• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.152-152
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• 2021

The importance of groundwater has long been recognized, but the ground water potential to become contaminated as a result of human activities has only been recognized in recently. Before 1980 it was thought that soils served as filters, preventing harmful substances deposited at the surface from migrating into groundwater. Today it is known that soils have a finite capacity to protect groundwater. It can be contaminated from divers sources. Therefore, Assessment of aquifer vulnerability to pollution is essential for the protection and management of groundwater and land use planning. In this study, we used DRASTIC and AVI for groundwater vulnerability to contamination assessment. the different methods were applied to the southern coastal sedimentary basin of Benin and DRASTIC method was modified in two different steps. First, we modified DRASTIC by adding land use parameter to include the actual pollution sources (DRASTIC_LcLu) and second, classic DRASTIC weights was modified using Shannon's entropy (Entropy weight DRASTIC). The reliability of the applied approaches was verified using nitrate (NO3^-) concentration and by comparing the overall vulnerability maps to the previous researches in the study area and in the world. The results from validation showed that the addition of landcover/land use parameter to the classic DRASTIC helps to improve the method for better definition of the vulnerable areas in the basin and also, the weight modification using entropy improved better the method because Entropy weight DRASTIC_LcLu showed the highest correlation with nitrate concentration in the study basin. In summary the weight modification using entropy approach reduced the uncertainty of the human subjectivity in assigning weights and ratings in the standard DRASTIC.

• Journal of Information Processing Systems
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• v.19 no.4
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• pp.407-416
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• 2023

In order to effectively relieve the traffic pressure of the city, ensure the smooth flow of freight and promote the development of the logistics industry, the selection of appropriate port logistics location is the basis of giving full play to the port logistics function. In order to better realize the selection of port logistics, this paper adopts the entropy weight method to set up a multi-dimensional evaluation index, and constructs the evaluation model of port logistics location. Then through the actual case, from the environmental dimension and economic competition dimension to make choices and analysis. The results show that port d has the largest logistics competitiveness and the highest relative proximity among the three indicators of hinterland city economic activity, hinterland economic structure, and port operation capacity of different port logistics locations, which has absolute advantages. It is hoped that the research results can provide a reference for the multi-dimensional selection of port logistics site selections.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.2
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• pp.55-61
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• 2003

The purpose of this paper is to propose a method for selecting the preferred alternatives of Multiple- Attribute Decision-Making(MADM) problem using the Entropy measure. A decision-maker who wants to estimate exactly the weight to be applied to her/his MADM problem is usually confronted with the embarrassing situation where, although there exist a variety of weighting methods, it is hard to find a right procedure to choose a pertinent value To remedy this uncomfortable situation, the Entropy measure commonly used in information theory, Is proposed as a tool that can be used by decision-makers to more efficiently select the preferred alternatives. As a result, the method proposed in the paper can be significant in that relatively easy to understand by decision-makers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4611-4624
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• 2020

Future network scenario will be a heterogeneous wireless network environment composed of multiple networks and multimode terminals (MMT). Seamless switching and optimal connectivity for MMT among different networks and different services become extremely important. Here, a vertical handoff algorithm combined an improved entropy weighting method based on grey relational analysis (GRA) is proposed. In which, the improved entropy weight method is used to obtain the objective weights of the network attributes, and GRA is done to rank the candidate networks in order to choose the best network. Through simulation and comparing the results with other vertical handoff decision algorithms, the number of handoffs and reversal phenomenon are reduced with the proposed algorithm, which shows a better performance.

• Earthquakes and Structures
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• v.14 no.2
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• pp.143-153
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• 2018

Correlation among different factors must be considered for selection of influencing factors in safety monitoring of high dam including positive correlation of variables. Therefore, a new factor selection method was constructed based on Copula entropy and mutual information theory, which was deduced and optimized. Considering the small sample size in high dam monitoring and distribution of daily monitoring samples, a computing method that avoids causality of structure as much as possible is needed. The two-dimensional normal information diffusion and fuzzy reasoning of pattern recognition field are based on the weight theory, which avoids complicated causes of the studying structure. Hence, it is used to dam safety monitoring field and simplified, which increases sample information appropriately. Next, a complete system integrating high dam monitoring and uncertainty prediction method was established by combining Copula entropy theory and information diffusion theory. Finally, the proposed method was applied in seepage monitoring of Nuozhadu clay core-wall rockfill dam. Its selection of influencing factors and processing of sample data were compared with different models. Results demonstrated that the proposed method increases the prediction accuracy to some extent.

