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Mechanomyography (MMG) is a low-frequency signal emitted during muscle contraction; it can overcome the inherently unreliable defects of electromyography (EMG) and electroencephalography (EEG). For MMG-based movement pattern recognition, this paper proposes an innovative kernel extreme learning machine (KELM) based on the chaotic particle swarm optimization (CPSO), namely CPSO–KELM. By using CPSO–KELM in MMG-based movement pattern recognition, the classification accuracy of upper-limb movement has been improved, and the results can be better applied to the control of passive rehabilitation training of the upper-limb exoskeleton, which can provide help for the upper extremity rehabilitation of stroke patients. In this paper, MMG which is used for pattern recognition research, is collected by accelerometers when the subjects performed seven types of upper-limb rehabilitation movements. After filtering and segmentation, six time-domain features are extracted for the MMG of each channel, then kernel principal component analysis (KPCA) and principal component analysis (PCA) are used to reduce the feature dimensions. By using different classifiers to build classification models, the average recognition accuracies of movement classification under different processing methods are obtained; it is found that for most classifiers, the recognition rate of MMG after KPCA dimensionality reduction is better than that of PCA, and the overall recognition rate of upper-limb movements using the CPSO–KELM classifier can reach 97.1%, which is better than support vector machine (SVM), back-propagation neural network (BPNN), linear discriminant algorithm (LDA) and other MMG common classifiers in recognition accuracy. Moreover, the experimental analysis shows that compared with genetic algorithm (GA) and particle swarm optimization (PSO), CPSO has faster convergence and smaller training error, and the final recognition accuracy proves that the performance of CPSO–KELM is better than those of GA–KELM and PSO–KELM. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive neural network acting like a proportional-integral-derivative (PID) controller that uses an intelligent meta-heuristic technique to improve the drug infusion rate (propofol) as a manipulated variable in closed-loop control of anesthesia systems using the Bispectral Index (BIS) as the primary controlled variable. The effect of propofol on the human body is modelled using the pharmacokinetic (PK) and pharmacodynamics (PD) models. A physiological dataset of patients, including gender, weight, height, age, and the like, determines the parameters of the PK/PD mathematical model. The proposed controller seeks to provide the optimal propofol control action, which is in charge of swiftly, precisely, and accurately maintaining a triad of hypnosis, analgesia, and neuromuscular blockade by infusing several drugs that are specific to each state. To train this neural network like a PID controller with the radial basis function (RBF) in a neuron, the meta-heuristic method is employed. The first technique is particle swarm optimization (PSO), which has been widely used in both data estimation and training because of its quick computing speed, while the second technique is the chaotic PSO algorithm, and the third technique is the modified CPSO algorithm (MCPSO). The fundamental proposed procedures of the MCPSO algorithm use the chaos method, including the coefficients of acceleration, and remove the two random parameters from the velocity update equation to generate more randomness in the search space to quickly solve the local minima problem. The PSO, CPSO, and MCPSO meta-heuristic algorithms use the mean square error (MSE) performance index to find and optimize the optimal or the nearly ideal gain parameters of the nonlinear neural network to function like a PID controller. The simulation results show that the proposed controller for different physiological dataset patients is characterized by its efficacy and resilience in terms of controlling the depth of the hypnosis state and the infusion rate of the anesthetic drug during surgery in order to avoid under- or over-dosing of the drug for the patient through the desired value of BIS (50) with minimizing the steady-state error, which is equal to zero without any oscillation. Moreover, the comparison results showed that the proposed RBF-NN-PID controller enhanced the time in one minute to reach the depth of anesthesia at the moderate hypnotic state when compared to the fractional-order adaptive high-gain controller, in which the time to reach the depth of anesthesia is two minutes. In contrast, the adaptive neuro-fuzzy controller reached the depth of anesthesia in three minutes. Therefore, the time was improved by 50% and 67%, respectively. In particular, the surgery BIS index was kept at the BIS desired 50 at the moderate hypnotic state without any error and with no oscillation at steady-state. [ABSTRACT FROM AUTHOR]

