• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.4
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• pp.172-178
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• 2006

In this paper, an advanced demand clustering algorithm which can explore the planned maintenance outage of generators in changed electricity industry is proposed. The major contribution of this paper can be captured in the development of the long-term estimates for the generation availability considering planned maintenance outage. Two conflicting viewpoints, one of which is reliability-focused and the other is economy-focused, are incorporated in the development of estimates of maintenance outage based on the advanced demand clustering algorithm. Based on the advanced clustering algorithm, in each demand cluster, conventional effective outage of generators which conceptually capture maintenance and forced outage of generators, are newly defined in order to properly address the characteristic of the planned maintenance outage in changed electricity markets. First, initial market demand is classified into multiple demand clusters, which are defined by the effective outage rates of generators and by the inherent characteristic of the initial demand. Then, based on the advanced demand clustering algorithm, the planned maintenance outages and corresponding effective outages of generators are reevaluated. Finally, the conventional demand clusters are newly classified in order to reflect the improved effective outages of generation markets. We have found that the revision of the demand clusters can change the number of the initial demand clusters, which cannot be captured in the conventional demand clustering process. Therefore, it can be seen that electricity market situations, which can also be classified into several groups which show similar patterns, can be more accurately clustered. From this the fundamental characteristics of power systems can be more efficiently analyzed, for this advanced classification can be widely applicable to other technical problems in power systems such as generation scheduling, power flow analysis, price forecasts, and so on.

• Journal of Preventive Medicine and Public Health
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• v.34 no.4
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• pp.417-426
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• 2001

Objectives : To analyse the psychosocial factors associated with hypertension management(drug treatment and life style modification) of newly detected cases and to understand and assess their behavioral intention or behaviors. Methods : The survey area was a combined urban and rural area in Chungnam province, Korea, and the sampling method was cluster sampling. Study subjects included 541 newly detected cases of hypertension rated above stage 2 by JNC-VI from a community survey. The first survey was applied to 383 of these patients in order to discern their psychosocial characteristics. A follow-up survey was given to 345 persons with an 11-month interval following monthly telephone counseling concerning medication and life style modification by trained nurses. The final study subjects for analysis comprised 271 persons after excluding cases of incomplete data and change of address. Results : Among the 85(33.2%) new patients who had intended to undergo drug treatment, 30(35.3%) persons were treated with antihypertensive agent after 11 - month interval, while among the patients with no intention to receive treatment, only 36(21.1%) persons were treated. Hypertensive patients with a high intention score revealed a high score in life style modification compliance as well. Seventy three percent of the variance of behavioral intention to undergo hypertension management was explained by the patients attitude toward performing the behavior and subjective norm associated with behaviors related to the theory of reasoned action in structural modeling. Actual behaviors related positively with behavioral intention. The coefficient of determination was 0.255. Conclusion : Improving the compliance level of hypertensive patients in respect to drug treatment or life style modification requires a build up of positive behavioral intention, and caregivers must pay more attention to eventually converting behavioral intention to actual behaviors.

• Analytical Science and Technology
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• v.34 no.2
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• pp.87-98
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• 2021

In this study, we developed the nanoparticle multi-generator by 3D printer fusion deposition modeling (FDM) method that can reliably generate and deliver nanoparticles at a constant concentration for inhalation risk assessment. A white ABS filament was used as the test material, and SMPS was used for concentration analysis such as particle size and particle distribution. In the case of particle size, the particle size was divided by 100 nm or less and 100 to 1,000 nm, and the number of particles concentration, mass concentration, median diameter of particles, geometric average particle diameter, etc were measured. The occurrence conditions were the extruder temperature, the extruding speed of the nozzle, and the air flow rate, and experiments were conducted according to the change of conditions including the manufacturer's standard conditions. In addition, the utility of inhalation risk assessment was reviewed through a stability maintenance experiment for 6 h. As a result of the experiment, the size of the nanoparticles increased as the discharger temperature increased, as the discharge speed of the nozzle increased, and as the air flow rate decreased. Also, a constant pattern was shown according to the conditions. Even when particles were generated for a long time (6 h), the concentration was kept constant without significant deviation. The distribution of the particles was approximately 80 % for particles of 60 nm to 260 nm, 1.7 % for 1 ㎛ or larger, 0.908 mg/㎥ for the mass concentration, 111 nm for MMAD and 2.10 for GSD. Most of the ABS particles were circular with a size of less than 10 nm, and these circular particles were aggregated to form a cluster of grape with a size of several tens to several hundred nm.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.387-393
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• 2022

