In order to improve the inadequate firing accuracy of the existing prototype of a 30-mm caliper gun overhead weapon station, a triple-index constrained optimization model with damper is developed with the consideration of a real-world operating environment. Furthermore, a method of deriving the characteristic parameters of the damper is proposed based on the triple indices, and the optimal damping parameters are obtained. Simulation results show that the dynamic characteristics of the automatic-firing muzzle of the overhead weapon station with the proposed damper have been significantly improved, where the dispersion variances of horizontal and pitching are decreased by more than 30%, indicating that the firing accuracy is considerably enhanced. The result can be applied for the development of overhead weapon stations in the future.

Forced unavailability of units not only influences economic indicators of nuclear power units like availability, but also seriously threatens the safe operation of units. It is significantly important for the safety and economy of nuclear power units to reduce forced shutdown times and shorten forced shutdown times of nuclear power units. In this paper, on the basis of European Pressurized Water Reactor (EPR) units for the third generation nuclear power, the application of VVP and ARE systems in EPR units is studied based on the Probabilistic Availability Assessment (PAA) method. Critical failure modes that influence forced unavailability of units are determined by Failure Mode and Effect Analysis (FMEA). Based on this, the unplanned shutdown time that influences unit availability is calculated, whether the unplanned unavailability of EPR units can meet the requirements is evaluated, critical weaknesses are identified, and methods of optimization are put forward. The results are a good reference for design and research, system configuration, and equipment selection of newly-built nuclear power units, and they also provide referable opinions and suggestions for improving the reliability of existing nuclear power units.

Camsof drive gear platesare key components of D type knotters.Wearis the main failure mode of cams. Accordingly, this paper studies the relationship between cams’ wear andthe reliability of a knot that is equippedin a D type knotter. Based on the theoretical and motional wear analysis of the cam mechanism assembledin a drive gear plate of a D type knotter, the mathematical relationship between cam wear and the displacement reliability of the cam follower is established. Moreover, the displacement reliability of the cam follower is calculated by joint simulation of the ADAMS virtual prototype and ANSYS. The followingconclusions are reached after a considerable number of simulations: (1)Thecam mechanism works at thestable wear stage at the time of design life. (2) The D type knotter will be unable to successfully tie knots anymore when the wear volume of the cam and contact surface of the follower reaches 2.3mm (the cam follower displacement reliability is 0.9463). (3)99% displacement reliability of the cam follower, at a workload of 20000 bales,requiresthe mean wear of the cam contact surfaceto be less than 0.87mm. A further conclusion is that maintenance and replacement measures are suggested to be taken at a workload of 20000 bales,to ensure that the displacement reliability of cam follower ismore than 99% andthe D type knot isworkable.

In the paper, the sensitivity of the processing parameters for the cutting force on the disc-mill cutter machining TC17 blisk-tunnel is studied. Firstly, based on response surface methodology (RSM), a quadratic regression model between the cutting force and the processing parameters (cutting speed, feed rate per tooth, and cutting height) was established. Then, the local sensitivity of each parameter for thecutting force was calculated using the regression model, and a sensitivity curve of single parameter was acquired. Stable and unstable regions of each parameter were obtained byanalyzing the sensitivity curve. Finalglobal sensitivity of the process parameterstothe cutting force was computed using theSobol index method. The main effect, total effect, and interactive effect indexes were acquired. The results suggested that the cutting force for the disc-mill cutter machining blisk-tunnel is sensitive to thecutting speed and the feed rate per tooth.There existsa remarkable interaction between thecutting speed, feed rate per tooth, and cutting height on the cutting force. The cutting speed with [30,50], feed rate per tooth with [0.02,0.065],and cutting height with [28,43] are stable regions for the cutting force.

Building a fault tree and calculating the sequence of bottom eventsimportance degree are key steps in fault diagnosis. Two improvements are made to the fuzzy fault tree in this study. The first is building the fault tree using Interpretive Structure Modeling (ISM)technology. The second istransforming triangular fuzzy numbers into binary connection numbers (BCN)through the uncertainty theory of set pair analysis,where the certainty coefficient is determined by the median of the triangular fuzzy number and the uncertainty coefficient is determined by the interval value described bythe upper and lower limitations.The formula of failure probability of the top event and the formula of probability importance of the bottom event are deduced with the binary connection number. This method reduces the calculation amount.A case studyis carried out to verify the feasibility and effectiveness of the method.

