAZE algorithm. Firstly, a new system architecture is proposed. It increases the system throughput, provides flexibility in image resolution, and offers trade-offs between speed and scaling robustness. The architecture consists of a two-dimensional pipeline array that fully utilizes computational similarities in octaves. Secondly, a substructure (block-serial discrete-time cellular neural network) that can realize a nonlinear filter is proposed. This structure decreases the memory demand through the removal of data dependency. Thirdly, a hardware-friendly descriptor is introduced in order to overcome the hardware design bottleneck through the polar sample pattern; a simplified method to realize rotation invariance is also presented. Finally, the proposed architecture is designed in TSMC 65 nm CMOS technology. The experimental results show a performance of 127 fps in full HD resolution at 200 MHz frequency. The peak performance reaches 181 GOPS and the throughput is double the speed of other state-of-the-art architectures.

A 181 GOPS AKAZE Accelerator Employing Discrete-Time Cellular Neural Networks for Real-Time Feature Extraction

Directory of Open Access Journals (Sweden)

Guangli Jiang

2015-09-01

Full Text Available This paper proposes a real-time feature extraction VLSI architecture for high-resolution images based on the accelerated KAZE algorithm. Firstly, a new system architecture is proposed. It increases the system throughput, provides flexibility in image resolution, and offers trade-offs between speed and scaling robustness. The architecture consists of a two-dimensional pipeline array that fully utilizes computational similarities in octaves. Secondly, a substructure (block-serial discrete-time cellular neural network that can realize a nonlinear filter is proposed. This structure decreases the memory demand through the removal of data dependency. Thirdly, a hardware-friendly descriptor is introduced in order to overcome the hardware design bottleneck through the polar sample pattern; a simplified method to realize rotation invariance is also presented. Finally, the proposed architecture is designed in TSMC 65 nm CMOS technology. The experimental results show a performance of 127 fps in full HD resolution at 200 MHz frequency. The peak performance reaches 181 GOPS and the throughput is double the speed of other state-of-the-art architectures.

Assigning Main Orientation to an EOH Descriptor on Multispectral Images

Directory of Open Access Journals (Sweden)

Yong Li

2015-07-01

Full Text Available This paper proposes an approach to compute an EOH (edge-oriented histogram descriptor with main orientation. EOH has a better matching ability than SIFT (scale-invariant feature transform on multispectral images, but does not assign a main orientation to keypoints. Alternatively, it tends to assign the same main orientation to every keypoint, e.g., zero degrees. This limits EOH to matching keypoints between images of translation misalignment only. Observing this limitation, we propose assigning to keypoints the main orientation that is computed with PIIFD (partial intensity invariant feature descriptor. In the proposed method, SIFT keypoints are detected from images as the extrema of difference of Gaussians, and every keypoint is assigned to the main orientation computed with PIIFD. Then, EOH is computed for every keypoint with respect to its main orientation. In addition, an implementation variant is proposed for fast computation of the EOH descriptor. Experimental results show that the proposed approach performs more robustly than the original EOH on image pairs that have a rotation misalignment.

An Incremental Classification Algorithm for Mining Data with Feature Space Heterogeneity

Directory of Open Access Journals (Sweden)

Yu Wang

2014-01-01

Full Text Available Feature space heterogeneity often exists in many real world data sets so that some features are of different importance for classification over different subsets. Moreover, the pattern of feature space heterogeneity might dynamically change over time as more and more data are accumulated. In this paper, we develop an incremental classification algorithm, Supervised Clustering for Classification with Feature Space Heterogeneity (SCCFSH, to address this problem. In our approach, supervised clustering is implemented to obtain a number of clusters such that samples in each cluster are from the same class. After the removal of outliers, relevance of features in each cluster is calculated based on their variations in this cluster. The feature relevance is incorporated into distance calculation for classification. The main advantage of SCCFSH lies in the fact that it is capable of solving a classification problem with feature space heterogeneity in an incremental way, which is favorable for online classification tasks with continuously changing data. Experimental results on a series of data sets and application to a database marketing problem show the efficiency and effectiveness of the proposed approach.

Fuzzy Relational Compression Applied on Feature Vectors for Infant Cry Recognition

Science.gov (United States)

Reyes-Galaviz, Orion Fausto; Reyes-García, Carlos Alberto

Data compression is always advisable when it comes to handling and processing information quickly and efficiently. There are two main problems that need to be solved when it comes to handling data; store information in smaller spaces and processes it in the shortest possible time. When it comes to infant cry analysis (ICA), there is always the need to construct large sound repositories from crying babies. Samples that have to be analyzed and be used to train and test pattern recognition algorithms; making this a time consuming task when working with uncompressed feature vectors. In this work, we show a simple, but efficient, method that uses Fuzzy Relational Product (FRP) to compresses the information inside a feature vector, building with this a compressed matrix that will help us recognize two kinds of pathologies in infants; Asphyxia and Deafness. We describe the sound analysis, which consists on the extraction of Mel Frequency Cepstral Coefficients that generate vectors which will later be compressed by using FRP. There is also a description of the infant cry database used in this work, along with the training and testing of a Time Delay Neural Network with the compressed features, which shows a performance of 96.44% with our proposed feature vector compression.

The Brazilian Telenovela: A Presentation of Its Format and its Main Features

Directory of Open Access Journals (Sweden)

Larissa Perfeito Barreto Redondo

2008-08-01

Full Text Available The telenovela produced by Rede Globo, wellknown as novela das oito, is one of the most broadcasting phenomenon in the world, because of its rating points, large production and originality. There are specific strategies and techniques which construct telenovela. What and how do they work? This is the question addressed in this study, that consists of a bibliography revision which put together data searched by authors who classified it study by study. The target was to identify the basic features that construct the format telenovela, and so classify the format, which has the power of communicating to a large audience. (Observation: it is known that telenovela is similar to soap operas.

Study on the P-wave feature time course as early predictors of paroxysmal atrial fibrillation

International Nuclear Information System (INIS)

Martínez, Arturo; Alcaraz, Raúl; Rieta, José J

2012-01-01

Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice, increasing the risk of stroke and all-cause mortality. Its mechanisms are poorly understood, thus leading to different theories and controversial interpretation of its behavior. In this respect, it is unknown why AF is self-terminating in certain individuals, which is called paroxysmal AF (PAF), and not in others. Within the context of biomedical signal analysis, predicting the onset of PAF with a reasonable advance has been a clinical challenge in recent years. By predicting arrhythmia onset, the loss of normal sinus rhythm could be addressed by means of preventive treatments, thus minimizing risks for the patients and improving their quality of life. Traditionally, the study of PAF onset has been undertaken through a variety of features characterizing P-wave spatial diversity from the standard 12-lead electrocardiogram (ECG) or from signal-averaged ECGs. However, the variability of features from the P-wave time course before PAF onset has not been exploited yet. This work introduces a new alternative to assess time diversity of the P-wave features from single-lead ECG recordings. Furthermore, the method is able to assess the risk of arrhythmia 1 h before its onset, which is a relevant advance in order to provide clinically useful PAF risk predictors. Results were in agreement with the electrophysiological changes taking place in the atria. Hence, P-wave features presented an increasing variability as PAF onset approximates, thus suggesting intermittently disturbed conduction in the atrial tissue. In addition, high PAF risk prediction accuracy, greater than 90%, has been reached in the two considered scenarios, i.e. discrimination between healthy individuals and PAF patients and between patients far from PAF and close to PAF onset. Nonetheless, more long-term studies have to be analyzed and validated in future works. (paper)

A Feature Fusion Based Forecasting Model for Financial Time Series

Science.gov (United States)

Guo, Zhiqiang; Wang, Huaiqing; Liu, Quan; Yang, Jie

2014-01-01

Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models. PMID:24971455

Definitions of engineered safety features and related features for nuclear power plants

International Nuclear Information System (INIS)

1986-01-01

In light water moderated, light water cooled nuclear power plants, definitions are given of engineered safety features which are designed to suppress or prevent dispersion of radioactive materials due to damage etc. of fuel at the times of power plant failures, and of related features which are designed to actuate or operate the engineered safety features. Contents are the following: scope of engineered safety features and of related features; classification of engineered safety features (direct systems and indirect systems) and of related features (auxiliaries, emergency power supply, and protective means). (Mori, K.)

Robustness of digitally modulated signal features against variation in HF noise model

Directory of Open Access Journals (Sweden)

Shoaib Mobien

2011-01-01

Full Text Available Abstract High frequency (HF band has both military and civilian uses. It can be used either as a primary or backup communication link. Automatic modulation classification (AMC is of an utmost importance in this band for the purpose of communications monitoring; e.g., signal intelligence and spectrum management. A widely used method for AMC is based on pattern recognition (PR. Such a method has two main steps: feature extraction and classification. The first step is generally performed in the presence of channel noise. Recent studies show that HF noise could be modeled by Gaussian or bi-kappa distributions, depending on day-time. Therefore, it is anticipated that change in noise model will have impact on features extraction stage. In this article, we investigate the robustness of well known digitally modulated signal features against variation in HF noise. Specifically, we consider temporal time domain (TTD features, higher order cumulants (HOC, and wavelet based features. In addition, we propose new features extracted from the constellation diagram and evaluate their robustness against the change in noise model. This study is targeting 2PSK, 4PSK, 8PSK, 16QAM, 32QAM, and 64QAM modulations, as they are commonly used in HF communications.

Semantic Labeling of User Location Context Based on Phone Usage Features

Directory of Open Access Journals (Sweden)

Helena Leppäkoski

2017-01-01

Full Text Available In mobile phones, the awareness of the user’s context allows services better tailored to the user’s needs. We propose a machine learning based method for semantic labeling that utilizes phone usage features to detect the user’s home, work, and other visited places. For place detection, we compare seven different classification methods. We organize the phone usage data based on periods of uninterrupted time that the user has been in a certain place. We consider three approaches to represent this data: visits, places, and cumulative samples. Our main contribution is semantic place labeling using a small set of privacy-preserving features and novel data representations suitable for resource constrained mobile devices. The contributions include (1 introduction of novel data representations including accumulation and averaging of the usage, (2 analysis of the effect of the data accumulation time on the accuracy of the place classification, (3 analysis of the confidence on the classification outcome, and (4 identification of the most relevant features obtained through feature selection methods. With a small set of privacy-preserving features and our data representations, we detect the user’s home and work with probability of 90% or better, and in 3-class problem the overall classification accuracy was 89% or better.

Temporal Feature Integration for Music Organisation

DEFF Research Database (Denmark)

Meng, Anders

2006-01-01

This Ph.D. thesis focuses on temporal feature integration for music organisation. Temporal feature integration is the process of combining all the feature vectors of a given time-frame into a single new feature vector in order to capture relevant information in the frame. Several existing methods...... for handling sequences of features are formulated in the temporal feature integration framework. Two datasets for music genre classification have been considered as valid test-beds for music organisation. Human evaluations of these, have been obtained to access the subjectivity on the datasets. Temporal...... ranking' approach is proposed for ranking the short-time features at larger time-scales according to their discriminative power in a music genre classification task. The multivariate AR (MAR) model has been proposed for temporal feature integration. It effectively models local dynamical structure...

A Novel Approach for Multi Class Fault Diagnosis in Induction Machine Based on Statistical Time Features and Random Forest Classifier

Science.gov (United States)

Sonje, M. Deepak; Kundu, P.; Chowdhury, A.

2017-08-01

Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.

ALGORITHMIC CONSTRUCTION SCHEDULES IN CONDITIONS OF TIMING CONSTRAINTS

Directory of Open Access Journals (Sweden)

Alexey S. Dobrynin

2014-01-01

Full Text Available Tasks of time-schedule construction (JSSP in various fields of human activities have an important theoretical and practical significance. The main feature of these tasks is a timing requirement, describing allowed planning time periods and periods of downtime. This article describes implementation variations of the work scheduling algorithm under timing requirements for the tasks of industrial time-schedules construction, and service activities.

Stochastic formalism-based seafloor feature discrimination using multifractality of time-dependent acoustic backscatter

Digital Repository Service at National Institute of Oceanography (India)

Haris, K.; Chakraborty, B.

Nonlin. Processes Geophys., 21, 101–113, 2014 www.nonlin-processes-geophys.net/21/101/2014/ doi:10.5194/npg-21-101-2014 © Author(s) 2014. CC Attribution 3.0 License. Nonlinear Processes in Geophysics O pen A ccess Stochastic formalism-based seafloor... shifted in time to align with the selected feature (Fig. 2). The aligned echo envelopes were averaged to obtain stable acoustic signals to Nonlin. Processes Geophys., 21, 101–113, 2014 www.nonlin-processes-geophys.net/21/101/2014/ K. Haris and B...

A red tide of Alexandrium fundyense in the Gulf of Maine

Science.gov (United States)

McGillicuddy, D. J.; Brosnahan, M. L.; Couture, D. A.; He, R.; Keafer, B. A.; Manning, J. P.; Martin, J. L.; Pilskaln, C. H.; Townsend, D. W.; Anderson, D. M.

2014-05-01

In early July 2009, an unusually high concentration of the toxic dinoflagellate Alexandrium fundyense occurred in the western Gulf of Maine, causing surface waters to appear reddish brown to the human eye. The discolored water appeared to be the southern terminus of a large-scale event that caused shellfish toxicity along the entire coast of Maine to the Canadian border. Rapid-response shipboard sampling efforts together with satellite data suggest the water discoloration in the western Gulf of Maine was a highly ephemeral feature of less than two weeks in duration. Flow cytometric analysis of surface samples from the red water indicated the population was undergoing sexual reproduction. Cyst fluxes downstream of the discolored water were the highest ever measured in the Gulf of Maine, and a large deposit of new cysts was observed that fall. Although the mechanisms causing this event remain unknown, its timing coincided with an anomalous period of downwelling-favorable winds that could have played a role in aggregating upward-swimming cells. Regardless of the underlying causes, this event highlights the importance of short-term episodic phenomena on regional population dynamics of A. fundyense.

Determination of flow times and longitudinal dispersion coefficients in the Main river using 3HHO as tracer

International Nuclear Information System (INIS)

Krause, W.J.; Mundschenk, H.

1989-01-01

Single discharges from nuclear power plants as well as discrete labeling with tritiated water are used to determine flow times, flow velocities and longitudinal dispersion coefficients in German rivers as shown here, for example, for the Main river. (orig.)

Feature coding for image representation and recognition

CERN Document Server

Huang, Yongzhen

2015-01-01

This brief presents a comprehensive introduction to feature coding, which serves as a key module for the typical object recognition pipeline. The text offers a rich blend of theory and practice while reflects the recent developments on feature coding, covering the following five aspects: (1) Review the state-of-the-art, analyzing the motivations and mathematical representations of various feature coding methods; (2) Explore how various feature coding algorithms evolve along years; (3) Summarize the main characteristics of typical feature coding algorithms and categorize them accordingly; (4) D

Robust Features Of Surface Electromyography Signal

Science.gov (United States)

Sabri, M. I.; Miskon, M. F.; Yaacob, M. R.

2013-12-01

Nowadays, application of robotics in human life has been explored widely. Robotics exoskeleton system are one of drastically areas in recent robotic research that shows mimic impact in human life. These system have been developed significantly to be used for human power augmentation, robotics rehabilitation, human power assist, and haptic interaction in virtual reality. This paper focus on solving challenges in problem using neural signals and extracting human intent. Commonly, surface electromyography signal (sEMG) are used in order to control human intent for application exoskeleton robot. But the problem lies on difficulty of pattern recognition of the sEMG features due to high noises which are electrode and cable motion artifact, electrode noise, dermic noise, alternating current power line interface, and other noise came from electronic instrument. The main objective in this paper is to study the best features of electromyography in term of time domain (statistical analysis) and frequency domain (Fast Fourier Transform).The secondary objectives is to map the relationship between torque and best features of muscle unit activation potential (MaxPS and RMS) of biceps brachii. This project scope use primary data of 2 male sample subject which using same dominant hand (right handed), age between 20-27 years old, muscle diameter 32cm to 35cm and using single channel muscle (biceps brachii muscle). The experiment conduct 2 times repeated task of contraction and relaxation of biceps brachii when lifting different load from no load to 3kg with ascending 1kg The result shows that Fast Fourier Transform maximum power spectrum (MaxPS) has less error than mean value of reading compare to root mean square (RMS) value. Thus, Fast Fourier Transform maximum power spectrum (MaxPS) show the linear relationship against torque experience by elbow joint to lift different load. As the conclusion, the best features is MaxPS because it has the lowest error than other features and show

Robust Features Of Surface Electromyography Signal

International Nuclear Information System (INIS)

Sabri, M I; Miskon, M F; Yaacob, M R

2013-01-01

Nowadays, application of robotics in human life has been explored widely. Robotics exoskeleton system are one of drastically areas in recent robotic research that shows mimic impact in human life. These system have been developed significantly to be used for human power augmentation, robotics rehabilitation, human power assist, and haptic interaction in virtual reality. This paper focus on solving challenges in problem using neural signals and extracting human intent. Commonly, surface electromyography signal (sEMG) are used in order to control human intent for application exoskeleton robot. But the problem lies on difficulty of pattern recognition of the sEMG features due to high noises which are electrode and cable motion artifact, electrode noise, dermic noise, alternating current power line interface, and other noise came from electronic instrument. The main objective in this paper is to study the best features of electromyography in term of time domain (statistical analysis) and frequency domain (Fast Fourier Transform).The secondary objectives is to map the relationship between torque and best features of muscle unit activation potential (MaxPS and RMS) of biceps brachii. This project scope use primary data of 2 male sample subject which using same dominant hand (right handed), age between 20–27 years old, muscle diameter 32cm to 35cm and using single channel muscle (biceps brachii muscle). The experiment conduct 2 times repeated task of contraction and relaxation of biceps brachii when lifting different load from no load to 3kg with ascending 1kg The result shows that Fast Fourier Transform maximum power spectrum (MaxPS) has less error than mean value of reading compare to root mean square (RMS) value. Thus, Fast Fourier Transform maximum power spectrum (MaxPS) show the linear relationship against torque experience by elbow joint to lift different load. As the conclusion, the best features is MaxPS because it has the lowest error than other features and

CMB anomalies and the effects of local features of the inflaton potential

Energy Technology Data Exchange (ETDEWEB)

Cadavid, Alexander Gallego [Kyoto University, Yukawa Institute for Theoretical Physics, Kyoto (Japan); ICRANet, Pescara (Italy); Universidad de Antioquia, Instituto de Fisica, Medellin (Colombia); Romano, Antonio Enea [Kyoto University, Yukawa Institute for Theoretical Physics, Kyoto (Japan); University of Torino, Department of Physics, Turin (Italy); Universidad de Antioquia, Instituto de Fisica, Medellin (Colombia); Gariazzo, Stefano [University of Torino, Department of Physics, Turin (Italy); INFN, Sezione di Torino, Turin (Italy); Instituto de Fisica Corpuscular (CSIC-Universitat de Valencia), Paterna, Valencia (Spain)

2017-04-15

Recent analysis of the WMAP and Planck data have shown the presence of a dip and a bump in the spectrum of primordial perturbations at the scales k = 0.002 Mpc{sup -1}, respectively. We analyze for the first time the effects of a local feature in the inflaton potential to explain the observed deviations from scale invariance in the primordial spectrum. We perform a best-fit analysis of the cosmic microwave background (CMB) radiation temperature and polarization data. The effects of the features can improve the agreement with observational data respect to the featureless model. The best-fit local feature affects the primordial curvature spectrum mainly in the region of the bump, leaving the spectrum unaffected on other scales. (orig.)

Ordinary and Activated Bone Grafts: Applied Classification and the Main Features

Directory of Open Access Journals (Sweden)

R. V. Deev

2015-01-01

Full Text Available Bone grafts are medical devices that are in high demand in clinical practice for substitution of bone defects and recovery of atrophic bone regions. Based on the analysis of the modern groups of bone grafts, the particularities of their composition, the mechanisms of their biological effects, and their therapeutic indications, applicable classification was proposed that separates the bone substitutes into “ordinary” and “activated.” The main differential criterion is the presence of biologically active components in the material that are standardized by qualitative and quantitative parameters: growth factors, cells, or gene constructions encoding growth factors. The pronounced osteoinductive and (or osteogenic properties of activated osteoplastic materials allow drawing upon their efficacy in the substitution of large bone defects.

Depth-time interpolation of feature trends extracted from mobile microelectrode data with kernel functions.

Science.gov (United States)

Wong, Stephen; Hargreaves, Eric L; Baltuch, Gordon H; Jaggi, Jurg L; Danish, Shabbar F

2012-01-01

Microelectrode recording (MER) is necessary for precision localization of target structures such as the subthalamic nucleus during deep brain stimulation (DBS) surgery. Attempts to automate this process have produced quantitative temporal trends (feature activity vs. time) extracted from mobile MER data. Our goal was to evaluate computational methods of generating spatial profiles (feature activity vs. depth) from temporal trends that would decouple automated MER localization from the clinical procedure and enhance functional localization in DBS surgery. We evaluated two methods of interpolation (standard vs. kernel) that generated spatial profiles from temporal trends. We compared interpolated spatial profiles to true spatial profiles that were calculated with depth windows, using correlation coefficient analysis. Excellent approximation of true spatial profiles is achieved by interpolation. Kernel-interpolated spatial profiles produced superior correlation coefficient values at optimal kernel widths (r = 0.932-0.940) compared to standard interpolation (r = 0.891). The choice of kernel function and kernel width resulted in trade-offs in smoothing and resolution. Interpolation of feature activity to create spatial profiles from temporal trends is accurate and can standardize and facilitate MER functional localization of subcortical structures. The methods are computationally efficient, enhancing localization without imposing additional constraints on the MER clinical procedure during DBS surgery. Copyright © 2012 S. Karger AG, Basel.

Improved design features of KSNP+ BOP Fluid System

International Nuclear Information System (INIS)

Park, Heung Gyu; Yoon, Kyung Sup

2002-01-01

KOPEC (Korea Power Engineering Co.) in conjunction with the client KHNP (Korea Hydro and Nuclear Power Co.) has been developing the KSNP + (Improved Korean Standard Nuclear Power Plants) design concept since 1998. The main objective of the KSNP + is to enhance safety and economy of KSNP. The design concepts of the KSNP + will be implemented in Shin-Kori Units 1 and 2 Shin-Wolsung Units 1 and 2. This paper provides on an introduction to the improved design features of the KSNP + BOP fluid system consisting of 45 design improvement items. The design improvement concepts of the BOP fluid system have been developed as follows: optimization of system configuration and capacity, simplification of system, and adoption of advanced design features. Improved design features of the BOP fluid system allow additional benefits due to making a contribution to the optimization of plant arrangement and the reduction of operating costs during the plant life time. In conclusion, design improvement to the BOP fluid system have contributed to the KSNP + design concept being more reliable, safe and economically competitive

A performance evaluation of point pair features

DEFF Research Database (Denmark)

Kiforenko, Lilita; Drost, Bertram; Tombari, Federico

2018-01-01

have low resolution data, where local histogram features show a higher performance than PPFs. We also found that PPFs compared to most local histogram features degrade faster under disturbances such as occlusion and clutter, however, PPFs still remain more descriptive on an absolute scale. The main...

On a Batch Arrival Queuing System Equipped with a Stand-by Server during Vacation Periods or the Repairs Times of the Main Server

Directory of Open Access Journals (Sweden)

Rehab F. Khalaf

2011-01-01

Full Text Available We study a queuing system which is equipped with a stand-by server in addition to the main server. The stand-by server provides service to customers only during the period of absence of the main server when either the main server is on a vacation or it is in the state of repairs due to a sudden failure from time to time. The service times, vacation times, and repair times are assumed to follow general arbitrary distributions while the stand-by service times follow exponential distribution. Supplementary variables technique has been used to obtain steady state results in explicit and closed form in terms of the probability generating functions for the number of customers in the queue, the average number of customers, and the average waiting time in the queue while the MathCad software has been used to illustrate the numerical results in this work.

Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet energy spectrum

Science.gov (United States)

Kong, Yun; Wang, Tianyang; Li, Zheng; Chu, Fulei

2017-09-01

Planetary transmission plays a vital role in wind turbine drivetrains, and its fault diagnosis has been an important and challenging issue. Owing to the complicated and coupled vibration source, time-variant vibration transfer path, and heavy background noise masking effect, the vibration signal of planet gear in wind turbine gearboxes exhibits several unique characteristics: Complex frequency components, low signal-to-noise ratio, and weak fault feature. In this sense, the periodic impulsive components induced by a localized defect are hard to extract, and the fault detection of planet gear in wind turbines remains to be a challenging research work. Aiming to extract the fault feature of planet gear effectively, we propose a novel feature extraction method based on spectral kurtosis and time wavelet energy spectrum (SK-TWES) in the paper. Firstly, the spectral kurtosis (SK) and kurtogram of raw vibration signals are computed and exploited to select the optimal filtering parameter for the subsequent band-pass filtering. Then, the band-pass filtering is applied to extrude periodic transient impulses using the optimal frequency band in which the corresponding SK value is maximal. Finally, the time wavelet energy spectrum analysis is performed on the filtered signal, selecting Morlet wavelet as the mother wavelet which possesses a high similarity to the impulsive components. The experimental signals collected from the wind turbine gearbox test rig demonstrate that the proposed method is effective at the feature extraction and fault diagnosis for the planet gear with a localized defect.

FEATURES OF CRISIS MANAGEMENT IN ENTERPRISES

Directory of Open Access Journals (Sweden)

K. D. Busygin

2014-01-01

Full Text Available The article considers the value of preventive management in modern conditions. The global fi nancial and economic crisis of 2008-2010. sharpened interest in the problems of crisis management. This interest is manifested at the level of individual businesses, and at the level of the economy as a whole. At the same time revealed a signifi cant drawback: the development of crisis management theory lags behind practice. Non-compliance of the existing theory to modern requirements leads to the fact that the known approaches are not based on theoretical positions and empirical evidence and best practices, and, consequently, do not diff er systematically, because of this requires further research in this direction. The analysis shows that crisis management is a complex control system, which has its own specifi c features. Feature development solutions in crisis situations caused by the fact that they can only wear improving change with the obligatory account the limiting parameters of sustainable livelihoods enterprise (structure funds, personnel, activity profi le, the main products, and others.

Comprehensive chlorophyll composition in the main edible seaweeds.

Science.gov (United States)

Chen, Kewei; Ríos, José Julián; Pérez-Gálvez, Antonio; Roca, María

2017-08-01

Natural chlorophylls present in seaweeds have been studied regarding their biological activities and health benefit effects. However, detailed studies regarding characterization of the complete chlorophyll profile either qualitatively and quantitatively are scarce. This work deals with the comprehensive spectrometric study of the chlorophyll derivatives present in the five main coloured edible seaweeds. The novel complete MS 2 characterization of five chlorophyll derivatives: chlorophyll c 2 , chlorophyll c 1 , purpurin-18 a, pheophytin d and phytyl-purpurin-18 a has allowed to obtain fragmentation patterns associated with their different structural features. New chlorophyll derivatives have been identified and quantified by first time in red, green and brown seaweeds, including some oxidative structures. Quantitative data of the chlorophyll content comes to achieve significant information for food composition databases in bioactive compounds. Copyright © 2017 Elsevier Ltd. All rights reserved.

Object-Based Paddy Rice Mapping Using HJ-1A/B Data and Temporal Features Extracted from Time Series MODIS NDVI Data.

Science.gov (United States)

Singha, Mrinal; Wu, Bingfang; Zhang, Miao

2016-12-22

Accurate and timely mapping of paddy rice is vital for food security and environmental sustainability. This study evaluates the utility of temporal features extracted from coarse resolution data for object-based paddy rice classification of fine resolution data. The coarse resolution vegetation index data is first fused with the fine resolution data to generate the time series fine resolution data. Temporal features are extracted from the fused data and added with the multi-spectral data to improve the classification accuracy. Temporal features provided the crop growth information, while multi-spectral data provided the pattern variation of paddy rice. The achieved overall classification accuracy and kappa coefficient were 84.37% and 0.68, respectively. The results indicate that the use of temporal features improved the overall classification accuracy of a single-date multi-spectral image by 18.75% from 65.62% to 84.37%. The minimum sensitivity (MS) of the paddy rice classification has also been improved. The comparison showed that the mapped paddy area was analogous to the agricultural statistics at the district level. This work also highlighted the importance of feature selection to achieve higher classification accuracies. These results demonstrate the potential of the combined use of temporal and spectral features for accurate paddy rice classification.

A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals.

Science.gov (United States)

Zarei, Roozbeh; He, Jing; Siuly, Siuly; Zhang, Yanchun

2017-07-01

Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the time complexity. This study develops a robust feature extraction method combining the principal component analysis (PCA) and the cross-covariance technique (CCOV) for the extraction of discriminatory information from the mental states based on EEG signals in BCI applications. We apply the correlation based variable selection method with the best first search on the extracted features to identify the best feature set for characterizing the distribution of mental state signals. To verify the robustness of the proposed feature extraction method, three machine learning techniques: multilayer perceptron neural networks (MLP), least square support vector machine (LS-SVM), and logistic regression (LR) are employed on the obtained features. The proposed methods are evaluated on two publicly available datasets. Furthermore, we evaluate the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that all three classifiers achieve high performance (above 99% overall classification accuracy) for the proposed feature set. Among these classifiers, the MLP and LS-SVM methods yield the best performance for the obtained feature. The average sensitivity, specificity and classification accuracy for these two classifiers are same, which are 99.32%, 100%, and 99.66%, respectively for the BCI competition dataset IVa and 100%, 100%, and 100%, for the BCI competition dataset IVb. The results also indicate the proposed methods outperform the most recently reported methods by at least 0.25% average accuracy improvement in dataset IVa. The execution time

Simulation of the Quantity, Variability, and Timing of Streamflow in the Dennys River Basin, Maine, by Use of a Precipitation-Runoff Watershed Model

Science.gov (United States)

Dudley, Robert W.

2008-01-01

The U.S. Geological Survey (USGS), in cooperation with the Maine Department of Marine Resources Bureau of Sea Run Fisheries and Habitat, began a study in 2004 to characterize the quantity, variability, and timing of streamflow in the Dennys River. The study included a synoptic summary of historical streamflow data at a long-term streamflow gage, collecting data from an additional four short-term streamflow gages, and the development and evaluation of a distributed-parameter watershed model for the Dennys River Basin. The watershed model used in this investigation was the USGS Precipitation-Runoff Modeling System (PRMS). The Geographic Information System (GIS) Weasel was used to delineate the Dennys River Basin and subbasins and derive parameters for their physical geographic features. Calibration of the models used in this investigation involved a four-step procedure in which model output was evaluated against four calibration data sets using computed objective functions for solar radiation, potential evapotranspiration, annual and seasonal water budgets, and daily streamflows. The calibration procedure involved thousands of model runs and was carried out using the USGS software application Luca (Let us calibrate). Luca uses the Shuffled Complex Evolution (SCE) global search algorithm to calibrate the model parameters. The SCE method reliably produces satisfactory solutions for large, complex optimization problems. The primary calibration effort went into the Dennys main stem watershed model. Calibrated parameter values obtained for the Dennys main stem model were transferred to the Cathance Stream model, and a similar four-step SCE calibration procedure was performed; this effort was undertaken to determine the potential to transfer modeling information to a nearby basin in the same region. The calibrated Dennys main stem watershed model performed with Nash-Sutcliffe efficiency (NSE) statistic values for the calibration period and evaluation period of 0.79 and 0

Acoustic Longitudinal Field NIF Optic Feature Detection Map Using Time-Reversal & MUSIC

Energy Technology Data Exchange (ETDEWEB)

Lehman, S K

2006-02-09

We developed an ultrasonic longitudinal field time-reversal and MUltiple SIgnal Classification (MUSIC) based detection algorithm for identifying and mapping flaws in fused silica NIF optics. The algorithm requires a fully multistatic data set, that is one with multiple, independently operated, spatially diverse transducers, each transmitter of which, in succession, launches a pulse into the optic and the scattered signal measured and recorded at every receiver. We have successfully localized engineered ''defects'' larger than 1 mm in an optic. We confirmed detection and localization of 3 mm and 5 mm features in experimental data, and a 0.5 mm in simulated data with sufficiently high signal-to-noise ratio. We present the theory, experimental results, and simulated results.

Magnetic mirror fusion systems: Characteristics and distinctive features

International Nuclear Information System (INIS)

Post, R.F.

1987-01-01

A tutorial account is given of the main characteristics and distinctive features of conceptual magnetic fusion systems employing the magnetic mirror principle. These features are related to the potential advantages that mirror-based fusion systems may exhibit for the generation of economic fusion power

Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods.

Science.gov (United States)

Qu, Kaiyang; Han, Ke; Wu, Song; Wang, Guohua; Wei, Leyi

2017-09-22

DNA-binding proteins play vital roles in cellular processes, such as DNA packaging, replication, transcription, regulation, and other DNA-associated activities. The current main prediction method is based on machine learning, and its accuracy mainly depends on the features extraction method. Therefore, using an efficient feature representation method is important to enhance the classification accuracy. However, existing feature representation methods cannot efficiently distinguish DNA-binding proteins from non-DNA-binding proteins. In this paper, a multi-feature representation method, which combines three feature representation methods, namely, K-Skip-N-Grams, Information theory, and Sequential and structural features (SSF), is used to represent the protein sequences and improve feature representation ability. In addition, the classifier is a support vector machine. The mixed-feature representation method is evaluated using 10-fold cross-validation and a test set. Feature vectors, which are obtained from a combination of three feature extractions, show the best performance in 10-fold cross-validation both under non-dimensional reduction and dimensional reduction by max-relevance-max-distance. Moreover, the reduced mixed feature method performs better than the non-reduced mixed feature technique. The feature vectors, which are a combination of SSF and K-Skip-N-Grams, show the best performance in the test set. Among these methods, mixed features exhibit superiority over the single features.

Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods

Directory of Open Access Journals (Sweden)

Kaiyang Qu

2017-09-01

Full Text Available DNA-binding proteins play vital roles in cellular processes, such as DNA packaging, replication, transcription, regulation, and other DNA-associated activities. The current main prediction method is based on machine learning, and its accuracy mainly depends on the features extraction method. Therefore, using an efficient feature representation method is important to enhance the classification accuracy. However, existing feature representation methods cannot efficiently distinguish DNA-binding proteins from non-DNA-binding proteins. In this paper, a multi-feature representation method, which combines three feature representation methods, namely, K-Skip-N-Grams, Information theory, and Sequential and structural features (SSF, is used to represent the protein sequences and improve feature representation ability. In addition, the classifier is a support vector machine. The mixed-feature representation method is evaluated using 10-fold cross-validation and a test set. Feature vectors, which are obtained from a combination of three feature extractions, show the best performance in 10-fold cross-validation both under non-dimensional reduction and dimensional reduction by max-relevance-max-distance. Moreover, the reduced mixed feature method performs better than the non-reduced mixed feature technique. The feature vectors, which are a combination of SSF and K-Skip-N-Grams, show the best performance in the test set. Among these methods, mixed features exhibit superiority over the single features.

Further improvement of human-machine interface for ABWR main control room

International Nuclear Information System (INIS)

Makino, S.

