• Journal of Information Science Theory and Practice
• /
• 제7권4호
• /
• pp.56-64
• /
• 2019

Planting intercropping in rubber plantations is another alternative for generating more income for farmers. However, farmers still lack the knowledge of choosing plants. In addition, information for decision making comes from many sources and is knowledge accumulated by the expert. Therefore, this research aims to create a decision support system for growing rubber trees for individual farmers. It aims to get the highest income and the lowest cost by using semantic web technology so that farmers can access knowledge at all times and reduce the risk of growing crops, and also support the decision supporting system (DSS) to be more intelligent. The integrated intercropping ontology and rule are a part of the decision-making process for selecting plants that is suitable for individual rubber plots. A list of suitable plants is important for decision variables in the allocation of planting areas for each type of plant for multiple purposes. This article presents designing and developing the intercropping ontology for DSS which defines a class based on the principle of intercropping in rubber plantations. It is grouped according to the characteristics and condition of the area of the farmer as a concept of the rubber plantation. It consists of the age of rubber tree, spacing between rows of rubber trees, and water sources for use in agriculture and soil group, including slope, drainage, depth of soil, etc. The use of ontology for recommended plants suitable for individual farmers makes a contribution to the knowledge management field. Besides being useful in DSS by offering options with accuracy, it also reduces the complexity of the problem by reducing decision variables and condition variables in the multi-objective optimization model of DSS.

• 한국정밀공학회지
• /
• 제28권4호
• /
• pp.482-489
• /
• 2011

In practical design process, designer needs to find an optimal solution by using full factorial discrete combination, rather than by using optimization algorithm considering continuous design variables. So, ANOVA(Analysis of Variance) based on an orthogonal array, i.e. Taguchi method, has been widely used in most parts of industry area. However, the Taguchi method is limited for the shape optimization by using CAE, because the multi-level and multi-objective optimization can't be carried out simultaneously. In this study, a combined method was proposed taking into account of multi-level computational orthogonal array and TOPSIS(Technique for Order preference by Similarity to Ideal Solution), which is known as a classical method of multiple attribute decision making and enables to solve various decision making or selection problems in an aspect of multi-objective optimization. The proposed method was applied to a case study of the multi-level shape optimization of lower arm used to automobile parts, and the design space was explored via an efficient application of the related CAE tools. The multi-level shape optimization was performed sequentially by applying both of the neural network model generated from seven-level four-factor computational orthogonal array and the TOPSIS. The weight and maximum stress of the lower arm, as the objective functions for the multi-level shape optimization, showed an improvement of 0.07% and 17.89%, respectively. In addition, the number of CAE carried out for the shape optimization was only 55 times in comparison to full factorial method necessary to 2,401 times.

• 한국직업건강간호학회지
• /
• 제11권1호
• /
• pp.63-80
• /
• 2002

Objectives: The objective of this study is to evaluate the job characteristics of container terminal workers by applying the Job Strain model, and to assess the relationship among the general characteristics, job characteristics and psychosocial distress. Methods: A self-administrated questionnaire survey was performed to the container terminal workers in Pusan. Among the 200 male workers who answered the questionnaires, white-collar workers and blue-collar workers were 100, respectively. Karaseks Job Content Questionnaire was utilized to evaluate the job characteristics and Psychosocial well-being index (PWI) was applied to measure the extent of their psychosocial stress. Results: In white-collar workers, the skill discretion, created skill, decision-making authority, decision-making latitude, psychological job demand, and supervisor support of the job characteristics were significantly high, while in blue-collar workers physical exertion was significantly high. The result of Psychosocial well-being index (PWI) reveals that blue-collar workers were more stressed than white-collar workers, especially, the indices of depression (factor 2), sleeping disturbance and anxiety (factor 3), General well-being and vitality (factor 4) were significantly increased; whereas, in white-collar workers, only the index of social performance and self-confidence (factor 1) was significantly increased. And PWI scores were significantly increased in the lower social support and psychological job demand. By the multiple logistic regression analysis for PWI, blue-collar workers had increased odds ratio of 2.66(95% CI;1.11-6.41) compared with white-collar workers. The unmarried workers increased odds ratio of 3.54(95% CI;1.18-10.62) compared with married workers. And workers who have not own house increased odds ratio of 2.35(95% CI;1.15-4.79) compared with workers who have own house. Particularly, odds ratio of work-shift in blue-collar workers was 11.10(2.14-57.64). Conclusion: Skill discretion, created skill, decision-making authority, decision-making latitude, psychological job demand, and supervisor support were increased in white-collar workers. Decreased skill discretion and increased physical exertion were found in blue-collar workers, which is supported the Job Strain model. Job stress of blue-collar workers was comparatively higher than that of white-collar workers, especially, skill discretion, decision-making authority, decision-making latitude, job insecurity, physical exertion were noticeable factors. Especially, sleeping, smoking, and work shifting turned out to be a main cause that increases stress. Therefore, in order to decrease the job stress, a health promotion program to change the health behaviors should be activated and an organized job stress management program should be introduced. Especially, working condition for blue-collar such as physical exertion and work-shift should be improved.

