• 방송공학회논문지
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• 제25권5호
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• pp.808-811
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• 2020

본 논문은 랜덤 무향 칼만 필터를 이용하여 키넥트 카메라 센서의 오차를 최소화하는 방법을 제안한다. RGB 값과 깊이(Depth) 정보를 제공하는 RGB+D 카메라는 센서의 오차로 인해 뻐대 검출 과정에서 성능 저하의 원인을 제공한다. 기존에는 다양한 필터링 기법을 이용하여 오차를 제거하였으나 비선형 잡음을 효과적으로 제거하는데 한계가 있었다. 이에 본 논문에서는 비선형 잡음 특성을 예측하고 업데이트하기 위해 랜덤 무향 칼만 필터를 적용하였으며 이를 바탕으로 뻐대 검출 성능을 높이고자 하였다. 실험 결과 제안한 방법은 기존의 방법에 비해 정량적 오차를 줄였으며 뼈대의 3D 검출 시 우수한 결과를 확인할 수 있었다.

• 한국인터넷방송통신학회논문지
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• 제16권6호
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• pp.141-146
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• 2016

굴착 현장 인접 구조물의 안정성을 확보하기 위해 현장 주변 지반의 변형을 모니터링하는 것은 중요하다. 지반특성과 굴착 깊이에 따라 굴착 중 벽체의 변형이 다르게 발생하며, 이를 정확하게 예측하는 것은 불가능하다. 따라서 굴착지지 벽체 후방에 인클리노미터를 탑재한 센서를 설치하여 이를 모니터링한다. 본 논문에서는 굴착과정에서 벽체의 변형을 모니터링하기 위해 무선 센서 노드를 사용한 모니터링 시스템을 설계하였다. 또한 배터리 기반의 센서 노드의 수명을 연장하기 위해 굴착 진행 과정과 인클리노미터의 계측 값에 따라 위험도 등급을 설정하고 해당 등급에 따라 활성/휴면 구간을 적응적으로 변경하는 기법을 제안하였다. 지연시간 분석을 통해 제안기법은 굴착현장과 같이 위험도가 다른 데이터가 동적으로 발생하는 환경에서 고정 듀티사이클링 기법에 비해 지연시간을 크게 줄일 수 있음을 확인한다.

• 한국해양공학회지
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• 제31권1호
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• pp.69-79
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• 2017

This paper presents the operations of a multi-beam echo sounder (MBES) installed on the deep-sea remotely operated vehicle (ROV) Hemire. Hemire explored hydrothermal vents in the Forecast volcano located near the Mariana Trench in March of in 2006. During these explorations, we acquired profiling points on the routes of the vehicle using the MBES. Information on the position, depth, and attitude of the ROV are essential to obtain higher accuracy for the profiling quality. However, the MBES installed on Hemire does not have its own position and depth sensors. Although it has attitude sensors for roll, pitch, and heading, the specifications of these sensors were not clear. Therefore, we had to merge the high-performance sensor data for the motion and position obtained from Hemire into the profiling data of the MBES. Then, we could properly convert the profiling points with respect to the Earth-fixed coordinates. This paper describes the integration of the MBES with Hemire, as well as the coordinate conversion between them. Bathymetric maps near the summit of the Forecast volcano were successfully collected through these processes. A comparison between the bathymetric maps from the MBES and those from the Onnuri Research Vessel, the mother ship of the ROV Hemire for these explorations, is also presented.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.1278-1282
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• 2009

In apartment type of housing, if resident's vehicle is registered in central control office and RFID TAG is issued, identification can be recognized from the time of entrance into parking lot and intelligent parking guide system can be activated based on the residents' profile. Parking Guide System leads a vehicle to the available parking space which is closest to the entrance gate of the vehicle's owner. And when residents forget where they parked their cars, they can query to the Parking Guide and Management System and get responses about the location. For the correct operation of this system, it is necessary to find out where the residents' cars have parked in real time and which lot is available for parking of other cars. RFID is very fancy solution for this system. RFID reader gathers the ID information in RFID TAGs in parked cars and updates the DB up to date. But, when non-residents' cars are parked inside apartment, RFID reader cannot identify them nor know the exact empty/occupied status of parking spaces because they don't react to RFID reader's query. So for the exact detection of empty/occupied status, we suggest the combined use of ultrasonic sensors and RFID. We designed a tree topology with intermediate data aggregators. The depth of tree is normally more than 3 from root (central office) to leaves (individual parking lots). The depth of 2 in tree topology brings about the bottleneck in communication and maintenance. We also designed the information fields used in RFID networks and Sensor Networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1706-1727
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• 2023

