• Title/Summary/Keyword: 데이터 구축

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Analysis-based Pedestrian Traffic Incident Analysis Based on Logistic Regression (로지스틱 회귀분석 기반 노인 보행자 교통사고 요인 분석)

  • Siwon Kim;Jeongwon Gil;Jaekyung Kwon;Jae seong Hwang;Choul ki Lee
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.23 no.2
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    • pp.15-31
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    • 2024
  • The characteristics of elderly traffic accidents were identified by reflecting the situation of the elderly population in Korea, which is entering an ultra-aging society, and the relationship between independent and dependent variables was analyzed by classifying traffic accidents of serious or higher and traffic accidents of minor or lower in elderly pedestrian traffic accidents using binomial variables. Data collection, processing, and variable selection were performed by acquiring data from the elderly pedestrian traffic accident analysis system (TAAS) for the past 10 years (from 13 to 22 years), and basic statistics and analysis by accident factors were performed. A total of 15 influencing variables were derived by applying the logistic regression model, and the influencing variables that have the greatest influence on the probability of a traffic accident involving severe or higher elderly pedestrians were derived. After that, statistical tests were performed to analyze the suitability of the logistic model, and a method for predicting the probability of a traffic accident according to the construction of a prediction model was presented.

A Study on Factors that Improve Waterfront Satisfaction: Targeting Residents of Apartment Houses in the Tokyo's Coastal Area (수변 만족도를 향상시키는 요인에 관한 연구: 도쿄 임해지역 집합주택 거주자를 대상으로)

  • Joohong Kim;Sekyung Oh
    • Land and Housing Review
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    • v.15 no.1
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    • pp.117-134
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    • 2024
  • This study examines the factors contributing to waterfront satisfaction among different generations, utilizing a survey on the use and perception of waterfront areas by residents of apartment houses in Tokyo's coastal region. The variables used in this study are developed through a review of existing literature, focusing on factors related to waterfront usage. Specifically, we select the proximity to the waterfront, duration of stay at the waterfront, and frequency of visits to the waterfront as key variables for investigation. In addition, the study further incorporates aspects related to waterfront recognition, such as the space, user behavior, and management status of the waterfront. Utilizing these variables, we carry out a correlation analysis to investigate the association between generational use and perception of the waterfront and their satisfaction with water-related experiences. The results identify three key factors to enhance waterfront satisfaction: First, to increase visits from both younger and older generations, improving pedestrian access to the waterfront is essential. Second, for middle-aged and older generations, creating waterfront areas closer to their residences can reduce travel distance and time, encouraging more frequent use. Third, maintaining clean and well-kept waterfront spaces is fundamental for all generations to enjoy relaxing experiences, thereby improving overall satisfaction.

Estimation of Frost Occurrence using Multi-Input Deep Learning (다중 입력 딥러닝을 이용한 서리 발생 추정)

  • Yongseok Kim;Jina Hur;Eung-Sup Kim;Kyo-Moon Shim;Sera Jo;Min-Gu Kang
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.26 no.1
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    • pp.53-62
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    • 2024
  • In this study, we built a model to estimate frost occurrence in South Korea using single-input deep learning and multi-input deep learning. Meteorological factors used as learning data included minimum temperature, wind speed, relative humidity, cloud cover, and precipitation. As a result of statistical analysis for each factor on days when frost occurred and days when frost did not occur, significant differences were found. When evaluating the frost occurrence models based on single-input deep learning and multi-input deep learning model, the model using both GRU and MLP was highest accuracy at 0.8774 on average. As a result, it was found that frost occurrence model adopting multi-input deep learning improved performance more than using MLP, LSTM, GRU respectively.

Implementation of an Automated Agricultural Frost Observation System (AAFOS) (농업서리 자동관측 시스템(AAFOS)의 구현)

  • Kyu Rang Kim;Eunsu Jo;Myeong Su Ko;Jung Hyuk Kang;Yunjae Hwang;Yong Hee Lee
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.26 no.1
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    • pp.63-74
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    • 2024
  • In agriculture, frost can be devastating, which is why observation and forecasting are so important. According to a recent report analyzing frost observation data from the Korea Meteorological Administration, despite global warming due to climate change, the late frost date in spring has not been accelerated, and the frequency of frost has not decreased. Therefore, it is important to automate and continuously operate frost observation in risk areas to prevent agricultural frost damage. In the existing frost observation using leaf wetness sensors, there is a problem that the reference voltage value fluctuates over a long period of time due to contamination of the observation sensor or changes in the humidity of the surrounding environment. In this study, a datalogger program was implemented to automatically solve these problems. The established frost observation system can stably and automatically accumulate time-resolved observation data over a long period of time. This data can be utilized in the future for the development of frost diagnosis models using machine learning methods and the production of frost occurrence prediction information for surrounding areas.

