Control method based on DRFNN sliding mode for multifunctional flexible multistate switch

To address the low accuracy and stability when applying classical control theory in distribution networks with distributed generation,a control method involving flexible multistate switches(FMSs)is proposed in this study.This approach is based on an improved double-loop recursive fuzzy neural network(DRFNN)sliding mode,which is intended to stably achieve multiterminal power interaction and adaptive arc suppression for single-phase ground faults.First,an improved DRFNN sliding mode control(SMC)method is proposed to overcome the chattering and transient overshoot inherent in the classical SMC and reduce the reliance on a precise mathematical model of the control system.To improve the robustness of the system,an adaptive parameter-adjustment strategy for the DRFNN is designed,where its dynamic mapping capabilities are leveraged to improve the transient compensation control.Additionally,a quasi-continuous second-order sliding mode controller with a calculus-driven sliding mode surface is developed to improve the current monitoring accuracy and enhance the system stability.The stability of the proposed method and the convergence of the network parameters are verified using the Lyapunov theorem.A simulation model of the three-port FMS with its control system is constructed in MATLAB/Simulink.The simulation result confirms the feasibility and effectiveness of the proposed control strategy based on a comparative analysis.

Inversion of Seabed Geotechnical Properties in the Arctic Chukchi Deep Sea Basin Based on Time Domain Adaptive Search Matching Algorithm

The chirp sub-bottom profiler,for its high resolution,easy accessibility and cost-effectiveness,has been widely used in acoustic detection.In this paper,the acoustic impedance and grain size compositions were obtained based on the chirp sub-bottom profiler data collected in the Chukchi Plateau area during the 11th Arctic Expedition of China.The time-domain adaptive search matching algorithm was used and validated on our established theoretical model.The misfit between the inversion result and the theoretical model is less than 0.067%.The grain size was calculated according to the empirical relationship between the acoustic impedance and the grain size of the sediment.The average acoustic impedance of sub-seafloor strata is 2.5026×10^(6) kg(s m^(2))^(-1)and the average grain size(θvalue)of the seafloor surface sediment is 7.1498,indicating the predominant occurrence of very fine silt sediment in the study area.Comparison of the inversion results and the laboratory measurements of nearby borehole samples shows that they are in general agreement.

Present situation and developing trend of coal mine automation and communication technology

Introduced developing process of coal mine automation and communication technology,analyzed present features and characteristics of coal mine automation and communication technology,and put forward a few key technical problems needed to be solved.

An Immune Self-adaptive Differential Evolution Algorithm with Application to Estimate Kinetic Parameters for Homogeneous Mercury Oxidation

A new version of differential evolution （DE） algorithm, in which immune concepts and methods are applied to determine the parameter setting, named immune self-adaptive differential evolution （ISDE）, is proposed to improve the performance of the DE algorithm. During the actual operation, ISDE seeks the optimal parameters arising from the evolutionary process, which enable ISDE to alter the algorithm for different optimization problems and improve the performance of ISDE by the control parameters＇ self-adaptation. The .performance of the proposed method is studied with the use of nine benchmark problems and compared with original DE algorithm ~nd-other well-known self-adaptive DE algorithms. The experiments conducted show that the ISDE clearly outperforms the other DE algorithms in all benchmark functions. Furthermore, ISDE is applied to develop the kinetic model for homogeneous mercury. （Hg） oxidation in flue gas, and satisfactory results are obtained.

An Identification Method Based on the Improved NLJ Algorithm and Its Application

The accurate model is the most important and basic condition for the application of advanced process control, but the conventional methods do not provide satisfactory results in the case of unstable processes. To effec-tively control these processes, a novel identification method (Model Parameters and Initial States Identification si-multaneously in closed loop —MPISI) is proposed. The model parameters and initial states of state equation can be simultaneously identified using this method. The results of simulation and application show that this method has the advantageous of disturbance-rejection and robustness. This method proposes a novel way for the optimization and the advanced control of the process systems.

