• Journal of Korean Society of Disaster and Security
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• v.12 no.2
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• pp.47-56
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• 2019

Recently, in order to overcome the difficulty of entering a fire source due to the occurrence of a large amount of smoke in the event of a fire in a parking lot, it has used that a method of discharge smoke using air supply, exhaust fans and jet fans installed for ventilation of parking lots. In this study, the variation of flow in the smoke layer was observed using CCTV under two conditions, in which only the air supply fan operates and the manned fan operates together, and the temperature around the plume was compared to Albert eq. to assess its suitability as a parking lot ventilation performance evaluation method. As a result, it was found that the smoke layer could be disturbed if the Jet Fan was operated at the same time, which could lead to the possibility of an initial evacuation disturbance. However, the additional operation of the Jet Fan has been confirmed by the observation CCTV that the emission performance is improved, which is believed to help conduct the suppression operation. The temperature around the plume was measured and compared to Alpert eq, and was analyzed to be about $2^{\circ}C$ lower at the center axis of the plume and $9.0^{\circ}C$ higher at 8 m in the direction of the discharge of smoke. The results of temperature measurements around the plume were lower than the maximum temperature expected in AS 4391 and did not exceed the expected temperature risk caused by the experiment. As with these results, the temperature risk from the progression of hot smoke tests is foreseeable, so it will be available as one of the general evaluation methods for assessing smoke control performance in a parking lot without relevant criteria.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1266-1271
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• 2022

Cats have strong wildness so they have a characteristic of hiding diseases well. The disease may have already worsened when the guardian finds out that the cat has a disease. It will be of great help in treating the cat's disease if the owner can recognize the cat's polydipsia, polyuria, and frequent urination more quickly. In this paper, 1) Efficient version of DeepLabCut for pose estimation, 2) YOLO v4 for object detection, 3) LSTM is used for behavior prediction, and 4) BoT-SORT is used for object tracking running on an artificial intelligence device. Using artificial intelligence technology, it predicts the cat's next, polyuria and frequency of urination through the analysis of the cat's behavior pattern from the home CCTV video and the weight sensor of the water bowl. And, through analysis of cat behavior patterns, we propose an application that reports disease prediction and abnormal behavior to the guardian and delivers it to the guardian's mobile and the server system.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1245-1254
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• 2020

In this study, the limitations of the existing water level detection algorithm using CCTV images were recognized and the water level detection algorithm was improved by applying the Region growing technique. It applied three techniques (Horizontal projection profile, Texture analysis, and Optical flow) to estimate the water area, and the results were analyzed in a comprehensive analysis to select the initial water area. The water level was then continuously detected by the Region growing technique, referring to the initial water area. As a result, it was possible to confirm that the exact level of water was detected without being affected by environmental factors compared to the existing level detection algorithm, which had frequent mis-detection phenomena depending on the surrounding environmental factors. In addition, the water level was detected in the video showing flooded roads in urban areas, not in the video of the river. These results are believed to be able to supplement the difficulty of monitoring at all times with limited manpower by automatically detecting the level of water through numerous CCTV footage installed throughout the country, and to contribute to laying the foundation for preventing disasters caused by torrential rains and typhoons in advance.

• Journal of the Korean Society of Costume
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• v.60 no.9
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• pp.26-40
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• 2010

The purpose of this study is the Tibetan folk costumes'characteristics and change of design in stage. The subjects are the Tibetan costumes in the Spring Festival Gala of CCTV. The basic characteristics of Tibetan folk costumes are fat waist, long sleeves, overlap, and right ren. Tibetan costumes consist of Tibetan gowns, aprons, shirts, belts, Tibetan hats, headgear, ornaments, all of these compose the traditional image of the Tibetan people. Because of the long-term closed survival, the development of Tibetan costumes has no much vertical differences and changes. The costumes of Weizang(衛藏), Ali(阿里), Gongbu(工布), Kham(康巴), Amdo(安多) have different features which are divided according to different dialects. Study the changes of design Tibetan stage costumes from 25times, we can get that during the 1986~1992, people emphasis on the activities of dancers, styles are simple, just to grasp the characteristics of the Tibetan costumes. In the 1993~1999, the dancers put on the real-life Tibetan clothing on stages. By the 2000s to now, Tibetan stage costumes have much more changes in forms and colors which are more complete and complex. The types of Tibetan costumes include Tibetan all clothing, they all express the changed forms and colors without exception. So in this period are artistic Tibetan folk costumes.

