Journal articles on the topic 'Fuzzy local weights'
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Javed, Umer, Muhammad Mohsin Riaz, Abdul Ghafoor, Syed Sohaib Ali, and Tanveer Ahmed Cheema. "MRI and PET Image Fusion Using Fuzzy Logic and Image Local Features." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/708075.
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An image fusion technique for magnetic resonance imaging (MRI) and positron emission tomography (PET) using local features and fuzzy logic is presented. The aim of proposed technique is to maximally combine useful information present in MRI and PET images. Image local features are extracted and combined with fuzzy logic to compute weights for each pixel. Simulation results show that the proposed scheme produces significantly better results compared to state-of-art schemes.
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Xu, Ce, Zhenbang Xu, and Mingyi Xia. "Obstacle Avoidance in a Three-Dimensional Dynamic Environment Based on Fuzzy Dynamic Windows." Applied Sciences 11, no. 2 (January 6, 2021): 504. http://dx.doi.org/10.3390/app11020504.
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This paper presents a real-time path planning approach for controlling the motion of space-based robots. The algorithm can plan three-dimensional trajectories for agents in a complex environment which includes numerous static and dynamic obstacles, path constraints, and/or performance constraints. This approach is extended based on the dynamic window approach (DWA). As the classic reactive method for obstacle avoidance, DWA uses an optimized function to select the best motion command. The original DWA optimization function consists of three weight terms. Changing the weights of these terms will change the behavior of the algorithm. In this paper, to improve the evaluation ability of the optimization function and the robot’s ability to adapt to the environment, a new optimization function is designed and combined with fuzzy logic to adjust the weights of each parameter of the optimization function. Given that DWA has the defect of local minima, which makes the robot hard to escape U-shaped obstacles, a dual dynamic window method and local goals are adopted in this article to help the robot escape local minima. By comparison, the proposed method is superior to traditional DWA and fuzzy DWA (F_DWA) in terms of computational efficiency, smoothness and security.
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Solgi, Ehsan, Hossein Gitinavard, and Reza Tavakkoli-Moghaddam. "Sustainable High-Tech Brick Production with Energy-Oriented Consumption: An Integrated Possibilistic Approach Based on Criteria Interdependencies." Sustainability 14, no. 1 (December 25, 2021): 202. http://dx.doi.org/10.3390/su14010202.
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Brick making contributes significantly to the of supply materials for the building industry. The majority of brick production sectors, especially in developing countries, employ polluting and energy-inefficient technologies. Due to the increasing pressures on manufacturing firms to improve economic performance and growing environmental protection issues, sustainable and clean production is the main concern for brick makers. This paper considers the technological, economic, environmental, social, and energy-oriented criteria to select the optimal brick production technologies. Therefore, technology selection is viewed as a multi-criteria group decision-making (MCGDM) problem. This research proposes a novel hybrid fuzzy MCGDM (HFMCGDM) model to tackle the problem. In this respect, first of all, the modified triangular fuzzy pair-wise comparison (MTFPC) method is proposed to compute the local weights of criteria and sub-criteria. Then, a fuzzy DEMATEL (FDEMATEL) method is presented to calculate the interdependencies between and within the criteria. Moreover, the integration of MTFPC and FDEMATEL methods is applied to calculate the global criteria weights. Afterward, a novel method is proposed to determine the experts’ weight. Considering the last aggregation approach to diminish data loss, a new version of a fuzzy TOPSIS method is proposed to find the local and global priorities of the candidates. Then, a case study is given to demonstrate the applicability and superiority of the proposed methodology. To get a deeper view about considering kilns, energy and environmental performance of which has been investigated. Moreover, a comparative analysis is presented to illuminate the merits of the proposed methodology. Eventually, a sensitivity analysis is conducted to peruse the influence of criteria weights on ranking order.
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PRASCEVIC, Natasa, and Zivojin PRASCEVIC. "APPLICATION OF FUZZY AHP FOR RANKING AND SELECTION OF ALTERNATIVES IN CONSTRUCTION PROJECT MANAGEMENT." Journal of Civil Engineering and Management 23, no. 8 (November 20, 2017): 1123–35. http://dx.doi.org/10.3846/13923730.2017.1388278.
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The construction project management (CPM) is very important and large segment of entire project management (PM). Realisation of construction projects is usually long term process which requests significant financial, material, human and other resources to fulfil contracted obligations and achieve a good quality of works. Therefore, making good decisions with the satisfaction of various criteria is one of the main conditions to achieve planed business objectives and finish the project in contracted time with good quality. This paper proposes a new procedure for determination of the weights of criteria and alternatives in the Fuzzy analytic hierarchy process (FAHP) with trapezoidal fuzzy number using a new method for finding eigenvelues and eigenvectors of the criteria and alternatives, which is based on expected values of the fuzzy numbers and their products. Local and global fuzzy weights of the alternatives are determined using linear programming. In the paper a formula for ranking fuzzy numbers by reduced generalized fuzzy mean is also proposed, since ranking by the coefficient of variation is not always reliable. In the presented case study, applying proposed method, from imprecise input data are obtained enough accurate and useful results for rational ranking of alternatives related to the project realization.
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Wan, Shao Song, Jian Cao, and Qun Song Zhu. "Research on Image Vectorization Based on Fuzzy Neural Network and Expert System." Advanced Materials Research 989-994 (July 2014): 4877–80. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4877.
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Because the traditional linear vectorization methods have some shortcomings including processing data slowly, being sensitive to noises and being easy to be distorted. Fuzzy rules and its inference mechanism are the assurance of achieving feature fusion. However, the self-learning function of FNN could train its weights; it is difficult to optimize fuzzy rules. Besides, the common FNN training algorithms have low constringency speed and are liable to run into the local optimization.PSO algorithm has high convergence speed and it is simpler on the operation and is more potential on optimizing FNN. Thus, PSO algorithm could be adapted to train FNN weights, and prune the redundancy links, optimize fuzzy rules base. In the paper we present an improving immune genetic algorithm based on chaos theory. The over-spread character and randomness of chaos can be used to initialize population and improve the searching speed, and the initial value sensitivity of chaos can be used to enlarge the searching space. To avoid the local optimization, the algorithm renews population and enhances the diversity of population by using density calculation of immune theory and adjusting new chaos sequence.
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Wang, Tien-Chin, Chin-Ying Huang, Shu-Li Huang, and Jen-Yao Lee. "Priority Weights for Predicting the Success of Hotel Sustainable Business Models." Sustainability 13, no. 24 (December 20, 2021): 14032. http://dx.doi.org/10.3390/su132414032.
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This study proposes the use of consistent fuzzy preference relations to evaluate the structure of hotel sustainable business model (HSBM) dimensions and the corresponding hierarchy of evaluation indicators, and predict the overall probability of success. As fuzzy preference relations require, a group of hotel professionals in Taiwan was asked to process pairwise comparisons using linguistic variables to determine the weights of dimensions and indicators. According to the results, finances were found to be the most important dimension, followed by human capital. The number of local cultural events in the hotel was identified as the most important indicator. The predictive values revealed the possibility for successful HSBM implementation, shedding light on the vision of sustainability for the hotel industry. The results of the present study contribute to the literature on sustainability by determining the importance and weights of dimensions and indicators for hotel business models, providing an example of the use of this strategic tool in generating and modifying sustainable business models for the hotel industry.
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Jiang, Zhenni, and Xiyu Liu. "A Novel Consensus Fuzzy K-Modes Clustering Using Coupling DNA-Chain-Hypergraph P System for Categorical Data." Processes 8, no. 10 (October 21, 2020): 1326. http://dx.doi.org/10.3390/pr8101326.
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In this paper, a data clustering method named consensus fuzzy k-modes clustering is proposed to improve the performance of the clustering for the categorical data. At the same time, the coupling DNA-chain-hypergraph P system is constructed to realize the process of the clustering. This P system can prevent the clustering algorithm falling into the local optimum and realize the clustering process in implicit parallelism. The consensus fuzzy k-modes algorithm can combine the advantages of the fuzzy k-modes algorithm, weight fuzzy k-modes algorithm and genetic fuzzy k-modes algorithm. The fuzzy k-modes algorithm can realize the soft partition which is closer to reality, but treats all the variables equally. The weight fuzzy k-modes algorithm introduced the weight vector which strengthens the basic k-modes clustering by associating higher weights with features useful in analysis. These two methods are only improvements the k-modes algorithm itself. So, the genetic k-modes algorithm is proposed which used the genetic operations in the clustering process. In this paper, we examine these three kinds of k-modes algorithms and further introduce DNA genetic optimization operations in the final consensus process. Finally, we conduct experiments on the seven UCI datasets and compare the clustering results with another four categorical clustering algorithms. The experiment results and statistical test results show that our method can get better clustering results than the compared clustering algorithms, respectively.
