• 한국의상디자인학회지
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• 제21권1호
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• pp.115-129
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• 2019

This study aimed to develop methods for technical design education that can be intimately connected to the industrial field. For this, technical design jobs performed in the fields of the domestic and foreign fashion industries and their required competences were examined, and educational methods based on constructivism were proposed. Korean fashion technical designers' works were identified, and then the fashion technical designer's responsibilities and qualifications were collected and analyzed from global employment sites. On the basis of the collection and analysis, hands-on staff members and education experts were interviewed about required competences for the actual business and possible suitable methods for education. The results of research showed that in the case of the US, job systems and relevant duties for technical designers were clearly defined by clothing brands, whereas in Korea, businesses were systematized around vendors, not brands, and as a result the businesses of technical package composition and specification proposals were not performed properly. This study organized the contents of technical design education into fit development and specification, the composition of technical design packages, the evaluation and approval of samples, fit schedule management and fitting, block pattern setting and pattern correction, sewing specifications appropriate for styles and materials, grading, technical terms, and production management. As for the technical design education models, the cognitive apprenticeship model, resource-based learning, the problem-based and anchored model, and the problem-based and resource-based models were proposed.

• 수산해양교육연구
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• 제21권1호
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• pp.16-27
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• 2009

The purpose of this study was to develop of learning strategy scale for college students. This study further classified several sub-areas and defined each concepts of learning strategy. Based upon the classification of each sub-areas, tentative test items were developed through the verification of validity by three professionals. A pilot study of the developed scale was administered to 239 college students. And the research collected major data from 1,012 college students. Data were analyzed to obtain item quality, reliability, and validity analysis. The results of this study were as follows. The scale for learning strategy was defined by eight factors and they were 'self-management strategy', 'examination-readiness strategy', 'cognitive strategy', 'memorizing strategy', 'reporting strategy', 'resource-utilization strategy', 'self-regulated strategy', 'cooperative learning strategy'. The results of the confirmatory factor analysis proved the eight factors in the learning strategy. And criterion validity evidence was also obtained from a correlation analysis of the level of academic achievement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• 인터넷정보학회논문지
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• 제18권5호
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• pp.69-77
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• 2017

본 논문에서는 재난상황에서 딥러닝과 증강현실 기술을 활용한 재난대응 방안과 그에 따른 네트워크 자원 확보 방안을 제안한다. 딥러닝과 증강현실 기술의 특징과 현황을 파악하고, 재난분야와의 연관성에 관하여 설명한다. 딥러닝 기술을 사용하여 재난 상황을 정확하게 인지하고 관련 재난 정보를 증강현실로 구현하여 재난대응 현장 및 통합지원본부, 재난안전대책본부 등에 제공함으로써 재난대응 역량을 강화할 수 있다. 각종 재난사례 중 화재상황을 중점으로, 딥러닝 기반 화재상황 인식 및 증강현실 정보제공을 통해 효과적으로 재난대응 역량을 강화할 수 있는 방안을 제시한다. 마지막으로, 본 논문의 재난대응 방안을 활용하기 위한 네트워크 자원 확보 기법을 제시한다.

• 농촌지도와개발
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• 제17권2호
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• pp.261-277
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• 2010

This study is undertaken to present the facilitation plan of English library that specialized the resource-based learning to provide learning that is suited for student's own learning style and self-leading learning as the method to learn contents required for individuals as a method for improving the English education environment in the rural areas. For this purpose, a study was conducted to find out the possibility of facilitating English library in the rural areas on the basis of consulting for structuring and operating English library in public libraries located in isolated areas clustered with low income class in the urban areas where the conditions are similar to the rural areas and results are shown as the followings. First, it displayed the possibility to have the rural area located with many closed schools or small-sized schools to facilitate the available facilities to build up the environment to specialize in English education that would be as comparable as any facilities in any urban setting. Second, it would enable the conditions to moderate the conflict on education environment for local residents who felt inequality in education by providing the benefit for fine education linked to public education through English library without going through private education. And third, English library that has the limitations in locality or economic means would actively participate by local educational institutions and volunteers to enhance the sense of master for the local residents and bring residents together to make positive impact on local economy facilitation.

