• Title/Summary/Keyword: artificial intelligence (AI)

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Prototypical Eye Shape Classification to Solve Life-and-Death Problem in Go, using Monte-Carlo Method and Point Pattern Matching (몬테카를로 방법과 점 패턴 매칭을 활용한 바둑에서의 사활문제 해결을 위한 원형 안형의 분류)

  • Lee, Byung-Doo
    • Journal of Korea Game Society
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    • v.21 no.6
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    • pp.31-40
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    • 2021
  • Go has a history of more than 2,500 years, and the life-and-death problems in Go is a fundamental problem domain that must be solved when implementing a computer Go. We attempted to determine the numbers of prototypical eye shapes with 3, 4, 5, and 6 eyes that are directly related to the life-and-death problems, and to classify the prototypical eye shapes represented in 4-tuple forms. Experiment was conducted by Monte-Carlo method and point pattern matching. According to the experimental results, the numbers of prototypical eye shapes were 2 for 3-eye, 5 for 4-eye, 12 for 5-eye, and 35 for 6-eye shapes. Further, using a 4-tuple form, we classified prototypical eye shapes into 1 for 3-eye, 3 for 4-eye, 4 for 5-eye, and 8 for 6-eye shapes.

Study of Black Ice Detection Method through Color Image Analysis (컬러 이미지 분석을 통한 블랙 아이스 검출 방법 연구)

  • Park, Pill-Won;Han, Seong-Soo
    • Journal of Platform Technology
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    • v.9 no.4
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    • pp.90-96
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    • 2021
  • Most of the vehicles currently under development and in operation are equipped with various IoT sensors, but some of the factors that cause car accidents are relatively difficult to detect. One of the major risk factors among these factors is black ice. Black ice is one of the factors most likely to cause major accidents, as it can affect all vehicles passing through areas covered with black ice. Therefore, black ice detection technique is essential to prevent major accidents. For this purpose, some studies have been carried out in the past, but unrealistic factors have been reflected in some parts, so research to supplement this is needed. In this paper, we tried to detect black ice by analyzing color images using the CNN technique, and we succeeded in detecting black ice to a certain level. However, there were differences from previous studies, and the reason was analyzed.

Development of SW-STEAM Education Program Using Monte Carlo Simulation: Focusing on Mendelian Inheritance (몬테카를로 시뮬레이션을 활용한 SW융합교육 프로그램 개발: 멘델의 유전 원리를 중심으로)

  • Kim, Bongchul;Yoo, Hyejin;Oh, Seungtak;Namgoong, Dongkook;Kim, Jonghoon
    • Journal of The Korean Association of Information Education
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    • v.26 no.2
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    • pp.97-104
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    • 2022
  • As the era of digital transformation begins in earnest, the importance of convergent thinking based on software, artificial intelligence, and big data is increasing. In line with these social needs, this study developed a 5th hour SW-STEAM education program using Monte Carlo simulation techniques for Mendelian inheritance in the field of life science. By programming and implementing Mendelian inheritance using Monte carlo simulation, the program was organized so that not only convergent thinking skills but also related knowledge could be understood in depth. In order to verify the validity of the developed education program, 11 experts in related fields were requested to test the content validity, and the validity was verified by meeting the CVR reference value of 0.59 suggested by Lawshe.

Development of External Expansion Devices and Convergence Contents for Future Education based on Software Teaching Tools (소프트웨어 교육용 교구 활용 미래 교육을 위한 융합 콘텐츠 및 외부 확장장치 개발)

  • Ju, Yeong-Tae;Kim, Jong-Sil;Kim, Eung-Kon
    • The Journal of the Korea institute of electronic communication sciences
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    • v.16 no.6
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    • pp.1317-1322
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    • 2021
  • Software in the era of the Fourth Industrial Revolution is becoming a key foundation in an intelligent information society. Therefore, it is necessary to study the new direction of manpower training and education that can cope with the times. To this end, the Ministry of Education reorganized the curriculum and is implementing software education based on a logical problem-solving process based on computing thinking skills rather than acquiring general ICT knowledge. However, there is a lack of securing high-quality educational content for software education, and there is also a lack of teaching aids that can be taught in connection with advanced IT technologies. To overcome this, this paper proposes the development of external expansion devices to expand educational content and functions capable of convergent software education such as artificial intelligence using coding robots for software education. Through this, effective software education is possible by improving the curriculum of the existing simple problem-solving method and developing various learning materials.