Based on data of the equivalent stress from the ANSYS for a loaded hollow shaft, the correlation between a mechanical part’s elastic modulus and the corresponding Von Mises stress is statistically verified in this paper. Using the Copula correlation theory, a static reliability model involving stress-strength interference is built. According to the performance degradation data of mechanical parts with long-life and high-reliability, deterministic time series models are used to extract the characteristic information of the distribution of degradation variables, and then a method is proposed for estimating the characteristic parameters of degradation strength and integrated stress. Two-stage maximum likelihood estimation is applied to determine the scalar degree of correlation between both, and then a reliability assessment of long-life mechanical parts is completed.

On-orbit maintainability is one of the most important methods to extend the lifecycle of the space station, as it ensures the safety of astronauts and minimizes operating costs. Based on this, maintainability verification plays an extremely significant role in on-orbit maintenance and can verify the correctness and validity of the on-orbit maintainability. By conducting a comparative analysis and reviewing best practices both at home and abroad, this paper embarks on the mission of the China manned space station, combined with the requirements of objects, test items, and environment that need to be verified. The authors propose a ground testing by-step classification methodology and a simplified zero-gravity simulation separated space platform to accommodate the validation of the entire process on the orbital replaceable units.

For a system subject to gradual degradation, it can be in one of three different states: normal, minor defective, or severe defective stages, which can overall be referred to as a three-stage failure process. For this system, periodic inspection may not be the most ideal policy.Fewer inspections will lead to lower costs if the system is in the normal stage, but if the system is in the defective stage, frequent inspections are recommended to prevent failure. Therefore, it may be more economical to take the sequential time${{T}_{j}}(j=0,1,2,\cdots )$as the inspection interval. In view of this, a preventive maintenance (PM) model subject to sequential inspection for a three-stage failure process is proposed. Two-level sequential inspections, postponed maintenance and opportunistic maintenance (OM), are introduced into the PM model. The minor inspection is taken at the successive time${{S}_{j}}(j=0,1,2,\cdots )$, where${{S}_{j}}=\sum\limits_{k=0}^{j}{{{T}_{k}}}$, ${{T}_{0}}=0$, and${{S}_{0}}=0.$ Minor inspection is an imperfect inspection that can identify the minor defective stage with a certain probability but can reveal other two stages completely. The nth major inspection is taken to substitute for the A_n ^{th$(n=0,1,2,\cdots )$} minor inspection, where ${{A}_{n}}=\sum\limits_{k=0}^{n}{{{N}_{k}}}$,${{N}_{0}}=0$, and${{A}_{0}}=0.$ Major inspection is a perfect inspection that can distinguish the state of the system perfectly. Once the severe defective stage is identified, the inspection is stopped and the maintenance action is postponed to the next OM if the time to the next OM is less than a threshold level; otherwise, the system is maintained immediately. A numerical example is given to demonstrate the proposed model by comparing with other models and analysing the influence of the parameters on the expected cost.

The performance of a composite wind turbine blade varies with its ply parameters. According to the design principle of the response surface method,different ply schemesarebuilt. Taking the optimal overall properties of the blade as goals, with ply parameters as independent variables and thestatic strength and stiffness of the blade as response variables, experimentsare designed using theTaguchi method in Minitab, and the static strength and stiffness of the blade aresimulated. The mapping relationship between ply parameters and the Tsai-Wu failure factor is determined, the maximum displacement isestablished through multivariate linear regression analysis, and the significant test of the mathematical model and its coefficients is performed. The influence of ply parameters on blade structure performance is analyzed. The correctness and effectiveness of the method are verified.

TSV technology can achieve heterogeneous integration by stacking different technologies and functions of logic chip, memory, MEMS, etc., as a system. There are many significant advantages for heterogeneous integration in terms of cost, performance, and time to market. TSV technology has the potential to improve 3D packaging. As the important physical connection and electrical connection between the chips, TSV’s reliability is undoubtedly the key to determine the reliability of TSV three-dimensional integrated devices. As a new interconnect technology, TSV technology faces many process difficulties and challenges. Its reliability has not been fully studied and guaranteed. The process optimization and reliability improvement of TSV have become a hot topic in recent years. Recognition process defects andanalysis of the failure mechanism play important roles in the optimization and improvement of design, production, and use of TSV three-dimensional integrated devices. In this paper, the square TSV and circular TSV with different ratios were researched by microphysical analysis and data analysis. The analysis results revealed the key technological factors and physical mechanism of formation of the TSV defects, which can support TSV device development, production, and reliable application.