Three Stage Equilibrium Model for Coal Gasification in Entrained Flow Gasifiers Based on Aspen Plus

A three stage equilibrium model is developed for coal gasification in the Texaco type coal gasifiersbased on Aspen Plus to calculate the composition of product gas, carbon conversion, and gasification teml^erature. The model is divided into three stages including pyrolysis and combustion stage, char gas reaction stage, and gas p.hase reaction stage. Part of the water produced in thepyrolysis and combust!on stag.e is assumed to be involved inthe second stage to react with the unburned carbon. Carbon conversion is then estimated in the second stage by steam participation ratio expressed as a function of temperature. And the gas product compositions are calculated from gas phase reactions in the third stage. The simulation results are consistent with published experimental data.

Deep Learning for EMG-based Human-Machine Interaction:A Review

Electromyography(EMG)has already been broadly used in human-machine interaction(HMI)applications.Determining how to decode the information inside EMG signals robustly and accurately is a key problem for which we urgently need a solution.Recently,many EMG pattern recognition tasks have been addressed using deep learning methods.In this paper,we analyze recent papers and present a literature review describing the role that deep learning plays in EMG-based HMI.An overview of typical network structures and processing schemes will be provided.Recent progress in typical tasks such as movement classification,joint angle prediction,and force/torque estimation will be introduced.New issues,including multimodal sensing,inter-subject/inter-session,and robustness toward disturbances will be discussed.We attempt to provide a comprehensive analysis of current research by discussing the advantages,challenges,and opportunities brought by deep learning.We hope that deep learning can aid in eliminating factors that hinder the development of EMG-based HMI systems.Furthermore,possible future directions will be presented to pave the way for future research.

θ-PSO: a new strategy of particle swarm optimization

Particle swarm optimization (PSO) is an efficient, robust and simple optimization algorithm. Most studies are mainly concentrated on better understanding of the standard PSO control parameters, such as acceleration coefficients, etc. In this paper, a more simple strategy of PSO algorithm called θ-PSO is proposed. In θ-PSO, an increment of phase angle vector replaces the increment of velocity vector and the positions are decided by the mapping of phase angles. Benchmark testing of nonlinear func- tions is described and the results show that the performance of θ-PSO is much more effective than that of the standard PSO.

Structure and erosion resistance of Ni60A/SiC coatting by laser cladding

The Ni60A and Ni60A/SiC coatings were obtained by laser cladding on 0.45% C steel. The microstructure and hardness of the coatings were studied by SEM and XRD. The erosion resistances of Ni60A and Ni60A/SiC coatings were also investigated. The results show that the structure of different coatings is up to the temperature gradient and solidifying velocity in metal-melting region during laser cladding process. The coatings consist of a cladding layer, in which dendritic crystal and bulky cell-like crystal exist mainly, and a thermo-affected layer. Ni60A/SiC coating has higher microhardness than that of Ni60A coating, which is mainly caused by SiC and complicated phases formed by Ni, Cr, Fe, C and Si. It is obvious from the erosion test that the Ni60A/SiC coating has high erosion resistance.

Science Letters:Visual odometry for road vehicles—feasibility analysis

Estimating the global position of a road vehicle without using GPS is a challenge that many scientists look forward to solving in the near future. Normally, inertial and odometry sensors are used to complement GPS measures in an attempt to provide a means for maintaining vehicle odometry during GPS outage. Nonetheless, recent experiments have demonstrated that computer vision can also be used as a valuable source to provide what can be denoted as visual odometry. For this purpose, vehicle motion can be estimated using a non-linear, photogrametric approach based on RAndom SAmple Consensus (RANSAC). The results prove that the detection and selection of relevant feature points is a crucial factor in the global performance of the visual odometry algorithm. The key issues for further improvement are discussed in this letter.

