• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3598-3614
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• 2020

With the increase of motor vehicles and tourism demand, some traffic problems gradually appear, such as traffic congestion, safety accidents and insufficient allocation of traffic resources. Facing these challenges, a model of Spatio-Temporal Dilated Convolutional Network (STDGCN) is proposed for assistance of extracting highly nonlinear and complex characteristics to accurately predict the future traffic flow. In particular, we model the traffic as undirected graphs, on which graph convolutions are built to extract spatial feature informations. Furthermore, a dilated convolution is deployed into graph convolution for capturing multi-scale contextual messages. The proposed STDGCN integrates the dilated convolution into the graph convolution, which realizes the extraction of the spatial and temporal characteristics of traffic flow data, as well as features of road occupancy. To observe the performance of the proposed model, we compare with it with four rivals. We also employ four indicators for evaluation. The experimental results show STDGCN's effectiveness. The prediction accuracy is improved by 17% in comparison with the traditional prediction methods on various real-world traffic datasets.

• Molecules and Cells
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• v.43 no.3
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• pp.215-221
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• 2020

The olfactory bulb (OB) has an extremely higher proportion of interneurons innervating excitatory neurons than other brain regions, which is evolutionally conserved across species. Despite the abundance of OB interneurons, little is known about the diversification and physiological functions of OB interneurons compared to cortical interneurons. In this review, an overview of the general developmental process of interneurons from the angles of the spatial and temporal specifications was presented. Then, the distinct features shown exclusively in OB interneurons development and molecular machinery recently identified were discussed. Finally, we proposed an evolutionary meaning for the diversity of OB interneurons.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.3
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• pp.161-170
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• 2013

Spatio-temporal distribution of soil moisture was studied to improve understanding of hydrological processes at hillslope scale. Using field measurements for three designated periods during the spring, summer and autumn seasons in 2010 obtained from Beomryunsa hillslope located at the Sulmachun watershed, correlation analysis was performed between soil moisture measurements and 18 different terrain attributes (e.g., curvatures and topographic index). The results of correlation analysis demonstrated distinct seasonal variation features of soil moisture in different depths with different terrain attributes and rainfall amount. The relationship between predicted flow lines and distribution of the soil moisture provided appropriate model structures and terrain indices.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.4
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• pp.11-19
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• 2000

Real-time extraction of traffic information such as the number of vehicles passing, speed, road-occupancy rate, distance between vehicles, and vehicle types from the traffic scenes acquired from the camera on the road, is a core component of the intelligent transportation system(lTS) We present a scheme of extracting the traffic informations based on the spatio-temporal image analysis, which is robust to the variation of weather conditions and the shades. The images of two detection regions for each traffic lane are classified into one of the three categories: the road, the vehicle, and the shade, using the statistical and structural features Quantitative traffic informations are retrieved by analysing the two spatio-temporal images. Since only the local images of detection regions are processed, the real-time operation of more than 30 frames per second is realized while ensuring the detection performance robust to the operating condition.

• Physical Therapy Korea
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• v.21 no.3
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• pp.80-88
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• 2014

Previous studies have reported that decreased cognitive ability has been consistently associated with significant declines in performance of one or both tasks under a dual-task walking condition. This study examined the relationship between specific cognitive abilities and the dual-task costs (DTCs) of spatio-temporal gait parameters in stroke patients. The spatio-temporal gait parameters were measured among 30 stroke patients while walking with and without a cognitive task (Stroop Word-Color Task) at the study participant's preferred walking speed. Cognitive abilities were measured using Computerized Neuropsychological Testing. Pearson's correlation coefficients (r) were calculated to quantify the associations between the neuropsychological measures and the DTCs in the spatio-temporal gait parameters. Moderate to strong correlations were found between the Auditory Continuous Performance test (ACPT) and the DTCs of the Single Support Time of Non-paretic (r=.37), the Trail Making A (TMA) test and the DTCs of Velocity (r=.71), TMA test and the DTCs of the Step Length of Paretic (r=.37), TMA test and the DTCs of the Step Length Non-paretic (r=.36), the Trail Making B (TMB) test and the DTCs of Velocity (r=.70), the Stroop Word-Color test and the DTCs of Velocity (r=-.40), Visual-span Backward (V-span B) test and the DTCs of Velocity (r=-.41), V-span B test and the DTCs of the Double Support Time of Non-paretic (r=.38), Digit-span Forward test and the DTCs of the Step Time of Non-paretic (r=-.39), and Digit-span Backward test and the DTCs of the Single Support Time of Paretic (r=.36). Especially TMA test and TMB test were found to be more strongly correlated to the DTCs of gait velocity than the other correlations. Understanding these cognitive features will provide guidance for identifying dual- task walking ability.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.413-415
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• 2022

