• 제어로봇시스템학회논문지
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• 제16권3호
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• pp.264-268
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• 2010

The purpose of this paper is to propose new techniques to match measured optical images by using the edge abstraction method and differentiation method based on image processing technology. To do this, we detect the matching template and non-matching template from each optical image. And then, we detect the edge parts of the overlaped image from comer edge abstraction method and remove noise image. At last, these data are related to applied first-order derivative operator. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• 반도체디스플레이기술학회지
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• 제10권3호
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• pp.99-103
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• 2011

The stereoscopic vision system is the algorithm to obtain the depth of target object of stereo vision image. This paper presents an efficient disparity matching method using blue edge filter and graph cut algorithm. We do recommend the use of the simple sobel edge operator. The application of B band sobel edge operator over image demonstrates result with somewhat noisy (distinct border). The basic technique is to construct a specialized graph for the energy function to be minimized such that the minimum cut on the graph also minimizes the energy (either globally or locally). This method has the advantage of saving a lot of data. We propose a preprocessing effective stereo matching method based on sobel algorithm which uses blue edge information and the graph cut, we could obtain effective depth map.

• 디지털산업정보학회논문지
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• 제17권3호
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• pp.9-15
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• 2021

In this paper, the Embedded MEMS system to the power apparatus used Edge Impulse machine learning tools and therefore an improved predictive system design is implemented. The proposed MEMS embedded system is developed based on nRF52840 system and the sensor with 3-Axis Digital Magnetometer, I2C interface and magnetic measurable range ±120 uT, BM1422AGMV which incorporates magneto impedance elements to detect magnetic field and the ARM M4 32-bit processor controller circuit in a small package. The MEMS embedded platform is consisted with Edge Impulse Machine Learning and system driver implementation between hardware and software drivers using SensorQ which is special queue including user application temporary sensor data. In this paper by experimenting, TensorFlow machine learning training output is applied to the power apparatus for analyzing the status such as "Normal, Warning, Hazard" and predicting the performance at level of 99.6% accuracy and 0.01 loss.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.418-420
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• 2019

다양하게 요구되고 있는 해양 사물 인터넷 서비스 제공을 위하여 필요한 복합 센서 장치와 edge device를 제안하였다. 특히 통신 3사의 상용망 외에 폐쇄망을 통해 센싱 정보의 관리와 처리가 가능하도록 설계 제안하였다.

• 시스템엔지니어링학술지
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• 제15권1호
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• pp.34-42
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• 2019

Receiving great attention of IoT and 4th industrial revolution, the necessity comes to the fore of the plant system which aims making it smart and effective. Smart Factory is the key realm of IoT to apply with the concept to optimize the entire process and it presents a new and flexible production paradigm based on the collected data from numerous sensors installed in a plant. Especially, the wireless sensor network technology is receiving attention as the key technology of Smart Factory, researches to interface those technology is actively in progress. In addition, IoT devices for plant industry security and high reliable network protocols are under development to cope with high-risk plant facilities. In the meanwhile, Blockchain can support high security and reliability because of the hash and hash algorithm in its core structure and transaction as well as the shared ledger among all nodes and immutability of data. With the reason, this research presents Blockchain as a method to preserve security and reliability of the wireless communication technology. In regard to that, it establishes some of key concepts of the possibility on the blockchain based IoT Edge devices for Plant O&M (Operations and Maintenance), and fulfills performance verification with test devices to present key indicator data such as transaction elapsed time and CPU consumption rate.

• 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.

• 한국통신학회논문지
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• 제41권7호
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• pp.789-798
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• 2016

본 논문에서는 home 소형셀 대비 넓은 커버리지를 갖고 많은 사용자를 서비스 하는 enterprise/urban 소형셀 환경에서 적용할 수 있는 사용자 컨텍스트 기반 캐시 알고리즘을 제안한다. 소형셀 캐시 기법은 소형셀 사용자의 웹 트래픽을 소형셀 내부에 위치한 저장 공간에 저장하는 방법으로 코어망 트래픽을 감소시키는 효과가 있다. 본 논문에서는 기존의 알고리즘과 달리 Mobile Edge Computing(MEC)의 개념을 적용하여 소형셀 내부가 아닌 edge server에 사용자 트래픽을 캐시하며 사용자 특성을 반영하기 위해 사용자를 그룹화한다. 또한, 그룹별 저장 공간의 크기를 달리하고, 캐시 업데이트 주기를 캐시 적중률에 따라 변경하여 코어망으로부터 제공받는 트래픽을 감소하고자 하였다. 성능 분석 결과 기존 알고리즘 대비 캐시 적중률 측면에서 약 11%, cache efficiency 측면에서 약 5.5%의 성능 향상을 확인할 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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• pp.826-842
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• 2024

As 5G and AI continue to develop, there has been a significant surge in the healthcare industry. The COVID-19 pandemic has posed immense challenges to the global health system. This study proposes an FL-supported edge computing model based on federated learning (FL) for predicting clinical outcomes of COVID-19 patients during hospitalization. The model aims to address the challenges posed by the pandemic, such as the need for sophisticated predictive models, privacy concerns, and the non-IID nature of COVID-19 data. The model utilizes the FATE framework, known for its privacy-preserving technologies, to enhance predictive precision while ensuring data privacy and effectively managing data heterogeneity. The model's ability to generalize across diverse datasets and its adaptability in real-world clinical settings are highlighted by the use of SHAP values, which streamline the training process by identifying influential features, thus reducing computational overhead without compromising predictive precision. The study demonstrates that the proposed model achieves comparable precision to specific machine learning models when dataset sizes are identical and surpasses traditional models when larger training data volumes are employed. The model's performance is further improved when trained on datasets from diverse nodes, leading to superior generalization and overall performance, especially in scenarios with insufficient node features. The integration of FL with edge computing contributes significantly to the reliable prediction of COVID-19 patient outcomes with greater privacy. The research contributes to healthcare technology by providing a practical solution for early intervention and personalized treatment plans, leading to improved patient outcomes and efficient resource allocation during public health crises.

• 전자공학회논문지 IE
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• 제44권1호
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• pp.15-19
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• 2007

본 연구는 전송 오차의 이미지 복원에 관한 방법으로 정지영상 또는 내부프레임 정정을 위한 모서리 방향 보간법에 기초한 오차 복원 기술의 개선을 목표로 한다. 여기서 제안된 방법은 블록의 모서리 방향 검출 방법은 스웨터의 손상된 부분을 남아 있는 부분과 맞추어가는 모서리 방향을 이용하는 것에 근거한다 처리 후 데이터 정보에 남은 에러 픽셀을 마지막 단계로 비선형 미디안 필터를 사용하여 보간 하였다. 실험 결과는 제안된 방법의 높은 회복 성향과 낮은 계산 시간은 실시간 영상 처리의 실현 가능성을 나타낸다.

• 한국정밀공학회지
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• 제13권3호
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• pp.150-157
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• 1996

In this paper, to develop a smart CAD/CAM system for systematically performing from the 3-D solid shape design of products to the CNC cutting operation of products by a machining center, a B-Rep solid modeler is realized based on the half edge data structure. Because the B-Rep solid modeler has the various capabilities related to the solid definition functions such as the creation operation of primitives and the translational and rotational sweep operation, the solid manipulation functions such as the split operation and the Boolean set operation, and the solid inversion function for effectively using the data structure, the 3-D solid shape of products can be easily designed and constructed. Also, besides the automatic generation of CNC code, the B-Rep solid modeler can be used as a powerful tool for realizing the automatic generation of finite elements, the interfer- ence check between solids, the structural design of machine tools and robots and so on.