• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.158-170
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• 2021

Virtualization technologies are being adopted and broadly utilized in many fields and at different levels. In cloud computing, achieving load balancing across large distributed virtual machines is considered a complex optimization problem with an essential importance in cloud computing systems and data centers as the overloading or underloading of tasks on VMs may cause multiple issues in the cloud system like longer execution time, machine failure, high power consumption, etc. Therefore, load balancing mechanism is an important aspect in cloud computing that assist in overcoming different performance issues. In this research, we propose a new approach that combines the advantages of different task allocation algorithms like Round robin algorithm, and Random allocation with different threshold techniques like the VM utilization and the number of allocation counts using least connection mechanism. We performed extensive simulations and experiments that augment different scheduling policies to overcome the resource utilization problem without compromising other performance measures like makespan and execution time of the tasks. The proposed system provided better results compared to the original round robin as it takes into consideration the dynamic state of the system.

• 한국BIM학회 논문집
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• 제10권4호
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• pp.70-80
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• 2020

In the MEP(Mechanical Electrical and Plumbing) field, the utilization of BIM-based drawings is lower than in the architectural and structural sectors, and the limited BIM collaboration problem caused by different levels of BIM utilization in each field is becoming a serious problem in adoption and utilizing BIM. Therefore, this study analyzed the current status of BIM application in the field of mechanical equipment in the construction industry and analyzed the practical problems and limitations of adoption and utilizing BIM from a corporate perspective based on Delphi analysis techniques. Based on the results of the analysis, the limitations of the current MEP BIM application were classified into economic, technical, institutional, and social factors to derive detailed items, and, accordingly, the improvement measures were classified as institutional, policy, and technical measures. As a result, the company intends to maximize the value of BIM utilization in the MEP field by presenting improvement plans to activate BIM in the field of mechanical equipment based on the opinions of the company, thereby laying the foundation for BIM in the construction industry by creating a collaborative BIM environment for each sector in the domestic construction industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권6호
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• pp.1398-1417
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• 2013

A method is proposed to reduce excess resources from a virtual machine(VM) while avoiding subsequent migrations for a computer cluster that provides cloud service. The proposed scheme cuts down on the resources of a VM based on the probability that migration may occur after a reduction. First, it finds a VM that can be scaled down by analyzing the history of the resource usage. Then, the migration probability is calculated as a function of the VM resource usage trend and the trend error. Finally, the amount of resources needed to eliminate from an underutilized VM is determined such that the migration probability after the resource reduction is less than or equal to an acceptable migration probability. The acceptable migration probability, to be set by the cloud service provider, is a criterion to assign a weight to the resource reduction either to prevent VM migrations or to enhance VM utilization. The results of simulation show that the proposed scheme lowers migration frequency by 31.6~60.8% depending on the consistency of resource demand while losing VM utilization by 9.1~21.5% compared to other known approaches, such as the static and the prediction-based methods. It is also verified that the proposed scheme extends the elapsed time before the first occurrence of migration after resource reduction 1.1~2.3-fold. In addition, changes in migration frequency and VM utilization are analyzed with varying acceptable migration probabilities and the consistency of resource demand patterns. It is expected that the analysis results can help service providers choose a right value of the acceptable migration probability under various environments having different migration costs and operational costs.

• 한국시뮬레이션학회논문지
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• 제22권1호
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• pp.77-86
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• 2013

클라우드 환경은 여러 개의 컴퓨팅 자원들을 이용하는 분산 컴퓨팅 환경의 일종으로 가상머신을 이용 하여 작업을 처리한다. 클라우드 환경은 작업 요청에 따르는 부하분산과 빠른 작업 처리를 위한 프로비저닝 기술을 이용하여 가상머신의 상태에 따라 작업을 할당 한다. 하지만, 클라우드 환경의 작업 스케줄링을 위해서는 가상머신의 성능에 따르는 애매모호한 상태에 대한 가용성의 정의가 필요하다. 본 논문에서는 클라우드 환경의 프로비저닝 스케줄링을 위해 퍼지 로직 기반의 자원평가를 이용한 가상머신 프로비저닝 스케줄링(FVPRE: Fuzzy logic driven Virtual machine Provisioning scheduling using Resource Evaluation)을 제안한다. FVPRE는 각 가상머신의 정의하기 어려운 성능의 상태를 분석하여 자원 가용성에 대한 값을 구체화하여 정확한 자원의 가용성 평가를 통해 효율적인 프로비저닝 스케줄링이 가능하다. FVPRE는 클라우드 환경의 작업 처리에 대해 높은 처리율과 활용율을 보인다.