With the continuous increase in global energy demand and growing environmental awareness, the utilization of renewable energy has become a worldwide consensus. In order to address the challenges posed by the intermittent and unpredictable nature of renewable energy in distributed power distribution networks, as well as to improve the economic and operational stability of distribution systems, this paper proposes the establishment of an active distribution network capable of accommodating renewable energy. The objective is to enhance the efficiency of new energy utilization. This study investigates optimal scheduling models for energy storage technologies and economic-operation dispatching techniques in distributed power distribution networks. Additionally, it develops a comprehensive demand response model, with real-time pricing and incentive policies aiming to minimize load peak–valley differentials. The control mechanism incorporates time-of-use pricing and integrates a chaos particle swarm algorithm for a holistic approach to solution finding. By coordinating and optimizing the control of distributed power sources, energy storage systems, and flexible loads, the active distribution network achieves minimal operational costs while meeting demand-side power requirements, striving to smooth out load curves as much as possible. Case studies demonstrate significant enhancements during off-peak periods, with an approximately 60% increase in the load power overall elevation of load factors during regular periods, as well as a reduction in grid loads during evening peak hours, with a maximum decrease of nearly 65 kW. This approach mitigates grid operational pressures and user expense, effectively enhancing the stability and economic efficiency in distribution network operations. [ABSTRACT FROM AUTHOR]

District heating system is the main way of heating in cities and towns in China. The development of district heating system still has the problems of low intelligence and low control accuracy, and there is the imbalance of heat supply and demand in heat distribution. Resulting in the energy consumed by the district heating system can account for more than half of the total energy consumption of the building. In order to alleviate this imbalance, this paper studies the control of heat distribution of each heat exchange station in the primary network. The heat model of primary network is established by recurrent neural network (RNN), and the data set used for modeling is the operation data of heat exchange station in reality. Combined with the heat load prediction model, a heat distribution strategy was proposed to optimize the primary flow of the heat exchange station. According to the predicted value, chaotic particle swarm optimization (CPSO) algorithm is used to optimize the primary flow sequence of each heat exchange station, and then the primary flow is adjusted to control the heat distribution of the secondary network. Finally, Simulink simulation model is used to simulate the water supply temperature of the secondary side of the heat exchange station. And analyze the operation status of the secondary side, the results verify the effectiveness of the strategy. The model simulation results show that the heat distribution scheme proposed in this paper can effectively distribute the heat of the heat exchange station according to the heat demand. [ABSTRACT FROM AUTHOR]

Abstract The wastewater treatment process (WWTP) is one of the most common links in chemical plants. However, the testing for diagnosing faults in wastewater treatment plants is expensive and time-consuming. Due to strong nonlinearity and variable autocorrelation, traditional WWTP diagnostic methods based on principal component analysis (PCA) can lead to low fault detection rates (FDR) or difficulty in determining the root cause of faults. In this paper, an improved dynamic kernel principal component analysis (DKPCA) and Granger causality (GC) analysis model that uses chaotic particle swarm optimization (CPSO) to detect WWTP and locate the root causes of faults is proposed. First, a kernel function is introduced to map a nonlinear matrix to a linear space. Then, the training data are extended through a time lag constant to solve the problem of nonlinear and variable autocorrelation in WWTP. Moreover, a novel fault candidate variables selection method, together with GC, is introduced to locate the root variables of the fault. The CPSO algorithm is employed to optimize DKPCA's kernel function parameters, enhancing the accuracy of fault monitoring and diagnosis models. Compared with traditional methods, the proposed method has a better fault detection rate, achieving 95.83% and 93.33% fault detection rates in simulated and real WWTP, respectively.

With the continuous increase in global energy demand and growing environmental awareness, the utilization of renewable energy has become a worldwide consensus. In order to address the challenges posed by the intermittent and unpredictable nature of renewable energy in distributed power distribution networks, as well as to improve the economic and operational stability of distribution systems, this paper proposes the establishment of an active distribution network capable of accommodating renewable energy. The objective is to enhance the efficiency of new energy utilization. This study investigates optimal scheduling models for energy storage technologies and economic-operation dispatching techniques in distributed power distribution networks. Additionally, it develops a comprehensive demand response model, with real-time pricing and incentive policies aiming to minimize load peak–valley differentials. The control mechanism incorporates time-of-use pricing and integrates a chaos particle swarm algorithm for a holistic approach to solution finding. By coordinating and optimizing the control of distributed power sources, energy storage systems, and flexible loads, the active distribution network achieves minimal operational costs while meeting demand-side power requirements, striving to smooth out load curves as much as possible. Case studies demonstrate significant enhancements during off-peak periods, with an approximately 60% increase in the load power overall elevation of load factors during regular periods, as well as a reduction in grid loads during evening peak hours, with a maximum decrease of nearly 65 kW. This approach mitigates grid operational pressures and user expense, effectively enhancing the stability and economic efficiency in distribution network operations.

