• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.7
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• pp.922-934
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• 1993

In this paper, statistical properties of the spatiotemporal center weighted median(CWM) filter for image sequences are investigated. It is statistically shown that the CWM filter preserves image structures under motion at the expense of noise suppression. To improve the CWM filter, a filter which can be effectively used in image sequence processing, the adaptive directional center weighted median filter (ADCWM), is proposed. This filter utilizes a multistage filtering structure based on adaptive symmetric order statistic(ASOS) operators which produce a pall of order statistics symmetric about the median. The ASOS's are selected by using adaptive parameters adjusted by local image statistics. It is shown experimentally that the proposed filter can preserve image structures while attenuating noise without the use of motion estimation.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1857-1862
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• 2016

Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2767-2780
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• 2016

Video-based human-activity recognition has become increasingly popular due to the prominent corresponding applications in a variety of fields such as computer vision, image processing, smart-home healthcare, and human-computer interactions. The essential goals of a video-based activity-recognition system include the provision of behavior-based information to enable functionality that proactively assists a person with his/her tasks. The target of this work is the development of a novel approach for human-activity recognition, whereby human-body-joint features that are extracted from depth videos are used. From silhouette images taken at every depth, the direction and magnitude features are first obtained from each connected body-joint pair so that they can be augmented later with motion direction, as well as with the magnitude features of each joint in the next frame. A generalized discriminant analysis (GDA) is applied to make the spatiotemporal features more robust, followed by the feeding of the time-sequence features into a Hidden Markov Model (HMM) for the training of each activity. Lastly, all of the trained-activity HMMs are used for depth-video activity recognition.

• The Transactions of the Korea Information Processing Society
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• v.5 no.9
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• pp.2281-2293
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• 1998

Spatial objects in the space II odd hale been changed bl eitber non-spiltial operations or spatial operations. For example, their states arc changed by the following operation: changing their owners, changing their owner's address, installing new constructions, changing precincts, splitting, and merging, The conventional geographic information system(GIS), however, did not also manage their histoncal information cecause it handles the snapshot image of spatial ohjects in the world. In this paper we therelore propose a spatiotemporal data model which is ahle to not un]y manage the historical information of spatial objects but also, support their historical intemlgation by extending a time dimension and a historical pointer for SDE(Spatial Database Engine) spatial data model. Finally, the proposed spatiotemporal data model using a layered time extension are going to contribute to manage the history of spatial objects in the world and retrieve them.

• The Journal of Korean Physical Therapy
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• v.32 no.3
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• pp.185-190
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• 2020

Purpose: Gait and cognitive impairment in stroke patients exacerbate fall risk and mobility difficulties during multi-task walking. Virtual reality can provide interesting and challenging training in a community setting. This study evaluated the effect of community-based virtual reality gait training (VRGT) using a 360-degree image on the gait ability of chronic stroke patients. Methods: Forty-five chronic stroke patients who were admitted to a rehabilitation hospital participated in this study. Patients meeting the selection criteria were randomly divided into a VRGT group (n=23) and a control group (n=22). Both these groups received general rehabilitation. The VRGT group was evaluated using a 360-degree image that was recorded for 50 minutes a day, 5 days per week for a total of 6 weeks after their training. The control group received general treadmill training for the same amount of time as that of the VRGT group. The improvement in the spatiotemporal parameters of gait was evaluated using a gait analyzer system before and after training. Results: The spatiotemporal gait parameters showed significant improvements in both groups compare with the baseline measurements (p<0.05), and the VRGT group showed more improvement than the control group (p<0.05). Conclusion: Community-based VRGT has been shown to improve the walking ability of chronic stroke patients and is expected to be used in rehabilitation of stroke patients in the future.

