• 디지털콘텐츠학회 논문지
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• 제11권2호
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• pp.265-276
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• 2010

이 연구의 목적은 지역 단위의 인적자원개발 정책 추진 주체인 지방자치단체를 중심으로 성인교육 부문의 지역 교육격차 실태에 대한 인식 수준을 분석하고, 이 연구에서 지역 교육격차 해소의 대안으로 제시하고 있는 e-Learning과 관련하여 지역별로 보유하고 있는 인프라 구축 실태와 활용 현황에 대하여 살펴보고자 하였다. 이를 위하여 지역 대비 수도권으로 분류되는 서울 경기를 제외한 전체 12개의 지방자치단체에서 운영하고 있는 인적자원개발센터를 대상으로 연구를 실시하였으며 다음과 같은 결과를 도출하였다. 첫째, 지방자치단체에서는 교육의 양적, 질적 측면에서 지역 간 교육 격차를 크게 인식하고 있는 것으로 나타났으며 양적 측면보다 질적 측면에서 지역 격차가 상대적으로 큰 것으로 분석되었다. 둘째, 지역 간 교육 프로그램 개설 수, 교 강사의 전문성, 교육훈련 효과성 측면의 차이가 매우 크다고 인식하였다. 셋째, 집합교육이 e-Learning 보다 지역 간, 지역 내 교육격차가 더 크게 발생하고 있는 것으로 나타났다. 넷째, e-Learning의 주요 영역별 기반 구축 수준은 하드웨어가 상대적으로 높은 반면 나머지 영역은 미흡한 것으로 분석되었다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권2호
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• pp.116-120
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• 2008

Clustering is a method for unsupervised learning. General clustering tools have been depended on statistical methods and machine learning algorithms. One of the popular clustering algorithms based on machine learning is the self organizing map(SOM). SOM is a neural networks model for clustering. SOM and extended SOM have been used in diverse classification and clustering fields such as data mining. But, SOM has had a problem determining optimal number of clusters. In this paper, we propose an improvement of SOM using gap statistic and probability distribution. The gap statistic was introduced to estimate the number of clusters in a dataset. We use gap statistic for settling the problem of SOM. Also, in our research, weights of feature nodes are updated by probability distribution. After complete updating according to prior and posterior distributions, the weights of SOM have probability distributions for optima clustering. To verify improved performance of our work, we make experiments compared with other learning algorithms using simulation data sets.

• 로봇학회논문지
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• 제19권1호
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• pp.117-129
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• 2024

This paper introduces collective navigation through a narrow gap using a curriculum-based deep reinforcement learning algorithm for a swarm of unmanned aerial vehicles (UAVs). Collective navigation in complex environments is essential for various applications such as search and rescue, environment monitoring and military tasks operations. Conventional methods, which are easily interpretable from an engineering perspective, divide the navigation tasks into mapping, planning, and control; however, they struggle with increased latency and unmodeled environmental factors. Recently, learning-based methods have addressed these problems by employing the end-to-end framework with neural networks. Nonetheless, most existing learning-based approaches face challenges in complex scenarios particularly for navigating through a narrow gap or when a leader or informed UAV is unavailable. Our approach uses the information of a certain number of nearest neighboring UAVs and incorporates a task-specific curriculum to reduce learning time and train a robust model. The effectiveness of the proposed algorithm is verified through an ablation study and quantitative metrics. Simulation results demonstrate that our approach outperforms existing methods.

• 산업융합연구
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• 제22권5호
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• pp.39-48
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• 2024

코로나19로 인하여 개학연기부터 장기화된 온라인 원격수업으로 학습 손실과 교육격차에 대한 연구는 많이 진행되었으며, 대부분 교육격차 현상에 대한 연구가 주를 이루었다. 향후 이와 같은 팬데믹 상황이 온다면, 교육격차 해소를 위한 근본적인 정책이 필요하다. 근본적인 해결책은 교육격차 현상에 대한 이해뿐만 아니라 그 현상의 이면의 구조를 파악해야 한다. 따라서 본 연구는 구조주의 관점에서 코로나19로 인한 교육격차를 시스템사고의 원형으로 모델링하고 그 구조를 파악하고자 하였다. 그리고 기존의 교육격차 해소를 위한 정책들로 발생한 의도하지 않은 결과를 살펴보았다. 향후 유사한 재난상황에 대응하기 위해 본 연구의 구조를 기반으로 디지털 격차해소를 위한 정책, 기초학력 지원, 원격수업에 대한 품질 향상, 자기주도 학습에 대해 논의하였다.

• IEIE Transactions on Smart Processing and Computing
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• 제5권5호
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• pp.358-365
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• 2016

We propose a novel application of random forest, a machine learning-based general classification algorithm, to analyze the influence of design attributes on the silicon-to-SPICE (S2S) gap. To improve modeling accuracy, we introduce magnification of learning data as well as randomization for the counting of design attributes to be used for each tree in the forest. From the automatically generated decision trees, we can extract the so-called importance and impact indices, which identify the most significant design attributes determining the S2S gap. We apply the proposed method to actual silicon data, and observe that the identified design attributes show a clear trend in the S2S gap. We finally unveil 10nm key fin-shaped field effect transistor (FinFET) structures that result in a large S2S gap using the measurement data from 10nm test vehicles specialized for model-hardware correlation.

