• 전기학회논문지P
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• 제62권1호
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• pp.45-49
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• 2013

High frequency system has been used for the purpose of skin care and obesity treatment, by high-frequency energy is applied to the human body generates deep heat. Conventional high frequency system could not selection control by depending on the body load fluctuations. Such as burns and side effects have been reported by system instability and then therapeutic effect is insufficient. During treatment, objective information about the status of the patient was no. Because of treatment methods are subjective, and so tailored treatments were impossible. In this paper, high frequency medical system with sinusoidal frequency characteristics without distortion of the Push pull switching scheme for pain relief therapy was designed. And control circuit that was designed by feedback using the output changes according to the body-load fluctuation. Last, power circuit for efficient control the heat generated from the hardware was proposed.

• Journal of information and communication convergence engineering
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• 제16권2호
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• pp.130-134
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• 2018

Wireless capsule endoscopy (WCE) is considered as recent technology for the detection cancer cells in the human digestive system. WCE sends the captured information from inside the body to a sensor on the skin surface through a wireless medium. In WCE, the design of low-power consumption devices is a challenging topic. In the Shannon-Nyquist sampling theorem, the number of samples should be at least twice the highest transmission frequency to reconstruct precise signals. The number of samples is proportional to the power consumption in wireless communication. This paper proposes compressive sensing as a method to reduce power consumption in WCE, by means of a trade-off between samples and reconstruction accuracy. The proposed scheme is validated under channel constraints, expressed as the realistic human body path loss. The results show that the proposed scheme achieves a significant reduction in WCE power consumption and achieves a faster computation time with low signal error reconstruction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4385-4399
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• 2021

The Internet of Things (IoT) eHealth systems composed by Wireless Body Area Network (WBAN) has emerged recently. Sensor nodes are placed around or in the human body to collect physiological data. WBAN has many different applications, for instance health monitoring. Since the limitation of the size of the battery, besides speed, reliability, and accuracy; design of WBAN protocols should consider the energy efficiency and time delay. To solve these problems, this paper adopt the end-edge-cloud orchestrated network architecture and propose a transmission based on reinforcement algorithm. The priority of sensing data is classified according to certain application. System utility function is modeled according to the channel factors, the energy utility, and successful transmission conditions. The optimization problem is mapped to Q-learning model. Following this online power control protocol, the energy level of both the senor to coordinator, and coordinator to edge server can be modified according to the current channel condition. The network performance is evaluated by simulation. The results show that the proposed power control protocol has higher system energy efficiency, delivery ratio, and throughput.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2008년도 춘계종합학술대회 A
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• pp.401-404
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• 2008

인간과 마찬가지로 가축도 체온의 변화로부터 질병의 유무를 판별할 수 있는 정보를 제공한다. 그 중 소의 경우, 유열, 중독, 설사, 식체, 만성장염, 감기, 폐렴, 탄저병 등의 질병을 예측 할 수 있다. 따라서 주기적으로 체온을 측정하고 분석함으로써 질병을 조기 발견하고 빠르게 대처하여 손실을 최소화 할 수 있는 가축용 체온 측정 시스템을 제안한다. 또한 이 시스템은 주기적인 자동 측정과 위험 상태 알림 기능을 갖추고 있으므로 줄어드는 인력에 대한 대안과 가축의 품질 관리에 유용하게 사용될 수 있을 것이다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.1305-1308
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• 2005

Recently, the basic research which is supported by engineering has been studied in order to develop oriental medicine scientifically. However, the research only has been limited to quantization, visualization and generalization of biological signal. In this paper, we studied about the effects to 12 Kyungmaks of Body based on meridian theory in oriental medicine after Taichi practice. we measured the heating time on the key measuring point of the meridian of the human body's left and right by using heating machine. After taichi practice than other stimulation, experimental results showed that disharmony state of meridian had changed to harmony state more quickly.

