• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.75-84
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• 2009

In this paper, the EPC Network as the most important technologies in the field of applied technology research and special attention to the use of RFID to better manage the disease in infected U-MAS (U-Medical Administrative Services) system was designed. U-MAS system, the Center for Disease Control in the illness, depending on the type of isolate and treat infected patients, recovery, discharge, isolation wards and intensive italian to manage and increase efficiency, manual and use a simple computer program improve the qualify of the current level, using RFID tags to improve the management of the patient everything that a little more and be out of the isolation ward, if competent disease management districts, such as the location to respond more quickly to facilitate the purpose is to contribute to. First, EPC Network and related technology for mobile RFID systems and related technology research. U-MAS system design offers. If you take advantage of the proposed U-MAS system for monitoring infectious disease patients and patients in the isolation ward, when the unauthorized departure location to shorten the time it takes to improve the effectiveness of disease management and present the elected effects was.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.3
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• pp.611-616
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• 2020

In order to prevent the further spread of the COVID-19 virus in big cities, this paper proposes a semantic diagnosis and tracking system based on Linked Data through the cluster analysis of the infection situation in Seoul, South Korea. This paper is mainly composed of three sections, information of infected people in Seoul is collected for the cluster analysis, important infected patient attributes are extracted to establish a diagnostic model based on random forest, and a tracking system based on Linked Data is designed and implemented. Experimental results show that the accuracy of our diagnostic model is more than 80%. Moreover, our tracking system is more flexible and open than existing systems and supports semantic queries.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.545-554
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• 2023

In modern industry, the Global Navigation Satellite System(GNSS) is utilized in various fields, where PNT information (P: Positioning, N: Navigation, T: Timing) is always provided and the accurate location estimation based on PNT information is required. In particular, in order to prevent the infection and the spread of the COVID-19 pandemic situation that began in 2019, the precise GNSS technology and various supporting techniques have been used, and, with active quarantine and efforts for the infection spread restrain around the world, we are facing the transition to an endemic situation. In fields of disease and E-health, the location information of users is absolutely necessary to track and monitor infectionous diseases and provide remote medical services, and GNSS plays a leading role in providing the accurate location information. This paper presents investigation results on the up-to-date research trends in which GNSS technologies are employed in the field of disease and E-health, and analyzes the results.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.186-198
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• 2016

HPAI(Highly Pathogenic Avian Influenza) is a contagious animal disease that spreads rapidly by diffusion after the first occurrence. The disease has brought tremendous social costs and economic losses. KAHIS (Korea Animal Health Information System) is the integrated system for supporting the task of preventing epidemics. They provide decision-support information, recording vehicle visiting times and facility location, etc., which is possible by enforcing registration of all livestock related facilities and vehicles. KAHIS has accumulated spatial and temporal information that enables effective tracing of potential disease trajectories and diffusion through vehicle movements. The contact network is created utilizing spatial and temporal information in KAHIS to inform facility connection via vehicle visitation. Based on the contact network, it is possible to infer spatial and temporal mechanism of disease spread and diffusion. The study objective is to empirically demonstrate how to utilize primary spatial and temporal information in KAHIS in the form of the contact network. Based on the contact network, facilities with the possibility of infection can be pinpointed within the potential spatial and temporal extent where the disease has spread and diffused. This aids the decision-making process in the task of preventing epidemics. By interpreting our demonstration results, policy implications were presented. Finally, some suggestions were made to comprehensively utilize the contact network to draw enhanced decision-support information.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.688-690
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• 2003

본 논문에서는 다해상도 2차원 실루엣 영상들을 이용하여 3차원 모델간의 형상 유사성을 비교하기 위한 방법을 제안한다. 제안 시스템은 포즈 정규화 모듈, 유사성 계산 모듈, 3차원 시각화 모듈로 구성된다. 형상 비교를 위해서 먼저, 3차원 인체 장기 모델을 입력으로 받아서 정규화를 수행하고, 다해상도 깊이맵을 획득한다. 이어서 유사성 비교를 위해 실루엣 영상을 추출한 후, 유사도 측정을 위해 시그니쳐를 측도로 사용한다. 최종적으로 계산된 결과들은 3차원 글리프 및 컬러 코딩을 이용하여 시각화된다. 본 논문에서 제시한 3차원 형상 비교 시스템은 전처리 단계에서의 정규화 수행을 통하여 스케일 및 회전 변환에 불변하는 특성을 보인다. 그리고 다양한 레벨의 깊이맵을 형상 비교에 사용하여 다해상도 기반의 유사성 평가를 지원하며, 평가 계산 속도와 정확성간의 유연성을 제공한다. 또한 3차원 히스토그램. 3차윈 글리프. 컬러 코딩 시각화 기법들과 2차원 실루엣 피킹 인터페이스를 통하여 인체 장기 모델간의 정량적 형상 차이를 사용자가 직관적으로 평가할 수 있도록 한다. 본 시스템은 차후 데이터베이스를 이용한 원격 진료 시스템에서의 질병 진단, 추적 관찰. 치료계획 등에 활용될 수 있을 것이다.

