• 제목/요약/키워드: Demand Clustering

검색결과 130건 처리시간 0.021초

앙상블 모델 기반의 기계 고장 예측 방법 (An Ensemble Model for Machine Failure Prediction)

  • 천강민;양재경
    • 산업경영시스템학회지
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    • 제43권1호
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    • pp.123-131
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    • 2020
  • There have been a lot of studies in the past for the method of predicting the failure of a machine, and recently, a lot of researches and applications have been generated to diagnose the physical condition of the machine and the parts and to calculate the remaining life through various methods. Survival models are also used to predict plant failures based on past anomaly cycles. In particular, special machine that reflect the fluid flow and process characteristics of chemical plants are connected to hundreds or thousands of sensors, so there are not many factors that need to be considered, such as process and material data as well as application of derivative variables. In this paper, the data were preprocessed through time series anomaly detection based on unsupervised learning to predict the abnormalities of these special machine. Next, clustering results reflecting clustering-based data characteristics were applied to produce additional variables, and a learning data set was created based on the history of past facility abnormalities. Finally, the prediction methodology based on the supervised learning algorithm was applied, and the model update was confirmed to improve the accuracy of the prediction of facility failure. Through this, it is expected to improve the efficiency of facility operation by flexibly replacing the maintenance time and parts supply and demand by predicting abnormalities of machine and extracting key factors.

군집화 기법을 이용한 GIS 열화 패턴 연구 (A Study on Degradation Pattern of GIS Using Clustering Methode)

  • 이덕진
    • 한국전기전자재료학회논문지
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    • 제31권4호
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    • pp.255-260
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    • 2018
  • In recent years, increasing electricity use has led to considerable interest in green energy. In order to effectively supply, cut off, and operate an electric power system, many electric power facilities such as gas insulation switch (GIS), cable, and large substation facilities with higher densities are being developed to meet demand. However, because of the increased use of aging electric power facilities, safety problems are emerging. Electromagnetic wave and leakage current detection are mainly used as sensing methods to detect live-line partial discharges. Although electromagnetic sensors are excellent at providing an initial diagnosis and very reliable, it is difficult to precisely determine the fault point, while leakage current sensors require a connection to the ground line and are very vulnerable to line noise. The partial discharge characteristic in particular is accompanied by statistical irregularity, and it has been reported that proper statistical processing of data is very important. Therefore, in this paper, we present the results of analyzing ${\Phi}-q-n$ cluster distributions of partial discharge characteristics by using K-means clustering to develop an expert partial discharge diagnosis system generated in a GIS facility.

대체가공경로를 가지는 부품-기계 군집 문제를 위한 일반화된 군집 알고리듬 (Generalized Clustering Algorithm for Part-Machine Grouping with Alternative Process Plans)

  • 김창욱;박윤선;전진
    • 대한산업공학회지
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    • 제27권3호
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    • pp.281-288
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    • 2001
  • We consider in this article a multi-objective part-machine grouping problem in which parts have alternative process plans and expected annual demand of each part is known. This problem is characterized as optimally determining part sets and corresponding machine cells such that total sum of distance (or dissimilarity) between parts and total sum of load differences between machines are simultaneously minimized. Two heuristic algorithms are proposed, and examples are given to compare the performance of the algorithms.

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일반거리산정방법을 이용한 다-물류센터의 최적 수송경로 계획 모델 (A Vehicle Routing Model for Multi-Supply Centers Based on Lp-Distance)

  • 황흥석
    • 산업공학
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    • 제11권1호
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    • pp.85-95
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    • 1998
  • This study is focussed on an optimal vehicle routing model for multi-supply centers in two-echelon logistic system. The aim of this study is to deliver goods for demand sites with optimal decision. This study investigated an integrated model using step-by-step approach based on relationship that exists between the inventory allocation and vehicle routing with restricted amount of inventory and transportations such as the capability of supply centers, vehicle capacity and transportation parameters. Three sub-models are developed: 1) sector-clustering model, 2) a vehicle-routing model based on clustering and a heuristic algorithm, and 3) a vehicle route scheduling model using TSP-solver based on genetic and branch-and-bound algorithm. Also, we have developed computer programs for each sub-models and user interface with visualization for major inputs and outputs. The application and superior performance of the proposed model are demonstrated by several sample runs for the inventory-allocation and vehicle routing problems.

