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Low Rate VLC Receiver Design Using NCP302 Voltage Detector for IoT/IoL Connected Smart Homes
Lee, Beomhee,Mariappan, Vinayagam,Khudaybergenov, Timur,Han, Jungdo,Cha, Jaesang The Institute of Internet 2018 Journal of Advanced Smart Convergence Vol.7 No.4
The Internet of Things (IoT) and Visible Light Communication (VLC) is opening up new services in lighting industry by integrating sensory network features in addition to standard illumination functionality. In this progressive developments, the next generation lighting devices for smart homes are capable to sense the environmental conditions and transfer the captured data through lights to gateway controller to access remotely. The smart home environmental sensor information's are few kbps only so VLC systems need to built-in with low rate light connectivity to transfer data to the gateway. To provide error free communication, the quality of a received light signal is important to be considered when designing an VLC receiver. Therefore, this paper proposes the design of robust low rate IoL receiver design using NCP302 voltage detector for micro controller to adapt the IoT/IoL front end module for system integration. To evaluate the proposed system performance, the Arduino UNO based IoT/IoL controller designed with lighting, sensors and lights connectivity interfaces. The experimental result shows that the robust interference rejection is feasible on proposed VOL receiver and possible to have an error-free communication up to 10 kbps at a low SNR using OOK modulation.
A Study on HMD-AR based Industrial Training System for Live Machinery Operation
Lee, Beomhee,Choi, Jinyeong,Choi, Byunghoon,Lee, Jisung,Min, Byungjun,Cho, Juphil The Institute of Internet 2018 International Journal of Internet, Broadcasting an Vol.10 No.1
As technological development is progressing recently, various technologies are actively being studied in the course of the 4th industrial revolution. So, even in the educational field, virtual reality and augmented reality technology are used in educational environments, but specialized additional equipment is required and the price is very expensive. Also, since a plurality of equipment are required for a large number of people, it is urgent to study the technology that can be effectively applied to the industrial education field. So in this paper, we propose an industrial training system for HMD-AR, MPEG-DASH and SOAP based HTTP based Live Machinery Operation using Smartphone to solve the problems of existing system.
A Study on HMD-AR based Industrial Training System for Live Machinery Operation
Beomhee Lee,Jinyeong Choi,Byunghoon Choi,Jisung Lee,Byungjun Min,Juphil Cho 한국인터넷방송통신학회 2018 International Journal of Internet, Broadcasting an Vol.10 No.1
As technological development is progressing recently, various technologies are actively being studied in the course of the 4th industrial revolution. So, even in the educational field, virtual reality and augmented reality technology are used in educational environments, but specialized additional equipment is required and the price is very expensive. Also, since a plurality of equipment are required for a large number of people, it is urgent to study the technology that can be effectively applied to the industrial education field. So in this paper, we propose an industrial training system for HMD-AR, MPEG-DASH and SOAP based HTTP based Live Machinery Operation using Smartphone to solve the problems of existing system.
Shin, Jaekwon,Kim, Jintae,Lee, Beomhee,Lee, Junghoon,Lee, Jisung,Jeong, Seongyeob,Chang, Soonwoong The Institute of Internet 2018 International journal of advanced smart convergenc Vol.7 No.1
Recently, as the demand for limited resources continues to rise and problems of resource depletion rise worldwide, the importance of renewable energy is gradually increasing. In order to solve these problems, various methods such as energy conservation and alternative energy development have been suggested, and biogas, which can utilize the gas produced from biomass as fuel, is also receiving attention as the next generation of innovative renewable energy. New and renewable energy using biogas is an energy production method that is expected to be possible in large scale because it can supply energy with high efficiency in compliance with energy supply method of recycling conventional resources. In order to more efficiently produce and manage these biogas, a biogas plant has emerged. In recent years, a large number of biogas plants have been installed and operated in various locations. Organic wastes corresponding to biogas production resources in a biogas plant exist in a wide variety of types, and each of the incoming raw materials is processed in different processes. Because such a process is required, the case where the biogas plant process is inefficiently operated is continuously occurring, and the economic cost consumed for the operation of the biogas production relative to the generated biogas production is further increased. In order to solve such problems, various attempts such as process analysis and feedback based on the feedstock have been continued but it is a passive method and very limited to operate a medium/large scale biogas plant. In this paper, we propose "CNN-based production yield prediction algorithm for increasing process efficiency of biogas plant" for efficient operation of biogas plant process. Based on CNN-based production yield forecasting, which is one of the deep-leaning technologies, it enables mechanical analysis of the process operation process and provides a solution for optimal process operation due to process-related accumulated data analyzed by the automated process.
