A full factorial experiment is performed for the conventional dry drilling of cFRP with spindle speed, feed rate and point angle as drilling parameters, response variables are thrust force and exit-delamination. Artif...
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A full factorial experiment is performed for the conventional dry drilling of cFRP with spindle speed, feed rate and point angle as drilling parameters, response variables are thrust force and exit-delamination. Artificial neural network (ANN) is developed to express thrust force and delamination factor as a function of drilling parameters. Multi-objective optimization of drilling parameters is accomplished based on Non-dominated Sorting Geneticalgorithm (NSGA-II) with thrust force, delamination factor and material removal rate as optimization objectives, delamination factor also serves as a constraint. The Pareto front of drilling response variables determined by NSGA-II consists of a large number of non-dominated solutions. In order to facilitate the experimental verification of optimization results, fuzzy c-means clustering algorithm is used to narrow down the solutions on the front to several representative ones. conformation tests are conducted and results show that the representative solutions can give satisfactory performance with achieving a trade-off among thrust force, exit-delamination and material removal rate.
In this paper we developed a modified Hidden Markov Model (HMM) to analyze the raw nanopore experimental data. Traditionally, prior to further analysis the measured nanopore data must be pre-filtered, but the filterin...
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In this paper we developed a modified Hidden Markov Model (HMM) to analyze the raw nanopore experimental data. Traditionally, prior to further analysis the measured nanopore data must be pre-filtered, but the filtering usually distorts the waveform of the blockage current, especially for rapid translocations and bumping blockages. The HMM is known to be robust with respect to strong noise and thus suitable for processing the raw nanopore data, but its performance is susceptible to the setting of initial parameters. To overcome this problem, we use the fuzzyc-means (FcM) algorithm to initialize the HMM parameters in this work. Then we use the Viterbi training algorithm to optimize the HMM. Finally, both the simulated and experimental data analysis results are presented to show the effectiveness of the proposed method for detection of the nanopore current blockage events in analytical chemistry.
Due to the increasing deployment of Internet of Things in the mining industry, portable gas monitoring devices have been widely used. According to the character of time series of gas stream, the paper studies on mathe...
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ISBN:
(纸本)9781728135557
Due to the increasing deployment of Internet of Things in the mining industry, portable gas monitoring devices have been widely used. According to the character of time series of gas stream, the paper studies on mathematics analysis method of time series similarity based on pattern beam and pattern set. combining with short-time stationary of gas data, the feature selection method of short-time gas data based on sliding time window is proposed. On the basis of the fuzzy c-means clustering algorithm, short-time gas stream in the fuzzy c-means clustering algorithm is put forward to analyze the convergence effects of the data of gas stream based on binary statistic and multivariate statistic, which provides qualified data that available for analysis and calculation for data correction of gas sensors afterward.
Adaptive E-learning platforms provide personalized learning process relying mainly on learning styles. The traditional approach to find learning styles depends on asking learners to self-evaluate their own attitudes a...
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Adaptive E-learning platforms provide personalized learning process relying mainly on learning styles. The traditional approach to find learning styles depends on asking learners to self-evaluate their own attitudes and behaviors through surveys and questionnaires. This approach presents several weaknesses including the lack of self-awareness of learners of their own preferences. Furthermore, the vast majority of learners experience boredom when they are asked to fill out the corresponding questionnaire. Besides that, traditional approach assumes that learning styles are fixed, and cannot change over time. In this paper, we propose a generic approach for detecting learning styles automatically according to a given learning styles model. In fact, our approach does not depend on a specific LSM. This work consists of two major steps. First, we extract learning sequences from learners log files using web usage mining techniques. Second, we classify the extracted learners' sequences according to a specific learning style model using clusteringalgorithms. To perform our approach we use Felder-Silverman Model as LSM and fuzzyc-means as a clusteringalgorithm. We have conducted an experimental study using a real-world dataset. The obtained results show that our approach outperforms traditional approach and provides promising results.
In order to analysis the output dynamiccharacteristics of dc offshore wind farm, a multimachine representation dynamic equivalent method based on an improved fuzzyc-means (IFcM) clusteringalgorithm is proposed. Fir...
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ISBN:
(纸本)9781728116754
In order to analysis the output dynamiccharacteristics of dc offshore wind farm, a multimachine representation dynamic equivalent method based on an improved fuzzyc-means (IFcM) clusteringalgorithm is proposed. First, characteristic variables which can characterize the dynamiccharacteristics of the input and state performance are researched. Second, an IFcM clusteringalgorithm is first put forward. Third, the dc offshore wind generators (DcOWGs) are divided into many groups by analyzing the characteristic variables data with the IFcM. Finally, DcOWGs in the same group are equivalent as one DcOWG to analyze the dynamiccharacteristics. Simulation results from MATLAB/SIMULINK verify the theoretical analysis.
It is really important to diagnose jaw tumor in its early stages to improve its prognosis. A differential diagnosis could be performed using X-ray images;therefore, accurate and fully automatic jaw lesions image segme...
