• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2007년도 학술대회
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• pp.191-195
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• 2007

Recently, landslides frequently happen at a natural slope during period of intensive rainfall. With rapidly increasing population of steep terrain in Korea, landslides have become one of the most significant natural hazards. Thus, it is necessary to protect people from landslides and to minimize the damage of houses, roads and other facilities. To accomplish this goal, many landslide prediction methods have been developed in the world. In this study, a simple landslide prediction system that enables people to escape the endangered area is developed. The system is focused to debris flows which happen frequently during periods of intensive rainfall at steep slopes in Kangwondo. This system is based on the wireless sensor network that is composed of sensor nodes, gateway, and server system. Sensor nodes that are composed of sensing part and communication part are newly developed to detect sensitive ground movement. Sensing part is designed to measure tilt angle and acceleration accurately, and communication part is deployed with Bluetooth (IEEE 802.15. I) module to transmit the data to the gateway. To verify the feasibility of this landslide prediction system, a series of laboratory tests is performed at a small-scale earth slope supplying rainfall by artificial rainfall dropping device. It is found that sensing nodes installed at slope can detect the ground motion when the slope failure starts. It is expected that the landslide prediction system by wireless senor network can provide early warnings when landslides such as debris flow occurs, and can be applied to ubiquitous computing city (U-City) that is characterized by disaster free.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2008년도 춘계 학술발표회 초청강연 및 논문집
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• pp.1324-1331
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• 2008

Recently, landslides have frequently occurred on natural slopes during periods of intense rainfall. With a rapidly increasing population on or near steep terrain in Korea, landslides have become one of the most significant natural hazards. Thus, it is necessary to protect people from landslides and to minimize the damage of houses, roads and other facilities. To accomplish this goal, many landslide prediction methods have been developed in the world. In this study, a simple landslide prediction system that enables people to escape the endangered area is introduced. The system is focused to debris flows which happen frequently during periods of intense rainfall. The system is based on the wireless sensor network (WSN) that is composed of sensor nodes, gateway, and server system. Sensor nodes and gateway are deployed with Microstrain G-Link system. Five wireless sensor nodes and gateway are installed at the man-made slope to detect landslide. It is found that the acceleration data of each sensor node can be obtained via wireless sensor networks. Additionally, thresholds to determine whether the slope will be stable or not are proposed using finite element analysis. It is expected that the landslide prediction system by wireless senor network can provide early warnings when landslides such as debris flow occurs.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 추계학술대회
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• pp.657-658
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• 2015

무선센서네트워크는 멀티홉 전송을 통해 데이터 전송이 이루어지며 센서 노드들이 배터리와 같은 한정된 전원으로 작동하기 때문에 에너지 효율적인 라우팅기법이 필요로 된다. 멀티홉 전송에 있어 특정 전송경로로 인해 특정 센서 노드들은 다른 노드보다도 많이 사용되며 이러한 불균형적 전력소모는 전체의 네트워크 생존시간이나 전체적인 사용에 영향을 줄 수 있다. 본 논문에서는 무선센서네트워크에 사용되고 있는 기존 라우팅 프로토콜의 설계 문제를 비교하고, 최적의 경로를 선택하는 동안 전송 레이트 및 잔량 노드들 모두의 고려하는 라우팅 프로토콜을 제안하고 시뮬레이션을 통해 이 라우팅 프로토콜은 전송 지연을 최소화하고 네트워크의 수명을 연장시킬 수 있음을 확인하였다.

• 한국정밀공학회지
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• 제28권6호
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• pp.670-677
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• 2011

This study is to estimate the joint torques without torque sensor using the EMG (Electromyogram) signal of agonist/antagonist muscle with Neural Network Back Propagation Algorithm during the elbow motion. Command Signal can be guessed by EMG signal. But it cannot calculate the joint torque. There are many kinds of field utilizing Back Propagation Learning Method. It is generally used as a virtual sensor estimated physical information in the system functioning through the sensor. In this study applied the algorithm to obtain the virtual senor values estimated joint torque. During various elbow movement (Biceps isometric contraction, Biceps/Triceps Concentric Contraction (isotonic), Biceps/Triceps Concentric Contraction/Eccentric Contraction (isokinetic)), exact joint torque was measured by KINCOM equipment. It is input to the (BP)algorithm with EMG signal simultaneously and have trained in a variety of situations. As a result, Only using the EMG sensor, this study distinguished a variety of elbow motion and verified a virtual torque value which is approximately(about 90%) the same as joint torque measured by KINCOM equipment.