• 아태비즈니스연구
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• 제11권3호
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• pp.261-271
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• 2020

Purpose - This study was to establish a unified tourism management class for university students based on Gangwon-do. Community based learning(CBL) was applied to provide a tangible and intangible resource of tourism resources the theoretical approaches and the actual experiences of the community. Design/methodology/approach - In order to design a unified tourism management module, this study applied qualitative research and quantitative research methods to collect information on the direction of the module. the study conducted in-depth interviews and then an online survey. Findings - According to the results of the study, the main parts should include necessity of unification, inter-Korean tourism, inter-Korean cooperation, inter-Korean economy, and international relations. Research implications or Originality - The overall composition of the unification tourism management class should be designed as the unification tourism management theory to acquire the subject knowledge, the field trip to the border area for experiential learning, and the assignment of the field study task to understand the community.

• 감성과학
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• 제13권1호
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• pp.225-234
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• 2010

본 연구는 효과적인 감성교육의 방향을 모색하기 위하여, 학습자의 긍정적 감성과 학습 및 동기의 관계에 대한 연구의 흐름을 고찰하였다. 이를 위하여 본 연구는 긍정적 감성의 정의를 살펴보고, 긍정적 감성이 인간의 인지적 영역, 창의성, 사회성, 삶의 만족도 등의 심리적 자원에 미치는 긍정적 효과, 긍정적 감성과 동기의 정적 관계에 대한 선행연구들, 긍정적 감성과 학습에 대한 연구들을 살펴보았다. 특히 본 연구는 교육에서의 긍정적 감성의 역할을 보다 과학적으로 탐구하기 위하여, 최신 뇌기반 학습과학(brain-based learning) 연구결과들을 살펴보았다. 즉 교육학, 심리학, 인지과학, 뇌과학 분야 등의 다양한 연구결과들을 고찰하여 교육에서의 긍정적 감성의 역할을 확인하였다. 또한 본 연구는 긍정적 감성이 실제적으로 긍정적 감성을 향상시키기 위한 구체적 교육 프로그램을 살펴보기 위하여, 자율성 지지적 환경에 대한 연구도 예시적으로 고찰하였다. 향후 긍정적 감성과 동기 및 학습의 유기적 상호작용을 극대화 하는 효과적인 감성교육의 방법론으로서 뇌기반 학습과학의 가능성과 한계점에 대하여 논의하였으며, 학교현장에서의 활용 방안 등에 대해서도 살펴보았다.

• 전자통신동향분석
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• 제35권5호
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• pp.14-29
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• 2020

The 6G mobile communication system will become a backbone infrastructure around 2030 for the future digital world by providing distinctive services such as five-sense holograms, ultra-high reliability/low-latency, ultra-high-precision positioning, ultra-massive connectivity, and gigabit-per-second data rate for aerial and maritime terminals. The recent remarkable advances in machine learning (ML) technology have recognized its efficiency in wireless networking fields such as resource management and cell-configuration optimization. Further innovation in ML is expected to play an important role in solving new problems arising from 6G network management and service delivery. In contrast, an approach to apply ML to a physical-layer (PHY) target tackles the basic problems in radio links, such as overcoming signal distortion and interference. This paper reviews the methodologies of ML-based PHY, relevant industrial trends, and candiate technologies, including future research directions and standardization impacts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.1035-1048
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• 2023

The IEEE 802.11 WLAN adopts a random backoff algorithm for its collision avoidance mechanism, and it is well known that the contention-based algorithm may suffer from performance degradation especially in congested networks. In this paper, we design an efficient backoff algorithm that utilizes a reinforcement learning method to determine optimal values of backoffs. The mobile nodes share a common contention window (CW) in our scheme, and using a Q-learning algorithm, they can avoid collisions by finding and implicitly reserving their optimal time slot(s). In addition, we introduce Frame Size Control (FSC) algorithm to minimize the possible degradation of aggregate throughput when the number of nodes exceeds the CW size. Our simulation shows that the proposed backoff algorithm with FSC method outperforms the 802.11 protocol regardless of the traffic conditions, and an analytical modeling proves that our mechanism has a unique operating point that is fair and stable.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.170-178
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• 2024

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either "abnormal" or "normal" using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.