• 정보처리학회논문지B
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• 제12B권2호
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• pp.203-208
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• 2005

같은 언어 변수에서 정의된 인접 언어 값을 다루기 위한 알고리즘을 제안하였다. 제안된 방법을 사용해서 인접 언어 값에 대한 개인의 주관성의 차이를 명확히 발견할 수 있다. 제안된 방법은 같은 언어 변수에서 정의된 인접 언어 값들 사이의 숨겨진 관계를 발견하기 위한 유용한 도구로 이용될 수 있다. 결과적으로 제안된 방법은 퍼지 전문가시스템, 퍼지 의사결정 트리, 퍼지 인지 지도등과 같은 퍼지 시스템 개발 분야에서 지식 획득의 정확성을 개선하기 위한 기초를 제공해 줄 수 있다. 본 논문에서 제안된 방법을 다수의 전문가들 사이의 집단 언어 평가에 적용하였다.

• Transactions on Electrical and Electronic Materials
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• 제13권1호
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• pp.10-15
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• 2012

In conventional, skin detection methods using for skin color definitions is based on prior knowledge. By experimentation, the threshold value for dividing the background from the skin region is determined subjectively. A drawback of such techniques is that their performance is dependent on a threshold value which is estimated from repeated experiments. To overcome this, the present paper introduces a skin region detection method. This method uses a histogram approximation based on the mean shift algorithm. This proposed method applies the mean shift procedure to a histogram of a skin map of the input image. It is generated by comparing with the standard skin colors in the $C_bC_r$ color space. It divides the background from the skin region by selecting the maximum value according to the brightness level. As the histogram has the form of a discontinuous function. It is accumulated according to the brightness values of the pixels. It is then, approximated by a Gaussian mixture model (GMM) using the Bezier curve technique. Thus, the proposed method detects the skin region using the mean shift procedure to determine a maximum value. Rather than using a manually selected threshold value, as in existing techniques this becomes the dividing point. Experiments confirm that the new procedure effectively detects the skin region.

• 대한기계학회논문집A
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• 제20권8호
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• pp.2436-2448
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• 1996

This paper presents the development of three types of network performance manager for IEEE 802.4 token bus networks that are a part of Manufacturing Automation Protocol(MAP). The performance managers attempt to keep the average data latency below a certain level specified for each priority class. All of the three performance managers are based on a set of fuzzy rules incorporating the knowledge on the relationship between data latency and parameters of the priority mechanism. These Fuzzy Network Performance Managers(FNPMs) have been evaluated via discrete event simulation to demonstrate their efficacy.

• 한국측량학회지
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• 제23권2호
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• pp.157-163
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• 2005

21세기 지식정보사회로 접어들어 GIS가 부각됨에 따라 GIS의 활성화를 위한 기본지리정보에 대한 중요성이 대두되고 있다. 기본지리정보를 관리 감독하는 국토지리정보원에서는 2000년부터 기본지리정보구축에 관한 연구 등을 수행하여, 기본지리정보의 범위 선정과 데이터모델 표준화가 이루어 졌으며, 기본지리정보 유통과 구축에 필요한 생산사양이 이루어짐에 따라 기본지리정보의 품질 및 활용성에 대한 검토가 필요한 실정이다. 본 연구에서는 시설물분야 기본지리정보 생산사양에 따른 기본지리정보를 3단계에 걸쳐 구축하였으며 기 구축된 교통, 수자원, 행정경계 기본지리정보와 통합하여 활용성 평가를 수행하였다.

• Nuclear Engineering and Technology
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• 제53권3호
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• pp.825-832
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• 2021

A simulation-based approach is proposed to study the protective actions taken by residents during nuclear emergencies using cognitive findings. Human perception-based behaviors are not heavily incorporated in the evacuation study for nuclear emergencies despite their known importance. This study proposes a generic framework of perception-based behavior simulation, in accordance with the ecological concept of affordance theory and a formal representation of affordance-based finite state automata. Based on the generic framework, a simulation model is developed to allow an evacuee to perceive available actions and execute one of them according to Newton's laws of motion. The case of a shadow evacuation under nuclear emergency is utilized to demonstrate the applicability of the proposed framework. The illustrated planning algorithm enables residents to compute not only prior knowledge of the environmental map, but also the perception of dynamic surroundings, using widely observed heuristics. The simulation results show that the temporal and spatial dynamics of the evacuation behaviors can be analyzed based on individual perception of circumstances, while utilizing the findings in cognitive science under unavoidable data restriction of nuclear emergencies. The perception-based analysis of the proposed framework is expected to enhance nuclear safety technology by complementing macroscopic analyses for advanced protective measures.