2001-01-01

Tokyo Electric Power Company (TEPCO) has developed main control room panels based on progress in C and I technology. ABWR type main control room panels (ABWR MCR PNLs) are categorized as third generation type domestic BWR MCR, that is, they are were developed step by step based on operating experience with the first and the second generation BWR. ABWR type main control room panels were applied to Kashiwazaki-Kariwa Nuclear Power Station Units Number 6 and 7 (K-6/7) for the first time. K-6/7 are the first advanced BWR (ABWR), which started commercial operation in November 1996 and July 1997, respectively. The concept of ABWR MCR design was verified through wooden mock-up panels, start-up tests and commercial operation. Though the K-6/7 design has borne fruit, we are planning to refine and standardize the design based on the following concepts: to maintain the plant operation and monitoring style of ABWR MCR PNLs; to introduce brand-new HMI technology and devices; to incorporate operators' advice in the design. This paper outlines the features and improvements of the K6/7 MCR PNLs design. (author)

Estimation of T2 relaxation time of breast cancer: Correlation with clinical, imaging and pathological features

Energy Technology Data Exchange (ETDEWEB)

Seo, Mirinae; Sohn, Yu Mee [Dept. of Radiology, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, Seoul (Korea, Republic of); Ryu, Jung Kyu; Jahng, Geon Ho; Rhee, Sun Jung; Oh, Jang Hoon; Won, Kyu Yeoun [Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul (Korea, Republic of)

2017-01-15

The purpose of this study was to estimate the T2* relaxation time in breast cancer, and to evaluate the association between the T2* value with clinical-imaging-pathological features of breast cancer. Between January 2011 and July 2013, 107 consecutive women with 107 breast cancers underwent multi-echo T2*-weighted imaging on a 3T clinical magnetic resonance imaging system. The Student's t test and one-way analysis of variance were used to compare the T2* values of cancer for different groups, based on the clinical-imaging-pathological features. In addition, multiple linear regression analysis was performed to find independent predictive factors associated with the T2* values. Of the 107 breast cancers, 92 were invasive and 15 were ductal carcinoma in situ (DCIS). The mean T2* value of invasive cancers was significantly longer than that of DCIS (p = 0.029). Signal intensity on T2-weighted imaging (T2WI) and histologic grade of invasive breast cancers showed significant correlation with T2* relaxation time in univariate and multivariate analysis. Breast cancer groups with higher signal intensity on T2WI showed longer T2* relaxation time (p = 0.005). Cancer groups with higher histologic grade showed longer T2* relaxation time (p = 0.017). The T2* value is significantly longer in invasive cancer than in DCIS. In invasive cancers, T2* relaxation time is significantly longer in higher histologic grades and high signal intensity on T2WI. Based on these preliminary data, quantitative T2* mapping has the potential to be useful in the characterization of breast cancer.

A lower dimensional feature vector for identification of partial discharges of different origin using time measurements

International Nuclear Information System (INIS)

Evagorou, Demetres; Kyprianou, Andreas; Georghiou, George E; Lewin, Paul L; Stavrou, Andreas

2012-01-01

Partial discharge (PD) classification into sources of different origin is essential in evaluating the severity of the damage caused by its activity on the insulation of power cables and their accessories. More specifically, some types of PD can be classified as having a detrimental effect on the integrity of the insulation while others can be deemed relatively harmless, rendering the correct classification of different PD types of vital importance to electrical utilities. In this work, a feature vector was proposed based on higher order statistics on selected nodes of the wavelet packet transform (WPT) coefficients of time domain measurements, which can compactly represent the characteristics of different PD sources. To assess its performance, experimental data acquired under laboratory conditions for four different PD sources encountered in power systems were used. The two learning machine methods, namely the support vector machine and the probabilistic neural network, employed as the classification algorithms, achieved overall classification rates of around 98%. In comparison, the utilization of the scaled, raw WPT coefficients as a feature vector resulted in classification accuracy of around 99%, but with a significantly higher number of dimensions (1304 to 16), validating the PD identification ability of the proposed feature. Dimensionality reduction becomes a key factor in online, real-time data collection and processing of PD measurements, reducing the classification effort and the data-storage requirements. Therefore, the proposed method can constitute a potential tool for such online measurements, after addressing issues related to on-site measurements such as the rejection of interference. (paper)

Effect of various features on the life cycle cost of the timing/synchronization subsystem of the DCS digital communications network

Science.gov (United States)

Kimsey, D. B.

1978-01-01

The effect on the life cycle cost of the timing subsystem was examined, when these optional features were included in various combinations. The features included mutual control, directed control, double-ended reference links, independence of clock error measurement and correction, phase reference combining, self-organization, smoothing for link and nodal dropouts, unequal reference weightings, and a master in a mutual control network. An overall design of a microprocessor-based timing subsystem was formulated. The microprocessor (8080) implements the digital filter portion of a digital phase locked loop, as well as other control functions such as organization of the network through communication with processors at neighboring nodes.

Odd time formulation of the Batalin-Vilkovisky method of quantization

International Nuclear Information System (INIS)

Dayi, O.F.

1988-08-01

By using a Grassmann odd parameter which behaves like time, it is shown that the main features of the Batalin-Fradkin method of quantization of reducible gauge theories can be formulated systematically. (author). 6 refs

Extracting time-frequency feature of single-channel vastus medialis EMG signals for knee exercise pattern recognition.

Directory of Open Access Journals (Sweden)

Yi Zhang

Full Text Available The EMG signal indicates the electrophysiological response to daily living of activities, particularly to lower-limb knee exercises. Literature reports have shown numerous benefits of the Wavelet analysis in EMG feature extraction for pattern recognition. However, its application to typical knee exercises when using only a single EMG channel is limited. In this study, three types of knee exercises, i.e., flexion of the leg up (standing, hip extension from a sitting position (sitting and gait (walking are investigated from 14 healthy untrained subjects, while EMG signals from the muscle group of vastus medialis and the goniometer on the knee joint of the detected leg are synchronously monitored and recorded. Four types of lower-limb motions including standing, sitting, stance phase of walking, and swing phase of walking, are segmented. The Wavelet Transform (WT based Singular Value Decomposition (SVD approach is proposed for the classification of four lower-limb motions using a single-channel EMG signal from the muscle group of vastus medialis. Based on lower-limb motions from all subjects, the combination of five-level wavelet decomposition and SVD is used to comprise the feature vector. The Support Vector Machine (SVM is then configured to build a multiple-subject classifier for which the subject independent accuracy will be given across all subjects for the classification of four types of lower-limb motions. In order to effectively indicate the classification performance, EMG features from time-domain (e.g., Mean Absolute Value (MAV, Root-Mean-Square (RMS, integrated EMG (iEMG, Zero Crossing (ZC and frequency-domain (e.g., Mean Frequency (MNF and Median Frequency (MDF are also used to classify lower-limb motions. The five-fold cross validation is performed and it repeats fifty times in order to acquire the robust subject independent accuracy. Results show that the proposed WT-based SVD approach has the classification accuracy of 91.85%±0

Bread crumb classification using fractal and multifractal features

OpenAIRE

Baravalle, Rodrigo Guillermo; Delrieux, Claudio Augusto; Gómez, Juan Carlos

2017-01-01

Adequate image descriptors are fundamental in image classification and object recognition. Main requirements for image features are robustness and low dimensionality which would lead to low classification errors in a variety of situations and with a reasonable computational cost. In this context, the identification of materials poses a significant challenge, since typical (geometric and/or differential) feature extraction methods are not robust enough. Texture features based on Fourier or wav...

A proposed framework on hybrid feature selection techniques for handling high dimensional educational data

Science.gov (United States)

Shahiri, Amirah Mohamed; Husain, Wahidah; Rashid, Nur'Aini Abd

2017-10-01

Huge amounts of data in educational datasets may cause the problem in producing quality data. Recently, data mining approach are increasingly used by educational data mining researchers for analyzing the data patterns. However, many research studies have concentrated on selecting suitable learning algorithms instead of performing feature selection process. As a result, these data has problem with computational complexity and spend longer computational time for classification. The main objective of this research is to provide an overview of feature selection techniques that have been used to analyze the most significant features. Then, this research will propose a framework to improve the quality of students' dataset. The proposed framework uses filter and wrapper based technique to support prediction process in future study.

A linear-time algorithm for Euclidean feature transform sets

NARCIS (Netherlands)

Hesselink, Wim H.

2007-01-01

The Euclidean distance transform of a binary image is the function that assigns to every pixel the Euclidean distance to the background. The Euclidean feature transform is the function that assigns to every pixel the set of background pixels with this distance. We present an algorithm to compute the

Site Features

Data.gov (United States)

U.S. Environmental Protection Agency — This dataset consists of various site features from multiple Superfund sites in U.S. EPA Region 8. These data were acquired from multiple sources at different times...

Textural features and SUV-based variables assessed by dual time point 18F-FDG PET/CT in locally advanced breast cancer.

Science.gov (United States)

Garcia-Vicente, Ana María; Molina, David; Pérez-Beteta, Julián; Amo-Salas, Mariano; Martínez-González, Alicia; Bueno, Gloria; Tello-Galán, María Jesús; Soriano-Castrejón, Ángel

2017-12-01

To study the influence of dual time point 18F-FDG PET/CT in textural features and SUV-based variables and their relation among them. Fifty-six patients with locally advanced breast cancer (LABC) were prospectively included. All of them underwent a standard 18F-FDG PET/CT (PET-1) and a delayed acquisition (PET-2). After segmentation, SUV variables (SUVmax, SUVmean, and SUVpeak), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained. Eighteen three-dimensional (3D) textural measures were computed including: run-length matrices (RLM) features, co-occurrence matrices (CM) features, and energies. Differences between all PET-derived variables obtained in PET-1 and PET-2 were studied. Significant differences were found between the SUV-based parameters and MTV obtained in the dual time point PET/CT, with higher values of SUV-based variables and lower MTV in the PET-2 with respect to the PET-1. In relation with the textural parameters obtained in dual time point acquisition, significant differences were found for the short run emphasis, low gray-level run emphasis, short run high gray-level emphasis, run percentage, long run emphasis, gray-level non-uniformity, homogeneity, and dissimilarity. Textural variables showed relations with MTV and TLG. Significant differences of textural features were found in dual time point 18F-FDG PET/CT. Thus, a dynamic behavior of metabolic characteristics should be expected, with higher heterogeneity in delayed PET acquisition compared with the standard PET. A greater heterogeneity was found in bigger tumors.

Spatial reorientation in rats (Rattus norvegicus): Use of geometric and featural information as a function of arena size and feature location

NARCIS (Netherlands)

Maes, J.H.R.; Fontanari, L.; Regolin, L.

2009-01-01

Rats were used in a spatial reorientation task to assess their ability to use geometric and non-geometric, featural, information. Experimental conditions differed in the size of the arena (small, medium, or large) and whether the food-baited corner was near or far from a visual feature. The main

Altered cerebral blood flow velocity features in fibromyalgia patients in resting-state conditions.

Science.gov (United States)

Rodríguez, Alejandro; Tembl, José; Mesa-Gresa, Patricia; Muñoz, Miguel Ángel; Montoya, Pedro; Rey, Beatriz

2017-01-01

The aim of this study is to characterize in resting-state conditions the cerebral blood flow velocity (CBFV) signals of fibromyalgia patients. The anterior and middle cerebral arteries of both hemispheres from 15 women with fibromyalgia and 15 healthy women were monitored using Transcranial Doppler (TCD) during a 5-minute eyes-closed resting period. Several signal processing methods based on time, information theory, frequency and time-frequency analyses were used in order to extract different features to characterize the CBFV signals in the different vessels. Main results indicated that, in comparison with control subjects, fibromyalgia patients showed a higher complexity of the envelope CBFV and a different distribution of the power spectral density. In addition, it has been observed that complexity and spectral features show correlations with clinical pain parameters and emotional factors. The characterization features were used in a lineal model to discriminate between fibromyalgia patients and healthy controls, providing a high accuracy. These findings indicate that CBFV signals, specifically their complexity and spectral characteristics, contain information that may be relevant for the assessment of fibromyalgia patients in resting-state conditions.

Nonmotor Features in Atypical Parkinsonism.

Science.gov (United States)

Bhatia, Kailash P; Stamelou, Maria

2017-01-01

Atypical parkinsonism (AP) comprises mainly multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD), which are distinct pathological entities, presenting with a wide phenotypic spectrum. The classic syndromes are now called MSA-parkinsonism (MSA-P), MSA-cerebellar type (MSA-C), Richardson's syndrome, and corticobasal syndrome. Nonmotor features in AP have been recognized almost since the initial description of these disorders; however, research has been limited. Autonomic dysfunction is the most prominent nonmotor feature of MSA, but also gastrointestinal symptoms, sleep dysfunction, and pain, can be a feature. In PSP and CBD, the most prominent nonmotor symptoms comprise those deriving from the cognitive/neuropsychiatric domain. Apart from assisting the clinician in the differential diagnosis with Parkinson's disease, nonmotor features in AP have a big impact on quality of life and prognosis of AP and their treatment poses a major challenge for clinicians. © 2017 Elsevier Inc. All rights reserved.

Biometric feature extraction using local fractal auto-correlation

International Nuclear Information System (INIS)

Chen Xi; Zhang Jia-Shu

2014-01-01

Image texture feature extraction is a classical means for biometric recognition. To extract effective texture feature for matching, we utilize local fractal auto-correlation to construct an effective image texture descriptor. Three main steps are involved in the proposed scheme: (i) using two-dimensional Gabor filter to extract the texture features of biometric images; (ii) calculating the local fractal dimension of Gabor feature under different orientations and scales using fractal auto-correlation algorithm; and (iii) linking the local fractal dimension of Gabor feature under different orientations and scales into a big vector for matching. Experiments and analyses show our proposed scheme is an efficient biometric feature extraction approach. (condensed matter: structural, mechanical, and thermal properties)

Real-Time Detection and Measurement of Eye Features from Color Images

Directory of Open Access Journals (Sweden)

Diana Borza

2016-07-01

Full Text Available The accurate extraction and measurement of eye features is crucial to a variety of domains, including human-computer interaction, biometry, and medical research. This paper presents a fast and accurate method for extracting multiple features around the eyes: the center of the pupil, the iris radius, and the external shape of the eye. These features are extracted using a multistage algorithm. On the first stage the pupil center is localized using a fast circular symmetry detector and the iris radius is computed using radial gradient projections, and on the second stage the external shape of the eye (of the eyelids is determined through a Monte Carlo sampling framework based on both color and shape information. Extensive experiments performed on a different dataset demonstrate the effectiveness of our approach. In addition, this work provides eye annotation data for a publicly-available database.

Probabilistic Slow Features for Behavior Analysis

NARCIS (Netherlands)

Zafeiriou, Lazaros; Nicolaou, Mihalis A.; Zafeiriou, Stefanos; Nikitidis, Symeon; Pantic, Maja

A recently introduced latent feature learning technique for time-varying dynamic phenomena analysis is the so-called slow feature analysis (SFA). SFA is a deterministic component analysis technique for multidimensional sequences that, by minimizing the variance of the first-order time derivative

Learning slow features for behavior analysis

NARCIS (Netherlands)

Zafeiriou, Lazaros; Nicolaou, Mihalis A.; Zafeiriou, Stefanos; Nikitids, Symeon; Pantic, Maja

2013-01-01

A recently introduced latent feature learning technique for time varying dynamic phenomena analysis is the socalled Slow Feature Analysis (SFA). SFA is a deterministic component analysis technique for multi-dimensional sequences that by minimizing the variance of the first order time derivative

Main Trends and Features of Social Protection in Countries of EU

OpenAIRE

Bondar Nataliya A.

2012-01-01

In the article it was identified the main areas of realization of social protection of disabled persons and it was considered the experience of states that are part of the EU, concerning conditions for various types of assistance within these areas. Identified deficiencies in the system of social protection in the Member States of the EU.В статье определены основные направления реализации социальной защиты инвалидов и рассмотрен опыт государств, которые входят в состав ЕС, относительно услови...

Innovations in individual feature history management - The significance of feature-based temporal model

Science.gov (United States)

Choi, J.; Seong, J.C.; Kim, B.; Usery, E.L.

2008-01-01

A feature relies on three dimensions (space, theme, and time) for its representation. Even though spatiotemporal models have been proposed, they have principally focused on the spatial changes of a feature. In this paper, a feature-based temporal model is proposed to represent the changes of both space and theme independently. The proposed model modifies the ISO's temporal schema and adds new explicit temporal relationship structure that stores temporal topological relationship with the ISO's temporal primitives of a feature in order to keep track feature history. The explicit temporal relationship can enhance query performance on feature history by removing topological comparison during query process. Further, a prototype system has been developed to test a proposed feature-based temporal model by querying land parcel history in Athens, Georgia. The result of temporal query on individual feature history shows the efficiency of the explicit temporal relationship structure. ?? Springer Science+Business Media, LLC 2007.

An Overview of Android Operating System and Its Security Features

OpenAIRE

Rajinder Singh

2014-01-01

Android operating system is one of the most widely used operating system these days. Android Operating System is mainly divided into four main layers: the kernel, libraries, application framework and applications. Its kernel is based on Linux. Linux kernel is used to manage core system services such as virtual memory, networking, drivers, and power management. In these paper different features of architecture of Android OS as well security features of Android OS are discussed.

Effect of mobile technology featuring visual scene displays and just-in-time programming on communication turns by preadolescent and adolescent beginning communicators.

Science.gov (United States)

Holyfield, Christine; Caron, Jessica Gosnell; Drager, Kathryn; Light, Janice

2018-03-05

Visual scene displays (VSDs) and just-in-time programming supports are augmentative and alternative communication (AAC) technology features with theoretical benefits for beginning communicators of all ages. The goal of the current study was to evaluate the effects of a communication application (app) on mobile technology that supported the just-in-time programming of VSDs on the communication of preadolescents and adolescents who were beginning communicators. A single-subject multiple-baseline across participant design was employed to evaluate the effect of the AAC app with VSDs programmed just-in-time by the researcher on the communication turns expressed by five preadolescents and adolescents (9-18 years old) who were beginning communicators. All five participants demonstrated marked increases in the frequency of their communication turns after the onset intervention. Just-in-time programming support and VSDs are two features that may positively impact communication for beginning communicators in preadolescence and adolescence. Apps with these features allow partners to quickly and easily capture photos of meaningful and motivating events and provide them immediately as VSDs with relevant vocabulary to support communication in response to beginning communicators' interests.

Features of a time domain simulation tool for rigid riser design

Energy Technology Data Exchange (ETDEWEB)

Morooka, Celso K.; Brandt, Dustin M. [Universidade Estadual de Campinas (UNICAMP), SP (Brazil). Faculdade de Engenharia Mecanica. Dept. de Engenharia de Petroleo; Matt, Cyntia G.C.; Franciss, Ricardo [PETROBRAS S.A., Rio de Janeiro, RJ (Brazil). Centro de Pesquisas

2008-07-01

This paper present a number of numerical implementations designed for the analysis of rigid riser's static and dynamic behavior that includes the effects of vortex induced vibrations (VIV) and marine hydrodynamic loads in time domain. Features include the ability to consider pipe with a free-span utilizing a soil/riser interaction model. An implementation of a numerical coupling scheme to couple the vertical riser and platform dynamics was developed to allow prediction of the sub sea Blow-Out Preventer (BOP) re-entry into a sub sea petroleum well when drilling different phases of deep and ultra-deep wells. The developments contains support for the consideration of the Self Standing Hybrid Riser (SSHR) configuration which has been shown to be a promising riser configuration in deep and ultra-deep waters. A graphical interface was also created to better grasp the results and aid in the modeling, processing and to help analyze the numerical simulations, contributing to enhance agility and quality of the riser design and analysis processes. (author)

FEATURES OF CONSOLIDATED FINANCIAL STATEMENTS: FOREIGN EXPERIENCE

Directory of Open Access Journals (Sweden)

S. V. KUCHER

2016-12-01

Full Text Available The article researches the features of preparation and submission of the consolidated financial statements of the world countries of different systems of accounting standardization in order to identify the areas of accounting improvement for the process of consolidation of financial reporting indicators. The main problems of consolidated financial statements preparation by business entities in Ukraine are determined. The author determines the theoretical and practical problems of consolidation of financial statements of organizational and methodical character. The comparative analysis of the features of standardization process of financial statements consolidation in the world countries is carried out. The main differences in the requirements for the formation of consolidated financial statements indicators of such countries as the French Republic, the Federal Republic of Germany, the Republic of Belarus and the People’s Republic of China are outlined. The main directions of scientific researches on the improvement of accounting and analytical support for the preparation of consolidated financial statements are formed.

Fundamental features and main problems of nuclear power and radiological safety law

International Nuclear Information System (INIS)

Moser, B.

1981-01-01

This report deals on a general basis with the legal spheres affected by the utilisation of nuclear energy and protection from ionising radiation. Following a historical survey of the development both in the field of national legisation in Austria and internationally, the five principal legal spheres are discussed in detail. These are administrative law, liability and insurance law, criminal law, constitutional law and international law. In the foreground of discussion is administrative law, which is mainly of a preventive nature. This also comprises radiological safety law. Next in importance is liability and insurance law, which, in contrast to the former, aims at compensation for damage. Criminal law is also intended to have a preventive effect. Finally, the author discusses the peaceful use of nuclear energy in relation to the constitutional law and the international law in force. (Auth.)

Simultenious binary hash and features learning for image retrieval

Science.gov (United States)

Frantc, V. A.; Makov, S. V.; Voronin, V. V.; Marchuk, V. I.; Semenishchev, E. A.; Egiazarian, K. O.; Agaian, S.

2016-05-01

Content-based image retrieval systems have plenty of applications in modern world. The most important one is the image search by query image or by semantic description. Approaches to this problem are employed in personal photo-collection management systems, web-scale image search engines, medical systems, etc. Automatic analysis of large unlabeled image datasets is virtually impossible without satisfactory image-retrieval technique. It's the main reason why this kind of automatic image processing has attracted so much attention during recent years. Despite rather huge progress in the field, semantically meaningful image retrieval still remains a challenging task. The main issue here is the demand to provide reliable results in short amount of time. This paper addresses the problem by novel technique for simultaneous learning of global image features and binary hash codes. Our approach provide mapping of pixel-based image representation to hash-value space simultaneously trying to save as much of semantic image content as possible. We use deep learning methodology to generate image description with properties of similarity preservation and statistical independence. The main advantage of our approach in contrast to existing is ability to fine-tune retrieval procedure for very specific application which allow us to provide better results in comparison to general techniques. Presented in the paper framework for data- dependent image hashing is based on use two different kinds of neural networks: convolutional neural networks for image description and autoencoder for feature to hash space mapping. Experimental results confirmed that our approach has shown promising results in compare to other state-of-the-art methods.

Optimum location of external markers using feature selection algorithms for real-time tumor tracking in external-beam radiotherapy: a virtual phantom study.

Science.gov (United States)

Nankali, Saber; Torshabi, Ahmad Esmaili; Miandoab, Payam Samadi; Baghizadeh, Amin

2016-01-08

In external-beam radiotherapy, using external markers is one of the most reliable tools to predict tumor position, in clinical applications. The main challenge in this approach is tumor motion tracking with highest accuracy that depends heavily on external markers location, and this issue is the objective of this study. Four commercially available feature selection algorithms entitled 1) Correlation-based Feature Selection, 2) Classifier, 3) Principal Components, and 4) Relief were proposed to find optimum location of external markers in combination with two "Genetic" and "Ranker" searching procedures. The performance of these algorithms has been evaluated using four-dimensional extended cardiac-torso anthropomorphic phantom. Six tumors in lung, three tumors in liver, and 49 points on the thorax surface were taken into account to simulate internal and external motions, respectively. The root mean square error of an adaptive neuro-fuzzy inference system (ANFIS) as prediction model was considered as metric for quantitatively evaluating the performance of proposed feature selection algorithms. To do this, the thorax surface region was divided into nine smaller segments and predefined tumors motion was predicted by ANFIS using external motion data of given markers at each small segment, separately. Our comparative results showed that all feature selection algorithms can reasonably select specific external markers from those segments where the root mean square error of the ANFIS model is minimum. Moreover, the performance accuracy of proposed feature selection algorithms was compared, separately. For this, each tumor motion was predicted using motion data of those external markers selected by each feature selection algorithm. Duncan statistical test, followed by F-test, on final results reflected that all proposed feature selection algorithms have the same performance accuracy for lung tumors. But for liver tumors, a correlation-based feature selection algorithm, in

Main neuroendocrine features and therapy in primary sleep troubles.

Science.gov (United States)

Amihăesei, Ioana Cristina; Mungiu, O C

2012-01-01

Insomnia is a sleep trouble in which the patient has difficulties in falling or in staying asleep. There are patients who fall asleep easily, but wake up too early; others have troubles in falling asleep and a third category has troubles with both falling and staying asleep. Independent of the type of insomnia, the final result is a poor-quality sleep, responsible for depressive or irritable mood, loss in concentration, learning and memory capacities. Sleep is essential to emotional and physical health. Inadequate sleep over a period of time is increasing the risks for obesity, diabetes, heart disease and depression. People suffering of chronic insomnia show an increased predisposition for psychiatric problems. People who had sleep troubles reported impaired ability to fulfill tasks involving memory, learning, logical reasoning and mathematical operations. New studies show that insomnia might be a result of the decrease of gamma-aminobutyric acid (GABA), a neurochemical responsible for the decrease of activity in many brain areas. Lower brain GABA levels were also found in people with major depressive disorder and anxiety disorders. Hypnotics, such as benzodiazepines are acting increasing the activity of the GABA neurons. Exposure to stress is associated with a greater risk for insomnia, with individual differences. Stress activates the sympathetic nervous system and the hypothalamic-pituitary-adrenal (HPA) axis. Increased activity of HPA axis is stimulating the secretion of corticotropin-releasing hormone, further inducing sleep disruption. Insomnia is also associated with depression and anxiety disorders, in which the HPA axis is characteristically overactive. People who show predisposition to sleep troubles have a hyperactive sympathetic nervous system, they are usually suffering from hyperarousal and they have a more intense response to stressful events. Primary sleep troubles (insomnia) has no apparent causes, is lasting more than one month, and is affecting

Effects of Lexical Features, Textual Properties, and Individual Differences on Word Processing Times during Second Language Reading Comprehension

Science.gov (United States)

Kim, Minkyung; Crossley, Scott A.; Skalicky, Stephen

2018-01-01

This study examines whether lexical features and textual properties along with individual differences on the part of readers influence word processing times during second language (L2) reading comprehension. Forty-eight Spanish-speaking adolescent and adult learners of English read nine English passages in a self-paced word-by-word reading…

Opinion mining feature-level using Naive Bayes and feature extraction based analysis dependencies

Science.gov (United States)

Sanda, Regi; Baizal, Z. K. Abdurahman; Nhita, Fhira

2015-12-01

Development of internet and technology, has major impact and providing new business called e-commerce. Many e-commerce sites that provide convenience in transaction, and consumers can also provide reviews or opinions on products that purchased. These opinions can be used by consumers and producers. Consumers to know the advantages and disadvantages of particular feature of the product. Procuders can analyse own strengths and weaknesses as well as it's competitors products. Many opinions need a method that the reader can know the point of whole opinion. The idea emerged from review summarization that summarizes the overall opinion based on sentiment and features contain. In this study, the domain that become the main focus is about the digital camera. This research consisted of four steps 1) giving the knowledge to the system to recognize the semantic orientation of an opinion 2) indentify the features of product 3) indentify whether the opinion gives a positive or negative 4) summarizing the result. In this research discussed the methods such as Naï;ve Bayes for sentiment classification, and feature extraction algorithm based on Dependencies Analysis, which is one of the tools in Natural Language Processing (NLP) and knowledge based dictionary which is useful for handling implicit features. The end result of research is a summary that contains a bunch of reviews from consumers on the features and sentiment. With proposed method, accuration for sentiment classification giving 81.2 % for positive test data, 80.2 % for negative test data, and accuration for feature extraction reach 90.3 %.

Improving scale invariant feature transform-based descriptors with shape-color alliance robust feature

Science.gov (United States)

Wang, Rui; Zhu, Zhengdan; Zhang, Liang

2015-05-01

Constructing appropriate descriptors for interest points in image matching is a critical aspect task in computer vision and pattern recognition. A method as an extension of the scale invariant feature transform (SIFT) descriptor called shape-color alliance robust feature (SCARF) descriptor is presented. To address the problem that SIFT is designed mainly for gray images and lack of global information for feature points, the proposed approach improves the SIFT descriptor by means of a concentric-rings model, as well as integrating the color invariant space and shape context with SIFT to construct the SCARF descriptor. The SCARF method developed is more robust than the conventional SIFT with respect to not only the color and photometrical variations but also the measuring similarity as a global variation between two shapes. A comparative evaluation of different descriptors is carried out showing that the SCARF approach provides better results than the other four state-of-the-art related methods.

The time-course of feature interference in agreement comprehension: Multiple mechanisms and asymmetrical attraction.

Science.gov (United States)

Tanner, Darren; Nicol, Janet; Brehm, Laurel

2014-10-01

Attraction interference in language comprehension and production may be as a result of common or different processes. In the present paper, we investigate attraction interference during language comprehension, focusing on the contexts in which interference arises and the time-course of these effects. Using evidence from event-related brain potentials (ERPs) and sentence judgment times, we show that agreement attraction in comprehension is best explained as morphosyntactic interference during memory retrieval. This stands in contrast to attraction as a message-level process involving the representation of the subject NP's number features, which is a strong contributor to attraction in production. We thus argue that the cognitive antecedents of agreement attraction in comprehension are non-identical with those of attraction in production, and moreover, that attraction in comprehension is primarily a consequence of similarity-based interference in cue-based memory retrieval processes. We suggest that mechanisms responsible for attraction during language comprehension are a subset of those involved in language production.

Improvement of main control room

International Nuclear Information System (INIS)

Chae, Sung Ki; Ham, Chang Sik; Kwon, Ki Chun

1991-07-01

Information display system, advanced alarm system and fiber optical communication system were developed to improve the main control room in nuclear power plant. Establishing the new hierachical information structure of plant operation data, plant overview status board(POSB) and digital indicator(DI) were designed and manufactured. The prototype advanced alarm system which employed the new alarm logics and algorithm compared with the conventional alarm system were developed and its effectiveness was proved. Optical communication system which has multi-drop feature and capability of upgrading to large-scale system by using BITBUS communication protocol which is proven technology, were developed. Reliability of that system was enhanced by using distributed control. (Author)

TH-E-BRF-05: Comparison of Survival-Time Prediction Models After Radiotherapy for High-Grade Glioma Patients Based On Clinical and DVH Features

International Nuclear Information System (INIS)

Magome, T; Haga, A; Igaki, H; Sekiya, N; Masutani, Y; Sakumi, A; Mukasa, A; Nakagawa, K

2014-01-01

Purpose: Although many outcome prediction models based on dose-volume information have been proposed, it is well known that the prognosis may be affected also by multiple clinical factors. The purpose of this study is to predict the survival time after radiotherapy for high-grade glioma patients based on features including clinical and dose-volume histogram (DVH) information. Methods: A total of 35 patients with high-grade glioma (oligodendroglioma: 2, anaplastic astrocytoma: 3, glioblastoma: 30) were selected in this study. All patients were treated with prescribed dose of 30–80 Gy after surgical resection or biopsy from 2006 to 2013 at The University of Tokyo Hospital. All cases were randomly separated into training dataset (30 cases) and test dataset (5 cases). The survival time after radiotherapy was predicted based on a multiple linear regression analysis and artificial neural network (ANN) by using 204 candidate features. The candidate features included the 12 clinical features (tumor location, extent of surgical resection, treatment duration of radiotherapy, etc.), and the 192 DVH features (maximum dose, minimum dose, D95, V60, etc.). The effective features for the prediction were selected according to a step-wise method by using 30 training cases. The prediction accuracy was evaluated by a coefficient of determination (R 2 ) between the predicted and actual survival time for the training and test dataset. Results: In the multiple regression analysis, the value of R 2 between the predicted and actual survival time was 0.460 for the training dataset and 0.375 for the test dataset. On the other hand, in the ANN analysis, the value of R 2 was 0.806 for the training dataset and 0.811 for the test dataset. Conclusion: Although a large number of patients would be needed for more accurate and robust prediction, our preliminary Result showed the potential to predict the outcome in the patients with high-grade glioma. This work was partly supported by the JSPS Core

Feature and Region Selection for Visual Learning.

Science.gov (United States)

Zhao, Ji; Wang, Liantao; Cabral, Ricardo; De la Torre, Fernando

2016-03-01

Visual learning problems, such as object classification and action recognition, are typically approached using extensions of the popular bag-of-words (BoWs) model. Despite its great success, it is unclear what visual features the BoW model is learning. Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questions is fundamental for understanding existing BoW models and inspiring better models for visual recognition. To answer these questions, this paper presents a method for feature selection and region selection in the visual BoW model. This allows for an intermediate visualization of the features and regions that are important for visual learning. The main idea is to assign latent weights to the features or regions, and jointly optimize these latent variables with the parameters of a classifier (e.g., support vector machine). There are four main benefits of our approach: 1) our approach accommodates non-linear additive kernels, such as the popular χ(2) and intersection kernel; 2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; 3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; and 4) we point out strong connections with multiple kernel learning and multiple instance learning approaches. Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube illustrate the benefits of our approach.

Main features of buildings and structures important to safety of units V1 and V2 of Bohunice NPP

International Nuclear Information System (INIS)

David, M.

1993-01-01

The program of seismic upgrading of Bohunice NPPs has been started in the year 1989 (after finishing of new seismic input). Since that time the seismic upgrading of Main building of NPP V1 has already been realized, structural as well as technological parts. Beside that the designs of seismic upgrading of other structures of NPP V1 and V2 have been completed. It has been proved that the seismic upgrading of NPPs with reactors WWER 440 is very complicated, but still possible, even in the case with high seismic intensity. It would be not possible to fulfill this complicated task without the help of IAEA Missions. The activities of IAEA experts in the program of Bohunice NPPs upgrading are appreciated very much

Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network.

Science.gov (United States)

Yoon, Jaehong; Lee, Jungnyun; Whang, Mincheol

2018-01-01

Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain-computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects' ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.

Text feature extraction based on deep learning: a review.

Science.gov (United States)

Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

2017-01-01

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

Comparative Analysis of the Main Business Intelligence Solutions

OpenAIRE

Alexandra RUSANEANU

2013-01-01

Nowadays, Business Intelligence solutions are the main tools for analyzing and monitoring the company’s performance at any organizational level. This paper presents a comparative analysis of the most powerful Business Intelligence solutions using a set of technical features such as infrastructure of the platform, development facilities, complex analysis tools, interactive dashboards and scorecards, mobile integration and complex implementation of performance management methodologies.

Discriminating Induced-Microearthquakes Using New Seismic Features

Science.gov (United States)

Mousavi, S. M.; Horton, S.

2016-12-01

We studied characteristics of induced-microearthquakes on the basis of the waveforms recorded on a limited number of surface receivers using machine-learning techniques. Forty features in the time, frequency, and time-frequency domains were measured on each waveform, and several techniques such as correlation-based feature selection, Artificial Neural Networks (ANNs), Logistic Regression (LR) and X-mean were used as research tools to explore the relationship between these seismic features and source parameters. The results show that spectral features have the highest correlation to source depth. Two new measurements developed as seismic features for this study, spectral centroids and 2D cross-correlations in the time-frequency domain, performed better than the common seismic measurements. These features can be used by machine learning techniques for efficient automatic classification of low energy signals recorded at one or more seismic stations. We applied the technique to 440 microearthquakes-1.7Reference: Mousavi, S.M., S.P. Horton, C. A. Langston, B. Samei, (2016) Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression, Geophys. J. Int. doi: 10.1093/gji/ggw258.

Improvement of the software Bernese for SLR data processing in the Main Metrological Centre of the State Time and Frequency Service

Science.gov (United States)

Tsyba, E.; Kaufman, M.

2015-08-01

Preparatory works for resuming operational calculations of the Earth rotation parameters based on the results of satellite laser ranging data processing (LAGEOS 1, LAGEOS 2) are to be completed in the Main Metrology Centre Of The State Time And Frequency Service (VNIIFTRI) in 2014. For this purpose BERNESE 5.2 software (Dach & Walser, 2014) was chosen as a base software which has been used for many years in the Main Metrological Centre of the State Time and Frequency Service to process phase observations of GLONASS and GPS satellites. Although in the BERNESE 5.2 software announced presentation the possibility of the SLR data processing is declared, it has not been fully implemented. In particular there is no such an essential element as corrective action (as input or resulting parameters) in the local time scale ("time bias"), etc. Therefore, additional program blocks have been developed and integrated into the BERNESE 5.2 software environment. The program blocks are written in Perl and Matlab program languages and can be used both for Windows and Linux, 32-bit and 64-bit platforms.

