State Classification Method Research Articles

Transitions between neocortical seizure and non-seizure-like states and their association with presynaptic glutamate release

The transition between seizure and non-seizure states in neocortical epileptic networks is governed by distinct underlying dynamical processes. Based on the gamma distribution of seizure and inter-seizure durations, over time, seizures are highly likely to self-terminate; whereas, inter-seizure durations have a low chance of transitioning back into a seizure state. Yet, the chance of a state transition could be formed by multiple overlapping, unknown synaptic mechanisms. To identify the relationship between the underlying synaptic mechanisms and the chance of seizure-state transitions, we analyzed the skewed histograms of seizure durations in human intracranial EEG and seizure-like events (SLEs) in local field potential activity from mouse neocortical slices, using an objective method for seizure state classification. While seizures and SLE durations were demonstrated to have a unimodal distribution (gamma distribution shape parameter >1), suggesting a high likelihood of terminating, inter-SLE intervals were shown to have an asymptotic exponential distribution (gamma distribution shape parameter <1), suggesting lower probability of cessation. Then, to test cellular mechanisms for these distributions, we studied the modulation of synaptic neurotransmission during, and between, the in vitro SLEs. Using simultaneous local field potential and whole-cell voltage clamp recordings, we found a suppression of presynaptic glutamate release at SLE termination, as demonstrated by electrically- and optogenetically-evoked excitatory postsynaptic currents (EPSCs), and focal hypertonic sucrose application. Adenosine A1 receptor blockade interfered with the suppression of this release, changing the inter-SLE shape parameter from asymptotic exponential to unimodal, altering the chance of state transition occurrence with time. These findings reveal a critical role for presynaptic glutamate release in determining the chance of neocortical seizure state transitions.

On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant

This paper presents an on-line diagnosis method for large photovoltaic (PV) power plants by using a machine learning algorithm. Most renewable energy output power is decreased due to the lack of management tools and the skills of maintenance engineers. Additionally, many photovoltaic power plants have a long down-time due to the absence of a monitoring system and their distance from the city. The IEC 61724-1 standard is a Performance Ratio (PR) index that evaluates the PV power plant performance and reliability. However, the PR index has a low recognition rate of the fault state in conditions of low irradiation and bad weather. This paper presents a weather-corrected index, linear regression method, temperature correction equation, estimation error matrix, clearness index and proposed variable index, as well as a one-class Support Vector Machine (SVM) method and a kernel technique to classify the fault state and anomaly output power of PV plants.

Recognition of classroom student state features based on deep learning algorithms and machine learning

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

A Smartphone-Based Drowsiness Detection and Warning System for Automotive Drivers

This paper presents a smartphone-based system for the detection of drowsiness in automotive drivers. The proposed framework uses three-stage drowsiness detection. The first stage uses the percentage of eyelid closure (PERCLOS) obtained through images captured by the front camera with a modified eye state classification method. The system uses near infrared lighting for illuminating the face of the driver during night-driving. The second step uses the voiced to the unvoiced ratio obtained from the speech data from the microphone, in the event PERCLOS crosses the threshold. A final verification stage is used as a touch response within a stipulated time to declare the driver as drowsy and subsequently sound an alarm. The device maintains a log file of the periodic events of the metrics along with the corresponding GPS coordinates. The system has three advantages over existing drowsiness detection systems. First, the three-stage verification process makes the system more reliable. The second advantage is its implementation on an Android smart-phone, which is readily available to most drivers or cab owners as compared to other general purpose embedded platforms. The third advantage is the use of SMS service to inform the control room as well as the passenger regarding the loss of attention of the driver. The framework provides 93.33% drowsiness state classification as compared to a single stage which gives 86.66%.

