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A closed solid-state fermentation system refers to a system that automatically and intelligently completes solid-state fermentation under closed and controllable conditions to stabilize product quality, reduce labor intensity and improve intelligence level. In recent years, with the transformation and upgrading of the traditional brewing industry and with the increasingly urgent need to promote intelligent manufacturing, the research and development of closed solid-state fermentation systems have attracted more and more attention from the industry and researchers. In this paper, the monitoring methods for closed solid-state fermentation systems are reviewed from three perspectives: the application of various monitoring equipment and methods, the development of various mathematical models and control strategies, and the optimization of monitoring equipment using various control strategies. In addition, this review introduces the regulation strategies for closed solid-state fermentation systems in order to provide a theoretical basis for improving existing solid-state fermentation systems and developing new ones in the future.

In the process of torpedo homing guidance, it is difficult for a single guidance method to adapt to different guidance phases and ensure the torpedo guidance effect effectively. For this reason, this paper designs a fuzzy combined guidance law based on the principle of fuzzy control by combining three different typical guidance methods, namely, fixed lead angle guidance method, proportional guidance method, and variable structure guidance method. The results of simulation and comparison in different environments show that the comprehensive performance of the fuzzy combined guidance law is better than that of other typical guidance laws, which can provide reference for the practical application of torpedo homing guidance.

The application of Caputo-Katugampola gH−differentiability to solve systems of fractional partial differential equations is investigated in this work. The existence and uniqueness of two types of gH−weak solutions for fuzzy fractional coupled partial differential equations are established. Lipschitz conditions are employed, and the Banach fixed-point theorem and mathematical induction are used in our approach. To address the challenges of the initial value problems, a matrix-form Cornwall’s inequality is developed. Additionally, a novel analysis of the continuous dependence of the coupled system’s solutions on the given conditions and approximate solutions is provided. An illustrative example is presented to validate the findings.

Abstract Reliance on renewable energy is increasing, and generating units are being added to the network. Since renewable power fluctuates greatly, the frequency deviation of the grid becomes a crucial problem with access to renewable power generation. The fluctuation of the renewable power output of the system puts forward a higher demand for load frequency control of the power grid to increase the penetration of renewable power in the system. PID controller has proven its effectiveness for the LFC due to its simple structure and clear concept. In this article, the virtual synchronous generator is introduced and a fuzzy self-tuned PID controller is proposed for inertia control. The proposed controller is implemented in light of the significant integration of renewable energy and virtual inertia. The efficacy of the suggested controller is evaluated against the traditional PID controller for the Egyptian Power System as a case study under various load disturbance scenarios. The control technique is employed for variable loads with photovoltaic and wind turbine generation systems. Three instances of load changes are studied and the controller design is performed based on grey wolf optimizer in each case. The overshot and integral time absolute error are considered as comparison measures. The new contribution is applied to the proposed controller for the grid and virtual inertia. In the case of many load variations imposed, the disturbances of residential and industrial loads varied from 0.05, 0.01, 0.15, and 0.02 pu. The maximum overshoot is 0.005 for the proposed controller and 0.0078 for the classic PID controller. The integral time absolute error is 0.06429 for the proposed controller and 0.11481 for the classic PID controller. The results demonstrate the efficacy of the proposed controller for inertia control with high penetration of renewable energy. The results show that the proposed fuzzy self-tuned PID controller has an overshot less than the classical PID controller by 25% and integral time absolute error by 45%. These results show that the use of the proposed fuzzy self-tune controller for the grid and inertia gave a better performance in terms of the overshot value and the integral time absolute error.

Abstract The development of soft computing methods has had a significant influence on the subject of autonomous intelligent agriculture. This paper offers a system for autonomous greenhouse navigation that employs a fuzzy control algorithm and a deep learning-based disease classification model for tomato plants, identifying illnesses using photos of tomato leaves. The primary novelty in this study is the introduction of an upgraded Deep Convolutional Generative Adversarial Network (DCGAN) that creates augmented pictures of disease tomato leaves from original genuine samples, considerably enhancing the training dataset. To find the optimum training model, four deep learning networks (VGG19, Inception-v3, DenseNet-201, and ResNet-152) were carefully compared on a dataset of nine tomato leaf disease classes. These models have validation accuracy of 92.32%, 90.83%, 96.61%, and 97.07%, respectively, when using the original PlantVillage dataset. The system then uses an enhanced dataset with ResNet-152 network design to achieve a high accuracy of 99.69%, as compared to the original dataset with ResNet-152’s accuracy of 97.07%. This improvement indicates the use of the proposed DCGAN in improving the performance of the deep learning model for greenhouse plant monitoring and disease detection. Furthermore, the proposed approach may have a broader use in various agricultural scenarios, potentially altering the field of autonomous intelligent agriculture.

【Objective】 Crops are vulnerable to drought and waterlogging at their seedling stages. Experience-based irrigation and drainage is not effective to alleviate water stresses that seedlings could face. This paper presents an automatic drainage and irrigation system we developed for seedlings of direct-seeded rice. 【Method】 The system includes a micro-sprinkler irrigation system and an automatic drainage device. They are controlled by a LoRa wireless communication network; the irrigation - drainage scheduling was calculated by the fuzzy control algorithm, based on the dynamics of soil moisture and growth of the seedlings measured in the field. The applicability of the system in the fields was evaluated using performance indicators and drainage device performance indicators. 【Result】 ① In the terms of performance, the delay in the response of the system was 0.398 s, the decision-making time was 1.348 s, and the errors of the information monitoring was ±1.48%. ② The automatic drainage device took 4-6 s to close or open, and the standby electric current was 21.7 mA. ③ Experimental results showed that the systems can control soil moisture at 85% of the field capacity 15 days after the seed-drilling; the survival rate of the seedlings was 83.9%. 【Conclusion】 The automatic irrigation and drainage system for seedlings of direct-drilled rice is energy-efficient. It can maintain soil moisture in the proximity of the soil surface around the designed value, alleviating water stress and improving the survival of the seedlings of the directly drilled rice.

