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Image Segmentation Method for Brassica Napus L. Based on pso and Color Space Model

电脑学刊 2020年第1期31卷 266-273页

作者： Chang-Hua Liu Shu-Fang Qin Xiao-Ming Wu Li-Xia Li Kai-Qiong Sun Ren-Feng Liu

Image processing technology is used to identify the lesions and wormholes of the leaves, and then the pests and diseases of the crops can be analyzed. The segmentation of the leaf images from the background image accurately is a key part of the recognition process. Therefore, a image segmentation method is proposed for Brassica napus L. based on pso algorithm and color space model. Firstly the color component image of the highlighted target area is obtained using the color space model. Then its optimal segmentation threshold is calculated using the pso algorithm based on the inter-class variance method. Finally, the image is split using the threshold segmentation method. The segmentation results of multiple leaf images show that the proposed method can improve the segmentation accuracy of the image of Brassica napus L., and the error rate is less than 10% and the average error rate of 20 images is 0.91242%. Therefore the proposed method satisfies the accuracy requirements of agricultural applications and has a better segmentation effect than the Otsu algorithm.

关键词： Brassica napus L color space model image segmentation pso algorithm

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A Review on Pomegranate Disease Classification Using Machine Learning and Image Segmentation Techniques

A Review on Pomegranate Disease Classification Using Machine...

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International Conference on Intelligent Computing and Control Systems (ICICCS)

作者： Kantale, Pooja Thakare, Shubhada Govt Coll Engn Amravati Dept Elect Engn Amravati Maharashtra India

ISBN: (纸本)9781728148762

The paramount motive of farming is to yield good crops without any disease present. The role of digital image processing along with image analysis is indispensable in the sector of agriculture. Preprogrammed awareness of plant malady and production of good plants is of substantial significance in agriculture industrialization. To prevent the loss of agricultural yield one has to recognize the malady. Manually, detection of plants malady is quite difficult, it required huge time for analyzing the malady present on the fruit. To tame this problem, a machine learning-based approach is recommended which can evaluate the image of the fruit to detect the disease. Image processing is the method which is successfully used for the recognition of plant malady. This paper, contribute a review on approach evolve for detection of diseases in plants using their images.

关键词： Pomegranate diseases Ensemble algorithm pso algorithm

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New Approach for Sediment Yield Forecasting with a Two-Phase Feedforward Neuron Network-Particle Swarm Optimization Model Integrated with the Gravitational Search algorithm

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WATER RESOURCES MANAGEMENT 2019年第7期33卷 2335-2356页

作者： Meshram, Sarita Gajbhiye Ghorbani, M. A. Deo, Ravinesh C. Kashani, Mahsa Hasanpour Meshram, Chandrashekhar Karimi, Vahid Rani Durgavati Univ Dept Math & Comp Sci Jabalpur India Univ Tabriz Dept Water Engn Tabriz Iran Near East Univ Fac Engn 10 Mersin TR-99138 Nicosia North Cyprus Turkey Univ Southern Queensland Inst Agr & Environm Sch Agr Computat & Environm Sci Springfield Qld 4300 Australia Univ Mohaghegh Ardabili Dept Water Engn Ardebil Iran

Predicting sediment yield is an important task for decision-makers in environmental monitoring and water management since the benefits of applying non-linear, artificial intelligence (AI) models for optimal prediction can be far reaching in real-life decision support systems. AI-based models are considered to be favorable predictive tools since the nonlinear nature of suspended sediment data series warrants the utilization of nonlinear predictive methods for feature extraction, and for accurate simulation of suspended sediment load. In this study, Artificial Neural Network (ANN) approaches are employed to estimate the monthly sediment load where the two-phase Feed-forward Neuron Network Particle Swarm Optimization Gravitational Search algorithm (FNN-psoGSA) is developed, and then evaluated in respect to 3 distinct algorithms: the Adaptive Neuro-Fuzzy Inference System (ANFIS), Feed-forward Neuron Network (FNN) and the single-phase Feed-forward Neuron Network Particle Swarm Optimization (FNN-pso). The study is carried out using the monthly rainfall, runoff and sediment data spanning a 10year period (2000-2009) where about 75% of data are used in model training phase, 25% in testing phase. Three statistical performance criteria namely: the mean absolute error (MAE), Nash-Sutcliffe coefficient (NSE) and the Willmott's Index (WI) and diagnostic plots visualizing the tested results are used to evaluate the performance of four AI-based models. The results reveal that the objective model (the two-phase FNN-psoGSA model) and the single-phase FNN-pso model yielded more precise results compared to the other forecast models. This result accorded to an NSE value of 0.612 (for the FNN-psoGSA model) vs. an NS value of 0.500, 0.331 and 0.244 for the FNN-pso, FNN and ANFIS models, and WI=0.832 vs. 0.771, 0.692 and 0.726, respectively The study also demonstrated that the FNN model generated slightly better results than the ANFIS model for the estimation of sediment load data but over

