• 환경영향평가
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• 제22권5호
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• pp.513-523
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• 2013

This purpose of this work is to explore the characteristics of urban thermal environment distribution with the case of Daejeon. To do that, this work applied GIS Spatial Statistics to the LandSAT images gathered from 2000 to 2011. The urban thermal environment distribution at the time point of 2 showed high spatial autocorrelation. Therefore, it is judged that spatial autocorrelation is needed to increase the reliability and explanatory power of the characteristics of thermal environment distribution. In the case of the thermal in Daejeon, its positive clustering appeared high at the time point of 2, and its clustering in 2011 more gradually decreased than that in 2000 to 2011. In particular, given the decrease in the core H-H region, it was found that the thermal environment of Daejeon was greatly improved. However, since the rise in the region L-L means another changed like construction of a new city, it is judged that it is necessary to come up with a proper plan. It is considered that this analysis of the characteristics of urban thermal environment distribution in consideration of spatial autocorrelation L-L be useful for providing a fundamental material necessary for the policy and project of thermal environment improvement.

• Journal of Electrical Engineering and Technology
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• 제9권5호
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• pp.1677-1685
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• 2014

Major problem in the high voltage power transmission line is the flashover due to polluted ceramic insulators which leads to failure of equipments, catastrophic fires and power outages. This paper deals with the development of a better diagnostic tool to predict the flashover and pollution severity of power transmission line insulators based on the wavelet transform and fuzzy c-means clustering approach. In this work, laboratory experiments were carried out on power transmission line porcelain insulators under AC voltages at different pollution conditions and corresponding leakage current patterns were measured. Discrete wavelet transform technique is employed to extract important features of leakage current signals. Variation of leakage current magnitude and distortion ratio at different pollution levels were analyzed. Fuzzy c-means algorithm is used to cluster the extracted features of the leakage current data. Test results clearly show that the flashover and pollution severity of power transmission line insulators can be effectively realized through fuzzy clustering technique and it will be useful to carry out preventive maintenance work.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.87-96
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• 2021

The demand for e-learning through video lectures is rapidly increasing due to its diverse advantages over the traditional learning methods. This led to massive volumes of web-based lecture videos. Indexing and retrieval of a lecture video or a lecture video topic has thus proved to be an exceptionally challenging problem. Many techniques listed by literature were either visual or audio based, but not both. Since the effects of both the visual and audio components are equally important for the content-based indexing and retrieval, the current work is focused on both these components. A framework for automatic topic-based indexing and search depending on the innate content of the lecture videos is presented. The text from the slides is extracted using the proposed Merged Bounding Box (MBB) text detector. The audio component text extraction is done using Google Speech Recognition (GSR) technology. This hybrid approach generates the indexing keywords from the merged transcripts of both the video and audio component extractors. The search within the indexed documents is optimized based on the Naïve Bayes (NB) Classification and K-Means Clustering models. This optimized search retrieves results by searching only the relevant document cluster in the predefined categories and not the whole lecture video corpus. The work is carried out on the dataset generated by assigning categories to the lecture video transcripts gathered from e-learning portals. The performance of search is assessed based on the accuracy and time taken. Further the improved accuracy of the proposed indexing technique is compared with the accepted chain indexing technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1276-1295
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• 2023

Sensor networks are now an essential aspect of wireless communication, especially with the introduction of new gadgets and protocols. Their ability to be deployed anywhere, especially where human presence is undesirable, makes them perfect choices for remote observation and control. Despite their vast range of applications from home to hostile territory monitoring, limited battery power remains a limiting factor in their efficacy. To analyze and transmit data, it requires intelligent use of available battery power. Several studies have established effective routing algorithms based on clustering. However, choosing optimal cluster heads and similarity measures for clustering significantly increases computing time and cost. This work proposes and implements a simple two-phase technique of route creation and maintenance to ensure route reliability by employing nature-inspired ant colony optimization followed by the fuzzy decision engine (FDE). Benchmark methods such as PSO, ACO and GWO are compared with the proposed HRCM's performance. The objective has been focused towards establishing the superiority of proposed work amongst existing optimization methods in a standalone configuration. An average of 15% improvement in energy consumption followed by 12% improvement in latency reduction is observed in proposed hybrid model over standalone optimization methods.