Wettability and Intermetallic Compounds of Sn-Ag-Cu-based Solder Pastes with Addition of Nano-additives (나노 첨가제에 따른 Sn-Ag-Cu계 솔더페이스트의 젖음성 및 금속간화합물)

  • Seo, Seong Min;Sri Harini, Rajendran;Jung, Jae Pil
    • Journal of the Microelectronics and Packaging Society
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    • v.29 no.1
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    • pp.35-41
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    • 2022
  • In the era of Fifth-Generation (5G), technology requirements such as Artificial Intelligence (AI), Cloud computing, automatic vehicles, and smart manufacturing are increasing. For high efficiency of electronic devices, research on high-intensity circuits and packaging for miniaturized electronic components is important. A solder paste which consists of small solder powders is one of common solder for high density packaging, whereas an electroplated solder has limitation of uniformity of bump composition. Researches are underway to improve wettability through the addition of nanoparticles into a solder paste or the surface finish of a substrate, and to suppress the formation of IMC growth at the metal pad interface. This paper describes the principles of improving the wettability of solder paste and suppressing interfacial IMC growth by addition of nanoparticles.

Research Trend of the Remote Sensing Image Analysis Using Deep Learning (딥러닝을 이용한 원격탐사 영상분석 연구동향)

  • Kim, Hyungwoo;Kim, Minho;Lee, Yangwon
    • Korean Journal of Remote Sensing
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    • v.38 no.5_3
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    • pp.819-834
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    • 2022
  • Artificial Intelligence (AI) techniques have been effectively used for image classification, object detection, and image segmentation. Along with the recent advancement of computing power, deep learning models can build deeper and thicker networks and achieve better performance by creating more appropriate feature maps based on effective activation functions and optimizer algorithms. This review paper examined technical and academic trends of Convolutional Neural Network (CNN) and Transformer models that are emerging techniques in remote sensing and suggested their utilization strategies and development directions. A timely supply of satellite images and real-time processing for deep learning to cope with disaster monitoring will be required for future work. In addition, a big data platform dedicated to satellite images should be developed and integrated with drone and Closed-circuit Television (CCTV) images.

Compact UWB Log Periodic Right Triangle-Shaped Dipole Array Antenna Appended With Strips (스트립이 추가된 소형 UWB 대수 주기 직각 삼각형-모양 다이폴 배열 안테나)

  • Yeo, Junho;Lee, Jong-Ig
    • Journal of Advanced Navigation Technology
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    • v.26 no.5
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    • pp.344-349
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    • 2022
  • A compact LPDA antenna consisting of right triangle-shaped dipole elements appended with strips is proposed for UWB applications. First, right triangle-shaped dipole elements are used instead of conventional strip dipole elements to reduce the width of the LPDA antenna. Second, the spacing between the LPDA elements is decreased to reduce the length of the LPDA antenna. Finally, strips are appended at the ends of the right triangle-shaped dipole elements in order to further reduce the width of the antenna. A prototype of the proposed antenna with 16 elements and gain > 4 dBi is fabricated on an FR4 substrate with dimensions of 44 mm×30 mm. Measured frequency band of the fabricated antenna is 2.99-14.76 GHz for a VSWR < 2, which ensures UWB operation, and measured gain range is 4.0-5.5 dBi with a front-to-back ratio larger than 10 dB. The length and width of the proposed compact LPDA antenna are reduced by 40.9% and 20.6%, respectively, compared to the conventional LPDA.

