• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.19-26
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• 2018

In recent years, the substantial increase in the population's average age leads to an exceeded number of older persons comparing with the number of any other age group. As a result, both industry and academia are focused on the development of several solutions aimed to guarantee a healthy and safe lifestyle to the elderly. Ambient Assisted Living (AAL) approach is the way to guarantee better life conditions for the aged and for monitoring their health conditions by the development of innovative technologies and services. AAL technologies can also provide more safety for the elderly, offering emergency response mechanisms, fall detection solutions, and video surveillance systems. Unfortunately, due to the sensitive nature of AAL data, AAL systems should satisfy security requirements such as integrity, confidentiality, availability, anonymity, and others. In this paper, we propose an adaptive authentication protocol for the AAL systems. The proposed authentication protocol not only supports several important security requirements needed by the AAL systems, but can also withstand various types of attacks. In addition, the security analysis results show that the proposed authentication protocol is more efficient and secure than the existing authentication protocols.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.3220-3226
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• 2011

In order to monitor internal risk factors such as fire, terror, etc. on the subway station, the surveillance systems using CCTV and various kinds of sensors have been implemented and recently, introduction of surveillance systems using an advanced IT technology, sensor network technology is tried on several areas. Since 2007, Korean government has made an effort to develop the intelligent surveillance and monitoring system, which can monitor fire, intrusion, passenger congestion, health-state of structure, etc., by using wireless sensor network technology and intelligent video analytic technique. For that purpose, this study carried out field wireless communication environment test on Chungmuro Station of Seoul Metro on the basis of ZigBee that is considered as a representative wireless sensor network before field application of the intelligent integrated surveillance system being developed, arranged and analyzed and ZigBee based wireless communication environment test results on the platform and waiting room of Chungmuro Station on this paper. Results of wireless spectrum analysis on the platform and waiting room showed that there is no radio frequency overlapped with that of ZigBee based sensor network and no frequency interference with adjacent frequencies separated 10MHz or more. As results of wireless data transmission test using ZigBee showed that data transmission is influenced by multi-path fading effect from the number and flow rate of passengers on the platform or the waiting room rather than effects from entrance and exit of the train to/from the platform, it should be considered when implementing the intelligent integrated surveillance system on the station.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.271-284
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• 2008

Self-healing is an approach to evaluating constraints defined in target system and to applying an appropriate strategy when violating he constrains. Today, the computing environment is very complex, so researches that endow a system with the self-healing's ability that recognizes problem arising in a target system are being an important issues. However, most of the existing researches are that self-healing developers need much effort and time to analyze and model constraints. Thus, this paper proposes an automated approach to determine problem arising in external and internal system environment. The approach proposes: 1) Specifying the target system through the models created in design phase of target system. 2) Automatically creating constraints for external and internal system environment, by using the specified contents. 3) Deriving a dependency model of a component based on the created internal state rule. 4) Translating the constraints and dependency model into code evaluating behaviors of the target system, and determinating problem level. 5) Monitoring an internal and external status of system based on the level of problem determination, and applying self-healing strategy when detecting abnormal state caused in the target system. Through these, we can reduce the efforts of self-healing developers to analyze target system, and heal rapidly not only abnormal behavior of target system regarding external and internal problem, but also failure such as system break down into normal state. To evaluate the proposed approach, through video conference system, we verify an effectiveness of our approach by comparing proposed approach's self-healing activities with those of the existing approach.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.2
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• pp.324-339
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• 2021

In this study, we determined the correlation between the wave characteristics and the change in the area of Haeundae Beach, conducted regression analysis between the wave characteristics and the change in beach area, and derived a formula for calculating the change in beach area. The change in beach area was calculated by applying the derived formula to wave observation data corresponding to a period of approximately 10 months, and the formula was subsequently validated by comparing the obtained results with the observed area. It is found that the error associated with the formula for calculating the change in beach area ranges from 1.5 m to 2.7 m based on the average beach width, and the correlation coefficient corresponding to the observed area ranges from 0.91 to 0.94. Furthermore, it is observed that the change in beach area is af ected by the wave direction in the western zone, wave height in the central zone, and wave height and wave period in the eastern zone. These results can contribute to understanding the impact of a coastal improvement project on the beach area fluctuation characteristics of Haeundae Beach and the ef ectiveness of such a coastal improvement project. By applying the aforementioned derived formula to highly accurate wave prediction data, the change in beach area can be calculated and incorporated for predicting significant long-term changes in beach areas. Furthermore, such a prediction can be considered as the basis for making decisions while establishing preemptive countermeasure policies to prevent coastal erosion.

• Journal of Cadastre & Land InformatiX
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• v.51 no.1
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• pp.53-65
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• 2021

C-ITS(Cooperative Intelligent Transport System) that pursues traffic safety and convenience uses various sensors to generate traffic information. Therefore, it is necessary to improve the sensor-related technology to increase the efficiency and reliability of the traffic information. Recently, the role of CCTV in collecting video information has become more important due to advances in AI(Artificial Intelligence) technology. In this study, we propose to identify and track dynamic objects(vehicles, people, etc.) in CCTV images, and to analyze and provide information about them in various environments. To this end, we conducted identification and tracking of dynamic objects using the Yolov4 and Deepsort algorithms, establishment of real-time multi-user support servers based on Kafka, defining transformation matrices between images and spatial coordinate systems, and map-based dynamic object visualization. In addition, a positional consistency evaluation was performed to confirm its usefulness. Through the proposed scheme, we confirmed that CCTVs can serve as important sensors to provide relevant information by analyzing road conditions in real time in terms of road infrastructure beyond a simple monitoring role.

