• Title/Summary/Keyword: Electronic learning

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A Smart Farm Environment Optimization and Yield Prediction Platform based on IoT and Deep Learning (IoT 및 딥 러닝 기반 스마트 팜 환경 최적화 및 수확량 예측 플랫폼)

  • Choi, Hokil;Ahn, Heuihak;Jeong, Yina;Lee, Byungkwan
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.12 no.6
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    • pp.672-680
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    • 2019
  • This paper proposes "A Smart Farm Environment Optimization and Yield Prediction Platform based on IoT and Deep Learning" which gathers bio-sensor data from farms, diagnoses the diseases of growing crops, and predicts the year's harvest. The platform collects all the information currently available such as weather and soil microbes, optimizes the farm environment so that the crops can grow well, diagnoses the crop's diseases by using the leaves of the crops being grown on the farm, and predicts this year's harvest by using all the information on the farm. The result shows that the average accuracy of the AEOM is about 15% higher than that of the RF and about 8% higher than the GBD. Although data increases, the accuracy is reduced less than that of the RF or GBD. The linear regression shows that the slope of accuracy is -3.641E-4 for the ReLU, -4.0710E-4 for the Sigmoid, and -7.4534E-4 for the step function. Therefore, as the amount of test data increases, the ReLU is more accurate than the other two activation functions. This paper is a platform for managing the entire farm and, if introduced to actual farms, will greatly contribute to the development of smart farms in Korea.

The Discourse for Academic Generation of 'School library Education Science' (문헌정보교육학의 학문적 생성 담론)

  • Hahm, Myung-Sik
    • Journal of the Korean Society for Library and Information Science
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    • v.42 no.4
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    • pp.223-241
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    • 2008
  • This study presents the discourse for academic generation of 'School Library Education Science' based on integrative research between library & information science and education science. The study discusses research fields and themes of 'School Library Education Science' as the base to define the roles of the school library and of teacher-librarians. It contributes to upgrading the research level of the school library. The research field in the school library analyzes and integrates both aspects of library & information science and of education science. The developed theories of 'School Library Education Science' present Library-Based Education and Library-Based Learning, Base Connectionism, Base Integrationism, Top456 Approach, Curriculum-Based Approach, Viewpoint Approach, and Education of Information and the Library as a Subject Matter. The trends in research of the school library analyze the thesis numbers of academic journals. The future research challenges of 'School Library Education Science' discuss the educational basics of the school library, operational management, electronic school libraries, and educational activities. Conclusively, this study analyzes the current situation of the school library & of teacher-librarians in Korea, and provides its solution by the aspects of 'School Library Education Science'.

Design and Implementation of the Customized Contents Organization Engine (맞춤형 콘텐츠 구성 엔진의 설계 및 구현)

  • Heo, Sun-Young;Kim, Eun-Gyung
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2009.10a
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    • pp.599-601
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    • 2009
  • In currently being adopted as a e-leaning standard, SCORM it is difficult to provide the customized contents to a learner by changing the learner's level at runtime, and to control selective studying. So, we designed and implemented the customized contents organization engine(CCOE) in order to complement SCORM's faults in this paper. The CCOE consists of a level evaluation module, a contents re-organization module and a question item selection module. A level evaluation module evaluates the learner's level based on a question item reaction theory. And a question item selection module selects some random items by each level or by considering the learner's level which is then provided to a studying before evaluation, a section evaluation, and a quiz. And then this module transmits the selected items to the contents reorganization module for providing the quiz. A contents re-organization module selects the customized contents based on the learner's level by searching the tagged difficulty to the content, and creates the sequence with the selected items and the transmitted items from the question item selection module. If proposed in this paper CCOE is applied, the higher effectiveness of learning is expected by providing the customized learning contents based on the re-evaluated learner's level by each section.