Peripheral Circulatory Features during High-Frequency Jet Ventilation

Directory of Open Access Journals (Sweden)

M. B. Kontorovich

2010-01-01

Full Text Available The paper gives the results of a study of peripheral circulatory features during high-frequency jet ventilation (HFJV. The main specific features of peripheral circulation and oxygen transport during HFJV are formulated on the basis of a study of cardiac output (impedance cardiography, peripheral vascular resistance, peripheral vascular blood filling (photoplethysmogram analysis, adaptive peripheral blood flow reactions (spectral analysis of peripheral vascular pulsation. HFJV gives rise to the peculiar pattern of peripheral hemodynamics and tissue gas exchange, which is characterized by higher oxygen uptake without a decrease in mixed venous blood saturation, with normal extraction coefficient and preserved low peripheral vascular resistance. During HFJV, unlike traditional ventilation, the main peripheral hemodynamic feature is the increased capillary bed blood volume caused by the blood flow involvement of reserve capillaries under control of volume (parasympathetic regulation of adaptive peripheral hemodynamic reactions. Key words: high-frequency jet ventilation, oxygen transport, peripheral hemodynamics.

Discovering highly informative feature set over high dimensions

KAUST Repository

Zhang, Chongsheng; Masseglia, Florent; Zhang, Xiangliang

2012-01-01

For many textual collections, the number of features is often overly large. These features can be very redundant, it is therefore desirable to have a small, succinct, yet highly informative collection of features that describes the key characteristics of a dataset. Information theory is one such tool for us to obtain this feature collection. With this paper, we mainly contribute to the improvement of efficiency for the process of selecting the most informative feature set over high-dimensional unlabeled data. We propose a heuristic theory for informative feature set selection from high dimensional data. Moreover, we design data structures that enable us to compute the entropies of the candidate feature sets efficiently. We also develop a simple pruning strategy that eliminates the hopeless candidates at each forward selection step. We test our method through experiments on real-world data sets, showing that our proposal is very efficient. © 2012 IEEE.

Discovering highly informative feature set over high dimensions

KAUST Repository

Zhang, Chongsheng

2012-11-01

For many textual collections, the number of features is often overly large. These features can be very redundant, it is therefore desirable to have a small, succinct, yet highly informative collection of features that describes the key characteristics of a dataset. Information theory is one such tool for us to obtain this feature collection. With this paper, we mainly contribute to the improvement of efficiency for the process of selecting the most informative feature set over high-dimensional unlabeled data. We propose a heuristic theory for informative feature set selection from high dimensional data. Moreover, we design data structures that enable us to compute the entropies of the candidate feature sets efficiently. We also develop a simple pruning strategy that eliminates the hopeless candidates at each forward selection step. We test our method through experiments on real-world data sets, showing that our proposal is very efficient. © 2012 IEEE.

Classifying Written Texts Through Rhythmic Features

NARCIS (Netherlands)

Balint, Mihaela; Dascalu, Mihai; Trausan-Matu, Stefan

2016-01-01

Rhythm analysis of written texts focuses on literary analysis and it mainly considers poetry. In this paper we investigate the relevance of rhythmic features for categorizing texts in prosaic form pertaining to different genres. Our contribution is threefold. First, we define a set of rhythmic

The effect of destination linked feature selection in real-time network intrusion detection

CSIR Research Space (South Africa)

Mzila, P

2013-07-01

Full Text Available techniques in the network intrusion detection system (NIDS) is the feature selection technique. The ability of NIDS to accurately identify intrusion from the network traffic relies heavily on feature selection, which describes the pattern of the network...

Feature Selection as a Time and Cost-Saving Approach for Land Suitability Classification (Case Study of Shavur Plain, Iran

Directory of Open Access Journals (Sweden)

Saeid Hamzeh

2016-10-01

Full Text Available Land suitability classification is important in planning and managing sustainable land use. Most approaches to land suitability analysis combine a large number of land and soil parameters, and are time-consuming and costly. In this study, a potentially useful technique (combined feature selection and fuzzy-AHP method to increase the efficiency of land suitability analysis was presented. To this end, three different feature selection algorithms—random search, best search and genetic methods—were used to determine the most effective parameters for land suitability classification for the cultivation of barely in the Shavur Plain, southwest Iran. Next, land suitability classes were calculated for all methods by using the fuzzy-AHP approach. Salinity (electrical conductivity (EC, alkalinity (exchangeable sodium percentage (ESP, wetness and soil texture were selected using the random search method. Gypsum, EC, ESP, and soil texture were selected using both the best search and genetic methods. The result shows a strong agreement between the standard fuzzy-AHP methods and methods presented in this study. The values of Kappa coefficients were 0.82, 0.79 and 0.79 for the random search, best search and genetic methods, respectively, compared with the standard fuzzy-AHP method. Our results indicate that EC, ESP, soil texture and wetness are the most effective features for evaluating land suitability classification for the cultivation of barely in the study area, and uses of these parameters, together with their appropriate weights as obtained from fuzzy-AHP, can perform good results for land suitability classification. So, the combined feature selection presented and the fuzzy-AHP approach has the potential to save time and money for land suitability classification.

Estimation of main diversification time-points of hantaviruses using phylogenetic analyses of complete genomes.

Science.gov (United States)

Castel, Guillaume; Tordo, Noël; Plyusnin, Alexander

2017-04-02

Because of the great variability of their reservoir hosts, hantaviruses are excellent models to evaluate the dynamics of virus-host co-evolution. Intriguing questions remain about the timescale of the diversification events that influenced this evolution. In this paper we attempted to estimate the first ever timing of hantavirus diversification based on thirty five available complete genomes representing five major groups of hantaviruses and the assumption of co-speciation of hantaviruses with their respective mammal hosts. Phylogenetic analyses were used to estimate the main diversification points during hantavirus evolution in mammals while host diversification was mostly estimated from independent calibrators taken from fossil records. Our results support an earlier developed hypothesis of co-speciation of known hantaviruses with their respective mammal hosts and hence a common ancestor for all hantaviruses carried by placental mammals. Copyright © 2017 Elsevier B.V. All rights reserved.

Time series analysis of diverse extreme phenomena: universal features

Science.gov (United States)

Eftaxias, K.; Balasis, G.

2012-04-01

The field of study of complex systems holds that the dynamics of complex systems are founded on universal principles that may used to describe a great variety of scientific and technological approaches of different types of natural, artificial, and social systems. We suggest that earthquake, epileptic seizures, solar flares, and magnetic storms dynamics can be analyzed within similar mathematical frameworks. A central property of aforementioned extreme events generation is the occurrence of coherent large-scale collective behavior with very rich structure, resulting from repeated nonlinear interactions among the corresponding constituents. Consequently, we apply the Tsallis nonextensive statistical mechanics as it proves an appropriate framework in order to investigate universal principles of their generation. First, we examine the data in terms of Tsallis entropy aiming to discover common "pathological" symptoms of transition to a significant shock. By monitoring the temporal evolution of the degree of organization in time series we observe similar distinctive features revealing significant reduction of complexity during their emergence. Second, a model for earthquake dynamics coming from a nonextensive Tsallis formalism, starting from first principles, has been recently introduced. This approach leads to an energy distribution function (Gutenberg-Richter type law) for the magnitude distribution of earthquakes, providing an excellent fit to seismicities generated in various large geographic areas usually identified as seismic regions. We show that this function is able to describe the energy distribution (with similar non-extensive q-parameter) of solar flares, magnetic storms, epileptic and earthquake shocks. The above mentioned evidence of a universal statistical behavior suggests the possibility of a common approach for studying space weather, earthquakes and epileptic seizures.

Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network

Directory of Open Access Journals (Sweden)

Jaehong Yoon

2018-01-01

Full Text Available Feature of event-related potential (ERP has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain–computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects’ ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.

Feature Quantization and Pooling for Videos

Science.gov (United States)

2014-05-01

less vertical motion. The exceptions are videos from the classes of biking (mainly due to the camera tracking fast bikers), jumping on a trampoline ...tracking the bikers; the jumping videos, featuring people on trampolines , the swing videos, which are usually recorded in profile view, and the walking

Optimum location of external markers using feature selection algorithms for real‐time tumor tracking in external‐beam radiotherapy: a virtual phantom study

Science.gov (United States)

Nankali, Saber; Miandoab, Payam Samadi; Baghizadeh, Amin

2016-01-01

In external‐beam radiotherapy, using external markers is one of the most reliable tools to predict tumor position, in clinical applications. The main challenge in this approach is tumor motion tracking with highest accuracy that depends heavily on external markers location, and this issue is the objective of this study. Four commercially available feature selection algorithms entitled 1) Correlation‐based Feature Selection, 2) Classifier, 3) Principal Components, and 4) Relief were proposed to find optimum location of external markers in combination with two “Genetic” and “Ranker” searching procedures. The performance of these algorithms has been evaluated using four‐dimensional extended cardiac‐torso anthropomorphic phantom. Six tumors in lung, three tumors in liver, and 49 points on the thorax surface were taken into account to simulate internal and external motions, respectively. The root mean square error of an adaptive neuro‐fuzzy inference system (ANFIS) as prediction model was considered as metric for quantitatively evaluating the performance of proposed feature selection algorithms. To do this, the thorax surface region was divided into nine smaller segments and predefined tumors motion was predicted by ANFIS using external motion data of given markers at each small segment, separately. Our comparative results showed that all feature selection algorithms can reasonably select specific external markers from those segments where the root mean square error of the ANFIS model is minimum. Moreover, the performance accuracy of proposed feature selection algorithms was compared, separately. For this, each tumor motion was predicted using motion data of those external markers selected by each feature selection algorithm. Duncan statistical test, followed by F‐test, on final results reflected that all proposed feature selection algorithms have the same performance accuracy for lung tumors. But for liver tumors, a correlation‐based feature

Slim Battery Modelling Features

Science.gov (United States)

Borthomieu, Y.; Prevot, D.

2011-10-01

Saft has developed a life prediction model for VES and MPS cells and batteries. The Saft Li-ion Model (SLIM) is a macroscopic electrochemical model based on energy (global at cell level). The main purpose is to predict the battery performances during the life for GEO, MEO and LEO missions. This model is based on electrochemical characteristics such as Energy, Capacity, EMF, Internal resistance, end of charge voltage. It uses fading and calendar law effects on energy and internal impedance vs. time, temperature, End of Charge voltage. Based on the mission profile, satellite power system characteristics, the model proposes the various battery configurations. For each configuration, the model gives the battery performances using mission figures and profiles: power, duration, DOD, end of charge voltages, temperatures during eclipses and solstices, thermal dissipations and cell failures. For the GEO/MEO missions, eclipse and solstice periods can include specific profile such as plasmic propulsion fires and specific balancing operations. For LEO missions, the model is able to simulate high power peaks to predict radar pulses. Saft's main customers have been using the SLIM model available in house for two years. The purpose is to have the satellite builder power engineers able to perform by themselves in the battery pre-dimensioning activities their own battery simulations. The simulations can be shared with Saft engineers to refine the power system designs. This model has been correlated with existing life and calendar tests performed on all the VES and MPS cells. In comparing with more than 10 year lasting life tests, the accuracy of the model from a voltage point of view is less than 10 mV at end Of Life. In addition, thethe comparison with in-orbit data has been also done. b This paper will present the main features of the SLIM software and outputs comparison with real life tests. b0

Progressive Red Shifts in the Late-Time Spectra of Type Ia Supernovae

Science.gov (United States)

Black, Christine; Fesen, Robert; Parrent, Jerod

2017-01-01

We examine the evolution of late-time, optical nebular features of Type Ia supernovae (SNe Ia) using a sample consisting of 160 spectra of 27 normal SNe Ia taken from the literature as well as unpublished spectra of SN 2008Q and ASASSN-14lp. Particular attention is given to nebular features between 4000-6000 Ang in terms of temporal changes in width and central wavelength. Analysis of the prominent late-time 4700 Ang feature shows a progressive central wavelength shift from ˜4600 Ang to longer wavelengths out to at least day +300 for our entire sample. We find no evidence for the feature’s red-ward shift slowing or halting at an [Fe III] blend centroid ˜4700 Ang as has been proposed. Two weaker adjacent features at around 4850 and 5000 Ang exhibit similar red shifts to that of the 4700 Ang feature. We conclude that the ubiquitous red shift of these common late-time SN Ia spectral features is not mainly due to a decrease in line velocities of forbidden Fe emissions, but the result of decreasing line velocities and opacity of permitted Fe absorption lines.

The main features of electrical stimulation of biological tissues by implant electrodes: study from engineering perspective and equipment development to produce

International Nuclear Information System (INIS)

Suarez Bagnasco, D.; Alvarez Alonso, J.; Suarez Antola, R.

2004-08-01

The main features of electrical stimulation of biological tissues by implant electrodes are studied.These electrodes are applied in neural prostheses and cardiac pacing.Threshold phenomena are stressed and some aspects related with implant electrode design are discussed. A fairly through theoretical research about the optimal pulse shape for electrical stimulation of biological tissues is done.The excitation functional is introduced as a criterium to identify threshold pulses of electric current. We obtain the optimal pulse shapes that minimize the energy dissipated in tissues, or the energy taken by the load seen by the pulse generator, amongst other criteria.We show how these pulse shapes can be determined from experimentally measured strength-duration (S-D) curves using rectangular pulses of current. The development of a prototype of a new equipment is described.The equipment may be used to measure S-D curves and with this information it is able to syntetize the abovementioned optimal pulse shapes. The top-down design process is presented, involving both hardware and software.The construction and assembling of the prototype, as well as the implementation of software are described.Some testing and measures with the prototype, including test with biological tissues are described and assessed

Primordial inhomogeneities in the expanding universe. II - General features of spherical models at late times

Science.gov (United States)

Olson, D. W.; Silk, J.

1979-01-01

This paper studies the density profile that forms around a spherically symmetric bound central core immersed in a homogeneous-background k = 0 or k = -1 Friedmann-Robertson-Walker cosmological model, with zero pressure. Although the density profile in the linearized regime is almost arbitrary, in the nonlinear regime certain universal features of the density profile are obtained that are independent of the details of the initial conditions. The formation of 'halos' ('holes') with densities greater than (less than) the average cosmological density is discussed. It is shown that in most regions 'halos' form, and universal values are obtained for the slope of the ln (density)-ln (radius) profile in those 'halos' at late times, independently of the shape of the initial density profile. Restrictions are derived on where it is possible for 'holes' to exist at late times and on how such 'holes' must have evolved.

Data driven analysis of rain events: feature extraction, clustering, microphysical /macro physical relationship

Science.gov (United States)

Djallel Dilmi, Mohamed; Mallet, Cécile; Barthes, Laurent; Chazottes, Aymeric

2017-04-01

The study of rain time series records is mainly carried out using rainfall rate or rain accumulation parameters estimated on a fixed integration time (typically 1 min, 1 hour or 1 day). In this study we used the concept of rain event. In fact, the discrete and intermittent natures of rain processes make the definition of some features inadequate when defined on a fixed duration. Long integration times (hour, day) lead to mix rainy and clear air periods in the same sample. Small integration time (seconds, minutes) will lead to noisy data with a great sensibility to detector characteristics. The analysis on the whole rain event instead of individual short duration samples of a fixed duration allows to clarify relationships between features, in particular between macro physical and microphysical ones. This approach allows suppressing the intra-event variability partly due to measurement uncertainties and allows focusing on physical processes. An algorithm based on Genetic Algorithm (GA) and Self Organising Maps (SOM) is developed to obtain a parsimonious characterisation of rain events using a minimal set of variables. The use of self-organizing map (SOM) is justified by the fact that it allows to map a high dimensional data space in a two-dimensional space while preserving as much as possible the initial space topology in an unsupervised way. The obtained SOM allows providing the dependencies between variables and consequently removing redundant variables leading to a minimal subset of only five features (the event duration, the rain rate peak, the rain event depth, the event rain rate standard deviation and the absolute rain rate variation of order 0.5). To confirm relevance of the five selected features the corresponding SOM is analyzed. This analysis shows clearly the existence of relationships between features. It also shows the independence of the inter-event time (IETp) feature or the weak dependence of the Dry percentage in event (Dd%e) feature. This confirms

[Feature extraction for breast cancer data based on geometric algebra theory and feature selection using differential evolution].

Science.gov (United States)

Li, Jing; Hong, Wenxue

2014-12-01

The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.

3D real-time monitoring system for LHD plasma heating experiment

International Nuclear Information System (INIS)

Emoto, M.; Narlo, J.; Kaneko, O.; Komori, A.; Iima, M.; Yamaguchi, S.; Sudo, S.

2001-01-01

The JAVA-based real-time monitoring system has been in use at the National Institute for Fusion Science, Japan, since the end of March 1988 to maintain stable operations. This system utilizes JAVA technology to realize its platform-independent nature. The main programs are written as JAVA applets and provide human-friendly interfaces. In order to enhance the system's easy-recognition nature, a 3D feature is added. Since most of the system is written mainly in JAVA language, we adopted JAVA3D technology, which was easy to incorporate into the current running systems. With this 3D feature, the operator can more easily find the malfunctioning parts of complex instruments, such as LHD vacuum vessels. This feature is also helpful for recognizing physical phenomena. In this paper, we present an example in which the temperature increases of a vacuum vessel after NBI are visualized

Design of the control room of the N4-type PWR: main features and feedback operating experience

International Nuclear Information System (INIS)

Peyrouton, J.M.; Guillas, J.; Nougaret, Ch.

2004-01-01

This article presents the design, specificities and innovating features of the control room of the N4-type PWR. A brief description of control rooms of previous 900 MW and 1300 MW -type PWR allows us to assess the change. The design of the first control room dates back to 1972, at that time 2 considerations were taken into account: first the design has to be similar to that of control rooms for thermal plants because plant operators were satisfied with it and secondly the normal operating situation has to be privileged to the prejudice of accidental situations just as it was in a thermal plant. The turning point was the TMI accident that showed the weight of human factor in accidental situations in terms of pilot team, training, procedures and the ergonomics of the work station. The impact of TMI can be seen in the design of 1300 MW-type PWR. In the beginning of the eighties EDF decided to launch a study for a complete overhaul of the control room concept, the aim was to continue reducing the human factor risk and to provide a better quality of piloting the plant in any situation. The result is the control room of the N4-type PWR. Today the cumulated feedback experience of N4 control rooms represents more than 20 years over a wide range of situations from normal to incidental, a survey shows that the N4 design has fulfilled its aims. (A.C.)

Solitary main pancreatic ductal calculus of possible biliary origin causing acute pancreatitis.

Science.gov (United States)

Chaparala, Ramakrishna Prasad Chowdary; Patel, Rafiuddin; Guthrie, James Ahsley; Davies, Mervyn Huw; Guillou, Pierre J; Menon, Krishna V

2005-09-10

Pancreatic ductal calculi are most often associated with chronic pancreatitis. Radiological features of chronic pancreatitis are readily evident in the presence of these calculi. However, acute pancreatitis due to a solitary main pancreatic ductal calculus of biliary origin is rare. A 59-year-old man presented with a first episode of acute pancreatitis. Contrast enhanced computerized tomography (CT) scan and endoscopic retrograde cholangiopancreatography (ERCP) revealed a calculus in the main pancreatic duct in the head of the pancreas causing acute pancreatitis. There were no features suggestive of chronic pancreatitis on CT scanning. The episode acute pancreatitis was managed conservatively. ERCP extraction of the calculus failed as the stone was impacted in the main pancreatic duct resulting in severe acute pancreatitis. Once this resolved, a transduodenal exploration and extraction of the pancreatic ductal calculus was performed successfully. Crystallographic analysis revealed the composition of the calculus was different to that seen in chronic pancreatitis, but more in keeping with a calculus of biliary origin. This could be explained by migration of the biliary calculus via the common channel into the main pancreatic duct. Following the operation the patient made an uneventful recovery and was well at two-year follow up. Acute pancreatitis due to a solitary main pancreatic ductal calculus of biliary origin is rare. Failing endoscopic extraction, transduodenal exploration and extraction is a safe option after resolution of acute pancreatitis.

Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

Directory of Open Access Journals (Sweden)

Mariela Cerrada

2015-09-01

Full Text Available There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%.

A generic approach for the automatic verification of featured, parameterised systems

OpenAIRE

Miller, A.; Calder, M.

2005-01-01

A general technique is presented that allows property based feature analysis of systems consisting of an arbitrary number of components. Each component may have an arbitrary set of safe features. The components are defined in a guarded command form and the technique combines model checking and abstraction. Features must fulfill certain criteria in order to be safe, the criteria express constraints on the variables which occur in feature guards. The main result is a generalisation theorem whic...

Understanding Legacy Features with Featureous

DEFF Research Database (Denmark)

Olszak, Andrzej; Jørgensen, Bo Nørregaard

2011-01-01

Java programs called Featureous that addresses this issue. Featureous allows a programmer to easily establish feature-code traceability links and to analyze their characteristics using a number of visualizations. Featureous is an extension to the NetBeans IDE, and can itself be extended by third...

Depth estimation of features in video frames with improved feature matching technique using Kinect sensor

Science.gov (United States)

Sharma, Kajal; Moon, Inkyu; Kim, Sung Gaun

2012-10-01

Estimating depth has long been a major issue in the field of computer vision and robotics. The Kinect sensor's active sensing strategy provides high-frame-rate depth maps and can recognize user gestures and human pose. This paper presents a technique to estimate the depth of features extracted from video frames, along with an improved feature-matching method. In this paper, we used the Kinect camera developed by Microsoft, which captured color and depth images for further processing. Feature detection and selection is an important task for robot navigation. Many feature-matching techniques have been proposed earlier, and this paper proposes an improved feature matching between successive video frames with the use of neural network methodology in order to reduce the computation time of feature matching. The features extracted are invariant to image scale and rotation, and different experiments were conducted to evaluate the performance of feature matching between successive video frames. The extracted features are assigned distance based on the Kinect technology that can be used by the robot in order to determine the path of navigation, along with obstacle detection applications.

76 FR 59151 - Announcement of Funding Awards; HOPE VI Main Street Grant Program, Fiscal Year (FY) 2009

Science.gov (United States)

2011-09-23

... central business district or ``Main Street'' area by replacing unused commercial space in buildings with... historic or traditional architecture or design features in Main Street areas; (3) enhance economic...

Design features of HTMR-hybrid toroidal magnet tokamak reactor

International Nuclear Information System (INIS)

Rosatelli, F.; Avanzini, P.G.; Derchi, D.; Magnasco, M.; Grattarola, M.; Peluffo, M.; Raia, G.; Brunelli, B.; Zampaglione, V.

1984-01-01

The HTMR (Hybrid Toroidal Magnet Tokamak Reactor) conceptual design is aimed to demonstrate the feasibility of a Tokamak reactor which could fulfil the scientific and technological objectives expected from next generation devices with size and costs as small as possible. A hybrid toroidal field magnet, made up by copper and superconducting coils, seems to be a promising solution, allowing a considerable flexibility in machine performances, so as to gain useful margins in front of the uncertainties in confinement time scaling laws and beta and plasma density limits. The optimization procedure for the hybrid magnet, configuration, the main design features of HTMR and the preliminary mechanical calculations of the superconducting toroidal coils are described. (author)

Histologic features of alopecias: part II: scarring alopecias.

Science.gov (United States)

Bernárdez, C; Molina-Ruiz, A M; Requena, L

2015-05-01

The diagnosis of disorders of the hair and scalp can generally be made on clinical grounds, but clinical signs are not always diagnostic and in some cases more invasive techniques, such as a biopsy, may be necessary. This 2-part article is a detailed review of the histologic features of the main types of alopecia based on the traditional classification of these disorders into 2 major groups: scarring and nonscarring alopecias. Scarring alopecias are disorders in which the hair follicle is replaced by fibrous scar tissue, a process that leads to permanent hair loss. In nonscarring alopecias, the follicles are preserved and hair growth can resume when the cause of the problem is eliminated. In the second part of this review, we describe the histologic features of the main forms of scarring alopecia. Since a close clinical-pathological correlation is essential for making a correct histopathologic diagnosis of alopecia, we also include a brief description of the clinical features of the principal forms of this disorder. Copyright © 2014 Elsevier España, S.L.U. and AEDV. All rights reserved.

Optimized Feature Extraction for Temperature-Modulated Gas Sensors

Directory of Open Access Journals (Sweden)

Alexander Vergara

2009-01-01

Full Text Available One of the most serious limitations to the practical utilization of solid-state gas sensors is the drift of their signal. Even if drift is rooted in the chemical and physical processes occurring in the sensor, improved signal processing is generally considered as a methodology to increase sensors stability. Several studies evidenced the augmented stability of time variable signals elicited by the modulation of either the gas concentration or the operating temperature. Furthermore, when time-variable signals are used, the extraction of features can be accomplished in shorter time with respect to the time necessary to calculate the usual features defined in steady-state conditions. In this paper, we discuss the stability properties of distinct dynamic features using an array of metal oxide semiconductors gas sensors whose working temperature is modulated with optimized multisinusoidal signals. Experiments were aimed at measuring the dispersion of sensors features in repeated sequences of a limited number of experimental conditions. Results evidenced that the features extracted during the temperature modulation reduce the multidimensional data dispersion among repeated measurements. In particular, the Energy Signal Vector provided an almost constant classification rate along the time with respect to the temperature modulation.

Tri-modal Person Re-identification with RGB, Depth and Thermal Features

DEFF Research Database (Denmark)

Møgelmose, Andreas; Bahnsen, Chris; Moeslund, Thomas B.

2013-01-01

Person re-identification is about recognizing people who have passed by a sensor earlier. Previous work is mainly based on RGB data, but in this work we for the first time present a system where we combine RGB, depth, and thermal data for re-identification purposes. First, from each of the three...... modalities, we obtain some particular features: from RGB data, we model color information from different regions of the body, from depth data, we compute different soft body biometrics, and from thermal data, we extract local structural information. Then, the three information types are combined in a joined...

Simultaneous Channel and Feature Selection of Fused EEG Features Based on Sparse Group Lasso

Directory of Open Access Journals (Sweden)

Jin-Jia Wang

2015-01-01

Full Text Available Feature extraction and classification of EEG signals are core parts of brain computer interfaces (BCIs. Due to the high dimension of the EEG feature vector, an effective feature selection algorithm has become an integral part of research studies. In this paper, we present a new method based on a wrapped Sparse Group Lasso for channel and feature selection of fused EEG signals. The high-dimensional fused features are firstly obtained, which include the power spectrum, time-domain statistics, AR model, and the wavelet coefficient features extracted from the preprocessed EEG signals. The wrapped channel and feature selection method is then applied, which uses the logistical regression model with Sparse Group Lasso penalized function. The model is fitted on the training data, and parameter estimation is obtained by modified blockwise coordinate descent and coordinate gradient descent method. The best parameters and feature subset are selected by using a 10-fold cross-validation. Finally, the test data is classified using the trained model. Compared with existing channel and feature selection methods, results show that the proposed method is more suitable, more stable, and faster for high-dimensional feature fusion. It can simultaneously achieve channel and feature selection with a lower error rate. The test accuracy on the data used from international BCI Competition IV reached 84.72%.

Decontamination of main coolant pumps

International Nuclear Information System (INIS)

Roofthooft, R.

1988-01-01

Last year a number of main coolant pumps in Belgian nuclear power plants were decontaminated. A new method has been developed to reduce the time taken for decontamination and the volume of waste to be treated. The method comprises two phases: Oxidation with permanganate in nitric acid and dissolution in oxalic acid. The decontamination of main coolant pumps can now be achieved in less than one day. The decontamination factors attained range between 15 and 150. (orig.) [de

Blind image quality assessment based on aesthetic and statistical quality-aware features

Science.gov (United States)

Jenadeleh, Mohsen; Masaeli, Mohammad Masood; Moghaddam, Mohsen Ebrahimi

2017-07-01

The main goal of image quality assessment (IQA) methods is the emulation of human perceptual image quality judgments. Therefore, the correlation between objective scores of these methods with human perceptual scores is considered as their performance metric. Human judgment of the image quality implicitly includes many factors when assessing perceptual image qualities such as aesthetics, semantics, context, and various types of visual distortions. The main idea of this paper is to use a host of features that are commonly employed in image aesthetics assessment in order to improve blind image quality assessment (BIQA) methods accuracy. We propose an approach that enriches the features of BIQA methods by integrating a host of aesthetics image features with the features of natural image statistics derived from multiple domains. The proposed features have been used for augmenting five different state-of-the-art BIQA methods, which use statistical natural scene statistics features. Experiments were performed on seven benchmark image quality databases. The experimental results showed significant improvement of the accuracy of the methods.

Classification of Targets and Distractors Present in Visual Hemifields Using Time-Frequency Domain EEG Features

Directory of Open Access Journals (Sweden)

Sweeti

2018-01-01

Full Text Available This paper presents a classification system to classify the cognitive load corresponding to targets and distractors present in opposite visual hemifields. The approach includes the study of EEG (electroencephalogram signal features acquired in a spatial attention task. The process comprises of EEG feature selection based on the feature distribution, followed by the stepwise discriminant analysis- (SDA- based channel selection. Repeated measure analysis of variance (rANOVA is applied to test the statistical significance of the selected features. Classifiers are developed and compared using the selected features to classify the target and distractor present in visual hemifields. The results provide a maximum classification accuracy of 87.2% and 86.1% and an average classification accuracy of 76.5 ± 4% and 76.2 ± 5.3% over the thirteen subjects corresponding to the two task conditions. These correlates present a step towards building a feature-based neurofeedback system for visual attention.

The Features of the Architectonic of Financial System

Directory of Open Access Journals (Sweden)

Bondarenko Olena S.

2017-10-01

Full Text Available The article is aimed at substantiating the features of function of a contemporary financial system of the State together with the need to develop its architectonic, taking into consideration the functions and objectives of socio-economic development. The features of function of the current financial system of Ukraine have been disclosed. The main factors of influence have been defined and the need to develop new approaches to the management of the components of financial system has been proven. The essence and feasibility of building the architectonic of financial system have been substantiated, the main directions of practical implementation have been characterized. Prospects for further research are developing a mechanism for building the architectonic of financial system and creating an efficient management instrumentarium for managing its components.

A Novel Technique for Shape Feature Extraction Using Content Based Image Retrieval

Directory of Open Access Journals (Sweden)

Dhanoa Jaspreet Singh

2016-01-01

Full Text Available With the advent of technology and multimedia information, digital images are increasing very quickly. Various techniques are being developed to retrieve/search digital information or data contained in the image. Traditional Text Based Image Retrieval System is not plentiful. Since it is time consuming as it require manual image annotation. Also, the image annotation differs with different peoples. An alternate to this is Content Based Image Retrieval (CBIR system. It retrieves/search for image using its contents rather the text, keywords etc. A lot of exploration has been compassed in the range of Content Based Image Retrieval (CBIR with various feature extraction techniques. Shape is a significant image feature as it reflects the human perception. Moreover, Shape is quite simple to use by the user to define object in an image as compared to other features such as Color, texture etc. Over and above, if applied alone, no descriptor will give fruitful results. Further, by combining it with an improved classifier, one can use the positive features of both the descriptor and classifier. So, a tryout will be made to establish an algorithm for accurate feature (Shape extraction in Content Based Image Retrieval (CBIR. The main objectives of this project are: (a To propose an algorithm for shape feature extraction using CBIR, (b To evaluate the performance of proposed algorithm and (c To compare the proposed algorithm with state of art techniques.

Hypothesis testing for differentially correlated features.

Science.gov (United States)

Sheng, Elisa; Witten, Daniela; Zhou, Xiao-Hua

2016-10-01

In a multivariate setting, we consider the task of identifying features whose correlations with the other features differ across conditions. Such correlation shifts may occur independently of mean shifts, or differences in the means of the individual features across conditions. Previous approaches for detecting correlation shifts consider features simultaneously, by computing a correlation-based test statistic for each feature. However, since correlations involve two features, such approaches do not lend themselves to identifying which feature is the culprit. In this article, we instead consider a serial testing approach, by comparing columns of the sample correlation matrix across two conditions, and removing one feature at a time. Our method provides a novel perspective and favorable empirical results compared with competing approaches. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Saliency image of feature building for image quality assessment

Science.gov (United States)

Ju, Xinuo; Sun, Jiyin; Wang, Peng

2011-11-01

The purpose and method of image quality assessment are quite different for automatic target recognition (ATR) and traditional application. Local invariant feature detectors, mainly including corner detectors, blob detectors and region detectors etc., are widely applied for ATR. A saliency model of feature was proposed to evaluate feasibility of ATR in this paper. The first step consisted of computing the first-order derivatives on horizontal orientation and vertical orientation, and computing DoG maps in different scales respectively. Next, saliency images of feature were built based auto-correlation matrix in different scale. Then, saliency images of feature of different scales amalgamated. Experiment were performed on a large test set, including infrared images and optical images, and the result showed that the salient regions computed by this model were consistent with real feature regions computed by mostly local invariant feature extraction algorithms.

Features of Woman Journal in Tatar Language

Directory of Open Access Journals (Sweden)

Aigul A. Guseinova

2017-11-01

Full Text Available The article studies the functional and typological features of woman magazine published in the city of Kazan of the Republic of Tatarstan (Russia. The magazine "Syuyumbike" is the only woman magazine for the Tatars, the people of the Turkic ethnos living in the central regions of the European part of Russia. The Tatars make up 3.87% of population in Russia. "Syuyumbike", as the magazine with a century-old history, spreads throughout Russia. Besides it is read by Tatars, compactly residing in Ukraine, Kazakhstan, Uzbekistan, the USA, Finland and Australia. In Soviet times, the circulation reached half a million copies, and it makes only 10 thousand at present, one fifth of which is distributed outside of Tatarstan. Despite the fact that the publishers of the magazine do not share their readers on the basis of gender, the main character of the magazine is a woman, an active member of modern society. The magazine, being the platform for the exchange of opinions among the representatives of Tatar nation from all over the world, serves to search for the solutions to various problems of our time. The identification of typological features makes it possible to determine the place of publication in the media system, it has an undeniable significance for the further development of newspaper and magazine market in the national languages of Russia. After the historical development analysis and the analysis of the magazine "Syuyumbike" current state, the authors made conclusions about the trends and the prospects of its further development.

Novel clinical features of nonconvulsive status epilepticus

Science.gov (United States)

Nagayama, Masao; Yang, Sunghoon; Geocadin, Romergryko G.; Kaplan, Peter W.; Hoshiyama, Eisei; Shiromaru-Sugimoto, Azusa; Kawamura, Mitsuru

2017-01-01

Nonconvulsive status epilepticus (NCSE) has rapidly expanded from classical features such as staring, repetitive blinking, chewing, swallowing, and automatism to include coma, prolonged apnea, cardiac arrest, dementia, and higher brain dysfunction, which were demonstrated mainly after the 2000s by us and other groups. This review details novel clinical features of NCSE as a manifestation of epilepsy, but one that is underdiagnosed, with the best available evidence. Also, we describe the new concept of epilepsy-related organ dysfunction (Epi-ROD) and a novel electrode and headset which enables prompt electroencephalography. PMID:28979770

Audiovisual laughter detection based on temporal features

NARCIS (Netherlands)

Petridis, Stavros; Nijholt, Antinus; Nijholt, A.; Pantic, M.; Pantic, Maja; Poel, Mannes; Poel, M.; Hondorp, G.H.W.