• Journal of the Korea Society for Simulation
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• v.25 no.1
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• pp.29-34
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• 2016

Many researchers have used cohort DB (database) to predict the occurrence of disease or to keep track of disease spread. Cohort DB is Big Data which has simply stored disease and health information as separated DB table sets. To measure the relations between health information, It is necessary to reconstruct cohort DB which follows research purpose. In this paper, XML descriptor, editor has been used to construct ontology-based Big Data cohort DB. Also, we have developed ontology based cohort DB search system to check results of relations between health information. XML editor has used 7 layered Ontology development 101 and OWL API to change cohort DB into ontology-based. Ontology-based cohort DB system can measure the relation of disease and health information and can be used effectively when semantic relations are found. We have developed ontology-based cohort DB search system which can measure the relations between disease and health information. And it is very effective when searched results are semantic relations.

• Journal of Platform Technology
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• v.11 no.4
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• pp.3-12
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• 2023

In the post-COVID era, the importance of quarantine measures is greatly emphasized, and accordingly, research related to the detection of mask wearing conditions and prevention of other infectious diseases using deep learning is being conducted. However, research on the detection and tracking of visitors to cultural facilities to prevent the spread of diseases is equally important, so research on this should be conducted. In this paper, a convolutional neural network-based object detection model is trained through transfer learning using a pre-collected dataset. The weights of the trained detection model are then applied to a multi-object tracking model to monitor visitors. The visitor detection model demonstrates results with a precision of 96.3%, recall of 85.2%, and an F1-score of 90.4%. Quantitative results of the tracking model include a MOTA (Multiple Object Tracking Accuracy) of 65.6%, IDF1 (ID F1 Score) of 68.3%, and HOTA (Higher Order Tracking Accuracy) of 57.2%. Furthermore, a qualitative comparison with other multi-object tracking models showcased superior results for the model proposed in this paper. The research of this paper can be applied to the hygiene systems within cultural facilities in the post-COVID era.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.959-968
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• 2019

In the agricultural industry, wireless sensor network technology has being applied by utilizing various sensors and embedded systems. In particular, a lot of researches are being conducted to diagnose diseases of crops early by using sensor network. There are some difficulties on traditional research how to diagnose crop diseases is not practical for agriculture. This paper proposes the algorithm which enables to investigate and analyze the crop leaf image taken by image camera and detect the infected area within the image. We applied the enhanced k-means clustering method to the images captured at horticulture facility and categorized the areas in the image. Then we used the edge detection and edge tracking scheme to decide whether the extracted areas are located in inside of leaf or not. The performance was evaluated using the images capturing tomato leaves. The results of performance evaluation shows that the proposed algorithm outperforms the traditional algorithms in terms of classification capability.

• Progress in Medical Physics
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• v.26 no.1
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• pp.18-27
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• 2015

Since a Compton camera has high detection sensitivity due to electronic collimation and a good energy resolution, it is a potential imaging system for nuclear medicine. In this study, we investigated the feasibility of a Compton camera for multi-tracer imaging and proposed a rotating Compton camera to satisfy Orlov's condition for 3D imaging. Two software phantoms of 140 and 511 keV radiation sources were used for Monte-Carlo simulation and then the simulation data were reconstructed by listmode ordered subset expectation maximization to evaluate the capability of multi-tracer imaging in a Compton camera. And the Compton camera rotating around the object was proposed and tested with different rotation angle steps for improving the limited coverage of the fixed conventional Compton camera over the field-of-view in terms of histogram of angles in spherical coordinates. The simulation data showed the separate 140 and 511 keV images from simultaneous multi-tracer detection in both 2D and 3D imaging and the number of valid projection lines on the conical surfaces was inversely proportional to the decrease of rotation angle. Considering computation load and proper number of projection lines on the conical surface, the rotation angle of 30 degree was sufficient for 3D imaging of the Compton camera in terms of 26 min of computation time and 5 million of detected event number and the increased detection time can be solved with multiple Compton camera system. The Compton camera proposed in this study can be effective system for multi-tracer imaging and is a potential system for development of various disease diagnosis and therapy approaches.

• 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.