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한지훈,전중환,김동주,장홍희,구자민,김영기,이스캇,김은정,이희천,이효종,연성찬,Han, Ji-hoon,Jeon, Jung-hwan,Kim, Dong-joo,Chang, Hong-hee,Koo, Ja-min,Kim, Young-ki,Lee, Scott-s,Kim, Eun-jung,Lee, Hee-chun,Lee, Hyo-jong,Yeon, Seong-chan 대한수의학회 2005 大韓獸醫學會誌 Vol.45 No.2
This study was carried out to find out how space allowance affect the social behavior of Korean native cattle (Bos taurus coreanae) steers. Twelve Korean native cattle (Bos taurus coreanae) steers were used as subjects, each of which was 30-month-old and observation period was from June to July 2003. Five (T1) and seven (T2) steers were allotted to two pens of $5m{\times}10m$ in a building with slate roof and open sides respectively. They were fed at 09:00 h and 16:00 h, twice a day. The behaviors of steers were recorded from 06:00 h to 17:00 h, using two color CCD cameras (Samsung SDC-411, Korea), one B/W CCD cameras (Samsung SBC-340, Korea), one multiplexer (Samsung SDM-081, Korea) and a time lapse VCR (Samsung SRV-30, Korea). The behaviors of each steer were recorded every 2 min using an instantaneous point sampling method. While the mean percentage of time budget in WA of T1 was lower than that of T2 (p<0.05), the mean percentage of time budget in SF of T1 was higher than that of T2 (p<0.05). When it gets hot, steers in T1 rested from 10:00 h to 14:00 h when it gets cool, showing 40~80% of LD rate while steers in T2 rested from 12:00 h, when it very hot to 17:00 h, showing 20~50% of LD rate, which is relatively low. Steers in T1 were fed from 06:00 h to 08:00 h when it was cool and from 16:00 h to 18:00 h, showing 20~45% of EA rate while steers in T2 were fed from 08:00 h to 14:00 h when it was hot, showing 25~50% of EA rate. In conclusion, it turned out that the number of steers affected their social behavior, and T1 was better environment than T2 in terms of welfare.
한지훈,우상하,박유경,최성훈,이윤규,이정희,김재수,이현종,Han, Ji Hoon,Woo, Sang Ha,Park, Yu-kyeong,Choi, Seong-Hun,Lee, Yun-kyu,Lee, Jung Hee,Kim, Jae Soo,Lee, Hyun-Jong The Korean Medicine Society for the Herbal Formula 2021 大韓韓醫學方劑學會誌 Vol.29 No.4
This case study reports the effects of complex Korean medicine treatment, including Nangan-jeon, on Lambert-Eaton myasthenic syndrome (LEMS) accompanied by chronic constipation and abdominal pain as the main symptoms. A 39-year-old woman diagnosed with LEMS with major symptoms, including chronic constipation and acute abdominal pain, received Western treatment. The treatment efficacy was weak and symptoms recurred, so the patient received outpatient treatment and 13 days of hospitalization for active Korean medicine treatment, including Nangan-jeon. During outpatient treatment and hospitalization, defecation frequency and the numeric rating scale (NRS) for abdominal pain and abdominal cold feeling were measured. The NRS for abdominal pain and cold decreased from 8 at admission to 3 and 0, respectively, at discharge. Defecation frequency increased significantly from once or twice a month to once every 2-3 days during hospitalization. This study results that complex Korean medicine treatment, including Nangan-jeon may be useful for treating patients who mainly complain of autonomic neurological symptoms, among patients diagnosed with LEMS. In addition, it is believed that it could be basic data applicable to more LEMS patient treatment cases.
2008-2012년의 제주지역 낙뢰 특성 및 낙뢰에 의한 풍력단지 낙뢰율 평가
한지훈(Han Ji-Hoon),고경남(Ko Kyung-Nam),허종철(Huh Jong-Chul) 한국태양에너지학회 2013 한국태양에너지학회 논문집 Vol.33 No.5
This paper presents the characteristics of lightning over established and scheduled wind farms of Jeju island as well as over specific range of entire Jeju Island. The lightning data for 5 years from 2008 to 2012 was obtained from IMPACT ESP which detects lightning. Lightning frequency, lightning strength and regional lightning events were analyzed in detail, and then the lightning maps of Jeju Island were created. The evaluation of lightning rate was made for all the wind farms of this study. Damage to wind turbines by lightning was found in the existing wind farms. As a result, the eastern part of Jeju Island had more lightning frequency than the western part of the Island. Also, the evaluation of lightning rate was good for all established and scheduled wind farms of Jeju Island. Hankyung is the best place for lightning safety, while precaution should be taken against lightning damage in Kimnyung. Lightning damage to wind turbines occurred in Samdal and Haengwon wind farms, which had the first and the second highest lightning rate of the five existing wind farms.