In order to improve the control accuracy and robustness of path tracking system for the transplanter, a fuzzy predictive function control method was proposed using feedback linearization in this study. The kinematic model of transplanter was established using Frenet coordinate system. The state feedback method was applied to the nonlinear transplanter system to make the closed loop system become linear system. The Morlet wavelet function was selected as the basis function in predictive function control. The control law of path tracking system for transplanter was designed by the predictive function control algorithm. The weighting coefficient of lateral error in the performance index function for the predictive function control was adjusted online by designing fuzzy rules according to the lateral error and the reference path curvature. The weighting coefficient of change rate of lateral error in the performance index function for the predictive function control was adjusted dynamically by designing fuzzy rules according to the lateral error and the change rate of lateral error. The simulation platform was built for path tracking control of transplanter using Matlab/Simulink software. The simulation results of the fuzzy predictive function control showed that the lateral errors of straight path tracking asymptotically approached zero, and there was no overshoot of actual driving curves at different operating speeds for the straight path tracking. In the straight path tracking, the in-line distance of the fuzzy predictive function control was 1.2, 2.3 and 3.3 m, and the in-line distance of the conventional model predictive control was 2.6, 2.3 and 4.6 m, respectively, when the operating speeds of transplanter were 0.5, 1.0 and 1.5 m/s, respectively. In the case of curve path tracking for the fuzzy predictive function control, the maximum absolute values of lateral error were 0.7, 2.4 and 5.1 cm, and the standard values of lateral error were 0.4, 1.5 and 2.8 cm, respectively, when the operating speeds of transplanter were 0.5, 1.0 and 1.5 m/s, respectively. In the case of curve path tracking for the conventional model predictive control, the maximum absolute values of lateral error were 4.3, 5.5 and 7.8 cm, and the standard values of lateral error were 3.1, 3.5 and 5.0 cm, respectively, when the operating speeds of transplanter were 0.5, 1.0 and 1.5 m/s, respectively. The average operation cycle of the fuzzy predictive function control algorithm was 0.012 s, which was 0.004 s less than that of the conventional model predictive control algorithm. Compared with the conventional model predictive control, the dynamic performance, control accuracy and robustness of path tracking system for transplanter were improved on the premise of ensuring the real-time performance by the fuzzy predictive function control. The automatic driving control system of transplanter was built to install the satellite antenna, satellite receiver, angle sensor, electric steering wheel, controller and vehicle-mounted touch screen on the transplanter. The field experiment was carried out with the automatic driving control system of transplanter. The field test results showed that the fuzzy predictive function control had the strong robustness to the changes of operating speed and reference path curvature. The transplanter tracked the reference path smoothly and effectively. The maximum absolute value of lateral error occurred near the intersection of the straight path and the curve path. Once the operating speeds of transplanter were 0.5, 1.0 and 1.5 m/s, the maximum absolute values of lateral error were 5.9, 7.5 and 9.8 cm, the standard values of lateral error for the straight path were 1.4, 1.7 and 2.7 cm, and the standard values of lateral error for the curve path were 2.5, 3.6 and 5.5 cm, respectively. The fuzzy predictive function control can fully meet the actual control requirements of transplanter, and provided a reference for the research on predictive control method of path tracking for transplanter. [ABSTRACT FROM AUTHOR]

The control system of a quadrotor aircraft is characterized by nonlinearity, strong coupling, and underactuation, making it susceptible to external disturbances that can affect flight performance. To address this issue, this paper proposes a novel control system based on inner–outer loop architecture. In this system, the outer loop position control adopts a PID controller optimized by Genetic Algorithm-based Particle Swarm Optimization (GA-PSO), while the inner loop attitude control employs a Linear Active Disturbance Rejection Controller (LADRC) with fuzzy algorithm-based adaptive tuning, forming a dual-loop control structure. Comparisons with traditional dual-loop cascaded PID controllers, conventional PID in the outer loop with LADRC in the inner loop, and conventional PID in the outer loop with fuzzy algorithm-based adaptive tuning in the inner loop demonstrate that the proposed control system can stably track the desired position and attitude angles under certain external disturbances, exhibiting excellent anti-disturbance capability and stability. [ABSTRACT FROM AUTHOR]

Reliance on renewable energy is increasing, and generating units are being added to the network. Since renewable power fluctuates greatly, the frequency deviation of the grid becomes a crucial problem with access to renewable power generation. The fluctuation of the renewable power output of the system puts forward a higher demand for load frequency control of the power grid to increase the penetration of renewable power in the system. PID controller has proven its effectiveness for the LFC due to its simple structure and clear concept. In this article, the virtual synchronous generator is introduced and a fuzzy self-tuned PID controller is proposed for inertia control. The proposed controller is implemented in light of the significant integration of renewable energy and virtual inertia. The efficacy of the suggested controller is evaluated against the traditional PID controller for the Egyptian Power System as a case study under various load disturbance scenarios. The control technique is employed for variable loads with photovoltaic and wind turbine generation systems. Three instances of load changes are studied and the controller design is performed based on grey wolf optimizer in each case. The overshot and integral time absolute error are considered as comparison measures. The new contribution is applied to the proposed controller for the grid and virtual inertia. In the case of many load variations imposed, the disturbances of residential and industrial loads varied from 0.05, 0.01, 0.15, and 0.02 pu. The maximum overshoot is 0.005 for the proposed controller and 0.0078 for the classic PID controller. The integral time absolute error is 0.06429 for the proposed controller and 0.11481 for the classic PID controller. The results demonstrate the efficacy of the proposed controller for inertia control with high penetration of renewable energy. The results show that the proposed fuzzy self-tuned PID controller has an overshot less than the classical PID controller by 25% and integral time absolute error by 45%. These results show that the use of the proposed fuzzy self-tune controller for the grid and inertia gave a better performance in terms of the overshot value and the integral time absolute error. [ABSTRACT FROM AUTHOR]