• 예술인문사회 융합 멀티미디어 논문지
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• 제9권11호
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• pp.1133-1144
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• 2019

최근 스마트 기기의 발전으로 인터넷상에 존재하는 이미지 데이터의 양이 급속하게 증가하는 상황에서 효과적인 이미지 검색을 위한 다양한 방법들이 연구되고 있다. 기존의 이미지 검색 방법들은 이미지에 존재하는 물체들을 단순하게 검출하여 각 물체들의 라벨 정보에 근거한 검색을 수행하기 때문에 사용자가 원하는 이미지와 검색 결과로 얻은 이미지 간에 의미적 차이인 시맨틱 갭(Semantic Gap)이 발생된다. 이미지 검색에서 발생하는 시맨틱 갭을 줄이기 위해, 본 논문에서는 딥러닝 기반의 다중 객체 분류 모듈과 사람의 행위를 분류하는 모듈을 연결하고, 이 모듈들에 행위 온톨로지를 결합하였다. 즉, 딥러닝과 행위 온톨로지의 결합을 기반으로 객체들 간의 연관성을 고려한 이미지 검색 시스템을 제안한다. 이미지에 포함된 동적인 행위를 고려하기 위해 Walking과 Running 데이터를 이용하여 실험한 결과를 분석하였다. 제안한 방법은 향후 이미지 검색 결과의 정확도를 높일 수 있는 영상의 자동 주석 생성 연구에 확장하여 적용할 수 있다.

• International Journal of Computer Science & Network Security
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• 제24권3호
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• pp.113-124
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• 2024

One of the goals of the Saudi Arabia 2030 vision is to ensure full employment of its citizens. Recruitment of graduates depends on the quality of skills that they may have gained during their study. Hence, the quality of education and ensuring that graduates have sufficient knowledge about the in-demand skills of the market are necessary. However, IT graduates are usually not aware of whether they are suitable for recruitment or not. This study builds a prediction model that can be deployed on the web, where users can input variables to generate predictions. Furthermore, it provides data-driven recommendations of the in-demand skills in the Saudi IT labor market to overcome the unemployment problem. Data were collected from two online job portals: LinkedIn and Bayt.com. Three machine learning algorithms, namely, Support Vector Machine, k-Nearest Neighbor, and Naïve Bayes were used to build the model. Furthermore, descriptive and data analysis methods were employed herein to evaluate the existing gap. Results showed that there existed a gap between labor market employers' expectations of Saudi workers and the skills that the workers were equipped with from their educational institutions. Planned collaboration between industry and education providers is required to narrow down this gap.

• 지식경영연구
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• 제11권4호
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• pp.41-66
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• 2010

This research examines the effect of the relatedness and the gap between Resources and mechanisms on effectiveness of inter-organizational knowledge transfer. According to the literature, there has been a competing theory between two claims; one is that inter-organizational knowledge transfer will be more effective due to the reduction of the transaction cost as the relatedness increases. And the other is that the mutual complementarity of different organizational characteristics will increase synergy. In total, the relatedness and the gap of the Resource and mechanism makes the inverted U-shaped relationship with the inter-organizational knowledge transfer. As the result of empirical analysis about 109 Korean-based Joint Ventures entered country, it shows that the relatedness of parent company's production Resources, learning mechanisms, and coordination mechanisms made the inverted U-shaped relations with the inter-organizational knowledge transfer and the gap of production Resources and adjustment mechanism formed the same relationship. However, the U-shaped relationship has been established in the relatedness of market Resources, but the gap of market Resources and the learning mechanism was not statistically significant. Through this study, I can draw a best conclusion that the inter-organizational knowledge transfer will be more effective when the relatedness and the gap of management resources and mechanisms is in optimal level. However, when it comes to market Resources, it can be inferred that the result could be the opposite because the partner country's market environment would be different.

• 방송공학회논문지
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• 제26권7호
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• pp.844-854
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• 2021

Semi-supervised learning (SSL) and few-shot learning (FSL) have shown impressive performance even then the volume of labeled data is very limited. However, SSL and FSL can encounter a significant performance degradation if the diversity gap between the labeled and unlabeled data is high. To reduce this diversity gap, we propose a novel scheme that relies on an autoencoder for generating pseudo examples. Specifically, the autoencoder is trained on a specific class using the available labeled data and the decoder of the trained autoencoder is then used to generate N samples of that specific class based on N random noise, sampled from a standard normal distribution. The above process is repeated for all the classes. Consequently, the generated data reduces the diversity gap and enhances the model performance. Extensive experiments on MNIST and FashionMNIST datasets for SSL and FSL verify the effectiveness of the proposed approach in terms of classification accuracy and robustness against adversarial attacks.

• International Journal of Advanced Culture Technology
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• 제6권4호
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• pp.303-308
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• 2018

Demand is increasing rapidly in recent years than supply to machine learning professionals. To alleviate this gap, user-friendly machine learning software that can be used by non-specialists has emerged, which is Machine Learning-as-a-Service(MLaaS). MLaaS provides services that enable businesses to easily leverage ML capabilities without expertise. In this paper, we will compare and analyze features, interfaces, supporting programming language, ML framework, and Machine Learning services of MLaaS, to help companies easily use ML service.