• Journal of Digital Convergence
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• v.20 no.2
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• pp.63-75
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• 2022

In this study, the smart docent interface provided at 25 exhibition halls at domestic and foreign were compared and analyzed based on affordance. Heuristic evaluation techniques were adopted, centered on a small number of experienced people, and six research participants were selected based on Jacob Nielsen's guidelines that five to eight people per set of research can achieve sufficient results. As a result of comparative analysis of affordance, smart docent used in the Oriental showed a higher average value than smart docent used in the Western, but a lower value for sensory affordance alone. In addition, the categories were subdivided to derive the affordance values for each country, and a singularity was found that the sensory affordance of smart docents used in the United States represents the highest value compared to other countries. In conclusion, this study was able to compare the four affordances of smart docents used in domestic and foreign exhibition halls while presenting directions for future behavioral improvement, which could provide practical implication for smart docent interface design and exhibition hall service strategies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.2979-2995
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• 2017

In recent years, there has been an increasing number of studies focused on identifying a set of spreaders to maximize the influence of spreading in complex networks. Although the k-core decomposition can effectively identify the single most influential spreader, selecting a group of nodes that has the largest k-core value as the seeds cannot increase the performance of the influence maximization because the propagation sphere of this group of nodes is overlapped. To overcome this limitation, we propose a neighborhood coreness cover and discount heuristic algorithm named "NCCDH" to identify a set of influential and decentralized seeds. Using this method, a node in the high-order shell with the largest neighborhood coreness and an uncovered status will be selected as the seed in each turn. In addition, the neighbors within the same shell layer of this seed will be covered, and the neighborhood coreness of the neighbors outside the shell layer will be discounted in the subsequent round. The experimental results show that with increases in the spreading probability, the NCCDH outperforms other algorithms in terms of the affected scale and spreading speed under the Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models. Furthermore, this approach has a superior running time.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.78-84
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• 1998

In this paper, a fuzzy traffic controller using genetic algorithms is presented. Conventional fuzzy traffic controllers use membership functions generated by humans. However, this approach does not guarantee the optimal solution to design the fuzzy controller. Genetic algorithm is a good problem solving method requiring domain-specific knowledge that is often heuristic. To find fuzzy membership functions showing good performance, a fitness function must be defined. However it's not easy in traffic control to define such a function as a numeric expression. Thus, we use simulation approach, namely, the fitness value of a solution is determined by use of a performance measure that is obtained by traffic simulator. The proposed method outperforms the conventional fuzzy controllers.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.19-33
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• 2001

Link weight analysis approach is suggested as a heuristic for selection of input nodes in artificial neural network for bankruptcy prediction. That is to analyze each input node\\\\`s link weight-absolute value of link weight between an input node and a hidden node in a well-trained neural network model. Prediction accuracy of three methods in this approach, -weak-linked-neurons elimination method, strong-linked-neurons selection method and integrated link weight model-is compared with that of decision tree and multivariate discrimination analysis. In result, the methods suggested in this study show higher accuracy than decision tree and multivariate discrimination analysis. Especially an integrated model has much higher accuracy than any individual models.

• Journal of Biosystems Engineering
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• v.23 no.4
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• pp.365-372
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• 1998

This study was carried out to develop a nutrient-solution mixing-and-supplying system, which used a low-cost metering device instead of expensive metering pumps and a fuzzy logic controller. A low cost and precise overflow-type metering device was developed and evaluated by testing the flow discharge for the automatic nutrient-solution mixing-and-supplying system for snail-scale hydroponic sewers. The fuzzy logic controllers, which could predict and meet the desired values of EC and supply rate of nutrient solution were developed and verified by simulation and experiment. this fuzzy logic controller, whose algorithm consists of four crisp inputs, two crisp outputs and nine rules, was developed to predict the desired value of EC and supply rate of nutrient solution and two crisp inputs, one crisp output and nine rules used to control EC to the desired values. The nutrient-solution mixing-and-supplying system showed satisfactory EC control performance with the maximum overshooting of 0.035 mS/cm and the maximum settling time of 15 minutes in case of increasing 0.7 mS/cm. also, the accuracy of the overflow-type metering device in terms of the full-scale error was 2.29% when using solenoid valve only and 0.2% when using solenoid valve and flow control valve together.