• Journal of Apiculture
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• v.34 no.1
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• pp.47-55
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• 2019

Honey bees are a vital part of the food chain as the most important pollinators for a broad palette of crops and wild plants. The climate change and colony collapse disorder (CCD) phenomenon make it challenging to develop ICT solutions to predict changes in beehive and alert about potential threats. In this paper, we report the test results of the bee-counting system which stands out against the previous analogues due to its comprehensive components including an improved dual infrared sensor to detect honey bees entering and leaving the hive, environmental sensors that measure ambient and interior, a wireless network with the bluetooth low energy (BLE) to transmit the sensing data in real time to the gateway, and a cloud which accumulate and analyze data. To assess the system accuracy, 3 persons manually counted the outgoing and incoming honey bees using the video record of 360-minute length. The difference between automatic and manual measurements for outgoing and incoming scores were 3.98% and 4.43% respectively. These differences are relatively lower than previous analogues, which inspires a vision that the tested system is a good candidate to use in precise apicultural industry, scientific research and education.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.498-509
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• 2023

Purpose: This paper proposes a method detecting the falling of a maintenance worker in the underground utility tunnel, by applying deep learning techniques using CCTV video, and evaluates the applicability of the proposed method to the worker monitoring of the utility tunnel. Method: Each rule was designed to detect the falling of a maintenance worker by using the inference results from pre-trained YOLOv5 and OpenPose models, respectively. The rules were then integrally applied to detect worker falls within the utility tunnel. Result: Although the worker presence and falling were detected by the proposed model, the inference results were dependent on both the distance between the worker and CCTV and the falling direction of the worker. Additionally, the falling detection system using YOLOv5 shows superior performance, due to its lower dependence on distance and fall direction, compared to the OpenPose-based. Consequently, results from the fall detection using the integrated dual deep learning model were dependent on the YOLOv5 detection performance. Conclusion: The proposed hybrid model shows detecting an abnormal worker in the utility tunnel but the improvement of the model was meaningless compared to the single model based YOLOv5 due to severe differences in detection performance between each deep learning model

• Nutrition Research and Practice
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• v.17 no.4
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• pp.717-734
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• 2023

BACKGROUND/OBJECTIVES: This study aimed to identify preschool children's eating behaviors associated with early childhood obesity and its multi-level, socio-ecological determinants. SUBJECTS/METHODS: In a cross-sectional study of 364 mothers of preschool children aged 3-5 years, these children's healthy eating behaviors were assessed using a validated preschool nutrition quotient (NQ-P) questionnaire. The children's overweight or obesity statuses were determined based on body mass index percentiles from the 2017 Korean National Growth Chart. The associations between the NQ-P score and risk of overweight or obesity were examined using multivariable logistic regression. The associations of individual, maternal, physical, and media environmental factors with the NQ-P score were also examined using multivariable linear regression. RESULTS: Preschool children with greater NQ-P scores were at a significantly lower risk of overweight or obesity (P < 0.01). The NQ-P score had a significantly positive association with maternal body mass index and an inverse association with household income (all P < 0.05). Maternal parenting and feeding practices exhibited associations with the NQ-P score. Positive associations were observed with "warm," "structured," and "autonomy-supportive" parenting as well as monitoring feeding practices (all P < 0.05). In addition, the NQ-P score had a significantly positive association with the childcare center's anti-obesogenic environment, such as the provision of nutritional and physical-activity support and vicinity of the built food environment to the home, including access to good-quality food, fruits and vegetables, and low-fat foods (all P < 0.05). Regarding media environments, the NQ-P score demonstrated more significant associations with viewing and eating and/or cooking content displayed on online video platforms (all P < 0.05) than with that on television. CONCLUSIONS: Our findings confirm the significance of healthy eating behaviors in early-childhood-obesity prevention and underscore the importance of multilevel maternal, physical, and media environmental interventions that effectively guide eating behaviors in preschool children.

• Journal of the Korean Geosynthetics Society
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• v.23 no.2
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• pp.43-52
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• 2024

Recent abnormal climate conditions have increased the risk of slope collapses, which frequently result in significant loss of life and property due to the absence of early prediction and warning dissemination. In this paper, we develop a slope condition analysis system using IoT sensors and AI-based camera to assess the condition of slopes. To develop the system, we conducted hardware and firmware design for measurement sensors considering the ground conditions of slopes, designed AI-based image analysis algorithms, and developed prediction and warning solutions and systems. We aimed to minimize errors in sensor data through the integration of IoT sensor data and AI camera image analysis, ultimately enhancing the reliability of the data. Additionally, we evaluated the accuracy (reliability) by applying it to actual slopes. As a result, sensor measurement errors were maintained within 0.1°, and the data transmission rate exceeded 95%. Moreover, the AI-based image analysis system demonstrated nighttime partial recognition rates of over 99%, indicating excellent performance even in low-light conditions. Through this research, it is anticipated that the analysis of slope conditions and smart maintenance management in various fields of Social Overhead Capital (SOC) facilities can be applied.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.4 no.3 s.8
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• pp.33-43
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• 2005

Traffic condition monitoring system serves as the foundation for all intelligent transportation system operation. Loop detectors and Video Image Processing are the most widely common technology approach to condition monitoring in korea Highways. Lane Usage is defined as the proportion of total link volume served by each lane. In this research, the lane Usage(LU) of two lane link for one day. Interval is 56% : 44%. The LU of three lane link is 39% : 37% : 24%. The LU of four lane link is 25% : 29% : 26% : 21%. These analysis reveal that each lane distributions of link are not same. This research investigates the general concept of lane usage by using collected loop detector data and the investigated that lane distribution is different by traffic lane and lane usage is consistent by time of day.