Industry 4.0 has not only brought about profound changes to the manufacturing industry itself but has also had a significant impact on the sustainable development of urban economies. By 2021, the urbanization rate of China's resident population will reach 64.72%, with the urban population rising from 170 million in 1978 to 910 million, and the built-up area of cities reaching 63,000 square kilometers. Industry 4.0 is an important driver for sustainable urban economic development, which can improve the efficiency, quality, innovation, and competitiveness of urban manufacturing industries and promote urban economic growth, employment, environment, and social welfare. This paper analyzes the mechanism of the impact of Industry 4.0 on the sustainable development of China's urban economy from the perspective of fuzzy control and soft computing, as well as the application practice of Industry 4.0 in the sustainable development of urban economy. The study shows that fuzzy control and soft computing, as an intelligent computing method capable of dealing with uncertainty, nonlinearity, and complexity, can effectively support the implementation of Industry 4.0 and improve the flexibility, adaptive, and innovative capabilities of the manufacturing industry, thus promoting the green growth, social equity, and resource efficiency of urban economy. The theoretical research and practical exploration with the sustainable development of urban economy provide some insights and lessons. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of frequency recovery in distributed battery energy storage systems (BESSs) and the balancing of the state of charge (SOC) after secondary control inputs have been subjected to false data injection attacks (FDI). A fuzzy high order differentiator (FHOD) observer based distributed resilient control is introduced with the aim of achieving frequency restoration and power adaptive allocation. In order to mitigate the performance degradation caused by the chattering of traditional sliding mode observers, a high order differentiator (HOD) is employed as an observer. By introducing fuzzy control logic to optimize the coefficients of the differentiator, the coefficient of the proposed differentiator is adjusted based on the tracking error, thereby reducing the transient overshoot of the observer when the attack signal changes, which approach effectively balances the response speed and observation accuracy. The simulation outcomes demonstrate that the suggested control strategy is able to mitigate the effects caused by complex and unbounded FDI attacks on the BESSs and exhibits superior transient performance compared to traditional HOD observer in response to changes in attack signal. [ABSTRACT FROM AUTHOR]

The article discusses the application of fuzzy control theory in non-linear networked systems to enhance security against cyber-attacks. It focuses on various control strategies, such as resilient fuzzy control synthesis and adaptive control, to address security issues like denial-of-service attacks and deception attacks. The research presented in the article covers a wide range of topics related to control and security of information physical systems, resilient fuzzy control, and adaptive control in non-linear multi-agent systems and energy security management. The special issue includes 17 peer-reviewed papers that explore innovative approaches to address security challenges in non-linear networked systems. [Extracted from the article]

This paper presents a fuzzy sliding mode control method for voltage control of islanded AC/DC hybrid microgrid. The T‐S fuzzy model is utilized to approximate initial non‐linear dynamic model of the microgrid. The fuzzy rules and membership functions are designed according to the coupling relationship between AC part and DC part. To reduce the influence of external disturbance and cyber‐attacks, an integral sliding mode controller is considered. Furthermore, a sufficient condition is proposed with H∞${H}_\infty $ attenuation performance to ensure the asymptotic stability of sliding motion. The set of gain matrices can be acquired via solving the linear matrix inequalities deduced from the Lyapunov stability analysis. The reachability to the proposed sliding mode surface can be guaranteed via switching control law based on a Lyapunov function. In addition, to eliminate chattering performance of the sliding mode control theory, the fuzzy logic controller is designed to optimize the switching region in boundary layers of saturation functions. Finally, the simulation results for the islanded AC/DC hybrid microgrid are implemented to verify the robustness and effectiveness of the designed fuzzy sliding mode control method. [ABSTRACT FROM AUTHOR]

This paper proposes a new type of echo state network called Long‐Term Memory Enhanced Echo State Network (LTME‐ESN). By extending the ideas of the forget gate and the input gate from the LSTM, LTME‐ESN modifies the equation for updating the state in a Leaky Integral Echo State Network (Leaky‐ESN). By regulating the accumulation of information, the construction of extended periods of dependency between states may be delayed, allowing for adaptive management of the data transported from the previous state to the present one. In order to optimize parameters by using Stochastic Gradient Descent (SGD), this research presents a necessary condition for LTME‐ESN to meet the properties of the echo state network. The study uses low‐frequency sinusoidal, high‐frequency sinusoidal, and chaotic time series to demonstrate the model's efficiency. Simulations show that LTME‐ESN outperforms Leaky‐ESN in prediction accuracy and variation. [ABSTRACT FROM AUTHOR]

In this paper, a proposed echo state network (ESN) model called F‐ESN is based on methane topology. Due to the neurons in the reservoir of the ESN model being random and sparsely connected, the stability of the system structure could be better, and the prediction accuracy could be higher. To solve this issue, the reservoir's neuronal connectivity is modified to a certain methane structure. A master neuron governs each little unit and communicates with the others to pass information. In addition, MFO optimized by adaptive dynamic operators is used to optimize the three parameters of ESN : leakage rate (a)$ (a)$, spectral radius of the connection weight matrix (ρ)$ (\rho)$, and input scale factor (sin)$ (s^{in})$. To verify the effectiveness of the proposed method, low‐frequency sin time series, high‐frequency SIN time series, and MG time series are simulated. The experimental results show that the methane topology can further improve the prediction accuracy of leakage echo state networks. [ABSTRACT FROM AUTHOR]

In this paper, the adaptive fuzzy finite‐time tracking control problem is studied for a class of non‐strict feedback high‐order time‐delay systems with unknown dead‐zone. The fuzzy logic systems are introduced to approximate the uncertain nonlinear functions in the system. The Lyapunov–Krasovskii (L–K) functional technology is used to compensate for the unknown time delays. In addition, based on the backstepping technique and finite‐time theory, an adaptive fuzzy finite‐time tracking controller is designed to ensure that the closed‐loop system is semi‐global practical finite‐time stability, and the tracking error can converge to a small neighbourhood around the origin within a finite time. Finally, the rationality of the proposed method is verified by simulation results. [ABSTRACT FROM AUTHOR]