• 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

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.790-792
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• 2013

정보화 시대가 빠르게 발전함에 따라 스마트 그리드 및 CCTV등 유 무선 통신망을 이용한 다양한 분야의 서비스가 이루어지고 있다. 이러한 서비스를 제공하는데 있어서 민감한 내용을 포함한 정보가 존재한다면 데이터에 대한 보안은 중요한 요소 일 것이다. 현재 보안기능이 탑재되어 있지 않은 장치에 보안 기능을 탑재하기 위해서는 하드웨어적인 요소를 추가 혹은 교체하거나 소프트웨어 또는 펌웨어 업데이트 방식을 선택하여 보안 기능을 추가할 수 있다. 본 논문에서는 소프트웨어 업데이트를 통하여 보안 기능을 제공하는 방식에 대해서 설명하며 범용적인 x86아키텍처와 ARM9아키텍처를 비교 분석하여 아키텍처 환경별 암호모듈 적용방안을 제시하고자 한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.9-13
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• 2019

In this paper, IoT based thermal sensor data and image sensor integrated environmental monitoring system for ship, and it is the monitoring system which can process and transmit the Full HD IP camera image and thermal data transmitted from the thermal module for processing and transmitting, and the viewer S/W is to be developed which provides in real time the information for actual surrounding temperature together with the image, and enables fire prediction which was impossible in the case of the existing equipment by estimating the temperature change as the thermal image is added to the image camera, and saves and analyzes all data while receiving the temperature data and image signal transmitted from the integrated thermal sensor environmental monitoring equipment for ship and displaying them as 2D on the monitoring system.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.70-77
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• 2017

Recently, Internet of Things (IoT) and deep learning techniques have affected video surveillance systems in various ways. The surveillance features that perform detection, tracking, and classification of specific objects in Closed Circuit Television (CCTV) video are becoming more intelligent. This paper presents real-time algorithm that can run in a PC environment using only a low power CPU. Traditional tracking algorithms combine background modeling using the Gaussian Mixture Model (GMM), Hungarian algorithm, and a Kalman filter; they have relatively low complexity but high detection errors. To supplement this, deep learning technology was used, which can be trained from a large amounts of data. In particular, an SRGB(Sequential RGB)-3 Layer CNN was used on tracked objects to emphasize the features of moving people. Performance evaluation comparing the proposed algorithm with existing ones using HOG and SVM showed move-in and move-out error rate reductions by 7.6 % and 9.0 %, respectively.

• Smart Media Journal
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• v.9 no.3
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• pp.122-129
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• 2020

The possibility that an asymptotic coronavirus-19 infected person around the world is not aware of his infection and can spread it to people around him is still a very important issue in that the public is not free from anxiety and fear over the spread of the epidemic. In this paper, the K-means clustering algorithm and deep learning-based crowd aggregation were proposed to determine the possibility of contact with confirmed cases of infectious diseases. As a result of 300 iterations of all input learning images, the PSNR value was 21.51, and the final MAE value for the entire data set was 67.984. This means the average absolute error between observations and the average absolute error of fewer than 4,000 people in each CCTV scene, including the calculation of the distance and infection rate from the confirmed patient and the surrounding persons, the net group of potential patient movements, and the prediction of the infection rate.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.6
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• pp.561-566
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• 2016

A subway platform is equipped with screen doors in oder to avoid accidents of passengers, where Area sensors are installed for detecting obstacles in the screen doors. However, there exist frequent operating errors in screen doors due to dusts, sunlight, snow, and bugs. It is required to develope a detection device which reduces errors and elaborates detection function. In this paper, we compared the detection rates of the Area sensor the 3D sensor using CCTV-based image data with installing sensors at the screen door in Munyang station Daegu, where 3D sensor is applied with the space division multiple detection algorithms. It is measured that the detection rate of 3D sensor and Area sensor is approximately 89.61% and 78.88%, respectively. The results confirmed that 3D senor has higher detection rate compared with Area sensor with the rate of 6.87~9.79%, and 3D sensor has benefit in the aspect of installation fee.