• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.6
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• pp.721-728
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• 2012

The tracked SUGVs(Small Unmanned Ground Vehicles) are frequently operated in the narrow slope such as stairs and trails. But due to the nature of the tracked vehicle which is steered using friction between the track and the ground and the limited field of view of driving cameras mounted on the lower position, it is not easy for SUGVs to trace narrow slopes. To properly trace inclined narrows, it is very important for SUGVs to keep it's heading direction to the slope. As a matter of factor, no roll value control of a SUGV can makes it's heading being located in the direction of the slope in general terrains. But, the problem is that we cannot directly control roll motion for SUGV. Instead we can control yaw motion. In this paper, a new slope driving method that enables the vehicle trace the narrow slopes with IMU sensor usually mounted in the SUGV is suggested which including an estimation technique of the desired yaw angle corresponding to zero roll angle. In addition, a fuzzy steering controller robust to changes in driving speed and the stair geometry is designed to simulate narrow slope driving with the suggested method. It is shown that the suggested method is quite effective through the simulation.

• Geomechanics and Engineering
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• v.31 no.4
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• pp.339-352
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• 2022

Studying slope stability is an important branch of civil engineering. In this way, engineers have employed machine learning models, due to their high efficiency in complex calculations. This paper examines the robustness of various novel optimization schemes, namely equilibrium optimizer (EO), Harris hawks optimization (HHO), water cycle algorithm (WCA), biogeography-based optimization (BBO), dragonfly algorithm (DA), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) for enhancing the performance of adaptive neuro-fuzzy inference system (ANFIS) in slope stability prediction. The hybrid models estimate the factor of safety (F_S) of a cohesive soil-footing system. The role of these algorithms lies in finding the optimal parameters of the membership function in the fuzzy system. By examining the convergence proceeding of the proposed hybrids, the best population sizes are selected, and the corresponding results are compared to the typical ANFIS. Accuracy assessments via root mean square error, mean absolute error, mean absolute percentage error, and Pearson correlation coefficient showed that all models can reliably understand and reproduce the F_S behavior. Moreover, applying the WCA, EO, GWO, and TLBO resulted in reducing both learning and prediction error of the ANFIS. Also, an efficiency comparison demonstrated the WCA-ANFIS as the most accurate hybrid, while the GWO-ANFIS was the fastest promising model. Overall, the findings of this research professed the suitability of improved intelligent models for practical slope stability evaluations.

• Journal of Korea Port Economic Association
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• v.28 no.1
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• pp.159-178
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• 2012

Recently, Global network expansion strategy of GTOs(Global Terminal Operator) coupled with each country's port policy, plays huge role for the evolution of modern container port. The Chinese ports can be regarded as the major markets to the GTOs. However, there are scant of researches for finding the key success factors of GTOs' strategies when they consider to invest in overseas. In this respect, the aims of this study were to draw out the evaluation variables for successful investment strategies of GTOs, and to calculate the selected target ports. The 14 variables are selected including the variable named 'development potentiality of a port' through literature reviews. Using the Factor Analysis (FA) based on selected variables, four principal factors were extracted such as 'ability for port operating and cargo generating', 'the trade route and volume', 'the calling potentiality for large vessels' and 'the possibility of utilization of existing infrastructure'. In addition, the weights of factors and variables are evaluated through Fuzzy AHP method. As a result, 'ability for port operating and cargo generating' is chosen as the most important factor among principal factors as scored 0.343, and 'the development potentiality of a port' (0.107) is represented as the most important variable among 14 detailed variables. In overall, from the Global Terminal Operator's point of view, Shanghai is ranked as most suitable port for operating new terminal among the top 5 Chinese ports.

• Journal of Korea Water Resources Association
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• v.41 no.5
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• pp.517-525
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• 2008

Regional rainfall quantile depends on the identification of hydrologically homogeneous regions. Various variables relevant to precipitation can be used to form regions. Since the type and number of variables may lead to improve the efficiency of partitioning, it is important to select those precipitation related variables, which represent most of the information from all candidate variables. Multivariate analysis techniques can be used for this purpose. Procrustes analysis which can decrease the dimension of variables based on their correlations, are applied in this study. 42 rainfall related variables are decreased into 21 ones by Procrustes analysis. Factor analysis is applied to those selected variables and then 5 factors are extracted. Fuzzy-c means technique classifies 68 stations into 6 regions. As a result, the GEV distributions are fitted to 6 regions while the lognormal and generalized logistic distributions are fitted to 5 regions. For the comparison purpose with previous results, rainfall quantiles based on generalized logistic distribution are estimated by at-site frequency analysis, index flood method, and regional shape estimation method.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.75-87
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• 2001

Inter-port competition is fiercer than in the past because of technological evolution in transport systems : the increasing side of containerships implies only a few calls in three or four ports at each end of the trade and the rest of the traffic being served by smaller feederships. It is therefore essential for big ports to be selected as one of these calls by the main shipowners, consortia and alliances to avoid rmarginalisation. In order to compete effectively, many ports have been obliged to modernise and extend considerably its existing ports or to build new port facilities. With the advent of major environmental legislation around the world, however, amenities such as fish and wildlife, clean air and water, access to the waterfront, and view protection took on greater importance. Ports are now being forced to incorporate environmental considerations into their planning and management functions in order to avoid additional costs or timing delays. The aim of this paper is to analyse the port value by which port comparison(or selection) will be made with HFP(Hierarchical Fuzzy Process) method. This was done by extracting and grouping the evaluation factors of port value by port experts : facility and location factor, logistics service factor environment and amenity factor, city and economic factor, and human and system factor. For empirical test of this method, 6 major ports in Northeast Asia were chosen and analysed. The order of importance for five evaluation factors were 1) facility and location factor 2) logistics service factor 3) human and system factor, 4) city and economic factor, and 5) environment and amenity factor. This means that geographical location and logistics services are still being considered as the most important factor to call the port by port users. even though environment and amenity factor shows relatively low figure. Among 6 major ports, Port of Kobe was ranked the first position in a comprehensive evaluation, while Ports of Busan and Kwangyang were 4th and 5th respectively. This implies that Port of Busan should make much efforts to enhance the existing facilities as well as management system.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.3
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• pp.22-33
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• 2020