Classifying the traffic state of urban expressways: A machine-learning approach

The classification of the urban traffic state and its application is an important part of intelligent transportation systems (ITS), which can not only help traffic managers grasp the traffic operation situation and analyze congestion, but also provide travelers with more traffic information and help them avoid congestion. Thus, an accurate traffic state classification method would be very practical for urban traffic management. The primary objective of this study is to classify the urban traffic state using a machine-learning method (i.e., the FCM clustering method, and the classification results can be determined from the corresponding clustering labels). In this approach, two parts are developed. First, a new classification indicator, i.e., the ample degree of road network is proposed, and it will make up a comprehensive classification indicator system with other parameters such as traffic flow, speed and occupancy. Then, the traditional fuzzy c-means (FCM) clustering approach is improved in two regards, i.e., the fuzzy membership function improvement and weighting processing of the samples, and these improvements can enhance the clustering performance. As a result, an improved machine-learning method (i.e., the improved FCM clustering approach) is developed and used to conduct the clustering analysis with real-world traffic flow data. Next, a case study of Shanghai is used to guide the study process, which consists of data processing, clustering analysis and method comparison. The other methods (e.g., the support vector machines (SVM) method, the decision tree method, the k-Nearest Neighbor (KNN) method and the traditional FCM clustering approach) are introduced to compare with the improved FCM clustering approach. The discussion shows the superiority of the proposed method (e.g., compared with the traditional FCM clustering approach, the objective function value of the improved method decreased by 31.11%, and cluster center error also show a descending trend), and it outperforms the other methods in classification performance (e.g., the overall classification accuracy of the improved FCM method increased by 10.10%, 5.45%, 30.92% and 35.66% in comparison with the traditional FCM method, SVM method, decision tree method and KNN method, respectively). Additionally, the NMI, ROC curve results also illustrated the superiority of the improved FCM method to other methods. These comaprison results suggest that the improved FCM clustering approach is feasible and the results can be well used in the advanced traffic management system, which may have the potential to serve as a reference for releasing accurate traffic state information and preventing traffic congestion and risk.

Machine Tool Volumetric Error Features Extraction and Classification Using Principal Component Analysis and K-Means

Volumetric errors (VE) are related to the machine tool accuracy state. Extracting features from the complex VE data provides with a means to characterize this data. VE feature classification can reveal the machine tool accuracy states. This paper presents a study on how to use principal component analysis (PCA) to extract the features of VE and how to use the K-means method for machine tool accuracy state classification. The proposed data processing methods have been tested with the VE data acquired from a five-axis machine tool with different states of malfunction. The results indicate that the PCA and K-means are capable of extracting the VE feature information and classifying the fault states including the C axis encoder fault, uncalibrated C axis encoder fault, and pallet location fault from the machine tool normal states. This research provides a new way for VE features extraction and classification.

An Innovative Method for Brain State Classification from EEG Data using Hybrid Learning Algorithm

An Innovative Method for Brain State Classification from EEG Data using Hybrid Learning Algorithm

Quantitative Analysis of Urban Regional Traffic Status

In order to monitor the real-time operation condition of urban region traffic flow, and to quickly identify regional traffic status, this paper adopts CNM (Clauset-Newman-Moore) Community Division Method of Complex Network to analyze traffic status information deeply implied from the regional road network traffic flow data, which aims to objectively develop the reasonable classification of regional traffic state with no classification criteria of determining regional traffic state. Combined with the regional road network traffic data from a certain city, the example analysis shows that this proposed method can easily provide the reasonable division of regional traffic condition and verifies the feasibility of the regional traffic state classification method. Besides, the example analysis gives the rough regional traffic status determination standard, laying theoretical basis for accurately judging the regional traffic state.

State recognition method for machining process of a large spot welder based on improved genetic algorithm and hidden Markov model

Large spot welder is an important equipment in rail transit equipment manufacturing industry, but having the problem of low utilization rate and low effectlvely machining rate. State monitoring can master its operating states real time and comprehensively, and providing data support for state recognition. Hidden Markov model is a state classification method, but it is sensitive to the initial model parameters and easy to trap into a local optima. Genetic algorithm is a global searching method; however, it is quite poor at hill climbing and also has the problem of premature convergence. In this paper, proposing the improved genetic algorithm, and combining improved genetic algorithm and hidden Markov model, a new method of state recognition method named improved genetic algorithm–hidden Markov model is proposed. In the proposed method, improved genetic algorithm is used for optimizing the initial parameters, and hidden Markov model as a classifier to recognize the operating states for machining process. This method is also compared with the other two recognition methods named adaptive genetic algorithm–hidden Markov model and hidden Markov model, in which adaptive genetic algorithm is similarly used for optimizing the initial parameters, however hidden Markov model (in both methods) as a classifier. Experimental results show that the proposed method is very effective, and the improved genetic algorithm–hidden Markov model recognition method is superior to the adaptive genetic algorithm–hidden Markov model and hidden Markov model recognition method.

Real-Time Vision Based Driver Drowsiness Detection Using Partial Least Squares Analysis

Robust eye state classification in real-time is very crucial for automatic driver drowsiness detection to avoid road accidents. In this paper, we propose partial least squares (PLS) analysis based eye state classification method and its real-time implementation on resource constraint digital video processor platform, to monitor the eye state during all time driving conditions. The drowsiness is detected using percentage of eye closure (PERCLOS) metric. In this approach, face in the infrared (IR) image is detected using Haar features based cascaded classifier and within the face, eye is detected. For binary eye state classification, PLS analysis is applied to obtain the low dimensional discriminative subspace, within which simple PLS regression score based classifier is used to classify test vector into open and closed. We compared our algorithm to recent methods on challenging test sequences and the result shows superior performance. The results obtained during on-vehicle testing show that the proposed system achieves significant improvement in classification accuracy at nearly 3 frames per second.