• Geomechanics and Engineering
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• v.19 no.3
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• pp.283-293
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• 2019

Evaluating the stability of the excavation face of the cross-river shield tunnel with good accuracy is considered as a nonlinear and multivariable complex issue. Understanding the stability evaluation method of the shield tunnel excavation face is vital to operate and control the shield machine during shield tunneling. Considering the instability mechanism of the excavation face of the cross-river shield and the characteristics of this engineering, seven evaluation indexes of the stability of the excavation face were selected, i.e., the over-span ratio, buried depth of the tunnel, groundwater condition, soil permeability, internal friction angle, soil cohesion and advancing speed. The weight of each evaluation index was obtained by using the analytic hierarchy process and the entropy weight method. The evaluation model of the cross-river shield construction excavation face stability is established based on the idea point method. The feasibility of the evaluation model was verified by the engineering application in a cross-river shield tunnel project in China. Results obtained via the evaluation model are in good agreement with the actual construction situation. The proposed evaluation method is demonstrated as a promising and innovative method for the stability evaluation and safety construction of the cross-river shield tunnel engineerings.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.7-21
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• 2019

In order to improve the edge segmentation effect of the level set image segmentation and avoid the influence of the initial contour on the level set method, a saliency level set image segmentation model based on local Renyi entropy is proposed. Firstly, the saliency map of the original image is extracted by using saliency detection algorithm. And the outline of the saliency map can be used to initialize the level set. Secondly, the local energy and edge energy of the image are obtained by using local Renyi entropy and Canny operator respectively. At the same time, new adaptive weight coefficient and boundary indication function are constructed. Finally, the local binary fitting energy model (LBF) as an external energy term is introduced. In this paper, the contrast experiments are implemented in different image database. The robustness of the proposed model for segmentation of images with intensity inhomogeneity and complicated edges is verified.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.88-95
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• 2017

In this paper, we present a saliency detection method using entropy weight and Weber contrast in the wavelet transform domain. Our method is based on the commonly exploited conventional algorithms that are composed of the local bottom-up approach and global top-down approach. First, we perform the multi-level wavelet transform for the CIE Lab color images, and obtain global saliency by adding the local Weber contrasts to the corresponding low-frequency wavelet coefficients. Next, the local saliency is obtained by applying Gaussian filter that is weighted by entropy of wavelet high-frequency subband. The final saliency map is detected by non-lineally combining the local and global saliencies. To evaluate the proposed saliency detection method, we perform computer simulations for two image databases. Simulations results show the proposed method represents superior performance to the conventional algorithms.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.73-82
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• 2005

This paper proposes a multi-level thresholding method using weighted FCM(Fuzzy C-Means) algorithm in color image. FCM algerian determines a more optimal thresholding value than the existing methods and can extend to multi-level thresholding. But FCM algerian is sensitive to noise because it doesn't include spatial information. To solve the problem, we can remove noise by applying a weight based on entropy that is obtained from neighboring pixels to FCM algerian. And we determine the optimal cluster number by using within-class distance in code image based on the clustered pixels of each color component. In the experiments, we show that the proposed method is more tolerant to noise and is more superior than the existing methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.1-8
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• 2006

This paper proposes an improved FCM(Fuzzy C-means) algorithm using intercluster and entropy-based weight in gray image. The fuzzy clustering methods have been extensively used in the image segmentation since it extracts feature information of the region. Most of fuzzy clustering methods have used the FCM algorithm. But, FCM algorithm is still sensitive to noise, as it does not include spatial information. In addition, it can't correctly classify pixels according to the feature-based distributions of clusters. To solve these problems, we applied a weight and intercluster to the traditional FCM algorithm. A weight is obtained from the entropy information based on the cluster's number of neighboring pixels. And a membership for one pixel is given based on the information considering the feature-based intercluster. Experiments has confirmed that the proposed method was more tolerant to noise and superior to existing methods.