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Devi, K. R. Kosala, and V. Deepa. "Prediction of Tetralogy of Fallot using Fuzzy Clustering." Recent Advances in Computer Science and Communications 13, no. 4 (October 19, 2020): 694–705. http://dx.doi.org/10.2174/2213275912666190612120344.
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Background: Congenital Heart Disease is one of the abnormalities in your heart's structure. To predict the tetralogy of fallot in a heart is a difficult task. Cluster is the collection of data objects, which are similar to one another within the same group and are different from the objects in the other clusters. To detect the edges, the clustering mechanism improve its accuracy by using segmentation, Colour space conversion of an image implemented in Fuzzy c-Means with Edge and Local Information. Objective: To predict the tetralogy of fallot in a heart, the clustering mechanism is used. Fuzzy c-Means with Edge and Local Information gives an accuracy to detect the edges of a fallot to identify the congential heart disease in an efficient way. Methods: One of the finest image clustering methods, called as Fuzzy c-Means with Edge and Local Information which will introduce the weights for a pixel value to increase the edge detection accuracy value. It will identify the pixel value within its local neighbor windows to improve the exactness. For evaluation , the Adjusted rand index metrics used to achieve the accurate measurement. Results: The cluster metrics Adjusted rand index and jaccard index are used to evaluate the Fuzzy c- Means with Edge and Local Information. It gives an accurate results to identify the edges. By evaluating the clustering technique, the Adjusted Rand index, jaccard index gives the accurate values of 0.2, 0.6363, and 0.8333 compared to other clustering methods. Conclusion: Tetralogy of fallot accurately identified and gives the better performance to detect the edges. And also it will be useful to identify more defects in various heart diseases in a accurate manner. Fuzzy c-Means with Edge and Local Information and Gray level Co-occurrence matrix are more promising than other Clustering Techniques.
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Abdulrahman, Yadgar. "A new approach to the fuzzy c-means clustering algorithm by automatic weights and local clustering." Passer Journal of Basic and Applied Sciences 3, no. 1 (January 1, 2021): 95–101. http://dx.doi.org/10.24271/psr.18.
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Li, Xiao Hui, and Hui Yu Yang. "Application of Fault Diagnosis for Air Blower Based on Genetic Fuzzy Neural Network." Applied Mechanics and Materials 401-403 (September 2013): 1336–40. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1336.
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The way of fault characteristic parameters fuzzy processing and optimizing the weights and thresholds of ANN by GA are studied. As a result, the convergent rate and convergent precision are greatly increased. Application to the fault diagnosis of a air blower system shows the new model overcomes the low learning rate and local optima of BP algorithm, and the fault diagnosis precision is effectively improved.
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Lin, Hua, Lu, Sun, and Chen. "An Adaptive Exposure Fusion Method Using fuzzy Logic and Multivariate Normal Conditional Random Fields." Sensors 19, no. 21 (October 31, 2019): 4743. http://dx.doi.org/10.3390/s19214743.
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High dynamic range (HDR) has wide applications involving intelligent vision sensing which includes enhanced electronic imaging, smart surveillance, self-driving cars, intelligent medical diagnosis, etc. Exposure fusion is an essential HDR technique which fuses different exposures of the same scene into an HDR-like image. However, determining the appropriate fusion weights is difficult because each differently exposed image only contains a subset of the scene’s details. When blending, the problem of local color inconsistency is more challenging; thus, it often requires manual tuning to avoid image artifacts. To address this problem, we present an adaptive coarse-to-fine searching approach to find the optimal fusion weights. In the coarse-tuning stage, fuzzy logic is used to efficiently decide the initial weights. In the fine-tuning stage, the multivariate normal conditional random field model is used to adjust the fuzzy-based initial weights which allows us to consider both intra- and inter-image information in the data. Moreover, a multiscale enhanced fusion scheme is proposed to blend input images when maintaining the details in each scale-level. The proposed fuzzy-based MNCRF (Multivariate Normal Conditional Random Fields) fusion method provided a smoother blending result and a more natural look. Meanwhile, the details in the highlighted and dark regions were preserved simultaneously. The experimental results demonstrated that our work outperformed the state-of-the-art methods not only in several objective quality measures but also in a user study analysis.
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Dymova, Ludmila, Krzysztof Kaczmarek, Pavel Sevastjanov, Łukasz Sułkowski, and Krzysztof Przybyszewski. "An Approach to Generalization of the Intuitionistic Fuzzy Topsis Method in the Framework of Evidence Theory." Journal of Artificial Intelligence and Soft Computing Research 11, no. 2 (January 29, 2021): 157–75. http://dx.doi.org/10.2478/jaiscr-2021-0010.
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Abstract A generalization of technique for establishing order preference by similarity to the ideal solution (TOPSIS) in the intuitionistic fuzzy setting based on the redefinition of intuitionistic fuzzy sets theory (A IFS) in the framework of Dempster-Shafer theory (DST) of evidence is proposed. The use of DST mathematical tools makes it possible to avoid a set of limitations and drawbacks revealed recently in the conventional Atanassov’s operational laws defined on intuitionistic fuzzy values, which may produce unacceptable results in the solution of multiple criteria decision-making problems. This boosts considerably the quality of aggregating operators used in the intuitionistic fuzzy TOPSIS method. It is pointed out that the conventional TOPSIS method may be naturally treated as a weighted sum of some modified local criteria. Because this aggregating approach does not always reflects well intentions of decision makers, two additional aggregating methods that cannot be defined in the framework of conventional A IFS based on local criteria weights being intuitionistic fuzzy values, are introduced. Having in mind that different aggregating methods generally produce different alternative rankings to obtain the compromise ranking, the method for aggregating of aggregation modes has been applied. Some examples are used to illustrate the validity and features of the proposed approach.
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Zhou, Xiang Nan, Fu Xin Chai, Hang Zhang, and Xin Min Xie. "Research on Evaluation Index System and Fuzzy Clustering Iterative Model for Local Water Conservancy Development." Applied Mechanics and Materials 730 (January 2015): 208–12. http://dx.doi.org/10.4028/www.scientific.net/amm.730.208.
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According to the current water construction features and requirements, 11 indexes on three aspects which involve in water environment protection, the flood management and disaster mitigation and effective utilization of water resources are selected to establish a set of objective and scientific evaluation index system of local water conservancy development. Based on its hierarchy characteristics, a fuzzy clustering iterative model is established to determine the index weights. Finally, the model is used to evaluate water conservancy development situation in 14 cities of Liaoning province. The rank scoring of water development of each city in Liaoning province is analyzed, and the key factor is found.
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Baptista, Renato Dias, Fernando Ferrari Putti, Giuliana Aparecida Santini Pigatto, Camila Pires Cremasco Gabriel, and Luís Roberto Almeida Gabriel Filho. "The Organizational Culture and Local Culture in the Internationalization Process: An Analysis Through Fuzzy Logic." International Journal of Social Science Studies 8, no. 2 (February 12, 2020): 41. http://dx.doi.org/10.11114/ijsss.v8i2.4562.
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The internationalization process of production has marked the world scenarios since the last decades and has influenced the complex interrelationships between the local culture and organizational culture. The culture shock was a recurring phenomenon and made the internationalization started to absorb elements of local culture as determining factors in expansion strategies. The aim of this paper is to analyze the favorable conditions of the organizational culture to manage the local culture. This is a preliminary study that aimed to integrate qualitative and quantitative factors with a fuzzy logic system. To achieve this goal, three elements were analyzed and interrelated: values, organizational structure, and management practices of human resources. A fuzzy mathematical model that considers the different weights to the elements analyzed leading to the identification of the favorability of the organizational culture in managing the local culture was developed. Petrobras, a transnational corporation with unity in Bolivia, whose major shareholder is the Brazilian government, was studied to empirically identify how the elements interact and are incorporated into internationalization strategies.
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HAMID MUHAMMED, HAMED. "USING WEIGHTED FIXED NEURAL NETWORKS FOR UNSUPERVISED FUZZY CLUSTERING." International Journal of Neural Systems 12, no. 06 (December 2002): 425–34. http://dx.doi.org/10.1142/s0129065702001321.