Aiming at the problems of complex process mechanism, excessive disturbance and difficult measurement of the weight control system of the rubber compound extrusion line in the tire industry, a control method combining Smith prediction and linear active disturbance rejection control (Smith-LADRC) was designed to realize the stable control of tread weight. The simulation model was established to compare the control effect with PID, LADRC and Smith-PID to verify its advantages. At the same time, the chaotic particle swarm algorithm (CPSO) was introduced to adjust the parameters for the difficulty of parameter adjustment of this controller. The experimental results show that compared with the above three control methods, Smith-LADRC can make the system have better anti-interference and robustness. The control purpose is well achieved, and the rapidity of the system output response is improved. Compared with manual adjustment, the system output can be better controlled, while the parameter adjustment time is saved and the control efficiency is improved. [ABSTRACT FROM AUTHOR]

The pumped storage unit has good adjustment characteristics of a fast power response and convenient start and stop, which provides support for the safe and stable operation of the power system. To this end, this paper proposes a fractional order PID (FOPID) optimization control method for the regional load frequency of pumped-storage power plants. Specifically, based on IEEE standards, this paper established a single-region model of pumped storage. Then, a fractional order PID (FOPID) controller was designed, and the parameters of the controller were optimized via using the chaos particle swarm optimization (CPSO) algorithm. The effectiveness of the proposed method is verified by example simulation in the two-zone model of the pumped storage based on IEEE standards. The results of the example show that the proposed method exhibits stronger robustness and stability in the regional load frequency control. [ABSTRACT FROM AUTHOR]

Copyright of Basic Sciences Journal of Textile Universities / Fangzhi Gaoxiao Jichu Kexue Xuebao is the property of Basic Sciences Journal of Textile Universities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The primary goal of a mobile robot is to reach the desired goal by traversing an optimized path defined according to some criteria such as time, distance, and safety of the robot from any obstacles that may be in its way. Therefore, the backbone of autonomous mobile robots (AMRs) is path planning and avoiding obstacles. Many algorithms for path planning and obstacle avoidance have been presented by many researchers and each of these algorithms has several benefits and drawbacks. This paper focuses on comparing the performance of several metaheuristic algorithms that result in a more efficient, smoother, and shorter path for the mobile robot to reach the target in a complex environment. These algorithms include particle swarm optimization (PSO), chaotic particle swarm optimization (CPSO), modified chaotic particle swarm optimization (MCPSO), and firefly algorithm (FA). On the other hand, the paper proposes a hybrid algorithm by combining the FA and the MCPSO, namely the (HFAMCPSO). To demonstrate the effectiveness of the proposed algorithm in terms of the optimum cost function and obtaining the shortest path length, the optimal solution is compared to those of other path planning algorithms. Moreover, inverse dynamic and kinematic modeling are utilized to obtain the best torque and the best velocity actions for the wheels of the autonomous mobile robot. The proposed hybrid (FAMCPSO) algorithm provides enhancement on the path length equals (43.3%) and (25.5%) compared to the radial cell decomposition (RCD) and the A* algorithm, respectively. Moreover, the enhancement on the path length equals (2.3%) and (22.7%) compared to the firefly algorithm (FA) and the genetic algorithm (GA), respectively. All methods are simulated in an environment with static obstacles using the 2018b MATLAB package. [ABSTRACT FROM AUTHOR]

Facing the challenges of grid stability brought by the large-scale access of new energy to the grid, as one of the important means of frequency regulation of the grid, the doubly-fed pumped-storage units (DFPSU) have great practical significance in the study of the joint frequency regulation strategy with the thermal power unit (TPU). In order to reduce the frequency regulation pressure on the grid caused by the large-scale entry of new energy sources, this paper proposes a joint frequency regulation strategy for the TPU and DFPSU. Based on establishing the optimal scheduling model of the system, taking into account the minimum total operating cost and the optimal time as goals, the chaotic particle swarm optimization (CPSO) is proposed to solve the model. Finally, through MATLAB simulation, the relevant examples are studied and analyzed, and the simulation results verify that the joint frequency regulation strategy in this paper can effectively improve the frequency regulation performance of the unit and the operation economy is good. [ABSTRACT FROM AUTHOR]

The type1 of diabetes is a chronic situation characterized by abnormally high glucose levels in the blood. Persons with diabetes characterized by no insulin secretion in the pancreas (ß-cell) which also known as insulin-dependent diabetic Mellitus (IDDM). In order to keep the levels of glucose in blood near the normal ranges (70–110mg/dl), the diabetic patients needed to inject by external insulin from time to time. In this paper, a Modified Second Order Sliding Mode Controller (MSOSMC) has been developed to control the concentration of blood glucose levels under a dis-turbing meal. The parameters of the suggested design controller are optimized by using chaotic particle swarm optimization (CPSO) technique, the model which is used to represent the artificial pancreas is a minimal model for Bergman. The simulation was performed on a MATLAB/SIMULINK to verify the performance of the suggested controller. The results showed the effectiveness of the proposed MSOSMC in controlling the behavior of glu-cose deviation to a sudden rise in blood glucose.