Development of a driver's emotion detection model using auto-encoder on driving behavior and psychological data

  • Eun-Seo, Jung;Seo-Hee, Kim;Yun-Jung, Hong;In-Beom, Yang;Jiyoung, Woo
    • Journal of the Korea Society of Computer and Information
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    • v.28 no.3
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    • pp.35-43
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    • 2023
  • Emotion recognition while driving is an essential task to prevent accidents. Furthermore, in the era of autonomous driving, automobiles are the subject of mobility, requiring more emotional communication with drivers, and the emotion recognition market is gradually spreading. Accordingly, in this research plan, the driver's emotions are classified into seven categories using psychological and behavioral data, which are relatively easy to collect. The latent vectors extracted through the auto-encoder model were also used as features in this classification model, confirming that this affected performance improvement. Furthermore, it also confirmed that the performance was improved when using the framework presented in this paper compared to when the existing EEG data were included. Finally, 81% of the driver's emotion classification accuracy and 80% of F1-Score were achieved only through psychological, personal information, and behavioral data.

Smart Livestock Research and Technology Trend Analysis based on Intelligent Information Technology to improve Livestock Productivity and Livestock Environment (축산물 생산성 향상 및 축산 환경 개선을 위한 지능정보기술 기반 스마트 축사 연구 및 기술 동향 분석)

  • Kim, Cheol-Rim;Kim, Seungchoen
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.22 no.4
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    • pp.133-139
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    • 2022
  • Recently, livestock farms in Korea are introducing data-based technologies to improve productivity, such as livestock environment and breeding management, safe livestock production, and animal welfare. In addition, the government has been conducting a smart livestock distribution project since 2017 through the modernization of ICT-based livestock facilities in order to improve the productivity of livestock products and improve the livestock environment as a policy. However, the current smart livestock house has limitations in connection, diversity, and integration between monitoring and control. Therefore, in order to intelligently systemize all processes of livestock with intelligent algorithms and remote control in order to link and integrate various monitoring and control, the Internet of Things, big data, artificial intelligence, cloud computing, and mobile It is necessary to develop a smart livestock system. In this study, domestic and foreign research trends related to smart livestock based on intelligent information technology were introduced and the limitations of domestic application of advanced technologies were analyzed. Finally, future intelligent information technology applicable to the livestock field was examined.

KAB: Knowledge Augmented BERT2BERT Automated Questions-Answering system for Jurisprudential Legal Opinions

  • Alotaibi, Saud S.;Munshi, Amr A.;Farag, Abdullah Tarek;Rakha, Omar Essam;Al Sallab, Ahmad A.;Alotaibi, Majid
    • International Journal of Computer Science & Network Security
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    • v.22 no.6
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    • pp.346-356
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    • 2022
  • The jurisprudential legal rules govern the way Muslims react and interact to daily life. This creates a huge stream of questions, that require highly qualified and well-educated individuals, called Muftis. With Muslims representing almost 25% of the planet population, and the scarcity of qualified Muftis, this creates a demand supply problem calling for Automation solutions. This motivates the application of Artificial Intelligence (AI) to solve this problem, which requires a well-designed Question-Answering (QA) system to solve it. In this work, we propose a QA system, based on retrieval augmented generative transformer model for jurisprudential legal question. The main idea in the proposed architecture is the leverage of both state-of-the art transformer models, and the existing knowledge base of legal sources and question-answers. With the sensitivity of the domain in mind, due to its importance in Muslims daily lives, our design balances between exploitation of knowledge bases, and exploration provided by the generative transformer models. We collect a custom data set of 850,000 entries, that includes the question, answer, and category of the question. Our evaluation methodology is based on both quantitative and qualitative methods. We use metrics like BERTScore and METEOR to evaluate the precision and recall of the system. We also provide many qualitative results that show the quality of the generated answers, and how relevant they are to the asked questions.