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A novel algorithm for unsupervised fuzzy clustering is introduced. The algorithm uses a so-called Weighted Fixed Neural Network (WFNN) to store important and useful information about the topological relations in a given data set. The algorithm produces a weighted connected net, of weighted nodes connected by weighted edges, which reflects and preserves the topology of the input data set. The weights of the nodes and the edges in the resulting net are proportional to the local densities of data samples in input space. The connectedness of the net can be changed, and the higher the connectedness of the net is chosen, the fuzzier the system becomes. The new algorithm is computationally efficient when compared to other existing methods for clustering multi-dimensional data, such as color images.
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Fan, Zhe Chao. "Applying the Methods of Environmental Isotopes and Fuzzy Clustering to Study the Leakage from Embankment." Applied Mechanics and Materials 90-93 (September 2011): 2691–95. http://dx.doi.org/10.4028/www.scientific.net/amm.90-93.2691.
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Several typical sections of some embankment were studied by the method of environmental isotopes in the paper. The leakage sources of groundwater were ascertained. The result showed that the groundwater of No.1 borehole at Jiangdu dockyard, No.23 borehole at Xiaocaitan section and No.29 borehole at Lianmengzhuang dock was recharged by river water, the water of Heiyutang pond near No.5 borehole was recharged by the local precipitation or groundwater forming by recent precipitation, and the groundwater of No.7 borehole at Wanshougong section was the mixture of river water and local precipitation. Then the analysis of fuzzy clustering was performed by applying fuzzy clustering method that environmental isotopes and hydrochemistry values were taken as index characteristic values and endued different weights. The quantitative analysis result was in agreement with the conclusion qualitatively judged by environmental isotopes and hydrochemistry.
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Sheng, Jie, Jihong Sun, Yongliang Bai, Zhan Liu, Helong Wei, Lianwei Li, Guohui Su, and Zhao Wang. "Evaluation of hydrocarbon potential using fuzzy AHP-based grey relational analysis: a case study in the Laoshan Uplift, South Yellow Sea, China." Journal of Geophysics and Engineering 17, no. 1 (December 16, 2019): 189–202. http://dx.doi.org/10.1093/jge/gxz107.
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Abstract Effective evaluations of hydrocarbon potential contribute to delineating promising target areas for further exploration. Sparse available data and known hydrocarbon reservoirs in frontier areas present considerable challenges to the weighting of geological factors when evaluating hydrocarbon potential. This study proposes a hydrocarbon potential mapping method that employs grey relational analysis (GRA) based on a fuzzy analytic hierarchy process (fuzzy AHP). GRA is a comprehensive evaluation method that represents the hydrocarbon favourability according to the proximity between evaluation targets and the ideal target based on multiple evaluation factors and weights among them. To overcome the uncertainty and vagueness in the weighting procedure, the fuzzy AHP technique relies on experts’ knowledge to define the relative importance of evaluation factors and exploits triangular fuzzy numbers to simulate experts’ judgements in pairwise comparisons. The fuzzy AHP-based GRA method was tested using an example in the Laoshan Uplift in the South Yellow Basin of China. This test application not only quantified the favourability of local traps but also revealed their spatial variations on favourability maps and indicated potential targets for further exploration. The results obtained by the fuzzy AHP-based GRA method were more reliable than that of entropy weight-based GRA and displayed a suitable consistency with known geological information, thus demonstrating that such a procedure could reveal the potential spatial features of hydrocarbon accumulations and support the evaluation of hydrocarbon potential in relatively unknown areas.
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Dehuri, Satchidananda, and Sung-Bae Cho. "Learning Fuzzy Network Using Sequence Bound Global Particle Swarm Optimizer." International Journal of Fuzzy System Applications 2, no. 1 (January 2012): 54–70. http://dx.doi.org/10.4018/ijfsa.2012010104.
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This paper proposes an algorithm for classification by learning fuzzy network with a sequence bound global particle swarm optimizer. The aim of this work can be achieved in two folded. Fold one provides an explicit mapping of an input features from original domain to fuzzy domain with a multiple fuzzy sets and the second fold discusses the novel sequence bound global particle swarm optimizer for evolution of optimal set of connection weights between hidden layer and output layer of the fuzzy network. The novel sequence bound global particle swarm optimizer can solve the problem of premature convergence when learning the fuzzy network plagued with many local optimal solutions. Unlike multi-layer perceptron with many hidden layers it has only single hidden layer. The output layer of this network contains one neuron. This network advocates a simple and understandable architecture for classification. The experimental studies show that the classification accuracy of the proposed algorithm is promising and superior to other alternatives such as multi-layer perceptron and radial basis function network.
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Zhou, Jianzhong, Wenlong Fu, Yongchuan Zhang, Han Xiao, Jian Xiao, and Chu Zhang. "Fault diagnosis based on a novel weighted support vector data description with fuzzy adaptive threshold decision." Transactions of the Institute of Measurement and Control 40, no. 1 (June 3, 2016): 71–79. http://dx.doi.org/10.1177/0142331216649656.
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The fault diagnosis of generator units is critical to guarantee the high efficiency of the electric system. However, detailed fault samples are difficult to obtain, and the distribution of fault samples usually shows the characteristics of unevenness and unbalance, which may lead to low fault diagnosis precision. Nevertheless, it has been seldom considered in the traditional classifier of fault diagnosis for generator units until now. In this paper, a novel fault classifier of weighted support vector data description (SVDD) with fuzzy adaptive threshold decision is proposed and applied in the fault diagnosis of generator units. To tackle the drawback that SVDD is sensitive to the distribution of samples, a novel SVDD model based on a complex weight is proposed. The complex weight is assigned with local density and size-based weight, while local density of each data point is obtained with the k-nearest neighbour approach and the size-based weight of each data point is computed according to the proportion of classes. Then the conventional SVDD is reformulated with the complex weights. Furthermore, new decision rules based on the relative distance and fuzzy adaptive threshold decision are applied to identify the class of testing samples. Finally, the proposed method is applied in the identification of several standard datasets, as well as the fault diagnosis for a turbo-generator unit. Experimental results and the engineering application reveal that the proposed method shows good performance in accuracy and universality, and is suitable for the fault diagnosis of generator units.
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MUHAMMED, HAMED HAMID. "UNSUPERVISED FUZZY CLUSTERING USING WEIGHTED INCREMENTAL NEURAL NETWORKS." International Journal of Neural Systems 14, no. 06 (December 2004): 355–71. http://dx.doi.org/10.1142/s0129065704002121.
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A new more efficient variant of a recently developed algorithm for unsupervised fuzzy clustering is introduced. A Weighted Incremental Neural Network (WINN) is introduced and used for this purpose. The new approach is called FC-WINN (Fuzzy Clustering using WINN). The WINN algorithm produces a net of nodes connected by edges, which reflects and preserves the topology of the input data set. Additional weights, which are proportional to the local densities in input space, are associated with the resulting nodes and edges to store useful information about the topological relations in the given input data set. A fuzziness factor, proportional to the connectedness of the net, is introduced in the system. A watershed-like procedure is used to cluster the resulting net. The number of the resulting clusters is determined by this procedure. Only two parameters must be chosen by the user for the FC-WINN algorithm to determine the resolution and the connectedness of the net. Other parameters that must be specified are those which are necessary for the used incremental neural network, which is a modified version of the Growing Neural Gas algorithm (GNG). The FC-WINN algorithm is computationally efficient when compared to other approaches for clustering large high-dimensional data sets.
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Yang, Tiejun, Jinfeng Cheng, and Chunhua Zhu. "A segmentation of pulmonary nodules based on improved fuzzy C-means clustering algorithm." MATEC Web of Conferences 232 (2018): 03011. http://dx.doi.org/10.1051/matecconf/201823203011.
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According to reports, lung cancer is gradually becoming the first cancer that threatens human life. The early stage of lung cancer is in the form of pulmonary nodules. The key issue in computer-aided diagnosis of lung tumors is to correct and accelerate rapid segmentation of diseased tissue. Therefore, this paper proposes a robust fuzzy c-mean clustering algorithm for pulmonary nodules segmentation, which can effectively improve the adaptive degree of local domain pixels. Since the information of the domain pixels does not necessarily have a positive correlation with the central pixels, the reference mechanism of domain window pixel information needs to be redefined. The robust fuzzy c-means clustering algorithm redefines the grayscale of the spatial pixel points in the domain and selects different fuzzy factors according to the reference standard. Based on this, the weights of different fuzzy factors are updated according to the characteristics of pixel points and gray fluctuation in pixel domain. The experimental results show that this method is superior to other typical algorithms in the segmentation of pulmonary nodules.