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A Study on the Quality Monitoring and Prediction of OTT Traffic in ISP (ISP의 OTT 트래픽 품질모니터링과 예측에 관한 연구)

  • Nam, Chang-Sup
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.14 no.2
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    • pp.115-121
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    • 2021
  • This paper used big data and artificial intelligence technology to predict the rapidly increasing internet traffic. There have been various studies on traffic prediction in the past, but they have not been able to reflect the increasing factors that induce huge Internet traffic such as smartphones and streaming in recent years. In addition, event-like factors such as the release of large-capacity popular games or the provision of new contents by OTT (Over the Top) operators are more difficult to predict in advance. Due to these characteristics, it was impossible for an ISP (Internet Service Provider) to reflect real-time service quality management or traffic forecasts in the network business environment with the existing method. Therefore, in this study, in order to solve this problem, an Internet traffic collection system was constructed that searches, discriminates and collects traffic data in real time, separate from the existing NMS. Through this, the flexibility and elasticity to automatically register the data of the collection target are secured, and real-time network quality monitoring is possible. In addition, a large amount of traffic data collected from the system was analyzed by machine learning (AI) to predict future traffic of OTT operators. Through this, more scientific and systematic prediction was possible, and in addition, it was possible to optimize the interworking between ISP operators and to secure the quality of large-scale OTT services.

Personal Credit Evaluation System through Telephone Voice Analysis: By Support Vector Machine

  • Park, Hyungwoo
    • Journal of Internet Computing and Services
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    • v.19 no.6
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    • pp.63-72
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    • 2018
  • The human voice is one of the easiest methods for the information transmission between human beings. The characteristics of voice can vary from person to person and include the speed of speech, the form and function of the vocal organ, the pitch tone, speech habits, and gender. The human voice is a key element of human communication. In the days of the Fourth Industrial Revolution, voices are also a major means of communication between humans and humans, between humans and machines, machines and machines. And for that reason, people are trying to communicate their intentions to others clearly. And in the process, it contains various additional information along with the linguistic information. The Information such as emotional status, health status, part of trust, presence of a lie, change due to drinking, etc. These linguistic and non-linguistic information can be used as a device for evaluating the individual's credit worthiness by appearing in various parameters through voice analysis. Especially, it can be obtained by analyzing the relationship between the characteristics of the fundamental frequency(basic tonality) of the vocal cords, and the characteristics of the resonance frequency of the vocal track.In the previous research, the necessity of various methods of credit evaluation and the characteristic change of the voice according to the change of credit status were studied. In this study, we propose a personal credit discriminator by machine learning through parameters extracted through voice.

Line Tracer Modeling for Educational Virtual Experiment (교육용 가상실험 라인 트레이서 모델링)

  • Ki, Jang-Geun;Kwon, Kee-Young
    • Journal of Software Assessment and Valuation
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    • v.17 no.2
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    • pp.109-116
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    • 2021
  • Traditionally, the engineering field has been dominated by face-to-face education focused on experimental practice, but demand for online learning has soared due to the rapid development of IT technology and Internet communication networks and recent changes in the social environment such as COVID-19. In order for efficient online education to be conducted in the engineering field, where the proportion of experimental practice is relatively high compared to other fields, virtual laboratory practice content that can replace actual experimental practice is very necessary. In this study, we developed a line tracer model and a virtual experimental software to simulate it for efficient online learning of microprocessor applications that are essential not only in the electric and electronic field but also in the overall engineering field where IT convergence takes place. In the developed line tracer model, the user can set various hardware parameter values in the desired form and write the software in assembly language or C language to test the operation on the computer. The developed line tracer virtual experimental software has been used in actual classes to verify its operation, and is expected to be an efficient virtual experimental practice tool in online non-face-to-face classes.

Adaptive-learning Code Allocation Technique for Improving Dimming Level and Reducing Flicker in Visible Light Communication (가시광통신에서 Dimming Level 향상 및 Flicker 감소를 위한 적응-학습 코드할당 기법)

  • Lee, Kyu-Jin;Han, Doo-Hee
    • Journal of Convergence for Information Technology
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    • v.12 no.2
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    • pp.30-36
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    • 2022
  • In this paper, when the lighting and communication functions of the visible light communication system are used at the same time, we propose a technique to reduce the dimming level and flicker of the lighting. Visible light communication must satisfy both communication and lighting performance. However, the existing data code method results in reducing the brightness of the entire lighting. This causes deterioration of lighting performance and flicker phenomenon. To solve this problem, in this paper, we propose an adaptive learning code allocation technique that allocates binary codes to transmitted characters and optimizes and matches the binary codes allocated according to the frequency of occurrence of alphabets in character strings. Through this, we studied a technique that can faithfully play the role of lighting as well as communication function by allocating codes so that the 'OFF' pattern does not occur continuously while maintaining the maximum dimming level of each character string. As a result of the performance evaluation, the frequency of occurrence of '1' increased significantly without significantly affecting the overall communication performance, and on the contrary, the frequency of consecutive '0' decreased, indicating that the lighting performance of the system was greatly improved.