Under Water Sensor Networks (UWSN) has gained attraction among various communities for its potential applications like acoustic monitoring, 3D mapping, tsunami detection, oil spill monitoring, and target tracking. Unlike terrestrial sensor networks, it performs an acoustic mode of communication to carry out collaborative tasks. Typically, surface sink nodes are deployed for aggregating acoustic phenomena collected from the underwater sensors through the multi-hop path. In this context, UWSN is constrained by factors such as lower bandwidth, high propagation delay, and limited battery power. Also, the vulnerabilities to compromise the aquatic environment are in growing numbers. The paper proposes an Energy-Efficient standalone Intrusion Detection System (EEIDS) to entail the acoustic environment against malicious attacks and improve the network lifetime. In EEIDS, attributes such as node ID, residual energy, and depth value are verified for forwarding the data packets in a secured path and stabilizing the nodes' energy levels. Initially, for each node, three agents are modeled to perform the assigned responsibilities. For instance, ID agent verifies the node's authentication of the node, EN agent checks for the residual energy of the node, and D agent substantiates the depth value of each node. Next, the classification of normal and malevolent nodes is performed by determining the score for each node. Furthermore, the proposed system utilizes the sheep-flock heredity algorithm to validate the input attributes using the optimized probability values stored in the training dataset. This assists in finding out the best-fit motes in the UWSN. Significantly, the proposed system detects and isolates the malicious nodes with tampered credentials and nodes with lower residual energy in minimal time. The parameters such as the time taken for malicious node detection, network lifetime, energy consumption, and delivery ratio are investigated using simulation tools. Comparison results show that the proposed EEIDS outperforms the existing acoustic security systems.

• 대한임베디드공학회논문지
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• 제13권2호
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• pp.73-81
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• 2018

This paper describes a device platform which the blind can wear to keep path and to get surrounding information during their independent walking. Compared to the existing technologies, the proposed device could be used indoors and outdoors, and maps need not be provided in advance. It is composed of a glasses type device equipped with image sensors, and a portable device that analyzes sensor data for sighted guiding. RGB images and depth images are extracted to generate a walking map based on feature points. It also can cope with the risk of collision with bollard, color cone by applying vertical obstacle detection technology based on floor detection.

• 한국토양비료학회지
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• 제39권2호
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• pp.109-115
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• 2006

점토경반층 토양에서 수량 변이의 특성을 파악하기 위하여 센서를 통하여 관측된 깊이별 토양경도 및 ECa와 작물의 수량과의 관련성을 분석한 결과, 깊이별 토양의 경도는 점토가 집적된 지표면으로부터 15-30 cm 지점의 점토경반층 (argillic horizon)에서 높게 나타내고 있으며 토양의 깊이가 깊은 지점에서 토양경도의 변이가 작게 나타났다. 1994년부터 2002년까지 콩과 옥수수 수량과 작물생육기의 강우량을 분석한 결과 7-8월의 강우량이 작물의 수량과 매우 밀접한 관계를 가지고 있으며 이 기간의 강우량이 150 mm 이하이면 작물이 수분 부족으로 수량이 낮아지는 것으로 나타났다. 지표로부터 15-45 cm 지점에서 토양의 경도와 ECa 가 작물의 수량과 매우 유의한 상관을 갖는 것으로 나타났으며 "drought boundary" 인 7-8월의 강우량이 150 mm를 기점으로 각기 반대의 상관을 보이는 것으로 나타났다. 측정된 토양의 깊이별 경도 값과 ECa 를 이용하여 수량 추정식을 계산 하였으며 추정식의 검증을 위하여 별도의 독립적인 자료를 이용하여 추정된 수량과 측정된 수량의 표준오차를 비교한 결과 측정된 수량에 대한 표준오차의 비율이 4-16% 로 나타났으며 7-8월의 강우량이 150 mm 이하로 건조한 경우에 표준오차가 같은 시기에 강우량이 150 mm 이상으로 습윤한 경우보다 현저하게 표준오차가 크게 나타났다. 결론적으로 신속하고 경제적으로 이용할 수 있는 센서 측정자료와 작물수량과의 관련성을 분석한 결과, 연구에 이용된 점토경반층 토양에서 센서를 이용하여 측정한 ECa 및 깊이별 토양경도와 작물 수량간에 통계적으로 유의한 상관이 있음을 알 수 있다.