The viscoelastic material constitutive relationship is relatively complex in the engineering practice. People often use the Prony series method to fit experimental data. A lower number of terms leads to lower accuracy, but a higher number of terms leads to difficulty of fitting.Thus, a custom neural network model was put forward to replace the traditional algorithm in the Prony series fitting process. Based on each specific form of the Prony series constructed corresponding activation function of the hidden layer in the neural network, the number of neurons in the neural network corresponded to the number of the Prony series. A numerical example showed that the custom neural network can achieve good fitting results. It is convenient and appropriate to select the number of terms and also shows rapid convergence and high accuracy.

To address the issue that conducting a reliability enhancement test for large-caliber guns is impossible based on actual equipment, a new kind of enhancement test bed for the breech mechanism is designed and established. By measuring the extreme working stress of the test bed, enhancement test stress and enhancement stress levels are determined. The cross sections of single stress enhancement tests and comprehensive stress enhancement tests are designed. The cartridge extracting process is selected to excite the potential failures. The wear of the shaft arm of the shell stop and the wear of the arm of the cocking shaft are measured and analyzed based on finite element analysis and micro examination tests. The practical failure process is illustrated. Under the combined action of impact sliding coupling wear and abrasive particle wear, there are flakes, pan furrows, and holes on the part surfaces, which result in outline variation and force transmission failure. The research idea of this paper provides a new kind of failure mode analysis method for large-caliber guns.

Impeller blowers are used to convey materials for various forage harvesters. As the main working component, the throwing impeller endures various static and dynamic loads while conveying the materials. This makes the throwing impeller prone to fatigue fracture,so it is very necessary to find a feasible model to estimate the fatigue life of the throwing impeller accurately. In order to obtain the accurate random cyclic load applied on the high-speed rotating impeller, the finite element analysis and the fluid-solid coupling method are adopted to calculate the stress distribution of the impeller under the combined action of the fluid-solid coupling flow field pressure, the centrifugal force, and the gravity. At the same time, the stress on the dangerous section of the impeller is measured by using the DH5909 wireless strain testing system and is compared with the calculated one. The contrast results show that the numerical calculation results are reliable. To accurately predict the fatigue life of the throwing impeller at the design stage, the two-parameter nominal stress model is deduced and combined with the linear cumulative damage model of Miner and the lognormal distribution model. Its two parameters of the average stress S_m and the stress amplitude S_a can be obtained through finite element analysis and do not have to be equivalent to a symmetrical cyclic load. Therefore, its precision of estimating the fatigue life is improved. By contrasting the rated and predicted fatigue lives of an impeller, it was found that the impeller’s actual rated lives are closer to the predicted lives of the Goodman and Gerber two-parameter nominal stress model than those of the conventional S-N curve. In particular,they are closer to the calculation results of the Gerber-type two-parameter nominal stress model. This shows that the Gerber-type two-parameter nominal stress model is more accurate and suitable to predict the fatigue life of the throwing impeller. These achievements will play a significant role in further optimizing the impeller and improving its reliability.

In order to solve the one-sidedness problem based on a single indicator for evaluating the status of health (SOH) and predicting the remaining useful life (RUL) of lithium-ion batteries, a new algorithm is developed where the different features are integrated on the basis of the beta function distribution. The data of the capacity, internal resistance, and constant current charging time (CCCT) of lithium-ion batteries are analyzed, and then the fused features are presented. The simulation includes the data fusion of different types of batteries and the comparison between the SOH of a single indicator and the SOH of two or three fused indicators. From the simulation results, the end-of-life of the three features after fusion is shorter than the capacity, which indicates that multi-indicators are closer to the real situation than a single indicator for SOH and RUL.

Passenger locomotives are developing rapidly in the direction of high speed, high power, energy saving, environmental protection, and light weight. In order to avoid locomotive failures such as fatigue fracture, the requirements for reliability and fatigue strength are becoming increasingly higher. The passenger locomotive running steadily and safely mainly depends on whether the design of the steering frame structure is reasonable. Based on the locomotive bogie by 3D solid modeling,the static strength and modal analysis of ANSYS finite element analysis software is applied to the reliability of the operation stage to make judgments and a reliability evaluation model frame is set up based on finite element analysis.