2008-01-01

Previous research on automatic laughter detection has mainly been focused on audio-based detection. In this study we present an audiovisual approach to distinguishing laughter from speech based on temporal features and we show that the integration of audio and visual information leads to improved

Breast cancer mitosis detection in histopathological images with spatial feature extraction

Science.gov (United States)

Albayrak, Abdülkadir; Bilgin, Gökhan

2013-12-01

In this work, cellular mitosis detection in histopathological images has been investigated. Mitosis detection is very expensive and time consuming process. Development of digital imaging in pathology has enabled reasonable and effective solution to this problem. Segmentation of digital images provides easier analysis of cell structures in histopathological data. To differentiate normal and mitotic cells in histopathological images, feature extraction step is very crucial step for the system accuracy. A mitotic cell has more distinctive textural dissimilarities than the other normal cells. Hence, it is important to incorporate spatial information in feature extraction or in post-processing steps. As a main part of this study, Haralick texture descriptor has been proposed with different spatial window sizes in RGB and La*b* color spaces. So, spatial dependencies of normal and mitotic cellular pixels can be evaluated within different pixel neighborhoods. Extracted features are compared with various sample sizes by Support Vector Machines using k-fold cross validation method. According to the represented results, it has been shown that separation accuracy on mitotic and non-mitotic cellular pixels gets better with the increasing size of spatial window.

New features of the Helioviewer Project

Science.gov (United States)

Ireland, J.; Zahniy, S.; Nicula, B.; Mueller, D.; Felix, S.; Verstringe, F.; Bourgoignie, B.

2016-12-01

This year saw the release of major new upgrades to the capabilities of helioviewer.org and JHelioviewer. The helioviewer.org interface was completely re-designed, and now provides image and feature/event time-lines and data download capabilities. JHelioviewer introduced interactive time-series, the ability to query different servers for different data, and image reprojection. We introduce the new features of these software releases and give use cases. We will summarize our latest usage statistics, and discuss what's coming up next for the Helioviewer Project. We will also be soliciting bug reports, requests for new features and comments on the effectiveness of helioviewer.org and JHelioviewer. What would you like to see next from the Helioviewer Project?

Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study.

Science.gov (United States)

Najdi, Shirin; Gharbali, Ali Abdollahi; Fonseca, José Manuel

2017-08-18

Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process. In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity. Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy. The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among

Design features of APWR in Japan

International Nuclear Information System (INIS)

Yamaguchi, H.; Aeba, Y.; Weiss, E.H.

1999-01-01

Development of the Advanced Pressurized Water Reactor (APWR) was executed in the Improvement and Standardization Program which was organized by the Ministry of International Trade and Industry, Japanese utilities (Hokkaido, Kansai, Shikoku, Kyushu Electric Power Companies and the Japan Atomic Power Company) and manufacturers (Mitsubishi Heavy Industries and Westinghouse Electric). Improvements in terms of safety, reliability, operability, maintainability and economy have been incorporated based on comprehensive evaluations of both the advanced technologies available today, and the experience associated with construction and operation of current PWR plants. The main design improvement features applied in APWR include a core design that contributes to effective use of uranium resource, safety enhancement in the engineered safeguard system, and reliability enhancement in the reactor internal structures. This paper briefly describes the main features of the APWR design focusing on the following two items: the radial reflector, which enhances reliability of the reactor internal structures as well as neutron economy in the core region; and an advanced accumulator, which enhances Emergency Core Cooling System (ECCS) reliability and contributes to system simplification due to passive low pressure injection function. (author)

Joint Feature Selection and Classification for Multilabel Learning.

Science.gov (United States)

Huang, Jun; Li, Guorong; Huang, Qingming; Wu, Xindong

2018-03-01

Multilabel learning deals with examples having multiple class labels simultaneously. It has been applied to a variety of applications, such as text categorization and image annotation. A large number of algorithms have been proposed for multilabel learning, most of which concentrate on multilabel classification problems and only a few of them are feature selection algorithms. Current multilabel classification models are mainly built on a single data representation composed of all the features which are shared by all the class labels. Since each class label might be decided by some specific features of its own, and the problems of classification and feature selection are often addressed independently, in this paper, we propose a novel method which can perform joint feature selection and classification for multilabel learning, named JFSC. Different from many existing methods, JFSC learns both shared features and label-specific features by considering pairwise label correlations, and builds the multilabel classifier on the learned low-dimensional data representations simultaneously. A comparative study with state-of-the-art approaches manifests a competitive performance of our proposed method both in classification and feature selection for multilabel learning.

A Comprehensive Optimization Strategy for Real-time Spatial Feature Sharing and Visual Analytics in Cyberinfrastructure

Science.gov (United States)

Li, W.; Shao, H.

2017-12-01

For geospatial cyberinfrastructure enabled web services, the ability of rapidly transmitting and sharing spatial data over the Internet plays a critical role to meet the demands of real-time change detection, response and decision-making. Especially for the vector datasets which serve as irreplaceable and concrete material in data-driven geospatial applications, their rich geometry and property information facilitates the development of interactive, efficient and intelligent data analysis and visualization applications. However, the big-data issues of vector datasets have hindered their wide adoption in web services. In this research, we propose a comprehensive optimization strategy to enhance the performance of vector data transmitting and processing. This strategy combines: 1) pre- and on-the-fly generalization, which automatically determines proper simplification level through the introduction of appropriate distance tolerance (ADT) to meet various visualization requirements, and at the same time speed up simplification efficiency; 2) a progressive attribute transmission method to reduce data size and therefore the service response time; 3) compressed data transmission and dynamic adoption of a compression method to maximize the service efficiency under different computing and network environments. A cyberinfrastructure web portal was developed for implementing the proposed technologies. After applying our optimization strategies, substantial performance enhancement is achieved. We expect this work to widen the use of web service providing vector data to support real-time spatial feature sharing, visual analytics and decision-making.

The advanced main control console for next japanese PWR plants

International Nuclear Information System (INIS)

Tsuchiya, A.; Ito, K.; Yokoyama, M.

2001-01-01

The purpose of the improvement of main control room designing in a nuclear power plant is to reduce operators' workload and potential human errors by offering a better working environment where operators can maximize their abilities. In order to satisfy such requirements, the design of main control board applied to Japanese Pressurized Water Reactor (PWR) type nuclear power plant has been continuously modified and improved. the Japanese Pressurized Water Reactor (PWR) Utilities (Electric Power Companies) and Mitsubishi Group have developed an advanced main control board (console) reflecting on the study of human factors, as well as using a state of the art electronics technology. In this report, we would like to introduce the configuration and features of the Advanced Main Control Console for the practical application to the next generation PWR type nuclear power plants including TOMARI No.3 Unit of Hokkaido Electric Power Co., Inc. (author)

OGUMI-A new mobile application to conduct common-pool resource experiments in continuous time.

Directory of Open Access Journals (Sweden)

Gunnar Brandt

Full Text Available OGUMI is an Android-based open source mobile application for conducting Common-Pool Resource Experiments, Choice Experiments, and Questionnaires in the field, in the laboratory, and online. A main feature of OGUMI is its capacity to capture real-time changes in human behaviour in response to a dynamically varying resource. OGUMI is simple (for example, likewise other existing software, it does not require expertise in behavioural game theory, stable, and extremely flexible with respect to the user-resource model running in the background. Here we present the motivation for the development of OGUMI and we discuss its main features with an example application.

Information Processing Features Can Detect Behavioral Regimes of Dynamical Systems

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Rick Quax

2018-01-01

Full Text Available In dynamical systems, local interactions between dynamical units generate correlations which are stored and transmitted throughout the system, generating the macroscopic behavior. However a framework to quantify exactly how these correlations are stored, transmitted, and combined at the microscopic scale is missing. Here we propose to characterize the notion of “information processing” based on all possible Shannon mutual information quantities between a future state and all possible sets of initial states. We apply it to the 256 elementary cellular automata (ECA, which are the simplest possible dynamical systems exhibiting behaviors ranging from simple to complex. Our main finding is that only a few information features are needed for full predictability of the systemic behavior and that the “information synergy” feature is always most predictive. Finally we apply the idea to foreign exchange (FX and interest-rate swap (IRS time-series data. We find an effective “slowing down” leading indicator in all three markets for the 2008 financial crisis when applied to the information features, as opposed to using the data itself directly. Our work suggests that the proposed characterization of the local information processing of units may be a promising direction for predicting emergent systemic behaviors.

Feature recognition and detection for ancient architecture based on machine vision

Science.gov (United States)

Zou, Zheng; Wang, Niannian; Zhao, Peng; Zhao, Xuefeng

2018-03-01

Ancient architecture has a very high historical and artistic value. The ancient buildings have a wide variety of textures and decorative paintings, which contain a lot of historical meaning. Therefore, the research and statistics work of these different compositional and decorative features play an important role in the subsequent research. However, until recently, the statistics of those components are mainly by artificial method, which consumes a lot of labor and time, inefficiently. At present, as the strong support of big data and GPU accelerated training, machine vision with deep learning as the core has been rapidly developed and widely used in many fields. This paper proposes an idea to recognize and detect the textures, decorations and other features of ancient building based on machine vision. First, classify a large number of surface textures images of ancient building components manually as a set of samples. Then, using the convolution neural network to train the samples in order to get a classification detector. Finally verify its precision.

Nitrogen chronology of massive main sequence stars

NARCIS (Netherlands)

Köhler, K.; Borzyszkowski, M.; Brott, I.; Langer, N.; de Koter, A.

2012-01-01

Context. Rotational mixing in massive main sequence stars is predicted to monotonically increase their surface nitrogen abundance with time. Aims. We use this effect to design a method for constraining the age and the inclination angle of massive main sequence stars, given their observed luminosity,

Wire Finishing Mill Rolling Bearing Fault Diagnosis Based on Feature Extraction and BP Neural Network

Directory of Open Access Journals (Sweden)

Hong-Yu LIU

2014-10-01

Full Text Available Rolling bearing is main part of rotary machine. It is frail section of rotary machine. Its running status affects entire mechanical equipment system performance directly. Vibration acceleration signals of the third finishing mill of Anshan Steel and Iron Group wire plant were collected in this paper. Fourier analysis, power spectrum analysis and wavelet transform were made on collected signals. Frequency domain feature extraction and wavelet transform feature extraction were made on collected signals. BP neural network fault diagnosis model was adopted. Frequency domain feature values and wavelet transform feature values were treated as neural network input values. Various typical fault models were treated as neural network output values. Corresponding relations between feature vector and fault omen were utilized. BP neural network model of typical wire plant finishing mill rolling bearing fault was constructed by training many groups sample data. After inputting sample needed to be diagnosed, wire plant finishing mill rolling bearing fault can be diagnosed. This research has important practical significance on enhancing rolling bearing fault diagnosis precision, repairing rolling bearing duly, decreasing stop time, enhancing equipment running efficiency and enhancing economic benefits.

The Advantage of Playing Home in NBA: Microscopic, Team-Specific and Evolving Features.

Science.gov (United States)

Ribeiro, Haroldo V; Mukherjee, Satyam; Zeng, Xiao Han T

2016-01-01

The idea that the success rate of a team increases when playing home is broadly accepted and documented for a wide variety of sports. Investigations on the so-called "home advantage phenomenon" date back to the 70's and ever since has attracted the attention of scholars and sport enthusiasts. These studies have been mainly focused on identifying the phenomenon and trying to correlate it with external factors such as crowd noise and referee bias. Much less is known about the effects of home advantage in the "microscopic" dynamics of the game (within the game) or possible team-specific and evolving features of this phenomenon. Here we present a detailed study of these previous features in the National Basketball Association (NBA). By analyzing play-by-play events of more than sixteen thousand games that span thirteen NBA seasons, we have found that home advantage affects the microscopic dynamics of the game by increasing the scoring rates and decreasing the time intervals between scores of teams playing home. We verified that these two features are different among the NBA teams, for instance, the scoring rate of the Cleveland Cavaliers team is increased ≈0.16 points per minute (on average the seasons 2004-05 to 2013-14) when playing home, whereas for the New Jersey Nets (now the Brooklyn Nets) this rate increases in only ≈0.04 points per minute. We further observed that these microscopic features have evolved over time in a non-trivial manner when analyzing the results team-by-team. However, after averaging over all teams some regularities emerge; in particular, we noticed that the average differences in the scoring rates and in the characteristic times (related to the time intervals between scores) have slightly decreased over time, suggesting a weakening of the phenomenon. This study thus adds evidence of the home advantage phenomenon and contributes to a deeper understanding of this effect over the course of games.

The Advantage of Playing Home in NBA: Microscopic, Team-Specific and Evolving Features.

Directory of Open Access Journals (Sweden)

Haroldo V Ribeiro

Full Text Available The idea that the success rate of a team increases when playing home is broadly accepted and documented for a wide variety of sports. Investigations on the so-called "home advantage phenomenon" date back to the 70's and ever since has attracted the attention of scholars and sport enthusiasts. These studies have been mainly focused on identifying the phenomenon and trying to correlate it with external factors such as crowd noise and referee bias. Much less is known about the effects of home advantage in the "microscopic" dynamics of the game (within the game or possible team-specific and evolving features of this phenomenon. Here we present a detailed study of these previous features in the National Basketball Association (NBA. By analyzing play-by-play events of more than sixteen thousand games that span thirteen NBA seasons, we have found that home advantage affects the microscopic dynamics of the game by increasing the scoring rates and decreasing the time intervals between scores of teams playing home. We verified that these two features are different among the NBA teams, for instance, the scoring rate of the Cleveland Cavaliers team is increased ≈0.16 points per minute (on average the seasons 2004-05 to 2013-14 when playing home, whereas for the New Jersey Nets (now the Brooklyn Nets this rate increases in only ≈0.04 points per minute. We further observed that these microscopic features have evolved over time in a non-trivial manner when analyzing the results team-by-team. However, after averaging over all teams some regularities emerge; in particular, we noticed that the average differences in the scoring rates and in the characteristic times (related to the time intervals between scores have slightly decreased over time, suggesting a weakening of the phenomenon. This study thus adds evidence of the home advantage phenomenon and contributes to a deeper understanding of this effect over the course of games.

Features of Computerized Procedure System of Shin-Kori unit 5 and 6

International Nuclear Information System (INIS)

Seong, Nokyu; Jung, Yeonsub; Sung, Chanho

2016-01-01

The Computerized Procedure System (CPS) is one of the Man Machine Interface (MMI) resources of Main Control Room (MCR) of the Advanced Power Reactor 1400 (APR1400). The CPS has been continuously improved since it was installed in Shin-Kori unit 3 and 4. The Korea Hydro Nuclear Power Central Research Institute (KHNP CRI) has found the points of improvement of CPS through CPS centered Human Factors Engineering Verification and Validation (HFE V and V) and Operating Experience Review (OER) of reference power plant. This paper shows the main features of CPS of Shin-Kori 5 and 6 unit. This paper shows the main features of CPS of Shin-Kori 5 and 6. These are some of improvements of CPS. This prototype of CPS currently is implementing in CRI. The respective function can be more detailed after testing the prototype. These features will be applied to Shin-Kori 5 and 6 CPS after HFE V and V

Features of Computerized Procedure System of Shin-Kori unit 5 and 6

Energy Technology Data Exchange (ETDEWEB)

Seong, Nokyu; Jung, Yeonsub; Sung, Chanho [KHNP CRI, Daejeon (Korea, Republic of)

2016-10-15

The Computerized Procedure System (CPS) is one of the Man Machine Interface (MMI) resources of Main Control Room (MCR) of the Advanced Power Reactor 1400 (APR1400). The CPS has been continuously improved since it was installed in Shin-Kori unit 3 and 4. The Korea Hydro Nuclear Power Central Research Institute (KHNP CRI) has found the points of improvement of CPS through CPS centered Human Factors Engineering Verification and Validation (HFE V and V) and Operating Experience Review (OER) of reference power plant. This paper shows the main features of CPS of Shin-Kori 5 and 6 unit. This paper shows the main features of CPS of Shin-Kori 5 and 6. These are some of improvements of CPS. This prototype of CPS currently is implementing in CRI. The respective function can be more detailed after testing the prototype. These features will be applied to Shin-Kori 5 and 6 CPS after HFE V and V.

ROMANIAN YOUNG ENTREPRENEURS FEATURES: AN EMPIRICAL SURVEY

Directory of Open Access Journals (Sweden)

Ceptureanu Sebastian Ion

2015-07-01

Full Text Available There are many studies linking entrepreneurship and economic development. For specialists and public decision makers, developing entrepreneurship seems to be an easy policy action, even though actions and results are rather debatable. Unfortunately the relevant literature is not so generous concerning youth entrepreneurship. Youth is one of the most vulnerable groups in society, especially in the current economic and demographic situation in European Union and worldwide. At the same time, youth is the period when most people engage in their first job, are gaining financial independence and are assuming new responsibilities and roles shaping their identity. With respect to this, starting their own business is a natural choice for many young people. When considering entrepreneurial potential of young Romanians, there is almost not any data available. This paper aims to disseminate the results of a survey focused on young entrepreneurs, designed to fill the gap in the literature about Romanian young entrepreneurs’ features. The empirical study was divided in five parts: A. Personality of young entrepreneurs, highlighting the main features of behaviour and personality of young entrepreneurs. B. Professional background, focusing on young entrepreneurs’ background and how it influences their interest and performance improvement. C. Risk and crisis acceptance, highlighting the ability of young entrepreneurs to deal with critical situations. D. Business and business environment, focusing on internal and environmental aspects of the business. E. Social - cultural attitude, highlighting the attitude of society (incentives and disincentives to entrepreneurial initiatives of young people. This are excerpts of results from the first part, regarding personality of Romanian young entrepreneurs, concerning issues like level of independence, capacity for innovation, self-confidence, decision making process, level of persistence flexibility of young

76 FR 38408 - Notice of Funding Availability (NOFA) for HUD's Fiscal Year 2011 HOPE VI Main Street Grants

Science.gov (United States)

2011-06-30

... traditional central business district or ``Main Street'' area by replacing unused commercial space in... areas; 2. Preserve historic or traditional architecture or design features in Main Street areas; 3...

Vibration Feature Extraction and Analysis for Fault Diagnosis of Rotating Machinery-A Literature Survey

Directory of Open Access Journals (Sweden)

Saleem Riaz

2017-02-01

Full Text Available Safety, reliability, efficiency and performance of rotating machinery in all industrial applications are the main concerns. Rotating machines are widely used in various industrial applications. Condition monitoring and fault diagnosis of rotating machinery faults are very important and often complex and labor-intensive. Feature extraction techniques play a vital role for a reliable, effective and efficient feature extraction for the diagnosis of rotating machinery. Therefore, developing effective bearing fault diagnostic method using different fault features at different steps becomes more attractive. Bearings are widely used in medical applications, food processing industries, semi-conductor industries, paper making industries and aircraft components. This paper review has demonstrated that the latest reviews applied to rotating machinery on the available a variety of vibration feature extraction. Generally literature is classified into two main groups: frequency domain, time frequency analysis. However, fault detection and diagnosis of rotating machine vibration signal processing methods to present their own limitations. In practice, most healthy ingredients faulty vibration signal from background noise and mechanical vibration signals are buried. This paper also reviews that how the advanced signal processing methods, empirical mode decomposition and interference cancellation algorithm has been investigated and developed. The condition for rotating machines based rehabilitation, prevent failures increase the availability and reduce the cost of maintenance is becoming necessary too. Rotating machine fault detection and diagnostics in developing algorithms signal processing based on a key problem is the fault feature extraction or quantification. Currently, vibration signal, fault detection and diagnosis of rotating machinery based techniques most widely used techniques. Furthermore, the researchers are widely interested to make automatic

Development and application of intelligent CAE system for cyclotron main magnet

International Nuclear Information System (INIS)

Zhang Tianjue; Chen Yong; Fan Mingwu

1993-01-01

The main magnet that represents the feature of the cyclotron is the most important part in a cyclotron construction. Though there are many codes devoted to solve magnetic field computation problems, the results from them are depended on user's skill and experience very much. To help cyclotron magnet designer get acceptable result an intelligent CAE system for cyclotron main magnet design and machining has been developed. A reasonable good results in design could be get even the designer is a beginner with the help from an expert knowledge library installed in the program. The codes include following functions: 1. Intelligent CAD; 2. 2D and 3D magnetic field computation; 3. Beam dynamics analysis; 4. CAM for main magnet

Space Shuttle main engine product improvement

Science.gov (United States)

Lucci, A. D.; Klatt, F. P.

1985-01-01

The current design of the Space Shuttle Main Engine has passed 11 certification cycles, amassed approximately a quarter million seconds of engine test time in 1200 tests and successfully launched the Space Shuttle 17 times of 51 engine launches through May 1985. Building on this extensive background, two development programs are underway at Rocketdyne to improve the flow of hot gas through the powerhead and evaluate the changes to increase the performance margins in the engine. These two programs, called Phase II+ and Technology Test Bed Precursor program are described. Phase II+ develops a two-tube hot-gas manifold that improves the component environment. The Precursor program will evaluate a larger throat main combustion chamber, conduct combustion stability testing of a baffleless main injector, fabricate an experimental weld-free heat exchanger tube, fabricate and test a high pressure oxidizer turbopump with an improved inlet, and develop and test methods for reducing temperature transients at start and shutdown.

Process algebra with timing : real time and discrete time

NARCIS (Netherlands)

Baeten, J.C.M.; Middelburg, C.A.; Bergstra, J.A.; Ponse, A.J.; Smolka, S.A.

2001-01-01

We present real time and discrete time versions of ACP with absolute timing and relative timing. The starting-point is a new real time version with absolute timing, called ACPsat, featuring urgent actions and a delay operator. The discrete time versions are conservative extensions of the discrete

Process algebra with timing: Real time and discrete time

NARCIS (Netherlands)

Baeten, J.C.M.; Middelburg, C.A.

1999-01-01

We present real time and discrete time versions of ACP with absolute timing and relative timing. The startingpoint is a new real time version with absolute timing, called ACPsat , featuring urgent actions and a delay operator. The discrete time versions are conservative extensions of the discrete

Main real time software for high-energy physics experiments

International Nuclear Information System (INIS)

Tikhonov, A.N.

1985-01-01

The general problems of organization of software complexes, as well as development of typical algorithms and packages of applied programs for real time systems used in experiments with charged particle accelerators are discussed. It is noted that numerous qualitatively different real time tasks are solved by parallel programming of the processes of data acquisition, equipment control, data exchange with remote terminals, data express processing and accumulation, operator's instruction interpretation, generation and buffering of resulting files for data output and information processing which is realized on the basis of multicomputer system utilization. Further development of software for experiments is associated with improving the algorithms for automatic recognition and analysis of events with complex topology and standardization of applied program packages

Main features of the geological structure of upper-Frasnian barrier reefs in relation to their petroleum content

Energy Technology Data Exchange (ETDEWEB)

Nikonov, N.I.

1981-01-01

Analysis of new geological geophysical and industrial materials has made it possible to distinguish in the boundary part of the late Devonician shoal carbonate shelf barrier reefs of various ages. In confines of individual distinguished reefs there have been established deposits of oil (Western Tebuksk, Pashsor and Khar'yatinsk). There are given prospecting features of classification of buried reefs and prognoses for the finding possible oil deposits in them (Beyaksk, Sandiveis reefs).

Design features of HTMR-Hybrid Toroidal Magnet Tokamak Reactor

International Nuclear Information System (INIS)

Rosatelli, F.; Avanzini, P.G.; Brunelli, B.; Derchi, D.; Magnasco, M.; Grattarola, M.; Peluffo, M.; Raia, G.; Zampaglione, V.

1985-01-01

The HTMR (Hybrid Toroidal Magnet Tokamak Reactor) conceptual design is aimed to demonstrate the feasibility of a Tokamak reactor which could fulfill the scientific and technological objectives expected from next generation devices (e.g. INTOR-NET) with size and costs as small as possible. An hybrid toroidal field magnet, made up by copper and superconducting coils, seems to be a promising solution, allowing a considerable flexibility in machine performances, so as to gain useful margins in front of the uncertainties in confinement time scaling laws and beta and plasma density limits. In this paper the authors describe the optimization procedure for the hybrid magnet configuration, the main design features of HTMR and the preliminary mechanical calculations of the superconducting toroidal coils

Stylistic Features of Comment in Arabic Blogosphere

Directory of Open Access Journals (Sweden)

Gabdulzyamil G. Zaynullin

2017-11-01

Full Text Available One of the most important issues in the study of the functioning of the Internet language is the definition of the features of each Internet genre presented in online communication, taking into account the linguocultural features of the language in question. This paper studies the genre of the Internet comments of the Arabic-speaking blogosphere and reveals its stylistic features. The most common goal of the comment is gratitude, followed by praise. We created a corpus of comments from blogs of various subjects, and then conducted the tagging, having identified the group to which we attributed a comment, depending on the subject and the communicative goal. With the help of the Lexico 3 software, the most frequent lexical units were identified, the lexical features of the comments were described, the main one being the widespread use of religionyms, and the relationship between the blog subject and the stylistic characteristics of communication was revealed. The article traces the correlation between the literary and colloquial functional style in the comments, and also draws a conclusion that the comments are of a conversational, informal character. The main devices of expressiveness that are characteristic for both network and pre-network communication were revealed, and the tendency of the analysts to observe in the comments a stable three-part composition (greeting, message, final formula. The influence of traditional Arabic rhetoric, as well as the epistolary genre, was preserved. The results of the paper can be used when studying other genres of Internet communication in Arabic and in comparative studies to create the linguistic software.

Working memory units are all in your head: Factors that influence whether features or objects are the favored units.

Science.gov (United States)

Vergauwe, Evie; Cowan, Nelson

2015-09-01

We compared two contrasting hypotheses of how multifeatured objects are stored in visual working memory (vWM); as integrated objects or as independent features. A new procedure was devised to examine vWM representations of several concurrently held objects and their features and our main measure was reaction time (RT), allowing an examination of the real-time search through features and/or objects in an array in vWM. Response speeds to probes with color, shape, or both were studied as a function of the number of memorized colored shapes. Four testing groups were created by varying the instructions and the way in which probes with both color and shape were presented. The instructions explicitly either encouraged or discouraged the use of binding information and the task-relevance of binding information was further suggested by presenting probes with both color and shapes as either integrated objects or independent features. Our results show that the unit used for retrieval from vWM depends on the testing situation. Search was fully object-based only when all factors support that basis of search, in which case retrieving 2 features took no longer than retrieving a single feature. Otherwise, retrieving 2 features took longer than retrieving a single feature. Additional analyses of change detection latency suggested that, even though different testing situations can result in a stronger emphasis on either the feature dimension or the object dimension, neither one disappears from the representation and both concurrently affect change detection performance. (c) 2015 APA, all rights reserved).

Variation as a main feature of norm

Directory of Open Access Journals (Sweden)

S. Poladova

2017-09-01

Full Text Available The past half-century has witnessed remarkable growth in the study of language variation, and it has now become a highly productive subfield of research in sociolinguistics. Variability is everywhere in language, from the unique details in each production of a sound or sign to the auditory or visual processing of the linguistic signal. All languages that we can observe today show variation; what is more, they vary in identical ways, namely geographically and socially. It’s no secret that languages like English are full of variation. So, the aim of the article is to detect the reasons of variation and to uncover rates of usage of different free variations for a given set of lexical items. The research work is carried out by using the descriptive, comparative methods by subjecting to analysis the specific language materials. The discovery of law of variation became a starting point for the evolution of linguistics. The problem of search of variation facts and its role in the functioning of language system concerns many specialists from the outset. The scope of the investigation was to set up a system out of chaos of phenomena. Currently, the fact of conditionality of variation by system relations existing in the language is considered to be established.

The TRISTAN timing system

International Nuclear Information System (INIS)

Urakawa, Junji; Ishii, Kazuhiro; Kadokura, Eiichi; Kawamoto, Takashi; Kikuchi, Mitsuo; Kikutani, Eiji

1990-01-01

The TRISTAN accelerator complex comprises four accelerators: a 200 MeV electron linac for positron production, a 2.5 GeV linac, an 8 GeV accumulation ring (AR) and a 30 GeV main ring (MR). The TRISTAN timing system is divided into fast and slow timing systems. The fast timing system supplies timing signals (fast timing) for devices whose operation is synchronized with bunched beams from either the linac or the AR. These signals are also used in various beam monitors and beam feedback systems. The slow timing system generates trigger signals (slow timing) in order to achieve synchronization between the magnetic field and the rf accelerating voltage of the AR or MR. These triggers are also used for the automatic operation of machines. The TRISTAN timing system fulfills the following features with the required flexibility and extensibility while in the operation mode: (1) the linac gun trigger signals and the AR revolution clock are synchronized within ≅ 100 ps in timing accuracy, and a short pulse (≅ 1.5 ns) from the linac is injected and accumulated into an arbitrarily selected bucket of AR for a long time; (2) bucket matching between the AR and MR is achieved within ±6 ps in timing accuracy and a single bunched beam from the AR is injected into an arbitrarily selected bucket of the MR; (3) the slow timing system manages the operation mode of the AR and MR with both flexibility and extensibility; (4) the synchronization signals are transmitted through coaxial cables over a circumference of 3 km from the main control room. (orig.)

Energy and the environment: a review of the main features

International Nuclear Information System (INIS)

Cottrell, A.

1976-01-01

Science and technology have so far failed to discover practicable methods of tapping, on a large scale, the almost limitless supplies of energy in the form of solar radiation and its geophysical effects, the hot rocks inside the earth, and the thermonuclear potential of the hydrogen in the oceans. The problem with the natural energy sources is primarily an economic one. Despite its immense technical difficulties, thermonuclear fusion power seems a better long-term prospect. Biological methods of capturing solar energy, through photosynthesis, are not very promising. To meet this country's needs for petrol and gas from methane made from wood, about half the land area would have to be covered with coniferous forests. Fossil oil and gas supplies are unlikely to meet world needs for more than about 30 years longer. Coal supplies are much more plentiful and there is a good case for a strong technical effort on the desulphurisation of coal, to make it environmentally more acceptable, and on the conversion of coal to high-grade oil and gas. There is not enough uranium to support a world programme of thermal neutron reactors for more than about 20 years. The fast breeder reactor can overcome this problem, but the large-scale usage of these, in the next century, will aggravate the already deep anxieties about the risks to Society from radioactivity and other nuclear hazards. It is thus important to press forward strongly with programmes of fusion nuclear power, which would be more acceptable socially and environmentally. Later in the next century, the effects of man's sctivities on the world's climate may become important. It is already time to begin now to investigate these scientifically. (author)

Feature hashing for fast image retrieval

Science.gov (United States)

Yan, Lingyu; Fu, Jiarun; Zhang, Hongxin; Yuan, Lu; Xu, Hui

2018-03-01

Currently, researches on content based image retrieval mainly focus on robust feature extraction. However, due to the exponential growth of online images, it is necessary to consider searching among large scale images, which is very timeconsuming and unscalable. Hence, we need to pay much attention to the efficiency of image retrieval. In this paper, we propose a feature hashing method for image retrieval which not only generates compact fingerprint for image representation, but also prevents huge semantic loss during the process of hashing. To generate the fingerprint, an objective function of semantic loss is constructed and minimized, which combine the influence of both the neighborhood structure of feature data and mapping error. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. Experimental results show good performance of our approach.

Nearest neighbor 3D segmentation with context features

Science.gov (United States)

Hristova, Evelin; Schulz, Heinrich; Brosch, Tom; Heinrich, Mattias P.; Nickisch, Hannes

2018-03-01

Automated and fast multi-label segmentation of medical images is challenging and clinically important. This paper builds upon a supervised machine learning framework that uses training data sets with dense organ annotations and vantage point trees to classify voxels in unseen images based on similarity of binary feature vectors extracted from the data. Without explicit model knowledge, the algorithm is applicable to different modalities and organs, and achieves high accuracy. The method is successfully tested on 70 abdominal CT and 42 pelvic MR images. With respect to ground truth, an average Dice overlap score of 0.76 for the CT segmentation of liver, spleen and kidneys is achieved. The mean score for the MR delineation of bladder, bones, prostate and rectum is 0.65. Additionally, we benchmark several variations of the main components of the method and reduce the computation time by up to 47% without significant loss of accuracy. The segmentation results are - for a nearest neighbor method - surprisingly accurate, robust as well as data and time efficient.

Adding a visualization feature to web search engines: it's time.

Science.gov (United States)

Wong, Pak Chung

2008-01-01

It's widely recognized that all Web search engines today are almost identical in presentation layout and behavior. In fact, the same presentation approach has been applied to depicting search engine results pages (SERPs) since the first Web search engine launched in 1993. In this Visualization Viewpoints article, I propose to add a visualization feature to Web search engines and suggest that the new addition can improve search engines' performance and capabilities, which in turn lead to better Web search technology.

Hybrid feature selection for supporting lightweight intrusion detection systems

Science.gov (United States)

Song, Jianglong; Zhao, Wentao; Liu, Qiang; Wang, Xin

2017-08-01

Redundant and irrelevant features not only cause high resource consumption but also degrade the performance of Intrusion Detection Systems (IDS), especially when coping with big data. These features slow down the process of training and testing in network traffic classification. Therefore, a hybrid feature selection approach in combination with wrapper and filter selection is designed in this paper to build a lightweight intrusion detection system. Two main phases are involved in this method. The first phase conducts a preliminary search for an optimal subset of features, in which the chi-square feature selection is utilized. The selected set of features from the previous phase is further refined in the second phase in a wrapper manner, in which the Random Forest(RF) is used to guide the selection process and retain an optimized set of features. After that, we build an RF-based detection model and make a fair comparison with other approaches. The experimental results on NSL-KDD datasets show that our approach results are in higher detection accuracy as well as faster training and testing processes.

The Utilization of Imaging Features in the Management of Intraductal Papillary Mucinous Neoplasms

Directory of Open Access Journals (Sweden)

Stefano Palmucci

2014-01-01

Full Text Available Intraductal papillary mucinous neoplasms (IPMNs represent a group of cystic pancreatic neoplasms with large range of clinical behaviours, ranging from low-grade dysplasia or borderline lesions to invasive carcinomas. They can be grouped into lesions originating from the main pancreatic duct, main duct IPMNs (MD-IPMNs, and lesions which arise from secondary branches of parenchyma, denominated branch-duct IPMNs (BD-IPMNs. Management of these cystic lesions is essentially based on clinical and radiological features. The latter have been very well described in the last fifteen years, with many studies published in literature showing the main radiological features of IPMNs. Currently, the goal of imaging modalities is to identify “high-risk stigmata” or “worrisome feature” in the evaluation of pancreatic cysts. Marked dilatation of the main duct (>1 cm, large size (3–5 cm, and intramural nodules have been associated with increased risk of degeneration. BD-IPMNs could be observed as microcystic or macrocystic in appearance, with or without communication with main duct. Their imaging features are frequently overlapped with cystic neoplasms. The risk of progression for secondary IPMNs is lower, and subsequently an imaging based follow-up is very often proposed for these lesions.

The development of real-time stability supports visual working memory performance: Young children's feature binding can be improved through perceptual structure.

Science.gov (United States)

Simmering, Vanessa R; Wood, Chelsey M

2017-08-01

Working memory is a basic cognitive process that predicts higher-level skills. A central question in theories of working memory development is the generality of the mechanisms proposed to explain improvements in performance. Prior theories have been closely tied to particular tasks and/or age groups, limiting their generalizability. The cognitive dynamics theory of visual working memory development has been proposed to overcome this limitation. From this perspective, developmental improvements arise through the coordination of cognitive processes to meet demands of different behavioral tasks. This notion is described as real-time stability, and can be probed through experiments that assess how changing task demands impact children's performance. The current studies test this account by probing visual working memory for colors and shapes in a change detection task that compares detection of changes to new features versus swaps in color-shape binding. In Experiment 1, 3- to 4-year-old children showed impairments specific to binding swaps, as predicted by decreased real-time stability early in development; 5- to 6-year-old children showed a slight advantage on binding swaps, but 7- to 8-year-old children and adults showed no difference across trial types. Experiment 2 tested the proposed explanation of young children's binding impairment through added perceptual structure, which supported the stability and precision of feature localization in memory-a process key to detecting binding swaps. This additional structure improved young children's binding swap detection, but not new-feature detection or adults' performance. These results provide further evidence for the cognitive dynamics and real-time stability explanation of visual working memory development. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Chronic actinic dermatitis - A study of clinical features

Directory of Open Access Journals (Sweden)

Somani Vijay

2005-01-01

Full Text Available Background: Chronic actinic dermatitis (CAD, one of the immune mediated photo-dermatoses, comprises a spectrum of conditions including persistent light reactivity, photosensitive eczema and actinic reticuloid. Diagnostic criteria were laid down about 20 years back, but clinical features are the mainstay in diagnosis. In addition to extreme sensitivity to UVB, UVA and/or visible light, about three quarters of patients exhibit contact sensitivity to several allergens, which may contribute to the etiopathogenesis of CAD. This study was undertaken to examine the clinical features of CAD in India and to evaluate the relevance of patch testing and photo-aggravation testing in the diagnosis of CAD. Methods: The clinical data of nine patients with CAD were analyzed. Histopathology, patch testing and photo-aggravation testing were also performed. Results: All the patients were males. The average age of onset was 57 years. The first episode was usually noticed in the beginning of summer. Later the disease gradually tended to be perennial, without any seasonal variations. The areas affected were mainly the photo-exposed areas in all patients, and the back in three patients. Erythroderma was the presenting feature in two patients. The palms and soles were involved in five patients. Patch testing was positive in seven of nine patients. Conclusions: The diagnosis of CAD mainly depended upon the history and clinical features. The incidence of erythroderma and palmoplantar eczema was high in our series. Occupation seems to play a role in the etiopathogenesis of CAD.