The pumped storage unit has good adjustment characteristics of a fast power response and convenient start and stop, which provides support for the safe and stable operation of the power system. To this end, this paper proposes a fractional order PID (FOPID) optimization control method for the regional load frequency of pumped-storage power plants. Specifically, based on IEEE standards, this paper established a single-region model of pumped storage. Then, a fractional order PID (FOPID) controller was designed, and the parameters of the controller were optimized via using the chaos particle swarm optimization (CPSO) algorithm. The effectiveness of the proposed method is verified by example simulation in the two-zone model of the pumped storage based on IEEE standards. The results of the example show that the proposed method exhibits stronger robustness and stability in the regional load frequency control.

In order to solve the multi-factor conflict problem in a cluster head selection process, to optimize it and prolong the network life cycle, a chaotic particle swarm optimization method based on the multi-factor conflict is proposed for clustering the sensor networks. The object of study is a sensor network with a hierarchical clustering network topology, which includes cluster member nodes, cluster head nodes and sink nodes. The energy consumption model of data acquisition and processing in the network nodes is constructed. Four kinds of multi-factor collision problems of the node residual energy, the inter-nodal energy balance and the distance between the base stations, as well as the probability of the node acting as a cluster head are discussed. An energy balance index based on the standard deviation of the node residual energy is introduced to construct an adaptive degree function to obtain the relationship between the current and the previous adaptive degrees of particles. Then, the inertia weight is determined. The chaotic particle swarm optimization algorithm based on the adaptive inertia weight is used to optimize the cluster head selection. The nodes in the communication area are regarded as the cluster members. The cluster head number can meet the optimal cluster head number, which further improves the network energy efficiency. The simulation results show that the death time of the first node, the half node and the last node of the sensor network clustered by this method is 83.33%, 34.14% and 43.14% longer than that of the LEACH method, respectively. The energy consumption of the sensor network clustering is low, the network life cycle is long and the clustering effect is good. [ABSTRACT FROM AUTHOR]

The double reduced beam section (DRBS) connection is a newly developed steel moment connection. In this paper, the chaotic particle swarm optimization technique was utilized to optimize this connection for the best seismic performance. The resulting optimum DRBS connection shapes showed up to 39% better energy dissipation and up to respectively 50% and 55% lower rupture index (RI) and equivalent plastic strain (PEEQ) at the beam-column interface, compared to the conventional RBS connection. Also, the optimum design parameters determined in this paper led to up to a 28% increase in the objective function (energy dissipation) and up to respectively 36% and 38% decreases in the RI and PEEQ with respect to the original DRBS connection, indicating noticeable seismic performance enhancements. The results indicated that changing the size of the beam and column sections does not lead to any significant change in the optimal shape of the beam flange cut or the seismic performance of the connection. [ABSTRACT FROM AUTHOR]

In this paper, a neuro-optimized numerical method is presented for approximation of HIV virus progression model in the human body. The model is composed of coupled nonlinear system of differential equations (DEs) containing healthy and infected T-Cells and HIV free virus particles. The coupled system is transformed into feedforward artificial neural network (ANN) with Mexican hat wavelet function in the hidden layers. Two meta-heuristic algorithms based on chaotic particle swarm optimization (CPSO) and its hybrid version with local search technique are exploited to tune the parameters of ANN in an unsupervised manner of error function. A comprehensive testbed is established to observe the virus growth per day with performance metric containing fitness value, computational time complexity and convergence. The proposed solutions are compared with state of art Runge–Kutta method and Legendre Wavelet Collocation Method (LWCM). The core advantages of the proposed scheme are getting the solution on continuous grid, consistent convergence, simplicity in implementation and handling strong nonlinearity effectively. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of mine gas concentration prediction, an improved multi-variate adaptive weighted least squares support vector machine(AWLSSVM) gas prediction model for chaotic particle swarm optimization is proposed, and multi-step prediction is realized. Firstly, the particle swarm optimization algorithm is analyzed. An improved chaotic particle swarm optimization algorithm with faster convergence speed and stronger global search ability is proposed. Aiming at the shortcomings of the weighted least squares support vector machine(WLSSVM)weight distribution, AWLSSVM is proposed according to the distribution characteristics of discrete points. Secondly, the chaotic theory is used to construct a sample set. Finally, the performance of the prediction model was evaluated by mine monitoring data. The results indicate that in the univariate prediction, the prediction of AWLSSVM is 5.3% and 6.7% higher than the least squares support vector machine and WLSSVM. The multivariate accuracy is increased by 39.3%, 49.6%, 55.9%, 59.7% and 62.5%, relative to the univariate five-step prediction.

