• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.446-447
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• 2022

대부분의 머신러닝 및 딥러닝 모델의 경우 하이퍼 파라미터 선택은 모델의 성능에 큰 영향을 미친다. 따라서 전문가들은 작업을 수행하기 위해 모델을 구축할 때 하이퍼 파라미터 튜닝을 수행하는 데 상당한 시간을 소비해야 한다. Hyperparameter Optimization(HPO)을 해결하기 위한 알고리즘은 많지만 대부분의 방법은 검색을 수행하기 위해 각 epoch에서 실제 실험 결과를 필요로 한다. 따라서 HPO 검색을 위한 시간과 계산 지원을 줄이기 위해 본 논문에서는 Multi-agent Proximal Policy Optimization(MAPPO) 강화 학습 알고리즘을 제안한다. 2개의 이미지 분류 데이터 세트에 대한 실험 결과는 우리의 모델이 속도와 정확성에서 다른 기존 방법보다 우수하다는 것을 보여준다.

• Journal of Pharmaceutical Investigation
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• v.39 no.4
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• pp.263-267
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• 2009

The purpose of this study is to evaluate the efficacy of stipulated cleaning process, and the prohibition of cross-contamination and microbiological contamination, which inadequate cleaning in multi-production could occur, through cleaning validation of multi-purpose facility used to produce five biopharmaceutical products as sterile injection. After production of five biopharmaceutical products such as hGH, rhGCSF, rhEPO, rhFSH and rhIFN using vial filling machine, the cleaning validation such as residual analysis of active ingredients or human serum albumin, measurement of total organic carbon (TOC), residual analysis of detergent and microbiological contamination were carried out. In the case of rhGH and rhGCSF clean validations, drug residues were not detected. Furthermore, in the case of rhEPO, rhFSH and rhIFN clean validations, human serum albumin residues were not detected. At TOC (total organic carbon) analysis, all clean validations gave the TOC of about average 137.93%, not more than 150% of acceptance criteria. At sodium analysis for the checking of residues of cleaning agent, sodium residues were not detected. In sterility test, they showed no microbiological contamination of bacteria and fungi. Thus, this cleaning validation was determined as successful in protection of cross-contamination and induction of safety in multi-purpose facility.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.378-388
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• 2016

International societies are currently working together to achieve the Climate-Smart Agriculture (CSA) initiative which aims the triple wins: (1) sustainably increasing agricultural productivity and incomes; (2) adapting and building resilience to climate change; and (3) mitigating greenhouse gases emissions. In terms of its scope and context, CSA follows the '3Nong (三農)' vision cast about 200 years ago by Dasan Jeong Yak-Yong who emphasized the triad of governance, management and monitoring towards comfortable, profitable and noble agriculture. Yet, the CSA provides the practical aims that facilitate the development of holistic indicators for quantitative evaluation and monitoring, on which decision-making support system is based. In this study, we introduce an agent-based model, i.e. Mathematical Programming Multi-Agent Systems (MP-MAS), as a tool for supporting the decision-making toward CSA. We have established the initial version of MP-MAS adapted for domestic use and present the preliminary results from an application to the rice farming case in Haenam, Korea. MP-MAS can support both farmers and policy-makers to consider diverse management options from multiple perspectives. When the modules for system resilience and carbon footprint are added, MP-MAS will serve as a robust tool that fulfills not only CSA but also Dasan's '3Nong' vision of sustainable agricultural-societal systems.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.9
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• pp.351-358
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• 2006

In this paper, DSPOCS(Digital Switchgear-Panel Operation and Control Solution) is designed, which is the intelligent inference based operation and control solution to obtain the safety and reliability of electric power supply in substation based on IED. DSPOCS is designed as a scheduled monitoring and control task and a real-time alarm inference task, and is interlinked with BRES(Bus Reconfiguration Expert System) in the required case. The intelligent alarm inference task consists of the alarm knowledge generation part and the real-time pattern matching part. The alarm knowledge generation part generates automatically alarm knowledge from DB saves it in alarm knowledge base. On the other hand, the pattern matching part inferences the real-time event by comparing the real-time event information furnished from IEDs of substation with the patterns of the saved alarm knowledge base.; Especially, alarm knowledge base includes the knowledge patterns related with fault alarm, the overload alarm and the diagnosis alarm. In order to design the database independently in substation structure, busbar is represented as a connectivity node which makes the more generalized graph theory possible. Finally, DSPOCS is implemented in MS Visual $C^{++}$, MFC, the effectiveness and accuracy of the design is verified by simulation study to the typical distribution substation.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.157-175
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• 2012

It is difficult to evaluate the performance of process innovation of e-procurement which has large scale and complex processes. The existing evaluation methods for measuring the effects of process innovation have been mainly done with statistically quantitative methods by analyzing operational data or with qualitative methods by conducting surveys and interviews. However, these methods have some limitations to evaluate the effects because the performance evaluation of e-procurement process innovation should consider the interactions among participants who are active either directly or indirectly through the processes. This study considers the e-procurement process as a complex system and develops a simulation model based on MAS(Multi-Agent System) to evaluate the effects of e-procurement process innovation. Multi-agent based simulation allows observing interaction patterns of objects in virtual world through relationship among objects and their behavioral mechanism. Agent-based simulation is suitable especially for complex business problems. In this study, we used Netlogo Version 4.1.3 as a MAS simulation tool which was developed in Northwestern University. To do this, we developed a interaction model of agents in MAS environment. We defined process agents and task agents, and assigned their behavioral characteristics. The developed simulation model was applied to G2B system (KONEPS: Korea ON-line E-Procurement System) of Public Procurement Service (PPS) in Korea and used to evaluate the innovation effects of the G2B system. KONEPS is a successfully established e-procurement system started in the year 2002. KONEPS is a representative e-Procurement system which integrates characteristics of e-commerce into government for business procurement activities. KONEPS deserves the international recognition considering the annual transaction volume of 56 billion dollars, daily exchanges of electronic documents, users consisted of 121,000 suppliers and 37,000 public organizations, and the 4.5 billion dollars of cost saving. For the simulation, we analyzed the e-procurement of process of KONEPS into eight sub processes such as 'process 1: search products and acquisition of proposal', 'process 2 : review the methods of contracts and item features', 'process 3 : a notice of bid', 'process 4 : registration and confirmation of qualification', 'process 5 : bidding', 'process 6 : a screening test', 'process 7 : contracts', and 'process 8 : invoice and payment'. For the parameter settings of the agents behavior, we collected some data from the transactional database of PPS and some information by conducting a survey. The used data for the simulation are 'participants (government organizations, local government organizations and public institutions)', 'the number of bidding per year', 'the number of total contracts', 'the number of shopping mall transactions', 'the rate of contracts between bidding and shopping mall', 'the successful bidding ratio', and the estimated time for each process. The comparison was done for the difference of time consumption between 'before the innovation (As-was)' and 'after the innovation (As-is).' The results showed that there were productivity improvements in every eight sub processes. The decrease ratio of 'average number of task processing' was 92.7% and the decrease ratio of 'average time of task processing' was 95.4% in entire processes when we use G2B system comparing to the conventional method. Also, this study found that the process innovation effect will be enhanced if the task process related to the 'contract' can be improved. This study shows the usability and possibility of using MAS in process innovation evaluation and its modeling.