As the core of product architecture, the vital components of complex machinery and electronics products are of great significance in the process of product development, design, and manufacturing. However, it is very difficult to identify the vital components from a large number of components. Based on this, a vital components identification method on the basis of complex networks is proposed. First, the weighted and directed complex network (WDCN) of the relationships between the components is established. Then, the modified LeaderRank algorithm is used to identify the vital nodes in the network; this algorithm has shown low computational complexity and high calculation efficiency. Finally, a classical disease outbreak model, SIR (Susceptible-Infective-Recovered), is used to verify the correctness of the identification results. In the case of complex electric blower products, the vital components are identified using the proposed method, and the final result shows the effectiveness of this method. Such identification lays the firm foundation for the dramatic improvement of the reliability and quality of complex machinery and electronics products.

Universal generating function (UGF) is a basic and important technology in the reliability evaluation of multi-state systems (MSSs). It has been widely noticed by reliability scholars and engineers since its introduction. In the process of reliability evaluation of MSSs with UGF, universal generating operators play a great role in synthesizing the system output performance rate. For many uncertain MSSs in actual engineering, when the connection structure between components is unknown and/or the performance relationship is unclear, the definition of the weighted universal generating function is proposed. By designing reliability evaluation indices and constructing a weighted universal generating function of MSSs, reliability parameters of MSSs can be evaluated. A real case of steam turbine power generation system in a repairable naval equipment system is conducted to illustrate the applications.

Earthquake ground motion is a random process, and each response of a structure can be implemented as a random process. A large scale structure of the long buried pressure pipelines have the random variation of the space-time seismic load. A random space-time seismic load model is established by studying the time-varying and spatial characteristics of seismic load. Combined with the theory of fluctuation, the dynamic response of the buried pipeline is analyzed, and the dynamic response of the buried pressure pipeline is established by using the Von-Mises strength theory under the internal pressure. Based on the simplified formula of theoretical analysis, the first order second moment reliability method and the first transcendental failure theory are used to analyze the dynamic reliability of buried pipeline under random space-time seismic load, and the application of the method is analyzed. Dynamic reliability analysis methods for real application are validated. The feasibility of the proposed method is verified by combining the Wenchuan Earthquake. The study shows that it is necessary to consider the characteristics of random space-time seismic load when seismic damage analysis of long buried pressure pipeline, which lays the foundation for the theory of random space-time vibration.

Aiming at the problem that the existing reliability logic model cannot accurately describe the logical relationship of reliability within the equipment system-of-systems (ESoS), the mission reliability model of the weighted parallel structure of ESoS was established based on the analysis of structure and characteristics of ESoS. Based on the traditional system structure, the model considered the mission reliability of each functional system and its contributions to the ESoS. The system contribution was taken as the system weight in the right-linkage structure, and the ESoS mission reliability evaluation model was derived according to the reliability status. Meanwhile, the system was modeled as a redundant system, and the solution of the model with the same and different failure probabilities were given. Through an example and comparative analysis, the rationality and validity of the method were verified.

In view of the large manpower and financial resources required for the maintenance test of the armored equipment of the army, as well as the long acquisition period of the maintenance test data, this paper focuses on the maintenance test method based on small sample size and establishes the maintenance test verification based on the Bayes small sample theory. Assessing the model and proposing an equipment maintenance test based on this method effectively reduces the number of samples needed to validate the indicators. At the same time, it is validated with the aid of model equipment maintainability tests. The accuracy is high, and maintenance and verification are reduced. The proposed method is of great reference value for reducing the cost of equipment testing and shortening the equipment development cycle in the development of army equipment.

As a dynamic non-destructive testing technology, acoustic emission (AE) detection technology can locate the early damage of equipment and provide data support for health management. In order to improve the accuracy of wireless AE-based location, this paper proposes a Taylor expansion least squares algorithm to concern network delay. Firstly, according to the planar four-point positioning structure, a wireless AE detection system based on star topology is constructed, and the Taylor expanded least squares localization algorithm is then proposed. Secondly, the network queuing delay is analyzed, and the algorithm is modified with weighting factors of delay. Finally, simulation results show that the accuracy of the network delay-weighted algorithm has been improved.

Vibration analysis is one of the most popular methods for state monitoring of rotating machines, and feature extraction is of much importance in the design of the monitoring system. In this paper, a new high-level feature extraction method based on correlograms for vibration signal analysis is proposed, and it includes two phases. Firstly, in the learning process, a codebook is created from training data using thek-means algorithm. Next, in the testing process, for a given data stream collected from a monitoring rotating machine, the correlogram in each cycle is obtained by comparing every data point with all codewords in the codebook at first; the entropy is then computed to form final high-level features to measure the state of the machine. A change decision can be made finally based on features extracted from null hypothesis testing. Based on an experimental setup used in our previous work, the proposed method is evaluated with application to the speed change monitoring of a rotating machine. Experimental results demonstrate the excellent performance and the priority of the method compared with ten typical features.