With the popularity of renewable energy sources, fault‐tolerant frequency stability of distributed generation systems has become a major challenge. This paper proposes an optimal frequency fault‐tolerant control method in the distributed generation system, utilizing adaptive dynamic programming combined with fuzzy critic estimation. First, based on the operational state of the system, a non‐linear dynamic model for the virtual synchronous generator is established. Then, considering the occurrence of actuator faults, the fault‐tolerant control problem is formulated as an optimal control problem, where a fault observer is designed to estimate the fault signals. Furthermore, the proposed adaptive dynamic programming method based on fuzzy critic estimation is employed to effectively solve the highly coupled nonlinear Hamilton–Jacobi–Bellman equation and ensure the state convergence of the system with uniform ultimate boundedness. The optimal fault‐tolerant control law ensures a bounded frequency error for the virtual synchronous generator system in the event of faults. Finally, the effectiveness of the optimal control method is verified by two sets of simulations. [ABSTRACT FROM AUTHOR]

A traditional dredging systems on grab dredgers are typically controlled manually, which leads to low controlprecision and poor dredging flatness. Relying on a control modification project for a specific grab dredger, the operationalcharacteristics of the grab dredger and the driving characteristics of the hydraulic system were analyzed. Adepth -keepingalgorithm based on model predictive control and a fine dredging control algorithm based on fuzzy control were proposed. Adepth-keeping and fine dredging control system based on Siemens PLC was designed, ultimately achieving fully automatic"one -touch fine dredging". Practical application on the dredger demonstrates that the fully automatic operation process ofdepth-keeping and fine dredging control system is stable and reliable, with high control accuracy and flat excavation surfaces,which meets the construction operational requirements and enhances the construction efficiency of grab dredger operations. [ABSTRACT FROM AUTHOR]

This paper studies the fuzzy $ H_\infty $ H ∞ control of nonlinear DC microgrids subject to the dynamic event-triggered mechanism (ETM), aperiodic DoS attacks, noises and mismatching premises. First, using the information of DC microgrid's T-S fuzzy model and aperiodic DoS attacks, a discrete-time resilient dynamic ETM is proposed, which can save constrained system resources, as well as actively exclude attack-induced dropouts and Zeno behaviour. Second, a fuzzy switched system model is built, which provides a unified platform to evaluate effects of all the affecting factors such as the dynamic ETM and DoS attacks. Third, by constructing a piecewise Lyapunov functional, criteria for exponential stability with guaranteed $ H_\infty $ H ∞ performance are obtained, and an event-triggered fuzzy injection current controller is further designed. Simulation results confirm that, in the presence of aperiodic DoS attacks and noises, the proposed controller can stabilise the nonlinear DC microgrids, while the dynamic ETM works well in reducing the triggering rate without dropouts. Tradeoffs can be made between control and communication resources, and the proposed fuzzy controller achieves shorter settling times and smaller overshoots than the robust controller. [ABSTRACT FROM AUTHOR]

This work presents a construction of a novel system for autonomous inspection of power transmission lines using a quadrotor helicopter. The aircraft follows the power lines at a constant distance and acquires high‐resolution images of the conductors, dampers, and towers. Monitoring of the vegetation under the power lines is also performed. The traditional inspection is carried out by helicopters equipped with high‐resolution cameras or by direct visual examination by personnel climbing over de‐energized power lines. However, visual inspection is time‐consuming, labor‐intensive, and dangerous, and often, helicopter cameras cannot capture the acute details needed for accurate and comprehensive examinations. This work successfully evaluates a mathematical model for the quadrotor helicopter used in autonomous inspection through simulations and flight experiments. Two control strategies for power line tracking are compared: a classical PID and a fuzzy PD control. Several simulations and experimental results confirm the efficiency of both control laws, but the fuzzy controller performs better under strong wind disturbances. The results of this work will be implemented by an electric power company to inspect electrical transmission facilities regularly. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The design of the loading path is one of the important research contents of the tube hydroforming process. Optimization of loading paths using optimization algorithms has received attention due to the inefficiency of only finite element optimization. In this paper, the hydroforming process of 5A02 aluminum alloy variable diameter tube was as the research object. Fuzzy control was used to optimize the loading path, and the fuzzy rule base was established based on FEM. The minimum wall thickness and wall thickness reduction rate were determined as input membership functions, and the axial feeds variable value of the next step was used as output membership functions. The results show that the optimized loading path greatly improves the uniformity of wall thickness and the forming effect compared with the linear loading path. The round corner lamination rate of the tube is 91.2% under the fuzzy control optimized loading path, which was increased by 47.1% and 22.6% compared with linear loading Path 1 and Path 2, respectively. Based on the optimized loading path in the experiment, the minimum wall thickness of the variable diameter tube was 1.32 mm and the maximum thinning rate was 12.4%. The experimental results were consistent with the simulation results, which verified the accuracy of fuzzy control. The research results provide a reference for improving the forming quality of thin-walled tubes and plates. [ABSTRACT FROM AUTHOR]

To optimize the acceleration performance of independently driven electric vehicles with four in-wheel motors, this paper proposes an anti-slip regulation (ASR) strategy based on dynamic road surface observer for more efficient tracking of the optimal slip ratio and enhanced vehicle acceleration. The method uses the Unscented Kalman Filter (UKF) observer to estimate vehicle speed and calculate the actual slip ratio, while a fuzzy controller based on the Burckhardt tire model identifies road surfaces. The road's peak adhesion coefficient and optimal slip ratio curve are fitted using a Back Propagation Neural Network (BPNN) optimized by Particle Swarm Optimization (PSO). The control strategy further refines torque management through an adaptive sliding mode control (ASMC) that integrates adaptive laws and a super-twisting sliding mode approach to track the optimal slip ratio. Joint simulations with MATLAB/Simulink and Carsim on low-adhesion, joint, and split road surfaces demonstrate that the strategy quickly and accurately identifies the optimal slip ratio across various road surfaces. This enables the tire slip ratio to approach the optimal value in minimal time, significantly improving vehicle dynamic performance. Compared to conventional sliding mode controllers, the optimized ASMC reduces chattering and improves control precision. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power is the property of Electric Power Editorial Office 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.)