Spatiotemporal Features for Asynchronous Event-based Data

Directory of Open Access Journals (Sweden)

Xavier eLagorce

2015-02-01

Full Text Available Bio-inspired asynchronous event-based vision sensors are currently introducing a paradigm shift in visual information processing. These new sensors rely on a stimulus-driven principle of light acquisition similar to biological retinas. They are event-driven and fully asynchronous, thereby reducing redundancy and encoding exact times of input signal changes, leading to a very precise temporal resolution. Approaches for higher-level computer vision often rely on the realiable detection of features in visual frames, but similar definitions of features for the novel dynamic and event-based visual input representation of silicon retinas have so far been lacking. This article addresses the problem of learning and recognizing features for event-based vision sensors, which capture properties of truly spatiotemporal volumes of sparse visual event information. A novel computational architecture for learning and encoding spatiotemporal features is introduced based on a set of predictive recurrent reservoir networks, competing via winner-take-all selection. Features are learned in an unsupervised manner from real-world input recorded with event-based vision sensors. It is shown that the networks in the architecture learn distinct and task-specific dynamic visual features, and can predict their trajectories over time.

Main hematological parameters of sturgeon species (Acipenseridae (review

Directory of Open Access Journals (Sweden)

M. Simon

2017-03-01

Full Text Available Purpose. To analyze scientific sources on the physiological, biochemical, ecological and genetic features of the main hematological paremeters and patterns of their variability in sturgeon species (Acipenseridae. To examine the fundamental aspects of lipid and protein metabolism in blood serum and the effect of biotic and abiotic factors on them. To highlight the common features of serum enzymes. Findings. A review of scientific papers revealed that although hematological parameters of sturgeons are generally similar to those of teleosts and mammals, there are a number of significant differences. In addition, many hematological parameters are characterized by species specificity, even within a family. Special attention is given to the variability of hematological parameters under the effect of factors of both internal and external environment. The paper describes the effects of sex and age, as well as the seasons of the year on the compositio of sturgeon blood. The fundamentals of the use of serum proteins in genetic and population studies are outlined. The features of the functioning of hemoglobin in sturgeon’s red blood cells are examined. The main hematological parameters involved in the formation and maturation of sexual products, and their effect on fertility are reviewed. For example, the spawners, which hadn’t put reproductive product, are characterized by a low rate of hemoglobin, increase in erythrocyte sedimentation speed and also a rise of the level of crude protein in blood and β-lipoproteid serum.The biochemical parameters (total protein and fractions, glucose, creatinine, cholesterol, the activity of some enzymes (alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, creatine kinase in serum are examined. Practical value. The systematized data on the main hematological parameters and patterns of their variability in sturgeon species will be useful for both scientits and fish farmers. This is due to the

Main Memory Implementations for Binary Grouping

OpenAIRE

May, Norman; Moerkotte, Guido

2005-01-01

An increasing number of applications depend on efficient storage and analysis features for XML data. Hence, query optimization and efficient evaluation techniques for the emerging XQuery standard become more and more important. Many XQuery queries require nested expressions. Unnesting them often introduces binary grouping. We introduce several algorithms implementing binary grouping and analyze their time and space complexity. Experiments demonstrate their performance.

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas.

Science.gov (United States)

Liu, Bo; Wu, Huayi; Wang, Yandong; Liu, Wenming

2015-01-01

Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1) using directional mathematical morphology to enhance the contrast between roads and non-roads; (2) using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction.

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas.

Directory of Open Access Journals (Sweden)

Bo Liu

Full Text Available Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1 using directional mathematical morphology to enhance the contrast between roads and non-roads; (2 using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction.

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas

Science.gov (United States)

Liu, Bo; Wu, Huayi; Wang, Yandong; Liu, Wenming

2015-01-01

Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1) using directional mathematical morphology to enhance the contrast between roads and non-roads; (2) using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction. PMID:26397832

Feature Selection, Flaring Size and Time-to-Flare Prediction Using Support Vector Regression, and Automated Prediction of Flaring Behavior Based on Spatio-Temporal Measures Using Hidden Markov Models

Science.gov (United States)

Al-Ghraibah, Amani

Solar flares release stored magnetic energy in the form of radiation and can have significant detrimental effects on earth including damage to technological infrastructure. Recent work has considered methods to predict future flare activity on the basis of quantitative measures of the solar magnetic field. Accurate advanced warning of solar flare occurrence is an area of increasing concern and much research is ongoing in this area. Our previous work 111] utilized standard pattern recognition and classification techniques to determine (classify) whether a region is expected to flare within a predictive time window, using a Relevance Vector Machine (RVM) classification method. We extracted 38 features which describing the complexity of the photospheric magnetic field, the result classification metrics will provide the baseline against which we compare our new work. We find a true positive rate (TPR) of 0.8, true negative rate (TNR) of 0.7, and true skill score (TSS) of 0.49. This dissertation proposes three basic topics; the first topic is an extension to our previous work [111, where we consider a feature selection method to determine an appropriate feature subset with cross validation classification based on a histogram analysis of selected features. Classification using the top five features resulting from this analysis yield better classification accuracies across a large unbalanced dataset. In particular, the feature subsets provide better discrimination of the many regions that flare where we find a TPR of 0.85, a TNR of 0.65 sightly lower than our previous work, and a TSS of 0.5 which has an improvement comparing with our previous work. In the second topic, we study the prediction of solar flare size and time-to-flare using support vector regression (SVR). When we consider flaring regions only, we find an average error in estimating flare size of approximately half a GOES class. When we additionally consider non-flaring regions, we find an increased average

A software solution for recording circadian oscillator features in time-lapse live cell microscopy.

Science.gov (United States)

Sage, Daniel; Unser, Michael; Salmon, Patrick; Dibner, Charna

2010-07-06

Fluorescent and bioluminescent time-lapse microscopy approaches have been successfully used to investigate molecular mechanisms underlying the mammalian circadian oscillator at the single cell level. However, most of the available software and common methods based on intensity-threshold segmentation and frame-to-frame tracking are not applicable in these experiments. This is due to cell movement and dramatic changes in the fluorescent/bioluminescent reporter protein during the circadian cycle, with the lowest expression level very close to the background intensity. At present, the standard approach to analyze data sets obtained from time lapse microscopy is either manual tracking or application of generic image-processing software/dedicated tracking software. To our knowledge, these existing software solutions for manual and automatic tracking have strong limitations in tracking individual cells if their plane shifts. In an attempt to improve existing methodology of time-lapse tracking of a large number of moving cells, we have developed a semi-automatic software package. It extracts the trajectory of the cells by tracking theirs displacements, makes the delineation of cell nucleus or whole cell, and finally yields measurements of various features, like reporter protein expression level or cell displacement. As an example, we present here single cell circadian pattern and motility analysis of NIH3T3 mouse fibroblasts expressing a fluorescent circadian reporter protein. Using Circadian Gene Express plugin, we performed fast and nonbiased analysis of large fluorescent time lapse microscopy datasets. Our software solution, Circadian Gene Express (CGE), is easy to use and allows precise and semi-automatic tracking of moving cells over longer period of time. In spite of significant circadian variations in protein expression with extremely low expression levels at the valley phase, CGE allows accurate and efficient recording of large number of cell parameters, including

Influence of design features on decommissioning of a large fast breeder reactor

International Nuclear Information System (INIS)

Fournie, J.-L.; Alary, C.; Maire, D.; Seroux, N. de; Peyrard, G.

1990-01-01

The evolution of FBR design in Europe shows that pool-type design will become the reference design for future FBR and the projected European Fast Reactor (EFR) is based on this concept. The identification of design features shows that the main contributors of the sodium and structures activity are the Co60 for gamma radiation source and low decay, Ni63, Nb94 and Ni59 for long time decay. So, the technical benefits of a Co content reduction are interesting for the high activated structures and for diagrid thimbles coating and we made proposals to lower Co content in steels or alloys and to substitute coatings. We identify measures which must facilitate both the sodium draining and the reactor block and internal cleaning: all which improve the gravity draining and the downing of the sodium flow make easier the penetration of cleaning products. The features, connected with the dismantling of the very activated internal structures, of the roof and of the lay-out, are mentioned. (author)

An effective method for cirrhosis recognition based on multi-feature fusion

Science.gov (United States)

Chen, Yameng; Sun, Gengxin; Lei, Yiming; Zhang, Jinpeng

2018-04-01

Liver disease is one of the main causes of human healthy problem. Cirrhosis, of course, is the critical phase during the development of liver lesion, especially the hepatoma. Many clinical cases are still influenced by the subjectivity of physicians in some degree, and some objective factors such as illumination, scale, edge blurring will affect the judgment of clinicians. Then the subjectivity will affect the accuracy of diagnosis and the treatment of patients. In order to solve the difficulty above and improve the recognition rate of liver cirrhosis, we propose a method of multi-feature fusion to obtain more robust representations of texture in ultrasound liver images, the texture features we extract include local binary pattern(LBP), gray level co-occurrence matrix(GLCM) and histogram of oriented gradient(HOG). In this paper, we firstly make a fusion of multi-feature to recognize cirrhosis and normal liver based on parallel combination concept, and the experimental results shows that the classifier is effective for cirrhosis recognition which is evaluated by the satisfying classification rate, sensitivity and specificity of receiver operating characteristic(ROC), and cost time. Through the method we proposed, it will be helpful to improve the accuracy of diagnosis of cirrhosis and prevent the development of liver lesion towards hepatoma.

Spatial Feature Reconstruction of Cloud-Covered Areas in Daily MODIS Composites

Directory of Open Access Journals (Sweden)

Stephan Paul

2015-04-01

Full Text Available The opacity of clouds is the main problem for optical and thermal space-borne sensors, like the Moderate-Resolution Imaging Spectroradiometer (MODIS. Especially during polar nighttime, the low thermal contrast between clouds and the underlying snow/ice results in deficiencies of the MODIS cloud mask and affected products. There are different approaches to retrieve information about frequently cloud-covered areas, which often operate with large amounts of days aggregated into single composites for a long period of time. These approaches are well suited for static-nature, slow changing surface features (e.g., fast-ice extent. However, this is not applicable to fast-changing features, like sea-ice polynyas. Therefore, we developed a spatial feature reconstruction to derive information for cloud-covered sea-ice areas based on the surrounding days weighted directly proportional with their temporal proximity to the initial day of interest. Its performance is tested based on manually-screened and artificially cloud-covered case studies of MODIS-derived polynya area data for the polynya in the Brunt Ice Shelf region of Antarctica. On average, we are able to completely restore the artificially cloud-covered test areas with a spatial correlation of 0.83 and a mean absolute spatial deviation of 21%.

Investigation of features in radon soil dynamics and search for influencing factors

Science.gov (United States)

Yakovlev, Grigorii; Cherepnev, Maxim; Nagorskiy, Petr; Yakovleva, Valentina

2018-03-01

The features in radon soil dynamics at two depths were investigated and the main influencing factors were revealed. The monitoring of radon volumetric activity in soil air was performed at experimental site of Tomsk Observatory of Radioactivity and Ionizing Radiation with using radon radiometers and scintillation detectors of alpha-radiation with 10 min sampling frequency. The detectors were installed into boreholes of 0.5 and 1 m depths. The analysis of the soil radon monitoring data has allowed revealing some dependencies at daily and annual scales and main influencing factors. In periods with clearly defined daily radon variations in the soil were revealed the next: 1) amplitude of the daily variations of the soil radon volumetric activity damps with the depth, that is related with the influence of convective fluxes in the soil; 2) temporal shift between times of occurrence of radon volumetric activity maximum (or minimum) values at 0.5 m and 1 m depths can reach 3 hours. In seasonal dynamics of the soil radon the following dependences were found: 1) maximal values are observed in winter, but minimal - in summer; 2) spring periods of snow melting are accompanied by anomaly increasing of radon volumetric activity in the soil up to about 3 times. The main influencing factors are atmospheric precipitations, temperature gradient in the soil and the state of upper soil layer.

Learning to Automatically Detect Features for Mobile Robots Using Second-Order Hidden Markov Models

Directory of Open Access Journals (Sweden)

Olivier Aycard

2004-12-01

Full Text Available In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T-intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.

A FRET-based real-time PCR assay to identify the main causal agents of New World tegumentary leishmaniasis.

Directory of Open Access Journals (Sweden)

Pablo Tsukayama

Full Text Available In South America, various species of Leishmania are endemic and cause New World tegumentary leishmaniasis (NWTL. The correct identification of these species is critical for adequate clinical management and surveillance activities. We developed a real-time polymerase chain reaction (PCR assay and evaluated its diagnostic performance using 64 archived parasite isolates and 192 prospectively identified samples collected from individuals with suspected leishmaniasis enrolled at two reference clinics in Lima, Peru. The real-time PCR assay was able to detect a single parasite and provided unambiguous melting peaks for five Leishmania species of the Viannia subgenus that are highly prevalent in South America: L. (V. braziliensis, L. (V. panamensis, L. (V. guyanensis, L. (V. peruviana and L. (V. lainsoni. Using kinetoplastid DNA-based PCR as a gold standard, the real-time PCR had sensitivity and specificity values of 92% and 77%, respectively, which were significantly higher than those of conventional tests such as microscopy, culture and the leishmanin skin test (LST. In addition, the real-time PCR identified 147 different clinical samples at the species level, providing an overall agreement of 100% when compared to multilocus sequence typing (MLST data performed on a subset of these samples. Furthermore, the real-time PCR was three times faster and five times less expensive when compared to PCR - MLST for species identification from clinical specimens. In summary, this new assay represents a cost-effective and reliable alternative for the identification of the main species causing NWTL in South America.

Prominent feature extraction for review analysis: an empirical study

Science.gov (United States)

Agarwal, Basant; Mittal, Namita

2016-05-01

Sentiment analysis (SA) research has increased tremendously in recent times. SA aims to determine the sentiment orientation of a given text into positive or negative polarity. Motivation for SA research is the need for the industry to know the opinion of the users about their product from online portals, blogs, discussion boards and reviews and so on. Efficient features need to be extracted for machine-learning algorithm for better sentiment classification. In this paper, initially various features are extracted such as unigrams, bi-grams and dependency features from the text. In addition, new bi-tagged features are also extracted that conform to predefined part-of-speech patterns. Furthermore, various composite features are created using these features. Information gain (IG) and minimum redundancy maximum relevancy (mRMR) feature selection methods are used to eliminate the noisy and irrelevant features from the feature vector. Finally, machine-learning algorithms are used for classifying the review document into positive or negative class. Effects of different categories of features are investigated on four standard data-sets, namely, movie review and product (book, DVD and electronics) review data-sets. Experimental results show that composite features created from prominent features of unigram and bi-tagged features perform better than other features for sentiment classification. mRMR is a better feature selection method as compared with IG for sentiment classification. Boolean Multinomial Naïve Bayes) algorithm performs better than support vector machine classifier for SA in terms of accuracy and execution time.

Analysis of the Main Factors Influencing Food Production in China Based on Time Series Trend Chart

Institute of Scientific and Technical Information of China (English)

Shuangjin; WANG; Jianying; LI

2014-01-01

Based on the annual sample data on food production in China since the reform and opening up,we select 8 main factors influencing the total food production( growing area,application rate of chemical fertilizer,effective irrigation area,the affected area,total machinery power,food production cost index,food production price index,financial funds for supporting agriculture,farmers and countryside),and put them into categories of material input,resources and environment,and policy factors. Using the factor analysis,we carry out the multi-angle analysis of these typical influencing factors one by one through the time series trend chart. It is found that application rate of chemical fertilizer,the growing area of food crops and drought-affected area become the key factors affecting food production. On this basis,we set forth the corresponding recommendations for improving the comprehensive food production capacity.

Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network

Science.gov (United States)

Wang, Li-Hua; Zhao, Xiao-Ping; Wu, Jia-Xin; Xie, Yang-Yang; Zhang, Yong-Hong

2017-11-01

With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform (STFT) to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adaptively extracted by using a convolutional neural network (CNN). The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by traditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.

MAIN PROBLEMS OF MOULDING OF SILUMINS. WAYS OF SOLUTION

Directory of Open Access Journals (Sweden)

E. I. Marukovich

2016-01-01

Full Text Available The main problems of silumin’ moulding are connected with insufficient modifying of casting structure, their considerable inclination to gas porosity, difficult to remove nonmetallic inclusions and these features are shown in the article. Protection of liquid silumin from influence of the air atmosphere; use of crucibles from aluminum oxide; refinement of fusion from nonmetallic inclusions and application of the accelerated hydrogen hardening of castings were used to solve these problems, as it is shown in the article.

COMPETITIVENESS OF UKRAINIAN FOOD ENTERPRISES. FEATURES OF ASSESSMENT.

Directory of Open Access Journals (Sweden)

K. Stasiukova

2017-10-01

Full Text Available Competitiveness of a product is a main factor for its commercial success in the market with a largenumber of manufacturers of similar products. The article describes features of assessment and analysis offood enterprises products. It offers an evaluation algorithm and the ways of competitiveness improvement.

The features of space-planning and outfitting decisions

Energy Technology Data Exchange (ETDEWEB)

Voronov, N.A.; Bezrukov, A.K.

1982-01-01

The features of space-planning and outfitting solutions for a primary housing which was assembled with the No 1 auxillary housing are examined. The primary factors which have given rise to an unusual design decision on the depth of the structure of the main housing (12 meters) are noted.

FEATURE EXTRACTION FOR EMG BASED PROSTHESES CONTROL

Directory of Open Access Journals (Sweden)

R. Aishwarya

2013-01-01

Full Text Available The control of prosthetic limb would be more effective if it is based on Surface Electromyogram (SEMG signals from remnant muscles. The analysis of SEMG signals depend on a number of factors, such as amplitude as well as time- and frequency-domain properties. Time series analysis using Auto Regressive (AR model and Mean frequency which is tolerant to white Gaussian noise are used as feature extraction techniques. EMG Histogram is used as another feature vector that was seen to give more distinct classification. The work was done with SEMG dataset obtained from the NINAPRO DATABASE, a resource for bio robotics community. Eight classes of hand movements hand open, hand close, Wrist extension, Wrist flexion, Pointing index, Ulnar deviation, Thumbs up, Thumb opposite to little finger are taken into consideration and feature vectors are extracted. The feature vectors can be given to an artificial neural network for further classification in controlling the prosthetic arm which is not dealt in this paper.

Transformations of working time as a factor of labour dehumanization

OpenAIRE

Tomaszewska-Lipiec, Renata

2015-01-01

Shortening working time and making it more flexible, which have been observed in Europe since mid-20th century, are perceived as the main features and conditions for labour humanization as well as the sign of social progress. The aim of the article has been showing negative consequences of this phenomenon,especially for maintaining balance between work and private life. It was based on hypothesis that the tendency to shorten the working time and to make it more flexible cannot be seen only as...

Multithreaded hybrid feature tracking for markerless augmented reality.

Science.gov (United States)

Lee, Taehee; Höllerer, Tobias

2009-01-01

We describe a novel markerless camera tracking approach and user interaction methodology for augmented reality (AR) on unprepared tabletop environments. We propose a real-time system architecture that combines two types of feature tracking. Distinctive image features of the scene are detected and tracked frame-to-frame by computing optical flow. In order to achieve real-time performance, multiple operations are processed in a synchronized multi-threaded manner: capturing a video frame, tracking features using optical flow, detecting distinctive invariant features, and rendering an output frame. We also introduce user interaction methodology for establishing a global coordinate system and for placing virtual objects in the AR environment by tracking a user's outstretched hand and estimating a camera pose relative to it. We evaluate the speed and accuracy of our hybrid feature tracking approach, and demonstrate a proof-of-concept application for enabling AR in unprepared tabletop environments, using bare hands for interaction.

A HYBRID FILTER AND WRAPPER FEATURE SELECTION APPROACH FOR DETECTING CONTAMINATION IN DRINKING WATER MANAGEMENT SYSTEM

Directory of Open Access Journals (Sweden)

S. VISALAKSHI

2017-07-01

Full Text Available Feature selection is an important task in predictive models which helps to identify the irrelevant features in the high - dimensional dataset. In this case of water contamination detection dataset, the standard wrapper algorithm alone cannot be applied because of the complexity. To overcome this computational complexity problem and making it lighter, filter-wrapper based algorithm has been proposed. In this work, reducing the feature space is a significant component of water contamination. The main findings are as follows: (1 The main goal is speeding up the feature selection process, so the proposed filter - based feature pre-selection is applied and guarantees that useful data are improbable to be detached in the initial stage which discussed briefly in this paper. (2 The resulting features are again filtered by using the Genetic Algorithm coded with Support Vector Machine method, where it facilitates to nutshell the subset of features with high accuracy and decreases the expense. Experimental results show that the proposed methods trim down redundant features effectively and achieved better classification accuracy.

A Semidefinite Programming Based Search Strategy for Feature Selection with Mutual Information Measure.

Science.gov (United States)

Naghibi, Tofigh; Hoffmann, Sarah; Pfister, Beat

2015-08-01

Feature subset selection, as a special case of the general subset selection problem, has been the topic of a considerable number of studies due to the growing importance of data-mining applications. In the feature subset selection problem there are two main issues that need to be addressed: (i) Finding an appropriate measure function than can be fairly fast and robustly computed for high-dimensional data. (ii) A search strategy to optimize the measure over the subset space in a reasonable amount of time. In this article mutual information between features and class labels is considered to be the measure function. Two series expansions for mutual information are proposed, and it is shown that most heuristic criteria suggested in the literature are truncated approximations of these expansions. It is well-known that searching the whole subset space is an NP-hard problem. Here, instead of the conventional sequential search algorithms, we suggest a parallel search strategy based on semidefinite programming (SDP) that can search through the subset space in polynomial time. By exploiting the similarities between the proposed algorithm and an instance of the maximum-cut problem in graph theory, the approximation ratio of this algorithm is derived and is compared with the approximation ratio of the backward elimination method. The experiments show that it can be misleading to judge the quality of a measure solely based on the classification accuracy, without taking the effect of the non-optimum search strategy into account.

Music Genre Classification using the multivariate AR feature integration model

DEFF Research Database (Denmark)

Ahrendt, Peter; Meng, Anders

2005-01-01

informative decisions about musical genre. For the MIREX music genre contest several authors derive long time features based either on statistical moments and/or temporal structure in the short time features. In our contribution we model a segment (1.2 s) of short time features (texture) using a multivariate...... autoregressive model. Other authors have applied simpler statistical models such as the mean-variance model, which also has been included in several of this years MIREX submissions, see e.g. Tzanetakis (2005); Burred (2005); Bergstra et al. (2005); Lidy and Rauber (2005)....

In-service inspection robot for PFBR main vessel- concept

Energy Technology Data Exchange (ETDEWEB)

Rajendran, S; Ramakumar, M S [Bhabha Atomic Research Centre, Mumbai (India). Div. of Remote Handling and Robotics

1994-12-31

In-service inspection (ISI) of critical components in a nuclear reactor is one of the foremost and important tasks which reveals the state of health of the system, thereby ensuring the safety of the plant, personnel and environment. Prototype Fast Breeder Reactor (PFBR) is designed as a pool type reactor. A safety vessel is provided in the design which envelopes the main reactor vessel. The ISI of the main vessel is mandatory and will be carried out by a robot which will operate on this annular gap. The design of the robot is such that it can crawl around the vessel and into the gap at the bottom of the vessel relying on friction grip. The mobile robot will carry a CCTV camera and the inspection technique packages into the interspace, position and orient these to carry out the ISI of the main vessel. The paper discusses about the design features of the robot including the gripping mechanism and the crawling sequence to perform ISI of the reactor vessel. 3 figs.

In-service inspection robot for PFBR main vessel- concept

International Nuclear Information System (INIS)

Rajendran, S.; Ramakumar, M.S.

1994-01-01

In-service inspection (ISI) of critical components in a nuclear reactor is one of the foremost and important tasks which reveals the state of health of the system, thereby ensuring the safety of the plant, personnel and environment. Prototype Fast Breeder Reactor (PFBR) is designed as a pool type reactor. A safety vessel is provided in the design which envelopes the main reactor vessel. The ISI of the main vessel is mandatory and will be carried out by a robot which will operate on this annular gap. The design of the robot is such that it can crawl around the vessel and into the gap at the bottom of the vessel relying on friction grip. The mobile robot will carry a CCTV camera and the inspection technique packages into the interspace, position and orient these to carry out the ISI of the main vessel. The paper discusses about the design features of the robot including the gripping mechanism and the crawling sequence to perform ISI of the reactor vessel. 3 figs

A software solution for recording circadian oscillator features in time-lapse live cell microscopy

Directory of Open Access Journals (Sweden)

Salmon Patrick

2010-07-01

Full Text Available Abstract Background Fluorescent and bioluminescent time-lapse microscopy approaches have been successfully used to investigate molecular mechanisms underlying the mammalian circadian oscillator at the single cell level. However, most of the available software and common methods based on intensity-threshold segmentation and frame-to-frame tracking are not applicable in these experiments. This is due to cell movement and dramatic changes in the fluorescent/bioluminescent reporter protein during the circadian cycle, with the lowest expression level very close to the background intensity. At present, the standard approach to analyze data sets obtained from time lapse microscopy is either manual tracking or application of generic image-processing software/dedicated tracking software. To our knowledge, these existing software solutions for manual and automatic tracking have strong limitations in tracking individual cells if their plane shifts. Results In an attempt to improve existing methodology of time-lapse tracking of a large number of moving cells, we have developed a semi-automatic software package. It extracts the trajectory of the cells by tracking theirs displacements, makes the delineation of cell nucleus or whole cell, and finally yields measurements of various features, like reporter protein expression level or cell displacement. As an example, we present here single cell circadian pattern and motility analysis of NIH3T3 mouse fibroblasts expressing a fluorescent circadian reporter protein. Using Circadian Gene Express plugin, we performed fast and nonbiased analysis of large fluorescent time lapse microscopy datasets. Conclusions Our software solution, Circadian Gene Express (CGE, is easy to use and allows precise and semi-automatic tracking of moving cells over longer period of time. In spite of significant circadian variations in protein expression with extremely low expression levels at the valley phase, CGE allows accurate and

Toward fast feature adaptation and localization for real-time face recognition systems

NARCIS (Netherlands)

Zuo, F.; With, de P.H.N.; Ebrahimi, T.; Sikora, T.

2003-01-01

In a home environment, video surveillance employing face detection and recognition is attractive for new applications. Facial feature (e.g. eyes and mouth) localization in the face is an essential task for face recognition because it constitutes an indispensable step for face geometry normalization.

Basic features of the predictive tools of early warning systems for water-related natural hazards: examples for shallow landslides

Directory of Open Access Journals (Sweden)

R. Greco

2017-12-01

Full Text Available To manage natural risks, an increasing effort is being put in the development of early warning systems (EWS, namely, approaches facing catastrophic phenomena by timely forecasting and alarm spreading throughout exposed population. Research efforts aimed at the development and implementation of effective EWS should especially concern the definition and calibration of the interpretative model. This paper analyses the main features characterizing predictive models working in EWS by discussing their aims and their features in terms of model accuracy, evolutionary stage of the phenomenon at which the prediction is carried out and model architecture. Original classification criteria based on these features are developed throughout the paper and shown in their practical implementation through examples of flow-like landslides and earth flows, both of which are characterized by rapid evolution and quite representative of many applications of EWS.

Basic features of the predictive tools of early warning systems for water-related natural hazards: examples for shallow landslides

Science.gov (United States)

Greco, Roberto; Pagano, Luca

2017-12-01

To manage natural risks, an increasing effort is being put in the development of early warning systems (EWS), namely, approaches facing catastrophic phenomena by timely forecasting and alarm spreading throughout exposed population. Research efforts aimed at the development and implementation of effective EWS should especially concern the definition and calibration of the interpretative model. This paper analyses the main features characterizing predictive models working in EWS by discussing their aims and their features in terms of model accuracy, evolutionary stage of the phenomenon at which the prediction is carried out and model architecture. Original classification criteria based on these features are developed throughout the paper and shown in their practical implementation through examples of flow-like landslides and earth flows, both of which are characterized by rapid evolution and quite representative of many applications of EWS.

Engineering features of the INTOR conceptual design

International Nuclear Information System (INIS)

Shannon, T.E.

1981-01-01

The INTOR engineering design has been strongly influenced by considerations for assembly and maintenance. A maintenance philosophy was established at the outset of the conceptual design to insure that the tokamak configuration would be developed to accommodate maintenance requirements. The main features of the INTOR design are summarized in this paper with primary emphasis on the impact of maintenance considerations

Artificially intelligent recognition of Arabic speaker using voice print-based local features

Science.gov (United States)

Mahmood, Awais; Alsulaiman, Mansour; Muhammad, Ghulam; Akram, Sheeraz

2016-11-01

Local features for any pattern recognition system are based on the information extracted locally. In this paper, a local feature extraction technique was developed. This feature was extracted in the time-frequency plain by taking the moving average on the diagonal directions of the time-frequency plane. This feature captured the time-frequency events producing a unique pattern for each speaker that can be viewed as a voice print of the speaker. Hence, we referred to this technique as voice print-based local feature. The proposed feature was compared to other features including mel-frequency cepstral coefficient (MFCC) for speaker recognition using two different databases. One of the databases used in the comparison is a subset of an LDC database that consisted of two short sentences uttered by 182 speakers. The proposed feature attained 98.35% recognition rate compared to 96.7% for MFCC using the LDC subset.

Detection of Leaks in Water Mains Using Ground Penetrating Radar

OpenAIRE

Alaa Al Hawari; Mohammad Khader; Tarek Zayed; Osama Moselhi

2016-01-01

Ground Penetrating Radar (GPR) is one of the most effective electromagnetic techniques for non-destructive non-invasive subsurface features investigation. Water leak from pipelines is the most common undesirable reason of potable water losses. Rapid detection of such losses is going to enhance the use of the Water Distribution Networks (WDN) and decrease threatens associated with water mains leaks. In this study, GPR approach was developed to detect leaks by implementing an appropriate imagin...

Compounding approach for univariate time series with nonstationary variances

Science.gov (United States)

Schäfer, Rudi; Barkhofen, Sonja; Guhr, Thomas; Stöckmann, Hans-Jürgen; Kuhl, Ulrich

2015-12-01

A defining feature of nonstationary systems is the time dependence of their statistical parameters. Measured time series may exhibit Gaussian statistics on short time horizons, due to the central limit theorem. The sample statistics for long time horizons, however, averages over the time-dependent variances. To model the long-term statistical behavior, we compound the local distribution with the distribution of its parameters. Here, we consider two concrete, but diverse, examples of such nonstationary systems: the turbulent air flow of a fan and a time series of foreign exchange rates. Our main focus is to empirically determine the appropriate parameter distribution for the compounding approach. To this end, we extract the relevant time scales by decomposing the time signals into windows and determine the distribution function of the thus obtained local variances.

TreeBASIS Feature Descriptor and Its Hardware Implementation

Directory of Open Access Journals (Sweden)

Spencer Fowers

2014-01-01

Full Text Available This paper presents a novel feature descriptor called TreeBASIS that provides improvements in descriptor size, computation time, matching speed, and accuracy. This new descriptor uses a binary vocabulary tree that is computed using basis dictionary images and a test set of feature region images. To facilitate real-time implementation, a feature region image is binary quantized and the resulting quantized vector is passed into the BASIS vocabulary tree. A Hamming distance is then computed between the feature region image and the effectively descriptive basis dictionary image at a node to determine the branch taken and the path the feature region image takes is saved as a descriptor. The TreeBASIS feature descriptor is an excellent candidate for hardware implementation because of its reduced descriptor size and the fact that descriptors can be created and features matched without the use of floating point operations. The TreeBASIS descriptor is more computationally and space efficient than other descriptors such as BASIS, SIFT, and SURF. Moreover, it can be computed entirely in hardware without the support of a CPU for additional software-based computations. Experimental results and a hardware implementation show that the TreeBASIS descriptor compares well with other descriptors for frame-to-frame homography computation while requiring fewer hardware resources.

Conceptual Content and Unattended Visual Features

Directory of Open Access Journals (Sweden)

Francisco Pereira

2009-08-01

Full Text Available McDowell (1994 proposed a philosophical theory about perceptual content −call it “conceptualism”− that states that in every case the content of a visual experience necessarily involves concepts that fully specify every single feature consciously and simultaneously available during the experience. In this paper I will question conceptualism, arguing that some visual experiences carry information about so many objects, properties and relations at the same time that it is unlikely for subjects to possess and implement concepts for every feature represented simultaneously by the experience at that time. If this is the case, then McDowell’s conceptualism is insufficiently grounded.

Roentgenologic features of the Meckel syndrome

Energy Technology Data Exchange (ETDEWEB)

Seppaenen, U.; Herva, R.

1983-09-21

The Meckel syndrome is an autosomal recessive lethal malformation syndrome. The main features are multicystic dysplastic kidneys, microcephaly with occipital encephalocele and polydactyly. This paper describes 6 new cases, with special reference to skeletal findings in postmortem total body radiographs Microcephaly with an occipital bone defect and encephalocele or hydrocephaly (1/6), short upper extremities, bell-shaped thorax with abdominal distension and postaxial polydactyly in the hands and feet were constant findings in these cases.

Action recognition using mined hierarchical compound features.

Science.gov (United States)

Gilbert, Andrew; Illingworth, John; Bowden, Richard

2011-05-01

The field of Action Recognition has seen a large increase in activity in recent years. Much of the progress has been through incorporating ideas from single-frame object recognition and adapting them for temporal-based action recognition. Inspired by the success of interest points in the 2D spatial domain, their 3D (space-time) counterparts typically form the basic components used to describe actions, and in action recognition the features used are often engineered to fire sparsely. This is to ensure that the problem is tractable; however, this can sacrifice recognition accuracy as it cannot be assumed that the optimum features in terms of class discrimination are obtained from this approach. In contrast, we propose to initially use an overcomplete set of simple 2D corners in both space and time. These are grouped spatially and temporally using a hierarchical process, with an increasing search area. At each stage of the hierarchy, the most distinctive and descriptive features are learned efficiently through data mining. This allows large amounts of data to be searched for frequently reoccurring patterns of features. At each level of the hierarchy, the mined compound features become more complex, discriminative, and sparse. This results in fast, accurate recognition with real-time performance on high-resolution video. As the compound features are constructed and selected based upon their ability to discriminate, their speed and accuracy increase at each level of the hierarchy. The approach is tested on four state-of-the-art data sets, the popular KTH data set to provide a comparison with other state-of-the-art approaches, the Multi-KTH data set to illustrate performance at simultaneous multiaction classification, despite no explicit localization information provided during training. Finally, the recent Hollywood and Hollywood2 data sets provide challenging complex actions taken from commercial movie sequences. For all four data sets, the proposed hierarchical

Structural health monitoring feature design by genetic programming

International Nuclear Information System (INIS)

Harvey, Dustin Y; Todd, Michael D

2014-01-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and other high-capital or life-safety critical structures. Conventional data processing involves pre-processing and extraction of low-dimensional features from in situ time series measurements. The features are then input to a statistical pattern recognition algorithm to perform the relevant classification or regression task necessary to facilitate decisions by the SHM system. Traditional design of signal processing and feature extraction algorithms can be an expensive and time-consuming process requiring extensive system knowledge and domain expertise. Genetic programming, a heuristic program search method from evolutionary computation, was recently adapted by the authors to perform automated, data-driven design of signal processing and feature extraction algorithms for statistical pattern recognition applications. The proposed method, called Autofead, is particularly suitable to handle the challenges inherent in algorithm design for SHM problems where the manifestation of damage in structural response measurements is often unclear or unknown. Autofead mines a training database of response measurements to discover information-rich features specific to the problem at hand. This study provides experimental validation on three SHM applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring. Performance comparisons with common feature choices for each problem area are provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems. (paper)

Optimized feature subsets for epileptic seizure prediction studies.

