• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2542-2547
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• 2005

In this paper, we propose a support vector machine (SVM) hyper and kernel parameter optimization method which is based on minimizing radius/margin bound which is a kind of estimation of leave-one-error. This method uses dynamic encoding algorithm for search (DEAS) and gradient information for better optimization performance. DEAS is a recently proposed optimization algorithm which is based on variable length binary encoding method. This method has less computation time than genetic algorithm (GA) based and grid search based methods and better performance on finding global optimal value than gradient based methods. It is very efficient in practical applications. Hand-written letter data of MNI steel are used to evaluate the performance.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.11
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• pp.1027-1033
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• 2012

Semantic-based information retrieval techniques understand the meanings of the concepts that users specify in their queries, but the traditional semantic matching methods based on the ontology tree have three weaknesses which may lead to many false matches, causing the falling precision. In order to improve the matching precision and the recall of the information retrieval, this paper proposes a multi-agent improved semantic similarity matching algorithm based on the ontology tree, which can avoid the considerable computation redundancies and mismatching during the entire matching process. The results of the experiments performed on our algorithm show improvements in precision and recall compared with the information retrieval techniques based on the traditional semantic similarity matching methods.

• Journal of Mechanical Science and Technology
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• v.17 no.2
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• pp.221-229
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• 2003

In the virtual environment, reality can be enhanced by offering the motion based on a motion simulator in harmony with visual and auditory modalities. In this research the Stewart-Gough-platform-based motion simulator has been developed. Implementation of vehicle dynamics is necessary in the motion simulator for realistic sense of motion, so bicycle dynamics is adopted in this research. In order to compensate for the limited range of the motion simulator compared with the real vehicle motion, washout algorithm composed of high-pass filter, low-pass filter and tilt coordination is usually employed. Generally, the washout algorithm is used with fixed parameters. In this research a new approach is proposed to tune the filter parameters based on fuzzy logic in real-time. The cutoff frequencies of the filters are adjusted according to the workspace margins and driving conditions. It is shown that the washout filter with the fuzzy-based parameters presents better performance than that with the fixed ones.

• Journal of applied mathematics & informatics
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• v.35 no.1_2
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• pp.191-204
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• 2017

A new fifth-order weighted Runge-Kutta algorithm based on heronian mean for solving initial value problem in ordinary differential equations is considered in this paper. Comparisons in terms of numerical accuracy and size of the stability region between new proposed Runge-Kutta(5,5) algorithm, Runge-Kutta (5,5) based on Harmonic Mean, Runge-Kutta(5,5) based on Contra Harmonic Mean and Runge-Kutta(5,5) based on Geometric Mean are carried out as well. The problems, methods and comparison criteria are specified very carefully. Numerical experiments show that the new algorithm performs better than other three methods in solving variety of initial value problems. The error analysis is discussed and stability polynomials and regions have also been presented.

• Journal of Intelligence and Information Systems
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• v.1 no.2
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• pp.57-71
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• 1995

This paper presents a rule extraction algorithm RE to acquire explicit rules from trained neural networks. The validity of extracted rules has been confirmed using 6 different data sets. Based on experimental results, we conclude that extracted rules from RE predict more accurately and robustly than neural networks themselves and rules obtained from an inductive learning algorithm do. Rule extraction algorithm for neural networks are important for incorporating knowledge obtained from trained networks into knowledge based systems. In lieu of this, the proposed RE algorithm contributes to the trend toward developing hybrid and versatile knowledge-based system including expert systems and knowledge-based decision su, pp.rt systems.