Facing the challenges of grid stability brought by the large-scale access of new energy to the grid, as one of the important means of frequency regulation of the grid, the doubly-fed pumped-storage units (DFPSU) have great practical significance in the study of the joint frequency regulation strategy with the thermal power unit (TPU). In order to reduce the frequency regulation pressure on the grid caused by the large-scale entry of new energy sources, this paper proposes a joint frequency regulation strategy for the TPU and DFPSU. Based on establishing the optimal scheduling model of the system, taking into account the minimum total operating cost and the optimal time as goals, the chaotic particle swarm optimization (CPSO) is proposed to solve the model. Finally, through MATLAB simulation, the relevant examples are studied and analyzed, and the simulation results verify that the joint frequency regulation strategy in this paper can effectively improve the frequency regulation performance of the unit and the operation economy is good.

Vibration signals contain abundant information which can reflect the running state of high-speed trains. Accurate vibration signal prediction can provide references for anomaly detection of the gearbox in high-speed trains. This paper develops a hybrid model combining ensemble empirical mode decomposition (EEMD) with auto regression (AR) and support vector regression (SVR) models. First, the EEMD method is applied to decompose the vibration acceleration signal of gearbox. Second, AR models are employed to predict the intrinsic mode functions and the outputs are aggregated as the final result of AR. Third, reconstruct phase space and establish SVR models to predict the components; The predictions are aggregated as the final result of SVR. Finally, the results predicted using the AR and SVR models are weighted and summed together, with the weights being optimized by the chaotic particle swarm optimization algorithm. The actual operation monitoring data are used to validate the hybrid model. Data analysis demonstrates that the proposed method has better approximation compared with the AR model, the SVR model and the RBF neural network model.

Ultra-short-term wind power probabilistic forecasting is of significance for stable power grid operation; however, it is still challenging due to the inherent nonlinearity and uncertainty. Most state-of-the-art methods have focused on achieving quantile prediction using a combination of linear quantile regression and nonlinear complex deep neural networks. Yet, these methods struggle with several dilemmas. Quantile regression deep neural networks require a complete training once for each quantile. The multi-training mode and complex structure of quantile regression deep neural network can lead to extremely high computational complexity. Most of the training of quantile regression deep neural networks are guided by the loss of each quantile, and the weights are adjusted by gradient descent in which the gradient explosion and quantile crossover may be encountered. Therefore, this paper proposes a non-crossing multi-output quantile regression deep neural network optimized by chaotic particle swarm optimization. It designs a multi-output deep neural network to output all quantile estimations simultaneously through one training, effectively solving the structural complexity problem of traditional quantile regression deep neural networks. Since quantile regression produces a non-differentiable loss function which significantly hinders model training, the proposed neural network is trained by chaotic particle swarm optimization. It not only achieves the effect of optimizing all quantile losses simultaneously, but also can significantly alleviate the dilemma of training in traditional neural network weight optimization. In addition, several non-crossing constraints are designed for avoiding quantile crossover. The proposed model is trained and tested on two real-world wind power case studies. The experiment results show that the proposed model shows superiority in performance criteria, training speed, and avoiding quantile crossover. • A non-crossing multi-output quantile regression deep neural network is designed. • It is used to perform ultra-short-term wind power probabilistic forecasting. • Chaotic particle swarm optimization is used to avoid gradient explosion. • A set of constraints is designed for avoiding quantile crossover. • Proposed method performs better than other methods in wind power case studies. [ABSTRACT FROM AUTHOR]

• Using subsidies and carbon trading jointly is more effective than their sole use. • A proper carbon quota is vital for firms to obey emission reduction constraints. • The subsidy limits for carbon emission reduction is significant on its reduction. • The subsidy rate can adjust the total carbon emissions and emission reduction. With the increasing demand for low-carbon cold chain logistics, corresponding subsidy policy and carbon emissions trading policy are needed to guide cold chain logistics enterprises in energy-saving and emission reduction transformation of cold storage. However, current research mainly focuses on energy-saving and emission reduction through improving the energy efficiency of equipment or optimizing the operation and management of cold chain logistics enterprises, rarely considering the government policy. This paper constructs a decision-making model of cold chain logistics system by using the method of bilevel programming. The upper level is the government's goal, pursuing the total cost minimization of the whole cold chain logistics system. The lower level is the goal of the cold chain logistics enterprise, pursuing the cost minimization of the enterprise. Chaotic particle swarm optimization (CPSO) was developed to improve this model, and this method was applied to the decision-making of cold chain logistics system in Wuhan City, Hubei Province, China. The study reveals: (1) The joint use of carbon emission reduction subsidies and carbon emission quota optimization measures is more effective in promoting energy-saving and emission reduction of enterprises than using them separately; (2) The carbon emission quota set by the government should not be too high or too low; (3) The government subsidy limits for carbon emission reduction have a significant impact on the carbon emission reduction of enterprises; (4) The subsidy rate per unit carbon emission reduction can adjust the total carbon emissions and total carbon emission reduction of enterprises. This study provides a scientific basis and inspires the government and enterprise in decision-making to improve the energy-saving and emission reduction of the whole society. [ABSTRACT FROM AUTHOR]