• 한국측량학회지
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• 제37권2호
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• pp.45-53
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• 2019

RGB-D SLAM (Simultaneous Localization and Mapping) refers to the technology of using deep camera as a visual sensor for SLAM. In view of the disadvantages of high cost and indefinite scale in the construction of maps for laser sensors and traditional single and binocular cameras, a method for creating three-dimensional map of indoor environment with deep environment data combined with RGB-D SLAM scheme is studied. The method uses a mobile robot system equipped with a consumer-grade RGB-D sensor (Kinect) to acquire depth data, and then creates indoor three-dimensional point cloud maps in real time through key technologies such as positioning point generation, closed-loop detection, and map construction. The actual field experiment results show that the average error of the point cloud map created by the algorithm is 0.0045m, which ensures the stability of the construction using deep data and can accurately create real-time three-dimensional maps of indoor unknown environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.4122-4144
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• 2021

In this fast-paced technological world, the Internet of Things is a ground breaking technology which finds an immense role in the present electronic world which includes different embedded sensors, devices and most other things which are connected to the Internet. The IoT devices are designed in a way that it helps to collect various forms of data from varied sources and transmit them in digitalized form. In modern era of IoT technology data security is a trending issue which greatly affects the confidentiality of important information. Keeping the issue in mind a novel light encryption strategy known as LCB is designed for IoT devices for optimal security. LCB exploits the benefits of Feistel structure and the architectural benefits of substitution permutation network both to give more security. Moreover, this newly designed technique is tested on (Virtex-7) XC7VX330T FPGA board and it takes much little area of 224 GE (Gate Equivalent) and is extremely fast with very less combinational path delay of 0.877 ns. An in-depth screening confirms the proposed work to promise more security to counter cryptographic attacks. Lastly the Avalanche Effect (AE) of LCB showed as 63.125% and 63.875% when key and plaintext (PT) are taken into consideration respectively.

• 대한원격탐사학회지
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• 제37권5_1호
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• pp.861-870
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• 2021

본 연구에서는 COMS (Communication, Oceanography and Meteorology Satellite) 위성의 GOCI(Geostationary Ocean Color Imager) 센서와 Himawari-8 위성의 AHI (Advanced Himawari Imager) 센서에서 산출되는 에어로졸 광학두께 (Aerosol Optical Depth; AOD)를 활용하여 단일화된 AOD 합성장을 생산하였다. 위성 간의 공간해상도와 위치좌표계가 다르기 때문에 이를 맞춰주는 전처리 작업을 선행하였다. 이후 지상관측 기반인 AERONET (AErosol RObotic NETwork)의 레벨 1.5 AOD 자료를 사용하여 각 위성과 AERONET과의 상관관계 분석 및 추세를 보간하여 기존 위성 AOD 보다 정확한 위성 AOD 자료를 생산하였다. 이후 합성과정을 진행하며 최종적으로 시공간적으로 더 완벽하고 정확한 AOD 합성장을 생산하였다. 생산된 AOD 합성장의 제곱근 평균 오차(Root Mean Square Error; RMSE)는 0.13, 평균 편향(mean bias)는 0.05로, 기존의 GOCI AOD (RMSE: 0.15, Mean bias: 0.11)와 AHI AOD (RMSE: 0.15, Mean bias: 0.05) 보다 나은 성능을 보였다. 또한 합성된 AOD는 단일위성에서 구름으로 인하여 관측되지 못한 지역에서 시공간적으로 보다 완벽하게 생산되었음을 확인하였다.