• 한국융합학회논문지
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• 제10권7호
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• pp.289-294
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• 2019

출소자의 재범률을 줄이기 위해서는 생계가 막막한 출소자들의 지역사회 재진입 성공률을 높이는 다양한 방안이 마련되어야 하며 이러한 방안 중 하나로, 적절한 직업훈련을 통한 취업 연계가 무엇보다 중요하다. 최근 미국 법무부에서 나온 연구결과에 의하면 출소 후 1년 동안 무직상태에 있는 전과자는 60% 정도이다. 이러한 상황에서 전과를 가지고 있는 장애인들의 고용가능성은 더욱더 낮을 수밖에 없다. 그야말로 "이중 장애물(Dual Barriers)"이 존재하는 것이다. 이에 본 연구는 기존 연구 리뷰를 바탕으로 미국의 장애인 재소자 및 출소자 직업재활 프로그램을 소개하면서 장애인 범죄자의 재범률을 낮추고 고용가능성을 높이기 위한 전략을 제언하고자 한다.

• 한국멀티미디어학회논문지
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• 제22권2호
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• pp.231-241
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• 2019

Bulk-loading an R-tree can be a good approach to build an efficient one. However, it takes a lot of time to bulk-load an R-tree for huge amount of data. In this paper, we propose a parallel R-tree construction scheme based on a Hadoop framework. The proposed scheme divides the data set into a number of partitions for which local R-trees are built in parallel via Map-Reduce operations. Then the local R-trees are merged into an global R-tree that covers the whole data set. While generating the partitions, it considers the spatial distribution of the data into account so that each partition has nearly equal amounts of data. Therefore, the proposed scheme gives an efficient index structure while reducing the construction time. Experimental tests show that the proposed scheme builds an R-tree more efficiently than the existing approaches.

• 품질경영학회지
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• 제46권4호
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• pp.1001-1014
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• 2018

Purpose: Autonomous driving cars, which are often represent the new convergence product, have been researched since the early years of 1900 but their safety assurance policies are yet to be implemented for real world practices. The primary purpose of this paper is to propose a modular concept based on which a safety assurance system can be designed and implemented for operating autonomous driving cars. Methods: We combine a set of key attributes of CE mark (European Assurance standard), E-Mark (Automobile safety assurance system), and A-SPICE (Automobile software assurance standard) into a modular approach. Results: Autonomous vehicles are emphasizing software safety, but there is no integrated safety certification standard for products and software. As such, there is complexity in the product and software safety certification process during the development phase. Using the concept of module, we were able to come up with an integrated safety certification system of product and software for practical uses in the future. Conclusion: Through the modular concept, both international and domestic standards policy stakeholders are expected to consider a new structure that can help the autonomous driving industries expedite their commercialization for the technology advanced market in the era of Industry 4.0.

• 인터넷정보학회논문지
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• 제22권5호
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• pp.27-33
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• 2021

With the broad adoption of the Internet of Things (IoT) in a variety of scenarios and application services, management and orchestration entities require upgrading the traditional architecture and develop intelligent models with ultra-reliable methods. In a heterogeneous network environment, mission-critical IoT applications are significant to consider. With erroneous priorities and high failure rates, catastrophic losses in terms of human lives, great business assets, and privacy leakage will occur in emergent scenarios. In this paper, an efficient resource slicing scheme for optimizing federated learning in software-defined IoT (SDIoT) is proposed. The decentralized support vector regression (SVR) based controllers predict the IoT slices via packet inspection data during peak hour central congestion to achieve a time-sensitive condition. In off-peak hour intervals, a centralized deep neural networks (DNN) model is used within computation-intensive aspects on fine-grained slicing and remodified decentralized controller outputs. With known slice and prioritization, federated learning communications iteratively process through the adjusted resources by virtual network functions forwarding graph (VNFFG) descriptor set up in software-defined networking (SDN) and network functions virtualization (NFV) enabled architecture. To demonstrate the theoretical approach, Mininet emulator was conducted to evaluate between reference and proposed schemes by capturing the key Quality of Service (QoS) performance metrics.

• 융합신호처리학회논문지
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• 제21권3호
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• pp.107-112
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• 2020

최근 교육현장 및 가정에서는 창의적 학습의 중요성이 높게 평가되면서 유아 및 청소년기 아동의 교육 수요가 증가하고 있지만, 단순한 암기교육 위주와 고전적인 교육방식은 학습자 중심적 측면에서 효과가 크지 않은 경우가 많다. 오랫동안 책상에 앉아 있는 학생들은 지루한 고전적인 학습방법을 선호하지 않으며, 현재 교육 현장에서도 융합 교육 트렌드에 부응하는 교육 방법 및 교육 콘텐츠가 부족한 것이 현실이다. 따라서 본 연구에서는 학생들이 재미있고 친숙한 접근방법을 통해 운동을 병행하며 학습할 수 있으며, 뇌 가소성(brain plasticity) 활성화를 통해 교육 콘텐츠를 구현할 수 있는 시스템을 만들었다.

