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IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)

作者： Sommart, Thanapong Tuektaewskul, Vanisara Suksompong, Prapun Thammasat Univ Sch Informat Comp & Commun Technol Sirindhorn Int Inst Technol Pathum Thani Thailand

ISBN: (纸本)9798350333374

Blind System Identification (BSI) is a major pattern recognition task in many fields, including digital communication systems. Recently, many machine learning techniques have been applied to identify modulation types of signals solely by the receiver without prior knowledge of the transmission protocols. In this paper, we propose Automatic subtractive clustering algorithm Model (ASCAM), a novel hybrid approach between subtractive clustering algorithm (SCA) and deep neural network for not only classifying the order of Quadrature Amplitude Modulation (QAM) from the corrupted baseband constellations, but also estimating the original symbols' locations in their constellations. For this model, a deep neural network based on parallel convolution is used in conjunction with a negative straight-through estimator to formulate this problem as a supervised task, regardless of the non-differentiability of SCA. Our simulation demonstrates that the model works well for QAMs with Rayleigh fading and additive white Gaussian noise (AWGN), and achieves better performance than other existing methods. It also generalizes its feature extraction well, as it can maintain its performance even when some symbols in the observed constellations are not present.

关键词： Blind System Identification Quadrature Amplitude Modulation subtractive clustering algorithm Deep Neural Network Straight-Through Estimator

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Fuzzy forecasting based on linear combinations of independent variables, subtractive clustering algorithm and artificial bee colony algorithm

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INFORMATION SCIENCES 2019年 484卷 350-366页

作者： Zeng, Shouzhen Chen, Shyi-Ming Teng, Mario Orlando Ningbo Univ Sch Business Ningbo 315211 Zhejiang Peoples R China Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei Taiwan

In this paper, we propose a new clustering-based fuzzy time series (FTS) forecasting method based on linear combinations of independent variables, the subtractive clustering algorithm and the artificial bee colony (ABC) algorithm. The subtractive clustering algorithm is used to automatically generate clusters of historical training data and get the cluster center of each cluster. The ABC algorithm is applied to obtain the optimal neighborhood radiuses of the subtractive clustering algorithm to get the optimal cluster center of each cluster of the historical training data. Based on the obtained cluster center of each cluster, the weighted contribution of each cluster with respect to each historical training datum is calculated. Finally, the proposed method constructs the linear combinations of independent variables of the historical training data using this weighted contribution to forecast the historical testing data. The proposed method gets higher forecasting accuracy rates than the existing methods for forecasting the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the enrollments of the University of Alabama and the daily percentage of CO2. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Artificial bee colony algorithm Fuzzy time series subtractive clustering algorithm TAIEX

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Research on the subtractive clustering algorithm for mobile ad hoc network based on the Akaike information criterion

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2019年第9期15卷

作者： Banteng, Liu Yang, Haibo Chen, Qiuxia Wang, Zhangquan Zhejiang Shuren Univ Coll Informat Engn Hangzhou 310015 Zhejiang Peoples R China

Large and dense mobile ad hoc networks often meet scalability problems, the hierarchical structures are needed to achieve performance of network such as cluster control structure. clustering in mobile ad hoc networks is an organization method dividing the nodes in groups, which are managed by the nodes called cluster-heads. As far as we know, the difficulty of clustering algorithm lies in determining the number and positions of cluster-heads. In this article, the subtractive clustering algorithm based on the Akaike information criterion is proposed. First, Akaike information criterion is introduced to formulate the optimal number of the cluster-heads. Then, subtractive clustering algorithm is used in mobile ad hoc networks to get several feasible clustering schemes. Finally, the candidate schemes are evaluated by the index of minimum of the largest within-cluster distance variance to determine the optimal scheme. The results of simulation show that the performance of the proposed algorithm is superior to widely referenced clustering approach in terms of average cluster-head lifetime.

关键词： Mobile ad hoc networks subtractive clustering algorithm Akaike information criterion

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A deep semantic segmentation-based algorithm to segment crops and weeds in agronomic color images

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Information Processing in Agriculture 2022年第3期9卷 355-364页

作者： Sovi Guillaume Sodjinou Vahid Mohammadi Amadou Tidjani Sanda Mahama Pierre Gouton Institute of Mathematics and Physical Sciences University of Aboney CalaviDangboBenin ImViA UFR Sciences et TechniquesUniversite de BourgogneFranche-ComteDijonFrance

In precision agriculture,the accurate segmentation of crops and weeds in agronomic images has always been the center of *** methods have been proposed but still the clean and sharp segmentation of crops and weeds is a challenging issue for the images with a high presence of *** work proposes a segmentation method based on the combination of semantic segmentation and K-means algorithms for the segmenta-tion of crops and weeds in color *** images of two different databases were used for the segmentation *** the thresholding technique,everything except plants was removed from the ***,semantic segmentation was applied using U-net followed by the segmentation of crops and weeds using the K-means subtractive *** comparison of segmentation performance was made for the proposed method and K-Means clustering and superpixels *** proposed algorithm pro-vided more accurate segmentation in comparison to other methods with the maximum accuracy of equivalent to 99.19%.Based on the confusion matrix,the true-positive and true-negative values were 0.9952 and 0.8985 representing the true classification rate of crops and weeds,*** results indicated that the proposed method successfully provided accurate and convincing results for the segmentation of crops and weeds in the images with a complex presence of weeds.

