• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.3
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• pp.206-212
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• 2009

This paper presents an optimized integrated task planning and control approach for manipulating a nonholonomic robot by mobile manipulators. Then, we derive a kinematics model and a mobility of the mobile manipulator(M2) platform considering it as the combined system of the manipulator and the mobile robot. to improve task execution efficiency utilizing the redundancy, optimal trajectory of the mobile manipulator(M2) platform are maintained while it is moving to a new task point. A cost function for optimality can be defined as a combination of the square errors of the desired and actual configurations of the mobile robot and of the task robot. In the combination of the two square errors, a newly defined mobility of a mobile robot is utilized as a weighting index. With the aid of the gradient method, the cost function is minimized, so the path trajectory that the M2 platform generates is optimized. The simulation results of the 2 ink planar nonholonomic M2 platform are given to show the effectiveness of the proposed algorithm.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.3
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• pp.69-77
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• 2006

This paper presents the service restoration in electric power distribution systems. The aim of the service restoration is to control an emergency taken place in distribution systems and to restore out-of service areas as soon as possible when a fault occurs in distribution systems. For this reason, new service restoration strategies in multi-outage area are suggested in this paper. The suggested algorithm consist of two schemes. One is an individual restoration scheme, the other is an integrated restoration scheme. The former determines a restoration order of outage areas based on a restoration index. Unless the former scheme can generate a feasible restoration plan, the latter one will try to find out a new configuration without an overloaded section through tie exchange method.

• Industrial Engineering and Management Systems
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• v.11 no.1
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• pp.54-69
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• 2012

Chan and Qi (SCM 8/3 (2003) 209) developed an innovative measurement method that aggregates performance measures in a supply chain into an overall performance index. The method is useful and makes a significant contribution to supply chain management. Nevertheless, it can be cumbersome in computation due to its highly complex algorithmic fuzzy model. In aggregating the performance information, weights used by Chan and Qi-which aim to address the imprecision of human judgments-are incompatible with weights in additive models. Furthermore, the default assumption of linearity of its scoring procedure could lead to an inaccurate assessment of the overall performance. This paper addresses these limitations by developing an alternative measurement that takes care of the above. This research integrates three different approaches to multiple criteria decision analysis (MCDA)-the multiattribute value theory (MAVT), the swing weighting method and the eigenvector procedure-to develop a comprehensive assessment of supply chain performance. One case study is presented to demonstrate the measurement of the proposed method. The performance model used in the case study relies on the Supply Chain Operations Reference (SCOR) model level 1. With this measurement method, supply chain managers can easily benchmark the performance of the whole system, and then analyze the effectiveness and efficiency of the supply chain.

• Geomechanics and Engineering
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• v.28 no.6
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• pp.573-584
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• 2022

Water inrush may occur during seaside urban tunnel excavation. Various factors affect the water inrush, and the water inrush mechanism is complex. In this study, nine evaluation indices having potential effects on water inrush were analysed. Specifically, the geographic and geomorphic conditions, unfavourable geology, distance from the tunnel to sea, strength of the surrounding rock, groundwater level, tidal action, cyclical footage, grouting pressure, and grouting reinforced region were analysed. Furthermore, a two-step interval risk assessment method for water inrush management during seaside urban tunnel excavation was developed by a multi-index system and interval risk assessment comprised of an interval analytic hierarchy process, fuzzy comprehensive evaluation, and relative superiority analysis. The novel assessment method was applied to the Haicang Tunnel successfully. A preliminary interval risk assessment method for water inrush was performed based on engineering geological conditions. As a result, the risk level fell into a risk level IV, which represents a section with high risk. Subsequently, a secondary interval risk assessment method was performed based on engineering geological conditions and construction conditions. The risk level of water inrush is reduced to a risk level II. The results agreed with the current tunnel situation, which verified the reliability of this approach.

• Clean Technology
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• v.29 no.2
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• pp.145-150
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• 2023

Tele-metering systems have been useful tools for managing domestic wastewater treatment plants (WWTP) over the last decade. They mostly generate water quality data for discharged water to ensure that it complies with mandatory regulations and they may be able to produce every operation parameter and additional measurements in the near future. A sub-big data group, comprised of about 150,000 data points from four domestic WWTPs, was ready to be classified and also analyzed to optimize the WWTP process. We used the Statistical Product and Service Solutions (SPSS) 25 package in order to statistically treat the data with linear regression and correlation analysis. The major independent variables for analysis were water temperature, sludge recycle rate, electricity used, and water quality of the influent while the dependent variables representing the water quality of the effluent included the total nitrogen, which is the most emphasized index for discharged flow in plants. The water temperature and consumed electricity showed a strong correlation with the total nitrogen but the other indices' mutual correlations with other variables were found to be fuzzy due to the large errors involved. In addition, a multilayer perceptron analysis method was applied to TMS data along with root mean square error (RMSE) analysis. This study showed that the RMSE in the SS, T-N, and TOC predictions were in the range of 10% to 20%.