Science.gov (United States)

Direito, Bruno; Ventura, Francisco; Teixeira, César; Dourado, António

2011-01-01

The reduction of the number of EEG features to give as inputs to epilepsy seizure predictors is a needed step towards the development of a transportable device for real-time warning. This paper presents a comparative study of three feature selection methods, based on Support Vector Machines. Minimum-Redundancy Maximum-Relevance, Recursive Feature Elimination, Genetic Algorithms, show that, for three patients of the European Database on Epilepsy, the most important univariate features are related to spectral information and statistical moments.

Pilonidal sinus disease - Etiological factors, pathogenesis and clinical features

Directory of Open Access Journals (Sweden)

Kazim Duman

2016-12-01

Full Text Available and lsquo;Pilonidal sinus' disease, which is most commonly seen in reproductive populations, such as young adults - mostly in males who are in their twenties - is actually a controversial disease in that there is no consensus on its many facets. It is sometimes seen as an infected abscess draining from an opening or a lesion extending to the perineum. It may also present as a draining fistula opening to skin. In terms of etiological factors, various theories (main theories being congenital and acquired have been established since it was first described, no universal understanding achieved. A long and significant post-operative care period with different lengths of recovery depending on the type of operation are quite prevalent with regards to recurrence and complication status. In order to prevent recurrence and improve the quality of life, etiological and predisposing factors as well as clinical features of sacrococcygeal pilonidal disease should be well known, a detailed differential diagnosis should be made, and a suitable and timely intervention should be performed. It was aimed here to explain the etiological factors, pathogenesis and clinical features of the disease that may present with various clinical symptoms. [Arch Clin Exp Surg 2016; 5(4.000: 228-232

Psychopathology of Lived Time: Abnormal Time Experience in Persons With Schizophrenia.

Science.gov (United States)

Stanghellini, Giovanni; Ballerini, Massimo; Presenza, Simona; Mancini, Milena; Raballo, Andrea; Blasi, Stefano; Cutting, John

2016-01-01

Abnormal time experience (ATE) in schizophrenia is a long-standing theme of phenomenological psychopathology. This is because temporality constitutes the bedrock of any experience and its integrity is fundamental for the sense of coherence and continuity of selfhood and personal identity. To characterize ATE in schizophrenia patients as compared to major depressives we interviewed, in a clinical setting over a period of 15 years, 550 consecutive patients affected by schizophrenic and affective disorders. Clinical files were analyzed by means of Consensual Qualitative Research (CQR), an inductive method suited to research that requires rich descriptions of inner experiences. Of the whole sample, 109 persons affected by schizophrenic (n = 95 acute, n = 14 chronic) and 37 by major depression reported at least 1 ATE. ATE are more represented in acute (N = 109 out of 198; 55%) than in chronic schizophrenic patients (N = 14 out of 103; 13%). The main feature of ATE in people with schizophrenia is the fragmentation of time experience (71 out of 109 patients), an impairment of the automatic and prereflexive synthesis of primal impression-retention-protention. This includes 4 subcategories: disruption of time flowing, déjà vu/vecu, premonitions about oneself and the external world. We contrasted ATE in schizophrenia and in major depression, finding relevant differences: in major depressives there is no disarticulation of time experience, rather timelessness because time lacks duration, not articulation. These core features of the schizophrenic pheno-phenotype may be related to self-disorders and to the manifold of characteristic schizophrenic symptoms, including so called bizarre delusions and verbal-acoustic hallucinations. © The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Real-time estimation of optical flow based on optimized haar wavelet features

DEFF Research Database (Denmark)

Salmen, Jan; Caup, Lukas; Igel, Christian

2011-01-01

-objective optimization. In this work, we build on a popular algorithm developed for realtime applications. It is originally based on the Census transform and benefits from this encoding for table-based matching and tracking of interest points. We propose to use the more universal Haar wavelet features instead...

Congenital and Adquired Abnormalities of Pediatric Trachea and Main-Steam Bronchi

International Nuclear Information System (INIS)

Vargas Bazurto, Maria Catalina; Varon, Humberto; Perez Alvarado, Maria Carolina; Puerta Ramirez, Andres Felipe; Ruales Fierro, Franco Libardo

2011-01-01

Tracheobronchial tree abnormalities can be first suspected in chest radiography; nonetheless, multidetector row computed tomography imaging constitutes a complementary diagnostic alternative for the evaluation of congenital and acquired tracheobronchial tree anomalies that allows the radiologist a closer approximation toward the correct diagnosis as well as the accurate description of its morphological features and differential diagnosis. We present a review of the main tracheobronchial tree pathology.

Chinese buffer material for high-level radiowaste disposal-basic features of GMZ-1

International Nuclear Information System (INIS)

Wen, Zhijian

2005-01-01

Radioactive wastes arising from a wide range of human activities are in many different physical and chemical forms, contaminated with varying radioactivity. Their common feature is the potential hazard associated with their radioactivity and the need to manage them in such a way as to protect the human environment. The geological disposal is regarded as the most reasonable and effective way to safety disposal high-level radioactive wastes in the world. The conceptual model of geological disposal in China is based on a multi-barrier system that combines an isolating geological environment with an engineered barrier system. The buffer is one of the main engineered barriers for HLW repository. The buffer material is expected to maintain its low water permeability, self-sealing property, radio nuclides adsorption and retardation property, thermal conductivity, chemical buffering property, overpack supporting property, stress buffering property over a long period of time. Bentonite is selected as the main content of buffer material that can satisfy above. GMZ deposit is selected as the candidate supplier for Chinese buffer material of High Level Radioactive waste repository. This paper presents geological features of GMZ deposit and basic property of GMZ Na bentonite. GMZ bentonite deposit is a super large scale deposits with high content of Montmorillonite (about 75%) and GMZ-1, which is Na-bentonite produced from GMZ deposit is selected as reference material for Chinese buffer material study

Main successes, achievements. Paths of development

Directory of Open Access Journals (Sweden)

A. A. Kubanova

2015-01-01

Full Text Available The article provides the overview of incidence of sexually transmitted infections and skin disorders over time in Russian Federation in 2004-2014 with its main positive tendencies; results of reorganisation of bed capacity of dermatovenerologic medical organizations; dermatovenerologic bed rates.

Parenting, relational aggression, and borderline personality features: associations over time in a Russian longitudinal sample.

Science.gov (United States)

Nelson, David A; Coyne, Sarah M; Swanson, Savannah M; Hart, Craig H; Olsen, Joseph A

2014-08-01

Crick, Murray-Close, and Woods (2005) encouraged the study of relational aggression as a developmental precursor to borderline personality features in children and adolescents. A longitudinal study is needed to more fully explore this association, to contrast potential associations with physical aggression, and to assess generalizability across various cultural contexts. In addition, parenting is of particular interest in the prediction of aggression or borderline personality disorder. Early aggression and parenting experiences may differ in their long-term prediction of aggression or borderline features, which may have important implications for early intervention. The currrent study incorporated a longitudinal sample of preschool children (84 boys, 84 girls) living in intact, two-parent biological households in Voronezh, Russia. Teachers provided ratings of children's relational and physical aggression in preschool. Mothers and fathers also self-reported their engagement in authoritative, authoritarian, permissive, and psychological controlling forms of parenting with their preschooler. A decade later, 70.8% of the original child participants consented to a follow-up study in which they completed self-reports of relational and physical aggression and borderline personality features. The multivariate results of this study showed that preschool relational aggression in girls predicted adolescent relational aggression. Preschool aversive parenting (i.e., authoritarian, permissive, and psychologically controlling forms) significantly predicted aggression and borderline features in adolescent females. For adolescent males, preschool authoritative parenting served as a protective factor against aggression and borderline features, whereas authoritarian parenting was a risk factor for later aggression.

Consistent Feature Extraction From Vector Fields: Combinatorial Representations and Analysis Under Local Reference Frames

Energy Technology Data Exchange (ETDEWEB)

Bhatia, Harsh [Univ. of Utah, Salt Lake City, UT (United States)

2015-05-01

This dissertation presents research on addressing some of the contemporary challenges in the analysis of vector fields—an important type of scientific data useful for representing a multitude of physical phenomena, such as wind flow and ocean currents. In particular, new theories and computational frameworks to enable consistent feature extraction from vector fields are presented. One of the most fundamental challenges in the analysis of vector fields is that their features are defined with respect to reference frames. Unfortunately, there is no single “correct” reference frame for analysis, and an unsuitable frame may cause features of interest to remain undetected, thus creating serious physical consequences. This work develops new reference frames that enable extraction of localized features that other techniques and frames fail to detect. As a result, these reference frames objectify the notion of “correctness” of features for certain goals by revealing the phenomena of importance from the underlying data. An important consequence of using these local frames is that the analysis of unsteady (time-varying) vector fields can be reduced to the analysis of sequences of steady (timeindependent) vector fields, which can be performed using simpler and scalable techniques that allow better data management by accessing the data on a per-time-step basis. Nevertheless, the state-of-the-art analysis of steady vector fields is not robust, as most techniques are numerical in nature. The residing numerical errors can violate consistency with the underlying theory by breaching important fundamental laws, which may lead to serious physical consequences. This dissertation considers consistency as the most fundamental characteristic of computational analysis that must always be preserved, and presents a new discrete theory that uses combinatorial representations and algorithms to provide consistency guarantees during vector field analysis along with the uncertainty

Main findings

International Nuclear Information System (INIS)

2014-01-01

Licensing regimes vary from country to country. When the license regime involves several regulators and several licenses, this may lead to complex situations. Identifying a leading organisation in charge of overall coordination including preparation of the licensing decision is a useful practice. Also, if a stepwise licensing process is implemented, it is important to fix in legislation decisions and/or time points and to identify the relevant actors. There is considerable experience in civil and mining engineering that can be applied when constructing a deep geological disposal facility. Specific challenges are, however, the minimization of disturbances to the host rock and the understanding of its long-term behavior. Construction activities may affect the geo-hydraulic and geochemical properties of the various system components which are important safety features of the repository system. Clearly defined technical specifications and an effective quality management plan are important in ensuring successful repository implementation which is consistent with safety requirements. Monitoring plan should also be defined in advance. The regulatory organization should prepare itself to the licensing review before construction by allocating sufficient resources. It should increase its competence, e.g., by interacting early with the implementer and through its own R and D. This will allow the regulator to define appropriate technical conditions associated to the construction license and to elaborate a relevant inspection plan of the construction work. After construction, obtaining the operational license is the most important and crucial step. Main challenges include (a) establishing sufficient confidence so that the methods for closing the individual disposal units comply with the safety objectives and (b) addressing the issue of ageing of materials during a 50-100 years operational period. This latter challenge is amplified when reversibility/retrievability is required

Automatic brain MR image denoising based on texture feature-based artificial neural networks.

Science.gov (United States)

Chang, Yu-Ning; Chang, Herng-Hua

2015-01-01

Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

Speech Emotion Feature Selection Method Based on Contribution Analysis Algorithm of Neural Network

International Nuclear Information System (INIS)

Wang Xiaojia; Mao Qirong; Zhan Yongzhao

2008-01-01

There are many emotion features. If all these features are employed to recognize emotions, redundant features may be existed. Furthermore, recognition result is unsatisfying and the cost of feature extraction is high. In this paper, a method to select speech emotion features based on contribution analysis algorithm of NN is presented. The emotion features are selected by using contribution analysis algorithm of NN from the 95 extracted features. Cluster analysis is applied to analyze the effectiveness for the features selected, and the time of feature extraction is evaluated. Finally, 24 emotion features selected are used to recognize six speech emotions. The experiments show that this method can improve the recognition rate and the time of feature extraction

Fusion of shallow and deep features for classification of high-resolution remote sensing images

Science.gov (United States)

Gao, Lang; Tian, Tian; Sun, Xiao; Li, Hang

2018-02-01

Effective spectral and spatial pixel description plays a significant role for the classification of high resolution remote sensing images. Current approaches of pixel-based feature extraction are of two main kinds: one includes the widelyused principal component analysis (PCA) and gray level co-occurrence matrix (GLCM) as the representative of the shallow spectral and shape features, and the other refers to the deep learning-based methods which employ deep neural networks and have made great promotion on classification accuracy. However, the former traditional features are insufficient to depict complex distribution of high resolution images, while the deep features demand plenty of samples to train the network otherwise over fitting easily occurs if only limited samples are involved in the training. In view of the above, we propose a GLCM-based convolution neural network (CNN) approach to extract features and implement classification for high resolution remote sensing images. The employment of GLCM is able to represent the original images and eliminate redundant information and undesired noises. Meanwhile, taking shallow features as the input of deep network will contribute to a better guidance and interpretability. In consideration of the amount of samples, some strategies such as L2 regularization and dropout methods are used to prevent over-fitting. The fine-tuning strategy is also used in our study to reduce training time and further enhance the generalization performance of the network. Experiments with popular data sets such as PaviaU data validate that our proposed method leads to a performance improvement compared to individual involved approaches.

Feature Extraction in Radar Target Classification

Directory of Open Access Journals (Sweden)

Z. Kus

1999-09-01

Full Text Available This paper presents experimental results of extracting features in the Radar Target Classification process using the J frequency band pulse radar. The feature extraction is based on frequency analysis methods, the discrete-time Fourier Transform (DFT and Multiple Signal Characterisation (MUSIC, based on the detection of Doppler effect. The analysis has turned to the preference of DFT with implemented Hanning windowing function. We assumed to classify targets-vehicles into two classes, the wheeled vehicle and tracked vehicle. The results show that it is possible to classify them only while moving. The feature of the class results from a movement of moving parts of the vehicle. However, we have not found any feature to classify the wheeled and tracked vehicles while non-moving, although their engines are on.

Legal positions of the Constitutional Court of Ukraine: main signs and definition

Directory of Open Access Journals (Sweden)

Romana Reva

2016-04-01

Full Text Available The practice of a single body of constitutional jurisdiction indicates on the necessity of a certain number of amendments to the current Law of Ukraine “On the Constitutional Court of Ukraine”. It is impossible to achieve the quality regulation of these issues without a thorough scientific analysis of basic features of the legal positions of the Constitutional Court of Ukraine. The purpose of the article is to analyze the legal positions of the Constitutional Court of Ukraine and scientific views on their signs, to identify and describe the main features of the legal positions of the Constitutional Court of Ukraine. There are the conclusions made that an important step in any legal position research is the establishment of the legal nature. The article discusses different scientific views on the basic features of the legal positions of the Constitutional Court of Ukraine. On the basis of the analysis of acts of the Constitutional Court of Ukraine and scientific works, there are defined, in particular, the following main features of the legal positions of the Constitutional Court of Ukraine: they are the result of interpretation and represent the most generalized, concentrated expression of the Constitutional Court of Ukraine understanding of the provisions of the Constitution of Ukraine, laws and/ or other regulations, which are carried out within the jurisdiction of the Constitutional Court of Ukraine; they are the basis for the final decision, which is set in the act of the Constitutional Court of Ukraine; they appear in the reasoning and/ or the operative parts of the decisions and conclusions and some rulings; they have a special legal force; they are obligatory, that are binding throughout the territory of Ukraine for all public authorities, local governments, enterprises, institutions and organizations, officials, citizens and their associations; suitable for further repeated use in solving similar cases; as opposed to the decisions of

Main circulator design features for HTR 100, HTR 500 and space heating plants

International Nuclear Information System (INIS)

Engel, J.; Glass, D.

1988-01-01

All design alternatives for modern high-temperature reactors have a common circulator concept: It is based on a vertical shaft design with a flying impeller. The circulators are equipped with active magnetic bearings and are driven by induction motors connected to variable-speed static converters. Due to their multiple functions during normal reactor operation and under accident conditions, extremely high requirements are made to safety-relevant circulators, since with the reactor pressurized as well as under depressurized conditions specified delivery heads and flow rates have to be ensured. The use of active magnetic bearings permits to obtain maintenance-free operation and functional safety to an extent which had not been achieved before. Magnetic bearings are therefore provided for the total range including primary gas circulators of a drive power of several MW as well as circulators for helium loops of reactor auxiliary systems. The essential feature for using active magnetic bearings is the retainer bearing technology, preventing contact between rotor and static circulator parts upon unintended deenergisation of the magnets. Results of current experiments are reported. Another aspect to be considered for reliable long-term operation for several decades is the effect of rotor dynamics. The various natural frequencies resulting from torsion and bending modes in view of a drive by a frequency-controlled induction motor have to be considered as well as the specific characteristics of the active magnetic bearings. Special attention has to be directed to the internal cooling loop so as to ensure that reactor temperature excursions in the event of deviation from normal operation can be overcome without damage. For circulator components exposed to temperature fields the design characteristics are determined by combining experimental and analytical methods. The coordination of all component parts is currently being optimized on a prototype circulator whose detailed

Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones.

Science.gov (United States)

Khan, Adil Mehmood; Siddiqi, Muhammad Hameed; Lee, Seok-Won

2013-09-27

Smartphone-based activity recognition (SP-AR) recognizes users' activities using the embedded accelerometer sensor. Only a small number of previous works can be classified as online systems, i.e., the whole process (pre-processing, feature extraction, and classification) is performed on the device. Most of these online systems use either a high sampling rate (SR) or long data-window (DW) to achieve high accuracy, resulting in short battery life or delayed system response, respectively. This paper introduces a real-time/online SP-AR system that solves this problem. Exploratory data analysis was performed on acceleration signals of 6 activities, collected from 30 subjects, to show that these signals are generated by an autoregressive (AR) process, and an accurate AR-model in this case can be built using a low SR (20 Hz) and a small DW (3 s). The high within class variance resulting from placing the phone at different positions was reduced using kernel discriminant analysis to achieve position-independent recognition. Neural networks were used as classifiers. Unlike previous works, true subject-independent evaluation was performed, where 10 new subjects evaluated the system at their homes for 1 week. The results show that our features outperformed three commonly used features by 40% in terms of accuracy for the given SR and DW.

Play as the main event in international and UK culture.

OpenAIRE

Woudhuysen, James

2003-01-01

Since Johan Huizinga’s Homo Ludens, 1938, few books have treated adult play at an abstract level using psychology. These works lack empirical statistics. On the other hand, most market research into consumer leisure lacks clear theoretical frameworks. ‘Play as the Main Event’ overcomes these twin deficiencies. It develops Huizinga and the major international theorists of play to define five distinctive features of contemporary play, applying this framework to five sub-sectors of consumer leis...

MRI features of placenta accreta

International Nuclear Information System (INIS)

Cao Manrui; Du Mu; Huang Yi; Liu Bingguang; Zhang Fangjing; Guo Jimin; Zhu Zhijun

2012-01-01

Objective: To investigate the MRI features of placenta accreta. Methods: From Apr 2009 to Jun 2011, 15 patients with placenta accrete received MRI examination. In them, placenta accreta was diagnosed based on clinical manifestations or postoperative histopathology. The MR features of placenta accreta in them (study group) were retrospectively analyzed and compared with those in 15 pregnant women without placenta accreta (control group) with Fisher exact test. Results: In the 15 patients with placenta accreta,uterine bulging and (or) a focal outward contour bulge was detected in 14 patients; heterogeneous signal intensity in the placenta was detected in 15 patients; dark intraplacental bands on T 2 -weighted images was detected in 15 patients; and increased subplacental vascularity was detected in 11 patients on T 1 - weighted images. In the study group, 14 patients showed at least three of the above four features, and in all of them uterine bulging and (or) a focal outward contour bulge, heterogeneous signal intensity in the placenta and dark intraplacental bands on T 2 -weighted images were detected; one patient showed heterogeneous signal intensity in the placenta, dark intraplacental bands on T 2 -weighted images and increased subplacental vascularity. In the control group,none patient had three of the above features.Uterine bulging and (or) a focal outward contour bulge, heterogeneous signal intensity in the placenta, dark intraplacental bands on T 2 -weighted images and increased subplacental vascularity were detected in 3, 6, 3 and 4 patients (P=0.000, 0.001, 0.000 and 0.027), respectively. Conclusions: The main MRI features of placenta accreta are uterine bulging and (or) a focal outward contour bulge, heterogeneous signal intensity in the placenta and dark intraplacental bands on T 2 -weighted images Besides, increased subplacental vascularity also could provide useful information for the diagnosis of placenta accreta. (authors)

Selection of individual features of a speech signal using genetic algorithms

Directory of Open Access Journals (Sweden)

Kamil Kamiński

2016-03-01

Full Text Available The paper presents an automatic speaker’s recognition system, implemented in the Matlab environment, and demonstrates how to achieve and optimize various elements of the system. The main emphasis was put on features selection of a speech signal using a genetic algorithm which takes into account synergy of features. The results of optimization of selected elements of a classifier have been also shown, including the number of Gaussian distributions used to model each of the voices. In addition, for creating voice models, a universal voice model has been used.[b]Keywords[/b]: biometrics, automatic speaker recognition, genetic algorithms, feature selection

Rhabdomyolysis featuring muscular dystrophies.

Science.gov (United States)

Lahoria, Rajat; Milone, Margherita

2016-02-15

Rhabdomyolysis is a potentially life threatening condition of various etiology. The association between rhabdomyolysis and muscular dystrophies is under-recognized in clinical practice. To identify muscular dystrophies presenting with rhabdomyolysis at onset or as predominant feature. We retrospectively reviewed clinical and laboratory data of patients with a genetically confirmed muscular dystrophy in whom rhabdomyolysis was the presenting or main clinical manifestation. Thirteen unrelated patients (males=6; females=7) were identified. Median age at time of rhabdomyolysis was 18 years (range, 2-47) and median duration between the first episode of rhabdomyolysis and molecular diagnosis was 2 years. Fukutin-related protein (FKRP) muscular dystrophy (n=6) was the most common diagnosis, followed by anoctaminopathy-5 (n=3), calpainopathy-3 (n=2) and dystrophinopathy (n=2). Four patients experienced recurrent rhabdomyolysis. Eight patients were asymptomatic and 3 reported myalgia and exercise intolerance prior to the rhabdomyolysis. Exercise (n=6) and fever (n=4) were common triggers; rhabdomyolysis was unprovoked in 3 patients. Twelve patients required hospitalization. Baseline CK levels were elevated in all patients (median 1200 IU/L; range, 600-3600). Muscular dystrophies can present with rhabdomyolysis; FKRP mutations are particularly frequent in causing such complication. A persistently elevated CK level in patients with rhabdomyolysis warrants consideration for underlying muscular dystrophy. Copyright © 2015 Elsevier B.V. All rights reserved.

Roentgenologic features of the Meckel syndrome

International Nuclear Information System (INIS)

Seppaenen, U.; Herva, R.

1983-01-01

The Meckel syndrome is an autosomal recessive lethal malformation syndrome. The main features are multicystic dysplastic kidneys, microcephaly with occipital encephalocele and polydactyly. This paper describes 6 new cases, with special reference to skeletal findings in postmortem total body radiographs Microcephaly with an occipital bone defect and encephalocele or hydrocephaly [1/6], short upper extremities, bell-shaped thorax with abdominal distension and postaxial polydactyly in the hands and feet were constant findings in these cases. (orig.)

Temporal resolution for the perception of features and conjunctions.

Science.gov (United States)

Bodelón, Clara; Fallah, Mazyar; Reynolds, John H

2007-01-24

The visual system decomposes stimuli into their constituent features, represented by neurons with different feature selectivities. How the signals carried by these feature-selective neurons are integrated into coherent object representations is unknown. To constrain the set of possible integrative mechanisms, we quantified the temporal resolution of perception for color, orientation, and conjunctions of these two features. We find that temporal resolution is measurably higher for each feature than for their conjunction, indicating that time is required to integrate features into a perceptual whole. This finding places temporal limits on the mechanisms that could mediate this form of perceptual integration.

Classifying three imaginary states of the same upper extremity using time-domain features.

Directory of Open Access Journals (Sweden)

Mojgan Tavakolan

Full Text Available Brain-computer interface (BCI allows collaboration between humans and machines. It translates the electrical activity of the brain to understandable commands to operate a machine or a device. In this study, we propose a method to improve the accuracy of a 3-class BCI using electroencephalographic (EEG signals. This BCI discriminates rest against imaginary grasps and elbow movements of the same limb. This classification task is challenging because imaginary movements within the same limb have close spatial representations on the motor cortex area. The proposed method extracts time-domain features and classifies them using a support vector machine (SVM with a radial basis kernel function (RBF. An average accuracy of 74.2% was obtained when using the proposed method on a dataset collected, prior to this study, from 12 healthy individuals. This accuracy was higher than that obtained when other widely used methods, such as common spatial patterns (CSP, filter bank CSP (FBCSP, and band power methods, were used on the same dataset. These results are encouraging and the proposed method could potentially be used in future applications including BCI-driven robotic devices, such as a portable exoskeleton for the arm, to assist individuals with impaired upper extremity functions in performing daily tasks.

The impact of the structural features of the rock mass on seismicity in Polish coal mines

Science.gov (United States)

Patyńska, Renata

2017-11-01

The article presents seismic activity induced in the coal mines of the Upper Silesian Coal Basin (GZW) in relation to the locations of the occurrence of rockbursts. The comparison of these measurements with the structural features of the rock mass of coal mines indicates the possibility of estimating the so-called Unitary Energy Expenditure (UEE) in a specific time. The obtained values of UEE were compared with the distribution of seismic activity in GZW mines. The level of seismic activity in the analysed period changed and depended on the intensity of mining works and diverse mining and geological conditions. Five regions, where tremors occurred (Bytom Trough, Main Saddle, Main Trough, Kazimierz Trough, and Jejkowice and Chwałowice Trough) which belong to various structural units of the Upper Silesia were analyzed. It was found out that rock bursts were recorded only in three regions: Main Saddle, Bytom Trough, and Jejkowice and Chwałowice Trough.

Monoenergetic time-dependent neutron transport in an infinite medium with time-varying cross sections

International Nuclear Information System (INIS)

Ganapol, B.D.

1987-01-01

For almost 20 yr, the main thrust of the author's research has been the generation of as many benchmark solutions to the time-dependent monoenergetic neutron transport equation as possible. The major motivation behind this effort has been to provide code developers with highly accurate numerical solutions to serve as standards in the assessment of numerical transport algorithms. In addition, these solutions provide excellent educational tools since the important physical features of neutron transport are still present even though the problems solved are idealized. A secondary motivation, though of equal importance, is the intellectual stimulation and understanding provided by the combination of the analytical, numerical, and computational techniques required to obtain these solutions. Therefore, to further the benchmark development, the added complication of time-dependent cross sections in the one-group transport equation is considered here

The Storm Time Evolution of the Ionospheric Disturbance Plasma Drifts

Science.gov (United States)

Zhang, Ruilong; Liu, Libo; Le, Huijun; Chen, Yiding; Kuai, Jiawei

2017-11-01

In this paper, we use the C/NOFS and ROCSAT-1 satellites observations to analyze the storm time evolution of the disturbance plasma drifts in a 24 h local time scale during three magnetic storms driven by long-lasting southward IMF Bz. The disturbance plasma drifts during the three storms present some common features in the periods dominated by the disturbance dynamo. The newly formed disturbance plasma drifts are upward and westward at night, and downward and eastward during daytime. Further, the disturbance plasma drifts are gradually evolved to present significant local time shifts. The westward disturbance plasma drifts gradually migrate from nightside to dayside. Meanwhile, the dayside downward disturbance plasma drifts become enhanced and shift to later local time. The local time shifts in disturbance plasma drifts are suggested to be mainly attributed to the evolution of the disturbance winds. The strong disturbance winds arisen around midnight can constantly corotate to later local time. At dayside the westward and equatorward disturbance winds can drive the F region dynamo to produce the poleward and westward polarization electric fields (or the westward and downward disturbance drifts). The present results indicate that the disturbance winds corotated to later local time can affect the local time features of the disturbance dynamo electric field.

Comparison of sEMG-Based Feature Extraction and Motion Classification Methods for Upper-Limb Movement

Directory of Open Access Journals (Sweden)

Shuxiang Guo

2015-04-01

Full Text Available The surface electromyography (sEMG technique is proposed for muscle activation detection and intuitive control of prostheses or robot arms. Motion recognition is widely used to map sEMG signals to the target motions. One of the main factors preventing the implementation of this kind of method for real-time applications is the unsatisfactory motion recognition rate and time consumption. The purpose of this paper is to compare eight combinations of four feature extraction methods (Root Mean Square (RMS, Detrended Fluctuation Analysis (DFA, Weight Peaks (WP, and Muscular Model (MM and two classifiers (Neural Networks (NN and Support Vector Machine (SVM, for the task of mapping sEMG signals to eight upper-limb motions, to find out the relation between these methods and propose a proper combination to solve this issue. Seven subjects participated in the experiment and six muscles of the upper-limb were selected to record sEMG signals. The experimental results showed that NN classifier obtained the highest recognition accuracy rate (88.7% during the training process while SVM performed better in real-time experiments (85.9%. For time consumption, SVM took less time than NN during the training process but needed more time for real-time computation. Among the four feature extraction methods, WP had the highest recognition rate for the training process (97.7% while MM performed the best during real-time tests (94.3%. The combination of MM and NN is recommended for strict real-time applications while a combination of MM and SVM will be more suitable when time consumption is not a key requirement.

Representation and Metrics Extraction from Feature Basis: An Object Oriented Approach

Directory of Open Access Journals (Sweden)

Fausto Neri da Silva Vanin

2010-10-01

Full Text Available This tutorial presents an object oriented approach to data reading and metrics extraction from feature basis. Structural issues about basis are discussed first, then the Object Oriented Programming (OOP is aplied to modeling the main elements in this context. The model implementation is then discussed using C++ as programing language. To validate the proposed model, we apply on some feature basis from the University of Carolina, Irvine Machine Learning Database.

Is hearing loss a feature of Joubert syndrome, a ciliopathy?

NARCIS (Netherlands)

Kroes, H.Y.; Van Zanten, B.G.A.; De Ru, S.A.; Boon, M.; Mancini, G.M.S.; van der Knaap, M.S.; Poll-The, B.; Lindhout, D.

2010-01-01

Objective: To assess if hearing loss is a feature of Joubert syndrome (JBS), one of the ciliopathies and therefore possibly associated with hearing loss. Design: Retrospective case series. Setting: University Children's Hospital. Patients: Dutch patients with JBS. Main outcome measures: Audiological

On feature augmentation for semantic argument classification of the Quran English translation using support vector machine

Science.gov (United States)

Khaira Batubara, Dina; Arif Bijaksana, Moch; Adiwijaya

2018-03-01

Research on the semantic argument classification requires semantically labeled data in large numbers, called corpus. Because building a corpus is costly and time-consuming, recently many studies have used existing corpus as the training data to conduct semantic argument classification research on new domain. But previous studies have proven that there is a significant decrease in performance when classifying semantic arguments on different domain between the training and the testing data. The main problem is when there is a new argument that found in the testing data but it is not found in the training data. This research carries on semantic argument classification on a new domain that is Quran English Translation by utilizing Propbank corpus as the training data. To recognize the new argument in the training data, this research proposes four new features for extending the argument features in the training data. By using SVM Linear, the experiment has proven that augmenting the proposed features to the baseline system with some combinations option improve the performance of semantic argument classification on Quran data using Propbank Corpus as training data.

Immunity-based optimal estimation approach for a new real time group elevator dynamic control application for energy and time saving.

Science.gov (United States)

Baygin, Mehmet; Karakose, Mehmet

2013-01-01

Nowadays, the increasing use of group elevator control systems owing to increasing building heights makes the development of high-performance algorithms necessary in terms of time and energy saving. Although there are many studies in the literature about this topic, they are still not effective enough because they are not able to evaluate all features of system. In this paper, a new approach of immune system-based optimal estimate is studied for dynamic control of group elevator systems. The method is mainly based on estimation of optimal way by optimizing all calls with genetic, immune system and DNA computing algorithms, and it is evaluated with a fuzzy system. The system has a dynamic feature in terms of the situation of calls and the option of the most appropriate algorithm, and it also adaptively works in terms of parameters such as the number of floors and cabins. This new approach which provides both time and energy saving was carried out in real time. The experimental results comparatively demonstrate the effects of method. With dynamic and adaptive control approach in this study carried out, a significant progress on group elevator control systems has been achieved in terms of time and energy efficiency according to traditional methods.

Immunity-Based Optimal Estimation Approach for a New Real Time Group Elevator Dynamic Control Application for Energy and Time Saving

Directory of Open Access Journals (Sweden)

Mehmet Baygin

2013-01-01

Full Text Available Nowadays, the increasing use of group elevator control systems owing to increasing building heights makes the development of high-performance algorithms necessary in terms of time and energy saving. Although there are many studies in the literature about this topic, they are still not effective enough because they are not able to evaluate all features of system. In this paper, a new approach of immune system-based optimal estimate is studied for dynamic control of group elevator systems. The method is mainly based on estimation of optimal way by optimizing all calls with genetic, immune system and DNA computing algorithms, and it is evaluated with a fuzzy system. The system has a dynamic feature in terms of the situation of calls and the option of the most appropriate algorithm, and it also adaptively works in terms of parameters such as the number of floors and cabins. This new approach which provides both time and energy saving was carried out in real time. The experimental results comparatively demonstrate the effects of method. With dynamic and adaptive control approach in this study carried out, a significant progress on group elevator control systems has been achieved in terms of time and energy efficiency according to traditional methods.

LBT Distributed Archive: Status and Features

Science.gov (United States)

Knapic, C.; Smareglia, R.; Thompson, D.; Grede, G.

2011-07-01

After the first release of the LBT Distributed Archive, this successful collaboration is continuing within the LBT corporation. The IA2 (Italian Center for Astronomical Archive) team had updated the LBT DA with new features in order to facilitate user data retrieval while abiding by VO standards. To facilitate the integration of data from any new instruments, we have migrated to a new database, developed new data distribution software, and enhanced features in the LBT User Interface. The DBMS engine has been changed to MySQL. Consequently, the data handling software now uses java thread technology to update and synchronize the main storage archives on Mt. Graham and in Tucson, as well as archives in Trieste and Heidelberg, with all metadata and proprietary data. The LBT UI has been updated with additional features allowing users to search by instrument and some of the more important characteristics of the images. Finally, instead of a simple cone search service over all LBT image data, new instrument specific SIAP and cone search services have been developed. They will be published in the IVOA framework later this fall.