• 대한방사선기술학회지:방사선기술과학
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• 제15권2호
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• pp.37-47
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• 1992

Magnetic Resonance Imaging(MRI) is one of the most expensive and sophisticated diagnostic tool and has been hailed as the most exciting event in medical imaging "since the introduction of X-rays", but a major disadvantage, high cost, is coming into focus especially in our country. To determine the status of distribution of MR imagers in Korea and to serve as a basic material for an efficient utilization of this Imaging machine, a retrospective survey of nationwide and regional(3 hospitals in Pusan) installations was performed. The results were as follows : 1. As of April 30, 1991, a total of 33 MRI units(24 for superconducting, 6 for permanent and 3 for resistive units) were set up and operated. 91% of the units were distributed in big cities with no one installation in 7 provinces among 12 provinces in our country. 85% of the units were imported. 2. Although 42.4% of the units were operated in Seoul, Taejeon had the best condition for the distribution of this imaging machine per population, hospital, and bed in Korea. 3. In Pusan : a) 5 units were operated with all superconducting magnet and medium magnetic field in type of machine. b) 80.1 % of the examinations were central nervous system(CNS). c) MRI examination occupied 1.4% of all radiographic examinations and the patients referred from other hospitals were composed of 23.4%% of all patients. 4. The average days under operating of MRI unit a week in Puasn were higher(5.5) than that of Seoul(4.5), but the average number of examinations and hours a week and a day, respectively(33, 8.4), was less than that of Seoul(57, 12.9). 5. The patients with positive MRI findings in a hospital(B) in Pusan was 74.5% on an average.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.589-594
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• 1991

Group Technology (GT) is a technique for identifying and bringing together related or similar components in a production process in order to take advantage of their similarities by making use of, for example, the inherent economies of flow production methods. The process of identification, from large variety and total of components, of the part families requiring similar manufacturing operations and forming the associated groups of machines is referred as 'machine-component grouping'. First part of this paper is devoted to describing a hierarchical divisive algorithm based on graph theory to find the natural part families. The objective is to form components into part families such that the degree of inter-relations is high among components within the same part family and low between components of different part families. Second part of this paper focuses on establishing cell design procedures. The aim is to create cells in which the most expensive and important machines-called key machine - have a reasonably high utilization and the machines should be allocated to minimize the intercell movement of machine loads. To fulfil the above objectives, 0-1 integer programming model is developed and the solution procedures are found. Next an attempt is made to test the feasibility of the proposed method. Several different problems appearing in the literature are chosen and the results air briefly showed.

• 한국정밀공학회지
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• 제24권4호
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• pp.84-92
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• 2007

Dynamic process planning requires not only more flexible capabilities of a CAPP system but also higher utility of the generated process plans. In order to meet the requirements, this paper develops an algorithm that can select machines for the machining operations by calculating the machine loads. The developed algorithm is based on the multi-objective genetic algorithm that gives rise to a set of optimal solutions (in general, known as the Pareto-optimal solutions). The objective is to satisfy both the minimization number of part movements and the maximization of machine utilization. The algorithm is characterized by a new and efficient method for nondominated sorting through K-means algorithm, which can speed up the running time, as well as a method of two stages for genetic operations, which can maintain a diverse set of solutions. The performance of the algorithm is evaluated by comparing with another multiple objective genetic algorithm, called NSGA-II and branch and bound algorithm.

• 한국정밀공학회지
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• 제33권12호
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• pp.965-970
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• 2016

Fuel used in the steel metallurgy industry is stored in huge stage systems called SILO. Fuel is released by RDM (Rotary Discharge Machine), at the place of utilization. RDM is located in the Silo, and is constituted of a main frame, driving part, discharging part and control part. RDM is combined to a direct motion on the rail in tunnel, having a rotary motion enabled by a motor. In this paper, we calculate the theoretical discharging capacity of RDM to confirm the correlation between design element and discharging capacity of RDM. Also, through structure analysis, we confirm the vulnerable point of RDM when it discharges the storage materials. We hope to apply these results to design a more efficient RDM.

• 산업경영시스템학회지
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• 제45권4호
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• pp.86-98
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• 2022

Recently, many studies have been conducted to improve quality by applying machine learning models to semiconductor manufacturing process data. However, in the semiconductor manufacturing process, the ratio of good products is much higher than that of defective products, so the problem of data imbalance is serious in terms of machine learning. In addition, since the number of features of data used in machine learning is very large, it is very important to perform machine learning by extracting only important features from among them to increase accuracy and utilization. This study proposes an anomaly detection methodology that can learn excellently despite data imbalance and high-dimensional characteristics of semiconductor process data. The anomaly detection methodology applies the LIME algorithm after applying the SMOTE method and the RFECV method. The proposed methodology analyzes the classification result of the anomaly classification model, detects the cause of the anomaly, and derives a semiconductor process requiring action. The proposed methodology confirmed applicability and feasibility through application of cases.

• Nuclear Engineering and Technology
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• 제55권5호
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• pp.1708-1717
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• 2023

The grade analysis of lead-zinc ore is the basis for the optimal development and utilization of deposits. In this study, a method combining Prompt Gamma Neutron Activation Analysis (PGNAA) technology and machine learning is proposed for lead-zinc mine borehole logging, which can identify lead-zinc ores of different grades and gangue in the formation, providing real-time grade information qualitatively and semi-quantitatively. Firstly, Monte Carlo simulation is used to obtain a gamma-ray spectrum data set for training and testing machine learning classification algorithms. These spectra are broadened, normalized and separated into inelastic scattering and capture spectra, and then used to fit different classifier models. When the comprehensive grade boundary of high- and low-grade ores is set to 5%, the evaluation metrics calculated by the 5-fold cross-validation show that the SVM (Support Vector Machine), KNN (K-Nearest Neighbor), GNB (Gaussian Naive Bayes) and RF (Random Forest) models can effectively distinguish lead-zinc ore from gangue. At the same time, the GNB model has achieved the optimal accuracy of 91.45% when identifying high- and low-grade ores, and the F₁ score for both types of ores is greater than 0.9.