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Ghaderi, F., and P. Pahlavani. "A NEW MULTIMODAL MULTI-CRITERIA ROUTE PLANNING MODEL BY INTEGRATING A FUZZY-AHP WEIGHTING METHOD AND A SIMULATED ANNEALING ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 203–9. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-203-2015.
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A multimodal multi-criteria route planning (MMRP) system provides an optimal multimodal route from an origin point to a destination point considering two or more criteria in a way this route can be a combination of public and private transportation modes. In this paper, the simulate annealing (SA) and the fuzzy analytical hierarchy process (fuzzy AHP) were combined in order to find this route. In this regard, firstly, the effective criteria that are significant for users in their trip were determined. Then the weight of each criterion was calculated using the fuzzy AHP weighting method. The most important characteristic of this weighting method is the use of fuzzy numbers that aids the users to consider their uncertainty in pairwise comparison of criteria. After determining the criteria weights, the proposed SA algorithm were used for determining an optimal route from an origin to a destination. One of the most important problems in a meta-heuristic algorithm is trapping in local minima. In this study, five transportation modes, including subway, bus rapid transit (BRT), taxi, walking, and bus were considered for moving between nodes. Also, the fare, the time, the user’s bother, and the length of the path were considered as effective criteria for solving the problem. The proposed model was implemented in an area in centre of Tehran in a GUI MATLAB programming language. The results showed a high efficiency and speed of the proposed algorithm that support our analyses.
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Yang, Shang Yang, Long Yun Zhang, and Yue Gao. "Comprehensive Evaluation Method of Surface Water Quality in Arid Region." Advanced Materials Research 403-408 (November 2011): 1517–20. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1517.
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With the building of the South-to-North Water Diversion Project, surface water resources in arid regions has been recharged effectively. Generally speaking, the surface water-supply resources in arid region include two parts, local water and secondary water, which makes its water quality evaluation has its own particularity and different from regular evaluation. So the surface water quality evaluation in arid region is unique.For it involves a wide range of complex factors in practice, the fuzzy set theory and fuzzy matter-element theory were applied to the study. The mathematical model was established based on the quantity demand of both local water and secondary water, and meanwhile taking the subjective and objective conditions into account carefully, so the weights of the two parts were obtained respectively. The results proved to be a good solution to solve the problem of water quality evaluation level in arid areas,and the research achievements also openup new ideas for comprehensive evaluation method of surface water quality in cities of arid region.
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Yılmaz, Hafize, and Özgür Kabak. "Prioritizing distribution centers in humanitarian logistics using type-2 fuzzy MCDM approach." Journal of Enterprise Information Management 33, no. 5 (July 9, 2020): 1199–232. http://dx.doi.org/10.1108/jeim-09-2019-0310.
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PurposeLocating disaster response centers is one of the key elements of efficient relief operations. The location and infrastructure of the candidate facilities usually conform to the required criteria at different levels. This study aims to identify the criteria for the main and local distribution center location problem separately and prioritize each candidate distribution center using a hybrid multiple criteria decision-making approach.Design/methodology/approachThe proposed model incorporates analytic hierarchy process (AHP) and Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) under interval type-2 fuzzy sets (IT2FSs) to overcome the uncertainty of experts` judgments and expressions in the evaluations of candidate distribution centers. In the proposed approach, weights of the criteria are determined using type-2 fuzzy AHP and the candidate distribution centers are prioritized using type-2 fuzzy TOPSIS.FindingsTransportation, cost, infrastructure and security are determined as the main criteria for the main distribution center location criteria. Cost, warehouse facilities and security are the main criteria for local distribution center location selection. Prioritization enables decision-makers to assess each alternative accordingly to be able to select the best locations/facilities for efficient disaster response operations.Originality/valueThis study proposes new multi-criteria decision support models for prioritizing disaster response distribution centers. IT2FSs are used to be able to reflect both the complexity and vagueness of disaster environment and expert opinions. Different support models are suggested for main and local distribution centers considering their different missions. The proposed methodology is applied in Istanbul city, Turkey, where a high-magnitude earthquake is expected.
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ZHOU, LIFANG, BIN FANG, WEISHENG LI, HENGXIN CHEN, and LIDOU WANG. "AN ADAPTIVE FUZZY FUSION FRAMEWORK FOR FACE RECOGNITION UNDER ILLUMINATION VARIATION BASED ON LOCAL MULTIPLE PATTERNS." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 01 (February 2013): 1356002. http://dx.doi.org/10.1142/s0218001413560028.
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Local binary pattern (LBP) operator offers an efficient way to recognize face under varying illumination, while it has the drawback of abandoning some important texture features. Local multiple patterns (LMP) has alleviated the problem by a hierarchical model. However, the LMP method can bring out the rapid expansion of feature dimension, so a special feature encoding method is adopted by this paper. Meanwhile, we find that the LMP features of different layers can be used to recognize face independently so that it would preserve more abundant recognition information. Most importantly, the contribution of the LMP features from different layers is blurry under varying illumination. We propose a fuzzy framework to fuse the recognition result of different layers and use adaptive weights to calculate contribution rates of different layers under varying illumination. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods on four databases such as Yale B, Extended Yale B, CMU PIE and Outdoor.
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Liu, Mei Rong, Yi Gang He, and Xiang Xin Li. "Fault Diagnosis of Analog Circuits Based on CFNN." Advanced Engineering Forum 6-7 (September 2012): 1045–50. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.1045.
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An analog circuits fault diagnosis method based on chaotic fuzzy neural network (CFNN) is presented. The method uses the advantage of the global movement characteristic inherent in chaos to overcome the shortcomings that BPNN is usually trapped to a local optimum and it has a low speed of convergence weights. The chaotic mapping was added into BPNN algorithm, and the initial value of the network was selected. The algorithm can effectively and reliably be used in analog circuit fault diagnosis by comparing the two methods and analyzing the results of the example.
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Bounabi, Mariem, Karim Elmoutaouakil, and Khalid Satori. "A new neutrosophic TF-IDF term weighting for text mining tasks: text classification use case." International Journal of Web Information Systems 17, no. 3 (April 8, 2021): 229–49. http://dx.doi.org/10.1108/ijwis-11-2020-0067.
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Purpose This paper aims to present a new term weighting approach for text classification as a text mining task. The original method, neutrosophic term frequency – inverse term frequency (NTF-IDF), is an extended version of the popular fuzzy TF-IDF (FTF-IDF) and uses the neutrosophic reasoning to analyze and generate weights for terms in natural languages. The paper also propose a comparative study between the popular FTF-IDF and NTF-IDF and their impacts on different machine learning (ML) classifiers for document categorization goals. Design/methodology/approach After preprocessing textual data, the original Neutrosophic TF-IDF applies the neutrosophic inference system (NIS) to produce weights for terms representing a document. Using the local frequency TF, global frequency IDF and text N's length as NIS inputs, this study generate two neutrosophic weights for a given term. The first measure provides information on the relevance degree for a word, and the second one represents their ambiguity degree. Next, the Zhang combination function is applied to combine neutrosophic weights outputs and present the final term weight, inserted in the document's representative vector. To analyze the NTF-IDF impact on the classification phase, this study uses a set of ML algorithms. Findings Practicing the neutrosophic logic (NL) characteristics, the authors have been able to study the ambiguity of the terms and their degree of relevance to represent a document. NL's choice has proven its effectiveness in defining significant text vectorization weights, especially for text classification tasks. The experimentation part demonstrates that the new method positively impacts the categorization. Moreover, the adopted system's recognition rate is higher than 91%, an accuracy score not attained using the FTF-IDF. Also, using benchmarked data sets, in different text mining fields, and many ML classifiers, i.e. SVM and Feed-Forward Network, and applying the proposed term scores NTF-IDF improves the accuracy by 10%. Originality/value The novelty of this paper lies in two aspects. First, a new term weighting method, which uses the term frequencies as components to define the relevance and the ambiguity of term; second, the application of NL to infer weights is considered as an original model in this paper, which also aims to correct the shortcomings of the FTF-IDF which uses fuzzy logic and its drawbacks. The introduced technique was combined with different ML models to improve the accuracy and relevance of the obtained feature vectors to fed the classification mechanism.
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Cai, Shunyao, Jiamin Fan, and Wei Yang. "Flooding Risk Assessment and Analysis Based on GIS and the TFN-AHP Method: A Case Study of Chongqing, China." Atmosphere 12, no. 5 (May 12, 2021): 623. http://dx.doi.org/10.3390/atmos12050623.