Soft Robotics:Morphology and Morphology-inspired Motion Strategy

Robotics has aroused huge attention since the 1950s.Irrespective of the uniqueness that industrial applications exhibit,conventional rigid robots have displayed noticeable limitations,particularly in safe cooperation as well as with environmental adaption.Accordingly,scientists have shifted their focus on soft robotics to apply this type of robots more effectively in unstructured environments.For decades,they have been committed to exploring sub-fields of soft robotics(e.g.,cutting-edge techniques in design and fabrication,accurate modeling,as well as advanced control algorithms).Although scientists have made many different efforts,they share the common goal of enhancing applicability.The presented paper aims to brief the progress of soft robotic research for readers interested in this field,and clarify how an appropriate control algorithm can be produced for soft robots with specific morphologies.This paper,instead of enumerating existing modeling or control methods of a certain soft robot prototype,interprets for the relationship between morphology and morphology-dependent motion strategy,attempts to delve into the common issues in a particular class of soft robots,and elucidates a generic solution to enhance their performance.

Improved SDT Process Data Compression Algorithm

Process data compression and trending are essential for improving control system performances. Swing Door Trending (SDT) algorithm is well designed to adapt the process trend while retaining the merit of simplicity. But it cannot handle outliers and adapt to the fluctuations of actual data. An Improved SDT (ISDT) algorithm is proposed in this paper. The effectiveness and applicability of the ISDT algorithm are demonstrated by computations on both synthetic and real process data. By applying an adaptive recording limit as well as outliers-detecting rules, a higher compression ratio is achieved and outliers are identified and eliminated. The fidelity of the algorithm is also improved. It can be used both in online and batch mode, and integrated into existing software packages without change.

An Improved Genetic Algorithm for Solving the Mixed⁃Flow Job⁃Shop Scheduling Problem with Combined Processing Constraints

The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied.Targeting this problem,the process state model of a mixed-flow production line is analyzed.On this basis,a mathematical model of a mixed-flow job-shop scheduling problem with combined processing constraints is established based on the traditional FJSP.Then,an improved genetic algorithm with multi-segment encoding,crossover,and mutation is proposed for the mixed-flow production line problem.Finally,the proposed algorithm is applied to the production workshop of missile structural components at an aerospace institute to verify its feasibility and effectiveness.

Dynamic analysis of elastic screen surface with multiple attached substructures and experimental validation

A feasible method to improve the reliability and processing efficiency of large vibrating screen via the application of an elastic screen surface with multiple attached substructures （ESSMAS） was proposed. In the ESSMAS, every screen rod, with ends embedded into elastomer, is coupled to the main screen structure in a relatively flexible manner. The theoretical analysis was conducted, which consists of establishing dynamic model promoted from the fuzzy structure theory as well as calculating for the equivalent stiffness of each attached structure. According to the numerical simulation using the NEWMARK-fl integration method, this assembling pattern significantly leads to the screen surface/rod having larger vibration intensity than that of the corresponding position on screen structure, which specifically, with an averaged acceleration amplitude increasing ratio of 11.37% in theoretical analysis and 20.27% in experimental test. The experimental results, within a tolerant error, also confirm the established model and demonstrate the feasibility of ESSMAS.

High-resolution boosted reconstruction of γ-ray spectra

Direct demodulation method(DDM) was applied to reconstruct γ-ray spectra. Boosted Richardson-Lucy iteration was introduced into DDM. Monte Carlo method(here GEANT 4) was proposed to calibrate response function and establish response matrix. First, gauss function was regarded as total energy peak. Spectra line was simulated with nine gauss functions. And afterwards DDM was applied to reconstruct the simulated spectra line and determine peak positions and areas. Compared with original spectra, for case that peak position interval was about 1/3 full width half maximum(FWHM), the error of rebuilding peak position was 2 channels. The rest of peaks could be searched accurately. The relative errors of all peaks' area were less than 4%. Then, three key factors, including noise, background, response matrix, were discussed. Finally, DDM was applied to calibrate the field NaI gamma spectrometer. The errors of U, Th, K were less than 5%. Comprehensive studies have shown that it is feasible to reconstruct gamma-ray spectra with DDM. DDM can significantly pseudo-improve energy resolution of gamma spectrometer, effectively decompose doublets whose peak potential interval is1/3 FHWM, and accurately search peak and calculate areas. DDM can restrain noise strongly but is greatly influenced by background. And DDM can improve the accuracy of qualitative and quantitative analysis in combination with the conventional spectrum analysis method.

Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation

In recent years, immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution for non-linear optimization problems in many engineering applications. However, IGA with deterministic mutation factor suffers from the problem of premature convergence. In this study, a modified self-adaptive immune genetic algorithm (MSIGA) with two memory bases, in which immune concepts are applied to determine the mutation parameters, is proposed to improve the searching ability of the algorithm and maintain population diversity. Performance comparisons with other well-known population-based iterative algorithms show that the proposed method converges quickly to the global optimum and overcomes premature problem. This algorithm is applied to optimize a feed forward neural network to measure the content of products in the combustion side reaction of p-xylene oxidation, and satisfactory results are obtained.

Enabling Technology of Multiagent Manufacturing System:A Novel Mode of Self-organizing IoT Manufacturing

As the manufacturing mode focuses more on network and community,the orders and production processes are becoming highly dynamic and unpredictable.The traditional manufacturing system cannot handle those exceptional events such as rush orders and machine breakdowns.Nevertheless,the multiagent manufacturing system(MAMS)becomes a critical pattern to deal with these disturbances in a real-time way.However,due to the lack of universality,MAMS is difficult to be applied to industrial sites.A new multiagent architecture and the relay cooperation model based on a positive process relation matrix are proposed to address this paper’s issue.An optimized contract net protocol(CNP)-based negotiation mechanism is developed to improve the efficiency of collaboration in the proposed architecture.Finally,a case study of self-organizing internet of things(Io T)manufacturing system is used to test the feasibility and effectiveness of the method.It is shown that the proposed self-organizing Io T manufacturing mode outperforms the traditional manufacturing system in terms of makespan and critical machine workload balancing under disturbances through comparison.

Analysis of system trustworthiness based on information flow noninterference theory

The trustworthiness analysis and evaluation are the bases of the trust chain transfer. In this paper the formal method of trustworthiness analysis of a system based on the noninterfer- ence （NI） theory of the information flow is studied. Firstly, existing methods cannot analyze the impact of the system states on the trustworthiness of software during the process of trust chain trans- fer. To solve this problem, the impact of the system state on trust- worthiness of software is investigated, the run-time mutual interfer- ence behavior of software entitles is described and an interference model of the access control automaton of a system is established. Secondly, based on the intransitive noninterference （INI） theory, a formal analytic method of trustworthiness for trust chain transfer is proposed, providing a theoretical basis for the analysis of dynamic trustworthiness of software during the trust chain transfer process. Thirdly, a prototype system with dynamic trustworthiness on a plat- form with dual core architecture is constructed and a verification algorithm of the system trustworthiness is provided. Finally, the monitor hypothesis is extended to the dynamic monitor hypothe- sis, a theorem of static judgment rule of system trustworthiness is provided, which is useful to prove dynamic trustworthiness of a system at the beginning of system construction. Compared with previous work in this field, this research proposes not only a formal analytic method for the determination of system trustworthiness, but also a modeling method and an analysis algorithm that are feasible for practical implementation.

Adaptive Extended Isogeometric Analysis for Steady-State Heat Transfer in Heterogeneous Media

Steady-state heat transfer problems in heterogeneous solid are simulated by developing an adaptive extended isogeometric analysis(XIGA)method based on locally refined non-uniforms rational B-splines(LR NURBS).In the XIGA,the LR NURBS,which have a simple local refinement algorithm and good description ability for complex geometries,are employed to represent the geometry and discretize the field variables;and some special enrichment functions are introduced into the approximation of temperature field,thus the computational mesh is independent of the material interfaces,which are described with the level setmethod.Similar to the approximation of temperature field,a temperature gradient recovery technique for heterogeneous media is proposed,and based on the Zienkiewicz–Zhu recovery technique a posteriori error estimator is defined to automatically identify the locally refined regions.The convergence and performance properties of the developed method are verified by using three numerical examples.The numerical results show that(1)The convergence speed of the adaptive local refinement is faster than that of the uniform global refinement;(2)The convergence rate of the high-order basis functions is faster than that of the low-order basis functions;and(3)The existing inclusions change the local distributions of the temperature,and the extreme values of the temperature gradients take place around the inclusion interfaces.