关键词： Weed coverage Semantic segmentation Convolutional neural network subtractive clustering algorithm Simple Linear Iterative clustering (SLIC) K-means

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subtractive clustering and phase correlation similarity measure for endmember extraction

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INFRARED PHYSICS & TECHNOLOGY 2020年 110卷 103452-103452页

作者： Palla, Parasuram Yadav Shetty, Amba Raghavendra, B. S. Narasimhadhan, A., V Natl Inst Technol Karnataka Surathkal India

Target identification using Remote Sensing techniques saves time, cost and reduces difficulties in field investigation. The endmember is a reference spectral response of a pure pixel in the hyperspectral image and is used for object identification/classification from hyperspectral data. Quality of endmembers selected influences classification accuracy. Though there have been several algorithms proposed for endmember extraction, choosing a benchmark algorithm requires further investigation. To the best of our knowledge, similarity measures have not been explored much in the extraction of spectrally distinct signatures called endmembers. In this paper, we propose a similarity measures based subtractive clustering algorithm (SM-SCA) for endmember extraction. The objective of this paper is to explore the applicability of a SM-SCA and effectiveness of different similarity measures in endmember extraction and to compare it's performance with classical endmember extraction algorithms. Implementation on airborne hyperspectral (Samson data and AVIRIS data over Cuprite region) and synthetic data proves that SM-SCA is capable of extracting endmembers of all the materials identified in the data, with appropriate similarity measure. Experimental results show that (i) the similarity measures are potential not only to discriminate but also in extraction of different endmember signatures and (ii) the proposed SM-SCA with phase correlation similarity measure perform comparable to the classical endmember extraction algorithms in identifying endmembers.

关键词： Endmember extraction subtractive clustering algorithm Similarity measures Phase correlation

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Locating minimum number of PMUs for pre- and post-disturbance monitoring of power systems

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第1期13卷 127-136页

作者： Koochi, Mohammad Hossein Rezaeian Esmaeili, Saeid Ledwich, Gerard Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld Australia

In this study, the post-disturbance coherency of buses is added to the conventional phasor measurement unit (PMU) placement problem to find the minimum number of PMUs at different depths of unobservability. To do that, different scenarios are defined based on probabilistic parameters. Then, in each scenario, central buses are determined based on the similarity of post-disturbance variations using subtractive clustering algorithm. The central bus in each area is the bus that its post-disturbance variations are similar to the highest number of buses in the area. The PMU placement problem is then solved considering the number of scenarios where each bus has been selected as a central bus. The resulted placement scheme is more suitable for monitoring the power system dynamics compared to the conventional method. In order to reduce the number of PMUs, the placement problem is solved for higher depths of unobservability. In each depth, the voltage phasor of unobservable buses is obtained using state estimation methods. The results of applying the proposed methodology on 68-bus, 16-machine system show its better performance compared to the conventional method.

关键词： phasor measurement pattern clustering power system state estimation probability power system reliability PMU placement problem predisturbance monitoring post-disturbance monitoring power system dynamics post-disturbance coherency phasor measurement unit placement problem probabilistic parameters post-disturbance variations subtractive clustering algorithm voltage phasor unobservable buses state estimation methods 68-bus system 16-machine system

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Application of adaptive neuro-fuzzy inference system for prediction of internal stability of soils

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EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING 2019年第2期23卷 153-171页

作者： Xue, Xinhua Xiao, Ming Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn Chengdu Sichuan Peoples R China Penn State Univ Dept Civil & Environm Engn University Pk PA 16802 USA

This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the assessment of internal stability of soils under seepage. The training of fuzzy system was performed by a hybrid method of back-propagation (BP) and least mean square algorithm, and the subtractive clustering algorithm was utilised for optimising the number of fuzzy rules. Experimental data on internal stability of soils in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the BP model, the particle swarm optimisation-BP model and the ANFIS model) were compared with the experimental data. The results show that the ANFIS model is a feasible, efficient and accurate tool for predicting the internal stability of soils according to Wan and Fell's criterion.

关键词： internal stability adaptive neuro-fuzzy inference system particle swarm optimisation back propagation subtractive clustering algorithm

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Neuro-fuzzy based approach for estimation of concrete compressive strength

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COMPUTERS AND CONCRETE 2018年第6期21卷 697-703页

作者： Xue, Xinhua Zhou, Hongwei Sichuan Univ State Key Lab Hydraul & Mt River Engn Coll Water Resource & Hydropower 24 South Sect 1Yihuan Rd Chengdu 610065 Sichuan Peoples R China

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

关键词： concrete compressive strength adaptive neuro-fuzzy inference system least squares method subtractive clustering algorithm

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Application of adaptive neuro-fuzzy inference system for prediction of internal stability of soils

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Revue Européenne de Génie Civil 2019年第2期23卷 153-171页

关键词： internal stability adaptive neuro-fuzzy inference system particle swarm optimisation back propagation subtractive clustering algorithm

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An Optimal Design Methodology of Adaptive Neuro-Fuzzy Inference System for Energy Load Forecasting - Hail city case study (Saudi Arabia) 18

An Optimal Design Methodology of Adaptive Neuro-Fuzzy Infere...

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4th International Conference on Engineering and MIS (ICEMIS)

作者： Hussein, Hisham Hail Univ Community Coll Dept Elect Engn Hail Saudi Arabia

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 clustering algorithm, 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.

关键词： Adaptive Neuro-Fuzzy inference system Energy load forecast Fuzzy C-means clustering algorithm Fuzzy inference system subtractive clustering algorithm

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