Features of clinical and radiographic appearances of SARS in children

International Nuclear Information System (INIS)

Zeng Jinjin; Sun Guoqiang; Shen Kunling; Yang Yonghong; Wei Xinmiao; Lei Gang

2003-01-01

Objective: To evaluate the features of clinical and radiographic appearances of SARS in children. Methods: The chest films obtained at clinical presentation and during treatment in 18 children with confirmed SARS were retrospectively evaluated. Results: The main X-ray manifestations included: (1) air-space opacity in 13/18; (2) round lesion with clear margin in 3/18; (3) ground-glass lesions in 2/18; (4) unilateral and single focal involvement was more common in children than in adults (5) no reticular shadow, lymphanopathy or pleural effusion was demonstrated; (6) radiographic changes of foci was not as rapid in children as in adults. The lesions migrated in 1 case. The average absorption time of the lesions was 19 days, and most of them had no remnant. Conclusion: Compare with that in adults , the clinical manifestation was not so severe in children with SARS, and most of the infected children had clear contact history. Chest X-ray appearance in affected children mainly showed unilateral involvement of the lungs with chiefly air-space infiltrates. Remnant lesion of lung is rare in children. Differential diagnosis of SARS in children includes mycoplasma pneumonia or adenovirus pneumonia

Fulfillment of the long-term safety functions by the different barriers during the main time frames after repository closure

International Nuclear Information System (INIS)

Preter, P. de; Lalieux, Ph.

2002-01-01

In general terms the basis long-term safety functions of a disposal system (i.e. the engineered barrier system, including the waste forms and the host rock) are the functions that the system as a whole or its constituents must fulfill in order to assure an adequate level of long-term radiological safety. The long-term safety functions of a disposal system constitute a generic and methodological tool that can be used in a double sense. In the first place these functions provide an a priori instrument for designing the system: the implementer must ensure that these safety functions are fulfilled by a series of robust system barriers and components. These functions can also be used as an a posteriori means to describe and assess in general terms the functioning of the system. In this way they are an important qualitative element to help to support the safety case and to identify further R and D priorities. By providing a general description of system functioning they are also a communication tool to stakeholders who are less familiar with the details of a safety case. Instead of limiting the description to a multi-barrier system, the safety functions enable to better explain how the different barriers contribute to one or more safety functions and by which processes this is performed. By doing so the system description moves from multi-barrier to multi-function. The aim of this paper is to provide such a general description of the system functioning for the Belgian case of deep disposal of high-level waste (mainly spent fuel or vitrified waste from fuel reprocessing) in the Boom Clay, o poorly-indurated argillaceous formation. From the detailed safety and performance evaluations the main time frames after repository closure are identified. Each time frame relates to a period during which the successive safety functions play a key role. Also, in each time frame the radiological impact on the environment is distinctly different. (authors)

At ISR Main Control Room

CERN Multimedia

1983-01-01

After 13 years the exploitation of the Intersecting Storage Rings as a beam-beam collider went to an end. In this last year the demands were very exacting, both in terms of operating time and diversified running conditions (Annual Report 1983 p. 123). Before dismantelement the photographer made a last tour, see photos 8310889X --> 8310667X. This photo shows the Main Control Room.

Frank hematuria as the presentation feature of acute leukemia

Directory of Open Access Journals (Sweden)

Suriya Owais

2010-01-01

Full Text Available Muco-cutaneous bleeding is a common presenting feature of acute leukemias. Mucosal bleeding usually manifests as gum bleeding and/or epistaxis but may occur in any mucosal surface of the body. Hematuria as an isolated or main presenting feature of acute leukemia is rare. We describe two cases of acute leukemia, a 19 year old male with acute lymphoblastic leukemia and a 52 year old male with acute myeloid leukemia, both presenting with gross hematuria. There was no demonstrable leukemic infiltration of the urinary tract on imaging studies. Hematuria in these patients was likely to be due to occult leukemic infiltration of the urinary system, aggravated by thrombocytopenia, as it subsided after starting chemotherapy. Our cases highlight that hematuria should be remembered as a rare presenting feature of acute leukemia.

Convolutional neural network features based change detection in satellite images

Science.gov (United States)

Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

2016-07-01

With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

Engineering safety features for high power experimental reactors

International Nuclear Information System (INIS)

Doval, A.; Villarino, E.; Vertullo, A.

2000-01-01

In the present analysis we will focus our attention in the way engineering safety features are designed in order to prevent fuel damage in case of abnormal or accidental situations. To prevent fuel damage two main facts must be considered, the shutdown of the reactor and the adequate core cooling capacity, it means that both, neutronic and thermohydraulic aspects must be analysed. Some neutronic safety features are common to all power ranges like negative feedback reactivity coefficients and the required number of control rods containing the proper absorber material to shutdown the reactor. From the thermohydraulic point of view common features are siphon-breaker devices and flap valves for those powers requiring cooling in the forced convection regime. For the high power reactor group, the engineering safety features specially designed for a generic reactor of 20 MW, will be presented here. From the neutronic point of view besides the common features, and to comply with our National Regulatory Authority, a Second Shutdown System was designed as a redundant shutdown system in case the control plates fail. Concerning thermohydraulic aspects besides the pump flywheels and the flap valves providing the natural convection loop, a metallic Chimney and a Chimney Water Injection System were supplied. (author)

Improved feature selection based on genetic algorithms for real time disruption prediction on JET

International Nuclear Information System (INIS)

Rattá, G.A.; Vega, J.; Murari, A.

2012-01-01

Highlights: ► A new signal selection methodology to improve disruption prediction is reported. ► The approach is based on Genetic Algorithms. ► An advanced predictor has been created with the new set of signals. ► The new system obtains considerably higher prediction rates. - Abstract: The early prediction of disruptions is an important aspect of the research in the field of Tokamak control. A very recent predictor, called “Advanced Predictor Of Disruptions” (APODIS), developed for the “Joint European Torus” (JET), implements the real time recognition of incoming disruptions with the best success rate achieved ever and an outstanding stability for long periods following training. In this article, a new methodology to select the set of the signals’ parameters in order to maximize the performance of the predictor is reported. The approach is based on “Genetic Algorithms” (GAs). With the feature selection derived from GAs, a new version of APODIS has been developed. The results are significantly better than the previous version not only in terms of success rates but also in extending the interval before the disruption in which reliable predictions are achieved. Correct disruption predictions with a success rate in excess of 90% have been achieved 200 ms before the time of the disruption. The predictor response is compared with that of JET's Protection System (JPS) and the ADODIS predictor is shown to be far superior. Both systems have been carefully tested with a wide number of discharges to understand their relative merits and the most profitable directions of further improvements.

Expanding the cardiac spectrum of Noonan syndrome with RIT1 variant: Left main coronary artery atresia causing sudden death.

Science.gov (United States)

Ramond, Francis; Duband, Sébastien; Croisille, Pierre; Cavé, Hélène; Teyssier, Georges; Adouard, Véronique; Touraine, Renaud

2017-06-01

Noonan syndrome is a well-known genetic condition associating congenital heart defects, short stature, and distinctive facial features. Pulmonary valve stenosis and hypertrophic cardiomyopathy are the most frequent cardiac abnormalities, the latter being associated with a higher mortality. Here we report for the first time, a case of congenital left main coronary artery atresia in a Noonan syndrome associated with RIT1 variant, leading to unrescued sudden death. This case-report supports the already-suspected severity of the RIT1-related Noonan syndrome compared to average Noonan syndrome, and should encourage clinicians to be very cautious with these patients. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Gulf of Maine - Control Points Used to Validate the Accuracies of the Interpolated Water Density Rasters

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This feature dataset contains the control points used to validate the accuracies of the interpolated water density rasters for the Gulf of Maine. These control...

[Relationship of motor deficits and imaging features in metastatic epidural spinal cord compression].

Science.gov (United States)

Liu, Shu-Bin; Liu, Yao-Sheng; Li, Ding-Feng; Fan, Hai-Tao; Huai, Jian-Ye; Guo, Jun; Wang, Lei; Liu, Cheng; Zhang, Ping; Cui, Qiu; Jiang, Wei-Hao; Cao, Yun-Cen; Jiang, Ning; Sui, Jia-Hong; Zhang, Bin; Zhou, Jiu

2010-06-15

To explore the relationship of motor deficits of the lower extremities with the imaging features of malignant spinal cord compression (MESCCs). From July 2006 through December 2008, 56 successive MESCC patients were treated at our department. All were evaluated by magnetic resonance imaging and computed tomography and were scored according to motor deficits Frankel grading on admission. Imaging assessment factors of main involved vertebrae were level of vertebral metastatic location, epidural space involvement, vertebral body involvement, lamina involvement, posterior protrusion of posterior wall, pedicle involvement, continuity of main involved vertebrae, fracture of anterior column, fracture of posterior wall, location in upper thoracic spine and/or cervicothoracic junction. Occurrence was the same between paralytic state of MESCCs and epidural space involvement of imaging features. Multiple regression equation showed that paralytic state had a linear regression relationship with imaging factors of lamina involvement (X1), posterior protrusion of posterior wall (X2), location in upper thoracic spine and/or cervicothoracic junction (X7) of main involved vertebrae. The optimal regression equation of paralytic state (Y) and imaging feature (X) was Y = -0.009 +0.639X, + 0.149X, +0.282X. Lamina involvement of main involved vertebrae has a greatest influence upon paralytic state of MESCC patients. Imaging factors of lamina involvement, posterior protrusion of posterior wall, location in upper thoracic spine and/or cervicothoracic junction of main involved vertebrae can predict the paralytic state of MESCC patients. MESCC with lamina involvement is more easily encroached on epidural space.

ESSENCE, FEATURES AND FUNCTIONS OF THE LABOUR DIGITAL MARKET

Directory of Open Access Journals (Sweden)

N. Azmuk

2015-06-01

Full Text Available In the article the author has presented the comparative analysis of both digital and traditional segments of the global labour market. The main functions of the digital labour market are social, economic and stimulant ones. The features of the digital labour market are digital employment, globality, high level of flexibility, large competition, dynamic changes of labour force and working places. The main kinds of digital employment are electronic free lance and electronic outsourcing. In the article the advantages and the risks of digital employment using have been determined.

Is hearing loss a feature of Joubert syndrome, a ciliopathy?

NARCIS (Netherlands)

Kroes, Hester Y.; Van Zanten, Bert G. A.; De Ru, Sander A.; Boon, Maartje; Mancini, Grazia M. S.; Van der Knaap, Marjo S.; Poll-The, Bwee Tien; Lindhout, Dick

Objective To assess if hearing loss is a feature of Joubert syndrome (JBS). one of the ciliopathies and therefore possibly associated with hearing loss Design: Retrospective case series. Setting University Children's Hospital Patients Dutch patients with JBS. Main outcome measures Audiological data.

Is hearing loss a feature of Joubert syndrome, a ciliopathy?

NARCIS (Netherlands)

Kroes, Hester Y.; van Zanten, Bert G. A.; de Ru, Sander A.; Boon, Maartje; Mancini, Grazia M. S.; van der Knaap, Marjo S.; Poll-The, Bwee Tien; Lindhout, Dick

2010-01-01

Objective To assess if hearing loss is a feature of Joubert syndrome (JBS). one of the ciliopathies and therefore possibly associated with hearing loss Design: Retrospective case series. Setting University Children's Hospital Patients Dutch patients with JBS. Main outcome measures Audiological data.

Features of financial support of reproduction processes in agriculture

Directory of Open Access Journals (Sweden)

Kudrina Valentina Aleksandrovna

2016-08-01

Full Text Available The article describes the features of the financing of reproduction processes in agriculture, arising from the specific production in the industry. Considered and analyzed the main sources of financial resources for the implementation of the reproduction processes in the agricultural sector, including bank lending, leasing, public financial support.

SCW Pressure-Channel Nuclear Reactor Some Design Features

Science.gov (United States)

Pioro, Igor L.; Khan, Mosin; Hopps, Victory; Jacobs, Chris; Patkunam, Ruban; Gopaul, Sandeep; Bakan, Kurtulus

Concepts of nuclear reactors cooled with water at supercritical pressures were studied as early as the 1950s and 1960s in the USA and Russia. After a 30-year break, the idea of developing nuclear reactors cooled with SuperCritical Water (SCW) became attractive again as the ultimate development path for water cooling. The main objectives of using SCW in nuclear reactors are: 1) to increase the thermal efficiency of modern Nuclear Power Plants (NPPs) from 30-35% to about 45-48%, and 2) to decrease capital and operational costs and hence decrease electrical energy costs (˜1000 US/kW or even less). SCW NPPs will have much higher operating parameters compared to modern NPPs (pressure about 25 MPa and outlet temperature up to 625°C), and a simplified flow circuit, in which steam generators, steam dryers, steam separators, etc., can be eliminated. Also, higher SCW temperatures allow direct thermo-chemical production of hydrogen at low cost, due to increased reaction rates. Pressure-tube or pressure-channel SCW nuclear reactor concepts are being developed in Canada and Russia for some time. Some design features of the Canadian concept related to fuel channels are discussed in this paper. The main conclusion is that the development of SCW pressure-tube nuclear reactors is feasible and significant benefits can be expected over other thermal-energy systems.

FEATURES OF WTO DISPUTE SETTLEMENT. THE STANDING OF THE EU

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Costin Horia Rogoveanu

2010-09-01

Full Text Available The WTO has an innovative system of dispute settlement, with the following features: sui-generis, integrated, resolving the disputes according to the WTO agreements, excluding unilateral solutions, interstate system. These features are detailed in the present article. Another level of analysis concerns the standing of the EU in the WTO, in general, and in the Geneva proceedings for dispute settlement, in particular. Generated by the quality of the European Communities statute as an original member of the Organisation, the EU has become one of the main users of the WTO dispute settlement system. One of the main challenges of the WTO dispute settlement mechanism is the implementation of decisions. In view of the cases assessed, while the execution record of the EU is a quite satisfactory one, it is apparent that implementation of decisions in more intricate cases creates difficulties at the Union level.

Is Reaction Time Variability in ADHD Mainly at Low Frequencies?

Science.gov (United States)

Karalunas, Sarah L.; Huang-Pollock, Cynthia L.; Nigg, Joel T.

2013-01-01

Background: Intraindividual variability in reaction times (RT variability) has garnered increasing interest as an indicator of cognitive and neurobiological dysfunction in children with attention deficit hyperactivity disorder (ADHD). Recent theory and research has emphasized specific low-frequency patterns of RT variability. However, whether…

Genetically modified crops and small-scale farmers: main opportunities and challenges

OpenAIRE

Azadi, Hossein; Samiee, Atry; Mahmoudi, Hossein; Jouzi, Zeynab; Rafiaani Khachak, Parisa; De Maeyer, Philippe; Witlox, Frank

2015-01-01

Although some important features of genetically modified (GM) crops such as insect resistance, herbicide tolerance, and drought tolerance might seem to be beneficial for small-scale farmers, the adoption of GM technology by smallholders is still slight. Identifying pros and cons of using this technology is important to understand the impacts of GM crops on these farmers. This article reviews the main opportunities and challenges of GM crops for small-scale farmers in developing countrie...

Exploratory Data Analysis of Acceleration Signals to Select Light-Weight and Accurate Features for Real-Time Activity Recognition on Smartphones

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Seok-Won Lee

2013-09-01

Full Text Available Smartphone-based activity recognition (SP-AR recognizes users’ activities using the embedded accelerometer sensor. Only a small number of previous works can be classified as online systems, i.e., the whole process (pre-processing, feature extraction, and classification is performed on the device. Most of these online systems use either a high sampling rate (SR or long data-window (DW to achieve high accuracy, resulting in short battery life or delayed system response, respectively. This paper introduces a real-time/online SP-AR system that solves this problem. Exploratory data analysis was performed on acceleration signals of 6 activities, collected from 30 subjects, to show that these signals are generated by an autoregressive (AR process, and an accurate AR-model in this case can be built using a low SR (20 Hz and a small DW (3 s. The high within class variance resulting from placing the phone at different positions was reduced using kernel discriminant analysis to achieve position-independent recognition. Neural networks were used as classifiers. Unlike previous works, true subject-independent evaluation was performed, where 10 new subjects evaluated the system at their homes for 1 week. The results show that our features outperformed three commonly used features by 40% in terms of accuracy for the given SR and DW.

Heat flow, heat transfer and lithosphere rheology in geothermal areas: Features and examples

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Ranalli, G.; Rybach, L.

2005-10-01

Surface heat flow measurements over active geothermal systems indicate strongly positive thermal anomalies. Whereas in "normal" geothermal settings, the surface heat flow is usually below 100-120 mW m - 2 , in active geothermal areas heat flow values as high as several watts per meter squared can be found. Systematic interpretation of heat flow patterns sheds light on heat transfer mechanisms at depth on different lateral, depth and time scales. Borehole temperature profiles in active geothermal areas show various signs of subsurface fluid movement, depending on position in the active system. The heat transfer regime is dominated by heat advection (mainly free convection). The onset of free convection depends on various factors, such as permeability, temperature gradient and fluid properties. The features of heat transfer are different for single or two-phase flow. Characteristic heat flow and heat transfer features in active geothermal systems are demonstrated by examples from Iceland, Italy, New Zealand and the USA. Two main factors affect the rheology of the lithosphere in active geothermal areas: steep temperature gradients and high pore fluid pressures. Combined with lithology and structure, these factors result in a rheological zonation with important consequences both for geodynamic processes and for the exploitation of geothermal energy. As a consequence of anomalously high temperature, the mechanical lithosphere is thin and its total strength can be reduced by almost one order of magnitude with respect to the average strength of continental lithosphere of comparable age and thickness. The top of the brittle/ductile transition is located within the upper crust at depths less than 10 km, acts as the root zone of listric normal faults in extensional environments and, at least in some cases, is visible on seismic reflection lines. These structural and rheological features are well illustrated in the Larderello geothermal field in Tuscany.

BIOLOGICAL FEATURES OF TARAN (RUTILUS HECKELII OF THE DNIEPER-BUG ESTUARY SYSTEM

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K. Geina

2016-03-01

Full Text Available Purpose. To analyze morphological variability and basic biological features of the modern stock of taran of the Dnieper-Bug estuary system in the conditions of the transformed Dnieper flow. Methodology. Morphological variability was determined based on the comparison of morphological features of roach Rutilus rutilus (Linnaeus, 1758 of the Kakhovka reservoir and semi-migratory taran Rutilus rutilus heckelii (Nordmann, 1840 of the Dnieper-Bug estuary system. As a main criterion of the evaluation of the taran stock biological state, we used age, sex structure, growth characteristics, fecundity and condition factor. Fish sampling was carried out at stationary monitoring-observation stations of the Institute of Fisheries NAAS of Ukraine. Field and cameral processing of the collected materials were performed based on conventional methods and guidelines. Findings. Morphological variability analysis demonstrated significant differences between Dnieper and Bug taran. The highest differences were observed for antroventral distance (td=11.19. Significant differences were also observed for antenanal and ventroanal distances td=4.05-4.14. No significant differences were found for meristic features. There were also significant differences between Dnieper-Bug taran and Kakhovka reservoir roach, which had formed a resident form after regulating the Dnieper River flow. Kakhovka reservoir roach is more deep-bodied with Н=32.79%, t-test value was 5.65. Pelvic fins were more shifted to the caudal fin (td=5.28 that resulted in significant difference (p<0.05 in ventroanal distance (td=4.26. Taran also had somewhat smaller length of the anal fin base (td=4.73 but its height was higher – td=5.78. The main peculiarity of the current biological state of taran stock is the domination of young age groups. The small number of fish in the boundary groups of the age series right wing with relative stability of growth features indicate on intensive pressure on the

Effective traffic features selection algorithm for cyber-attacks samples

Science.gov (United States)

Li, Yihong; Liu, Fangzheng; Du, Zhenyu

2018-05-01

By studying the defense scheme of Network attacks, this paper propose an effective traffic features selection algorithm based on k-means++ clustering to deal with the problem of high dimensionality of traffic features which extracted from cyber-attacks samples. Firstly, this algorithm divide the original feature set into attack traffic feature set and background traffic feature set by the clustering. Then, we calculates the variation of clustering performance after removing a certain feature. Finally, evaluating the degree of distinctiveness of the feature vector according to the result. Among them, the effective feature vector is whose degree of distinctiveness exceeds the set threshold. The purpose of this paper is to select out the effective features from the extracted original feature set. In this way, it can reduce the dimensionality of the features so as to reduce the space-time overhead of subsequent detection. The experimental results show that the proposed algorithm is feasible and it has some advantages over other selection algorithms.

Stokes polarimetry of main-line OH emission from stellar masers

International Nuclear Information System (INIS)

Claussen, M.J.; Fix, J.D.

1982-01-01

Main-line OH emission has been measured in all four Stokes parameters from seven late-type variable stars and the F8 supergiant IRC+10420. Linearly polarized features were detected in UX Cyg, U Ori, and IRC+10420 at 1665 MHz. The linearly polarized features in UX Cyg and IRC +10420, when combined with adjacent circularly polarized features suggest Zeeman patterns. A polarization pattern in IRC+10420 is probably the best example of a complete Zeeman pattern yet observed in stellar masers, although it appears to lack the shifted linear (sigma) components. This study, combined with other recent work, shows that linearly polarized features in stellar sources are uncommon. Only about 10% of the stellar OH sources show linearly polarized features. As an aid in accounting for the observed polarization properties of stellar OH masers, model mass flows were calculated using magnetic field structures similar to that of the solar wind. Conclusions drawn from this model were: (1) unpolarized or weakly circularly polarized emission from sources can arise from the entire circumstellar shell; (2) circular polarization without linear polarization can be produced either by emission from the entire shell or by enhanced OH densities in small regions of the shell provided there are sufficient free electrons present to depolarize the linear components; and (3) Zeeman patterns which include both circular and linear polarizations can be produced in OH density enhancements if electron densities are low. The electron densities required for effective Faraday depolarization yield emission measures of the order of 10 9 pc cm -6 . Given the large distances of stellar OH masers, the thermal continuum emission from such depolarizing electrons would probably be undetectable

Designing attractive gamification features for collaborative storytelling websites.

Science.gov (United States)

Hsu, Shang Hwa; Chang, Jen-Wei; Lee, Chun-Chia

2013-06-01

Gamification design is considered as the predictor of collaborative storytelling websites' success. Although aforementioned studies have mentioned a broad range of factors that may influence gamification, they neither depicted the actual design features nor relative attractiveness among them. This study aims to identify attractive gamification features for collaborative storytelling websites. We first constructed a hierarchical system structure of gamification design of collaborative storytelling websites and conducted a focus group interview with eighteen frequent users to identify 35gamification features. After that, this study determined the relative attractiveness of these gamification features by administrating an online survey to 6333 collaborative storytelling websites users. The results indicated that the top 10 most attractive gamification features could account for more than 50% of attractiveness among these 35 gamification features. The feature of unpredictable time pressure is important to website users, yet not revealed in previous relevant studies. Implications of the findings were discussed.

Polish Qualitative Sociology: The General Features and Development

OpenAIRE

Konecki, Krzysztof Tomasz

2005-01-01

The article explores the development of Polish qualitative sociology in Poland by presenting its main intellectual routes and some of the general features of Polish sociology. Romanticism and inductionmethod are crucial elements for the development of this discipline in Poland and contribute to its. unigueness. The role of Florian Znaniecki in creating the Polish qualitative sociology is also underlined. Krzysztof Konecki

Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction

International Nuclear Information System (INIS)

Garzon, Benjamin; Emblem, Kyrre E.; Mouridsen, Kim; Nedregaard, Baard; Due-Toennessen, Paulina; Nome, Terje; Hald, John K.; Bjoernerud, Atle; Haaberg, Asta K.; Kvinnsland, Yngve

2011-01-01

Background. A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. Purpose. To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients. Material and Methods. T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. Results. Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant (P < 0.0001). Conclusion. Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients

Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction

Energy Technology Data Exchange (ETDEWEB)

Garzon, Benjamin (Dept. of Circulation and Medical Imaging, NTNU, Trondheim (Norway)), email: benjamin.garzon@ntnu.no; Emblem, Kyrre E. (The Interventional Center, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway); Dept. of Radiology, MGH-HST AA Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston (United States)); Mouridsen, Kim (Center of Functionally Integrative Neuroscience, Aarhus Univ., Aarhus (Denmark)); Nedregaard, Baard; Due-Toennessen, Paulina; Nome, Terje; Hald, John K. (Dept. of Radiology and Nuclear Medicine, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway)); Bjoernerud, Atle (The Interventional Center, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway)); Haaberg, Asta K. (Dept. of Circulation and Medical Imaging, NTNU, Trondheim (Norway); Dept. of Medical Imaging, St Olav' s Hospital, Trondheim (Norway)); Kvinnsland, Yngve (NordicImagingLab, Bergen (Norway))

2011-11-15

Background. A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. Purpose. To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients. Material and Methods. T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. Results. Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant (P < 0.0001). Conclusion. Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients

SPOKEN-LANGUAGE FEATURES IN CASUAL CONVERSATION A Case of EFL Learners‘ Casual Conversation

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Aris Novi

2017-12-01

Full Text Available Spoken text differs from written one in its features of context dependency, turn-taking organization, and dynamic structure. EFL learners; however, sometime find it difficult to produce typical characteristics of spoken language, particularly in casual talk. When they are asked to conduct a conversation, some of them tend to be script-based which is considered unnatural. Using the theory of Thornburry (2005, this paper aims to analyze characteristics of spoken language in casual conversation which cover spontaneity, interactivity, interpersonality, and coherence. This study used discourse analysis to reveal four features in turns and moves of three casual conversations. The findings indicate that not all sub-features used in the conversation. In this case, the spontaneity features were used 132 times; the interactivity features were used 1081 times; the interpersonality features were used 257 times; while the coherence features (negotiation features were used 526 times. Besides, the results also present that some participants seem to dominantly produce some sub-features naturally and vice versa. Therefore, this finding is expected to be beneficial to provide a model of how spoken interaction should be carried out. More importantly, it could raise English teachers or lecturers‘ awareness in teaching features of spoken language, so that, the students could develop their communicative competence as the native speakers of English do.

What goes through the gate? Exploring interference with visual feature binding.

Science.gov (United States)

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

2011-05-01

A series of experiments explored the mechanisms determining the encoding and storage of features and objects in visual working memory. We contrasted the effects of three types of visual suffix on cued recall of a display of colored shapes. The suffix was presented after the display and before the recall cue. The latter was either the color or shape of one of the objects and signaled recall of the object's other feature. In Experiments 1 and 2, we found a larger effect of 'plausible' suffixes comprising features (color and shape) drawn from the experimental set, relative to the effect of 'implausible' suffixes comprising features outside the experimental set. Experiment 3 extended this pattern by showing that 'semi-plausible' suffixes containing only one feature (either color or shape) from the experimental set had an equivalent effect to those with both features from the set. Reduction in accuracy was mainly due to an increase in recall of suffix features, rather than within-display confusions. The findings suggest a feature-based filtering process in visual working memory, with any stimuli that pass through this filter serving to directly overwrite existing object representations. Copyright © 2011 Elsevier Ltd. All rights reserved.

Personality Disorder Features and Insomnia Status amongst Hypnotic-Dependent Adults

Science.gov (United States)

Ruiter, Megan E.; Lichstein, Kenneth L.; Nau, Sidney D.; Geyer, James

2012-01-01

Objective To determine the prevalence of personality disorders and their relation to insomnia parameters among persons with chronic insomnia with hypnotic dependence. Methods Eighty-four adults with chronic insomnia with hypnotic dependence completed the SCID-II personality questionnaire, two-weeks of sleep diaries, polysomnography, and measures of insomnia severity, impact, fatigue severity, depression, anxiety, and quality of life. Frequencies, between-subjects t-tests and hierarchical regression models were conducted. Results Cluster C personality disorders were most prevalent (50%). Obsessive-compulsive personality disorder (OCPD) was most common (n=39). These individuals compared to participants with no personality disorders did not differ in objective and subjective sleep parameters. Yet, they had poorer insomnia-related daytime functioning. OCPD and Avoidant personality disorders features were associated with poorer daytime functioning. OCPD features were related to greater fatigue severity, and overestimation of time awake was trending. Schizotypal and Schizoid features were positively associated with insomnia severity. Dependent personality disorder features were related to underestimating time awake. Conclusions Cluster C personality disorders were highly prevalent in patients with chronic insomnia with hypnotic dependence. Features of Cluster C and A personality disorders were variously associated with poorer insomnia-related daytime functioning, fatigue, and estimation of nightly wake-time. Future interventions may need to address these personality features. PMID:22938862

Personality disorder features and insomnia status amongst hypnotic-dependent adults.

Science.gov (United States)

Ruiter, Megan E; Lichstein, Kenneth L; Nau, Sidney D; Geyer, James D

2012-10-01

To determine the prevalence of personality disorders and their relation to insomnia parameters among persons with chronic insomnia with hypnotic dependence. Eighty-four adults with chronic insomnia with hypnotic dependence completed the SCID-II personality questionnaire, two-weeks of sleep diaries, polysomnography, and measures of insomnia severity, impact, fatigue severity, depression, anxiety, and quality of life. Frequencies, between-subjects t-tests and hierarchical regression models were conducted. Cluster C personality disorders were most prevalent (50%). Obsessive-Compulsive personality disorder (OCPD) was most common (n=39). These individuals compared to participants with no personality disorders did not differ in objective and subjective sleep parameters. Yet, they had poorer insomnia-related daytime functioning. OCPD and Avoidant personality disorders features were associated with poorer daytime functioning. OCPD features were related to greater fatigue severity, and overestimation of time awake was trending. Schizotypal and Schizoid features were positively associated with insomnia severity. Dependent personality disorder features were related to underestimating time awake. Cluster C personality disorders were highly prevalent in patients with chronic insomnia with hypnotic dependence. Features of Cluster C and A personality disorders were variously associated with poorer insomnia-related daytime functioning, fatigue, and estimation of nightly wake-time. Future interventions may need to address these personality features. Copyright © 2012 Elsevier B.V. All rights reserved.

Search asymmetry: a diagnostic for preattentive processing of separable features.

Science.gov (United States)

Treisman, A; Souther, J

1985-09-01

The search rate for a target among distractors may vary dramatically depending on which stimulus plays the role of target and which that of distractors. For example, the time required to find a circle distinguished by an intersecting line is independent of the number of regular circles in the display, whereas the time to find a regular circle among circles with lines increases linearly with the number of distractors. The pattern of performance suggests parallel processing when the target has a unique distinguishing feature and serial self-terminating search when the target is distinguished only by the absence of a feature that is present in all the distractors. The results are consistent with feature-integration theory (Treisman & Gelade, 1980), which predicts that a single feature should be detected by the mere presence of activity in the relevant feature map, whereas tasks that require subjects to locate multiple instances of a feature demand focused attention. Search asymmetries may therefore offer a new diagnostic to identify the primitive features of early vision. Several candidate features are examined in this article: Colors, line ends or terminators, and closure (in the sense of a partly or wholly enclosed area) appear to be functional features; connectedness, intactness (absence of an intersecting line), and acute angles do not.

Aging, selective attention, and feature integration.

Science.gov (United States)

Plude, D J; Doussard-Roosevelt, J A

1989-03-01

This study used feature-integration theory as a means of determining the point in processing at which selective attention deficits originate. The theory posits an initial stage of processing in which features are registered in parallel and then a serial process in which features are conjoined to form complex stimuli. Performance of young and older adults on feature versus conjunction search is compared. Analyses of reaction times and error rates suggest that elderly adults in addition to young adults, can capitalize on the early parallel processing stage of visual information processing, and that age decrements in visual search arise as a result of the later, serial stage of processing. Analyses of a third, unconfounded, conjunction search condition reveal qualitatively similar modes of conjunction search in young and older adults. The contribution of age-related data limitations is found to be secondary to the contribution of age decrements in selective attention.

Long-lasting modulation of feature integration by transcranial magnetic stimulation

NARCIS (Netherlands)

Scharnowski, Frank; Rueter, Johannes; Jolij, Jacob; Hermens, Frouke; Kammer, Thomas; Herzog, Michael H.

The human brain analyzes a visual object first by basic feature detectors. On the objects way to a conscious percept, these features are integrated in subsequent stages of the visual hierarchy. The time course of this feature integration is largely unknown. To shed light on the temporal dynamics of

Long-lasting modulation of feature integration by transcranial magnetic stimulation

NARCIS (Netherlands)

Scharnowski, Frank; Rueter, Johannes; Jolij, Jacob; Hermens, Frouke; Kammer, Thomas; Herzog, Michael H.

2009-01-01

The human brain analyzes a visual object first by basic feature detectors. On the objects way to a conscious percept, these features are integrated in subsequent stages of the visual hierarchy. The time course of this feature integration is largely unknown. To shed light on the temporal dynamics of

Cultural Diffusion Was the Main Driving Mechanism of the Neolithic Transition in Southern Africa

Science.gov (United States)

Jerardino, Antonieta; Fort, Joaquim; Isern, Neus; Rondelli, Bernardo

2014-01-01

It is well known that the Neolithic transition spread across Europe at a speed of about 1 km/yr. This result has been previously interpreted as a range expansion of the Neolithic driven mainly by demic diffusion (whereas cultural diffusion played a secondary role). However, a long-standing problem is whether this value (1 km/yr) and its interpretation (mainly demic diffusion) are characteristic only of Europe or universal (i.e. intrinsic features of Neolithic transitions all over the world). So far Neolithic spread rates outside Europe have been barely measured, and Neolithic spread rates substantially faster than 1 km/yr have not been previously reported. Here we show that the transition from hunting and gathering into herding in southern Africa spread at a rate of about 2.4 km/yr, i.e. about twice faster than the European Neolithic transition. Thus the value 1 km/yr is not a universal feature of Neolithic transitions in the world. Resorting to a recent demic-cultural wave-of-advance model, we also find that the main mechanism at work in the southern African Neolithic spread was cultural diffusion (whereas demic diffusion played a secondary role). This is in sharp contrast to the European Neolithic. Our results further suggest that Neolithic spread rates could be mainly driven by cultural diffusion in cases where the final state of this transition is herding/pastoralism (such as in southern Africa) rather than farming and stockbreeding (as in Europe). PMID:25517968

Photolineations, folding and breaking tectonics in the Vest main anticline in the northern Ruhr Carboniferous

Energy Technology Data Exchange (ETDEWEB)

Adler, R E

1978-10-01

Discusses the use of photolineaments and other structural features obtained from ERTS-1 and LANDSAT satellites for determining the transition zone between the 'Vest' main anticline and the Liffe Syncline, the core area of the Auguste-Victoria anticline, and the Ludinghausen syncline.

Distinguish self- and hetero-perceived stress through behavioral imaging and physiological features.

Science.gov (United States)

Spodenkiewicz, Michel; Aigrain, Jonathan; Bourvis, Nadège; Dubuisson, Séverine; Chetouani, Mohamed; Cohen, David

2018-03-02

Stress reactivity is a complex phenomenon associated to multiple and multimodal expressions. Response to stressors has an obvious survival function and may be seen as an internal regulation to adapt to threat or danger. The intensity of this internal response can be assessed as the self-perception of the stress response. In species with social organization, this response also serves a communicative function, so-called hetero-perception. Our study presents multimodal stress detection assessment - a new methodology combining behavioral imaging and physiological monitoring for analyzing stress from these two perspectives. The system is based on automatic extraction of 39 behavioral (2D+3D video recording) and 62 physiological (Nexus-10 recording) features during a socially evaluated mental arithmetic test. The analysis with machine learning techniques for automatic classification using Support Vector Machine (SVM) show that self-perception and hetero-perception of social stress are both close but different phenomena: self-perception was significantly correlated with hetero-perception but significantly differed from it. Also, assessing stress with SVM through multimodality gave excellent classification results (F1 score values: 0.9±0.012 for hetero-perception and 0.87±0.021 for self-perception). In the best selected feature subsets, we found some common behavioral and physiological features that allow classification of both self- and hetero-perceived stress. However, we also found the contributing features for automatic classifications had opposite distributions: self-perception classification was mainly based on physiological features and hetero-perception was mainly based on behavioral features. Copyright © 2017. Published by Elsevier Inc.