Microtubules Shape GPCR Spatiotemporal Membrane Organization and Function by Scaffolding Cortical Signaling Hubs

G-protein coupled receptors (GPCR) activity is precisely regulated in space and time, however the exact molecular mechanisms regulating GPCR localization within the plasma membrane are still poorly defined. Combining single receptor tracking and a diffusion state classification method together with imaging of early downstream signaling events, we explored the activity of two GPCR subtypes, the prostanoid EP2 and EP4 receptors. Although both receptors trigger the Gαs-cAMP pathway, we found that EP4-mediated cAMP production is stronger but transient, whereas EP2 induces shallow but continuous cAMP levels. This is structured by the dynamic localization of slowly diffusing EP4, but not EP2, in elongated areas of the cell membrane that are templated by the underlying cortical microtubule network and allow EP4 to interact with its downstream interactors. These results reveal a new paradigm of dynamic plasma membrane organization based on sub-membraneous microtubules that scaffold hubs for GPCR signaling initiation and attenuation.

HMM-based state classification of a user with a walking support system using visual PCA features

The improvement of safety and dependability in systems that physically interact with humans requires investigation with respect to the possible states of the user’s motion and an attempt to recognize these states. In this study, we propose a method for real-time visual state classification of a user with a walking support system. The visual features are extracted using principal component analysis and classification is performed by hidden Markov models, both for real-time fall detection (one-class classification) and real-time state recognition (multi-class classification). The algorithms are used in experiments with a passive-type walker robot called “RT Walker” equipped with servo brakes and a depth sensor (Microsoft Kinect). The experiments are performed with 10 subjects, including an experienced physiotherapist who can imitate the walking pattern of the elderly and people with disabilities. The results of the state classification can be used to improve fall-prevention control algorithms for walking support systems. The proposed method can also be used for other vision-based classification applications, which require real-time abnormality detection or state recognition.

A Remark on the Most Informative EEG Signal Components in a Super-scalable Method for Functional State Classification based on the Wavelet Decomposition

Technologies for automated real-time classification of functional states are essential for supervision over operators of critical infrastructure, stress resistance evaluation, functional studies of sportsmen. They also open new horizons for automated educational applications and applications for phobia therapy, especially supported by virtual reality technologies, as they allow the automated adaptation of tasks in real time. In 2012 a novel approach for the functional state automated classification based on electroencephalographic (EEG) data was introduced by E.D. Livshitz et al. The approach efficiently utilizes CDF 9/7 wavelet decomposition instead of classical Fourier analysis and provides a promising classification reliability. In this paper connections between a set of estimators that were identified as the most informative for this approach and expert knowledge used in EEG data analysis and manual functional state classification are studied. The formalization of these connections is based on a fact that in spite of substantial differences between CDF wavelets and standard trigonometric functions used in Fourier analysis wavelet-based estimators have a good localization in frequency domain.

Detecting driver drowsiness using feature-level fusion and user-specific classification

Accurate classification of eye state is a prerequisite for preventing automobile accidents due to driver drowsiness. Previous methods of classification, based on features extracted for a single eye, are vulnerable to eye localization errors and visual obstructions, and most use a fixed threshold for classification, irrespective of variations in the driver’s eye shape and texture. To address these deficiencies, we propose a new method for eye state classification that combines three innovations: (1) extraction and fusion of features from both eyes, (2) initialization of driver-specific thresholds to account for differences in eye shape and texture, and (3) modeling of driver-specific blinking patterns for normal (non-drowsy) driving. Experimental results show that the proposed method achieves significant improvements in detection accuracy.

Classification of Mental States Based on Spatiospectral Patterns of Brain Electrical Activity

Abstract: Classification of human thought is an emerging research field that may allow us to understand human brainfunctions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introducea new approach to classify various mental states from noninvasive electrophysiological recordings of human brainactivity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signalsrecorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spa-tiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-relatedsynchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current(input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices.Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For theverification of our approach, eight participants took part in the present study; their EEG data were recorded whilethey performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequentlybetween trials in the same class than th ose in different classes, indicating that th ese spatiospectral pattern maps couldbe used to classify different mental states. The classification accuracy was evaluated for each participant from boththe proposed approach and a conventional mental state classification method based on the inter-hemispheric spectralpower asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (± 9.64%) wasattained for the proposed method; whereas an average accuracy of 57% (± 5.68%) was attained for the conventionalmethod (significance was assessed by the one-tail paired t-test, p < 0.01), showing that the proposed simple clas-sification approach might be one of the promising methods in discriminating various mental states.Key words:brain-computer interface (BCI), spatiospectral pattern, mental state classification, mind reading, elec-troencephalography (EEG)