Purpose. To derive mathematical expressions that, using the available information, will allow forecasting the levels of electricity consumption by the city’s outdoor lighting network in the main possible scenarios for several years ahead, as well as when developing an energyefficient smart control system for the electrotechnical complex of lighting complex. Methodology. Creating an effective intelligent outdoor lighting control system involves the use of the following methods. First, using the empirical measurement method, information on illumination, electricity consumption, car and pedestrian traffic is obtained. Statistical methods are used to identify patterns and relationships between the measured values, as well as to make subsequent forecasts. For intelligent control of outdoor lighting, a decisionmaking method based on fuzzy inference is used, which allows one, based on information about the operating conditions of the outdoor lighting network, to determine the recommended value of the current or value of lighting devices and the required power source. This approach will ensure maximum system efficiency. Findings. The obtained analytical dependencies for forecasting the electricity consumption, which are based on data from different time intervals, have determination coefficients of 66.8 and 88.1 %, respectively. The simulation of the operation of a fuzzy control system for the electricity consumption of outdoor lighting on the example of an operated part of the road operated and illuminated by ten 100 W LED lamps for summer and winter nights with different discrete control steps confirms the possibility of achieving the efficiency of outdoor lighting when using the proposed controllability option. The combinedpowered control system is more efficient, reducing electricity consumption in summer and winter by more than 70 % compared to traditional control schemes. Originality. A fuzzy control system for the electrical complex of outdoor lighting in cities is improved, which takes into account the electricity tariff in addition to the level of illumination and the car or pedestrian traffic when generating the control action for the LED driver and determining the rational power source (grid or grid/battery) for lighting devices. Practical value. The architecture of the system for controlling electricity consumption by electrical receivers of lighting networks based on the fuzzy inference algorithm is developed, which is recommended for use to ensure an increase in the energy efficiency of this class of municipal consumers. [ABSTRACT FROM AUTHOR]

In order to enhance the accuracy of temperature control of the patenting furnace, a new temperature control system (TCS) based on the fuzzy adaptive (FA)–proportion integral differential (PID) control algorithm is proposed. The system takes temperature error value and error rate as input and automatically adjusts the PID control parameter value according to the signal of the furnace temperatures to realize adaptive adjustment. Based on the parameter of the FA from simulations, the results of online application showed the temperature fluctuation reduced from 14 to 4°C in Zone I, and it decreased from 15 to 5°C in Zone II. Thus, the accuracy control of operational ability of the TCS was improved obviously. Following practical application, the microstructure analysis of the steel wire postpatenting demonstrated the creation of a more consistently distributed sorbite with enhanced properties by leveraging the new FA‐PID system. [ABSTRACT FROM AUTHOR]

Phalaenopsis has been widely popular ornamental plants, due to its unique appearance and excellent market prospects. The commonly used breeding for phalaenopsis is tissue culture at the same time. However, this task involves high repetition and labor intensity. It is still challenging to increase the production of butterfly orchids. This study aims to improve the yield of tissue culture for the labor-saving in phalaenopsis production. A visual servo platform was proposed with the robotic arm gripping. The picking and planting of tissue-cultured seedlings were realized on the conveyor belt. The visual robotic arm was constructed as a 6-degree-of-freedom robotic arm with eyes on the hand, according to the uncalibrated visual servo control. Firstly, the images were captured from the camera. Image detection was implemented to obtain the image coordinates of the roots, stems, and leaves of phalaenopsis tissue-cultured seedlings. Then, the image data was analyzed to determine the angle between the tissue-cultured seedlings and the end clamp of the robotic arm, as well as the gripping point. According to this angle, the end posture of the robotic arm was obtained during gripping. Afterward, the difference in picking point coordinates was calculated between the current and expected image for the tissue-cultured seedlings. The obtained data was then fed into the fuzzy as an input. The acceleration value was derived to control the speed at the end of the robotic arm from the input quantity. The end gripper of the robotic arm was used to track the tissue-cultured seedlings during planting. The acceleration control was utilized to remain relatively stationary in the planting experiment. The gripping of tissue-cultured seedlings was realized to estimate the posture of the gripper after tracking. The tracking speed was also maintained to plant in the given and fixed culture medium. The accuracy of the original YOLOv8 was improved to identify the roots, particularly for the diverse root morphology of Phalaenopsis tissue-cultured seedlings. AKConv and DSConv modules were introduced to extract the root features, resulting in an improved AKDS_YOLOv8. Compared with the original YOLOv8, the recognition accuracy of AKDS_YOLOv8 increased by 8.6 percentage point for seedling roots, the recall value increased by 10.7 percentage point, and the average accuracy increased by 7.4 percentage point, when the intersection-over-union ratio of the real box and the detection box was set to 50%. The recognition performance was improved significantly. A dual-input single-output fuzzy was utilized by the platform. The input was taken as the difference between the actual and expected pixel coordinates of the target, as well as the variation in this difference. The membership function was used to obtain the membership degrees of different fuzzy sets, corresponding to the difference and the variation in difference. The resulting data was then fed into the fuzzy control rule table. The image deblurring was employed to determine the acceleration at the end of the robotic arm in the direction of the conveyor belt. 80 repeated planting experiments were conducted to verify the success rate of planting. A success rate of 82.5% was achieved under the failure situations. The platform can effectively pick and plant phalaenopsis tissue- cultured seedlings on the conveyor belt, indicating the promising application value. [ABSTRACT FROM AUTHOR]

