• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.551-558
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• 2021

In this paper, we made the integrated intelligent air ventilation of the actuator module that can be remotely controlled based on IoT and sensors. we implemented a ventilation window system by configuring an algorithm design and a driving circuit to control the operation of the actuator to open and close the ventilation port based on a predetermined number of data that detects indoor gas/CO2/humidity temperature and outdoor fine dust related indoor/outdoor environment. It is difficult to store, manage, and analyze data due to the large number of sensors and conditions for the transmission data of indoor air circulation module. The remote monitoring and remote wireless control screens were constructed to automate the separation and operation conditions by extracting and managing the state. We apply MQTT to enhance big data transmission and construct the system using Rocket MQ to ensure safe transmission of operational big data against system errors.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.809-818
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• 2024

This paper proposes an action plan for local governments to safely utilize artificial intelligence technology in various local government policies. The proposed method analyzes cases of application of artificial intelligence-related laws and policies in Gyeonggi Province, Seoul City, and New York City, and then presents matters that local governments should consider when utilizing AI technology in their policies. This paper applies the AILocalism-Korea analysis methodology, which is a modified version of the AILocalsm analysis methodology[1] presented by TheGovLab at New York University. AILocalism-Korea is an analysis methodology created to analyze the current activities of each local government in the fields of legal system, public procurement, mutual cooperation, and citizen participation, and to suggest practical alternatives in each area. In this paper, we use this analysis methodology to present 9 action plans that local governments should take based on safe and reliable use of artificial intelligence. By utilizing various AI technologies through the proposed plan in local government policies, it will be possible to realize reliable public services.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.149-156
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• 2021

This study started from the question of what we think about big data as the term "big data" was used and discussed in our daily lives in the era of the 4th industrial revolution. For the analysis, the final 30 Q samples were selected based on prior research related to big data, and 23 respondents were secured for Q analysis, and the following results were derived. First, the explanatory power of each type was 34.30% for , 8.03% for , 7.21% for , and 6.24% for , showing a total of 55.69%. Second, the Q sample emphasized by respondents by each type shows various occupational distributions in , and for 'big data', it is 'digital' and future'. So they were named 「Digital Type」. In , the distribution of 'social workers' was high, and for 'big data', 'future', 'collaboration', 'welfare', 'local residents', and 'defense' were emphasized. It was named 「welfare type」. In , the job distribution of respondents appeared evenly, and it was named as 「Convergence Type」. Because it emphasized statements such as 'convergence', 'digital', 'future', and 'sports'. is composed of association officials, sports instructors, and graduate students, and was named 「Artificial Intelligence Type」, because it emphasizes 'artificial intelligence', 'new paradigm', 'network', and 'sports'. In the age of knowledge industrialization and knowledge informatization that followed industrialization and informatization, how to process and utilize the numerous data accumulated over the years is an important task. Right now, in sports, more than anything else, it is necessary to continuously seek ways to utilize and activate accumulated big data.

• Convergence Security Journal
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• v.21 no.1
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• pp.93-100
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• 2021

Damage caused by intelligent cyber attacks not only disrupts system operations and leaks information, but also entails massive economic damage. Recently, cyber attacks have a distinct goal and use advanced attack tools and techniques to accurately infiltrate the target. In order to minimize the damage caused by such an intelligent cyber attack, it is necessary to block the cyber attack at the beginning or during the attack to prevent it from invading the target's core system. Recently, technologies for predicting cyber attack paths and analyzing risk level of cyber attack using big data or artificial intelligence technologies are being studied. In this paper, a cyber attack path analysis method using attack tree and RFI is proposed as a basic algorithm for the development of an automated cyber attack path prediction system. The attack path is visualized using the attack tree, and the priority of the path that can move to the next step is determined using the RFI technique in each attack step. Based on the proposed mechanism, it can contribute to the development of an automated cyber attack path prediction system using big data and deep learning technology.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.91-103
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• 2024

