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하계 전기, 전자연합학술회의 및 산학협동 심포지엄 초록

  • 대한전기학회
    • 전기의세계
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    • v.27 no.5
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    • pp.33-54
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    • 1978
  • (차례) 1.산학협동심포지업 (1)우리나라에서의 연구개발과 산학협동 (2)산학협동과 산업계의 역할 (3)산학협동의 현황과 진로 2.학술회의A (1)전력게통의 계층구조와 협조원리에 관한 연구 (2)2중층괴상회전자 유도전동기의 이론해석 (3)초고주파가열장치에 사용하는 철공진변압기의 해석적 설계 (4)한국전기기시험연구소 대전력단락 시험설비설계 (5)직류전동기제어를 위한 Thyristor Chopper정류회로에 관한 연구 (6)선로의 개폐정보를 포함하는 전력계통의 상태추정 (7)단일신경세포에 대한 ITEM 신호 특성 3.학술회의B (1)MMM-1 Computer System의 설계 및 제작 (2)Adaptive Delta Modulation System의 성능비교 연구 (3)6GHZ FMD마이크로파 무선전송장치의 개발 (4)적선도에 의한 회로망함수의 결정 (5)동맥혈압의 해석과 그의 전기적 유사모델 (6)피부감각의 정보전달 특성에 관하여 (7)선형직접회로의 공정설계 및 그 특성 조성 (8)DH L.D의 전기적포화현상에 관한 이론적 해석 (9)Potocoupler를 이용한 Isolator 4.학술회의C (1)Al-Al$_{2}$O$_{3}$ -Al박막구조의 전기적 특성 (2)이종금속에 샌드위치된 고분자물질의 단락전조 (3)유전체가 일부체워진 직 6면체의 캐비티의 다중모오드 해석 (4)반도체 가스 검지소자의 제조 및 그의 전기적 특성 (5)실리콘 산화공정에 대한 실험적 고찰 (6)진공증착법에 의한 InSb 박막제도에서 열처리효과 (7)(Ba$_{1}$-xBix) Tio$_{3}$ PTC thermistor의 첨가량의 최적건안 (8)금속박막증착시 두께조절 5.특별강연회 (1)일본에 있어서의 절력계통공학연구 (2)Linear Motor의 최근개발동향량도 높았다. valine과 leucine 및 aspartic acid, glycine과 glutamic acid, leucine과 aspartic acid 간에는 고도의 정상관, glycine과 serine, valine과 phenylalanine, threonine과 proline, phenylalanine과 arginine, methionine과 glutamic acid, histidine과 lysine 간에는 유의 정상관, 그리고 isoleucine과 lysine 간에는 유의한 부상관이 있었다. 4. lysine 함량은 단백질 함량과 정산곤, isoleucine 함량은 단빅질 함량과 부상관을 보였으며, alanine, valine, leucine 함량은 지방함량과 각각 유의한 정산관을 보였다. 5. 대두 단백질은 7.5% acrylamide gel 전기영동에 의해 품종에 따라 12~16개의 구성분으로 분리되었으며, 이들중 주구성분들은 상대이동도가 0.06(a), 0.14(b). 0.24(d) 이었고, 구성분 b의 함량이 품종간에 가장 변이가 컸으며, 구성분 b는 그밖의 주요 구성분들의 함량과 부의 상관이 있었고, 구성분 a는 단백질 함량과 정상관이 있었다. 6. 종실단백질 구성분들의 조합 특성 면에서 공시 86품종은 11개 유형군으로 분류되었으며, 우리나라와 일본품종은 미국품종에 비해 단백질구성분 조성이 훨씬 다양하였다. 7. 이동도가 매우 빠른 단백질 구성분 o(Rm 0.77) p(Rm 0.81)를 모두 갖고 있는 품종은 3품종, 모두 갖고 있지 않은 품종은 1품종이었고, 나머지 82품종은 o나 p중 한 구성분을 갖고 있었으며 그 분포율은 30 : 65 이었는데 미국계 품종은 우리나라 품종에 비해 구성분 o를 간고 있는 비율이 현저히 적었다. 8. 대두 종실은 개화후 22일까지 완만히, 그 이후 20~30일간 급속히