• 산업경영시스템학회지
• /
• 제34권2호
• /
• pp.112-120
• /
• 2011

The early phase of design intrinsically contains multiple sources of uncertainty in describing design, and nevertheless the decision-making process at this phase exerts a critical effect upon drawing a successful design. This paper proposes a set-based design approach for multi-objective design problem under uncertainty. The proposed design approach consists of four design processes including set representation, set propagation, set modification, and set narrowing. This approach enables the flexible and robust design while incorporating designer's preference structure. In contrast to existing optimization techniques, this approach generates a ranged set of design solutions that satisfy changing sets of performance requirements.

• 산업공학
• /
• 제17권3호
• /
• pp.249-260
• /
• 2004

The objective of this study is to measure and to predict the efficiency of participating groups of BK 21 by using DEA. DEA is a methodology to measure and to evaluate the relative efficiency of a homogeneous set of decision-making units (DMUs) in a process which uses multiple inputs to produce multiple outputs. In order to reflect the effect of the environmental factors of BK 21, we consider not only a general DEA model but also a logistic model for DEA. As a result, location of participating groups of BK 21 turns out to be significant. Our proposed approach can predict the efficiency of a new BK 21 group with given environmental factors. It is expected that these models can give a feedback for effective management of BK 21.

• 지능정보연구
• /
• 제17권4호
• /
• pp.1-17
• /
• 2011

오늘날 글로벌한 경쟁에서 고객의 만족도를 유지하고 생산성과 효율을 높이기 위해서 대량맞춤(mass customization)이 많은 선도기업에서 채택되고 있다. 대량맞춤은 제품가족과 제품 플랫폼에 근거하여 기업으로 하여금 새로운 제품을 유연하고 효율적이며 고객 요구에 신속히 대응하는 것을 용이하게 한다. 따라서 제품 플랫폼에 기반한 제품가족 전략이 대량맞춤을 실현화하는데 적절한 방법이다. 제품가족이란 다양한 시장의 요구를 충족하기 위해서 공통의 특성, 구성부품, 서브 시스템을 공유하는 일련의 유사한 제품군으로 정의된다. 이 연구의 목표는 제품가족 설계 전략을 이용하여 고객의 요구를 충족시키는 제품의 구성부품간의 우선순위를 찾아내고자 하는 것이다. 신 제품 개발을 위한 의사결정 과정은 피드백을 가지는 다 변량 의사결정 모형을 필요로 한다. 이를 위해서 분석적 네트워크 과정(analytic network process) 방법을 이용하여 의사결정 모델과 절차를 수립하였다. 구현을 위해서 제품가족 모델에 적합한 소형 PC인 넷북 제품을 선정하고, 각 제품가족에 대한 구성부품에 대하여 제안된 방법에 따라서 우선순위를 도출하였다. 구현결과를 QFD 모델을 이용하여 고객요구사항과 구성부품간의 관계를 분석하고 평가하였다.

• 한국건축시공학회지
• /
• 제16권4호
• /
• pp.331-339
• /
• 2016

건축물의 고층화, 복합화, 대형화에 따라 다양한 공법이 개발되고 있어 주요 공종에 대한 공법 선정의 중요성이 대두되고 있다. 그러나 프로젝트의 특성을 충분히 고려하지 못하고 있고 주요 공법의 선정을 위한 객관적 기준이나 자료 또한 부족한 실정이며, 실무자의 경험과 직관에만 의존하여 선정이 이루어지고 있는 점이 지적되어 왔다. 이러한 문제점을 해소하기 위해 퍼지, AHP, CBR 등 인공지능이론을 이용한 주요 공종의 공법 선정을 위한 다양한 연구가 진행되었다. 그러나 실무에서 공법 선정 시 공종별 특성 및 현장별 조건을 고려하여 주요 공종마다 각기 다른 여러 가지 공법 선정 모델을 적용하기는 어렵다. 이에 본 연구에서는 매트릭스 분석과 선형변환을 이용하여, 실무에서 활용이 용이한 범용적인 성격의 의사결정지원 모델을 제시하고, 사례 연구를 통해 흙막이 공법 선정 과정에 적용하여 연구모델의 정합성을 검증하였다.