• 대한안전경영과학회지
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• 제16권1호
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• pp.119-127
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• 2014

This study tried to develop a basis for quantitative index of working postures associated with WMSDs (Work-related Musculoskeletal Disorders) that could overcome realistic restriction during application of typical checklists for WMSDs evaluation. The baseline data(for a total of 603 jbs) for this study was obtained from automobile manufacturing company. Specifically, data for back posture was analyzed in this study to have a better and more objective method in terms of job relevance than typical methods such as OWAS, RULA, and REBA. Major statistical tools were clustering, logistic regression and so on. The main results in this study could be summarized as follows; 1) The relationship between working posture and WMSDs symptom at back was statistically significant based on the results from logistic regression, 2) Based on clustering analysis, three levels for WMSDs risk at back were produced for flexion as follows: low risk(< $18.5^{\circ}$), medium risk($18.5^{\circ}{\sim}36.0^{\circ}$), high risk(> $36.0^{\circ}$), 3) The sensitivities on risk levels of back flexion was 93.8% while the specificities on risk levels of back flexion was 99.1%. The results showed that the data associated with back postures in this study could provide a good basis for job evaluation of WMSDs at back. Specifically, this evaluation methodology was different from the methods usually used at WMSDs study since it tried to be based on direct job relevance from real working situation. Further evaluation for other body parts as well as back would provide more stability and reliability in WMSDs evaluation study.

• 한국안전학회지
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• 제28권1호
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• pp.95-101
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• 2013

This study tried to develop a basis for quantitative index of working postures associated with WMSDs(Work-related Musculoskeletal Disorders) that could overcome realistic restriction during application of typical checklists for WMSDs evaluation. The baseline data for this study was obtained from automobile manufacturing company(A total of 603 jobs were observed). Specifically, data for shoulder postures was analyzed to have a better and more objective method in terms of job relevance than typical methods such as OWAS, RULA, and REBA. Major statistical tools were Clustering, Logistic regression and so on. The main results in this study could be summarized as follows; 1) The relationships between working postures and WMSDs symptoms at shoulder were statistically significant based on the results from logistic regression. 2) Based on clustering analysis, three levels for WMSDs risk at shoulder were produced for both flexion and abduction were statistically significant. Specific results were as follows; Shoulder flexion: low risk(< $37.7^{\circ}$), medium risk($37.7^{\circ}{\sim}70.0^{\circ}$), high risk(> $70.0^{\circ}$) Shoulder abduction: low risk(< $26.5^{\circ}$), medium risk($26.5^{\circ}{\sim}56.8^{\circ}$), high risk(> $56.8^{\circ}$). 3) The sensitivities on risk levels of shoulder flexion and abduction were 64.0% and 20.6% respectively while the specificities on risk levels of shoulder flexion and abduction were 99.1% and 99.3% respectively. The results showed that the data associated with shoulder postures in this study could provide a good basis for job evaluation of WMSDs at shoulder. Specifically, this evaluation methodology was different from the methods usually used at WMSDs study since it tried to be based on direct job relevance from real working situation. Further evaluation for other body parts as well as shoulder would provide more stability and reliability in WMSDs evaluation study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권10호
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• pp.4883-4901
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• 2016

Energy efficiency in Wireless Sensor Networks (WSNs) is very appealing research area due to serious constrains on resources like storage, processing, and communication power of the sensor nodes. Due to limited capabilities of sensing nodes, such networks are composed of a large number of nodes. The higher number of nodes increases the overall performance in data collection from environment and transmission of packets among nodes. In such networks the nodes sense data and ultimately forward the information to a Base Station (BS). The main issues in WSNs revolve around energy consumption and delay in relaying of data. A lot of research work has been published in this area of achieving energy efficiency in the network. Various techniques have been proposed to divide such networks; like grid division of network, group based division, clustering, making logical layers of network, variable size clusters or groups and so on. In this paper a new technique of group based WSNs is proposed by using some features from recent published protocols i.e. "Energy-Efficient Multi-level and Distance Aware Clustering (EEMDC)" and "Energy-Efficient Multi-level and Distance Aware Clustering (EEUC)". The proposed work is not only energy-efficient but also minimizes the delay in relaying of data from the sensor nodes to BS. Simulation results show, that it outperforms LEACH protocol by 38%, EEMDC by 10% and EEUC by 13%.