Genetic features of circular bacteriocins produced by Gram-positive bacteria

NARCIS (Netherlands)

Maqueda, Mercedes; Sánchez-Hidalgo, Marina; Fernández, Matilde; Montalbán-López, Manuel; Valdivia, Eva; Martínez-Bueno, Manuel

This review highlights the main genetic features of circular bacteriocins, which require the co-ordinated expression of several genetic determinants. In general terms, it has been demonstrated that the expression of such structural genes must be combined with the activity of proteins involved in

Accuracy of locating circular features using machine vision

Science.gov (United States)

Sklair, Cheryl W.; Hoff, William A.; Gatrell, Lance B.

1992-03-01

The ability to automatically locate objects using vision is a key technology for flexible, intelligent robotic operations. The vision task is facilitated by placing optical targets or markings in advance on the objects to be located. A number of researchers have advocated the use of circular target features as the features that can be most accurately located. This paper describes extensive analysis on circle centroid accuracy using both simulations and laboratory measurements. The work was part of an effort to design a video positioning sensor for NASA's Flight Telerobotic Servicer that would meet accuracy requirements. We have analyzed the main contributors to centroid error and have classified them into the following: (1) spatial quantization errors, (2) errors due to signal noise and random timing errors, (3) surface tilt errors, and (4) errors in modeling camera geometry. It is possible to compensate for the errors in (3) given an estimate of the tilt angle, and the errors from (4) by calibrating the intrinsic camera attributes. The errors in (1) and (2) cannot be compensated for, but they can be measured and their effects reduced somewhat. To characterize these error sources, we measured centroid repeatability under various conditions, including synchronization method, signal-to-noise ratio, and frequency attenuation. Although these results are specific to our video system and equipment, they provide a reference point that should be a characteristic of typical CCD cameras and digitization equipment.

Specific features of accounting the time and spatial distribution of absorbed dose during the assessment of radiation casualties in current circumstances

International Nuclear Information System (INIS)

Chernyavskyij, I.Yu.

2015-01-01

This article presents an attempt to assess the necessity of accounting the spatial and time distribution of absorbed dose of mixed radiations of main radiation factors for the correct estimation of the troops' capabilities in the system of military dosimetry

Some Main Features of Wittgenstein´s Philosophy

OpenAIRE

Brock, Steen

2006-01-01

Der er tale om to kapitler fra et bogmanuskript om Wittgensteins filosofi. Teksten laver en systematisk kobling mellem sprogfilosofien, matematikfilosofien, psykologiens filosofi og naturvidenskabs-filosofien. Two chapters from a book manuscript, where four parts of Wittgenstein´s philosophy are systematically interconnected, philosophy of language, mathematics, psychology and natural science

Some Main Features of Wittgenstein´s Philosophy

DEFF Research Database (Denmark)

Brock, Steen

Two chapters from a book manuscript, where four parts of Wittgenstein´s philosophy are systematically interconnected, philosophy of language, mathematics, psychology and natural science......Two chapters from a book manuscript, where four parts of Wittgenstein´s philosophy are systematically interconnected, philosophy of language, mathematics, psychology and natural science...

The analysis of image feature robustness using cometcloud

Directory of Open Access Journals (Sweden)

Xin Qi

2012-01-01

Full Text Available The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval.

Critical product features' identification using an opinion analyzer.

Science.gov (United States)

Shamim, Azra; Balakrishnan, Vimala; Tahir, Muhammad; Shiraz, Muhammad

2014-01-01

The increasing use and ubiquity of the Internet facilitate dissemination of word-of-mouth through blogs, online forums, newsgroups, and consumer's reviews. Online consumer's reviews present tremendous opportunities and challenges for consumers and marketers. One of the challenges is to develop interactive marketing practices for making connections with target consumers that capitalize consumer-to-consumer communications for generating product adoption. Opinion mining is employed in marketing to help consumers and enterprises in the analysis of online consumers' reviews by highlighting the strengths and weaknesses of the products. This paper describes an opinion mining system based on novel review and feature ranking methods to empower consumers and enterprises for identifying critical product features from enormous consumers' reviews. Consumers and business analysts are the main target group for the proposed system who want to explore consumers' feedback for determining purchase decisions and enterprise strategies. We evaluate the proposed system on real dataset. Results show that integration of review and feature-ranking methods improves the decision making processes significantly.

Error Analysis of a Fractional Time-Stepping Technique for Incompressible Flows with Variable Density

KAUST Repository

Guermond, J.-L.; Salgado, Abner J.

2011-01-01

In this paper we analyze the convergence properties of a new fractional time-stepping technique for the solution of the variable density incompressible Navier-Stokes equations. The main feature of this method is that, contrary to other existing algorithms, the pressure is determined by just solving one Poisson equation per time step. First-order error estimates are proved, and stability of a formally second-order variant of the method is established. © 2011 Society for Industrial and Applied Mathematics.

Croatian repository construction project - present status and main obstacles

International Nuclear Information System (INIS)

Kucar Dragicevic, S.; Subasic, D.; Schaller, A.; Lokner, V.; Cerskov Klika, M.

1999-01-01

Croatia has been preparing backgrounds for the construction of the repository for low and intermediate radioactive waste on its territory, almost for a decade, now. In the name of Hrvtaska elektroprivreda, the co-owner of the NE Krsko, APO has been co-ordinating and organising numerous activities and projects during that time period. Siting process, safety assessment, disposal technology and repository design and public acceptance issues are the main fields of activities. The overall status of the project at the moment, including the overview of the present status of the main four aspects of activities, will be presented. Relatively, big and important progress made on the project work out during the last two years, as well as the main obstacles we were faced with during that time period, will be discussed.(author)

Enhancing facial features by using clear facial features

Science.gov (United States)

Rofoo, Fanar Fareed Hanna

2017-09-01

The similarity of features between individuals of same ethnicity motivated the idea of this project. The idea of this project is to extract features of clear facial image and impose them on blurred facial image of same ethnic origin as an approach to enhance a blurred facial image. A database of clear images containing 30 individuals equally divided to five different ethnicities which were Arab, African, Chines, European and Indian. Software was built to perform pre-processing on images in order to align the features of clear and blurred images. And the idea was to extract features of clear facial image or template built from clear facial images using wavelet transformation to impose them on blurred image by using reverse wavelet. The results of this approach did not come well as all the features did not align together as in most cases the eyes were aligned but the nose or mouth were not aligned. Then we decided in the next approach to deal with features separately but in the result in some cases a blocky effect was present on features due to not having close matching features. In general the available small database did not help to achieve the goal results, because of the number of available individuals. The color information and features similarity could be more investigated to achieve better results by having larger database as well as improving the process of enhancement by the availability of closer matches in each ethnicity.

Spatial features register: toward standardization of spatial features

Science.gov (United States)

Cascio, Janette

1994-01-01

As the need to share spatial data increases, more than agreement on a common format is needed to ensure that the data is meaningful to both the importer and the exporter. Effective data transfer also requires common definitions of spatial features. To achieve this, part 2 of the Spatial Data Transfer Standard (SDTS) provides a model for a spatial features data content specification and a glossary of features and attributes that fit this model. The model provides a foundation for standardizing spatial features. The glossary now contains only a limited subset of hydrographic and topographic features. For it to be useful, terms and definitions must be included for other categories, such as base cartographic, bathymetric, cadastral, cultural and demographic, geodetic, geologic, ground transportation, international boundaries, soils, vegetation, water, and wetlands, and the set of hydrographic and topographic features must be expanded. This paper will review the philosophy of the SDTS part 2 and the current plans for creating a national spatial features register as one mechanism for maintaining part 2.

Design features and operation experience of the main circulating pumps for the ''Loviisa'' NPP with the WWER-440 reactor

International Nuclear Information System (INIS)

Iofs, D.; Kujyala, I.; Timperi, I.; Shlejfer, G.; Vistbakka, V.; Prudovskij, A.M.; Turetskij, L.I.; Vorona, P.N.

1980-01-01

Technical characteristics and the operation of main circulating pumps (MCP) designed and mounted at the ''Loviisa'' NPP by Finnish firms ''Alstrem'' and ''Stremberg'' are described. The above MCP have specific advantages over similar pumps mounted at other NPP with pressurized water cooled reactors. This is a possibility of substitution of potentially most damaged units (bearing and pump shaft sealing) for several hours, without MCP disassembly as a whole as well as using rolling bearings together with the original electromagnetic unloading system from the axial force instead of usually employed in similar MCP radial thrust slip bearings. The two year operation experience has confirmed the efficiency and reliability of ''Loviisa'' NPP main circulating pumps

Deep Learning Methods for Underwater Target Feature Extraction and Recognition

Directory of Open Access Journals (Sweden)

Gang Hu

2018-01-01

Full Text Available The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved.

A Comparison Study on Multidomain EEG Features for Sleep Stage Classification

Directory of Open Access Journals (Sweden)

Yu Zhang

2017-01-01

Full Text Available Feature extraction from physiological signals of EEG (electroencephalogram is an essential part for sleep staging. In this study, multidomain feature extraction was investigated based on time domain analysis, nonlinear analysis, and frequency domain analysis. Unlike the traditional feature calculation in time domain, a sequence merging method was developed as a preprocessing procedure. The objective is to eliminate the clutter waveform and highlight the characteristic waveform for further analysis. The numbers of the characteristic activities were extracted as the features from time domain. The contributions of features from different domains to the sleep stages were compared. The effectiveness was further analyzed by automatic sleep stage classification and compared with the visual inspection. The overnight clinical sleep EEG recordings of 3 patients after the treatment of Continuous Positive Airway Pressure (CPAP were tested. The obtained results showed that the developed method can highlight the characteristic activity which is useful for both automatic sleep staging and visual inspection. Furthermore, it can be a training tool for better understanding the appearance of characteristic waveforms from raw sleep EEG which is mixed and complex in time domain.

Analysis of wheezes using wavelet higher order spectral features.

Science.gov (United States)

Taplidou, Styliani A; Hadjileontiadis, Leontios J

2010-07-01

Wheezes are musical breath sounds, which usually imply an existing pulmonary obstruction, such as asthma and chronic obstructive pulmonary disease (COPD). Although many studies have addressed the problem of wheeze detection, a limited number of scientific works has focused in the analysis of wheeze characteristics, and in particular, their time-varying nonlinear characteristics. In this study, an effort is made to reveal and statistically analyze the nonlinear characteristics of wheezes and their evolution over time, as they are reflected in the quadratic phase coupling of their harmonics. To this end, the continuous wavelet transform (CWT) is used in combination with third-order spectra to define the analysis domain, where the nonlinear interactions of the harmonics of wheezes and their time variations are revealed by incorporating instantaneous wavelet bispectrum and bicoherence, which provide with the instantaneous biamplitude and biphase curves. Based on this nonlinear information pool, a set of 23 features is proposed for the nonlinear analysis of wheezes. Two complementary perspectives, i.e., general and detailed, related to average performance and to localities, respectively, were used in the construction of the feature set, in order to embed trends and local behaviors, respectively, seen in the nonlinear interaction of the harmonic elements of wheezes over time. The proposed feature set was evaluated on a dataset of wheezes, acquired from adult patients with diagnosed asthma and COPD from a lung sound database. The statistical evaluation of the feature set revealed discrimination ability between the two pathologies for all data subgroupings. In particular, when the total breathing cycle was examined, all 23 features, but one, showed statistically significant difference between the COPD and asthma pathologies, whereas for the subgroupings of inspiratory and expiratory phases, 18 out of 23 and 22 out of 23 features exhibited discrimination power, respectively

Attention to internal face features in unfamiliar face matching.

Science.gov (United States)

Fletcher, Kingsley I; Butavicius, Marcus A; Lee, Michael D

2008-08-01

Accurate matching of unfamiliar faces is vital in security and forensic applications, yet previous research has suggested that humans often perform poorly when matching unfamiliar faces. Hairstyle and facial hair can strongly influence unfamiliar face matching but are potentially unreliable cues. This study investigated whether increased attention to the more stable internal face features of eyes, nose, and mouth was associated with more accurate face-matching performance. Forty-three first-year psychology students decided whether two simultaneously presented faces were of the same person or not. The faces were displayed for either 2 or 6 seconds, and had either similar or dissimilar hairstyles. The level of attention to internal features was measured by the proportion of fixation time spent on the internal face features and the sensitivity of discrimination to changes in external feature similarity. Increased attention to internal features was associated with increased discrimination in the 2-second display-time condition, but no significant relationship was found in the 6-second condition. Individual differences in eye-movements were highly stable across the experimental conditions.

Combining high-speed SVM learning with CNN feature encoding for real-time target recognition in high-definition video for ISR missions

Science.gov (United States)

Kroll, Christine; von der Werth, Monika; Leuck, Holger; Stahl, Christoph; Schertler, Klaus

2017-05-01

For Intelligence, Surveillance, Reconnaissance (ISR) missions of manned and unmanned air systems typical electrooptical payloads provide high-definition video data which has to be exploited with respect to relevant ground targets in real-time by automatic/assisted target recognition software. Airbus Defence and Space is developing required technologies for real-time sensor exploitation since years and has combined the latest advances of Deep Convolutional Neural Networks (CNN) with a proprietary high-speed Support Vector Machine (SVM) learning method into a powerful object recognition system with impressive results on relevant high-definition video scenes compared to conventional target recognition approaches. This paper describes the principal requirements for real-time target recognition in high-definition video for ISR missions and the Airbus approach of combining an invariant feature extraction using pre-trained CNNs and the high-speed training and classification ability of a novel frequency-domain SVM training method. The frequency-domain approach allows for a highly optimized implementation for General Purpose Computation on a Graphics Processing Unit (GPGPU) and also an efficient training of large training samples. The selected CNN which is pre-trained only once on domain-extrinsic data reveals a highly invariant feature extraction. This allows for a significantly reduced adaptation and training of the target recognition method for new target classes and mission scenarios. A comprehensive training and test dataset was defined and prepared using relevant high-definition airborne video sequences. The assessment concept is explained and performance results are given using the established precision-recall diagrams, average precision and runtime figures on representative test data. A comparison to legacy target recognition approaches shows the impressive performance increase by the proposed CNN+SVM machine-learning approach and the capability of real-time high

Designing high energy accelerators under DOE's 'New Culture' for environment and safety: An example, the Fermilab 150 GeV Main Injector proton synchrotron

International Nuclear Information System (INIS)

Fowler, W.B.

1991-01-01

Fermilab has initiated a design for a new Main Injector (150 GeV proton synchrotron) to take the place of the current Main Ring accelerator. 'New Culture' environmental and safety questions are having to be addressed. The paper details the necessary steps that have to be taken in order to obtain the permits which control the start of construction. Obviously these depend on site-specific circumstances, however some steps are universally applicable. In the example, floodplains and wetlands are affected and therefore the National Environmental Policy Act (NEPA) compliance is a significant issue. The important feature is to reduce the relevant regulations to a concise set of easily understandable requirements. The effort required and the associated time line are presented so that other new accelerator proposals can benefit from the experience gained from this example

Improved feature selection based on genetic algorithms for real time disruption prediction on JET

Energy Technology Data Exchange (ETDEWEB)

Ratta, G.A., E-mail: garatta@gateme.unsj.edu.ar [GATEME, Facultad de Ingenieria, Universidad Nacional de San Juan, Avda. San Martin 1109 (O), 5400 San Juan (Argentina); JET EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, Avda. Complutense, 40, 28040 Madrid (Spain); JET EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom); Murari, A. [Associazione EURATOM-ENEA per la Fusione, Consorzio RFX, 4-35127 Padova (Italy); JET EFDA, Culham Science Centre, OX14 3DB Abingdon (United Kingdom)

2012-09-15

Highlights: Black-Right-Pointing-Pointer A new signal selection methodology to improve disruption prediction is reported. Black-Right-Pointing-Pointer The approach is based on Genetic Algorithms. Black-Right-Pointing-Pointer An advanced predictor has been created with the new set of signals. Black-Right-Pointing-Pointer The new system obtains considerably higher prediction rates. - Abstract: The early prediction of disruptions is an important aspect of the research in the field of Tokamak control. A very recent predictor, called 'Advanced Predictor Of Disruptions' (APODIS), developed for the 'Joint European Torus' (JET), implements the real time recognition of incoming disruptions with the best success rate achieved ever and an outstanding stability for long periods following training. In this article, a new methodology to select the set of the signals' parameters in order to maximize the performance of the predictor is reported. The approach is based on 'Genetic Algorithms' (GAs). With the feature selection derived from GAs, a new version of APODIS has been developed. The results are significantly better than the previous version not only in terms of success rates but also in extending the interval before the disruption in which reliable predictions are achieved. Correct disruption predictions with a success rate in excess of 90% have been achieved 200 ms before the time of the disruption. The predictor response is compared with that of JET's Protection System (JPS) and the ADODIS predictor is shown to be far superior. Both systems have been carefully tested with a wide number of discharges to understand their relative merits and the most profitable directions of further improvements.

Medical features of the radiological accident in Chernobyl

International Nuclear Information System (INIS)

Oliveira, A.R. de

1987-01-01

The main medical features concerning the recent nuclear accident occurred in Chernobyl power station is summarized. The first measures taken by the Soviet medical authorities to minimize the effects of ionizing radiation on the victims are briefly commented on. The specialized laboratory analyses and therapeutic procedures adopted by the physicians during the course of the acute phase of the major syndromes are also discussed. (author) [pt

The main trends of dynamics of incomes of Russians in times of economic crisis

Directory of Open Access Journals (Sweden)

O. L. Petryakova

2016-01-01

Full Text Available A research objective which result was this article is the analysis of dynamics of the income of families during the last economic crisis in Russia and influence of change of the standard of living on performance by a family of the main functions, first of all reproductive, zhizneokhranitelny and educational. Now quite steady growth of birth rate, improvement of the indicators characterizing family trouble (refusals of the born children, deprivation of the parental rights, deviant behavior of children and teenagers and health of children and teenagers is observed, however, as a result of decrease in the standard of living, increase in employment of parents, violation of this favorable tendency is possible. The research is based on the analysis of statistical and sociological information, including results of polls of the population, in him the research of ranks of dynamics, graphic and tabular methods is applied. In article sources of the income of the population, first of all – the salary and social payments exerting the greatest impact on the level of the income in general are considered. On the basis of the carried-out analysis the main tendencies characterizing extent of fall of the income of families with children proceeding from this research are formulated, it is possible to speak about increase of needs of families in measures of economic support. However, at the same time becomes the negative moment on the one hand, extremely low knowledge of families of already available measures of such help, and with another – their low assessment and unwillingness to participate in these or those programs offered by the state. In turn it is the factor worsening financial position of families too. High prosperity, material security still (as well as the 90th years, as well as at the beginning of this century are on an equal basis with a family and children the leading value of Russians. It is connected, first of all

Feature-based attentional modulation of orientation perception in somatosensation

Directory of Open Access Journals (Sweden)

Meike Annika Schweisfurth

2014-07-01

Full Text Available In a reaction time study of human tactile orientation detection the effects of spatial attention and feature-based attention were investigated. Subjects had to give speeded responses to target orientations (parallel and orthogonal to the finger axis in a random stream of oblique tactile distractor orientations presented to their index and ring fingers. Before each block of trials, subjects received a tactile cue at one finger. By manipulating the validity of this cue with respect to its location and orientation (feature, we provided an incentive to subjects to attend spatially to the cued location and only there to the cued orientation. Subjects showed quicker responses to parallel compared to orthogonal targets, pointing to an orientation anisotropy in sensory processing. Also, faster reaction times were observed in location-matched trials, i.e. when targets appeared on the cued finger, representing a perceptual benefit of spatial attention. Most importantly, reaction times were shorter to orientations matching the cue, both at the cued and at the uncued location, documenting a global enhancement of tactile sensation by feature-based attention. This is the first report of a perceptual benefit of feature-based attention outside the spatial focus of attention in somatosensory perception. The similarity to effects of feature-based attention in visual perception supports the notion of matching attentional mechanisms across sensory domains.

Discrete Feature Model (DFM) User Documentation

Energy Technology Data Exchange (ETDEWEB)

Geier, Joel (Clearwater Hardrock Consulting, Corvallis, OR (United States))

2008-06-15

software, the geometry of discrete features and their hydrologic properties are defined as a mesh composed of triangular, finite elements. Hydrologic boundary conditions arc prescribed as a simulation sequence, which permits specification of conditions ranging from simple, steady-state flow to complex situations where both the magnitude and type of boundary conditions may vary over time

Discrete Feature Model (DFM) User Documentation

International Nuclear Information System (INIS)

Geier, Joel

2008-06-01

geometry of discrete features and their hydrologic properties are defined as a mesh composed of triangular, finite elements. Hydrologic boundary conditions arc prescribed as a simulation sequence, which permits specification of conditions ranging from simple, steady-state flow to complex situations where both the magnitude and type of boundary conditions may vary over time

Electricity market price spike analysis by a hybrid data model and feature selection technique

International Nuclear Information System (INIS)

Amjady, Nima; Keynia, Farshid

2010-01-01

In a competitive electricity market, energy price forecasting is an important activity for both suppliers and consumers. For this reason, many techniques have been proposed to predict electricity market prices in the recent years. However, electricity price is a complex volatile signal owning many spikes. Most of electricity price forecast techniques focus on the normal price prediction, while price spike forecast is a different and more complex prediction process. Price spike forecasting has two main aspects: prediction of price spike occurrence and value. In this paper, a novel technique for price spike occurrence prediction is presented composed of a new hybrid data model, a novel feature selection technique and an efficient forecast engine. The hybrid data model includes both wavelet and time domain variables as well as calendar indicators, comprising a large candidate input set. The set is refined by the proposed feature selection technique evaluating both relevancy and redundancy of the candidate inputs. The forecast engine is a probabilistic neural network, which are fed by the selected candidate inputs of the feature selection technique and predict price spike occurrence. The efficiency of the whole proposed method for price spike occurrence forecasting is evaluated by means of real data from the Queensland and PJM electricity markets. (author)

FEATURE SELECTION METHODS BASED ON MUTUAL INFORMATION FOR CLASSIFYING HETEROGENEOUS FEATURES

Directory of Open Access Journals (Sweden)

Ratri Enggar Pawening

2016-06-01

Full Text Available Datasets with heterogeneous features can affect feature selection results that are not appropriate because it is difficult to evaluate heterogeneous features concurrently. Feature transformation (FT is another way to handle heterogeneous features subset selection. The results of transformation from non-numerical into numerical features may produce redundancy to the original numerical features. In this paper, we propose a method to select feature subset based on mutual information (MI for classifying heterogeneous features. We use unsupervised feature transformation (UFT methods and joint mutual information maximation (JMIM methods. UFT methods is used to transform non-numerical features into numerical features. JMIM methods is used to select feature subset with a consideration of the class label. The transformed and the original features are combined entirely, then determine features subset by using JMIM methods, and classify them using support vector machine (SVM algorithm. The classification accuracy are measured for any number of selected feature subset and compared between UFT-JMIM methods and Dummy-JMIM methods. The average classification accuracy for all experiments in this study that can be achieved by UFT-JMIM methods is about 84.47% and Dummy-JMIM methods is about 84.24%. This result shows that UFT-JMIM methods can minimize information loss between transformed and original features, and select feature subset to avoid redundant and irrelevant features.

Enhanced HMAX model with feedforward feature learning for multiclass categorization

Directory of Open Access Journals (Sweden)

Yinlin eLi

2015-10-01

Full Text Available In recent years, the interdisciplinary research between neuroscience and computer vision has promoted the development in both fields. Many biologically inspired visual models are proposed, and among them, the Hierarchical Max-pooling model (HMAX is a feedforward model mimicking the structures and functions of V1 to posterior inferotemporal (PIT layer of the primate visual cortex, which could generate a series of position- and scale- invariant features. However, it could be improved with attention modulation and memory processing, which are two important properties of the primate visual cortex. Thus, in this paper, based on recent biological research on the primate visual cortex, we still mimic the first 100-150 milliseconds of visual cognition to enhance the HMAX model, which mainly focuses on the unsupervised feedforward feature learning process. The main modifications are as follows: 1 To mimic the attention modulation mechanism of V1 layer, a bottom-up saliency map is computed in the S1 layer of the HMAX model, which can support the initial feature extraction for memory processing; 2 To mimic the learning, clustering and short-term memory to long-term memory conversion abilities of V2 and IT, an unsupervised iterative clustering method is used to learn clusters with multiscale middle level patches, which are taken as long-term memory; 3 Inspired by the multiple feature encoding mode of the primate visual cortex, information including color, orientation, and spatial position are encoded in different layers of the HMAX model progressively. By adding a softmax layer at the top of the model, multiclass categorization experiments can be conducted, and the results on Caltech101 show that the enhanced model with a smaller memory size exhibits higher accuracy than the original HMAX model, and could also achieve better accuracy than other unsupervised feature learning methods in multiclass categorization task.

Enhanced HMAX model with feedforward feature learning for multiclass categorization.

Science.gov (United States)

Li, Yinlin; Wu, Wei; Zhang, Bo; Li, Fengfu

2015-01-01

In recent years, the interdisciplinary research between neuroscience and computer vision has promoted the development in both fields. Many biologically inspired visual models are proposed, and among them, the Hierarchical Max-pooling model (HMAX) is a feedforward model mimicking the structures and functions of V1 to posterior inferotemporal (PIT) layer of the primate visual cortex, which could generate a series of position- and scale- invariant features. However, it could be improved with attention modulation and memory processing, which are two important properties of the primate visual cortex. Thus, in this paper, based on recent biological research on the primate visual cortex, we still mimic the first 100-150 ms of visual cognition to enhance the HMAX model, which mainly focuses on the unsupervised feedforward feature learning process. The main modifications are as follows: (1) To mimic the attention modulation mechanism of V1 layer, a bottom-up saliency map is computed in the S1 layer of the HMAX model, which can support the initial feature extraction for memory processing; (2) To mimic the learning, clustering and short-term memory to long-term memory conversion abilities of V2 and IT, an unsupervised iterative clustering method is used to learn clusters with multiscale middle level patches, which are taken as long-term memory; (3) Inspired by the multiple feature encoding mode of the primate visual cortex, information including color, orientation, and spatial position are encoded in different layers of the HMAX model progressively. By adding a softmax layer at the top of the model, multiclass categorization experiments can be conducted, and the results on Caltech101 show that the enhanced model with a smaller memory size exhibits higher accuracy than the original HMAX model, and could also achieve better accuracy than other unsupervised feature learning methods in multiclass categorization task.

LC-IMS-MS Feature Finder: detecting multidimensional liquid chromatography, ion mobility and mass spectrometry features in complex datasets.

Science.gov (United States)

Crowell, Kevin L; Slysz, Gordon W; Baker, Erin S; LaMarche, Brian L; Monroe, Matthew E; Ibrahim, Yehia M; Payne, Samuel H; Anderson, Gordon A; Smith, Richard D

2013-11-01

The addition of ion mobility spectrometry to liquid chromatography-mass spectrometry experiments requires new, or updated, software tools to facilitate data processing. We introduce a command line software application LC-IMS-MS Feature Finder that searches for molecular ion signatures in multidimensional liquid chromatography-ion mobility spectrometry-mass spectrometry (LC-IMS-MS) data by clustering deisotoped peaks with similar monoisotopic mass, charge state, LC elution time and ion mobility drift time values. The software application includes an algorithm for detecting and quantifying co-eluting chemical species, including species that exist in multiple conformations that may have been separated in the IMS dimension. LC-IMS-MS Feature Finder is available as a command-line tool for download at http://omics.pnl.gov/software/LC-IMS-MS_Feature_Finder.php. The Microsoft.NET Framework 4.0 is required to run the software. All other dependencies are included with the software package. Usage of this software is limited to non-profit research to use (see README). rds@pnnl.gov. Supplementary data are available at Bioinformatics online.

Analysis of Feature Extraction Methods for Speaker Dependent Speech Recognition

Directory of Open Access Journals (Sweden)

Gurpreet Kaur

2017-02-01

Full Text Available Speech recognition is about what is being said, irrespective of who is saying. Speech recognition is a growing field. Major progress is taking place on the technology of automatic speech recognition (ASR. Still, there are lots of barriers in this field in terms of recognition rate, background noise, speaker variability, speaking rate, accent etc. Speech recognition rate mainly depends on the selection of features and feature extraction methods. This paper outlines the feature extraction techniques for speaker dependent speech recognition for isolated words. A brief survey of different feature extraction techniques like Mel-Frequency Cepstral Coefficients (MFCC, Linear Predictive Coding Coefficients (LPCC, Perceptual Linear Prediction (PLP, Relative Spectra Perceptual linear Predictive (RASTA-PLP analysis are presented and evaluation is done. Speech recognition has various applications from daily use to commercial use. We have made a speaker dependent system and this system can be useful in many areas like controlling a patient vehicle using simple commands.

Features of Internal Audit in Pharmaceutical Industry

OpenAIRE

Tsvetanova, Yulia

2014-01-01

The review highlights the main features of internal audit by focus on distribution of medicinal products. Recent data suggest internal audit as an antidote to effects of economic recession. The present review reveals internal audit as a tool for competitiveness through implementation of good practices. The purpose of the review is to describe the advantages of internal audit in new institutional frame. The object of analysis is the distribution practice, and more concrete, the wholesale di...

Features of monitoring system of physical state of urban bridges.

Directory of Open Access Journals (Sweden)

A.V. Bilchenko

2011-12-01

Full Text Available Abstract, the main features of urban bridge, structure are presented. The proposals concerning specialized management creation for exploitation, maintenance and reconstruction of bridges are developed. The essence of the new approach designed for the change of urban bridge structures physical state assessment system is stated.

Neutron radiography and other NDE tests of main rotor helicopter blades

International Nuclear Information System (INIS)

Beer, F.C. de; Coetzer, M.; Fendeis, D.; Silva, A. da Costa E

2004-01-01

A few nondestructive examination (NDE) techniques are extensively being used worldwide to investigate aircraft structures for all types of defects. The detection of corrosion and delaminations, which are believed to be the major initiators of defects leading to aircraft structural failures, are addressed by various NDE techniques. In a combined investigation by means of visual inspection, X-ray radiography and shearography on helicopter main rotor blades, neutron radiography (NRad) at SAFARI-1 research reactor operated by Necsa, was performed to introduce this form of NDE testing to the South African aviation industry to be evaluated for applicability. The results of the shearography, visual inspection and NRad techniques are compared in this paper. The main features and advantages of neutron radiography, within the framework of these investigations, will be highlighted

An Improved Rotation Forest for Multi-Feature Remote-Sensing Imagery Classification

Directory of Open Access Journals (Sweden)

Yingchang Xiu

2017-11-01

Full Text Available Multi-feature, especially multi-temporal, remote-sensing data have the potential to improve land cover classification accuracy. However, sometimes it is difficult to utilize all the features efficiently. To enhance classification performance based on multi-feature imagery, an improved rotation forest, combining Principal Component Analysis (PCA and a boosting naïve Bayesian tree (NBTree, is proposed. First, feature extraction was carried out with PCA. The feature set was randomly split into several disjoint subsets; then, PCA was applied to each subset, and new training data for linear extracted features based on original training data were obtained. These steps were repeated several times. Second, based on the new training data, a boosting naïve Bayesian tree was constructed as the base classifier, which aims to achieve lower prediction error than a decision tree in the original rotation forest. At the classification phase, the improved rotation forest has two-layer voting. It first obtains several predictions through weighted voting in a boosting naïve Bayesian tree; then, the first-layer vote predicts by majority to obtain the final result. To examine the classification performance, the improved rotation forest was applied to multi-feature remote-sensing images, including MODIS Enhanced Vegetation Index (EVI imagery time series, MODIS Surface Reflectance products and ancillary data in Shandong Province for 2013. The EVI imagery time series was preprocessed using harmonic analysis of time series (HANTS to reduce the noise effects. The overall accuracy of the final classification result was 89.17%, and the Kappa coefficient was 0.71, which outperforms the original rotation forest and other classifier ensemble results, as well as the NASA land cover product. However, this new algorithm requires more computational time, meaning the efficiency needs to be further improved. Generally, the improved rotation forest has a potential advantage in

Biosensor method and system based on feature vector extraction

Science.gov (United States)

Greenbaum, Elias [Knoxville, TN; Rodriguez, Jr., Miguel; Qi, Hairong [Knoxville, TN; Wang, Xiaoling [San Jose, CA

2012-04-17

A method of biosensor-based detection of toxins comprises the steps of providing at least one time-dependent control signal generated by a biosensor in a gas or liquid medium, and obtaining a time-dependent biosensor signal from the biosensor in the gas or liquid medium to be monitored or analyzed for the presence of one or more toxins selected from chemical, biological or radiological agents. The time-dependent biosensor signal is processed to obtain a plurality of feature vectors using at least one of amplitude statistics and a time-frequency analysis. At least one parameter relating to toxicity of the gas or liquid medium is then determined from the feature vectors based on reference to the control signal.

[Physiological features of skin ageing in human].

Science.gov (United States)

Tikhonova, I V; Tankanag, A V; Chemeris, N K

2013-01-01

The issue deals with the actual problem of gerontology, notably physiological features of human skin ageing. In the present review the authors have considered the kinds of ageing, central factors, affected on the ageing process (ultraviolet radiation and oxidation stress), as well as the research guidelines of the ageing changes in the skin structure and fuctions: study of mechanical properties, microcirculation, pH and skin thickness. The special attention has been payed to the methods of assessment of skin blood flow, and to results of investigations of age features of peripheral microhemodynamics. The laser Doppler flowmetry technique - one of the modern, noninvasive and extensively used methods for the assessmant of skin blood flow microcirculation system has been expanded in the review. The main results of the study of the ageing changes of skin blood perfusion using this method has been also presented.

Comparative analysis of features of Polish and Lithuanian Day-ahead electricity market prices

International Nuclear Information System (INIS)

Bobinaite, Viktorija; Juozapaviciene, Aldona; Staniewski, Marcin; Szczepankowski, Piotr

2013-01-01

The goal of this article is to better understand the processes of electricity market price formation in Poland and Lithuania through an analysis of the features (volatility and spikes) of Lithuanian and Polish day-ahead electricity market prices and to assess how acquired electricity price features could affect the achievement of the main goals of the national energy policy. The following indicators have been calculated to determine electricity market price volatility: the oscillation coefficient, the coefficient of variation, an adjusted coefficient of variation, the standard deviation indicator, the daily velocity indicator (based on the overall average price) and the daily velocity indicator (based on the daily average price). Critical values for electricity market price have been calculated to evaluate price spikes. This analysis reveals that electricity market-price volatility is moderate in Poland and high in Lithuania. Electricity price spikes have been an observable phenomenon both in Lithuanian and in Polish day-ahead electricity markets, but they are more common in Lithuania, encompassing 3.15% of the time period analysed in Poland and 4.68% of the time period analysed in Lithuania. Volatile, spiking and increasing electricity prices in day-ahead electricity markets in Lithuania and Poland create preconditions and substantiate the relevance of implementation of the national energy policies and measures. - Highlights: • Moderate and seasonal volatility. • spiking market price and. • stable average price

Implementation of special engineering safety features for severe accident management. New SAMG approach

International Nuclear Information System (INIS)

Grigorov, D.; Borisov, E.; Mancheva, K.

2012-01-01

Conclusions: As a result of the thermohydraulic analysis conducted the following main conclusions are formulated: The operator actions for accident management are effective and allow reaching conditions for application of the new engineering safety features for SAMG; The new engineering safety features application is effective and prevents severe core damage for Scenario 1. For the Scenario 2 they prevents degradation and relocation of the reactor core for a long period of time (in the analysis this period is 10 h, but the unit could be kept in safe condition for longer time which is not specifically analysed).The maximal fuel cladding temperature for Scenario 1 reaches 558 o C. This low fuel cladding temperature gradient is achieved by applying a complex of operator actions which prevent any core damage. If the additional discharge line with DN 100 mm from the PRZ is not opened then a severe core damage occurs; The maximal fuel cladding temperature for Scenario 2 reaches 1307 o C. One of the possibilities for keeping this temperature below 1200 o C is to mount second line (the first SFP line is between YT12S03.S04) from the SFP to the TQ22 pipeline which is connected to YT14B01 hydroaccumulator line, between the check valves YT14S03.S04