The article reflects short-term perspectives on the use of information and communication technologies in the training of teachers for higher education. Education is characterized by conservatism, so aspects of systematic development of the industry are relevant to this cluster of social activity. Therefore, forecasting the introduction of innovative elements of ICT training is in demand for the educational environment. Forecasting educational trends are most relevant exactly in the issues of training future teachers of higher education because these specialists are actually the first to implement the acquired professional skills in pedagogical activities. The article aims to consider the existing potential of ICT-based learning, its implementation in the coming years, and promising innovative educational elements that may become relevant for the educational space in the future. The tasks of scientific exploration are to show the optimal formats of synergy between traditional and innovative models of learning. Based on already existing experience, extrapolation of conditions of educational process organization with modeling realities of using information and communication technologies in various learning dimensions should be carried out. Educational trends for the next 3 years are a rather tentative forecast because, as demonstrated by the events associated with the COVID-19 pandemic, the socio-cultural space is very changeable. Consequently, the dynamism of the educational environment dictates the need for a value-based awareness of the information society and the practical use of technological advances. Thus, information and communication technologies are a manifestation of innovative educational strategies of today and become an important component along with traditional aspects of educational process organization. Future higher education teachers should develop a training strategy taking into account the expediency of the ICT component.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3836-3854
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• 2022

The student group division is benefit for universities to do the student management based on the group profile. With the widespread use of student smart cards on campus, especially where students living in campus residence halls, students' daily activities on campus are recorded with information such as smart card swiping time and location. Therefore, it is feasible to depict the students with the daily activity data and accordingly group students based on objective measuring from their campus behavior with some regular student attributions collected in the management system. However, it is challenge in feature representation due to diverse forms of the student data. To effectively and comprehensively represent students' behaviors for further student group division, we proposed to adopt activity data from student smart cards and student attributes as input data with taking account of activity and attribution relationship types from different perspective. Specially, we propose a novel student group division method based on a multi-view student attribute heterogeneous information network (MSA-HIN). The network nodes in our proposed MSA-HIN represent students with their multi-dimensional attribute information. Meanwhile, the edges are constructed to characterize student different relationships, such as co-major, co-occurrence, and co-borrowing books. Based on the MSA-HIN, embedded representations of students are learned and a deep graph cluster algorithm is applied to divide students into groups. Comparative experiments have been done on a real-life campus dataset collected from a university. The experimental results demonstrate that our method can effectively reveal the variability of student attributes and relationships and accordingly achieves the best clustering results for group division.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.99-109
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• 2010

Existing research about automated wafer transport management strategy for semiconductor manufacturing equipment was mainly focused on dispatching rules which is optimized to specific system layout, process environment or transfer patterns. But these methods can cause problem as like requiring additional rules or changing whole transport management strategy when applied to new type of process or system. In addition, a lack of consideration for interconnectedness of the added rules can cause unexpected deadlock. In this study, in order to improve these problems, propose dynamic priority based transfer job decision making algorithm which is applicable with regardless of system lay out and transfer patterns. Also, extra rule handling part proposed to support special transfer requirement which is available without damage to generality for maintaining a consistent scheduling policies and minimize loss of stability due to expansion and lead to improve productivity at the same time. Simulation environment of Twin-slot type semiconductor equipment was built In order to measure performance and examine validity about proposed wafer scheduling algorithm.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.261-268
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• 2021

Cloud computing is a computing paradigm in which users can utilize computing resources in a pay-as-you-go manner. In a cloud system, resources can be dynamically scaled up and down to the user's on-demand so that the total cost of ownership can be reduced. The Modeling and Simulation (M&S) technology is a renowned simulation-based method to obtain engineering analysis and results through CAE software without actual experimental action. In general, M&S technology is utilized in Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Multibody dynamics (MBD), and optimization fields. The work procedure through M&S is divided into pre-processing, analysis, and post-processing steps. The pre/post-processing are GPU-intensive job that consists of 3D modeling jobs via CAE software, whereas analysis is CPU or GPU intensive. Because a general-purpose desktop needs plenty of time to analyze complicated 3D models, CAE software requires a high-end CPU and GPU-based workstation that can work fluently. In other words, for executing M&S, it is absolutely required to utilize high-performance computing resources. To mitigate the cost issue from equipping such tremendous computing resources, we propose HEMOS-Cloud service, an integrated cloud and cluster computing environment. The HEMOS-Cloud service provides CAE software and computing resources to users who want to experience M&S in business sectors or academics. In this paper, the economic ripple effect of HEMOS-Cloud service was analyzed by using industry-related analysis. The estimated results of using the experts-guided coefficients are the production inducement effect of KRW 7.4 billion, the value-added effect of KRW 4.1 billion, and the employment-inducing effect of 50 persons per KRW 1 billion.

