• Journal of the Korean Institute of Landscape Architecture
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• v.32 no.2
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• pp.11-24
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• 2004

The purpose of this study is to present data on the human activities responding to the physical environment of ‘Ulsan’s Grand Parks by evaluating user behavior and activity, visiting motivations, and user satisfaction. This study was conducted using multi-methods such as interviews and questionnaires surveys. The main findings of this study can be summarized in six parts as follows: \circled1 Behavior patterns showed that the users visited the park left within 30 minutes and the frequency of visits was 1 or 2 times per week. They spent their time mostly on ‘walking’ and ‘picnicking’. The users visited regardless of the seasons or the day of the week. \circled2 The priorities for improvements were analyzed as follows: the users expressed their demands for ‘shadowing facilities’ like shelters and pergolas because they used these facilities frequently. Also, the users would like an ‘event program’, ‘sign system’ and ‘guide program’. \circled3 Analysis of the the user’s perception of the park showed that they perceive the park as ‘representative source of the landscape and open space in Ulsan and place for making contact with nature. \circled4 In examining the visiting motivations of the users of Ulsan Grand Park, it was revealed that people use the park for ‘time with family and friends’, ‘to escape from city life’, ‘to relieve fatigue. As a result of factor analysis, 4 factors were identified such as ‘physical motive(MF1)’, ‘exploratory motive(MF2)’, ‘social motive(MF3)’ and ‘emotional motive(MF4). \circled5 Park users’ evaluation for park facilities showed that people are satisfied with most of the facilities and especially, they have high level of satisfaction for ‘footpaths’, ‘squares’ and ‘picnicspace’. The evaluation of the park user’s of activity reveals that they are content with nearly all the variables. Especially, they have high level of satisfaction for the variables of ‘convenience for dynamic activities’, ‘making of a beautiful atmosphere, ‘accessibility from the outside’ and, ‘convenience in group activitie. Factor analysis of the park user’s of activity revealed 5 factors such as ‘convenience and interest factor (AF1)’, ‘park maintenance, management and use program(AF2)’, ‘visual beauty(AF3)’, ‘safety and accessibility(AF4)’ and ‘crowding(AF5)’. \circled6 Regression analysis was employed to get the predictor factors of overall satisfaction with a result of 60.0%($R^2$). The variance was explained as ‘quality of the picnic space’, ‘convenience and interest factor while using the park’, ‘park program for maintain and management in the park’, ‘visually beauty while using the park’, ‘safety and accessibility of the parks’, ‘quality of the pond’, ‘crowding’, ‘quality of the square’.

• Journal of IKEEE
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• v.23 no.1
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• pp.112-119
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• 2019

In this paper, we propose a deep learning structure suitable for embedded system. The flame detection process of the proposed deep learning structure consists of four steps : flame area detection using flame color model, flame image classification using deep learning structure for flame color specialization, $N{\times}N$ cell separation in detected flame area, flame image classification using deep learning structure for flame shape specialization. First, only the color of the flame is extracted from the input image and then labeled to detect the flame area. Second, area of flame detected is the input of a deep learning structure specialized in flame color and is classified as flame image only if the probability of flame class at the output is greater than 75%. Third, divide the detected flame region of the images classified as flame images less than 75% in the preceding section into $N{\times}N$ units. Fourthly, small cells divided into $N{\times}N$ units are inserted into the input of a deep learning structure specialized to the shape of the flame and each cell is judged to be flame proof and classified as flame images if more than 50% of cells are classified as flame images. To verify the effectiveness of the proposed deep learning structure, we experimented with a flame database of ImageNet. Experimental results show that the proposed deep learning structure has an average resource occupancy rate of 29.86% and an 8 second fast flame detection time. The flame detection rate averaged 0.95% lower compared to the existing deep learning structure, but this was the result of light construction of the deep learning structure for application to embedded systems. Therefore, the deep learning structure for flame detection proposed in this paper has been proved suitable for the application of embedded system.

• Therapeutic Science for Rehabilitation
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• v.11 no.2
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• pp.77-91
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• 2022

Objective : This study aimed to assess the effects of occupation-based lifestyle intervention programs on older adults in the local community. Methods : Nine community-dwelling older individuals participated in this study using a one-group pre-post design. The occupation-based lifestyle intervention program consisted of 12 group sessions, and one individual session was conducted for seven weeks. Occupational balance, activity occupancy, activity participation, depression, health-related quality of life, and program satisfaction were assessed. Results : The average attendance rate of the nine participants was 10.11 (SD=1.36). Overall occupational balance (p=.012), activity participation status (p=.008), performance (p=.012), and satisfaction with activity participation (p=.008) were increased. Furthermore, the results showed changes in leisure time (p=.008) and rest time (p=.008). Finally, there were some improvements in the overall health-related quality of life (p=.034) and depression scores (p=.012). Conclusions : Occupation-based lifestyle intervention programs positively affected occupational balance, activity occupancy, activity participation, depression, and health-related quality of life in community-dwelling older adults. This research suggests promising benefits and feasibility of the program for community-dwelling older adults.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.2
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• pp.34-44
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• 2007