• Journal of Information Technology Applications and Management
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• v.25 no.4
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• pp.145-156
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• 2018

This study proposes a content-based recommendation algorithm (NRA) for pet food. The proposed algorithm tries to recommend appropriate or inappropriate feed by using collective intelligence based on user experience and prior knowledge of experts. Based on the physical and health status of the dogs, this study suggests what kind of nutrients are necessary for the dogs and the most recommended pet food containing these nutrients. Performance evaluation was performed in terms of recall, precision, F1 and AUC. As a result of the performance evaluation, the AUC and F1 value of the proposed NRA was 15% and 42% higher than that of the baseline model, respectively. In addition, the performance of NRA is shown higher for recommendation of normal dogs than disease dogs.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.130-136
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• 2021

In recent years, much study has been conducted in robotic grasping. The grasping algorithms based on deep learning have shown better grasping performance than the traditional ones. However, deep learning-based algorithms require a lot of data and time for training. In this study, a grasping algorithm using an artificial neural network-based graspability estimator is proposed. This graspability estimator can be trained with a small number of data by using a neural network based on the residual blocks and point clouds containing the shapes of objects, not RGB images containing various features. The trained graspability estimator can measures graspability of objects and choose the best one to grasp. It was experimentally shown that the proposed algorithm has a success rate of 90% and a cycle time of 12 sec for one grasp, which indicates that it is an efficient grasping algorithm.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.401-409
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• 2023

This study compared and analyzed the performance of a distributed area search algorithm and a distributed probability search algorithm based on information exchange between ships. The distributed algorithm is a method that can search for an optimal avoidance route based on information exchange between ships. In the distributed area search algorithm, only a ship with the maximum cost reduction among neighboring ships has priority, so the next expected location can be changed. The distributed stochastic search algorithm allows a non-optimal value to be searched with a certain probability so that a new value can be searched. A circular-based ship collision avoidance model was used for the ship-to-ship collision avoidance experiment. The experimental method simulated the distributed area search algorithm and the distributed stochastic search algorithm while increasing the number of ships from 2 to 50 that were the same distance from the center of the circle. The calculation time required for each algorithm, sailing distance, and number of message exchanges were compared and analyzed. As a result of the experiment, the DSSA(Distributed Stochastic Search Algorithm) recorded a 25%calculation time, 88% navigation distance, and 84% of number of message exchange rate compared to DLSA.

• Journal of the Korean Solar Energy Society
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• v.34 no.4
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• pp.23-29
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• 2014

Recently, in power system studies, Multiple Energy Carriers (MECs) such as Energy Hub has been broadly utilized in power system planners and operators. Particularly, Energy Hub performs one of the most important role as the intermediate in implementing the MECs. However, it still needs to be put under examination in both modeling and operating concerns. For instance, a probabilistic optimization model is treated by a robust global optimization technique such as multi-agent genetic algorithm (MAGA) which can support the online economic dispatch of MECs. MAGA also reduces the inevitable uncertainty caused by the integration of selected input energy carriers. However, MAGA only considers current state of the integration of selected input energy carriers in conjunctive with the condition of smart grid environments for decision making in Energy Hub. Thus, in this paper, we propose an immune algorithm based Multiple Energy Carriers System which can adopt the learning process in order to make a self decision making in Energy Hub. In particular, the proposed immune algorithm considers the previous state, the current state, and the future state of the selected input energy carriers in order to predict the next decision making of Energy Hub based on the probabilistic optimization model. The below figure shows the proposed immune algorithm based Multiple Energy Carriers System. Finally, we will compare the online economic dispatch of MECs of two algorithms such as MAGA and immune algorithm based MECs by using Real Time Digital Simulator (RTDS).

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.1
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• pp.31-40
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• 2020

In this study, we developed an algorithm for automatic travel itinerary planning based on expert recommendation. The proposed algorithm generates an itinerary by patterning a number of travel routes based on the automatic itinerary generation method based on the routes recommended by travel experts. To evaluate the proposed algorithm, we generated 30 itinerary for Singapore, Bankok, and Da Nang using both algorithms and analyzed the mean difference of trip distances with t-test and interater reliability of those itineraries. The result shows that the itineraries based on the proposed algorithm is not different from that of VRP(Vehicle routing problem) algorithm and interater reliability is high enough to show that the proposed algorithm is effective enough for real-world usage.