• The Journal of Asian Finance, Economics and Business
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• v.7 no.2
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• pp.309-316
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• 2020

This study aims to measure the relationship between corporate social responsibility (CSR) and affective commitment (ACO), normative commitment (NCO), and organizational performance in food processing enterprises (FPEs) in the Mekong River Delta, Vietnam. To test the initial model proposed in this paper, a total of 422 owners, directors and managers of FPEs were interviewed from some provinces in the Mekong River Delta. The method of exploratory factor analysis (EFA) is initially employed, then confirmatory factor analysis (CFA) and structure equation modelling (SEM) are used. The results of SEM showed that higher affective commitment was correlated with normative commitment. The results showed that four aspects of CSR toward employees, customers, environment and legal are significant factors. As a result, ACO and NCO act as mediators between CSR and organizational performance. This finding provides strong evidence of the important role of CSR to support positive impacts on ACO, NCO, and orgazational performance (OP). In addition, the success of the organizational performance is also found by contributions of CSR and NCO to its changes. Although ACO does not directly affect performance, it has a positive effect on the NCO. Therefore, it is necessary to enhance the implementation of CSR to promote implementation of organizational commitments.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.36-43
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• 2019

Turning, a main machining process, is a widespread process in metal cutting industries. Many researchers have investigated the effects of process parameters on the machining process. In the turning process, input variables including cutting speed, feed, and depth of cut are generally used. Surface roughness and electric current consumption are used as output variables in this study. We construct a simulation model for the turning process using a neural network, which predicts the output values based on input values. In the neural network, obtaining the appropriate set of weights, which is called training, is crucial. In general, back propagation (BP) is widely used for training. In this study, techniques such as ant colony optimization (ACO) and particle swarm optimization (PSO) as well as BP were used to obtain the weights in the neural network. Particularly, two combined techniques of ACO_BP and PSO_BP were utilized for training the neural network. Finally, the performances of the two techniques are compared with each other.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.1
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• pp.43-49
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• 2008

In this paper, a method using the Ant Colony Optimization(ACO) is proposed for reducing the power consumption of memory ECC checker circuitry which provide Single-Error Correcting and Double-Error Detecting(SEC-DED). The H-matrix which is used to generate SEC-DED codes is optimized to provide the minimum switching activity with little to no impact on area or delay using the symmetric property and degrees of freedom in constructing H-matrix of Hsiao codes. Experiments demonstrate that the proposed method can provide further reduction of power consumption compared with the previous works.

• ETRI Journal
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• v.30 no.1
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• pp.129-140
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• 2008

In this paper, we propose a novel ant colony optimization (ACO)-based test scheduling method for testing network-on-chip (NoC)-based systems-on-chip (SoCs), on the assumption that the test platform, including specific methods and configurations such as test packet routing, generation, and absorption, is installed. The ACO metaheuristic model, inspired by the ant's foraging behavior, can autonomously find better results by exploring more solution space. The proposed method efficiently combines the rectangle packing method with ACO and improves the scheduling results by dynamically choosing the test-access-mechanism widths for cores and changing the testing orders. The power dissipation and variable test clock mode are also considered. Experimental results using ITC'02 benchmark circuits show that the proposed algorithm can efficiently reduce overall test time. Moreover, the computation time of the algorithm is less than a few seconds in most cases.

• Journal of Computing Science and Engineering
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• v.8 no.4
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• pp.199-206
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• 2014

The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.319-322
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• 2004

물류/택배업계의 공급사슬관리에서 차량에 의한 고객의 요구 서비스 시간대 만족은 고객의 재고수준을 낮추고 또한 서비스 수준의 향상에 매우 중요한 제약조건이다. 최근에 소개된 메타휴리스틱인 개미해법(Ant Colony Optimization: ACO)은 NP-hard 문제의 해공간 탐색에 있어서 상당한 장점을 가지고 있으나, 시간대 제약이 있는 차량경로문제(Vehicle Routing Problems with Time Windows: VRPTW)에 대한 적용은 아주 미비한 실정이다. 따라서, 본 연구에서는 ACO 를 VRPTW에 적용하여 최선의 차량경로 해를 구하기 위한 여러 변형을 제시하고, 이들의 영향을 다양한 실험문제를 이용하여 분석하고자 한다. 계산실험 결과, 기본 ACO 에 여러 설계 요소들을 추가함에 따라 계산시간이 다소 증가하지만 보다 우수한 차량경로 해를 구할 수 있었다.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.9
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• pp.884-891
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• 2009