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Flood risk assessment and mapping is required for management and mitigation of flood in mountain cities. However, the specific characteristics of population, society, economy, environment, transportation and other disaster-bearing bodies in various regions of mountain cities are significantly different, which increases the uncertainty of risk assessment index weight and risk assessment accuracy. To overcome these problems, the triangular fuzzy number-based analytical hierarchy process (TFN-AHP) was employed to determine the weights of eleven indexes influencing flooding. Further, the geographic information system (GIS) spatial statistics technique was introduced to investigate global regional risk pattern, as well as to identify local risk hot spots. Experiments were conducted using open data of Chongqing, China. From the results, it was observed that the TFN-AHP has a higher efficiency in flood risk assessment on mountain cities than the AHP method. The dynamically changing risk pattern and risk hot spots were explored, and the results are generally consistent with seasonal characteristics of precipitation. Lastly, sensitivity analysis of assessment factors’ weights was conducted. The comparative consequences indicate that TFN-AHP can better assess the flooding risk and can be successfully applied to urban development policy.
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Anitha, J., P. V. G. D. Prasad Reddy, and M. S. Prasad Babu. "An Approach for Summarizing Hindi Text Through a Hybrid Fuzzy Neural Network Algorithm." Journal of Information & Knowledge Management 13, no. 04 (December 2014): 1450036. http://dx.doi.org/10.1142/s0219649214500361.
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Text summarization is one of the most discussed topic in the field in information exchange and retrieval. Recently, the need for local language based text summarization methods are increasing. In this paper, a method for text summarization in Hindi language is plotted with help of extraction methods. The proposed approach is uses three major algorithms, fuzzy classifier, neural network and global search optimization (GSO). The fuzzy classifier and neural network are used for generating sentence score. The GSO algorithm is used with the neural network, in order to optimize the weights in the neural network. A hybrid score is generated from fuzzy method and neural network for each input sentences. Finally, based on the hybrid score from fuzzy classifier and neural network, the summary of the given input records are generated. An experimental analysis of the proposed approach will subjected based on the evaluation parameters precision, recall. Later on experimental analysis are conducted on the proposed approach in order to evaluate the performance. According to the experimental analysis, the proposed approach achieved an average precision rate 0.90 and average recall rate of 0.88 for compression rate 20%. The comparative analysis also provided reasonable results to prove the efficiency of the proposed approach.
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Zamali Tarmudi, Shamsatun Nahar Ahmad, Nurul ‘Aini Harun, and Nor Siti Khadijah Arunah. "Sustainable Quality of Life Criteria for Ledang National Park Community: Fuzzy Approach." Insight Journal 8 (April 7, 2021): 120–37. http://dx.doi.org/10.24191/ij.v8i0.113.
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Recently, there is a rising concern among researchers regarding the issue of sustainable criteria for quality of life (QoL). As it gains more interest, the issue becomes highly debated worldwide especially with regards to the national park community area. The aim of this study is to assess the identified sustainable criteria for QoL, particularly for the surrounding community of Ledang National Park (LNP) using the fuzzy Analytical Network Process (ANP). The fuzzy ANP was employed based on graded mean integration of representation and canonical representation of multiple operations to derive both local and global weights. To show the feasibility of the proposed method, three decision makers (DMs) were identified from the relevant agencies to assess three main criteria using linguistic evaluation via pairwise comparison process. In addition, nine sub-criteria were also investigated and analysed thoroughly using six steps of the fuzzy ANP towards achieving the sustainable criteria for QoL assessment. Based on the numerical analysis, it was found that the sub-criterion health(c32) has the highest global weight with a score of 0.184, which indicates that this sub-criterion is the biggest contributor to achieve the sustainability of QoL. The results also revealed the overall total score of 73.14%, thus placing LNP in the ‘moderately sustainable’ category. In the future, the entire investigated sub-criteria are suggested to be maintained and used to measure the sustainability of QoL. The findings from this study can be used to guide and assist the relevant authorities for future development planning in studied areas.
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Chen, Wei-Ling, Chung-Dann Kan, Chia-Hung Lin, Ying-Shin Chen, and Yi-Chen Mai. "Hypervolemia screening in predialysis healthcare for hemodialysis patients using fuzzy color reason analysis." International Journal of Distributed Sensor Networks 13, no. 1 (January 2017): 155014771668509. http://dx.doi.org/10.1177/1550147716685090.
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Maintaining adequate dry weight and fluid volume balance is an important issue for dialysis patients. Malnutrition and sodium intake are the primary factors that cause fluid volume imbalance and changes in body weights. Inadequate dry weight control results in higher levels of blood pressures and is related to various complications, such as volume overload, hypertension, congestive symptoms, and cardiovascular diseases. Moreover, inadequate fluid removal provokes hypotension during dialysis treatment. Thus, we propose an early warning tool based on fuzzy color reason analysis in predialysis healthcare for hypervolemia screening. The anthropometric method is a rapid, non-invasive, and simple technique for estimating the total body water. In this study, Watson standard formula is employed to estimate cross-sectional standard of total body water with the patient characteristics, including gender, age, height, and weight. In contrast to the experienced anthropometric formulas, Watson formula has less than 2% of margin errors and provides a criterion as a reference manner to estimate the total body water in patient’s normal dry weight. In addition, inadequate dry weight and total body water controls will lead to higher blood pressures. The systolic blood pressure is also an indicator to evaluate pre-hypertension of 120–139 mmHg and hypertension of greater than or equal to 140 mmHg. Therefore, the levels of two indicators, total body water and systolic blood pressure, are parameterized with fuzzy membership grades to describe the normal and the specific ranges of undervolemia and hypervolemia. A color reason analysis utilizes a hue–saturation–value color model to design a color perceptual manner for separating normal condition from hypervolemia or undervolemia. Normalized hue angle and saturation value provide a promising visual representation with color codes to realize the patients’ diagnosis. Dialysis patients with hypertension demonstrated that the proposed model can be used in clinical applications. In addition, a healthcare chair is carried out to measure blood pressure and weight in predialysis. The proposed assistant tool integrates an electronic pressure monitor and an electronic weight monitor, and fuzzy color reason analysis is also intended to be established in an intelligent vehicle via a WiFi wireless local area network for cloud computing.
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Wang, Jian Wei, and Jian Ming Zhang. "An Improved Adaptive Evolutionary Algorithm for Multi-Objective Optimization." Applied Mechanics and Materials 303-306 (February 2013): 1494–500. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1494.
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Aiming at effectively overcoming the disadvantages of traditional evolutionary algorithm which converge slowly and easily run into local extremism, an improved adaptive evolutionary algorithms is proposed. Firstly, in order to choose the optimal objective fitness value from the population in every generation, the absolute and relative fitness are defined. Secondly, fuzzy technique is adopted to adjust the weights of objective functions, crossover probability, mutation probability, crossover positions and mutation positions during the iterative process. Finally, three classical test functions are given to illustrate the validity of improved adaptive evolutionary algorithm, simulation results show that the diversity and practicability of the optimal solution set are better by using the proposed method than other multi-objective optimization methods.
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Hosseini-Golgoo, Seyed Mohsen, H. Bozorgi, A. Saberkari, and S. Rahbarpour. "Analyzing the Response of a Temperature Modulated Tin-Oxide Gas Sensor Using Local Linear Neuro-Fuzzy Model for Gas Detection." Key Engineering Materials 543 (March 2013): 129–32. http://dx.doi.org/10.4028/www.scientific.net/kem.543.129.
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A resistive gas sensor (RGS) under temperature modulation regime is considered as a system for gas detection. Five target gases including Methanol, Ethanol, 2-Propanol, 1-Butanol, and Hydrogen each at 11 concentration levels, were selected for diagnosis using a single commercial gas sensor. For modulating the sensor, a staircase containing five voltage steps each with 20s plateau is applied to micro-heater of the sensor. This, in turn, alters both the temperature and the resistance profiles of the sensing layer which are considered as the input and the output of the defined system, respectively. In this way, five systems corresponding to five steps of the system input can be distinguished. Next, each system under the influence of the examined target gases is modeled with neuro-fuzzy network. Local linear model tree (LOLIMOT) used as learning algorithm of the systems and weights of the trained networks utilized as the features of the sensor in presence of target gas. Mapping these feature vectors using linear discriminant analysis showed successful classification of all target gases.
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Zhang, Wen Dan, Zhi Xia Jiang, Yan Zhong Li, and Su Zhang. "Application of the Multi-Level Fuzzy Comprehensive Evaluation Method to Software Companies’ Choice of Competitive Strategies." Advanced Materials Research 989-994 (July 2014): 5228–31. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.5228.