Harmony Search 알고리즘 기반 HMM 구조 최적화에 의한 얼굴 정서 인식 시스템 개발

본 논문에서는 얼굴 표정에서 나타나는 동적인 정서상태 변화를 고려한 얼굴 영상 기반 정서 인식 연구를 제안한다. 본 연구는 얼굴 영상 기반 정서적 특징 검출 및 분석 단계와 정서 상태 분류/인식 단계로 구분할 수 있다. 세부 연구의 구성 중 첫 번째는 Facial Action Units (FAUs)과 결합한 Active Shape Model (ASM)을 이용하여 정서 특징 영역 검출 및 분석기법의 제안이며, 두 번째는 시간에 따른 정서 상태의 동적 변화를 고려한 정확한 인식을 위하여 Hidden Markov Model(HMM) 형태의 Dynamic Bayesian Network를 사용한 정서 상태 분류 및 인식기법의 제안이다. 또한, 최적의 정서적 상태 분류를 위한 HMM의 파라미터 학습 시 Harmony Search (HS) 알고리즘을 이용한 휴리스틱 최적화 과정을 적용하였으며, 이를 통하여 동적 얼굴 영상 변화를 기반으로 하는 정서 상태 인식 시스템을 구성하고 그 성능의 향상을 도모하였다. In this paper, we propose an study of the facial emotion recognition considering the dynamical variation of emotional state in facial image sequences. The proposed system consists of two main step: facial image based emotional feature extraction and emotional state classification/recognition. At first, we propose a method for extracting and analyzing the emotional feature region using a combination of Active Shape Model (ASM) and Facial Action Units (FAUs). And then, it is proposed that emotional state classification and recognition method based on Hidden Markov Model (HMM) type of dynamic Bayesian network. Also, we adopt a Harmony Search (HS) algorithm based heuristic optimization procedure in a parameter learning of HMM in order to classify the emotional state more accurately. By using all these methods, we construct the emotion recognition system based on variations of the dynamic facial image sequence and make an attempt at improvement of the recognition performance.

An adhesive bond state classification method for a composite skin-to-spar joint using chaotic insonification

The combination of chaotically amplitude-modulated ultrasonic waves and time series prediction algorithms has shown the ability to locate and classify various bond state damage conditions of a composite bonded joint. This study examines the ability of a new two-part supervised learning classification scheme not only to classify disbond size but also to classify whether a bond for which there is no baseline data is undamaged or has some form of disbond. This classification is performed using data from a similarly configured composite bond for which baseline data are available. The test structures are analogous to a wing skin-to-spar bonded joint. An active excitation signal is imparted to the structure through a macro fiber composite (MFC) patch on one side of the bonded joint and sensed using an equivalent MFC patch on the opposite side of the joint. There is an MFC actuator/sensor pair for each bond condition to be identified. The classification approach compares features derived from an autoregressive (AR) model coefficient vector cross-assurance criterion.

Метод класифікації стану лісів за матеріалами аерокосмічної зйомки на основі принципу злиття даних

Метод класифікації стану лісів за матеріалами аерокосмічної зйомки на основі принципу злиття даних

Efficient state classification of finite-state Markov chains

This paper presents an efficient method for state classification of finite-state Markov chains using binary-decision diagram-based symbolic techniques. The method exploits the fundamental properties of a Markov chain and classifies the state space by iteratively applying reachability analysis. We compare our method with the state-of-the-art technique, which requires the transitive closure of the transition relation of a Markov chain. Experiments in over a dozen synchronous and asynchronous systems and queueing networks demonstrate that our method dramatically reduces the CPU time needed and solves much larger problems because of the reduced memory requirements.

State-dependent spike detection: concepts and preliminary results

In traditional methods of spike detection, spikes are defined in absolute terms (duration, amplitude) or relative to a few seconds of background. These methods result in many false positive detections during long-term epilepsy monitoring because of numerous artefacts and non-epileptic transients. To reduce significantly false detection, we propose to render spike detection sensitive to the state of the EEG. We thus defined 5 states (active wakefulness, quiet wakefulness, desynchronized EEG, phasic EEG and slow EEG) and designed a method for automatic state classification. We then designed procedures for identification of non-epileptic transients (eye blinks, EMG, alpha, spindles, vertex sharp waves). These procedures are to be applied only in the state in which they are likely to occur (e.g., eye blinks in wakefulness). We present preliminary results from 14 recordings each lasting 100 min, which indicate a state classification reliability of 85–90%, reduction in false detection of 65–90% if state classification were perfect; true spikes lost as a result of these procedures were under 5%. These results are encouraging and validate the concept of a spike detection system which analyses a wide temporal and spatial context before deciding the significance of a wave form.