In this paper, a novel control and prediction algorithm is proposed to solve the problem of decoupling control and response delay in the AHC system of electric winch, which effectively improves the compensation accuracy and response speed of the AHC system. The innovation of the control strategy is mainly as follows: 1) Long short-term memory (LSTM) neural network is used for predicting the ship heave motion. The Bayesian algorithm is used to optimize the hyperparameters to improve the prediction accuracy.2) In the design of the AHC controller, the main speed feedforward is used to directly govern the system's output. The position loop adopts the active disturbance rejection control (ADRC) to improve the anti-disturbance and position-tracking ability of the AHC system.3) For the numerous parameters of ADRC, the chaotic particle Swarm optimization (CPSO) algorithm is used to tune parameters. The simulation results show that the algorithm is effective in improving the robustness and compensation efficiency of the system. Finally, the experiment is carried out on the AHC experimental platform of the full-size winch. The compensation results before and after prediction are compared and analyzed. • This paper adopts BO-LSTM to improve the accuracy of ship heave motion prediction. • AHC controller adopts the main speed feedforward to direct control system output. • The position loop uses CPSO for parameter tuning of ADRC controller. • The mathematical model of winch driven by asynchronous motor is deduced completely. • The effectiveness of the proposed algorithm is verified on a full-size winch. [ABSTRACT FROM AUTHOR]

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a chaotic particle swarm optimization (CPSO) algorithm combined with data mining method for transient stability preventive control. The data mining method is utilized to approximate the security region considering transient stability. Therefore, the application effects of different input features and data-mining classifiers are compared first. Then, a two-stage support vector machine (SVM) approach is proposed to generate two models, including a linear SVM model with controllable features provides preventive adjustment rules, and a more accurate SVM model to approximate the actual security region. Finally, the CPSO in combination with the two-stage SVM is proposed to calculate the optimal preventive control strategies. Comprehensive studies are conducted on a 16-machine 68-bus system and 48-machine 140-bus system to verify the effectiveness. [ABSTRACT FROM AUTHOR]

Rivers provide many services to humans, however, globally, they have been historically impacted from over-exploitation for human needs. Such impacts are likely to increase into the future leading to poorer river health and the service functions provided by rivers are being lost. This study focused on the river health evaluation for river protection and sustainable water management. Eight indices were selected to structure an evaluation index system, consisting of physical, chemical, and biological elements, for river health evaluation. Among them, a new index, Ratio of Environmental flow to Streamflow (RES), was proposed to evaluate the satisfaction degree of environmental flow and to establish a common standard for the comparison of habitat quality among different rivers. A combined model consisted of fuzzy matter-element and projection pursuit models was established for the evaluation of river health, which was adopted to convert several indices in the evaluation index system to a comprehensive indicator. The Huai River Basin was selected as the case study area. The results showed that the river health of the whole Huai River was at the medium level. Four river reaches such as Reach I, II, IV and V were in the middle level, while only Reach III was at the sub-healthy level. This study is expected to provide scientific knowledge for the improvement of river health and water allocation. [ABSTRACT FROM AUTHOR]

MEMS (Micro Electro Mechanical System) gyroscopes have been widely applied to various fields, but MEMS gyroscope random drift has nonlinear and non-stationary characteristics. It has attracted much attention to model and compensate the random drift because it can improve the precision of inertial devices. This paper has proposed to use wavelet filtering to reduce noise in the original data of MEMS gyroscopes, then reconstruct the random drift data with PSR (phase space reconstruction), and establish the model for the reconstructed data by LSSVM (least squares support vector machine), of which the parameters were optimized using CPSO (chaotic particle swarm optimization). Comparing the effect of modeling the MEMS gyroscope random drift with BP-ANN (back propagation artificial neural network) and the proposed method, the results showed that the latter had a better prediction accuracy. Using the compensation of three groups of MEMS gyroscope random drift data, the standard deviation of three groups of experimental data dropped from 0.00354°/s, 0.00412°/s, and 0.00328°/s to 0.00065°/s, 0.00072°/s and 0.00061°/s, respectively, which demonstrated that the proposed method can reduce the influence of MEMS gyroscope random drift and verified the effectiveness of this method for modeling MEMS gyroscope random drift. [ABSTRACT FROM AUTHOR]