• 한국멀티미디어학회논문지
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• 제19권9호
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• pp.1690-1697
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• 2016

In a beacon-enabled Zigbee network, the slotted CSMA/CA mechanism based on the super frame structure fairly provides communication chance for each node and makes a reasonable usage of the available energy. In the case of wireless nano sensors that are implanted into the target human body area for detecting disease symptoms or virus, such a nano-network requires a similar type of channel sharing and transmission of short length event-driven data. In this paper, for nano-network's in-body applications, we aim to design conceptually a new super frame derived from the existing beacon-enabled Zigbee MAC protocol. And we analyze the efficiency of the proposed super frame in the aspect of practical deployment.

• 한국정보통신학회논문지
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• 제25권12호
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• pp.1964-1967
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• 2021

Recently, to improve the effectiveness of education to learn from textbooks, immersive 3D environments such as virtual reality(VR) has been widely used for education. In this paper, in order to intuitively present education about content scenarios on changes in the human body according to food intake, we consist an immersive virtual reality environment to express the same life-size organs. The participants in our educational system showed higher results in all items compared to the existing textbook-based education such as immersion, understanding, and quality of education program. Also it was found the importance of interactivity to increase the effectiveness of immersive class.

• 한국전자통신학회논문지
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• 제17권1호
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• pp.111-118
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• 2022

컴퓨터 비전 연구에서 2차원 인체 자세는 매우 광범위한 연구 방향으로 특히 자세 추적과 행동 인식에서 유의미한 분야다. 인체 자세 표적 획득은 이미지에서 인체 목표를 정확히 찾는 방법을 연구하는 것이 핵심이며 인체 자세 인식은 인공지능(AI)에 적용하는 한편 일상생활에 활용되고 있어서 매우 중요한 연구의의가 있다. 인체 자세 인식 효과의 우수성의 기준은 인식 과정의 성공률과 정확도에 의해 결정된다. 본 연구의 인체 자세 인식에서는 딥러닝 전용 데이터셋인 MS COCO를 기반하여 인체를 17개의 키 포인트로 구분하였다. 다음으로 주요 특징에 대한 세분화 마스크(segmentation mask) 방법을 사용하여 인식률을 개선하였다. 최종적으로 신경망 모델을 설계하고 간단한 단계별 학습부터 효율적인 학습에 이르기까지 많은 수의 표본을 학습시키는 알고리즘을 제안하여 정확도를 향상할 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1189-1204
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• 2018

Detecting and capturing 3D human structures from the intensity-based image sequences is an inherently arguable problem, which attracted attention of several researchers especially in real-time activity recognition (Real-AR). These Real-AR systems have been significantly enhanced by using depth intensity sensors that gives maximum information, in spite of the fact that conventional Real-AR systems are using RGB video sensors. This study proposed a depth-based routine-logging Real-AR system to identify the daily human activity routines and to make these surroundings an intelligent living space. Our real-time routine-logging Real-AR system is categorized into two categories. The data collection with the use of a depth camera, feature extraction based on joint information and training/recognition of each activity. In-addition, the recognition mechanism locates, and pinpoints the learned activities and induces routine-logs. The evaluation applied on the depth datasets (self-annotated and MSRAction3D datasets) demonstrated that proposed system can achieve better recognition rates and robust as compare to state-of-the-art methods. Our Real-AR should be feasibly accessible and permanently used in behavior monitoring applications, humanoid-robot systems and e-medical therapy systems.

• Journal of information and communication convergence engineering
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• 제11권2호
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• pp.124-131
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• 2013

Extraction and representation of postures and/or gestures from human activities in videos have been a focus of research in this area of action recognition. With various applications cropping up from different fields, this paper seeks to improve the performance of these action recognition machines by proposing a shape-based silhouette-edge descriptor for the human body. Information entropy, a method to measure the randomness of a sequence of symbols, is used to aid the selection of vital key postures from video frames. Morphological operations are applied to extract and stack edges to uniquely represent different actions shape-wise. To classify an action from a new input video, a Hausdorff distance measure is applied between the gallery representations and the query images formed from the proposed procedure. The method is tested on known public databases for its validation. An effective method of human action annotation and description has been effectively achieved.