Since human joints can be used as useful information for analyzing human behavior as a component of the human body, many studies have been conducted on human action recognition using joint information. However, it is a very complex problem to recognize human action that changes every moment using only each independent joint information. Therefore, an additional information extraction method to be used for learning and an algorithm that considers the current state based on the past state are needed. In this paper, we propose a human action recognition technique considering the positional relationship of connected joints and the change of the position of each joint over time. Using the pre-trained joint extraction model, position information of each joint is obtained, and bone information is extracted using the difference vector between the connected joints. In addition, a simplified neural network is constructed according to the two types of inputs, and spatio-temporal features are extracted by adding LSTM. As a result of the experiment using a dataset consisting of 9 behaviors, it was confirmed that when the action recognition accuracy was measured considering the temporal and spatial relationship features of each joint, it showed superior performance compared to the result using only single joint information.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.2
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• pp.126-132
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• 2009

This study examined the differences in spatio-temporal parameters, joint angle, ground reaction force (GRF), and joint power according to the changes of gait speed for trans-tibial amputees to investigate the features of the energy-storing foot for sports. The subjects walked at normal speed and at fast speed, wearing a single-axis type foot (Korec) and an energy-storing foot for sports (Renegade) respectively. The results showed that Renegade yielded faster gait speed as well as more symmetric gait pattern, compared to Korec. However, as gait speed was increased, there was no significant difference in kinematics, ground reaction force, and joint power between two artificial foots. This was similar to the results from previous studies regarding the energy-storing foot, where the walking velocity and gait symmetry have been improved. Nevertheless, the result of this study differed from the previous ones which reported that joint angle, joint power, and GRF increased as the gait speed increased except spatio-temporal parameters.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.927-939
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• 2020

Gait recognition can identify people's identity from a long distance, which is very important for improving the intelligence of the monitoring system. Among many human features, gait features have the advantages of being remotely available, robust, and secure. Traditional gait feature extraction, affected by the development of behavior recognition, can only rely on manual feature extraction, which cannot meet the needs of fine gait recognition. The emergence of deep convolutional neural networks has made researchers get rid of complex feature design engineering, and can automatically learn available features through data, which has been widely used. In this paper,conduct feature metric learning in the three-dimensional space by combining the three-dimensional convolution features of the gait sequence and the Siamese structure. This method can capture the information of spatial dimension and time dimension from the continuous periodic gait sequence, and further improve the accuracy and practicability of gait recognition.

• Proceedings of the ESK Conference
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• 1995.10a
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• pp.46-55
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• 1995

The multimedia documents becomes the most natural user interface for CSCW(Conputer Supported Cooperative Work) in distributed environment. The objective of this study is to propose a multimedia document architecture and to develop a system that can manage it well. The new architecture is for revisable documents and is the basic layer for hypermedia documents. A good document architecture for CSCW must support pointing, marking, and editing over a part of documents. The user views, version control, and full- content search are also desirable features. In this paper, we discuss the basic concept of a new document architecture for CSCW. We also present the user interfaces for spatio-temporal compositions of multimedia documents.

• International Journal of Contents
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• v.4 no.2
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• pp.24-28
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• 2008

In this paper, we propose a new approach for content-based video retrieval using non-parametric based motion classification in the shot-based video indexing structure. Our system proposed in this paper has supported the real-time video retrieval using spatio-temporal feature comparison by measuring the similarity between visual features and between motion features, respectively, after extracting representative frame and non-parametric motion information from shot-based video clips segmented by scene change detection method. The extraction of non-parametric based motion features, after the normalized motion vectors are created from an MPEG-compressed stream, is effectively fulfilled by discretizing each normalized motion vector into various angle bins, and by considering the mean, variance, and direction of motion vectors in these bins. To obtain visual feature in representative frame, we use the edge-based spatial descriptor. Experimental results show that our approach is superior to conventional methods with regard to the performance for video indexing and retrieval.