Energy management strategy play a crucial role in the optimal control of energy in pure electric logistics vehicles with hybrid energy storage system (HESS). Aiming at the problem that the energy management strategy of HESS cannot be reasonably allocated according to different slope conditions, this paper adds the road slope coefficient to the input of fuzzy control and designs a fuzzy controller that automatically adjusts the output power of ultracapacitor based on different road slope coefficients. Then, a chaotic particle swarm optimization (CPSO) method is used to optimize the designed fuzzy controller. Finally, the simulation test is carried out under urban dynamometer driving schedule (UDDS) urban road driving cycle by using Matlab/Simulink. The results show that the improved control strategy can increase the energy utilization by 3.63\% and reduce the battery power consumption by 3.97%. Furthermore, the proposed strategy can improve 30.66% energy utilization and 18.70% ultracapacitor power storage at the downhill situation, and can reduce the energy consumption of the whole vehicle by 5.23%, the average current by 6.19%, and the lithium-ion battery power consumption by 2.20% under the actual urban road gradient. [ABSTRACT FROM AUTHOR]

In this paper, the Unified Power Flow Controller (UPFC) is enhanced with a Chaotic Particle Swarm Optimization (CPSO) Damping Controller in order to mitigate the Low Frequency Oscillations (LFO) in a Single Machine Infinite Bus (SMIB) power system. The designed damping controller is an optimized lead-lag controller, which extracts the speed deviation of the generator rotor and generates the output feedback signal, which aims to modulate the reference values of the UPFC normal controller to achieve the best damping of LFO. In order to examine the better damping option, the damping controller is applied to both series and shunt converter of the UPFC and the results are comprehensively compared in three different operating points. Simulation results are performed in MATLAB/Simulink in three different cases and a Performance Index (PI) analysis is carried out.

This paper established a geophysical retrieval algorithm for sea surface wind vector, sea surface temperature, columnar atmospheric water vapor, and columnar cloud liquid water from WindSat, using the measured brightness temperatures and a matchup database. To retrieve the wind vector, a chaotic particle swarm approach was used to determine a set of possible wind vector solutions which minimize the difference between the forward model and the WindSat observations. An adjusted circular median filtering function was adopted to remove wind direction ambiguity. The validation of the wind speed, wind direction, sea surface temperature, columnar atmospheric water vapor, and columnar liquid cloud water indicates that this algorithm is feasible and reasonable and can be used to retrieve these atmospheric and oceanic parameters. Compared with moored buoy data, the RMS errors for wind speed and sea surface temperature were 0.92 m s and 0.88°C, respectively. The RMS errors for columnar atmospheric water vapor and columnar liquid cloud water were 0.62 mm and 0.01 mm, respectively, compared with F17 SSMIS results. In addition, monthly average results indicated that these parameters are in good agreement with AMSR-E results. Wind direction retrieval was studied under various wind speed conditions and validated by comparing to the QuikSCAT measurements, and the RMS error was 13.3°. This paper offers a new approach to the study of ocean wind vector retrieval using a polarimetric microwave radiometer. [ABSTRACT FROM AUTHOR]

The compound active clamp zero voltage soft switching (CACZVS) three-phase power factor correction (PFC) converter has many advantages, such as high efficiency, high power factor, bi-directional energy flow, and soft switching of all the switches. Triple closed-loop PI controllers are used for the three-phase power factor correction converter. The control objectives of the converter include a fast transient response, high accuracy, and unity power factor. There are six parameters of the controllers that need to be tuned in order to obtain multi-objective optimization. However, six of the parameters are mutually dependent for the objectives. This is beyond the scope of the traditional experience based PI parameters tuning method. In this paper, an improved chaotic particle swarm optimization (CPSO) method has been proposed to optimize the controller parameters. In the proposed method, multi-dimensional chaotic sequences generated by spatiotemporal chaos map are used as initial particles to get a better initial distribution and to avoid local minimums. Pareto optimal solutions are also used to avoid the weight selection difficulty of the multi-objectives. Simulation and experiment results show the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, we propose a new variable coded hierarchical fuzzy model (VCHFM) for handling classification problems. The proposed hierarchical framework classification model synergistically integrates the standard fuzzy inference system and DNA coding with supervised learning. The VCHFM automatically generates fuzzy rules from numerical data and membership functions using both the feature extraction unit and the inference unit. Furthermore, three modified algorithms are employed by the proposed VCHFM. The implementation of this model comprises four stages. First, a genetic algorithm procedure is used to determine the distribution of fuzzy sets for each feature variable in the feature extraction unit. Second, the membership functions are adjusted by DNA computing. Third, chaotic particle swarm optimization is used to regulate the weighting grade of the principal output node in the inference unit. Finally, a multi-objective optimum fitness function is used to ensure the best classification rate with the minimum number and length of rules. We validated the proposed VCHFM by classifying five benchmark datasets: the UCI Pima Indians Diabetes, Glass, Wisconsin Breast Cancer, Wine, and Iris datasets. The computer simulation results demonstrate that the proposed VCHFM can obtain a sufficiently high classification rate, unlike other models proposed in previous studies. [ABSTRACT FROM AUTHOR]

