• Title/Summary/Keyword: artificial intelligence management

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Artificial Intelligence in Library Instruction (인공지능을 이용한 도서관 이용자 교육)

  • Tak, Hae-Kyung
    • Journal of Information Management
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    • v.27 no.3
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    • pp.41-60
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    • 1996
  • Export system using artificial intelligence give the technology for the varied library user instruction. Expert system showing problem solving process give educational effectives. In this paper, expert system are reviewed to discuss the application possibility in education and the model of intelligent tutoring system(ITS) applying artificial intelligence is presented.

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Artificial Intelligence Image Segmentation for Extracting Construction Formwork Elements (거푸집 부재 인식을 위한 인공지능 이미지 분할)

  • Ayesha Munira, Chowdhury;Moon, Sung-Woo
    • Journal of KIBIM
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    • v.12 no.1
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    • pp.1-9
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    • 2022
  • Concrete formwork is a crucial component for any construction project. Artificial intelligence offers great potential to automate formwork design by offering various design options and under different criteria depending on the requirements. This study applied image segmentation in 2D formwork drawings to extract sheathing, strut and pipe support formwork elements. The proposed artificial intelligence model can recognize, classify, and extract formwork elements from 2D CAD drawing image and training and test results confirmed the model performed very well at formwork element recognition with average precision and recall better than 80%. Recognition systems for each formwork element can be implemented later to generate 3D BIM models.

A Study on Artificial Intelligence Learning Data Generation Method for Structural Member Recognition (구조부재 인식을 위한 인공지능 학습데이터 생성방법 연구)

  • Yoon, Jeong-Hyun;Kim, Si-Uk;Kim, Chee-Kyeong
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2022.04a
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    • pp.229-230
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    • 2022
  • With the development of digital technology, construction companies at home and abroad are in the process of computerizing work and site information for the purpose of improving work efficiency. To this end, various technologies such as BIM, digital twin, and AI-based safety management have been developed, but the accuracy and completeness of the related technologies are insufficient to be applied to the field. In this paper, the learning data that has undergone a pre-processing process optimized for recognition of construction information based on structural members is trained on an existing artificial intelligence model to improve recognition accuracy and evaluate its effectiveness. The artificial intelligence model optimized for the structural member created through this study will be used as a base technology for the technology that needs to confirm the safety of the structure in the future.

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A Study on the Feasibility of IoT and AI-based elderly care system application

  • KANG, Minsoo;KIM, Baek Seob;SEO, Jin Won;KIM, Kyu Ho
    • Korean Journal of Artificial Intelligence
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    • v.9 no.2
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    • pp.15-21
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    • 2021
  • This paper conducted a feasibility study by applying an Internet of Things and Artificial intelligence-based management system for the elderly living alone in an aging society. The number of single-person families over the age of 50 is expected to increase, and problems such as health, safety, and loneliness may occur due to aging. Therefore, by establishing an IoT-based care system for the elderly living alone, a stable service was developed through securing a rapid response system for the elderly living alone and automatically reporting 119. The participants of the demonstration test were subjects under the jurisdiction of the "Seongnam Senior Complex," and the data collection rate between the IoT sensor and the emergency safety gateway was high. During the demonstration period, as a result of evaluating the satisfaction of the IoT-based care system for the elderly living alone, 90 points were achieved. We are currently in the COVID-19 situation. Therefore, the number of elderly living alone is continuously increasing, and the number of people who cannot benefit from care services will continue to occur. Also, even if the COVID-19 situation is over, the epidemic will happen again. So the care system is essential. The elderly care system developed in this way will provide safety management services based on artificial intelligence-based activity pattern analysis, improving the quality of in-house safety services.

A Study on the Field Application and Prospect of Artificial Intelligence and Bio-Sensing Technology in Physical Therapy: Focusing on Customized Rehabilitation Treatment (물리치료 분야에서 인공지능 및 바이오센싱 기술의 현장적용 및 전망에 관한 연구: 맞춤형 재활치료를 중심으로)

  • Kyung-Tae Yoo
    • Journal of the Korean Society of Physical Medicine
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    • v.18 no.3
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    • pp.73-84
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    • 2023
  • PURPOSE: This study analyzed the impact of AI and biosensors on physical therapy, identifying the stage of customized technology development and future prospects. AI and biosensors improve the efficiency, establish customized treatment plans, and expand patient treatment opportunities. The study employed a literature review by searching databases and collecting research. METHODS: This study searched various databases related to the topic, collected existing research, papers, and reports, evaluated the literature, and summarize the results. RESULTS: Exercise therapy utilizing artificial intelligence can provide personalized and optimal exercise plans while monitoring rehabilitation progress. In addition, biosensors such as EMG sensors and accelerometers can monitor the individual progress in physical therapy, particularly in stroke patients, which can help improve physical therapy strategy and promote patient recovery. CONCLUSION: This study suggested that artificial intelligence can be applied in many areas of physical therapy, such as exercise therapy, customized treatment plans, rehabilitation and management, pain management, neuro rehabilitation, and auxiliary devices. Using AI technology, it is possible to analyze and improve exercise and posture, retrain the central nervous system, establish customized treatment plans for individual patients, predict and compare patient progress before and after treatment, and provide customized pain analysis and treatment methods. In addition, AI can provide neuro rehabilitation programs and customized auxiliary devices.