Design of Authentication Mechinism for Command Message based on Double Hash Chains (이중 해시체인 기반의 명령어 메시지 인증 메커니즘 설계)

  • Park Wang Seok;Park Chang Seop
    • Convergence Security Journal
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    • v.24 no.1
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    • pp.51-57
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    • 2024
  • Although industrial control systems (ICSs) recently keep evolving with the introduction of Industrial IoT converging information technology (IT) and operational technology (OT), it also leads to a variety of threats and vulnerabilities, which was not experienced in the past ICS with no connection to the external network. Since various control command messages are sent to field devices of the ICS for the purpose of monitoring and controlling the operational processes, it is required to guarantee the message integrity as well as control center authentication. In case of the conventional message integrity codes and signature schemes based on symmetric keys and public keys, respectively, they are not suitable considering the asymmetry between the control center and field devices. Especially, compromised node attacks can be mounted against the symmetric-key-based schemes. In this paper, we propose message authentication scheme based on double hash chains constructed from cryptographic hash function without introducing other primitives, and then propose extension scheme using Merkle tree for multiple uses of the double hash chains. It is shown that the proposed scheme is much more efficient in computational complexity than other conventional schemes.

AutoML Machine Learning-Based for Detecting Qshing Attacks Malicious URL Classification Technology Research and Service Implementation (큐싱 공격 탐지를 위한 AutoML 머신러닝 기반 악성 URL 분류 기술 연구 및 서비스 구현)

  • Dong-Young Kim;Gi-Seong Hwang
    • Smart Media Journal
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    • v.13 no.6
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    • pp.9-15
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    • 2024
  • In recent trends, there has been an increase in 'Qshing' attacks, a hybrid form of phishing that exploits fake QR (Quick Response) codes impersonating government agencies to steal personal and financial information. Particularly, this attack method is characterized by its stealthiness, as victims can be redirected to phishing pages or led to download malicious software simply by scanning a QR code, making it difficult for them to realize they have been targeted. In this paper, we have developed a classification technique utilizing machine learning algorithms to identify the maliciousness of URLs embedded in QR codes, and we have explored ways to integrate this with existing QR code readers. To this end, we constructed a dataset from 128,587 malicious URLs and 428,102 benign URLs, extracting 35 different features such as protocol and parameters, and used AutoML to identify the optimal algorithm and hyperparameters, achieving an accuracy of approximately 87.37%. Following this, we designed the integration of the trained classification model with existing QR code readers to implement a service capable of countering Qshing attacks. In conclusion, our findings confirm that deriving an optimized algorithm for classifying malicious URLs in QR codes and integrating it with existing QR code readers presents a viable solution to combat Qshing attacks.

A Study on the Activation of Pet Plant Kit Industry - Catering to the Demands of Industry Professionals - (반려식물 키트 산업의 활성화 방안에 관한 연구 - 산업 종사자의 수요를 중심으로 -)

  • Roh, Hoi-Eun;Lim, Chae-Jun;Lee, Min-Ji;Jo, Jang-Hwan
    • Journal of the Korean Institute of Landscape Architecture
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    • v.52 no.3
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    • pp.46-58
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    • 2024
  • The purpose of this study is to understand the current status of the pet plant kit industry and determine the priorities for support policies to revitalize the industry. SWOT analysis assessed the industry's current state, and the Analytic Hierarchy Process (AHP) was used with industry professionals to prioritize support policies. The SWOT analysis results indicated that SO strategies involve leveraging government support policies to enhance marketing and developing eco-friendly DIY products. WO strategies include launching advertising campaigns to increase market recognition and establishing strategic partnerships to expand distribution. ST strategies focus on strengthening price competitiveness and proposing unique values, while WT strategies involve improving production processes and enhancing product quality based on consumer feedback. The AHP analysis identified 3 top-level and 12 sub-level evaluation items, with data collected from 17 expert surveys. The results showed the 'entry phase' (0.482), 'activation phase' (0.397), and 'advanced phase' (0.121) were prioritized, with 'organizing seminars' (0.181) as the most crucial subcategory and 'support for kit development' (0.020) as the least. The pet plant kit industry is in its early stages, and appropriate policy incubation can help activate the garden industry. This study provides foundational information on the industry's needs for activation.

