• Title/Summary/Keyword: Artificial Intelligence

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A Study on the Overseas Case Analysis of Evacuation Guidance System for High-rise and Underground Complexes (초고층 및 지하연계 복합건축물 피난유도 시스템의 국외 사례 분석 연구)

  • Choi, Byeong-Yun;Kim, Dong-Oh;Seo, Jeong-Wan;Kang, Boo-Seong
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2023.11a
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    • pp.117-118
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    • 2023
  • This study is a basic research study to establish an effective evacuation guidance system in case of fire in high-rise and underground-linked complex buildings. As a result of the analysis of overseas cases, it is judged that it is necessary to develop an evacuation guidance system using artificial intelligence.

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The use of ChatGPT in occupational medicine: opportunities and threats

  • Chayma Sridi;Salem Brigui
    • Annals of Occupational and Environmental Medicine
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    • v.35
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    • pp.42.1-42.4
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    • 2023
  • ChatGPT has the potential to revolutionize occupational medicine by providing a powerful tool for analyzing data, improving communication, and increasing efficiency. It can help identify patterns and trends in workplace health and safety, act as a virtual assistant for workers, employers, and occupational health professionals, and automate certain tasks. However, caution is required due to ethical concerns, the need to maintain confidentiality, and the risk of inconsistent or inaccurate results. ChatGPT cannot replace the crucial role of the occupational health professional in the medical surveillance of workers and the analysis of data on workers' health.

Parking Lot Occupancy Detection using Deep Learning and Fisheye Camera for AIoT System

  • To Xuan Dung;Seongwon Cho
    • Smart Media Journal
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    • v.13 no.1
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    • pp.24-35
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    • 2024
  • The combination of Artificial Intelligence and the Internet of Things (AIoT) has gained significant popularity. Deep neural networks (DNNs) have demonstrated remarkable success in various applications. However, deploying complex AI models on embedded boards can pose challenges due to computational limitations and model complexity. This paper presents an AIoT-based system for smart parking lots using edge devices. Our approach involves developing a detection model and a decision tree for occupancy status classification. Specifically, we utilize YOLOv5 for car license plate (LP) detection by verifying the position of the license plate within the parking space.

Computer-aided polyp characterization in colonoscopy: sufficient performance or not?

  • Natalie Halvorsen;Yuichi Mori
    • Clinical Endoscopy
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    • v.57 no.1
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    • pp.18-23
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    • 2024
  • Computer-assisted polyp characterization (computer-aided diagnosis, CADx) facilitates optical diagnosis during colonoscopy. Several studies have demonstrated high sensitivity and specificity of CADx tools in identifying neoplastic changes in colorectal polyps. To implement CADx tools in colonoscopy, there is a need to confirm whether these tools satisfy the threshold levels that are required to introduce optical diagnosis strategies such as "diagnose-and-leave," "resect-and-discard" or "DISCARD-lite." In this article, we review the available data from prospective trials regarding the effect of multiple CADx tools and discuss whether they meet these thresholds.

Digital Transformation: Using D.N.A.(Data, Network, AI) Keywords Generalized DMR Analysis (디지털 전환: D.N.A.(Data, Network, AI) 키워드를 활용한 토픽 모델링)

  • An, Sehwan;Ko, Kangwook;Kim, Youngmin
    • Knowledge Management Research
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    • v.23 no.3
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    • pp.129-152
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    • 2022
  • As a key infrastructure for digital transformation, the spread of data, network, artificial intelligence (D.N.A.) fields and the emergence of promising industries are laying the groundwork for active digital innovation throughout the economy. In this study, by applying the text mining methodology, major topics were derived by using the abstract, publication year, and research field of the study corresponding to the SCIE, SSCI, and A&HCI indexes of the WoS database as input variables. First, main keywords were identified through TF and TF-IDF analysis based on word appearance frequency, and then topic modeling was performed using g-DMR. With the advantage of the topic model that can utilize various types of variables as meta information, it was possible to properly explore the meaning beyond simply deriving a topic. According to the analysis results, topics such as business intelligence, manufacturing production systems, service value creation, telemedicine, and digital education were identified as major research topics in digital transformation. To summarize the results of topic modeling, 1) research on business intelligence has been actively conducted in all areas after COVID-19, and 2) issues such as intelligent manufacturing solutions and metaverses have emerged in the manufacturing field. It has been confirmed that the topic of production systems is receiving attention once again. Finally, 3) Although the topic itself can be viewed separately in terms of technology and service, it was found that it is undesirable to interpret it separately because a number of studies comprehensively deal with various services applied by combining the relevant technologies.