Development of deep autoencoder-based anomaly detection system for HANARO

  • Seunghyoung Ryu;Byoungil Jeon ;Hogeon Seo ;Minwoo Lee;Jin-Won Shin;Yonggyun Yu
    • Nuclear Engineering and Technology
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    • v.55 no.2
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    • pp.475-483
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    • 2023
  • The high-flux advanced neutron application reactor (HANARO) is a multi-purpose research reactor at the Korea Atomic Energy Research Institute (KAERI). HANARO has been used in scientific and industrial research and developments. Therefore, stable operation is necessary for national science and industrial prospects. This study proposed an anomaly detection system based on deep learning, that supports the stable operation of HANARO. The proposed system collects multiple sensor data, displays system information, analyzes status, and performs anomaly detection using deep autoencoder. The system comprises communication, visualization, and anomaly-detection modules, and the prototype system is implemented on site in 2021. Finally, an analysis of the historical data and synthetic anomalies was conducted to verify the overall system; simulation results based on the historical data show that 12 cases out of 19 abnormal events can be detected in advance or on time by the deep learning AD model.

Development of a training program to strengthen the competency of training designers in the era of artificial intelligence (인공지능시대 연수디자이너 역량 강화 연수프로그램 개발)

  • Hong, Yuna;Lim, Eugene;Ha, Young-ja
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.18 no.1
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    • pp.65-77
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    • 2022
  • The purpose of this study is to develop a competency strengthen program with the aim of training designers who respond to changing national policies in the era of artificial intelligence. To this end, this study was conducted through a total of five steps. First, through literature research and analysis of prior research and literature related to the role of training designers and HRD managers was investigated and analyzed, and based on related prior research, theoretical and academic grounds for training designer competency modeling were prepared. In the second stage, a draft training program was developed to strengthen the competency of training designers based on the needs analysis of stakeholders and advice from education experts. In the third stage, a pilot program was conducted based on the draft training program. In step 4, the effectiveness verification was confuted and the deficiencies of the training program were derived from the pilot program. Finally, the final draft of the training program for strengthening the competency of training designers in the era of artificial intelligence was developed. The final training program consisted of 10 modules and was designed to run for three days. The details of specific training program are presented in the article.

Effective E-Learning Practices by Machine Learning and Artificial Intelligence

  • Arshi Naim;Sahar Mohammed Alshawaf
    • International Journal of Computer Science & Network Security
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    • v.24 no.1
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    • pp.209-214
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    • 2024
  • This is an extended research paper focusing on the applications of Machine Learing and Artificial Intelligence in virtual learning environment. The world is moving at a fast pace having the application of Machine Learning (ML) and Artificial Intelligence (AI) in all the major disciplines and the educational sector is also not untouched by its impact especially in an online learning environment. This paper attempts to elaborate on the benefits of ML and AI in E-Learning (EL) in general and explain how King Khalid University (KKU) EL Deanship is making the best of ML and AI in its practices. Also, researchers have focused on the future of ML and AI in any academic program. This research is descriptive in nature; results are based on qualitative analysis done through tools and techniques of EL applied in KKU as an example but the same modus operandi can be implemented by any institution in its EL platform. KKU is using Learning Management Services (LMS) for providing online learning practices and Blackboard (BB) for sharing online learning resources, therefore these tools are considered by the researchers for explaining the results of ML and AI.

Case Study on Artificial Intelligence and Risk Management - Focusing on RAI Toolkit (인공지능과 위험관리에 대한 사례 연구 - RAI Toolkit을 중심으로)

  • Sunyoung Shin
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.24 no.1
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    • pp.115-123
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    • 2024
  • The purpose of this study is to contribute to how the advantages of artificial intelligence (AI) services and the associated limitations can be simultaneously overcome, using the keywords AI and risk management. To achieve this, two cases were introduced: (1) presenting a risk monitoring process utilizing AI and (2) introducing an operational toolkit to minimize the emerging limitations in the development and operation of AI services. Through case analysis, the following implications are proposed. First, as AI services deeply influence our lives, the process are needed to minimize the emerging limitations. Second, for effective risk management monitoring using AI, priority should be given to obtaining suitable and reliable data. Third, to overcome the limitations arising in the development and operation of AI services, the application of a risk management process at each stage of the workflow, requiring continuous monitoring, is essential. This study is a research effort on approaches to minimize limitations provided by advancing artificial intelligence (AI). It can contribute to research on risk management in the future growth and development of the related market, examining ways to mitigate limitations posed by evolving AI technologies.

Development of Facility Layout Design Algorithm Based on Artificial Intelligence Concept (인공지능 개념을 이용한 공장 설비배치 알고리즘 개발)

  • Kim, Hwan-Seong;Lee, Sang-Yong
    • Journal of Korean Society for Quality Management
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    • v.19 no.1
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    • pp.151-162
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    • 1991
  • The purpose of this study is to propose a facility layout design algorithm based on artificial intelligence concept, and then to develop a computer program which is more practical than any other conventional facility layout design systems. The algorithm is composed of five step layout procedures; knowledge and data input, knowledge interpretation, priority determination, inference of layout design, and evaluation, In the step of priority determination, the algorithm is divided into single row and multi row layout problem. In the step of inference of layout design, alternatives are generated by constraints-directed reasoning and depth first search method based on artificial intelligence concept. Alternatives are evaluated by the moving cost and relationship value by interactive man-machine interface in the step of evaluation. As a case study, analytical considerations over conventional programs such as CRAFT and CORELAP was investigated and compared with algorithm propsed in this study. The proposed algorithm in this study will give useful practical tool for layout planner. The computer progran was written in C language for IBM PC-AT.

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