Aiming at the complexity, nonlinearity, and non-stationarity of the rolling bearing vibration signal, a fault diagnosis method based on Ensemble Empirical Mode Decomposition (EEMD) and Gauss Bernoulli Deep Belief Network (GBDBN) model is proposed. The method first carries out EEMD on the vibration signal; second, the eigenvalues of each intrinsic mode function (IMF) are statistically analyzed; then, the feature vectors are constructed by selecting less change features; finally, the normalized feature vectors are input into the GBDBN to identify the fault types. The experimental results show that the proposed methodachievesmore than 90% recognition rate of fault types and has better fault diagnosis ability, which can provide convenience for maintenance.

Contact fatigue is one of the main causes of the failure of gear transmission systems. Considering the gear separation phenomenon, a contact damage-torsional vibration coupling dynamic model is proposed with nonlinear backlash, time-varying meshing stiffness, contact stress model, and contact damage model based on gear contact geometry, S-N curve, and reliability theory. Using the model established, the numerical simulation is investigated to analyze the degradation process of the gear system under a fixed-speed condition. The research results show that the degradation process of gear contact fatigue has a non-linear relation with rotational speed and rotation cycle. The contact stress shows that there is no obvious gear separation phenomenon at n=1000 and n=3000, but there are obvious gear separation phenomena at n=2000. The remaining useful life of gear teeth changes more violently and the total life is lowest at n=2000.

In the research process of ID3 algorithm, some deficiencies were found. RLID3 algorithm is on the improvement of ID3 algorithm in terms of the number of leaf nodes. RLID3 algorithm uses ensemble learning method to integrate multiple incremental RLID3 model and the predictive ability of the algorithm is further improved.Incre_RLID3 is an incremental learning algorithm that is based on a decision tree constructed by RLID3. It adjusts construction of the tree using incremental data set. The goal of this algorithm is to use the new data on the basis of the original decision tree. In order to further improve the accuracy of the algorithm, this paper proposes an ensemble algorithm PAR_WT. The basic idea of this algorithm is to use the data set to generate multiple RLID3 decision tree. Then, the test samples are classified by each decision tree. Finally, combined with the PAR_WT algorithm and Incre_RLID3 algorithm, an incremental ensemble algorithm Incre_RLID3_ENM algorithm with incremental learning ability is obtained.

In order to satisfy the needs of people’s intelligent home environment, this paper proposes an intelligent home control system based on gesture recognition technology. To obtain and recognize gestures of human by the depth data,skeleton data and 3D point clouds uses Kinect.The Arduino microprocessor is used to process the received data to realize the intelligent control of home appliances. The body mass index BMI was generated by the acquired biological characteristics, and detects the user’s physical condition. The experimental results show that the system can achieve effective control of household appliances and accurately measurehuman biological characteristics by receiving and recognizing human body posture.It proves that the system is innovative and practical.

In most scenes, color images have richer information than grayscale images. This paper presents a method of grayscale image pseudo coloring that constructed and trained an end-to-end deep learning model based on dense neural network aims to extract all kinds of information and features (such as classification information and detail feature information). Entering a grayscale picture to the trained networkcan generate a full and vibrant vivid color picture. By constantly training the entire network on a wide variety of data sets, you will get the most adaptable, high-performance pseudo color network. The experiments show that the method proposed has a higher utilization of features and can obtain a satisfactory coloring effect. Compared with the current advanced pseudo color methods, it has also made remarkable improvements, and to a certain extent, the problem during the coloring processing have been improved, such as color overflow, loss of details, low contrast etc.

Recently, more and more researchers have focused on the problem about how to automatically distinguish text images from non-text ones. Most of previous works have originated from local features, which are computational expensive, and usually employ GPU in their procedure. To address this problem, we propose a new and simple but effective scheme from an overall perspective. In the proposed scheme, a sort of holistic feature is first extracted from Fourier spectrum, which describes the characteristic of the image or the sub-image as a whole without local feature extraction; then, random forests are utilized to classify images into text and non-text ones. Experimental results in several public datasets demonstrate that this scheme is efficient and effective.