Approaches to Applying Social Network Analysis to the Army's Information Sharing System: A Case Study (육군 정보공유체계에 사회관계망 분석을 적용하기 위한방안: 사례 연구)

  • GunWoo Park
    • The Journal of the Convergence on Culture Technology
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    • v.9 no.5
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    • pp.597-603
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    • 2023
  • The paradigm of military operations has evolved from platform-centric warfare to network-centric warfare and further to information-centric warfare, driven by advancements in information technology. In recent years, with the development of cutting-edge technologies such as big data, artificial intelligence, and the Internet of Things (IoT), military operations are transitioning towards knowledge-centric warfare (KCW), based on artificial intelligence. Consequently, the military places significant emphasis on integrating advanced information and communication technologies (ICT) to establish reliable C4I (Command, Control, Communication, Computer, Intelligence) systems. This research emphasizes the need to apply data mining techniques to analyze and evaluate various aspects of C4I systems, including enhancing combat capabilities, optimizing utilization in network-based environments, efficiently distributing information flow, facilitating smooth communication, and effectively implementing knowledge sharing. Data mining serves as a fundamental technology in modern big data analysis, and this study utilizes it to analyze real-world cases and propose practical strategies to maximize the efficiency of military command and control systems. The research outcomes are expected to provide valuable insights into the performance of C4I systems and reinforce knowledge-centric warfare in contemporary military operations.

A study of Artificial Life Art as Behavior-oriented Art (행동 지향적 예술로서의 인공생명 아트 연구)

  • Park, Nam-Sik;Jung, Moon-Ryul
    • 한국HCI학회:학술대회논문집
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    • 2009.02a
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    • pp.1081-1086
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    • 2009
  • 기술의 발전은 사회에 많은 변화를 일으키고 있다. 또한 기술의 발전은 예술 영역에 있어서도 형식과 내용에 많은 변화와 영향을 주고 있다. 컴퓨터 아트, 인터액티브 아트, 뉴 미디어 아트라고 불리는 새로운 예술 장르들이 탄생하였으며 예술가들은 다양한 기술과 접목하여 새로운 작품을 만들어 내고 있다. 뉴 미디어 아트의 중요한 특징 중 하나는 상호작용성인데 이것은 예술작품, 예술가, 그리고 관람자의 수용방식에 결정적인 변화를 가져왔다. 즉 뉴미디어 아트서의 예술작품은 완성태가 아닌 과정(process)으로 주어지고, 예술가는 작업의 초안자 또는 작업의 맥락을 규정하는 자로 규정되며, 작품과 관람자간의 상호작용이 무엇보다 강조된다. 그러나 기존의 뉴 미디어 작품에서 일어나는 상호작용성은 미리 계산된 범위 안에서 일어나는 제약이 있기에 진정한 상호작용성이라고 보기 힘들다는 비판도 있다. 이런 상호작용성은 공학적 세계관에 갇힌 닫힌 시스템으로서의 상호작용성이라고 말하며 미적인 상호 작용성의 도구로서 열린 시스템으로서의 새로운 작품의 필요성을 제시한 예술가들이 있다. 본 논문은 이러한 예술가들의 발자취를 따라 더 본질적인 미학적 상호작용성에 대한 고민과 함께 그에 따른 새로운 상호작용적 예술인 행동지향적 예술로서 인공지능, 인공생명 아트에 대하여 살펴보고자 한다.

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Comparison of Artificial Neural Networks for Low-Power ECG-Classification System

  • Rana, Amrita;Kim, Kyung Ki
    • Journal of Sensor Science and Technology
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    • v.29 no.1
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    • pp.19-26
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    • 2020
  • Electrocardiogram (ECG) classification has become an essential task of modern day wearable devices, and can be used to detect cardiovascular diseases. State-of-the-art Artificial Intelligence (AI)-based ECG classifiers have been designed using various artificial neural networks (ANNs). Despite their high accuracy, ANNs require significant computational resources and power. Herein, three different ANNs have been compared: multilayer perceptron (MLP), convolutional neural network (CNN), and spiking neural network (SNN) only for the ECG classification. The ANN model has been developed in Python and Theano, trained on a central processing unit (CPU) platform, and deployed on a PYNQ-Z2 FPGA board to validate the model using a Jupyter notebook. Meanwhile, the hardware accelerator is designed with Overlay, which is a hardware library on PYNQ. For classification, the MIT-BIH dataset obtained from the Physionet library is used. The resulting ANN system can accurately classify four ECG types: normal, atrial premature contraction, left bundle branch block, and premature ventricular contraction. The performance of the ECG classifier models is evaluated based on accuracy and power. Among the three AI algorithms, the SNN requires the lowest power consumption of 0.226 W on-chip, followed by MLP (1.677 W), and CNN (2.266 W). However, the highest accuracy is achieved by the CNN (95%), followed by MLP (76%) and SNN (90%).