• Journal of Communications and Networks
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• v.14 no.6
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• pp.672-681
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• 2012

A potential breakthrough of the electrification of the vehicle fleet will incur a steep rise in the load on the electrical power grid. To avoid huge grid investments, coordinated charging of those vehicles is a must. In this paper, we assess algorithms to schedule charging of plug-in (hybrid) electric vehicles as to minimize the additional peak load they might cause. We first introduce two approaches, one based on a classical optimization approach using quadratic programming, and a second one, market based coordination, which is a multi-agent system that uses bidding on a virtual market to reach an equilibrium price that matches demand and supply. We benchmark these two methods against each other, as well as to a baseline scenario of uncontrolled charging. Our simulation results covering a residential area with 63 households show that controlled charging reduces peak load, load variability, and deviations from the nominal grid voltage.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.735-738
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• 1997

The traffic congestion caused by the exploding increase of vehicles became one of the severest social problems. Among the various approaches to solve this problem, controlling the length of traffic signals appropriately according to the individual traffic situation would be the most plausible and cost-effective method. To design a traffic signal controller which has such a property as adaptive decision-making process, we adopt fuzzy logic control method(fuzzy traffic signal controller), Moreover, using genetic algorithms we obtain an optimized fuzzy traffic signal controller (GA-fuzzy traffic signal controller). To evaluate and validate the proposed fuzzy and GA-fuzzy traffic signal controller, simulation results are presented.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1049-1058
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• 2022

HAR(Human Action Recognition) such as anomaly and object detection has become a trend in research field(s) that focus on utilizing Artificial Intelligence (AI) methods to analyze patterns of human action in crime-ridden area(s), media services, and industrial facilities. Especially, in real-time system(s) using video streaming data, HAR has become a more important AI-based research field in application development and many different research fields using HAR have currently been developed and improved. In this paper, we propose and analyze a deep-learning-based HAR that provides more efficient scheme(s) using an intelligent AI models, such system can be applied to media services using RGB video streaming data usage without feature extraction pre-processing. For the method, we adopt Slowfast based on the Deep Neural Network(DNN) model under an open dataset(HMDB-51 or UCF101) for improvement in prediction accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.16-24
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• 1997

This paper proposes a modified GA whose individuals have their own ages. Thus, a chromosome will die only when the age becomes zero, as a result, the population size of this method increases according to the generations. This helps a GA to preserve the good characteristics of a few chromosomes during several generations if the ages are evaluated with fitness values. As a result, the performance of the method is better than that of existing ones. A multi-modal function optimization problem is employed to simulate the performance of this method. To show the effective:~esso f ageing paradigm, three ageing evaluation methods are introduced. A paper whose itlea is similar to that of ours have been published in a conference. We also experimented a method that showed the best performance in the paper. Original simple GA was also experimented and the performance is compared with others. However, the perforniance of the previous method shows worse than that of our methods in some aspects because the previous method didn't take the fitness value into account in the selection process.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.555-565
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• 1999

The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.386-393
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• 2012

Portfolio selection methods based on stochastic receding horizon approach, which were recently reported in the field of financial engineering, can explicitly consider the dynamic characteristics of wealth evolution and various constraints in the process of performing optimal portfolio selection. In view of the theoretical value, versatility, and effectiveness that receding horizon approach has achieved in many engineering problems, dynamic portfolio selection methods based on stochastic receding horizon optimization technique have the possibility of becoming an important breakthrough. This paper observes through theoretical investigations that the SDP(semi-definite program)-based portfolio selection procedure can be simplified, and has obtained meaningful performance on returns from simulation studies applying the simplified version to Korean financial markets.

• Journal of Korean Society of Transportation
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• v.29 no.4
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• pp.113-123
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• 2011

Traffic signal control is one of the most cost-effective means of improving urban mobility. With the recent progress of ITS (Intelligent Transportation System) and the installation of the real time traffic control systems, traffic signal control is conducted in online and real time. Normally, time-of-day (TOD) signal control is used with the system, but no definite methodology has yet been available for efficient TOD signal planing designing. Such method and process are in need to optimize the traffic signal timing plan. This paper proposes the optimization of TOD signal timings on arterials. The effects of the signal timings from the proposed method were assessed in the field. The proposed includes the methods determining the separation of the TOD break points and the TOD intervals. Those were tested on an arterial consisting of ten coordinated signalized intersections. It was found from the test results that the proposed TOD signal timing plans outperformed the previous signal timings.

• Journal of Intelligence and Information Systems
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• v.12 no.3
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• pp.33-46
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• 2006

In order to decrease the number of handles, speed up retrieval operations, and manage products efficiently, the investment of facilities such as the installation of the storage equipment and the enlargement of the storage area may be attempted. However, the same objectives can be accomplished by utilizing the existing storage area efficiently. In many types of storage facilities, because of the limitation of storage areas, products are usually piled up, which may cause rehandles of inventories. Rehandles influence significantly the handling efficiency of warehouses. This study develops methods for minimizing rehandles of inventories to improve the operational efficiency of warehouses. A mixed storage problem is addressed for minimizing the expected number of rehandles. Optimization models are proposed and the genetic algorithm is applied to solve the problem.

• Structural Engineering and Mechanics
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• v.75 no.5
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• pp.633-642
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• 2020

Artificial neural networks (ANNs) are known as intelligent methods for modeling the behavior of physical phenomena because of it is a soft computing technique and takes data samples rather than entire data sets to arrive at solutions, which saves both time and money. ANN is successfully used in the civil engineering applications which are suitable examining the complicated relations between variables. Functionally graded materials (FGMs) are advanced composites that successfully used in various engineering design. The FGMs are nonhomogeneous materials and made of two different type of materials. In the present study, the bending analysis of functionally graded material (FGM) beams presents on theoretical based on combination of mixed-finite element method, Gâteaux differential and Timoshenko beam theory. The main idea in this study is to build a model using ANN with four parameters that are: Young's modulus ratio (E_t/E_b), a shear correction factor (k_s), power-law exponent (n) and length to thickness ratio (L/h). The output data is the maximum displacement (w). In the experiments: 252 different data are used. The proposed ANN model is evaluated by the correlation of the coefficient (R), MAE and MSE statistical methods. The ANN model is very good and the maximum displacement can be predicted in ANN without attempting any experiments.

• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1306-1313
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• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.