• Steel and Composite Structures
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• v.49 no.6
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• pp.645-666
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• 2023

Due to the steadily declining supply of natural coarse aggregates, the concrete industry has shifted to substituting coarse aggregates generated from byproducts and industrial waste. Oil palm shell is a substantial waste product created during the production of palm oil (OPS). When considering the usage of OPSC, building engineers must consider its uniaxial compressive strength (UCS). Obtaining UCS is expensive and time-consuming, machine learning may help. This research established five innovative hybrid AI algorithms to predict UCS. Aquila optimizer (AO) is used with methods to discover optimum model parameters. Considered models are artificial neural network (AO - ANN), adaptive neuro-fuzzy inference system (AO - ANFIS), support vector regression (AO - SVR), random forest (AO - RF), and extreme gradient boosting (AO - XGB). To achieve this goal, a dataset of OPS-produced concrete specimens was compiled. The outputs depict that all five developed models have justifiable accuracy in UCS estimation process, showing the remarkable correlation between measured and estimated UCS and models' usefulness. All in all, findings depict that the proposed AO - XGB model performed more suitable than others in predicting UCS of OPSC (with R², RMSE, MAE, VAF and A_15-index at 0.9678, 1.4595, 1.1527, 97.6469, and 0.9077). The proposed model could be utilized in construction engineering to ensure enough mechanical workability of lightweight concrete and permit its safe usage for construction aims.

• The KIPS Transactions:PartA
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• v.13A no.5 s.102
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• pp.465-472
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• 2006

Recently, many suggestions have been made in image segmentation methods for extracting human organs or disease affected area from huge amounts of medical image datasets. However, images from some areas, such as brain, which have multiple structures with ambiruous structural borders, have limitations in their structural segmentation. To address this problem, clustering technique which classifies voxels into finite number of clusters is often employed. This, however, has its drawback, the influence from noise, which is caused from voxel by voxel operations. Therefore, applying image enhancing method to minimize the influence from noise and to make clearer image borders would allow more robust structural segmentation. This research proposes an efficient structural segmentation method by filtering based clustering to extract detail structures such as white matter, gray matter and cerebrospinal fluid from brain MR. First, coherence enhancing diffusion filtering is adopted to make clearer borders between structures and to reduce the noises in them. To the enhanced images from this process, fuzzy c-means clustering method was applied, conducting structural segmentation by assigning corresponding cluster index to the structure containing each voxel. The suggested structural segmentation method, in comparison with existing ones with clustering using Gaussian or general anisotropic diffusion filtering, showed enhanced accuracy which was determined by how much it agreed with the manual segmentation results. Moreover, by suggesting fine segmentation method on the border area with reproducible results and minimized manual task, it provides efficient diagnostic support for morphological abnormalities in brain.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.295-304
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• 2020

The IPCC (Intergovernmental Panel on Climate Change) recommended the importance of preventive measures against extreme weather, and heat waves are one of the main themes for establishing preventive measures. In this study, we tried to analyze the heat vulnerable areas by considering not only spatial characteristics but also social characteristics. Energy consumption, popu lation density, normalized difference vegetation index, waterfront distance, solar radiation, and road distribution were examined as variables. Then, by selecting a suitable model, SLM (Spatial Lag Model), available variables were extracted. Then, based on the Fuzzy theory, the degree of vulnerability to heat waves was analyzed for each variable, and six variables were superimposed to finally derive the heat vulnerable area. The study site was selected as the Daegu area where the effects of the heat wave were high. In the case of vulnerable areas, it was confirmed that the existing urban areas are mainly distributed in Seogu, Namgu, and Dalseogu of Daegu, which are less affected by waterside and vegetation. It was confirmed that both spatial and social characteristics should be considered in policy support for reducing heat waves in Daegu.

• Review of Culture and Economy
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• v.17 no.2
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• pp.63-80
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• 2014

This study evaluates the importance of negotiation agendas and the possibility of reaching the Korea-China FTA agreement. It assesses the current situation of trade barriers in the audiovisual service sector of China and then examines a survey of practitioners and experts to screen important regulations. The results are as follows: First, considering the national economic situation in Korea and the environment of the Chinese trade barrier, an important agenda is to enable the direct service of online games and to reach a co-production agreement in the audiovisual service sector. Second, an agenda regarding the co-production agreement of an audio-visual service sector has high potential to be realized, followed by agendas regarding online game and music services. In the broadcasting and film service sectors, with their high cultural identity, a mutual cooperative approach is needed. Korea bringing up the agenda regarding online service may allow it to gain a net benefit for the next FATs. To realize a mutual cooperative approach, it is necessary to form a frame of mutual interests and cooperation through a co-production agreement of audio-visual service. If both countries agree to acknowledge co-produced content as each country's contents, both would benefit.

• Smart Structures and Systems
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• v.14 no.5
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• pp.785-809
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• 2014

In this paper, an Adaptive nerou-based inference system (ANFIS) is being used for the prediction of shear strength of high strength concrete (HSC) beams without stirrups. The input parameters comprise of tensile reinforcement ratio, concrete compressive strength and shear span to depth ratio. Additionally, 122 experimental datasets were extracted from the literature review on the HSC beams with some comparable cross sectional dimensions and loading conditions. A comparative analysis has been carried out on the predicted shear strength of HSC beams without stirrups via the ANFIS method with those from the CEB-FIP Model Code (1990), AASHTO LRFD 1994 and CSA A23.3 - 94 codes of design. The shear strength prediction with ANFIS is discovered to be superior to CEB-FIP Model Code (1990), AASHTO LRFD 1994 and CSA A23.3 - 94. The predictions obtained from the ANFIS are harmonious with the test results not accounting for the shear span to depth ratio, tensile reinforcement ratio and concrete compressive strength; the data of the average, variance, correlation coefficient and coefficient of variation (CV) of the ratio between the shear strength predicted using the ANFIS method and the real shear strength are 0.995, 0.014, 0.969 and 11.97%, respectively. Taking a look at the CV index, the shear strength prediction shows better in nonlinear iterations such as the ANFIS for shear strength prediction of HSC beams without stirrups.