Sign system is one of the most widely used guide media in scenic spots. It plays vital role in introducing cultural values of destinations to tourists with better visit experience. The purpose of this study is to derive the influence factors of the sign system of Mount Tai scenic area for tourists, analyze the satisfaction of tourists, and provide suggestions for the sign system of Mount Tai Mountaineering Road to improve tourists' satisfaction in the future. The evaluation items of Mount Tai Mountaineering Road sign system were derived from the previous studies and then subdivided comprehensively. Survey by questionnaires was carried out to obtain the influence factors. In order to understand the satisfaction degree of tourists, fuzzy comprehensive evaluation was implemented. The research results of this study are summarized as follows. First, four influence factors of the sign system on Mountaineering Road of Mount Tai were concluded as the interpretation content, appearance modeling, interpretation methods and layout management. Second, the order of weight values of influence factors was the interpretation content, appearance modeling, interpretation methods and layout management respectively from high to low, which means that tourists paid more attention to practicality and aesthetics. Third, the satisfaction degree of the tourists on the sign system was different. The satisfaction level for the three factors (interpretation content, appearance modeling, layout management) was good, while the satisfaction for interpretation method was medium. The reason was that it failed to deepen the understanding of tourists on the natural and cultural values of Mount Tai Mountaineering Road. These results indicate great significance to provide theoretical basis for the later readjustment and design of the sign system and to improve the overall satisfaction of tourists on tourism experience.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.420-425
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• 1999

The maximum demand controller is an electrical equipment installed at the consumer side of power system for monitoring the electrical energy consumed during every integrating period and preventing the target maximum demand (MD) being exceeded by disconnecting sheddable loads. By avoiding the peak loads and spreading the energy requirement the controller contributes to maximizing the utility factor of the generator systems. It results in not only saving the energy but also reducing the budget for constructing the natural base facilities by keeping thc number of generating plants ~ninimumT. he conventional MD controllers often bring about the large number of control actions during the every inteyating period and/or undesirable loaddisconnecting operations during the beginning stage of the integrating period. These make the users aviod the MD controllers. In this paper. fuzzy control technique is used to get around the disadvantages of the conventional MD control system. The proposed MD controller consists of the predictor module and the fuzzy MD control module. The proposed forecasting method uses the SOFM neural network model, differently from time series analysis, and thus it has inherent advantages of neural network such as parallel processing, generalization and robustness. The MD fuzzy controller determines the sensitivity of control action based on the time closed to the end of the integrating period and the urgency of the load interrupting action along the predicted demand reaching the target. The experimental results show that the proposed method has more accurate forecastinglcontrol performance than the previous methods.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.192-207
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• 2023

Commercialization of level-4 (Lv.4) autonomous driving applications requires the definition of a safe road environment under which autonomous vehicles can operate safely. Thus, a risk assessment model is required to determine whether the operation of autonomous vehicles can provide safety to is sufficiently prepared for future real-life traffic problems. Although the risk factors of autonomous vehicles were selected and graded, the decision-making method was applied as qualitative data using a survey of experts in the field of autonomous driving due to the cause of the accident and difficulty in obtaining autonomous driving data. The fuzzy linguistic representation of decision-makers and the fuzzy analytic hierarchy process (AHP), which converts uncertainty into quantitative figures, were implemented to compensate for the AHP shortcomings of the multi-standard decision-making technique. Through the process of deriving the weights of the upper and lower attributes, the road alignment, which is a physical infrastructure, was analyzed as the most important risk factor in the operation risk of autonomous vehicles. In addition, the operation risk of autonomous vehicles was derived through the example of the risk of operating autonomous vehicles for the 5 areas to be evaluated.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.343-350
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• 2000

In this paper, we propose the Multi-FPNN(Fuzzy Polynomial Neural Networks) model based on FNN and PNN(Polyomial Neural Networks) for optimal system identifacation. Here FNN structure is designed using fuzzy input space divided by each separated input variable, and urilized both in order to get better output performace. Each node of PNN structure based on GMDH(Group Method of Data handing) method uses two types of high-order polynomials such as linearane and quadratic, and the input of that node uses three kinds of multi-variable inputs such as linear and quadratic, and the input of that node and Genetic Algorithms(GAs) to identify both the structure and the prepocessing of parameters of a Multi-FPNN model. Here, HCM clustering method, which is carried out for data preproessing of process system, is utilized to determine the structure method, which is carried out for data preprocessing of process system, is utilized to determance index with a weighting factor is used to according to the divisions of input-output space. A aggregate performance inddex with a wegihting factor is used to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of this aggregate abjective function which it is acailable and effective to design to design and optimal Multi-FPNN model. The study is illustrated with the aid of two representative numerical examples and the aggregate performance index related to the approximation and generalization abilities of the model is evaluated and discussed.