• 디지털융복합연구
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• 제11권11호
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• pp.169-175
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• 2013

스마트 유연근무는 개인이 직장과 가정에서 요구되는 역할 균형을 가능하도록 하는 주요 정책수단으로 최근 관심을 받아 왔다. 본 연구는 유연근무를 시범적으로 시행하고 있는 광역자치단체 공무원들을 대상으로 스마트 유연근무제 유형에 대해 살펴보고, 조직단위 별 스마트 유연근무 유형을 분석하고 근로 장소 유연성, 근로시간 유연성 과 근로 장소 및 시간 융합 유연성 등 스마트 유연근무제 유형별 성공요인을 분석하였다. 분석결과 직장영역과 가정영역 별 유연근무의 특성에 차이가 있는 것으로 나타났고 구체적으로 가정 및 직장 융합 영역이 유연근무에 가장 중요한 성공요인으로 밝혀졌으며 이에 따른 정책적 시사점을 도출하였다.

• 한국경영과학회지
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• 제18권3호
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• pp.129-149
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• 1993

The high level of activity in the development and maintenance of computer software makes the scheduling of software projects an importnat factor in reducing operating costs and increasing competitiveness. Software activity is labor intensive. Scheduling management of hours of software work is complicated by ther interdependencies between the segments of work, and the uncertainties of the work itself. This paper discusses issues of scheduling in software engineering management, and presents a modular decomposition model for software project scheduling, taking advantage of the facility for decomposition of a software project into relatively independent work segment modules. Modular decomposition makes it possible to treat scheduling as clustering and sequencing in the context of integer programming. A heuristic algorithm for the model is presented with some computational experiments.

• 한국정보과학회논문지:데이타베이스
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• 제37권2호
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• pp.71-81
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• 2010

데이터의 군집을 찾아내는 문제는 패턴 인식, 이미지 처리, 시장 조사 등 많은 응용 분야에서 널리 사용되고 있다. 군집의 질을 결정하는 핵심 요소로는 유사 측도, 차원의 개수 등이 있다. 유사 측도는 데이터의 특성을 반영하여 다르게 정의되어야 하는데, 대부분 기존의 연구들은 데이터를 특징 지어주는 속성이 수치형으로 주어진 경우에 국한되어 있었다. 속성이 범주형으로 주어진 경우도 실생활에 많이 존재하지만, 범주형 변수에 대한 속성값의 유사성은 값의 순서가 고유하게 정해지지 않아서 정의하기 어렵다. 이에 더하여, 고차원 데이터에 대해서는 데이터 점들이 희박하게 위치하여 가까운 점과 먼 점간의 차이가 거의 없고, 군집화 결과가 좋지 않을 수 있다. 이 문제를 해결하기 위해 부분 차원 군집화 방법이 제안되어 왔다. 부분 차원 군집화 방법은 각 군집을 발견하기에 적합한 부분 차원을 선택하면서 군집화를 수행하는 방법이다. 본 논문에서는 범주형 속성으로 특징지어진 고차원 데이터를 부분 차원 군집화하기 위한 새로운 유사 측도를 제안한다. 유사 측도는 각 군집은 다른 군집과 구별되는 특정 정보를 잘 표현할 수 있어야 한다는 기본적인 가정 하에 속성들 사이의 상관성을 반영하여 정의되었다. 이들 모두를 반영한 유사측도는 기존에 존재하지 않았다는 점에서 본 연구는 의미가 있다. 실제 데이터 집합을 군집화하는 실험을 통해 제안하는 방법이 다른 군집화 방법보다 저차원 데이터와 고차원 데이터 모두에 대해 좀 더 정확한 군집 결과를 얻을 수 있음을 보였다.