Evaluation of Attention Level on College Students by Application of Cryotherapy in the Posterior Region of Neck (후경부 냉동요법 적용을 통한 대학생의 주의력 수준 평가)

  • Ji Hong Chang
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.16 no.5
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    • pp.272-278
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    • 2023
  • Attention, an important element of human cognitive function, is an important cognitive function that focuses only on necessary stimuli from various stimuli through sensory systems. Although various studies have shown that environmental conditions may affect the level of attention, the effect on attention by applying cooling directly to the skin has not been studied much. Differences in attention depending on when and whether cryotherapy was applied during learning and relaxation activities were examined among 36 subjects. The FAIR attention test, a visual attention evaluation tool, was used, and statistical analysis was conducted on the results of the sub-index of FAIR: Performance index, Quality index, and Continuity index. Among participants who performed rest activities, the application of cryotherapy appeared to have no effect on any subindex. Among the participants who performed learning activities, the group that applied pre-cryotherapy (P=469.0, C=435.4)) and the group that applied post-cryotherapy (P=457.4, C=425.4) showed higher Performance index and Continuity index than the group that did not apply cryotherapy (P=335.8, C=301.7). From these results, it can be concluded that cryotherapy maintains the level of alertness and awakens attention, albeit to a limited extent.

A Study on Applying the Nonlinear Regression Schemes to the Low-GloSea6 Weather Prediction Model (Low-GloSea6 기상 예측 모델 기반의 비선형 회귀 기법 적용 연구)

  • Hye-Sung Park;Ye-Rin Cho;Dae-Yeong Shin;Eun-Ok Yun;Sung-Wook Chung
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.16 no.6
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    • pp.489-498
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    • 2023
  • Advancements in hardware performance and computing technology have facilitated the progress of climate prediction models to address climate change. The Korea Meteorological Administration employs the GloSea6 model with supercomputer technology for operational use. Various universities and research institutions utilize the Low-GloSea6 model, a low-resolution coupled model, on small to medium-scale servers for weather research. This paper presents an analysis using Intel VTune Profiler on Low-GloSea6 to facilitate smooth weather research on small to medium-scale servers. The tri_sor_dp_dp function of the atmospheric model, taking 1125.987 seconds of CPU time, is identified as a hotspot. Nonlinear regression models, a machine learning technique, are applied and compared to existing functions conducting numerical operations. The K-Nearest Neighbors regression model exhibits superior performance with MAE of 1.3637e-08 and SMAPE of 123.2707%. Additionally, the Light Gradient Boosting Machine regression model demonstrates the best performance with an RMSE of 2.8453e-08. Therefore, it is confirmed that applying a nonlinear regression model to the tri_sor_dp_dp function during the execution of Low-GloSea6 could be a viable alternative.

Development of a Simulator for Optimizing Semiconductor Manufacturing Incorporating Internet of Things (사물인터넷을 접목한 반도체 소자 공정 최적화 시뮬레이터 개발)

  • Dang, Hyun Shik;Jo, Dong Hee;Kim, Jong Seo;Jung, Taeho
    • Journal of the Korea Society for Simulation
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    • v.26 no.4
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    • pp.35-41
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    • 2017
  • With the advances in Internet over Things, the demand in diverse electronic devices such as mobile phones and sensors has been rapidly increasing and boosting up the researches on those products. Semiconductor materials, devices, and fabrication processes are becoming more diverse and complicated, which accompanies finding parameters for an optimal fabrication process. In order to find the parameters, a process simulation before fabrication or a real-time process control system during fabrication can be used, but they lack incorporating the feedback from post-fabrication data and compatibility with older equipment. In this research, we have developed an artificial intelligence based simulator, which finds parameters for an optimal process and controls process equipment. In order to apply the control concept to all the equipment in a fabrication sequence, we have developed a prototype for a manipulator which can be installed over an existing buttons and knobs in the equipment and controls the equipment communicating with the AI over the Internet. The AI is based on the deep learning to find process parameters that will produce a device having target electrical characteristics. The proposed simulator can control existing equipment via the Internet to fabricate devices with desired performance and, therefore, it will help engineers to develop new devices efficiently and effectively.