• Journal of information and communication convergence engineering
• /
• 제20권1호
• /
• pp.22-30
• /
• 2022

The reinforcement learning algorithm has proven its potential in solving sequential decision-making problems under uncertainties, such as finding paths to route data packets in wireless sensor networks. With reinforcement learning, the computation of the optimum path requires careful definition of the so-called reward function, which is defined as a linear function that aggregates multiple objective functions into a single objective to compute a numerical value (reward) to be maximized. In a typical defined linear reward function, the multiple objectives to be optimized are integrated in the form of a weighted sum with fixed weighting factors for all learning agents. This study proposes a reinforcement learning -based routing protocol for wireless sensor network, where different learning agents prioritize different objective goals by assigning weighting factors to the aggregated objectives of the reward function. We assign appropriate weighting factors to the objectives in the reward function of a sensor node according to its hop-count distance to the sink node. We expect this approach to enhance the effectiveness of multi-objective reinforcement learning for wireless sensor networks with a balanced trade-off among competing parameters. Furthermore, we propose SDN (Software Defined Networks) architecture with multiple controllers for constant network monitoring to allow learning agents to adapt according to the dynamics of the network conditions. Simulation results show that our proposed scheme enhances the performance of wireless sensor network under varied conditions, such as the node density and traffic intensity, with a good trade-off among competing performance metrics.

• 한국산학기술학회논문지
• /
• 제13권9호
• /
• pp.3841-3848
• /
• 2012

획득대안 분석 프로세스는 비용, 일정, 성능 및 위험이라는 제약사항 내에서 다양한 후보 대안들 가운데에서 최적의 대안을 선택하는 것이다. 신규 무기체계 획득을 위해 사용하고 있는 기존의 대안 분석 방법은 일반적으로 요구사항 분석, 설계 조합, 그리고 비용 추정을 통해 수행되고 있다. 본 논문은 함정 설계개념 정제 및 물자적 대안분석 단계에서 다목표 의사결정 방법을 기반으로 개선된 획득대안 분석 방법을 제시한 것이다. 이번 연구에서는 시스템공학 원리를 기반으로 효과도 분석, 사업 비용 추정, 그리고 위험도 평가 기법을 활용하여 차세대 다목적 훈련지원함에 대한 실질적인 응용 및 적용 연구를 수행하였다.

• Journal of Preventive Medicine and Public Health
• /
• 제55권2호
• /
• pp.193-204
• /
• 2022

Objectives: The primary objective of this study was to examine the effect of women's empowerment on the immunization of Indonesian children. The secondary objective was to examine the effect of wealth as a factor modifying this association. Methods: We utilized data from the 2017 Indonesian Demographic and Health Survey (IDHS). The subjects were married women with children aged 12-23 months (n=3532). Complete immunization was defined using the 2017 IDHS definition. Multiple components of women's empowerment were measured: enabling resources, decision-making involvement, and attitude toward intimate partner violence. The primary analysis was conducted using binomial logistic regression. Model 1 represented only the indicators of women's empowerment and model 2 controlled for socio-demographic variables. Subgroup analyses were conducted for each wealth group. Results: The primary analysis using model 1 identified several empowerment indicators that facilitated complete immunization. The analysis using model 2 found that maternal education and involvement in decision-making processes facilitated complete immunization in children. Subgroup analyses identified that wealth had a modifying effect. The indicators of women's empowerment were strong determinants of complete immunization in lower wealth quintiles but insignificant in middle-income and higher-income quintiles. Conclusions: To our knowledge, this study is the first to explore women's empowerment as a determinant of child immunization in Indonesia. The results indicate that women's empowerment must be considered in Indonesia's child immunization program. Women's empowerment was not found to be a determinant in higher wealth quintiles, which led us to rethink the conceptual framework of the effect of women's empowerment on health outcomes.