• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.37 no.4
• /
• pp.239-244
• /
• 2014

The conventional clustering approaches are mostly based on minimizing total dissimilarity of input and output. However, the clustering approach may not be helpful in some cases of clustering decision making units (DMUs) with production feature converting multiple inputs into multiple outputs because it does not care converting functions. Data envelopment analysis (DEA) has been widely applied for efficiency estimation of such DMUs since it has non-parametric characteristics. We propose a new clustering method to identify groups of DMUs that are similar in terms of their input-output profiles. A real world example is given to explain the use and effectiveness of the proposed method. And we calculate similarity value between its result and the result of a conventional clustering method applied to the example. After the efficiency value was added to input of K-means algorithm, we calculate new similarity value and compare it with the previous one.

• Smart Structures and Systems
• /
• v.20 no.1
• /
• pp.99-114
• /
• 2017

In cognitive science, it is illustrated how the collective opinions of a group of individuals answers to questions involving quantity estimation. One example of this approach is introduced in this article as Star Graph (SG) algorithm. This graph describes the details of communication among individuals to share their information and make a new decision. A new labyrinthine network of neighbors is defined in the decision-making process of the algorithm. In order to prevent getting trapped in local optima, the neighboring networks are regenerated in each iteration of the algorithm. In this algorithm, the normal distribution is utilized for a group of agents with the best results (guidance group) to replace the existing infeasible solutions. Here, some new functions are introduced to provide a high convergence for the method. These functions not only increase the local and global search capabilities but also require less computational effort. Various benchmark functions and engineering problems are examined and the results are compared with those of some other algorithms to show the capability and performance of the presented method.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.7
• /
• pp.1183-1190
• /
• 2007

This paper proposes the improved fault decision algorithm using DWT(Discrete Wavelet Transform) and ANNs for the FRTU(Feeder Remote Terminal Unit) on the feeder in the power distribution system. Generally, the FRTU has the fault decision scheme detecting the phase fault, the ground fault. Especially FRTU has the function for 2000ms. This function doesn't operate FI(Fault Indicator) for the Inrush current generated in switching time. But it has a defect making it impossible for the FI to be operated from the real fault current in inrush restraint time. In such a case, we can not find the fault zone from FI information. Accordingly, the improved fault recognition algorithm is needed to solve this problem. The DWT analysis gives the frequency and time-scale information. The neural network system as a fault recognition was trained to distinguish the inrush current from the fault status by a gradient descent method. In this paper, fault recognition algorithm is improved by using voltage monitoring system, DWT and neural network. All of the data were measured in actual 22.9kV power distribution system.

• Wind and Structures
• /
• v.31 no.3
• /
• pp.241-253
• /
• 2020

The inerter-based vibration absorber (IVA) is an enhanced variation of Tuned Mass Damper (TMD). The parametric optimization of absorbers in the previous research mainly considered only two decision variables, namely frequency ratio and damping ratio, and aimed to minimize peak displacement and acceleration individually under the excitation of the across-wind load. This paper extends these efforts by minimizing two conflicting objectives simultaneously, i.e., the extreme displacement and acceleration at the top floor, under the constraint of the physical mass. Six decision variables are optimized by adopting a constrained multi-objective evolutionary algorithm (CMOEA), i.e., NSGA-II, under fluctuating across- and along-wind loads, respectively. After obtaining a set of optimal individuals, a decision-making approach is employed to select one solution which corresponds to a Tuned Mass Damper Inerter/Tuned Inerter Damper (TMDI/TID). The optimization procedure is applied to parametric optimization of TMDI/TID installed in a 340-meter-high building under wind loads. The case study indicates that the optimally-designed TID outperforms TMDI and TMD in terms of wind-induced vibration mitigation under different wind directions, and the better results are obtained by the CMOEA than those optimized by other formulae. The optimal TID is proven to be robust against variations in the mass and damping of the host structure, and mitigation effects on acceleration responses are observed to be better than displacement control under different wind directions.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.6 no.8
• /
• pp.1259-1268
• /
• 2002

One of the major goals suggested in distributed system is to improve the performance of the system through the load balancing of whole system. Load balancing among systems improves the rate of processor utilization and reduces the turnaround time of system. In this paper, we design the rule of decision-making and information interchange based on knowledge based mechanism which makes optimal load balancing by sharing the future load state information inferred from past and present information of each nodes. The result of performance evaluation shows that utilization of processors is balanced, the processing time is improved and reliability and availability of systems are enhanced. The proposed mechanism in this paper can be utilized in the design of load balancing algorithm in distributed operating systems.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.9
• /
• pp.3293-3311
• /
• 2015

Cloud services are required to be composed as a single service to fulfill the workflow applications. Service composition in Cloud raises new challenges caused by the diversity of users with different QoS requirements and vague preferences, as well as the development of cloud computing having geographically distributed characteristics. So the selection of the best service composition is a complex problem and it faces trade-off among various QoS criteria. In this paper, we propose a Cloud service composition approach based on evolutionary algorithms, i.e., NSGA-II and MOPSO. We utilize the combination of multi-objective evolutionary approaches and Decision-Making method (AHP) to solve Cloud service composition optimization problem. The weights generated from AHP are applied to the Crowding Distance calculations of the above two evolutionary algorithms. Our algorithm beats single-objective algorithms on the optimization ability. And compared with general multi-objective algorithms, it is able to precisely capture the users' preferences. The results of the simulation also show that our approach can achieve a better scalability.

• Agribusiness and Information Management
• /
• v.7 no.1
• /
• pp.1-9
• /
• 2015

The purpose of this study is to develop a growth prediction model that can predict growth and development information influencing the production of citrus fruits: the growth model algorithm that can predict floral leaf ratio, number of fruit sets, fruit width, and overweight depending on the main period of growth and development with consideration of the applied weather factors. Every year, large scale of manpower was mobilized to investigate the production of outdoor-grown citrus fruits, but it was limited to recycling the data without an observation supporting system to systemize the database. This study intends to create a systematical database based on the basic data obtained through the observation supporting system in application of an algorithm according to the accumulated long term data and prepare a base for its continuous improvement and development. The importance of the observed data is increasingly recognized every year, and the citrus fruit observation supporting system is important for utilizing an effective policy and decision making according to various applications and analysis results through an interconnection and an integration of the investigated statistical data. The citrus fruit is a representative crop having a great ripple effect in Jeju agriculture. An early prediction of the growth and development information influencing the production of citrus fruits may be helpful for decision making in supply and demand control of agricultural products.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.231-252
• /
• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2004.11a
• /
• pp.309-313
• /
• 2004

This paper presents an algorithm of detecting changed intervals with step-type shape in motor's speed response data. The proposed method is composed of 4 parts such as noise filtering, decision making of reference value's change, finding entrance point of steady state and detecting changed intervals. According to simulation results for three cases, we see that changed intervals can be found well.

• Proceedings of the Korea Multimedia Society Conference
• /
• 2004.05a
• /
• pp.326-329
• /
• 2004

Content adaptation is an effective solution to support the quality of service over multimedia services over heterogeneous environments. This paper deals with the accuracy and the real-time requirement, two important issues in making decision on content adaptation. From our previous problem formulation, we propose an optimal algorithm and a fast approximation based on the Viterbi algorithm of dynamic programming. Through extensive experiments, we show that the proposed algorithms can enable accurate adaptation decisions, and especially they can support the real-time processing.