• Journal of the Korean earth science society
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• v.43 no.6
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• pp.691-711
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• 2022

The state-of-the-art Earth system model as a virtual Earth is required for studies of current and future climate change or climate crises. This complex numerical model can account for almost all human activities and natural phenomena affecting the atmosphere of Earth. The Unified Model (UM) from the United Kingdom Meteorological Office (UK Met Office) is among the best Earth system models as a scientific tool for studying the atmosphere. However, owing to the expansive numerical integration cost and substantial output size required to maintain the UM, individual research groups have had to rely only on supercomputers. The limitations of computer resources, especially the computer environment being blocked from outside network connections, reduce the efficiency and effectiveness of conducting research using the model, as well as improving the component codes. Therefore, this study has presented detailed guidance for installing a new version of the UM on high-performance parallel computers (Linux clusters) owned by individual researchers, which would help researchers to easily work with the UM. The numerical integration performance of the UM on Linux clusters was also evaluated for two different model resolutions, namely N96L85 (1.875° ×1.25° with 85 vertical levels up to 85 km) and N48L70 (3.75° ×2.5° with 70 vertical levels up to 80 km). The one-month integration times using 256 cores for the AMIP and CMIP simulations of N96L85 resolution were 169 and 205 min, respectively. The one-month integration time for an N48L70 AMIP run using 252 cores was 33 min. Simulated results on 2-m surface temperature and precipitation intensity were compared with ERA5 re-analysis data. The spatial distributions of the simulated results were qualitatively compared to those of ERA5 in terms of spatial distribution, despite the quantitative differences caused by different resolutions and atmosphere-ocean coupling. In conclusion, this study has confirmed that UM can be successfully installed and used in high-performance Linux clusters.

• Korean Journal of Social Welfare
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• v.64 no.1
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• pp.31-52
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• 2012

This study has three objectives. First, it examines the relationship between organizational fairness and organizational commitment perceived by social workers in Korea. Second, it simultaneously examines mediating effects of organizational trust and organizational cynicism in the relationship between organizational fairness and organizational commitment. Third, it also examines the effect of social workers' perceived work value on the combined model by considering the unique characteristics of social work profession. This study employs the stratified cluster sampling method on social workers with more than two year work experiences in their current social service agencies that are located in Seoul and Kyungki province; finally it analyzes the responses from 564 social workers by using the method of structural equation modeling. This study has the following results: (1) there is a positive causal relationship between organizational fairness and organizational commitment perceived by social workers; (2) there is also a positive causal relationship between social workers' perceived work value and organizational commitment; and (3) in the mediating effects of organizational trust and organizational cynicism, there are no mediating effects in the relationship between organizational fairness and organizational commitment. This study discusses the importance of social workers' perceived work value and theoretical and practical implications of the results.

• Korean Security Journal
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• no.14
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• pp.125-140
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• 2007

This study was to analyze structural equation modeling of organizational culture, human resource management and organization performance in the private security corporation. To attain the goal of the study described above paragraphs, the employee of private security corporation located in Seoul, 2006 year were set as a collected group. Then, using the cluster random sampling method, finally drew out 300 peoples and analyzed 250 peoples in total. The material collection device was the brochure named . The validity of instrument was confirmed by confirmatory factor analysis(CFA) The result of reliability check up was here below; Chronbach' ${\alpha}=.724$. To analyze materials, correlation analysis, CFA, SEM were used as statistic analysis techniques. The conclusion based on above study method and the result of material analysis are follows; First, organizational culture influences on the human resource management. Second, organizational culture influences on the organizational performance. Third, human resource management influences on the organizational performance. Particularly, organizational culture influences indirectly on organizational performance throughout human resource management. Human resource management is very important variable mediating organizational culture and performance.