关键词： Neural networks pso algorithm GSA algorithm Sediment load Modelling

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Total organic carbon content prediction based on support-vector-regression machine with particle swarm optimization

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2019年 180卷 699-706页

作者： Rui, Jianwen Zhang, Hongbing Zhang, Dailu Han, Feilong Guo, Qiang Hohai Univ Coll Earth Sci & Engn Nanjing 211100 Jiangsu Peoples R China

Total organic carbon (TOC) is a significant factor to evaluate the hydrocarbon potential of the unconventional reservoir, while it is difficult to obtain the continuous and accurate prediction result of TOC by traditional methods due to the non-linear relationship between TOC content and wireline log data. However, support vector regress machine is an efficient supervised learning artificial intelligence algorithm, and performs well to deal with non-linear relationship dataset. Therefore, in order to explore a more efficient way for TOC content prediction, the support vector regress machine was tried to predict TOC content through wireline log data. To further verify this, some data tests have been conducted on an offshore exploration prior well data in the northern of Beibu Gulf Basin. After analyzing various kernel functions and optimization methods by cross-validation and error analysis, it can be indicated that the Gaussian kernel function and the particle swarm optimization (pso) algorithm which used the linear decreasing inertia coefficient and mutated particles are suitable for SVR model and model parameter selection, respectively. Finally, the prediction result of SVR with the best model and model parameters is compared with the results of an improved Delta logR method and multilayer perceptron method. The comparison results show that SVR has a higher correlation coefficient than other methods. In conclusion, the prediction result of SVR can be thought of as an efficient and reliable method for TOC content prediction. Through the continuous prediction of organic carbon content, the qualitative and quantitative evaluation of high-quality source rocks in this area can be realized, which is of great practical significance for determining the exploration field and the search for favorable target reservoirs.

关键词： TOC content Support vector regress Delta logR pso algorithm

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A pso-RRBF neural network for BOD prediction in sewage treatment 38

A PSO-RRBF neural network for BOD prediction in sewage treat...

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38th Chinese Control Conference (CCC)

作者： Chu, Minghui Li, Wenjing Qiao, Junfei Beijing Univ Technol Fac Informat Technol Beijing 100024 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China

ISBN: (纸本)9789881563972

Aiming at the question that the effluent biochemical oxygen demand (BOD) in sewage treatment is difficult to measure accurately in real time, a soft-measurement model of recursive radial basis function (RRBF) neural network based on pso algorithm (pso-RRBF) is proposed to predict the effluent BOD. Firstly, the pso algorithm is put forward to determine not only the input variables but also the structure of the RRBF effectively. Secondly, the gradient descent method is used to adjust the weights, center and width. Finally, the soft-measurement model is applied to the actual sewage treatment process. The experimental results show that the soft-measurement model has a more compact structure and its accuracy is improved compared with other models.

关键词： pso algorithm recursive RBF gradient descent method effluent BOD soft measurement

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Automatic ECG arrhythmias classification scheme based on the conjoint use of the multi-layer perceptron neural network and a new improved metaheuristic approach

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IET SIGNAL PROCESSING 2019年第8期13卷 726-735页

作者： Bouaziz, Fatiha Oulhadj, Hamouche Boutana, Daoud Siarry, Patrick Jijel Univ Elect Dept BP 98 Ouled Aissa Jijel 18000 Algeria Jijel Univ Mechatron Lab LMT BP 98 Ouled Aissa Jijel 18000 Algeria Paris EST Creteil Univ Lab Images Signals & Intelligent Syst LISSI Paris France

The authors have proposed a new automatic classification scheme based on the conjoint use of the multi-layer perceptron (MLP) neural network and an enhanced particle swarm optimisation (Epso) algorithm for its training. In this work, six predominant categories of heartbeats from MIT-BIH database are considered, which are: normal, premature ventricular contraction, atrial premature contraction, right bundle branch block, left bundle branch block and paced beats. First, the authors have applied the standard particle swarm optimisation (pso) algorithm to select the network structure for each features vector. Then, the relevant electrocardiogram (ECG) features to the studied arrhythmias were chosen, which suited to the optimised training performance of the classifier. The recognition performance of the proposed Epso-MLP classification system is evaluated considering two different versions of the Epso algorithm. In the first version (Epsow), the inertia weight factor of the pso algorithm is proposed to be a variable with iterations. However, two pso parameters are taken to be variables in the second version of the improved learning algorithm (Epsowc). The obtained experimental results prove the enhancement of the convergence ability of the MLP neural network and confirm the superiority of the proposed Epso-MLP classification scheme on comparison with the other last published classification systems.