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It is really important to diagnose jaw tumor in its early stages to improve its prognosis. A differential diagnosis could be performed using X-ray images;therefore, accurate and fully automatic jaw lesions image segmentation is a challenging and essential task. The aim of this work was to develop a novel, fully automatic and effective method for jaw lesions in panoramic X-ray image segmentation. The hybrid fuzzyc-means and Neutrosophic approach is used for segmenting jaw image and detecting the jaw lesion region in panoramic X-ray images which may help in diagnosing jaw lesions. Area error metrics are used to assess the performance and efficiency of the proposed approach from different aspects. Both efficiency and accuracy are analyzed. Specificity, sensitivity and similarity analyses are conducted to assess the robustness of the proposed approach. comparing the proposed approach with the Hybrid Firefly algorithm with the fuzzyc-means, and the Artificial Bee colony with the fuzzyc-meansalgorithm, the proposed approach produces the most identical lesion region to the manual delineation by the Oral Pathologist and shows better performance (FP rate is 6.1%, TP rate is 90%, specificity rate is 0.9412, sensitivity rate is 0.9592 and similarity rate is 0.9471). (c) 2016 Ain Shams University.
Automotive image segmentation systems are becoming an important tool in the medical field for disease diagnosis. The white blood cell (WBc) segmentation is crucial, because it plays an important role in the determinat...
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Automotive image segmentation systems are becoming an important tool in the medical field for disease diagnosis. The white blood cell (WBc) segmentation is crucial, because it plays an important role in the determination of the diseases and helps experts to diagnose the blood disease disorders. The precise segmentation of the WBcs is quite challenging because of the complex contents in the bone marrow smears. In this paper, a novel neural network (NN) classifier is proposed for the classification of the bone marrow WBcs. The proposed NN classifier integrates the fractional gravitation search (FGS) algorithm for updating the weight in the radial basis function mapping for the classification of the WBc based on the cell nucleus feature. The experimentation of the proposed FGS-RBNN classifier is carried on the images collected from the publically available dataset. The performance of the proposed methodology is evaluated over the existing classifier approaches using the measures accuracy, sensitivity, and specificity. The results show that the classification using the nucleus features alone can be utilized to achieve the classification with the better accuracy. Moreover, the classification performance of the proposed FGS-RBNN is better than the existing classifiers, and it is proved to be the efficacious classifier with a classification accuracy of 95%.
In this paper, it is shown that accurate load forecasts are vital for short, medium and long-term operations. The energy load forecast has its impact on different outcomes and decisions for power generation companies....
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ISBN:
(纸本)9781450363921
In this paper, it is shown that accurate load forecasts are vital for short, medium and long-term operations. The energy load forecast has its impact on different outcomes and decisions for power generation companies. It also has its influence on electricity market prices. The purpose of this research is to develop an energy load forecasting model to predict future electricity loads for energy load management. The forecasting model is based on a straightforward sequential methodology by implementing subtractive clusteringalgorithm, fuzzy c-means clustering algorithm and eventually an adaptive Neuro-fuzzy inference system architecture for generating the best fuzzy inference system using historical energy load data. In addition, the influence of different weather factors on energy loads such as dry-bulb temperature is counted in.
In order to make the RBF hidden layer centres being established more adaptively and avoid the blindness, this paper proposes a fusion algorithm in order to optimize the parameters of the RBF neural network used in rec...
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In order to make the RBF hidden layer centres being established more adaptively and avoid the blindness, this paper proposes a fusion algorithm in order to optimize the parameters of the RBF neural network used in recognizing the state of coal flotation. Firstly, in the optimization algorithm, the improved immune algorithm was used to determine the center position and the number of hidden layer of RBF neural network. Before this, the immune algorithm has been improved in several aspects, such as the initial population selection algorithm and the method for segment selection of affinity thresholds. In addition, the antibody removal mechanism, antibody immune mechanism and antibody concentration regulation principle had also been added in immune algorithm. Secondly, in virtue of combining a fuzzy c-means clustering algorithm, the centers of the hidden layer were optimized accurately. Through the sample verification, the RBF neural network obtained by the fusion algorithm was proved to have been improved significantly in the accuracy of identifying the coal flotation state and has better generalization ability.
To solve the problem of fuzzyclassification of manufacturing resources in a cloud manufacturing environment, a hybrid algorithm based on geneticalgorithm (GA), simulated annealing (SA) and fuzzyc-meansclustering a...
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To solve the problem of fuzzyclassification of manufacturing resources in a cloud manufacturing environment, a hybrid algorithm based on geneticalgorithm (GA), simulated annealing (SA) and fuzzy c-means clustering algorithm (FcM) is proposed. In this hybrid algorithm, classification is based on the processing feature and attributes of the manufacturing resource;the inner and outer layers of the nested loops are solving it, GA obtains the best classification number in the outer layer;the fitness function is constructed by fuzzyclusteringalgorithm (FcM), carrying out the selection, crossover and mutation operation and SA cooling operation. The final classification results are obtained in the inner layer. Using the hybrid algorithm to solve 45 kinds of manufacturing resources, the optimal classification number is 9 and the corresponding classification results are obtained, proving that the algorithm is effective.
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