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Contrast Media Side Effects Prediction Study using Artificial Intelligence Technique (인공지능 기법을 이용한 조영제 부작용 예측 연구)

  • Sang-Hyun Kim
    • Journal of the Korean Society of Radiology
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    • v.17 no.3
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    • pp.423-431
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    • 2023
  • The purpose of this study is to analyze the factors affecting the classification of the severity of contrast media side effects based on the patient's body information using artificial intelligence techniques to be used as basic data to reduce the degree of contrast medium side effects. The data used in this study were 606 examiners who had no contrast medium side effects in the past history survey among 1,235 cases of contrast medium side effects among 58,000 CT scans performed at a general hospital in Seoul. The total data is 606, of which 70% was used as a training set and the remaining 30% was used as a test set for validation. Age, BMI(Body Mass Index), GFR(Glomerular Filtration Rate), BUN(Blood Urea Nitrogen), GGT(Gamma Glutamyl Transgerase), AST(Aspartate Amino Transferase,), and ALT(Alanine Amiono Transferase) features were used as independent variables, and contrast media severity was used as a target variable. AUC(Area under curve), CA(Classification Accuracy), F1, Precision, and Recall were identified through AdaBoost, Tree, Neural network, SVM, and Random foest algorithm. AdaBoost and Random Forest show the highest evaluation index in the classification prediction algorithm. The largest factors in the predictions of all models were GFR, BMI, and GGT. It was found that the difference in the amount of contrast media injected according to renal filtration function and obesity, and the presence or absence of metabolic syndrome affected the severity of contrast medium side effects.

Development of Elementary Maker Education Program using WeDo Robot (WeDo 로봇 활용 초등 메이커 교육 프로그램 개발)

  • Kweon, Soonhwan;Park, Jungho
    • 한국정보교육학회:학술대회논문집
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    • 2021.08a
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    • pp.335-340
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    • 2021
  • This study conducted research on creating an environment for maker education programs for robot and SW education, development and application of maker education programs for low-grade elementary school students in farming and fishing villages. Based on the preceding maker education model, the OMCSI model was developed for the lower grade level of elementary school, and based on this, five WeDo-utilized elementary maker education programs were developed. From April 1, 2020 to October 30, 2020, the results of applying the elementary school maker education program using WeDo Robot 2.0 to 10 second graders of 10 Elementary School in Gyeongsangnam-do are as follows. The average increased by 3.40 points (t=-2.378, p=0.034) and the average increased by 3.30 points (t=-2.329, p=0.040). The average was also increased by 3.40 points (t=-2.458, p=0.038). Finally, it rose to 3.70 points (t=-2.449, p=0.037) for its reasoning ability. That is, all four sub-elements of computing thinking had a significant probability of 0.04, indicating statistical significant differences between scores of pre-post computing thinking. Therefore, the Elementary Maker Education Program using WeDo robots has worked very effectively to improve students' computing thinking skills.

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Case Study on the Pre-Service Earth Science Teachers' Faults Discrimination on Geological Map using Eye Tracker (시선 추적기를 활용한 지질도에서 예비 지구과학교사들의 단층 판별에 대한 사례 연구)

  • Woong Hyeon Jeon;Duk Ho Chung;Chul Min Lee
    • Journal of the Korean earth science society
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    • v.44 no.3
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    • pp.210-221
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    • 2023
  • The purpose of this study is to evaluate the content knowledge and problem solving process used by pre-service earth science teachers while discriminating faults on geological maps. For this, we collected and evaluated data on fixation duration and gaze plot, while pre-service earth science teachers (N=12) solved the problem on faults interpretation using an eye tracker (Tobii Pro Glass 2 model). The results were as follows. First, most of the pre-service earth science teachers know the concepts of the normal and reverse fault but they do not know the procedural knowledge essential for fault interpretation on geological maps. Second, the pre-service earth science teachers did not draw a geological cross-sectional map to interpret the fault on the geological map and interpreted the fault based on two-dimensional information collected from the geological map rather than three-dimensional information. Therefore, it is essential to improve the teaching and learning environment so that pre-service earth science teachers who will become earth science teachers in the future can learn procedural knowledge essential to comprehend natural phenomena including understanding natural phenomena. The results of this study can substantially help organize a new earth science curriculum or develop materials on teachers' education in the future.