Demand response (DR) can ensure electricity supply security by shifting or shedding loads, which plays an important role in a power system with a high proportion of renewable energy sources. Industrial loads are vital participants in DR, but it is difficult to assess DR potential because of many complex factors. In this paper, a new method based on fuzzy control is given to assess the DR potential of industrial loads. A complete assessment framework including four steps is presented. Firstly, the industrial load data are preprocessed to mitigate the influence of noisy and transmission losses, and then the K-means algorithm considering the optimal cluster number is used to calculate baseline load of industrial load. Subsequently, an open-loop fuzzy controller is designed to predict the response factor of different industrial loads. Three strongly correlated indicators, namely peak load rate, electricity intensity, and load flexibility, are selected as the input of fuzzy control, which represents response willingness. Finally, the baseline load of diverse clustering scenarios and the response factor are used to calculate the DR potential of different industrial loads. The proposed method takes into account both economic and technical factors comprehensively, and thus, the results better represent the available DR potential in real-world situations. To demonstrate the effectiveness of the proposed method, the case of a medium-sized city in China is studied. The simulation focuses on the top eight industrial types, and the results show they can contribute about 189 MW available DR potential. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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 speed control of wind turbine blade gluing travel mechanism directly affects the gluing quality and stability. Addressing issues such as inadequate adaptive response, insufficient control precision, and weak resistance to interference during the adhesive application process, an analysis of the overall layout and structural components of the adhesive application equipment was conducted. Subsequently, the mathematical model of state equation controlling voltage input and rack-speed output and the fuzzy proportional-integral-derivative( PID) controller with Kalman-genetic optimization were established. Genetic algorithms were employed to optimize key parameters within the fuzzy controller, thereby achieving precise control of the motion system's speed. The integration of a Kalman filter algorithm was also introduced to mitigate the impact of external disturbances on the system. The walking mechanism prototype was used to verify the fuzzy PID control algorithm optimized by genetic algorithm. The experimental results show that the fuzzy PID control algorithm optimized based on genetic algorithm has an overshoot of 0. 8% and a regulation time of 0. 153 s, which is an improvement compared to both fuzzy PID and PID control algorithms. The fuzzy PID based on Kalman genetic optimization improves the response time by 10 times and reduces interference by 16 times compared to the genetically optimized fuzzy PID. The Kalman genetic algorithm optimized fuzzy PID improves the control accuracy, stability and anti-interference ability of the system, which improves the gluing quality and gluing efficiency of the gluing equipment. [ABSTRACT FROM AUTHOR]

The development of soft computing methods has had a significant influence on the subject of autonomous intelligent agriculture. This paper offers a system for autonomous greenhouse navigation that employs a fuzzy control algorithm and a deep learning-based disease classification model for tomato plants, identifying illnesses using photos of tomato leaves. The primary novelty in this study is the introduction of an upgraded Deep Convolutional Generative Adversarial Network (DCGAN) that creates augmented pictures of disease tomato leaves from original genuine samples, considerably enhancing the training dataset. To find the optimum training model, four deep learning networks (VGG19, Inception-v3, DenseNet-201, and ResNet-152) were carefully compared on a dataset of nine tomato leaf disease classes. These models have validation accuracy of 92.32%, 90.83%, 96.61%, and 97.07%, respectively, when using the original PlantVillage dataset. The system then uses an enhanced dataset with ResNet-152 network design to achieve a high accuracy of 99.69%, as compared to the original dataset with ResNet-152's accuracy of 97.07%. This improvement indicates the use of the proposed DCGAN in improving the performance of the deep learning model for greenhouse plant monitoring and disease detection. Furthermore, the proposed approach may have a broader use in various agricultural scenarios, potentially altering the field of autonomous intelligent agriculture. [ABSTRACT FROM AUTHOR]

Summary: As a significant research issue in control and science field, chaos synchronization has attracted wide attention in recent years. However, it is difficult for traditional control methods to realize synchronization in predefined time and resist external interference effectively. Inspired by the excellent performance of zeroing neural network (ZNN) and the wide application of fuzzy logic system (FLS), a noise‐tolerant fuzzy‐type zeroing neural network (NTFTZNN) with fuzzy time‐varying convergent parameter is proposed for the synchronization of chaotic systems in this paper. Notably the fuzzy parameter generated from FLS combined with traditional convergent parameter embedded into this NTFTZNN can adjust the convergence rate according to the synchronization errors. For the sake of emphasizing the advantages of NTFTZNN model, other three sets of contrast models (FTZNN, VPZNN, and PTZNN) are constructed for the purpose of comparison. Besides, the predefined‐time convergence and noise‐tolerant ability of NTFTZNN model are distinctly demonstrated by detailed theoretical analysis. Furthermore, synchronization simulation experiments including two chaotic systems with different dimensions are provided to verify the related mathematical theories. Finally, the schematic of NTFTZNN model for chaos synchronization is accomplished completely through Simulink, further accentuating its effectiveness and potentials in practical applications. [ABSTRACT FROM AUTHOR]

Although field medical microgrids have been widely studied as an important component of future medical power systems, current sharing control in field medical microgrids under false information injection (FDI) attacks has rarely been researched. Based on this, this paper proposes a distributed fuzzy control method for power sharing in field medical microgrids considering communication networks under FDI attacks. First, the field medical microgrid is modeled as a multi-bus DC microgrid system with power coupling. To provide voltage control and initial current equalization, fractional order PI control is applied. In order to reduce the model complexity, the concept of block modeling is employed to transform the model into a linear heterogeneous multi-agent system. Secondly, a fully distributed current sharing fuzzy control strategy is proposed. It can precisely realize current sharing control and reduce the communication bandwidth. Finally, the proposed control strategy is verified by simulation results. [ABSTRACT FROM AUTHOR]

The parameter variations of magnetically coupled resonant wireless power transmission (MCR‐WPT) system cause frequency splitting and resonant frequency shift, which lead to the system detuning and reduce the transmission efficiency. To solve the problems, a frequency tracking control method based on the phase difference between the primary‐side voltage and the secondary‐side current (Frequency Tracking Control‐the primary‐side voltage and the secondary‐side current, FTC‐PS) in this paper. According to the characteristics that the phase difference between the voltage of transmitting side and the current of receiving side is not affected by frequency splitting, the phase difference is controlled at 90°, so that the system can achieve the maximum transmission efficiency under the condition of frequency splitting and resonant frequency shift. The experimental results show that the method can make the system always work at the natural resonant frequency when the parameters change, and the transmission efficiency is not affected by the frequency splitting phenomenon. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

For lithium batteries in fast charging and other scenarios, natural cooling is usually difficult to ensure that the battery temperature is working in the appropriate range, which is very likely to cause the battery temperature is too high or even thermal runaway. The thermal management system integration scheme for different working conditions is proposed, and the vehicle thermal management system model is constructed by AMESim, the compressor speed of the air conditioning system is taken as the regulating quantity, and the compressor speed is regulated by PID control and fuzzy control according to the feedback battery temperature, and the temperature change of the controlled target is analysed by using the high temperature in summer and the intense driving condition US06 as the testing condition. The results show that the designed thermal management integration method and control strategy can meet the cooling requirements of each subsystem, and the fuzzy control can obtain better control effect than PID control. [ABSTRACT FROM AUTHOR]