이상치 데이터를 고려한 DT-CNN 기반의 전동기 고장 예측
한지훈(Ji-Hoon Han),최동진(Dong-Jin Choi),박상욱(Sang-Uk Park),홍선기(Sun-Ki Hong) 제어로봇시스템학회 2020 제어·로봇·시스템학회 논문지 Vol.26 No.11
One of the major problems with the existing motor failure prediction system is to assume that all motors with the same fault condition have the same or a similar signal. This is a problem that arises because it is impossible to measure all the countless types of motors and data of driving conditions and failures. It is difficult to implement a general-purpose failure prediction system with an existing system having limited data and limited output. Data that have a large difference because they do not exist in the existing system are called outlier data. In previous studies, the problem arising from the outlier data has not been considered. To solve this problem, a system designed by separating the failure diagnosis model and the failure prediction model is proposed. The diagnostic model of the proposed system can detect data that are not inside big data using a decision-tree convolution neural network (DT-CNN). By using the diagnostic model and the predictive model in series, it is possible to analyze data in a non-measured state more efficiently. Additionally, a method for averaging the outputs of the diagnostic and predictive models is proposed. Through this, the deep learning algorithm can obtain in effect of applying the filter. Furthermore, the average values can be used to confirm the long-term signal change trend. The proposed system improves the problems of the existing failure prediction and enables more practical failure prediction.
한지훈(Han Ji Hoon),박경배(Park Gyong Bae),곽승욱(Kwak Seung Uk),김정일(Kim Jeong II),정근원(Jeong Keun Won),송인근(Song In Keun),이광배(Lee Kwang Bae),김현욱(Kim Hyen Uk) 한국정보처리학회 1999 정보처리학회논문지 Vol.6 No.1
Uses can share information and use resources effectively by using TCP/IP-based networks. So, a protocol to manage complex networks effectively is needed. For the management of the distributed networks, the SNMP(Simple Network Management Protocol) has been adopted as an international standard in 1989, and the SNMPv2 in which a security function was added was published in 1993. There are two encryption schemes in SNMPv2, the one is a DES using symmetric encryption scheme and the other is a MD5(Message Digest5) hash function for authentication. But the DES has demerits that a key length is a few short and the encryption and the authentication is executed respectively. In order to solve these problems, we use a RSA cryptography in this paper. In this paper, we examine the items related with SNMP. In addition to DES and MD5 proposed in SNMPv3, we enhance security functionality be adopting RSA, a public key algorithm executing the encryption and the authentication simultaneously. The proposed SNMPv3 security module is written in JAVA under Windows NT environment.
딥 러닝에 의한 전동기 기계적 고장 수준 결정을 위한 전동기 진동/전류 데이터 특성 분석 연구
한지훈(Ji-Hoon Han),박상욱(Sang-Uk Park),홍선기(Sun-Ki Hong) 대한전기학회 2021 전기학회논문지 Vol.70 No.10
In the classic motor fault diagnosis system, a method of analyzing the differences between the normal and collected state signals of the motor to be diagnosed was used and the method can diagnose only the limited situations because the diagnosis is based on the frequency of the mechanical failure. In order to compensate for this, some studies on a system that performs more specialized fault diagnosis through deep learning algorithms were carried out. However, the level of failure cannot be determined because these studies consider only the signals that have a great influence on motor operation. To solve this problem, the characteristics of vibration and current signals are analyzed to develop a deep learning algorithm suitable for fault level determination. The characterized signals are used for fault diagnosis and prediction. Fault diagnosis based on vibration signal is carried out through DT-CNN (Decision Tree Convolutional Neural Network). In addition, it is checked whether the current signal in the initial failure state, which is relatively insensitive to failure, can be classified through a deep learning algorithm. The proposed data utilization performance was evaluated through an induction motor and the analyzed signal-based fault diagnosis system is expected to enable a more precise diagnosis compared to the existing system.
PDMS 채널 내부에 성장된 산화아연 나노막대를 이용한 H7N9 인플루엔자 바이러스 전기화학 면역센서
한지훈 ( Ji Hoon Han ),이동영 ( Dong Young Lee ),박정호 ( Jung Ho Pak ) 한국센서학회 2014 센서학회지 Vol.23 No.4
In this study, we propose an immunosensor using zinc oxide nanorods (NRs) inside PDMS channel for detecting the influenza A virus subtype H7N9. ZnO with high isoelectric point (IEP, ~9.5) makes it suitable for immobilizing proteins with low IEP. In this proposed H7N9 immunosensor structure ZnO NRs were grown on the PDMS channel inner surface to immobilize H7N9 capture antibody. A sandwich enzyme-linked immunosorbent assay (ELISA) method with was used 3,3`,5,5` tetramethylbenzidine (TMB) for detecting H7N9 influenza virus. The immunosensor was evaluated by amperometry at various H7N9 influenza antigen concentrations (1 pg/ml-1 ng/ml). The redox peak voltage and current were measured by amperometry with ZnO NWs and without ZnO NWs inside PDMS channel. The measurement results of the H7N9 immunosensor showed that oxidation peak current of TMB at 0.25 V logarithmically increased from 2.3 to 3.8 uA as the H7N9 influenza antigen concentration changed from 1 pg/ml to 1 ng/ml. And then we demonstrated that ZnO NRs inside PDMS channel can improve the sensitivity of immunosensor to compare non-ZnO NRs inside PDMS channel.