Coverage Analysis of VHF Aviation Communication Network for Initial UAM Operations Considering Real Terrain Environments (실제 지형 환경을 고려한 초기 UAM 운용을 위한 VHF 항공통신 커버리지 분석)

  • Seul-Ae Gwon;Seung-Kyu Han;Young-Ho Jung
    • Journal of Advanced Navigation Technology
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    • v.28 no.1
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    • pp.102-108
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    • 2024
  • In the initial stages of urban air mobility (UAM) operations, compliance with existing visual flight rules and instrument flight regulations for conventional human-crewed aircraft is crucial. Additionally, voice communication between the on board pilot and relevant UAM stakeholders, including vertiports, is essential. Consequently, very high frequency (VHF) aviation voice communication must be consistently provided throughout all phases of UAM operations. This paper presents the results of the VHF communication coverage analysis for the initial UAM demonstration areas, encompassing the Hangang River and Incheon Ara-Canal corridors, as well as potential vertiport candidate locations. By considering the influence of terrain and buildings through the utilization of a digital surface model (DSM), communication quality prediction results are obtained for the analysis areas. The three-dimensional coverage analysis results indicate that stable coverage can be achieved within altitude corridors ranging from 300 m to 600 m. However, there are shaded areas in the low-altitude vertiport regions due to the impact of high-rise buildings. Therefore, additional research to ensure stable coverage around vertiports in the lower altitude areas is required.

Information Security Job Skills Requirements: Text-mining to Compare Job Posting and NCS (정보보호 직무 수행을 위해 필요한 지식 및 기술: 텍스트 마이닝을 이용한 구인광고와 NCS의 비교)

  • Hyo-Jung Jun;Byeong-Jo Park;Tae-Sung Kim
    • Information Systems Review
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    • v.25 no.3
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    • pp.179-197
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    • 2023
  • As a sufficient workforce supports the industry's growth, workforce training has also been carried out as part of the industry promotion policy. However, the market still has a shortage of skilled mid-level workers. The information security disclosure requires organizations to secure personnel responsible for information security work. Still, the division between information technology work and job areas is unclear, and the pay is not high for responsibility. This paper compares job keywords in advertisements for the information security workforce for 2014, 2019, and 2022. There is no difference in the keywords describing the job duties of information security personnel in the three years, such as implementation, operation, technical support, network, and security solution. To identify the actual needs of companies, we also analyzed and compared the contents of job advertisements posted on online recruitment sites with information security sector knowledge and skills defined by the National Competence Standards used for comprehensive vocational training. It was found that technical skills such as technology development, network, and operating system are preferred in the actual workplace. In contrast, managerial skills such as the legal system and certification systems are prioritized in vocational training.

The Influencing Mechanism of Manufacturing SMEs' Smart Factory Advancement Acceptance Intention: Based on the Information Systems Success Model (중소제조기업의 스마트팩토리 고도화수용의도 영향 메커니즘: 정보시스템 성공모형을 기반으로)

  • Yoon Jae Kim;Chang-Geun Jeong;Sung-Byung Yang
    • Information Systems Review
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    • v.25 no.3
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    • pp.199-220
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    • 2023
  • Projects to deploy and diffuse smart factories in South Korea are aimed at enhancing national manufacturing competitiveness. However, a significant portion of deployed companies remain at the basic stage and struggle to utilize smart factories regularly. Existing studies have primarily focused on the technical aspects of smart factories, using data analytics and case studies, leading to a gap in empirical research on continuous use and upgrade intentions. This study identifies key factors influencing smart factory usage and user satisfaction, drawing on the Information Systems Success Model (ISSM) and previous research. It empirically examines the impact of these factors on continuous use intention, management performance, and advancement acceptance intention through smart factory usage and user satisfaction. A structural equation model is employed to validate the research hypotheses, using survey data from 287 small and medium-sized manufacturing enterprises (SMEs) that have adopted smart factories. Results demonstrate that system quality, information quality, service quality, and government support significantly affect smart factory usage, while service quality and government support influence user satisfaction. Furthermore, smart factory usage and user satisfaction have positive effects on management performance, continuous use intention, and subsequently advancement acceptance intention. This study provides novel insights by demonstrating the specific impact mechanisms of smart factory user satisfaction on the business and the intentions of manufacturing SMEs regarding continuous use and advancement acceptance, leveraging the ISSM.