This study investigates some of swarm intelligence algorithms to tackle a traditional damage detection problem having stiffness degradation or damage in mechanical structures. Particle swarm(PSO) and ant colony optimization(ACO) methods have been exploited for localizing and estimating the location and extent damages in a structure. Both PSO and ACO are population-based, stochastic algorithms that have been developed from the underlying concept of swarm intelligence and search heuristic. A finite element (FE) model updating is implemented to minimize the difference in a set of natural frequencies between measured and baseline vibration data. Stiffness loss of certain elements is considered to simulate structural damages in the FE model. It is numerically shown that PSO and ACO algorithms successfully completed the optimization process of model updating in locating unknown damages in a truss structure.

• Korean Management Science Review
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• v.26 no.3
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• pp.55-65
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• 2009

The objective of this paper is to propose an ant colony optimization (ACO) for clustering design in wireless sensor network problem. This proposed ACO approach is designed to deal with the dynamics of the sensor nodes which can be adaptable to topological changes to any network graph in a time. Long communication distances between sensors and a sink in a sensor network can greatly consume the energy of sensors and reduce the lifetime of a network. We can greatly minimize the total communication distance while minimizing the number of cluster heads using proposed ACO. Simulation results show that our proposed method is very efficient to find the best solutions comparing to the optimal solution using CPLEX in 100, 200, and 400 node sensor networks.

• Journal of Information Processing Systems
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• v.10 no.2
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• pp.271-282
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• 2014

The advancement of knowledge society has enabled the social network community (SNC) to be perceived as another space for learning where individuals produce, share, and apply content in self-directed ways. The content generated within social networks provides information of value for the participants in real time. Thus, this study proposes the social network community activity-based content model (SoACo Model), which takes SNC-based activities and embodies them within learning objects. The SoACo Model consists of content objects, aggregation levels, and information models. Content objects are composed of relationship-building elements, including real-time, changeable activities such as making friends, and participation-activity elements such as "Liking" specific content. Aggregation levels apply one of three granularity levels considering the reusability of elements: activity assets, real-time, changeable learning objects, and content. The SoACo Model is meaningful because it transforms SNC-based activities into learning objects for learning and teaching activities and applies to learning management systems since they organize activities -- such as tweets from Twitter -- depending on the teacher's intention.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.512-515
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• 2010

온톨로지는 시맨틱 웹의 핵심 기술로써 시맨틱 웹이 소개된 이후 다양한 온톨로지 생성 방법의 연구가 이루어져 왔다. 그러나 온톨로지는 작성이 어렵고 난해한 면이 있어 소수의 전문가 집단에 의해서만 만들어지고 있는 것이 현실이다. 본 논문에서는 웹 2.0 기반 환경에서 사용자들이 생성한 온톨로지를 수집하여 대중 온톨로지를 완성하는 새로운 온톨로지 생성 방법을 제안한다. 본 논문에서는 집단지성을 이용한 최적화 기법 중 한가지인 ACO 를 온톨로지 생성 분야에 적용시켜 전문가가 아닌 일반 사용자들이 작성한 낮은 수준의 온톨로지를 모아 완성된 형태의 온톨로지를 생성한다. 그리고 본 논문에서 제안한 방법을 통해 만들어진 온톨로지의 신뢰성을 검증하기 위하여 전문가 집단이 만든 온톨로지에 대해 정확도와 재현율을 계산하여 보인다. 본 논문에서 제시하는 방법은 복잡하고 난해한 기존 온톨로지의 제작 방법의 단점을 효과적으로 해결하며 대중적으로 시맨틱 웹이 활용될 수 있는 환경을 구축할 수 있다.