In this study, we propose an outlier detection method that considers the spatial distribution of intelligent compaction measurement values (ICMVs) to address the high variability of ICMVs measured continuously across an entire construction area. The proposed method initially identified cases where the CMV at a specific location decreased despite an increase in the number of compaction passes. Among these, values that significantly differed from those measured within a 1.5-m radius were classified as outliers. Applying this method to CMV data obtained from field tests, we found that it effectively excluded the influence of changes in roller operating conditions unrelated to compaction quality while considering the inherent heterogeneity of the soil. However, after removing the outliers, the coefficient of variation of CMV (21.4%-26.3%) remained higher than the 20% suggested by relevant standards. Further field tests are needed to modify the proposed outlier detection method and to establish reasonable criteria for the variability of ICMV.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.11
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• pp.1036-1044
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• 2012

Thanks to progressive development of IT technology, the number of intelligent devices communicating each other through an In-Vehicle Network(IVN) has been steadily increasing. It is expected that the required network bandwidth and network nodes for vehicle control in 2015 will be increased by two times and one and half times as compared to in 2010, respectively. As a result, many researchers in automotive industry has showed a significant interest on industrial Ethernets, such as EtherCAT and TTEthernet. This paper addresses an analysis on transmission delay and fault recovery performance with an EtherCAT network which is being considered as an IVN. A mathematical model based on the analysis is verified through a set of experiments using an experimental network setup.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.146-152
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• 2010

In this paper, we implemented the intelligent healthcare system self-diagnosis that can achieve self-diagnosis by measured bio-signal(blood pressure, blood sugar, body fat monitor) after the recognize a user to access using RFID. The implemented healthcare self-diagnosis intelligent system is consist of kiosk structure that is RFID reader, bio-signal measuring instrument(hemadynamometer, glucometer, body fat monitor), computer for a part of database server and printer for print the result of self-diagnosis. It can achieve self-diagnosis of a user after compare and analyze the measured data and information of a user from database. The implemented system can make simple self-diagnosis even if not take a physical examination at hospital and apply to company, school, etc.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.133-142
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• 2021

With the recent advancements of deep learning, companies such as smart home, healthcare, and intelligent transportation systems are utilizing its functionality to provide high-quality services for vehicle detection, emergency situation detection, and controlling energy consumption. To provide reliable services in such sensitive systems, deep learning models are required to have high accuracy. In order to develop a deep learning model for analyzing previously mentioned services, developers should utilize the state of the art deep learning models that have already been verified for higher accuracy. The developers can verify the accuracy of the referenced model by validating the model on the dataset. For this validation, the developer needs structural information to document and apply deep learning models, including metadata such as learning dataset, network architecture, and development environments. In this paper, we propose a description language that represents the network architecture of the deep learning model along with its metadata that are necessary to develop a deep learning model. Through the proposed description language, developers can easily verify the accuracy of the referenced deep learning model. Our experiments demonstrate the application scenario of a deep learning description document that focuses on the license plate recognition for the detection of illegally parked vehicles.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.619-621
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• 2020

본 논문에서는 메이커 교육을 기초로 인공지능 챗봇 수업을 개발하였다. 세부적으로 R. M. Gagne(1985)에 9가지 이론을 기초로 정보교과 문제해결과 프로그래밍 단원의 지도안을 작성 후 평가를 제시하였다. 연구 내용 분석 결과 교육현장에서 인공지능 교육의 필요성이 강조되며 확고한 플랫폼 구축(인공지능 플랫폼)과 빅데이터 분석·확보하여 개인 맞춤형 서비스 제공이 필요하다. 본 논문을 토대로 인공지능 교육의 체계적인 연구 활성화에 시발점이 되었으면 한다.

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.11a
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• pp.151-154
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• 2006

본 연구는 공급사슬관리하에서 부품 및 원재료 공급기업에서 고객기업인 제조기업의 주문 사항과 공급기업내 생산관련 제약사항을 동시에 고려하여 최적의 생산 체제를 구축할 수 있는 방법론을 제시한다. 본 연구에서는 수주 및 비용 데이터베이스로부터 주문 및 생산관련 데이터를 SOM 신경망분석을 통해 그룹핑하고 고객기업군별로 특성분석을 통해 이에 맞는 생산체제를 구축할 것을 제안하였다. 공급사슬관리 환경하에서 원재료/부품 공급기업이 고객기업의 주문 요구와 내부 생산상의 제약을 동시에 고려함으로써 SCM 적용 성과를 극대화할 수 있다는 점에서 본 연구는 의미가 있다.