Many urban cities deployed ITS technologies to improve the efficiency of traffic operation and management including a real-time franc control system (i.e., COSMOS). The system adopted loop detector system to collect traffic information such as volume, occupancy time, degree of saturation, and queue length. This paper investigated the applicability of detector information within COSMOS to represent the congestion level of the links. Initially, link travel times obtained from the field study were related with each of detector information. Results showed that queue length was highly correlated with link travel time, and direct link travel time estimation using the spot speed data produced high estimation error rates. From this analysis, a procedure was proposed to estimate congestion level of the links using both degree of saturation and queue length information.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.191-198
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• 2024

In this paper, we proposes a user management system for efficient operation of the combat system within naval ship. Recently, naval ships have seen performance enhancements through various sensors, features, and continuous system development. This progress in the system has led to an increase in multi-funstion consoles that can manipulate various sensors and features within naval ship, consequently increasing the number of operators for these consoles. Therefore, a user management system that can control and manage multi-function consoles and operators in real-time is necessary for efficient management within naval ship. This paper suggests a user management system that can effectively manage the real-time situation of users accessing multi-function consoles. Additionally, a parallelization method using GPUs to reduce the CPU workload in operating various functions of the combat system is proposed. The proposed user management system has shown a performance improvement where the response time decreased by approximately 82% and the occupancy reduced by approximately 20% compared to the method using CPUs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.8
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• pp.679-686
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• 2013

Power efficiency becomes more important in wireless LANs as the mobile stations send more data with limited batteries. It has been known that the IEEE 802.11 PSM is not efficient in high load networks: AP cannot deliver buffered packets to a PS station immediately and it can lead the station to stay in active state quite long and result in energy waste. Moreover, it is inefficient that only one data frame is retrieved by a PS-POLL frame. In this paper, we propose a time slotted scheme to enhance the PSM, in which a mobile station can reserve time slots to receive data frames. Our mechanism can reduce collisions by reservation and decrease the channel occupancy by transmitting multiple data frames via one PS-POLL. The analytic model and simulation results show that proposed scheme reduces power consumption significantly and enhances the performance of PSM.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.29-38
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• 2018

This study aims to propose a method for the estimation of operational delay cost using transportation card data in bus accidents. Average operational delay time from bus accidents was surveyed among 12 bus companies through an interview method. Then, the operational delay cost was estimated using actual traffic accident data and transportation card data. Results showed that average loss time per bus accident was found to be 45 minutes. In addition, total occupancy of 659 was estimated for the accidents investigated using transportation card data, resulting a total loss time of 494.25 hours. An estimated operational delay cost was 186.9 thousand won per accident, which was 6.37% of social agency cost. The magnitude of this number implied that operational delay cost may have a significant impact on traffic accident cost if included.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.85-94
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• 2004

Loop detection systems have been used in real-time signal control system to collect traffic information for estimating queue lengths. The queue length algorithm uses speed as a key variable estimated from occupancy time and average vehicle length. The measurement of average vehicle length is affected from the lengths of feeder cable, but their effects have not yet been evaluated. In this study, the variability of average vehicle length due to the lengths of feeder cable is assessed through a field study, and a practical guidelines is proposed. By applying this result, the operational performance of real-time signal control system could be improved.

• International Journal of Highway Engineering
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• v.20 no.4
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• pp.47-58
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• 2018

PURPOSES : Seoul introduced public bicycles to reduce environmental pollution and create a healthy society. Because the use of bicycles is highly weather dependent, and bicycles are rented by the people, member characteristics and seasonal influences should be considered. This study analyzed bicycle traffic characteristics considering seasonal and member characteristics and highlighted some implications. METHODS : The Yeouido and Sangam districts, which have multiple business districts, were taken as the areas of interest. In order to reflect seasonal and membership characteristics, the traffic volume, time of use, and characteristics of each zone were categorized by season (spring, summer, autumn, winter) and membership type (season, daily, group). In addition, we analyzed the pattern of traffic volume and usage time according to the traffic purpose after separating rental locations into residential, business, subway, and park, reflecting the land characteristics. RESULTS : The results revealed that seasonal characteristics were high for bicycle traffic, time of use, and occupancy rate for park locations in spring and autumn. In terms of membership characteristics, group and daily users appeared as major visitors for park locations, and the trends of commuter pass users showed that bicycle use meets the purpose of introducing public bicycles. CONCLUSIONS : Traffic characteristics differed according to seasonal and membership characteristics. It is necessary to involve and extend the users of the commuter pass. Situations in which commuter pass users cannot function as a group or in which daily users monopolize bicycles (especially near parks, near subway stations, etc.) must be avoided.