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This paper attempts to employ the matrix of Strengths, Weaknesses, Opportunities, Threats (SWOT) to establish an evaluative index system for software enterprises in Jilin Province. The SWOT analysis is performed in combination with the Analytic Hierarchy Process (AHP) to determine the relative weights of various criteria in the analytic hierarchy as they are related to the overarching goal. The ultimate assessment of the developmental status of those provincial IT enterprises from the local (departmental/segmental) to the global (corporate/industrial) level will then be derived by applying a fuzzy comprehensive evaluation matrix in accordance with the principle of maximum associations. This approach promises to overcome drawbacks commonly found in conventional hierarchical analyses, where frequent adjustments and tests are often required to make results from judgmental matrix consistent. Examples with concrete analytic results have shown that this innovative, synthetic method is of promising guiding significance to corporate strategic formulations.
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Tu, Shiyu, and Guangwei Huang. "Study of Urban Community Emergency Management in Huizhou, China." IOP Conference Series: Earth and Environmental Science 973, no. 1 (January 1, 2022): 012015. http://dx.doi.org/10.1088/1755-1315/973/1/012015.
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Abstract This paper focuses on the study of urban community emergency management in Huizhou, China. An index system for urban community emergency management evaluation which has two levels is established in the study. The index system consists of six primary indicators and twenty-two secondary indicators. The weights of the indicators are determined by using the Analytic Hierarchy Process (AHP). The established evaluation system is used to evaluate the emergency management ability of a community called Xuefu Community in Huizhou, China. Purpose sampling is used in the evaluation. A questionnaire with twenty-three questions is designed and all community staff are invited to answer the questionnaire. Fuzzy Comprehensive Evaluation method is adopted to calculate the scores and resulted are analysed. Suggestions are given to the Xuefu Community and local government based on the analysis.
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Zhou, Lian, and Fuyuan Xiao. "DCM: D Number Extended Cognitive Map. Application on Location Selection in SCM." INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL 14, no. 5 (November 17, 2019): 753. http://dx.doi.org/10.15837/ijccc.2019.5.3585.
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Offshore outsourcing is a widely used management technique for performing business functions with the aim of reducing labor and transportation costs. The selection of locations has a significant influence on the supply chain’s resilience and qualities, but the influence of multiple external factors on the supply chain’s performance in local places in a complex and uncertain environment has not been examined. In this study, we investigated the influence of external factors in a highly uncertain and complicated situation in which relationships between external factors and supply chain resilience are complicated. Furthermore, we proposed a novel model to select locations from a comprehensive perspective. Specifically, the fuzzy cognitive map (FCM) is utilized to simulate the dynamic influence process where the adjacency is aggregated by D numbers. The weights of different resilience capabilities are considered from the perspective of maximizing benefits by using the decision-making trial and evaluation laboratory-analytic network processes (DEMATEL-ANP) model. By comparing the distance to the ideal solutions, we selected the best alternative location. Our results differ from the general case, which reveals that the weights of different capabilities influence selections.
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Кравцов, Дмитрий, Dmitriy Kravtsov, Евгений Леонов, and Evgeniy Leonov. "MODEL OF LINGUISTIC ONTOLOGY WITH FUZZY SEMANTIC RELATIONS GENERATED ON BASIS OF WIKIPEDIA." Bulletin of Bryansk state technical university 2016, no. 1 (March 31, 2016): 134–39. http://dx.doi.org/10.12737/18304.
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The application without knowledge of an ontological type allows updating considerably quality of problem solutions in natural language processing. A number of researchers use Wikipedia as a basis for the formation of such resources. This paper reports the formalization method of Wikipedia structures and linguistic ontology used in the developed by the authors system of the linguistic ontology formation a specified subject field from Wikipedia. The papers and references connecting them serve a purpose for formation of a weighted graph of ontology to the graph nodes correspond notions, and to the ribs of graph – fuzzy semantic relations between them. The references obtain different weights depending on entering this or that information unit on a page. By a graph of relations it is possible to estimate numerically the degree of semantic proximity of two arbitrary concepts. For this purpose it is possible to use different measures of semantic proximity. Recursive measures possess considerable computational complexity at insignificant improvement of quality in test problem solution in comparison with nonrecursive local measures of the Dice measure type that is unacceptable for the ontology large enough. From these considerations the Dice weighted measure is chosen as a basic one for the system under development.
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Lima, Abitante, Pons, and Senne. "A Spatial Fuzzy Multicriteria Analysis of Accessibility: A Case Study in Brazil." Sustainability 11, no. 12 (June 20, 2019): 3407. http://dx.doi.org/10.3390/su11123407.
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Accessibility is a well-established concept in sustainable transportation literature; however, it is often measured through accessibility models that are still being developed. This article presents an accessibility evaluation model that applies multiple attributes, fuzzy functions, and spatial analysis tools. The model determines indices that reflect an average level of attractiveness for each potential destination (deemed a location of interest—LI). Each destination has different weights based on its degree of importance. Moreover, the model was developed in two phases: The first considered cost–distance metrics, and the second incorporated ground friction factors. The application of the model provides great contribution to the region under study (Campos do Jordão, a city located in a mountainous region of the state of São Paulo), thus presenting some implications for sustainable urban planning and mobility policies, especially in segregated areas with mixed inhabitant populations between tourists and local residents. The results have shown that special attention should be paid to planning new school facilities and city transportation systems. Most of these services are currently concentrated in the city’s downtown area, making access to urban facilities inefficient and unfair. Using the results in urban projects, the allocation of future urban facilities or the reallocation of current urban facilities contributes to reduced impacts on urban mobility caused by individual motorized transportation in daily activities.
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Mishra, Satyasis, Premananda Sahu, and Manas Ranjan Senapati. "APSO based LLRBFNN Model and Enhanced Fuzzy C Means algorithm for Brain Tumor Detection and Classification from Magnetic Resonance Image." International Journal of Engineering & Technology 8, no. 4 (November 5, 2019): 490. http://dx.doi.org/10.14419/ijet.v8i4.20445.
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This paper presents a novel APSO (Accelerated Particle Swarm Optimization) Predicated LLRBFNN (Local Linear Radial Basis Function Neural Network) model for automatic encephalon tumor detection and classification. The enhanced fuzzy c means algorithm (EnFCM) has been proposed for image segmentation and the GLCM (Gray Level Co-occurrence Matrix) technique for feature extraction from MR (Magnetic Resonance) images. This research work aims to utilize the hybrid models and algorithms for relegation and segmentation of encephalon tumors from the MR images. The extracted features have been alimented as input to the proposed APSO predicated LLRBFNN model for relegation of benign and malignant tumors. In this research work the proposed LLRBFNN model weights are optimized by utilizing APSO training which will provide unique solution to mitigation the hectic task of radiologist from manual detection of encephalon tumors from MR Images. Additionally the centers of the LLRBFNN model are culled by the Enhanced Fuzzy C Means algorithm and updated by the APSO algorithm. The results of proposed PSO predicated LLRBFNN model has been compared with PSO-LLRBFNN model, APSO-RBFNN and PSO-RBFNN model and the comparison results are presented. The experimental results obtained from the proposed model shows better relegation results as compared to the subsisting models proposed anteriorly.
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Wang, Bin, Linghui Xia, Dongmei Song, Zhongwei Li, and Ning Wang. "A Two-Round Weight Voting Strategy-Based Ensemble Learning Method for Sea Ice Classification of Sentinel-1 Imagery." Remote Sensing 13, no. 19 (October 2, 2021): 3945. http://dx.doi.org/10.3390/rs13193945.
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Sea ice information in the Arctic region is essential for climatic change monitoring and ship navigation. Although many sea ice classification methods have been put forward, the accuracy and usability of classification systems can still be improved. In this paper, a two-round weight voting strategy-based ensemble learning method is proposed for refining sea ice classification. The proposed method includes three main steps. (1) The preferable features of sea ice are constituted by polarization features (HH, HV, HH/HV) and the top six GLCM-derived texture features via a random forest. (2) The initial classification maps can then be generated by an ensemble learning method, which includes six base classifiers (NB, DT, KNN, LR, ANN, and SVM). The tuned voting weights by a genetic algorithm are employed to obtain the category score matrix and, further, the first coarse classification result. (3) Some pixels may be misclassified due to their corresponding numerically close score value. By introducing an experiential score threshold, each pixel is identified as a fuzzy or an explicit pixel. The fuzzy pixels can then be further rectified based on the local similarity of the neighboring explicit pixels, thereby yielding the final precise classification result. The proposed method was examined on 18 Sentinel-1 EW images, which were captured in the Northeast Passage from November 2019 to April 2020. The experiments show that the proposed method can effectively maintain the edge profile of sea ice and restrain noise from SAR. It is superior to the current mainstream ensemble learning algorithms with the overall accuracy reaching 97%. The main contribution of this study is proposing a superior weight voting strategy in the ensemble learning method for sea ice classification of Sentinel-1 imagery, which is of great significance for guiding secure ship navigation and ice hazard forecasting in winter.