• Korean Journal of Cognitive Science
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• v.6 no.2
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• pp.27-45
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• 1995

In this paper, we suggest the "Fuzzy Behavior Knowledge Space(FBKS)" and explain how to utilize the FBKS when aggregating decisions of individual classifiers. The concept of "Behavior Knowledge Space(BKS)" is known to be the best method in the context that each classifier offers only one class label as its decision. However. the BKS does not considers measurement value of class label. Furthermore, it does not allow the heuristic knowledge of human experts to be embedded when combining multiple decisions. The FBKS eliminates such drawbacks of the BKS by adapting the fwzy concepts. Our method applies to the classification results that contain both class labels and associated measurement values. Experimental results confirm that the FBKS could be a very promising tool in pattern recognition areas.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.190-196
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• 2006

In this paper, the nonlinear damping force model is made to identify the properties of the ER damper using higher order spectrum. The higher order spectral analysis is used to investigate the nonlinear frequency coupling phenomena with the damping force signal according to the sinusoidal excitation of the damper. Also, this paper presents an inverse model of the ER damper, i.e., the model can predict the required voltage so that the ER damper can produce the desired force for the requirement of vibration control of vehicle suspension systems. The inverse model has been constructed by using piecewise linear damping force model. In this paper, the fuzzy logic control based on heuristic knowledge is combined with the skyhook control. And it is simulated for a quarter car model. The acceleration of the sprung mass is included in the premise part of the fuzzy rules to reduce the vertical acceleration RMS value of the sprung mass. Then scaling factors and membership functions are tuned using genetic algorithm to obtain optimal performance.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.2
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• pp.81-86
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• 2014

In this paper, a jointly optimal group replacement and spare provisioning policy is presented. Most maintenance policies assume that the spare inventory is always available, but in practice the maintenance schedule is affected by the availability of spare inventory. We present a maintenance-inventory model which jointly optimizes the group replacement interval and spare ordering quantity. Group replacement policy is used when a group of units are put in operation simultaneously. The operating fleet is replaced altogether at a predetermined number of units are failed. A sufficient level of spare inventory is carried to perform a number of group replacement. A cost rate expression which considers the group maintenance cost and inventory holding cost is derived and a heuristic method for searching the optimum value of decision variables is suggested. Numerical examples demonstrate the analytical results and the performance of the presented model.

• Journal of Korea Game Society
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• v.18 no.4
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• pp.99-106
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• 2018

Board games have many game characters and many state spaces. Therefore, games must be long learning. This paper used reinforcement learning algorithm. But, there is weakness with reinforcement learning. At the beginning of learning, reinforcement learning has the drawback of slow learning speed. Therefore, we tried to improve the learning speed by using the heuristic using the knowledge of the problem domain considering the game tree when there is the same best value during learning. In order to compare the existing character the improved one. I produced a board game. So I compete with one-sided attacking character. Improved character attacked the opponent's one considering the game tree. As a result of experiment, improved character's capability was improved on learning speed.

• Journal of Advanced Navigation Technology
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• v.14 no.2
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• pp.284-288
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• 2010

Video surveillance is widely used in establishing the societal security network. In this paper, intrusion detection based on visual information acquired by static camera is proposed. Proposed approach uses background model constructed by approximated median filter(AMF) to find a foreground candidate, and detected object is calculated by analyzing motion information. Motion detection is determined by the relative size of 2D object in RGB space, finally, the threshold value for detecting object is determined by heuristic method. Experimental results showed that the performance of intrusion detection is better one when the spatio-temporal candidate informations change abruptly.