• 한국건설관리학회논문집
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• 제20권1호
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• pp.105-113
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• 2019

하나의 건설 프로젝트에는 다양한 작업들이 상호의존적으로 진행되어 복잡한 프로세스를 이룬다. 한 작업에서 공사변동이 발생하면 추가적으로 다른 작업들도 영향을 받게 되며 신속하게 진행되는 건설현장 특성상 매번 변동에 대한 합의를 도출하기는 매우 어렵다. 이에 본 연구에서는 시공 과정에 발생하는 공사변동에 대한 근본원인을 파악하고 주요 공사변동요인에 해당하는 작업들에 대하여 인접행렬을 구축하여 네트워크 분석을 실시하였다. 이를 통해 작업들 간 관계론적 특성과 구조적 위치를 파악하였다. 이러한 시공 프로세스에서의 작업 네트워크 분석을 통해 복잡한 시공 과정에서의 작업흐름을 안정화 시키고자 하며 공정 계획의 신뢰성과 프로젝트 성과를 향상시키고자 한다.

• 한국통신학회논문지
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• 제36권2A호
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• pp.139-148
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• 2011

본 논문은 멀티밴드 해양통신망에서 선적 정보를 주기적으로 전송할 때 발생하는 비용을 최소화하기 위해 가용한 네트워크의 전송 비용과 주어진 허용 가능한 최대 지연 범위 이내에서 예상되는 최소 평균 전송 비용을 비교하여 전송 시점을 결정하는 방안을 제시한다. 이때 전송 시점과 해당 네트워크의 선택 과정을 Markov Decision Process (MDP)로 모델링하며, 이에 따라 각 밴드에서의 채널 상태를 2-State Markov Chain으로 모델링하고 평균 전송 비용을 Stochastic Dynamic Programming을 통해 계산한다. 이를 통해 최소 비용의 망 선택 방식이 도출되었으며, 제안된 방식을 사용할 때 고정 주기를 사용하여 정보를 전송하는 방식에 비해 상당한 망 사용 비용을 절감할 수 있음을 컴퓨터 시뮬레이션을 통해 보인다.

• Journal of applied mathematics & informatics
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• 제41권1호
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• pp.95-105
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• 2023

To make the network energy efficient and to protect the network from malignant user's energy efficient grid based secret key sharing scheme is proposed. The cost function is evaluated to select the optimal nodes for carrying out the data transaction process. The network is split into equal number of grids and each grid is placed with certain number of nodes. The node cost function is estimated for all the nodes present in the network. Once the optimal energy proficient nodes are selected then the data transaction process is carried out in a secured way using malicious nodes filtration process. Therefore, the message is transmitted in a secret sharing method to the end user and this process makes the network more efficient. The proposed work is evaluated in network simulated and the performance of the work are analysed in terms of energy, delay, packet delivery ratio, and false detection ratio. From the result, we observed that the work outperforms the other works and achieves better energy and reduced packet rate.

• 감성과학
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• 제21권3호
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• pp.83-102
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• 2018

The process model of emotion regulation suggests that cognitive reappraisal and expressive suppression engage at different time points in the regulation process. Although multiple brain regions and networks have been identified for each strategy, no articles have explored changes in network characteristics or network connectivity over time. The present study examined (a) the whole-brain network and six other resting-state networks, (b) their modularity and global efficiency, which is an index of the efficiency of information exchange across the network, (c) the degree and betweenness centrality for 160 brain regions to identify the hub nodes with the most control over the entire network, and (d) the intra-network and inter-network functional connectivity (FC). Such investigations were performed using a traditional large-scale FC analysis and a relatively recent sliding window correlation analysis. The results showed that the right inferior orbitofrontal cortex was the hub region of the whole-brain network for both strategies. The present findings of temporally altering functional activity of the networks revealed that the default mode network (DMN) activated at the early stage of reappraisal, followed by the task-positive networks (cingulo-opercular network and fronto-parietal network), emotion-processing networks (the cerebellar network and DMN), and sensorimotor network (SMN) that activated at the early stage of suppression, followed by the greater recruitment of task-positive networks and their functional connection with the emotional response-related networks (SMN and occipital network). This is the first study that provides neuroimaging evidence supporting the process model of emotion regulation by revealing the temporally varying network efficiency and intra- and inter-network functional connections of reappraisal and suppression.