• Journal of Environmental Impact Assessment
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• v.18 no.3
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• pp.161-170
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• 2009

As urban growth continues, the earth ecosystem is increasingly dependent on the patterns of urban growth. The impact intensity from urban growth is expected to change predictably with distance from the urban center. However we can't fully understand yet how urban development pattern affects urban ecosystem. In researches about urban ecosystem, it is important to relate the spatial pattern of urbanization to ecological processes. So we used gradient analysis with time data; 1980's, 1990's and 2000's. We attempted to quantify the urban spatiotemporal impacts in Daejeon-city and Cheonan-city, Korea, along a 75km long and 3km wide transect. Through the results, we found the impacts range of urbanization with urban development process of two cities. When the urban growth was concentrated on in both cities, the impacts intensity and range were much stronger and wider. As a result, in urban planning or green space planning, we have to consider suitable urban development forms with surrounding areas, and make legal clauses which limits landuse change. This quantifying the urban gradient is an important step in understanding urban ecology.

• Environmental Engineering Research
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• v.25 no.2
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• pp.147-162
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• 2020

The main objective of this study is to estimate the levels of pollution to which the community is presently exposed and to model the regimes of local air quality. Diurnal, daily, and monthly variations of NO, NO₂, SO₂, and O₃ were thoroughly investigated in three areas; namely, residential, industrial, and terminal in Ras Al-Khafji. There is obvious diurnal variation in the concentration of these pollutants that clearly follows the diurnal variation of atmospheric temperature and main anthropogenic and industrial activities. Correlation analysis showed that meteorological conditions play a vital role in shaping the pattern and transportation of air pollutants and photochemical processes affecting O₃ formation and destruction. Bivariate polar plots, an effective graphical tool that utilizes air pollutant concentrations' dependence on wind speed and wind direction, were used to identify prevailing emission sources. Non-buoyant ground-level sources like domestic heating and street transport emissions, various industrial stacks, and airport-related activities were considered dominant emission sources in observatory sites. This study offers valuable and detailed information on the status of air quality, which has considerable, quantifiable, and important public health benefits.

• Journal of the Korea Computer Industry Society
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• v.5 no.1
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• pp.109-122
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• 2004

It is the emergence of multimedia based on information revolution that foretells and develops the largest change in the transfiguration of a modern design. In this paper, the basic principle and characteristics of a new design model is compared and analyzed, and the change of communication based on information form in order to examine the concept and characteristics of digital as today's new media in detail is considered in an changed environment. And also a new theoretical ground is derived about ‘virtual reality’ and ‘interaction’ which is the important characteristics of a digital media so as to compare the property of digital with that of analogue, analyze them and specify the concept and characteristics of digital. Thus the validity of this study is proved by a new interpretation methode of ‘spatiotemporal structure’ on the digital design on the basis of the above mentioned things

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.5
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• pp.327-330
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• 2016

Currently, the world's elderly population continues to grow at a dramatic rate. As the number of senior citizens increases, detection of someone falling has attracted increasing attention for visual surveillance systems. This paper presents a novel fall-detection algorithm using motion estimation and an integrated spatiotemporal energy map of the object region. The proposed method first extracts a human region using a background subtraction method. Next, we applied an optical flow algorithm to estimate motion vectors, and an energy map is generated by accumulating the detected human region for a certain period of time. We can then detect a fall using k-nearest neighbor (kNN) classification with the previously estimated motion information and energy map. The experimental results show that the proposed algorithm can effectively detect someone falling in any direction, including at an angle parallel to the camera's optical axis.

• Journal of Power Electronics
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• v.16 no.2
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• pp.610-620
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• 2016

The compound active clamp zero voltage soft switching (CACZVS) three-phase power factor correction (PFC) converter has many advantages, such as high efficiency, high power factor, bi-directional energy flow, and soft switching of all the switches. Triple closed-loop PI controllers are used for the three-phase power factor correction converter. The control objectives of the converter include a fast transient response, high accuracy, and unity power factor. There are six parameters of the controllers that need to be tuned in order to obtain multi-objective optimization. However, six of the parameters are mutually dependent for the objectives. This is beyond the scope of the traditional experience based PI parameters tuning method. In this paper, an improved chaotic particle swarm optimization (CPSO) method has been proposed to optimize the controller parameters. In the proposed method, multi-dimensional chaotic sequences generated by spatiotemporal chaos map are used as initial particles to get a better initial distribution and to avoid local minimums. Pareto optimal solutions are also used to avoid the weight selection difficulty of the multi-objectives. Simulation and experiment results show the effectiveness and superiority of the proposed method.