Identifying characteristic changes in club convergence of China's urban pollution emission: A spatial-temporal feature analysis

2021 ◽  
Vol 98 ◽  
pp. 105243
Author(s):  
Yang Song ◽  
Dayu Liu ◽  
Qiaoru Wang
Keyword(s):  
Urban Pollution ◽  
Feature Analysis ◽  
Club Convergence ◽  
Pollution Emission ◽  
Temporal Feature

scholarly journals Crowd Anomaly Detection Using Motion Based Spatio-Temporal Feature Analysis

2017 ◽  
Vol 7 (3) ◽  
pp. 737 ◽  
Author(s):  
Basavaraj G M ◽  
Ashok Kusagur
Keyword(s):  
Behavior Analysis ◽  
False Positive Rate ◽  
Security System ◽  
Visual Surveillance ◽  
Feature Analysis ◽  
Crowd Behavior ◽  
Crowd Behavior Analysis ◽  
Positive Rate ◽  
Spatio Temporal ◽  
Temporal Feature

<p>Recently, the demand for surveillance system is increasing in real time application to enhance the security system. These surveillance systems are mainly used in crowded places such as shopping malls, sports stadium etc. In order to support enhance the security system, crowd behavior analysis has been proven a significant technique which is used for crowd monitoring, visual surveillance etc. For crowd behavior analysis, motion analysis is a crucial task which can be achieved with the help of trajectories and tracking of objects. Various approaches have been proposed for crowd behavior analysis which has limitation for densely crowded scenarios, a new object entering the scene etc. In this work, we propose a new approach for abnormal crowd behavior detection. Proposed approach is a motion based spatio-temporal feature analysis technique which is capable of obtaining trajectories of each detected object.  We also present a technique to carry out the evaluation of individual object and group of objects by considering relational descriptors based on their environmental context. Finally, a classification is carried out for detection of abnormal or normal crowd behavior by following patch based process. In the results, we have reported that proposed model is able to achieve better performance when compared to existing techniques in terms of classification accuracy, true positive rate, and false positive rate.</p>

scholarly journals Temporal feature analysis method describing for automata models of computation systems

2021 ◽  
Vol 2094 (3) ◽  
pp. 032043
Author(s):  
M P Sinev ◽  
M A Mitrokhin ◽  
A I Martyshkin ◽  
I N Doroshenko ◽  
A V Dubravin ◽  
...  
Keyword(s):  
Measurement Accuracy ◽  
Time Measurement ◽  
Feature Analysis ◽  
Analysis Method ◽  
Work Process ◽  
Final State ◽  
Models Of Computation ◽  
Temporal Features ◽  
Temporal Feature ◽  
Modified Algorithm

Abstract The paper reported the temporal feature analysis method describing for automata models of computation systems. The method is based on from source algorithm to modified algorithm expanding makes it possible to take characteristics of work process related to execution time measurement. The paper considers the transition method from source algorithm representation to modification a final state machine with additional states providing registration of temporal features. The paper demonstrates method usage example on computer system authorization algorithm, approves an algorithm complexity for features registration O(n) for n-states automata algorithm. The method can be used for a large class algorithm, but is recommended to apply it to an algorithm, separated on procedure thus mean of commands amount should be more than 500 for more time measurement accuracy.

Feature-Based Hand Gesture Recognition Using an FMCW Radar and its Temporal Feature Analysis

2018 ◽  
Vol 18 (18) ◽  
pp. 7593-7602 ◽  
Author(s):  
Si-Jung Ryu ◽  
Jun-Seuk Suh ◽  
Seung-Hwan Baek ◽  
Songcheol Hong ◽  
Jong-Hwan Kim
Keyword(s):  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Feature Analysis ◽  
Hand Gesture ◽  
Fmcw Radar ◽  
Feature Based ◽  
Temporal Feature

Phonomotor Versus Semantic Feature Analysis Treatment for Anomia in 58 Persons With Aphasia: A Randomized Controlled Trial

2019 ◽  
Vol 62 (12) ◽  
pp. 4464-4482 ◽  
Author(s):  
Diane L. Kendall ◽  
Megan Oelke Moldestad ◽  
Wesley Allen ◽  
Janaki Torrence ◽  
Stephen E. Nadeau
Keyword(s):  
Randomized Controlled Trial ◽  
Response Latency ◽  
Controlled Trial ◽  
Semantic Knowledge ◽  
Semantic Feature ◽  
Feature Analysis ◽  
Group Differences ◽  
Randomized Controlled ◽  
Confrontation Naming ◽  
Semantic Feature Analysis

Purpose The ultimate goal of anomia treatment should be to achieve gains in exemplars trained in the therapy session, as well as generalization to untrained exemplars and contexts. The purpose of this study was to test the efficacy of phonomotor treatment, a treatment focusing on enhancement of phonological sequence knowledge, against semantic feature analysis (SFA), a lexical-semantic therapy that focuses on enhancement of semantic knowledge and is well known and commonly used to treat anomia in aphasia. Method In a between-groups randomized controlled trial, 58 persons with aphasia characterized by anomia and phonological dysfunction were randomized to receive 56–60 hr of intensively delivered treatment over 6 weeks with testing pretreatment, posttreatment, and 3 months posttreatment termination. Results There was no significant between-groups difference on the primary outcome measure (untrained nouns phonologically and semantically unrelated to each treatment) at 3 months posttreatment. Significant within-group immediately posttreatment acquisition effects for confrontation naming and response latency were observed for both groups. Treatment-specific generalization effects for confrontation naming were observed for both groups immediately and 3 months posttreatment; a significant decrease in response latency was observed at both time points for the SFA group only. Finally, significant within-group differences on the Comprehensive Aphasia Test–Disability Questionnaire ( Swinburn, Porter, & Howard, 2004 ) were observed both immediately and 3 months posttreatment for the SFA group, and significant within-group differences on the Functional Outcome Questionnaire ( Glueckauf et al., 2003 ) were found for both treatment groups 3 months posttreatment. Discussion Our results are consistent with those of prior studies that have shown that SFA treatment and phonomotor treatment generalize to untrained words that share features (semantic or phonological sequence, respectively) with the training set. However, they show that there is no significant generalization to untrained words that do not share semantic features or phonological sequence features.

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