• Design & Manufacturing
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• 제17권4호
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• pp.72-78
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• 2023

Injection molding is a process widely used in various industries because of its high production speed and ease of mass production during the plastic manufacturing process, and the product is molded by injecting molten plastic into the mold at high speed and pressure. Since process conditions such as resin and mold temperature mutually affect the process and the quality of the molded product, it is difficult to accurately predict quality through mathematical or statistical methods. Recently, studies to predict the quality of injection molded products by applying artificial neural networks, which are known to be very useful for analyzing nonlinear types of problems, are actively underway. In this study, structural optimization of neural networks was conducted by applying multi-task learning techniques according to the characteristics of the input and output parameters of the artificial neural network. A structure reflecting the characteristics of each process step was applied to the input parameters, and a structure reflecting the quality characteristics of the injection molded part was applied to the output parameters using multi-tasking learning. Building an artificial neural network to predict the three qualities (mass, diameter, height) of injection-molded product under six process conditions (melt temperature, mold temperature, injection speed, packing pressure, pacing time, cooling time) and comparing its performance with the existing neural network, we observed enhancements in prediction accuracy for mass, diameter, and height by approximately 69.38%, 24.87%, and 39.87%, respectively.

• 산업경영시스템학회지
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• 제25권5호
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• pp.67-74
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• 2002

Technology Management and Research & Development(R&D) have been one of the most difficult divisions for measurement and evaluation. In spite of these difficulties, the importance of R&D has been dramatically increased. It is very difficult to manage more efficiently and effectively than any other departments of production, finance, marketing and so on. As criticizing the shortcomings of the traditional evaluation system in making decisions for corporate management which has only been focused on financial indices, so Kaplan & Norton has suggested the Balanced Scorecard(BSC) which can be managed Critical Success Factors(CSF) in accordance with corporate's strategy. The Analytic Network Process(ANP), based on the Analytic Hierarchy Process, allows the decision makers to leap beyond the traditional hierarchy to the interdependent environment of network modeling. Based on BSC, this study has developed the evaluation system for R&D which has used ANP transforming quantitative and qualitative indices to the quantifying scales in evaluating R&D.

• 한국농공학회논문집
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• 제52권5호
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• pp.109-118
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• 2010

The objectives of this study are to analyze and compare importance degrees of rural amenity resources for rural development project using AHP and ANP (Analytic Network Process) which can be applied a complex decision making problem. For this study, I chose the 5 rural development project types and the 10 rural amenity resources as major criteria and formed the ANP network from relations with criteria. The importance degree matrix were derived by the results of AHP and several ANP analysis. As the results of this study, the importance degrees of 10 rural amenity resources are determined and the indigenous product resource is identified as the most important resource in general rural development project.

• 기술혁신연구
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• 제14권2호
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• pp.143-165
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• 2006

By analyzing five in-depth case studies of suppliers in newly emerging Vietnam's motorcycle industry, this paper explores the differences in patterns and processes of capability improvement of suppliers who participate in different kinds of supplier-assembler network with different structures. The paper finds the correlation between the kinds of suppliers' capabilities improved and the structure of networks they participate in. While suppliers in arm-length networks can improve more upstream capabilities (structure design, process desist), suppliers in embedded networks can improve more downstream capabilities (process design, process setup, process maintenance and delivery control). Two capability improvement patterns of firms in newly emerging economy are indicated. The first pattern is asymmetrical improvement, either upstream or downstream capabilities, by participating in either arm-length or embedded networks. This pattern obstructs the suppliers to meet the requirements of new buyers rho come from different kinds of network. The second pattern is symmetrical improvement by joining both arm-length and embedded networks.

• 제어로봇시스템학회논문지
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• 제4권4호
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• pp.551-560
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• 1998

This paper presents a neural network model representing complex hydro-thermo-dynamic characteristics of a steam generator in nuclear power plants. The key modeling processes include training data gathering process, analysis of system dynamics and determining of the neural network structure, training process, and the final process for validation of the trained model. In this paper, we suggest a training data gathering method from an unstable steam generator so that the data sufficiently represent the dynamic characteristics of the plant over a wide operating range. In addition, we define the inputs and outputs of neural network model by analyzing the system dimension, relative degree, and inputs/outputs of the plant. Several types of neural networks are applied to the modeling and training process. The trained networks are verified by using a class of test data, and their performances are discussed.

• 한국생산제조학회지
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• 제7권3호
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• pp.14-21
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• 1998

The in-process detection of the state of cutting tool is one of the most important technical problem in Intelligent Machining System. This paper presents a method of detecting the state of cutting tool in turning process, by using Artificial Neural Network. In order to sense the state of cutting tool. the sensor fusion of an acoustic emission sensor and a force sensor is applied in this paper. It is shown that AErms and three directional dynamic mean cutting forces are sensitive to the tool wear. Therefore the six pattern features that is, the four sensory signal features and two cutting conditions are selected for the monitoring system with Artificial Neural Network. The proposed monitoring system shows a good recogniton rate for the different cutting conditions.