• 제목/요약/키워드: Approach of Network

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금형연마작업에서 신경망을 이용한 표면거칠기 추정 (Estimation of Surface Roughness using Neural Network in Polishing Operation of Mold and Die)

  • 조규갑;강용우
    • 한국정밀공학회지
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    • 제19권4호
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    • pp.73-78
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    • 2002
  • This paper presents a neural network approach to estimate the surface roughness by considering the relationship between the polishing operation parameters and the surface roughness. The neural network model predicts the post-machining surface roughness by using several factors such as pre-machining surface roughness, pressure, feed rate, spindle speed, and the number of polishing as inputs. In this paper, the several neural network models are implemented to estimate the surface roughness by using actual experimental data. The experimental results show that the neural network approach is more appropriate to represent the polishing characteristics of mold and die compared with the results obtained by the approach using exponential function.

타부 리스트가 결합된 유전자 알고리즘을 이용한 트리형 네트워크의 경제적 설계 (Economic Design of Tree Network Using Tabu List Coupled Genetic Algorithms)

  • 이성환;이한진;염창선
    • 산업경영시스템학회지
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    • 제35권1호
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    • pp.10-15
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    • 2012
  • This paper considers an economic design problem of a tree-based network which is a kind of computer network. This problem can be modeling to be an objective function to minimize installation costs, on the constraints of spanning tree and maximum traffic capacity of sub tree. This problem is known to be NP-hard. To efficiently solve the problem, a tabu list coupled genetic algorithm approach is proposed. Two illustrative examples are used to explain and test the proposed approach. Experimental results show evidence that the proposed approach performs more efficiently for finding a good solution or near optimal solution in comparison with a genetic algorithm approach.

Network Analysis in Systems Epidemiology

  • Park, JooYong;Choi, Jaesung;Choi, Ji-Yeob
    • Journal of Preventive Medicine and Public Health
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    • 제54권4호
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    • pp.259-264
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    • 2021
  • Traditional epidemiological studies have identified a number of risk factors for various diseases using regression-based methods that examine the association between an exposure and an outcome (i.e., one-to-one correspondences). One of the major limitations of this approach is the "black-box" aspect of the analysis, in the sense that this approach cannot fully explain complex relationships such as biological pathways. With high-throughput data in current epidemiology, comprehensive analyses are needed. The network approach can help to integrate multi-omics data, visualize their interactions or relationships, and make inferences in the context of biological mechanisms. This review aims to introduce network analysis for systems epidemiology, its procedures, and how to interpret its findings.

생리적 신호를 이용한 통증 인식을 위한 딥 러닝 네트워크 (Deep Learning Network Approach for Pain Recognition Using Physiological Signals)

  • ;이귀상;양형정;김수형
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2021년도 추계학술발표대회
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    • pp.1001-1004
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    • 2021
  • Pain is an unpleasant experience for the patient. The recognition and assessment of pain help tailor the treatment to the patient, and they are also challenging in the medical. In this paper, we propose an approach for pain recognition through a deep neural network applied to pre-processed physiological. The proposed approach applies the idea of shortcut connections to concatenate the spatial information of a convolutional neural network and the temporal information of a recurrent neural network. In addition, our proposed approach applies the attention mechanism and achieves competitive performance on the BioVid Heat Pain dataset.

공급사슬네트워크에서 시뮬레이션과 유전알고리즘을 이용한 통합생산분배계획에 대한 연구 (Study of Integrated Production-Distribution Planning Using Simulation and Genetic Algorithm in Supply Chain Network)

  • 임석진
    • 대한안전경영과학회지
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    • 제22권4호
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    • pp.65-74
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    • 2020
  • Many of companies have made significant improvements for globalization and competitive business environment The supply chain management has received many attentions in the area of that business environment. The purpose of this study is to generate realistic production and distribution planning in the supply chain network. The planning model determines the best schedule using operation sequences and routing to deliver. To solve the problem a hybrid approach involving a genetic algorithm (GA) and computer simulation is proposed. This proposed approach is for: (1) selecting the best machine for each operation, (2) deciding the sequence of operation to product and route to deliver, and (3) minimizing the completion time for each order. This study developed mathematical model for production, distribution, production-distribution and proposed GA-Simulation solution procedure. The results of computational experiments for a simple example of the supply chain network are given and discussed to validate the proposed approach. It has been shown that the hybrid approach is powerful for complex production and distribution planning in the manufacturing supply chain network. The proposed approach can be used to generate realistic production and distribution planning considering stochastic natures in the actual supply chain and support decision making for companies.