With the rapid growth of the video data’s amount, how to efficiently retrieve useful information has become very urgent. As the base of video indexing and searching, video annotation has great significance for its application prospect and research value. In the semantic detection, moving object detection and tracing is the basis. In the paper, adaptive Gaussian Mixture Model is used to background model; Camshift and Kalman filter are used to trace the players and ball. The implement of the algorithms is all based on Visual C++ and Visual c#2008. OpenCV and Aforge.net class base are also used. Experimental result shows that the method annotates well.

In the drawing process of undergroundhigh-voltage power supply system, the position of the bus is to complete the drawing of the underground high-voltage power supply system through the direct alignment between the high voltage outlet switch graphic element and the high voltage outlet switch graphic element, there is no independent bus graphic element. The existing topology analysis method of the high-voltagegrid ofcoal mine based on incidence matrix cannot complete topology analysis and construct topology model of the above undergroundhigh-voltage power supply system diagrams. Network topology model of high-voltage power supply system of coal minebased on node coincidence and incidence matrix, which can solve the above problems effectively, is proposed. The simulation results show that the model can easily complete the topology identification of the power supply network of the high-voltage power supply system as well as realize the function of automatic short-circuit calculation. It has the characteristics of simplicity and high efficiency.

Feature selection is the key issue of unstructured data mining related fields. This paper presents a dimensionality reduction method which uses a rough set as the feature selection tool. Different from previous rough set based classification algorithm, it takes feature co-occurrence into account when make attribution reduction to get a more accurate feature subset. The novel method called Feature Co-occurrence Quick Reduction algorithm is in this article. Experimental results show it has a high efficiency in dimensionality reduction—time consumption by approximately 23% less than traditional rough set based dimensionality reduction methods. Moreover, classification based on the feature set selected by Feature Co-occurrence Quick Reduction algorithm is more precise. The proposed algorithm is helpful to us for refining knowledge from massive unstructured data.

Vehicle routing problem (VRP) is the key issue of logistics system optimization. As a classical combinatorial optimization problem, it belonged to the typical NP-hard problem and remained unsolved. In this paper, the novel bat algorithm is proposed to solve VRP. The improvement is based on the combination of dynamic inertia weight and time factor. It can take full advantages of dynamic search by the random velocity and random step-size. Furthermore, with the real-number encoding approach, the discrete VRP can be converted into a quasi-continuous one. The procedure of the optimal searching in multidimensional continuous space can be implemented directly. Experimental results indicate that improved bat algorithm performs well for vehicle routing problem.

At present, there are many problems in the existing content-based medical image retrieval (CBMIR) algorithms. The most important problem is the lack of feature extraction, resulting in the imperfect expression of semantic information and the lack of data-based learning ability. Meanwhile, the characteristic dimension is high, which affects the performance of image retrieval. In order to solve these problems, this paper presents a fully convolutional dense network (FCDN) algorithm, which solves the gap between the extracted low-level features and high-level semantic features. In order to improve the accuracy and efficiency of retrieval, the concept of Joint distance is proposed in this paper. Since the image information of lung nodules extracted from different layers of the network is different, the minimum Joint distance is selected by comparing the minimum Hamming distances of the layers 4, 17 and 25 of the similar images retrieved. Compared with other methods, the average accuracy of the lung nodule image retrieval can reach 91.17% under the 64-bit hash code length, the average time for retrieving a lung slice is 4.8×10 ^-5s,The search results not only express the rich semantic features of the image, but also improve the retrieval efficiency. And the retrieval performance is better than other network structures to help doctors assist in diagnosis.

Estimating the mixing matrix is a research that focuses on underdetermined blind source separation. In order to get a more accurate estimated mixing matrix, we investigate a novel algorithm for mixing matrix estimation. Firstly, a new method for detecting single source points was introduced. Then, we reckoned the signal quantity and initial clustering centers by adopting an improved clustering method based on the potential function. Finally, the point density theory and the initial clustering centers were utilized to get more accurate clustering centers and estimated the mixing matrix. The simulation results illustrate that we can obtain the more accurate and stable estimation of the mixing matrix by using the proposed algorithm.

The development of computer technology has led to many intelligent algorithms; the chaos algorithm is one of them. It has played a great role in promoting many fields with its superior performance. The electric power control system of electrical automation equipment can not only optimize the control system but also minimize the consumption of electrical energy. PID controller is often used in the design of the power control system. The control effect of PID controller is greatly influenced by the parameters. The traditional parameter setting method is more complicated and the setting is more difficult. Therefore, a method of optimal design of the power control system for electrical automation equipment based on chaos theory is proposed.