A novel support vector machine (SVM) model by combining kernel principal component analysis (KPCA) with improved chaotic particle swarm optimization (ICPSO) is proposed to deal with intrusion detection. The proposed method, in which multi-layer SVM classifier is employed to estimate whether the action is an attack, KPCA is applied as a preprocessor of SVM to reduce the dimension of feature vectors and shorten training time. To shorten the training time and improve the performance of SVM, N-RBF is employed to reduce the noise generated by feature differences, and ICPSO is presented to optimize the punishment factor C, kernel parameters $$\sigma $$ and the tube size $$\varepsilon $$ of SVM, which introduces chaos optimization and premature processing mechanism. Experimental results illustrate that the improved SVM model has faster computational time and higher predictive accuracy, and it can also shorten the training time and improve the performance of SVM. [ABSTRACT FROM AUTHOR]

A power system has critical values which are the limit values of voltage stability. These values are the highest active power taken by the load busses, voltage amplitude and the angle of the busses. In this research the critical values in electric power systems are defined with use of Chaotic Particle Swarm Optimization (CPSO) algorithm. In this study CPSO has been aimed to use logistic map and Henon map as chaotic maps to control the values of the parameters in velocity update formulation. Initially, critical values of voltage stability have been found by simple Particle Swarm Optimization (PSO). Then the same values have been found with CPSO. Accordingly, the results have been evaluated and observed that the stability critical values found by CPSO can be used to produce good potential solutions. Simulation results are promising and show the effectiveness of the applied approach. [ABSTRACT FROM AUTHOR]

Municipal Solid Waste (MSW) is an increasing concern at any municipality in the world, and is one of the primary factors that contribute greatly to the rising of climate change and global warming. MSW collection and disposal especially in the context of developing countries are indeed the urgent requirements for the sustainable development of environment and landscape, which rule over the quality-of-life and life expectancy of human being. In this paper, we concentrate on MSW collection at Danang city, which is one of four largest municipalities in Vietnam having high quantity of the average waste load per person and is bearing negative impacts of climate change such as severe weather conditions and natural disasters as a result. A novel vehicle routing model for the MSW collection problem at Danang city is presented. A novel hybrid method between Chaotic Particle Swarm Optimization and ArcGIS is proposed to generate optimal solutions from the vehicle routing model of Danang. Experimental results on the real dataset of Danang show that the proposed hybrid method obtains better total collected waste quantity than the relevant ones including the manual MSW collection procedure that is currently applied at this city. [ABSTRACT FROM AUTHOR]

Currently, the method of optimizing the wavelet neural network with particle swarm plays a certain role in improving the convergence speed and accuracy; however, it is not a good solution for problems of turning into local extrema and poor global search ability. To solve these problems, this paper, based on the particle swarm optimization, puts forward an improved method, which is introducing the chaos mechanism into the algorithm of chaotic particle swarm optimization. Through a series of comparative simulation experiments, it proves that applying this algorithm to optimize the wavelet neural network can successfully solve the problems of turning into local extrema, and improve the convergence speed of the network, in the meantime, reduce the output error and improve the search ability of the algorithm. In general, it helps a lot to improve the overall performance of the wavelet neural network. [ABSTRACT FROM AUTHOR]

Abstract: The time series of particulate matter at urban intersection consists of complex linear and nonlinear patterns and are difficult to forecast. Artificial neural networks (ANNs) have been applied to air quality forecasting in urban areas, but they have limited accuracy owing to their potential convergence to a local minimum and over-fitting. Chaotic particle swarm optimization (CPSO) algorithm is chaos-based searching algorithms and can recognize nonlinear patterns. Hence, a novel hybrid model combining ANN and CPSO algorithm is proposed to improve forecast accuracy. The proposed model, together with the ANN model with the traditional algorithms (Levenberg–Marquardt and PSO), is examined with the measured data in spring and winter respectively. The proposed model is found to provide the best results among them, implying that the hybrid model can be an effective tool to improve the particulate matter forecasting accuracy. Additionally, the proposed model is found to perform better for fine particles than for coarse particles. The model is also verified to predict better in winter than in spring. The outputs of these findings demonstrate the potential of the proposed model to be applied to forecast the trends of air pollution in similar meso-to mega-cities. [Copyright &y& Elsevier]