A comprehensive approach to flow-based seismic risk analysis of water transmission network

  • Yoon, Sungsik;Lee, Young-Joo;Jung, Hyung-Jo
    • Structural Engineering and Mechanics
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    • 제73권3호
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    • pp.339-351
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    • 2020
  • Earthquakes are natural disasters that cause serious social disruptions and economic losses. In particular, they have a significant impact on critical lifeline infrastructure such as urban water transmission networks. Therefore, it is important to predict network performance and provide an alternative that minimizes the damage by considering the factors affecting lifeline structures. This paper proposes a probabilistic reliability approach for post-hazard flow analysis of a water transmission network according to earthquake magnitude, pipeline deterioration, and interdependency between pumping plants and 154 kV substations. The model is composed of the following three phases: (1) generation of input ground motion considering spatial correlation, (2) updating the revised nodal demands, and (3) calculation of available nodal demands. Accordingly, a computer code was developed to perform the hydraulic analysis and numerical modelling of water facilities. For numerical simulation, an actual water transmission network was considered and the epicenter was determined from historical earthquake data. To evaluate the network performance, flow-based performance indicators such as system serviceability, nodal serviceability, and mean normal status rate were introduced. The results from the proposed approach quantitatively show that the water network is significantly affected by not only the magnitude of the earthquake but the interdependency and pipeline deterioration.

비이진 연관행렬 기반의 부품-기계 그룹핑을 위한 효과적인 인공신경망 접근법 (Effective Artificial Neural Network Approach for Non-Binary Incidence Matrix-Based Part-Machine Grouping)

  • 원유경
    • 한국경영과학회지
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    • 제31권4호
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    • pp.69-87
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    • 2006
  • This paper proposes an effective approach for the part-machine grouping(PMG) based on the non-binary part-machine incidence matrix in which real manufacturing factors such as the operation sequences with multiple visits to the same machine and production volumes of parts are incorporated and each entry represents actual moves due to different operation sequences. The proposed approach adopts Fuzzy ART neural network to quickly create the Initial part families and their machine cells. A new performance measure to evaluate and compare the goodness of non-binary block diagonal solution is suggested. To enhance the poor solution due to category proliferation inherent to most artificial neural networks, a supplementary procedure reassigning parts and machines is added. To show effectiveness of the proposed approach to large-size PMG problems, a psuedo-replicated clustering procedure is designed. Experimental results with intermediate to large-size data sets show effectiveness of the proposed approach.

Large-Scale Integrated Network System Simulation with DEVS-Suite

  • Zengin, Ahmet
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제4권4호
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    • pp.452-474
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    • 2010
  • Formidable growth of Internet technologies has revealed challenging issues about its scale and performance evaluation. Modeling and simulation play a central role in the evaluation of the behavior and performance of the large-scale network systems. Large numbers of nodes affect simulation performance, simulation execution time and scalability in a weighty manner. Most of the existing simulators have numerous problems such as size, lack of system theoretic approach and complexity of modeled network. In this work, a scalable discrete-event modeling approach is described for studying networks' scalability and performance traits. Key fundamental attributes of Internet and its protocols are incorporated into a set of simulation models developed using the Discrete Event System Specification (DEVS) approach. Large-scale network models are simulated and evaluated to show the benefits of the developed network models and approaches.

A Coordinated Heuristic Approach for Virtual Network Embedding in Cloud Infrastructure

  • Nia, Nahid Hamzehee;Adabi, Sepideh;Nategh, Majid Nikougoftar
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제11권5호
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    • pp.2346-2361
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    • 2017
  • A major challenge in cloud infrastructure is the efficient allocation of virtual network elements on top of substrate network elements. Path algebra is a mathematical framework which allows the validation and convergence analysis of the mono-constraint or multi-constraint routing problems independently of the network topology or size. The present study proposes a new heuristic approach based on mathematical framework "paths algebra" to map virtual nodes and links to substrate nodes and paths in cloud. In this approach, we define a measure criterion to rank the substrate nodes, and map the virtual nodes to substrate nodes according to their ranks by using a greedy algorithm. In addition, considering multi-constraint routing in virtual link mapping stage, the used paths algebra framework allows a more flexible and extendable embedding. Obtained results of simulations show appropriate improvement in acceptance ratio of virtual networks and cost incurred by the infrastructure networks.

인공신경망 기초 의사결정트리 분류기에 의한 시계열모형화에 관한 연구 (A Neural Network-Driven Decision Tree Classifier Approach to Time Series Identification)

  • 오상봉
    • 한국시뮬레이션학회논문지
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    • 제5권1호
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    • pp.1-12
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    • 1996
  • We propose a new approach to classifying a time series data into one of the autoregressive moving-average (ARMA) models. It is bases on two pattern recognition concepts for solving time series identification. The one is an extended sample autocorrelation function (ESACF). The other is a neural network-driven decision tree classifier(NNDTC) in which two pattern recognition techniques are tightly coupled : neural network and decision tree classfier. NNDTc consists of a set of nodes at which neural network-driven decision making is made whether the connecting subtrees should be pruned or not. Therefore, time series identification problem can be stated as solving a set of local decisions at nodes. The decision values of the nodes are provided by neural network functions attached to the corresponding nodes. Experimental results with a set of test data and real time series data show that the proposed approach can efficiently identify the time seires patterns with high precision compared to the previous approaches.

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