• 한국정보시스템학회지:정보시스템연구
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• 제31권2호
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• pp.45-62
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• 2022

Purpose Smart farms play a leading role in changing the safety food culture for the citizens. The purpose of this study is to investigate the factors that are important to covid 19-response in the case of ICT smart farm. To do so, we classified the factors as operating effect aspect and industrial wave effect aspect of the smart farm. Design/methodology/approach This study was conducted by visiting Geumsan County, which is attempting to perform a smart farm. Through interviewing farmers representatives based on their operational effect expectations on the smart farm, we derived the industrial crash effect factors and thereafter designed the research model. This study applied AHP, which is an expert decision-making method cans be used to measure relative importance for determining priorities. After interviewing the experts with smart farm, we obtained the factors which are important to smart farm development. Findings According to analysis, the productivity improvement factor was ranked as the most important among the operational effect items. This is consistent with the ultimate goal of smart farms with ICT convergence technology, which is increase the profitability of agriculture. The second place is the factor in the development of infrastructure and infrastructure, and the third and fifth positions were export expansion, environmentally friendly management, and job creation in terms of operational effectiveness.

• 대한안전경영과학회지
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• 제24권4호
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• pp.61-70
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• 2022

In spite of lab safety act for over 10 years, over 100 safety accidents in the laboratory have been constantly occurring. The ideal safety management system is to prevent accidents by differential classifying and managing laboratory regulatory materials according to the risk level. In order to approach this system, in-depth interviews with safety managers were first conducted to identify the current status of safety management in domestic university laboratories. And then through comparative analysis of safety management systems in domestic and foreign laboratories, a new regulatory substance classification standard based on the analysis of the hazards and the classification of risk grades, and a safety management system are proposed. From this study, it will contribute to the creation of a safe laboratory environment by differential classification and management laboratory regulatory materials based on the risk level.

• 인터넷정보학회논문지
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• 제23권2호
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• pp.1-7
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• 2022

Machine learning (ML) algorithms have been intended to seamlessly collaborate for enabling intelligent networking in terms of massive service differentiation, prediction, and provides high-accuracy recommendation systems. Mobile edge computing (MEC) servers are located close to the edge networks to overcome the responsibility for massive requests from user devices and perform local service offloading. Moreover, there are required lightweight methods for handling real-time Internet of Things (IoT) communication perspectives, especially for ultra-reliable low-latency communication (URLLC) and optimal resource utilization. To overcome the abovementioned issues, this paper proposed an intelligent scheme for traffic steering based on the integration of MEC and lightweight ML, namely support vector machine (SVM) for effectively routing for lightweight and resource constraint networks. The scheme provides dynamic resource handling for the real-time IoT user systems based on the awareness of obvious network statues. The system evaluations were conducted by utillizing computer software simulations, and the proposed approach is remarkably outperformed the conventional schemes in terms of significant QoS metrics, including communication latency, reliability, and communication throughput.

• 스마트미디어저널
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• 제12권1호
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• pp.32-46
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• 2023

Natural Language Processing (NLP) has grown tremendously in recent years. Typically, bilingual, and multilingual translation models have been deployed widely in machine translation and gained vast attention from the research community. On the contrary, few studies have focused on translating between spoken and sign languages, especially non-English languages. Prior works on Sign Language Translation (SLT) have shown that a mid-level sign gloss representation enhances translation performance. Therefore, this study presents a new large-scale Korean sign language dataset, the Museum-Commentary Korean Sign Gloss (MCKSG) dataset, including 3828 pairs of Korean sentences and their corresponding sign glosses used in Museum-Commentary contexts. In addition, we propose a translation framework based on self-supervised learning, where the pretext task is a text-to-text from a Korean sentence to its back-translation versions, then the pre-trained network will be fine-tuned on the MCKSG dataset. Using self-supervised learning help to overcome the drawback of a shortage of sign language data. Through experimental results, our proposed model outperforms a baseline BERT model by 6.22%.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.160-160
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• 2022

Floods are the most common natural disasters and are annually causing severe destructions worldwide. Human activities, along with expected increased extreme precipitation patterns as a result of climate change enhance the future potential of floods. There are proven evidence that infrastructure based responses to flood disaster is no longer achieving optimum mitigation and have created a false sense of security. Nature-based solutions(NBS) is a widely accepted sustainable and efficient approach for disaster risk reduction and involves the protection, restoration, or management of natural and semi-natural ecosystems to tackle the climate and natural crisis. Adoption of NBS in decision-making, especially in developing nations is limited due to a lack of sufficient scenario-based studies, research, and technical knowledge. This study explores the knowledge gap and challenges on NBS adoption with case study of developing nation, specially for flood management, by the study of multiple scenario analysis in the context of climate, land-use change, and policies. Identification and quantification of the strength of natural ecosystems for flood resilience and water management can help to prioritize NBS in policymaking leading to sustainable measures for integrated flood management.