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서비스 부문의 기술혁신목적별 정부 지원제도의 활용도 분석 연구 (Data Mining for the Effectiveness of Government Support Strategies for Technology Innovation in Service Sectors)

  • 황두현;김우진;손소영
    • 산업공학
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    • 제21권2호
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    • pp.237-246
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    • 2008
  • In today's competitive global environment, technological innovation is an important issue. Many countries are devising national level strategies to further strengthen industrial capacity in support of innovative companies. South Korea is no exception, and multiple strategies are in place to aid innovative development in the private sector. This study postulates that such national level strategies are applied differently depending on the innovation goal pursued by the service sector in Korea. We use data mining methods to test such research hypothesis. Factor analysis is used for clustering of various service companies, while association rule is used in finding the relationship per each cluster. The results show that national level strategies are underutilized and unequally distributed. This may be attributed to the disparity between the demand and needs of the private sector and the opinion of the government, which lead to underutilized and indistinguishable strategies.

배달과 수집을 수행하는 차량경로문제 휴리스틱에 관한 연구: 수도권 레미콘 운송사례 (Heuristic for the Pick-up and Delivery Vehicle Routing Problem: Case Study for the Remicon Truck Routing in the Metropolitan Area)

  • 지창훈;김미이;이영훈
    • 경영과학
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    • 제24권2호
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    • pp.43-56
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    • 2007
  • VRP(Vehicle Routing Problem) is studied in this paper, where two different kinds of missions are to be completed. The objective is to minimize the total vehicle operating distance. A mixed integer programming formulation and a heuristic algorithm for a practical use are suggested. A heuristic algorithm consists of three phases such as clustering, constructing routes, and adjustment. In the first phase, customers are clustered so that the supply nodes are grouped with demand nodes to be served by the same vehicle. Vehicle routes are generated within the cluster in the second phase. Clusters and routes are adjusted in the third phase using the UF (unfitness) rule designed to determine the customers and the routes to be moved properly. It is shown that the suggested heuristic algorithm yields good performances within a relatively short computational time through computational experiment.

Reinforcement learning multi-agent using unsupervised learning in a distributed cloud environment

  • Gu, Seo-Yeon;Moon, Seok-Jae;Park, Byung-Joon
    • International Journal of Internet, Broadcasting and Communication
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    • 제14권2호
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    • pp.192-198
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    • 2022
  • Companies are building and utilizing their own data analysis systems according to business characteristics in the distributed cloud. However, as businesses and data types become more complex and diverse, the demand for more efficient analytics has increased. In response to these demands, in this paper, we propose an unsupervised learning-based data analysis agent to which reinforcement learning is applied for effective data analysis. The proposal agent consists of reinforcement learning processing manager and unsupervised learning manager modules. These two modules configure an agent with k-means clustering on multiple nodes and then perform distributed training on multiple data sets. This enables data analysis in a relatively short time compared to conventional systems that perform analysis of large-scale data in one batch.

Clustering of PV Load Patterns Based on Any Colony Centroid Model

  • Munshi, Amr
    • International Journal of Computer Science & Network Security
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    • 제22권5호
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    • pp.67-72
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    • 2022
  • There has been a significant growth in global population and industrialization, as a consequence demand for electricity is increasing rapidly and the power systems need to increase the electricity generation. Currently, most of generated electricity is generated from fossil fuels. However, there are many financial and environmental concerns associated with the generation of electricity from such resource. Photovoltaic )PV) solar as a renewable resource is promising. The power output of PV systems is mainly affected by the solar irradiation and ambient temperature. This paper attempts at reducing the burden and improving the accuracy of the extensive simulations related to integrating PV systems into the electrical grid.

SOM을 이용한 제품수명주기 기반 서비스 수요예측 (Product Life Cycle Based Service Demand Forecasting Using Self-Organizing Map)

  • 장남식
    • 지능정보연구
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    • 제15권4호
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    • pp.37-51
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    • 2009
  • 서비스 인적자원 운용의 효율성 제고와 부품 또는 시설 할당의 적정성 향상을 위해 서비스센터를 통해 접수되는 서비스 요청 건수를 보다 정확하게 예측하고자 하는 필요성이 제조업을 중심으로 증가하고 있다. 본 연구에서는 제품의 특성을 반영하여 제품수명주기 별로 제품들을 군집화하고 군집 별로 적절한 예측모형을 구축한 후 예측 값을 통합하는 개별예측방식을 LCD 모니터 제조사의 사례를 통해 제시한다. 또한 예측 결과를 총량방식 및 기존에 기업이 사용하고 있는 방식과 비교. 평가하여 우수성을 증명함으로써 제품이나 산업의 특성을 반영한 맞춤형 수요예측 기법 도입의 필요성을 부각하고, 그에 따른 이론적, 실무적 가이드라인을 제공한다.

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AREA 활용 전력수요 단기 예측 (Short-term Forecasting of Power Demand based on AREA)

  • 권세혁;오현승
    • 산업경영시스템학회지
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    • 제39권1호
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    • pp.25-30
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    • 2016
  • It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.