Jaekwon Shin,Jintae Kim,Beomhee Lee,Junghoon Lee,Jisung Lee,Seongyeob Jeong,Soonwoong Chang 한국인터넷방송통신학회 2018 Journal of Advanced Smart Convergence Vol.7 No.1
Recently, as the demand for limited resources continues to rise and problems of resource depletion rise worldwide, the importance of renewable energy is gradually increasing. In order to solve these problems, various methods such as energy conservation and alternative energy development have been suggested, and biogas, which can utilize the gas produced from biomass as fuel, is also receiving attention as the next generation of innovative renewable energy. New and renewable energy using biogas is an energy production method that is expected to be possible in large scale because it can supply energy with high efficiency in compliance with energy supply method of recycling conventional resources. In order to more efficiently produce and manage these biogas, a biogas plant has emerged. In recent years, a large number of biogas plants have been installed and operated in various locations. Organic wastes corresponding to biogas production resources in a biogas plant exist in a wide variety of types, and each of the incoming raw materials is processed in different processes. Because such a process is required, the case where the biogas plant process is inefficiently operated is continuously occurring, and the economic cost consumed for the operation of the biogas production relative to the generated biogas production is further increased. In order to solve such problems, various attempts such as process analysis and feedback based on the feedstock have been continued but it is a passive method and very limited to operate a medium/large scale biogas plant. In this paper, we propose "CNN-based production yield prediction algorithm for increasing process efficiency of biogas plant" for efficient operation of biogas plant process. Based on CNN-based production yield forecasting, which is one of the deep-leaning technologies, it enables mechanical analysis of the process operation process and provides a solution for optimal process operation due to process-related accumulated data analyzed by the automated process.
Development of Machine Learning-Based Clinical Decision Support System for Hepatocellular Carcinoma
( Gwang Hyeon Choi ),( Jihye Yun ),( Jonggi Choi ),( Danbi Lee ),( Ju Hyun Shim ),( Young-suk Lim ),( Han Chu Lee ),( Young-hwa Chung ),( Yung Sang Lee ),( Beomhee Park ),( Namkug Kim ),( Kang Mo Kim 대한간학회 2020 춘·추계 학술대회 (KASL) Vol.2020 No.1
Aims: There is a significant discrepancy between the actual choice for initial treatment option for hepatocellular carcinoma (HCC) and recommendations from the currently used BCLC staging system. The aim of this study is to develop a machine learning-based clinical decision support system (CDSS) for recommending initial treatment option in HCC and predicting overall survival (OS). Methods: From hospital records of 1021 consecutive patients with HCC treated at a single center in Korea between January 2010 and October 2010, we collected information on 61 pretreatment variables, initial treatment, and survival status. Twenty pretreatment key variables were finally selected. We developed the CDSS from the derivation set (N=813) using random forest method and validated it in the validation set (N=208). Results: Among the 1021 patients (mean age: 56.9 years), 81.8% were male and hepatitis B virus was positive in 77.0%. Patients with BCLC stages 0, A, B, C, and D were 13.4%, 26.0%, 18.0%, 36.6%, and 6.3%, respectively. The 6 multistep classifier models were developed for treatment decision in a hierarchical manner, and it showed good performance with 76.6-88.4% of accuracy. We also developed 7 survival prediction models for each treatment option, which showed good prediction ability for OS with C-index values ranging from 0.684-0.959. Conclusions: Our newly developed HCC-CDSS model showed good performance in terms of treatment recommendation and overall survival prediction. Our HCC-CDSS model may be used as a guidance in deciding the initial treatment option for HCC.
아쿠아포닉 시스템에서 인공배지가 질산화작용에 미치는 영향
이현진 ( Hyounjin Lee ),윤범희 ( Beomhee Yoon ),백정현 ( Jeonghyeon Baek ),최은영 ( Eunyoung Choi ) 한국농업기계학회 2019 한국농업기계학회 학술발표논문집 Vol.24 No.1
본 연구는 아쿠아포닉스 시스템에서 인공 배지인 하이드로볼이 bio-filter 대체재로써 질산화 작용에 미치는 영향을 알아보기 위하여 어종 진주린(Carassius auratus)과 상추 품종인 로메인(Lactuca sativa L.,)을 사용하여 실시하였다. 모든 처리구에서 암모니아태 질소(NH<sub>4</sub>-N)에서 아질산염(NO<sub>2</sub>-N)으로의 전환은 유의차가 없었지만, 인공 여과기 처리구와 하이드로볼 처리구에서 질산염(NO<sub>3</sub>-N)의 농도가 높게 나타났다. NO<sub>3</sub>-N 농도는 인공 여과기 설치구에서는 증가 속도가 하이드로볼 처리구와 유사하였으나 4일 만에 급격히 감소되는 반면, 하이드로볼 처리구는 감소 시점은 유사하나 10일 이상 지속되는 경향을 보였다. 식물 생육은 인공 여과기 처리구에서 수질의 pH 수준이 낮아지면서 잎끝마름 현상이 나타나 품질이 저하되었다. 본 연구의 결과들을 종합해 볼 때 아쿠아포닉스에서 하이드로볼이 bio-filter를 대체할 수 있을 것으로 판단되며 더 세밀한 실험이 필요하다.