AI Security Plan for Public Safety Network App Store (재난안전통신망 앱스토어를 위한 AI 보안 방안 마련)

  • Jung, Jae-eun;Ahn, Jung-hyun;Baik, Nam-kyun
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.10a
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    • pp.458-460
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    • 2021
  • The provision and application of public safety network in Korea is still insufficient for security response to the mobile app of public safety network in the stages of development, initial construction, demonstration, and initial service. The available terminals on the Disaster Safety Network (PS-LTE) are open, Android-based, dedicated terminals that potentially have vulnerabilities that can be used for a variety of mobile malware, requiring preemptive responses similar to FirstNet Certified in U.S and Google's Google Play Protect. In this paper, before listing the application service app on the public safety network mobile app store, we construct a data set for malicious and normal apps, extract features, select the most effective AI model, perform static and dynamic analysis, and analyze Based on the result, if it is not a malicious app, it is suggested to list it in the App Store. As it becomes essential to provide a service that blocks malicious behavior app listing in advance, it is essential to provide authorized authentication to minimize the security blind spot of the public safety network, and to provide certified apps for disaster safety and application service support. The safety of the public safety network can be secured.

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A Study on the Establishment of Entropy Source Model Using Quantum Characteristic-Based Chips (양자 특성 기반 칩을 활용한 엔트로피 소스 모델 수립 방법에 관한 연구)

  • Kim, Dae-Hyung;Kim, Jubin;Ji, Dong-Hwa
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.10a
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    • pp.140-142
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    • 2021
  • Mobile communication technology after 5th generation requires high speed, hyper-connection, and low latency communication. In order to meet technical requirements for secure hyper-connectivity, low-spec IoT devices that are considered the end of IoT services must also be able to provide the same level of security as high-spec servers. For the purpose of performing these security functions, it is required for cryptographic keys to have the necessary degree of stability in cryptographic algorithms. Cryptographic keys are usually generated from cryptographic random number generators. At this time, good noise sources are needed to generate random numbers, and hardware random number generators such as TRNG are used because it is difficult for the low-spec device environment to obtain sufficient noise sources. In this paper we used the chip which is based on quantum characteristics where the decay of radioactive isotopes is unpredictable, and we presented a variety of methods (TRNG) obtaining an entropy source in the form of binary-bit series. In addition, we conducted the NIST SP 800-90B test for the entropy of output values generated by each TRNG to compare the amount of entropy with each method.

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Context-Dependent Video Data Augmentation for Human Instance Segmentation (인물 개체 분할을 위한 맥락-의존적 비디오 데이터 보강)

  • HyunJin Chun;JongHun Lee;InCheol Kim
    • KIPS Transactions on Software and Data Engineering
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    • v.12 no.5
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    • pp.217-228
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    • 2023
  • Video instance segmentation is an intelligent visual task with high complexity because it not only requires object instance segmentation for each image frame constituting a video, but also requires accurate tracking of instances throughout the frame sequence of the video. In special, human instance segmentation in drama videos has an unique characteristic that requires accurate tracking of several main characters interacting in various places and times. Also, it is also characterized by a kind of the class imbalance problem because there is a significant difference between the frequency of main characters and that of supporting or auxiliary characters in drama videos. In this paper, we introduce a new human instance datatset called MHIS, which is built upon drama videos, Miseang, and then propose a novel video data augmentation method, CDVA, in order to overcome the data imbalance problem between character classes. Different from the previous video data augmentation methods, the proposed CDVA generates more realistic augmented videos by deciding the optimal location within the background clip for a target human instance to be inserted with taking rich spatio-temporal context embedded in videos into account. Therefore, the proposed augmentation method, CDVA, can improve the performance of a deep neural network model for video instance segmentation. Conducting both quantitative and qualitative experiments using the MHIS dataset, we prove the usefulness and effectiveness of the proposed video data augmentation method.