Aiming at the requirements of the marine work platform for real-time control, time-varying speed and the time-varying torque control of the motor-driven active heave compensation device, this paper introduces a composite control strategy based on the switched reluctance motor (SRM)-driven active heave compensation device. As for the compensation error caused by the time lag in the real-time system, the model prediction trajectory algorithm is used to predict the compensation displacement obtained using the dynamic model. The next time, the control parameters are then provided for the SRM control system in advance to reduce the compensation error. The SRM control strategy selects a double closed-loop compound control strategy of Back Propagation (BP) fuzzy neural network Proportion Integration Differentiation (PID) control. Its outer speed loop uses a fuzzy controller to quickly track a wide range of speed changes. The torque inner loop uses BP neural network adaptive PID control. This helps to reduce torque ripple and to ensure that the electromagnetic torque output of the SRM remains stable. Finally, the system feasibility is verified by setting different wave parameters. The simulation results show that the simulation conditions can reach 97.5% and 96.4% under the 3 and 4 wave levels, respectively. The simulation effect is satisfying, which verifies the feasibility of the proposed scheme. [ABSTRACT FROM AUTHOR]

To control the oil film stiffness of a hydrostatic center frame precisely, an electrohydraulic servo oil film thickness control system was designed. It employs multiple groups of controllable oil cavities to regulate and control the oil film thickness. A mathematical model of the control system was established, and system model identification and verification were performed. A two-stage self-correcting fuzzy proportional-integral-derivative (PID) control strategy was devised and used in the model obtained by identification. An online fuzzy inference algorithm was designed for real-time online adjustment of the fuzzy control rules. Finally, a MATLAB simulation model was established. Compared with conventional fuzzy PID control, two-stage self-correcting fuzzy PID composite control has faster dynamic response characteristics, smaller overshooting, and higher steady-state accuracy. It significantly improves the synchronous control of the film thickness in the oil film control process. [ABSTRACT FROM AUTHOR]

This paper focuses on the combined path following (PF) and direct yaw-moment control (DYC) problem of unmanned electric vehicles (UEVs) based on an event-triggered Takagi–Sugeno (T–S) fuzzy method. First, to describe the parameter uncertainties and nonlinear tire dynamics of UEVs, a T–S fuzzy dynamic model is developed for performance analysis and controller design of the vehicle lateral dynamic system. Second, as the signal transmission inevitably suffers from delay and data dropouts because of the limited network bandwidth, an event-triggered scheme is constructed to complete the communication scheduling and save the communication resource. Third, considering that there is an unavoidable asynchronous constraint between the fuzzy system and the fuzzy controller due to the communication delay, a hierarchical event-triggered fuzzy controller design approach is given via a new Lyapunov functional. Moreover, a differential distribution scheme is introduced to obtain the desired torque of each tire of the vehicle. Finally, experimental results validate that the presented controller in this paper is able to improve the path following accuracy by 61.93% with a resource economization 92.04% compared with the existing method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of East China University of Science & Technology is the property of Journal of East China University of Science & Technology Editorial Office 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 digital culture business benefits from quick technical updates, digital manufacturing, communication networks, and customized consumption, which promotes new supply and consumption. The innovation and development of the digital cultural industry cannot be separated from the physical industry. The efficient production of the physical industry can ensure the innovative development of digital cultural industry. Therefore, to improve the production efficiency of physical industry, this research proposes a temperature control model combining whale optimization algorithm and fuzzy proportional integral differential algorithm based on the traditional temperature control in ceramic firing and wine making process. Firstly, fuzzy control theory is used to improve the proportional integral differential algorithm, and a fuzzy proportional integral differential model with adaptive adjustment function is established. On this basis, to quickly confirm the optimal parameters of the proportional integral differential model, the whale optimization algorithm is introduced to further optimize its weight factors. It was verified that the temperature control adjustment model combining whale optimization algorithm and fuzzy proportional integral differential model was 2.77 s, which was shortened by 1.11 s, 2.37 s and 3.13 s, respectively than the other three models. Therefore, the constructed temperature control model can effectively realize high-precision intelligent temperature control in industrial production, laying the cornerstone for promoting the innovative development of digital culture industry. The proposed model can improve the production efficiency of physical industries, promote digital transformation, and optimize the innovative development of digital cultural industries. [ABSTRACT FROM AUTHOR]

The theory of Hedge Algebra (HA) has attracted the attention of numerous scientists, who have conducted extensive research into it. The capacity to calculate the semantic value of linguistic terms in HA enables the resolution of fuzzy inference issues and the development of fuzzy controllers based on HA (Hedge Algebra Controller - HAC). This paper presents a newly proposed methodology for the design of HAC to control an active damping system. The objective of the controller optimization is to adjust the fuzziness parameters of the hedge algebra structure. The optimization technique adopted in this study is the Particle Swarm Optimization algorithm (PSO), which is a popular and effective algorithm for optimizing multi-parameter systems. The optimal HAC was used to control the active damping system. Simulation results demonstrate that the HAC functions effectively in the presence of varying road disturbances. [ABSTRACT FROM AUTHOR]

Continuum manipulators with controllable shape and exerting force are attractive devices. Utilizing great features of shape memory alloys (SMAs), thin continuum modules are developed. SMAs are embedded around an elastic rod and activated through a control algorithm to set a desired shape. However, complex behavior of SMAs in such a multi-input multi-output system, make the control challenging. Additionally, simultaneous control of position and the force applied by the module, as a challenging problem, has not been investigated so far. Handling this problem may increase the application of continuum robots when utilized as manipulators or when pass through narrow and complex canals with sensitive wall. In this research, position and force control of such continuum module is under focus for achieving a more practical tool for better non-invasive medical devices. Further to the position control, the amount of force applied to the environment is adjusted in different locations of the workspace through a novel fuzzy controller. The results indicate the possibility of simultaneous control of the position and force using fuzzy controller with a reasonable accuracy of 0.5° for angle and 0.05 N for the force. The concluded results may be utilized in developing smarter soft robots. [ABSTRACT FROM AUTHOR]

In the paper, a proportional derivative (PD) controller and a fuzzy system tuning gains from proportional integral derivative controller are applied to stabilize an unmanned aerial vehicle (UAV), to control the attitude. Inputs of fuzzy logical controller consist of the speed required for the distance between the current position of quadcopter and the defined reference point and differences between orientation angles and variance in differences. Outputs of fuzzy logical controller consist of the proportional integral derivative coefficients which make pitch, roll, yaw and height values. The fuzzy-PD control algorithm is real-time applied to the quadcopter in MATLAB/Simulink environment. Based on data from experimental studies, although both classical proportional integral derivative controller and fuzzy-PD controller have accomplished to track a defined trajectory with the quadcopter. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology 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.)