• Journal of Information Processing Systems
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• 제16권6호
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• pp.1238-1249
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• 2020

Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.

• Molecules and Cells
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• 제44권11호
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• pp.843-850
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• 2021

The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr.

• 동의생리병리학회지
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• 제35권5호
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• pp.132-138
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• 2021

Artificial intelligence technology sheds light on new ways of innovating acupuncture research. As acupoint selection is specific to target diseases, each acupoint is generally believed to have a specific indication. However, the specificity of acupoint selection may be not always same with the specificity of acupoint indication. In this review, we propose that the specificity of acupoint indication can be inferred from clinical data using reverse inference. Using forward inference, the prescribed acupoints for each disease can be quantified for the specificity of acupoint selection. Using reverse inference, targeted diseases for each acupoint can be quantified for the specificity of acupoint indication. It is noteworthy that the selection of an acupoint for a particular disease does not imply the acupoint has specific indications for that disease. Electronic medical record includes various symptoms and chosen acupoint combinations. Data mining approach can be useful to reveal the complex relationships between diseases and acupoints from clinical data. Combining the clinical information and the bodily sensation map, the spatial patterns of acupoint indication can be further estimated. Interoperable medical data should be collected for medical knowledge discovery and clinical decision support system. In the era of artificial intelligence, machine learning can reveal the associations between diseases and prescribed acupoints from large scale clinical data warehouse.

• 한국프랜차이즈경영연구
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• 제13권1호
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• pp.19-33
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• 2022

Purpose: Based on the resource-based view and the competitive advantage theory, the study views marketing capabilities (product, pricing, delivery/inventory, and promotional support) as sources of competitive advantage (differentiation advantage and low-cost advantage) and examines their impacts on competitive advantage, which in turn, will influence non-business and business performance. Research design, data and methodology: Data were collected from 149 representatives of franchising companies in South Korea and analyzed with SmartPLS 3.3.7. Results: First, promotional support and product have a significant impact on differentiation advantage. Second, pricing and promotional support have a significant impact on low-cost advantage. Third, differentiation advantage has an influence on non-financial and financial business performance. Fourth, low-cost advantage has an impact on non-financial performance but has no significant direct impact on financial performance. Fifth, non-financial performance is related to financial performance. Finally, the result of IPMA shows that importance and performance values of exogeneous variables are different depending on firm size. Conclusions: The findings suggest that franchisors should focus on different marketing capabilities depending on their strategic focus and objectives. Finally, the findings based on an IPMA suggest that small companies perceive low-cost advantage as important, while their counterparts do not. Several theoretical and managerial implications are offered.

• The Journal of Asian Finance, Economics and Business
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• 제10권2호
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• pp.355-365
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• 2023

This study examined the relationship between corporate governance and risk disclosure via a systematic literature review and bibliometric visualization analysis. The study aimed to present evidence of risk disclosure intellectual structure, volume, and development knowledge trends. Data was extracted from Scopus and analyzed with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and RTools. In turn, 64 articles were extracted from the Scopus database. The results demonstrated that the number of corporate governance and risk disclosure publications increased significantly from 2015 to 2019 compared to before 2015. RTools revealed the most prominent journals, authors, and interests in the field. The co-occurrences map was constructed based on 208 keywords from 64 articles, where the keywords were required to appear once in the research. Interestingly, the keyword search yielded new concepts relatively unexplored in the risk disclosure field. The 13 clusters were generated, which contained 1987 total links and 1567 direct citations. Based on the scientific analysis discussion, corporate governance and risk disclosure is an interesting topic that has produced many publications. Applying research keywords arguably aided in producing and publishing papers in top journals. Despite the number of publications decreasing due to the COVID-19 pandemic, the pandemic also presented new opportunities for future research.