Liaohe National Park based on big data visualization Visitor Perception Study

  • Qi-Wei Jing;Zi-Yang Liu;Cheng-Kang Zheng
    • Journal of the Korea Society of Computer and Information
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    • v.28 no.4
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    • pp.133-142
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    • 2023
  • National parks are one of the important types of protected area management systems established by IUCN and a management model for implementing effective conservation and sustainable use of natural and cultural heritage in countries around the world, and they assume important roles in conservation, scientific research, education, recreation and driving community development. In the context of big data, this study takes China's Liaohe National Park, a typical representative of global coastal wetlands, as a case study, and using Python technology to collect tourists' travelogues and reviews from major OTA websites in China as a source. The text spans from 2015 to 2022 and contains 2998 reviews with 166,588 words in total. The results show that wildlife resources, natural landscape, wetland ecology and the fishing and hunting culture of northern China are fully reflected in the perceptions of visitors to Liaohe National Park; visitors have strong positive feelings toward Liaohe National Park, but there is still much room for improvement in supporting services and facilities, public education and visitor experience and participation.

Design of a designated lane enforcement system based on deep learning (딥러닝 기반 지정차로제 단속 시스템 설계)

  • Bae, Ga-hyeong;Jang, Jong-wook;Jang, Sung-jin
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.10a
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    • pp.236-238
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    • 2022
  • According to the current Road Traffic Act, the 2020 amendment bill is currently in effect as a system that designates vehicle types for each lane for the purpose of securing road use efficiency and traffic safety. When comparing the number of traffic accident fatalities per 10,000 vehicles in Germany and Korea, the number of traffic accident deaths in Germany is significantly lower than in Korea. The representative case of the German autobahn, which did not impose a speed limit, suggests that Korea's speeding laws are not the only answer to reducing the accident rate. The designated lane system, which is observed in accordance with the keep right principle of the Autobahn Expressway, plays a major role in reducing traffic accidents. Based on this fact, we propose a traffic enforcement system to crack down on vehicles violating the designated lane system and improve the compliance rate. We develop a designated lane enforcement system that recognizes vehicle types using Yolo5, a deep learning object recognition model, recognizes license plates and lanes using OpenCV, and stores the extracted data in the server to determine whether or not laws are violated.Accordingly, it is expected that there will be an effect of reducing the traffic accident rate through the improvement of driver's awareness and compliance rate.

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A Study on the Structural Relationship between Employee Services and Store Loyalty (종업원 서비스와 점포충성도간의 구조적 관계에 관한 연구)

  • Yoon, Sung-Wook;Suh, Geun-Ha
    • Asia Marketing Journal
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    • v.6 no.3
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    • pp.59-81
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    • 2004
  • Store loyalty is increasingly being recognized as a path to long-term business profitability. Customer contact employees deliver a service firm's promises and create an important image for the firm. A major purpose of this study is to investigate the effects of customer service and product value on store loyalty. In order to test research hypotheses, data were collected through surveys administered to 300 apparel store customers. Two hundred thirty nine usable data were used for the analysis. The findings of this research are as follows: First, a employee's voluntary service(EVS) has a positive impact on interpersonal r elationship, which then affects switching barrier and store loyalty. Second, a employee's regular service(ERS) has an influence on store satisfaction, which in turn affect store loyalty. Third, product value is shown to be a significant antecedent to store satisfaction, which have a direct effect on store loyalty. The study concludes with implications, contributions, and limitations of the research and the empirical findings of this research should be beneficial to marketing practitioners and retailing businessmen in developing effective marketing strategies.

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