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Wang, Shen, Sun, Zhang, Zhang, Jia, and Zhang. "A Pricing Model for Groundwater Rights in Ningxia, China Based on the Fuzzy Mathematical Model." International Journal of Environmental Research and Public Health 16, no. 12 (June 19, 2019): 2176. http://dx.doi.org/10.3390/ijerph16122176.
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To reduce groundwater overexploitation and alleviate water shortages, market mechanisms are introduced to allocate water rights. Scientific and reasonable pricing of groundwater rights is key to ensuring the effectiveness of the groundwater market. Because of the complexity and uncertainty of water resources, this study calculates the price of groundwater rights based on the value of water resources with an evaluation indicator system. The system includes 14 indicators developed with a fuzzy mathematics model addressing three dimensions: environment, society, and economy. The weights of the indicators are determined through the analytic network process (ANP) and the entropy method. The results show that the price of groundwater rights in Ningxia, China increased from 5.11 yuan/m3 to 5.73 yuan/m3 between 2013 and 2017; this means the price was basically stable, with a slight increase. The ratio of residents’ water fee expenditures to real disposable income also remained essentially stable, fluctuating around 0.37%, far below the normal level. These data demonstrated that the current regional water price policy does not reflect the true value of groundwater resources; there is room to increase urban water prices. Local governments need speed up water price system reforms and adopt water rights systems to optimize water resource allocations.
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Jahangiri, Katayoun, Hasti Borgheipour, Saeid Bahramzadeh Gendeshmin, Amirhossein Matin, and Ghazaleh Monazami Tehrani. "Site selection criteria for temporary sheltering in urban environment." International Journal of Disaster Resilience in the Built Environment 11, no. 1 (November 11, 2019): 58–70. http://dx.doi.org/10.1108/ijdrbe-06-2018-0025.
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Purpose The inevitable occurrence of natural disasters and crisis arising from them causes a lot of losses globally, particularly in disaster-prone countries such as Iran. One of the main issues considered by organizations involved in crisis management is the selection of suitable sites for temporary sheltering for disaster victims. This study aims to choose safe places to establish temporary sheltering in urban environment. Design/methodology/approach Initially, relevant factors are identified by reviewing literature and through consultation with disaster experts. Next, the important layers were collected and analytical hierarchy process was used to assess the criteria weights based on their effectiveness on selection of safe sites for temporary sheltering. Finally, for integrating layers of factors, overlay and fuzzy models were used in Geographic Information System (GIS) environment, and subsequently, a proper map was prepared and suitable areas were identified. Findings 7 main criteria and 19 sub-criteria were selected to provide safe places for temporary sheltering. The results of fuzzy model in this study provide more accurate and limited safe areas for temporary sheltering when compared to index overlay model. Originality/value The results of this study will help decision-makers and local and regional managers to reduce the vulnerability of at-risk communities in urban environments. Moreover, choosing appropriate places for temporary shelters would help build community disaster resilience according to these criteria.
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Wu, Tingting, and Minlin Wen. "An English Teaching Ability Assessment Method Based on Fuzzy Mean-Shift Clustering." Scientific Programming 2022 (May 4, 2022): 1–11. http://dx.doi.org/10.1155/2022/4219249.
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This paper presents an in-depth study and analysis of the assessment of English teaching ability using the algorithm of fuzzy mean-shift clustering. The paper proposes an automatic scoring model for Chinese-English sentence-level interpretation based on semantic scoring. The automatic scoring model calculates candidates’ Chinese-English sentence interpretation scores by fusing the feature parameters at both the phonological and content levels. Adaptive weights are introduced to fuse the current pixel and the neighborhood mean with adaptive weighting, and the weighted entropy constraint term is embedded in the clustering objective function to solve the selection problem of the weighting parameters. Finally, the graphical fuzzy division information of the neighboring pixels is used to construct the local spatial information constraint term of the current pixel, and the graphical fuzzy division term of the current pixel is adjusted to correct the clustering center obtained from the iteration. Fluency is selected as the feature scoring parameter at the speech level, and the automatic scoring model directly scores the fluency features of the candidates’ recordings; two feature scoring parameters, keywords, and sentence semantics are selected at the content level, and the content features are scored after converting the candidates’ recordings to text by manual conversion. The zero-energy product method is used to extract speech features to calculate fluency feature scores; the semantic scoring model introduced in this paper is used to calculate keyword and sentence semantic feature scores; finally, the random forest algorithm is used to fuse the above three feature scoring parameters to obtain the total quality score of Chinese-English interpretation. Considering the correlation of neighborhood pixel affiliation, the KL scatter and affiliation space information is used to supervise the current pixel affiliation, to further improve the segmentation accuracy of the algorithm; finally, segmentation tests are conducted on synthetic images, medical images, and remote sensing images. The results show that the proposed algorithm has a stronger noise suppression ability and can obtain more satisfactory segmentation results than other robust fuzzy clustering algorithms.
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Zhao, Wenbin, Changlai Xiao, Yunxu Chai, Xiaoya Feng, Xiujuan Liang, and Zhang Fang. "Application of a New Improved Weighting Method, ESO Method Combined with Fuzzy Synthetic Method, in Water Quality Evaluation of Chagan Lake." Water 13, no. 10 (May 20, 2021): 1424. http://dx.doi.org/10.3390/w13101424.
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The existing weighting methods mainly comprise subjective and objective weighting and have a certain degree of subjectivity, with certain requirements for the professional ability of the users and unstable results. Therefore, an improved weighting method based on the entropy weight, over-standard multiple, and single-factor evaluation methods, referred to as the ESO method, is proposed. The advantages and advancements of the ESO method are demonstrated in this study by combining it with the fuzzy synthetic evaluation method to evaluate the water quality of Chagan Lake wetland from 2007 to 2016. The main conclusions of this study are as follows: 1. The ESO method has more comprehensive consideration factors, lower requirements for the professional ability of users, and more stable weighting results than the traditional weighting method. Therefore, it is highly suitable for beginners and frontline staff who are not professionally qualified and cannot accurately conduct subjective weighting. Meanwhile, owing to the amendment rule and emphasis on the local weight of the sample in the ESO method, it is applicable to time-series samples. 2. The ESO method better allocates the amendment weights to indicators with a higher degree of pollution; thus, the final comprehensive evaluation results are relatively conservative. However, in contrast to the single-factor evaluation, the conservatism of ESO method is the result of the comprehensive effect of all samples; thus, the conservative result of the ESO method is more reasonable. 3. The water quality of Chagan Lake in 2009 and 2015 was class IV, which did not meet the standard, while that in remaining the eight years was class III, which met the requirements of the national 13th Five-Year Plan. The results of this study can provide a new approach to weighting calculation methods and a basis for the protection and treatment of the ecological environment of the Chagan Lake wetland.
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Shoukry, Samir N., David R. Martinelli, and Jennifer A. Reigle. "Universal Pavement Distress Evaluator Based on Fuzzy Sets." Transportation Research Record: Journal of the Transportation Research Board 1592, no. 1 (January 1997): 180–86. http://dx.doi.org/10.3141/1592-20.