• 대한조선학회논문집
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• 제45권3호
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• pp.329-336
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• 2008

Basic performances of wireless sensor network (WSN) and power line communication (PLC) confirmed in the test bed ashore are investigated in the 3000-ton class training ship of Korea Maritime University. The ubiquitous technologies can be considered for the provision of safety and convenience in a ship. We employed WSN and PLC, as the ubiquitous technologies, at the two areas within the training ship to estimate the realization of the ubiquitous environments in a ship. The experiments show rather good results in terms of data transfer rate. However, more detailed studies concerning the connection between WSN and PLC, noises induced to power line and fading effects are required to improve the quality and the stability of the communication for the ubiquitous environments.

• 한국방재학회:학술대회논문집
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• 한국방재학회 2008년도 정기총회 및 학술발표대회
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• pp.369-372
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• 2008

Recently, landslides have frequently occurred on natural slopes during periods of intense rainfall. With a rapidly increasing population on or near steep terrain in Korea, landslides have become one of the most significant natural hazards. Thus, it is necessary to protect people from landslides and to minimize the damage of houses, roads and other facilities. To accomplish this goal, many landslide prediction methods have been developed in the world. In this study, a simple landslide prediction system that enables people to escape the endangered area is introduced. The system is focused to debris flows which happen frequently during periods of intense rainfall. The system is based on the wireless sensor network (WSN) that is composed of sensor nodes, gateway, and server system. Sensor nodes comprising a sensing part and a communication part are developed to detect ground movement. Sensing part is designed to measure inclination angle and acceleration accurately, and communication part is deployed with Bluetooth (IEEE 802.15.1) module to transmit the data to the gateway. To verify the feasibility of this landslide prediction system, a series of experimental studies was performed at a small-scale earth slope equipped with an artificial rainfall dropping device. It is found that sensing nodes installed at slope can detect the ground motion when the slope starts to move. It is expected that the landslide prediction system by wireless senor network can provide early warnings when landslides such as debris flow occurs.

• 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.

• 한국산업정보학회논문지
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• 제9권2호
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• pp.65-73
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• 2004

최근에 통신 기술과 하드웨어 기술의 발전으로 아주 작은 센서노드로 무선 센서 네트워크를 구성하는 것이 가능하게 되었다. 무선 센서 노드들은 이용 가능한 자원이 극히 제한되기 때문에, 노드들은 장시간 동안 활동하기 위해서 에너지 소모를 최소로 하는 것이 관건이다. 무선 센서 네트워크는 애드 혹 망과 매우 유사하지만, 통신, 전력소모 그리고 계산능력 측면에서 제약을 받는다. 각 노드들의 응용계층에서는 적은 양의 데이터를 생성하고, 느린 속도로 전송되는 특징을 가진다. 각각의 센서 노드는 소스(source)와 싱크(sink)가 될 수 있는 일반적인 애드 혹 환경과는 달리 하나의 기지국(base station)이 싱크의 역할을 하고 싱크를 제외한 노드들은 소스의 역할을 하게 된다. 또한 무선 센서 네트워크는 설치된 후 지속적으로 주변을 관찰하고 고정된 상태로 있는 것이 대부분이다. 기존 애드 혹 망에서 라우팅 프로토콜은 이러한 무선 센서 네트워크의 특징을 만족할 수 없다. 본 논문에서는 무선 센서 네트워크의 통신 형태의 특징을 고려하여 트리 기반 라우팅 프로토콜을 확장한 에너지 측면에서 효율적인 라우팅 프로토콜을 제안한다.

• 대한원격탐사학회지
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• 제38권1호
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• pp.103-110
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• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• 한국컴퓨터정보학회논문지
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• 제24권3호
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• pp.11-17
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• 2019

In this paper, we propose a method to recognize the moving direction in the indoor environment by using the sensing system equipped with passive infrared (PIR) sensors and a deep learning algorithm. A PIR sensor generates a signal that can be distinguished according to the direction of movement of the user. A sensing system with four PIR sensors deployed by $45^{\circ}$ increments is developed and installed in the ceiling of the room. The PIR sensor signals from 6 users with 10-time experiments for 8 directions were collected. We extracted the raw data sets and performed experiments varying the number of sensors fed into the deep learning algorithm. The proposed sensing system using deep learning algorithm can recognize the users' moving direction by 99.2 %. In addition, with only one PIR senor, the recognition accuracy reaches 98.4%.