关键词： learning (artificial intelligence) particle swarm optimisation multilayer perceptrons electrocardiography medical signal processing signal classification multilayer perceptron neural network improved metaheuristic approach automatic classification scheme enhanced particle swarm optimisation algorithm MIT-BIH database normal contraction premature ventricular contraction atrial premature contraction left bundle branch block standard particle swarm optimisation algorithm network structure features vector recognition performance Epso-MLP classification system Epso algorithm pso algorithm pso parameters improved learning algorithm MLP neural network Epso-MLP classification scheme published classification systems

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Stable control strategy for a second-order nonholonomic planar underactuated mechanical system

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2019年第11期50卷 2126-2141页

作者： Lai, Xuzhi Xiong, Peiyin Wu, Min China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan Peoples R China

This paper presents a stable control strategy for a planar four-link active-passive-active-active (APAA) underactuated mechanical system. The control objective is to move its end-point from any initial position to any target position. First, the controllers are designed to move the first link to the target angle which ensures the target position is within the reachable area of the planar three-link passive-active-active (PAA) system. Meantime, the system is reduced to a planar virtual Pendubot. Then, the periodic controllers are designed based on the nilpotent approximation model to make the first link return to the target angle and all angular velocities converge to zeroes, where the fuzzy modulating is added to adjust the convergence of angular velocities. Thus, the system is reduced to a planar virtual PAA system. Next, the control is divided into two stages, and based on the angle constraint relationships, the target angles of the planar virtual PAA system for the target position are obtained by using the online particle swarm optimisation algorithm. Each stage controllers are designed according to the target angles, so the end-point of the planar APAA system is controlled to the target position. Finally, the validity of the control strategy is demonstrated via simulations.

关键词： Underactuated mechanical system position control nilpotent approximation pso algorithm

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pso Based Improved Topology for MLI Considering Low THD and Low Switching Loss 10

PSO Based Improved Topology for MLI Considering Low THD and ...

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10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Mohanty, Rupali Chatterjee, Debashis Suman, Swati Sengupta, Gopinath Jadavpur Univ Elect Engn Dept Kolkata India IIT Guwahati Ctr Energy Gauhati India

ISBN: (纸本)9781538659069

Multilevel inverters have wide range of applications including renewable energy in modern industrial scenario. They can be used in high voltage and high power applications. The output voltage contains lesser harmonics and it provides greater efficiency as compared to classical inverter. By implementing different switching algorithms the output voltage can be generated with less THD and different topologies can been adopted for higher level inverter with less number of semiconductor switches. In this paper an improved topology of multilevel inverter has been proposed with reduced switch count, and by implementing pso algorithm the voltage THD has been reduced. The switching losses and efficiency has determined.

关键词： Reduced MLI Total harmonics distortion pso algorithm Device loss Modulation index

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Optimization Research for Receivers' Arrangement of LBL Acoustic Positioning System with pso algorithm

Optimization Research for Receivers' Arrangement of LBL Acou...

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2nd International Conference on Measurement, Instrumentation and Automation (ICMIA 2013)

作者： Xiao Chang-run Zhang Wen-zhao Univ Naval Engn Dept Naval Architecture & Ocean Engn Wuhan Peoples R China

ISBN: (纸本)9783037857502

Three optimization models were proposed according to the problems emerged from self-propelled model test. And the object function to evaluated acoustic positioning's precision of some given receiver's arrays was created. The genetic algorithm code to solve this optimization problem was developed and validated by optimization model 1 afterwards. The optimum arrangements for model 1 and model 3 were acquired by calculating such code. The cross arrays was certificated to be the optimum ones. One new method to arrange acoustic receivers in self-propelled model test was drafted.

关键词： LBL acoustic positioning pso algorithm receivers' arrangement

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Transient Model Parameters Identification of Transformer Based on pso algorithm

Transient Model Parameters Identification of Transformer Bas...

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3rd International Conference on Electric Power and Energy Conversion Systems (EPECS)

作者： Valii, Mohammad Bigdeli, Mehdi Hojjatiparast, Farid Islamic Azad Univ Dept Elect Engn Zanjan Branch Zanjan Iran Islamic Azad Univ Dept Elect Engn Sci & Res Branch Zanjan Iran

ISBN: (纸本)9781479906871;9781479906888

In this paper a novel model for analyzing the transient state of the distribution transformers is proposed. The presented model is as simple that the simulation process can be conducted so fast and easy and also its application in the form of a two port element in power network is possible. By considering the complexity of the analytical methods, Particles Swarm Optimization (pso) algorithm is applied for estimation of the parameters of the transient model of the transformers. In order to do that, related tests were performed on a 2.5 MVA and 6300/420 V distribution transformers, after that, the desired parameters were estimated by the implemented pso algorithm. Finally, by comparing the experimental and the estimated values, the reliability of the pso algorithm in this case was evaluated. Also, a comparison between the obtained values in this research and the results of Genetic algorithm (GA) and the analytical method were carried out. The result reveals the more capabilities and accuracies of the pso algorithm.

关键词： Transformer Transient Measurement Parameter Estimation pso algorithm

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