Slope protection frame construction vehicle is construction machinery equipment for soil slotting and cement concrete sliding form of highway slope. Aiming at the problem of motion control of rotating arm of slope protection frame construction vehicle in the slope environment, a position feedback control strategy based on fuzzy multi-parameter adaptive PID(proportional integral derivative) was designed. In consideration of the difference between the two chambers of the asymmetric hydraulic cylinder, the linear mathematical model of valve controlled asymmetrical hydraulic cylinder was established using area difference equivalent modeling method. By defining fuzzy subset, selecting membership function and establishing fuzzy rules, position control of asymmetrical hydraulic cylinder of rotating arm was realized using fuzzy multi-parameter adaptive PID control algorithm. Simulation and experiments show that fuzzy multiparameter adaptive PID based on the area difference equivalent mathematical model can effectively enhance position tracking control accuracy of hydraulic cylinder, and the effectiveness of the proposed control algorithm was verified. [ABSTRACT FROM AUTHOR]

The track tracking effect of intelligent vehicle directly affects the safety of vehicle and passengers. In the process of intelligent vehicle track tracking, the track tracking accuracy is related to many factors such as track curvature, road friction coefficient, and longitudinal speed change. In this paper, a new lateral and longitudinal coupling control algorithm is proposed. Based on the vehicle dynamics model and the optimal control theory, combined with the fuzzy control theory, the Fuzzy Linear Quadratic Regulator (FLQR) lateral optimal controller is designed, and feed-forward control and predictive controller are added. According to the real-time tracking lateral error and fuzzy control algorithm fed back by the system, the weight coefficient of the lateral displacement deviation in the cost function is dynamically adjusted; considering the coupling effect of lateral and longitudinal controllers of vehicle trajectory tracking control, a model predictive control (MPC) longitudinal speed controller is designed based on MPC theory, considering acceleration constraint and acceleration variation constraint, and taking lateral stability as evaluation index. A joint simulation platform is built based on CarSim and Simulink. The simulation results show that the designed lateral and longitudinal coupling controller of FLQR + MPC has better track tracking accuracy and can improve the driving stability of the vehicle; finally, the tracking effect of the designed algorithm is verified by real vehicle experiments. The maximum error of the designed controller algorithm in real vehicle tracking is 0.56 m, and the tracking effect is good. [ABSTRACT FROM AUTHOR]

When the tractor is undertaking the forestry carrying operation, due to the complex road conditions in the forest, it mostly runs at a low speed, has a high steering demand, and often has a load unit, so it pays more attention to its driving stability and steering sensitivity. In this paper, CarSim software is used to build the physical model and load unit model of the tractor, the control strategy of the tractor is designed based on Simulink software combined with the two-degree-of-freedom dynamic model, and the two-degree-of-freedom model of tractor is established. The braking torque feedback control is carried out by combining the yaw rate and the side deflection angle of centroid with the fuzzy control theory. In addition, the proportional control strategy of front and rear wheel angle is introduced according to the four-wheel steering mode of the tractor. Under the CarSim-Simlulink co-simulation platform, combined with the complex road conditions of forestry operations, the low-speed driving and steering experiments of tractor under different control strategies are carried out. The results show that the control strategy adopted in this paper can effectively reduce the steering radius of the tractor under complex road conditions, increase the steering speed, and limit the side deflection angle of centroid to a small range. The tractor has high stability, the steady state value of yaw rate is closer to the ideal state, and the steering is more sensitive. [ABSTRACT FROM AUTHOR]

This paper presents an innovative method to tackle the predictive-control challenges associated with large-scale fuzzy polynomial systems comprising interconnected polynomial fuzzy systems. This study models large-scale nonlinear fuzzy systems in a polynomial framework, which can reduce the number of fuzzy rules. We derive the conditions for controller synthesis in the main theorem using the Lyapunov theory and sum-of-squares technique. Simulation results confirm the validity and efficiency of this approach. [ABSTRACT FROM AUTHOR]

This research introduces an innovative approach for End-to-End steering angle prediction and its control in electric power steering (EPS) systems. The methodology integrates transfer learning-based computer vision techniques for prediction and control with fuzzy signatures-enhanced fuzzy systems. Fuzzy signatures are unique multidimensional data structures that represent data symbolically. This enhancement enables the fuzzy systems to effectively manage the inherent imprecision and uncertainty in various driving scenarios. The ultimate goal of this work is to assess the efficiency and performance of this combined approach by highlighting the pivotal role of steering angle prediction and control in the field of autonomous driving systems. Specifically, within EPS systems, the control of the motor directly influences the vehicle's path and maneuverability. A significant breakthrough of this study is the successful application of transfer learning-based computer vision techniques to extract respective visual data without the need for large datasets. This represents an advancement in reducing the extensive data collection and computational load typically required. The findings of this research reveal the potential of this approach within EPS systems, with an MSE score of 0.0386 against 0.0476, by outperforming the existing NVIDIA model. This result provides a 22.63% better Mean Squared Error (MSE) score than NVIDIA's model. The proposed model also showed better performance compared with all other three references found in the literature. Furthermore, we identify potential areas for refinement, such as decreasing model loss and simplifying the complex decision model of fuzzy systems, which can represent the symmetry and asymmetry of human decision-making systems. This study, therefore, contributes significantly to the ongoing evolution of autonomous driving systems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai University of Engineering Science / Shanghai Gongcheng Jishu Daxue Xuebao is the property of Journal of Shanghai University of Engineering Science Editorial Office 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.)