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Setting priorities for pavement maintenance and rehabilitation depends on the availability of a universal scale for assessing the condition of every element in the network. The condition of a pavement section has traditionally been assessed by several condition indexes. The present serviceability index (PSI) is one common evaluator used to describe the functional condition with respect to ride quality. Pavement condition index is another index commonly used to describe the extent of distress on a pavement section. During the decision-making process, both classes of indexes are needed to evaluate the overall status of a pavement section in comparison to other sections in the network. Traditionally, regression techniques were used for the development of functions that relate condition indexes to the information recorded in the pavement management database. This approach produces mathematical functions that are limited to a particular database. The functions so developed may also suffer from inaccuracies due to errors in data collection and recording. There is a need for a more generalized approach for the evaluation of pavement conditions to enable efficient management of large transportation networks. The development of a universal measure capable of formally assessing the condition of a pavement section within the universe of pavement conditions is described. This is accomplished by the fusion of a set of fuzzy membership functions that describe different parameters in the database with the perception of each parameter’s significance. The model output is the fuzzy distress index (FDI), which combines the extent of structural distress with traditional performance parameters such as roughness to describe the overall status of the pavement section. The behavior of FDI over time is examined for a random sample of pavement sections and is compared with trends in the corresponding PSI values (PSI was used only because it was readily available in the database). The results indicate that the flexible, universal FDI is a consistent and accurate measure of the overall pavement condition. The set of generated membership functions describing the different extents of every distress type can be easily standardized over the 50 states, allowing the model to be implemented on any pavement at any location. Also, the parameter weights used in the assessment may be easily adjusted (increased or decreased) to reflect changes in maintenance policies or budget availability at the local, state, or national decision-making level. Moreover, the concept allows for the omission of any number of parameters that might not be available in a particular pavement management database.
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Damjanović, Milanko, Željko Stević, Dragan Stanimirović, Ilija Tanackov, and Dragan Marinković. "IMPACT OF THE NUMBER OF VEHICLES ON TRAFFIC SAFETY: MULTIPHASE MODELING." Facta Universitatis, Series: Mechanical Engineering 20, no. 1 (April 8, 2022): 177. http://dx.doi.org/10.22190/fume220215012d.
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Traffic safety is one of the key issues nowadays, given the fact that a large number of people lose their lives in traffic accidents every day. There are various influential factors in the occurrence of traffic accidents, the number of vehicles being one of them. This paper assesses the traffic safety in Montenegro in the period 1998-2020 by applying the multiphase modeling with a purpose to obtain comparative results which enable implementation of adequate strategies. A total of six scenarios were formed with two inputs and two outputs in a DEA (Data Envelopment Analysis) model, with the number of registered vehicles per year being an input in all scenarios. In addition, as inputs, the scenarios included AADT (Annual Average Daily Traffic), passengers in road transport, passenger-km by road transport, goods transported by road, tone-km by road, and passengers in local transport. The number of traffic accidents with casualties, the number of traffic accidents with material damage, the number of fatal cases and the number of injured persons, depending on a scenario, were observed as outputs. After the DEA model, IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) was applied to determine the weights of inputs and outputs, while the final state of traffic safety by years was determined using the MARCOS (Measurement of alternatives and ranking according to COmpromise solution) method.
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Nuriyev, Aziz M. "Fuzzy MCDM models for selection of the tourism development site: the case of Azerbaijan." F1000Research 11 (March 14, 2022): 310. http://dx.doi.org/10.12688/f1000research.109709.1.
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Background: One of the vital issues in promoting the sustainable tourism industry in developing countries, including Azerbaijan, is the well-grounded selection of tourism sites. Applying traditional approaches as a solution to this task, does not provide a relevant result in all cases in these countries due to local specifics of the tourism, the incompleteness of statistical data, the high-level uncertainty of the internal and external environment, and the questionable reliability of the available information. Methods: Since the statistical data are limited, and conventional formalization tools used for uncertainty description do not consider the reliability degree of the data, it is suggested to make decisions based on the Z-extension of fuzzy logic. A Delphi panel with the expert group is conducted to obtain the information required for the model development. Fuzzy Z-information-based TOPSIS and PROMETHEE methods are applied for the problem solution. Within these approaches Z-number-based procedures of the decision matrix normalization, defining the distance between solutions and the preference function, and swing weights determination are realized. Direct computations with Z-numbers are implemented. Results: By applying Z-number-based multi-criteria decision-making methods, five potential regions of Azerbaijan have been evaluated for six criteria. The criteria reflect government policy to the development of the regions, economical, geographical, environmental factors, and infrastructure of the locations. Derived solutions are comparable in sense of sites ranking, and similar results were obtained using both methods. Direct calculations allow obtaining results based on the linguistic Z-evaluations of experts without distorting transformations. Conclusion: The managerial decision-making problems in the tourism sector, raised due to the aforementioned barriers, can be successfully resolved by applying Z-number-based multi-criteria approaches. The obtained results allow increasing a range of the decision-making tasks under a high degree of uncertainty to be solved for sustainable development studies and other areas.
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Wang, Yijie, Linzao Hou, Mian Li, and Ruixiang Zheng. "A Novel Fire Risk Assessment Approach for Large-Scale Commercial and High-Rise Buildings Based on Fuzzy Analytic Hierarchy Process (FAHP) and Coupling Revision." International Journal of Environmental Research and Public Health 18, no. 13 (July 5, 2021): 7187. http://dx.doi.org/10.3390/ijerph18137187.
Full textAbstract:
In recent years, much more emphasis than before has been placed on fire safety regulations by the local and central authorities of China, which makes fire risk assessments more important. In this paper we propose a new fire risk assessment approach for large-scale commercial and high-rise buildings that aims to evaluate the performances of their fire safety systems; this should improve the fire risk management and public safety in those buildings. According to the features of large-scale commercial and high-rise buildings, a fire-risk indexing system was built, and based on it we established a scientific fire risk evaluation system. To this end, the fuzzy analytic hierarchy process (FAHP) was used to assign a reasonable weight to each fire risk factor in the evaluation system. In addition, we revised the original scores by analyzing the coupling relationships among the fire risk factors. To validate our system, we selected 11 buildings in Shandong province and collected their fire safety data. Then, we calculated the final scores for the fire safety management of those buildings, and the results show that: (1) our fire risk evaluation system can assign reasonable weights; (2) the proposed evaluation system is comprehensive and has strong interpretability, since it exploits the coupling relationships among the risk factors. The novelty of the proposed approach lies in that it integrates opinions from multiple experts and utilizes coupling relationships among the factors. Further, the feedback from the approach can find not only the weaknesses in fire risk management, but also the potential causes of fires. As a result, the feedback from our assessment can assist the safety chiefs and inspectors with improving fire risk management.
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Yu, Haiying, Chuan Liang, Ping Li, Kaijie Niu, Faxing Du, Junhu Shao, and Yuyong Liu. "Evaluation of Waterlogging Risk in an Urban Subway Station." Advances in Civil Engineering 2019 (October 7, 2019): 1–12. http://dx.doi.org/10.1155/2019/5393171.
Full textAbstract:
Most cities in the world have extensive underground facilities, including public transport and commercial facilities, such as shopping malls, subways, and parks. Safety of underground space has been an important aspect of urban safety. Due to the influences of global climate change and human activities, waterlogging disasters in cities are becoming increasingly serious and underground facilities easily suffer from waterlogging disasters. Moreover, waterlogging disasters in cities can cause different degrees of damage to construction period and operating period of urban subway stations. By using the optimal combination weighting method combining subjective and objective weighting, this study assigned weights to evaluation factors. Based on this, a fuzzy synthetic evaluation model for waterlogging risk in the construction and operating periods of urban subway stations was established. Furthermore, the model was applied and verified to be effective in Chengdu Metro Line 4, Sichuan province, China, and the evaluation results coincided with the actual situation. The model in the study provides a new idea for evaluating waterlogging risks in urban subway stations, and these results can offer important information for local government to strengthen management on waterlogging risks.
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JAGANNATHAN, RUPA, SANJA PETROVIC, ANGELA MCKENNA, and LOUISE NEWTON. "A NOVEL TWO PHASE RETRIEVAL MECHANISM FOR A CLINICAL CASE BASED REASONING SYSTEM FOR RADIOTHERAPY TREATMENT PLANNING." International Journal on Artificial Intelligence Tools 21, no. 04 (August 2012): 1240017. http://dx.doi.org/10.1142/s0218213012400179.
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This paper presents a decision support system for radiotherapy treatment planning for head, neck and brain cancer. The aim of a treatment plan is to apply radiation to kill tumor cells, while minimizing the damage to healthy tissue and critical organs. Since treatment planning is a complex decision making process that relies heavily on the subjective experience of clinicians, we propose the use of case-based reasoning (CBR), in which problems are solved based on the solutions of similar past problems. This paper focuses on the case retrieval process of a CBR system. The attributes, which describe the cases, are selected by assessing their effect on the performance of the CBR system. We have developed a context sensitive local weighting scheme that assigns weights to attributes based on their value and the values of other attributes in the target case. A novel two phase retrieval mechanism is developed, in which each phase is optimized to retrieve a particular part of the solution. We also present an original use of fuzzy logic in order to represent nonlinearity in the similarity measure. Experiments, which evaluate the similarity measure using real brain cancer patient cases, show promising results.