Traditional unscented Kalman filtering (UKF) cannot solve the filtering problem for nonlinear systems with colored measurement noises and one-step randomly delayed measurements. To fix this problem, a new UKF algorithm is proposed in this paper. First, a system model with one-step randomly delayed measurements and colored measurement noises is established, wherein a first order Markov sequence model for whitening colored noises and an independently identical distributed Bernoulli variable for modeling one-step randomly delayed measurements is introduced. Second, an UKF is proposed for the above established models through unscented transformation by calculating the nonlinear states posterior mean and covariance based on the Bayesian filter framework. Specially, the proportional symmetric sampling method is used in the new UKF algorithm. Finally, the effectiveness and superiority of the proposed method is verified via simulation.

In the foam nickel process, texture is the indicator of foam nickel performance. In order to recognize foam nickel surface defects accurately and provide guidance for production operations, this paper proposes a method for extracting foam nickel image textures based on multi-scale texture analysis. First, nonsubsampled contourlet (NSCT) is used to carry out foam nickel image multi-scale decomposition, and the low-frequency and high-frequency components following decomposition are used to characterize different defect details. Then, the Haralick eigenvalue, which measures the foam nickel image texture information at each sub-band, is calculated. The KPCA and optimal DAG-SVM are adopted in order to reduce the parameter dimension and clarify defects. Tests are carried out on the foam nickel surface image samples, including crack, scratch, pollution, leakage, and indentation tests. The results indicate that the method proposed in this paper can extract different pieces of detailed texture information and can achieve a defect-identifying accuracy of up to 88.9%.

In this study, we propose a model and an adaptive backstepping tracking control method for omnidirectional rehabilitative training walker. The aim of the study is to design a stable tracking controller that can guarantee accurate tracking motion of the omnidirectional walker considering the interaction forces of the user and walker. A novel fuzzy model identification method was proposed to describe the interaction forces by using the reduced values of tracking performance. Further, an adaptive backstepping controller was developed to compensate the interaction forces on the basis of the identified model and adapt the change of user’s mass. The asymptotic stability of the trajectory tracking error and the velocity tracking error were guaranteed. As an application, simulation and experiment results were provided to illustrate the effectiveness of the proposed design procedures.

Several disease presentations are linked to a mismatch between the real body and the body’s internal representation of itself. In order to develop effective rehabilitation therapies, it is necessary to understand the mechanisms underlying changes in body representation. In this study, we focused on changes in body representation of the upper limb as a large part of the body and investigated the conditions under which such changes occur. Participants were presented four conditions which differentially affected their sense of ownership and agency, including a movement condition related to their sense of agency, and a visual hand information condition related to the sense of ownership. In the experiment, participants were asked to move their upper limb forward and backward on a manipulandum. Results of the study showed that visual hand position affected changes in body representation relevant to both conscious and unconscious body parts. In addition, changes in the representation of the unconscious body part occurred with, and were dependent on, active movement.

Recently, the concept of virtual data center (VDC) has attracted significant attention from researchers. VDC is made up of virtual nodes and virtual links with guaranteed bandwidth. It offers elasticity and flexibility, which means VDC can adjust resources dynamically according to different requirements. Existing studies focus on how to design the optimal embedding algorithm to achieve high success rate for the virtual data center request. However, due to the resource of physical data center changes over time, the optimal solution may become sub-optimal. In this paper, we study the problem of virtual data center migration and propose an energy-aware virtual data center migration algorithm, called CA-VDCM-ACO. This novel algorithm leverages the migration technique to further reduce the energy consumption with the success rate for the physical data center guaranteed. The extensive experiments show that our algorithm is very effective to reduce the energy consumption.

The intent of the parameter learning is to ensure the accuracy of intuitionistic fuzzy belief rule-based systems (IFBRBSs) considering both weight and reliability. The main contribution is that distinguish reliability and weight respectively treated as intrinsic and extrinsic properties of evidence. A parameter learning method considering both reliability and weight determined by internal and external conflicts (PL-RW-IEC) is proposed. Evidence reasoning with reliability and weight is introduced as a basis of the learning process. After learning, the mean square error (MSE) between the real output and the simulated output decreases 75 times. Compared to the parameter learning considering both reliability and weight determined by Dempster’s conflict (PL-RW-DC) and compared to the parameter learning not considered reliability (PL-NR), the PL-RW-IEC method gets the most accurate result according to the MSE.

In this study, the phenomenon of uneven gas distribution at different sections in the continuous annealing process, which affects the instability of the section furnace temperature and can cause accidents that exceed safety thresholds for a long period, was analyzed to establish a furnace temperature prediction model and a multi-objective optimization method for section gas was proposed. First, the industrial production process was analyzed to extract key factors that affect furnace temperature and combine them with the SVR algorithm to establish a prediction model for furnace temperature. Then, a multi-objective optimization constraint set and optimization objective function were constructed based on the constraints of the production process and equipment conditions. Finally, based on the prediction model, the constraint set, and the objective function, a multi-objective optimization algorithm was employed to optimize section gas based on the NSGA-III. The experimental verification and production results demonstrate that a model constructed using actual collected data yields excellent prediction results. When the multi-objective optimization method was implemented and put into production, the steel coil over-temperature alarm ratio was reduced and the average over-temperature alarm time was greatly reduced. The proposed method improves the production environment and ensures that the procss is safe and stable.

When the state of the robot reaches the smooth sliding plane, the current algorithm will generate high-frequency chattering, resulting in larger tracking error and longer response time. To solve these problems, we have proposed a trajectory tracking and control algorithm based on exponential reaching rate. The coordinate system of parallel robot system is established, and the kinetic energy and potential energy of the system are calculated. The results are brought into the Lagarnge equation to find the dynamic model of the system. The power amplifier, electro-hydraulic servo valve, hydraulic cylinder and its load are taken as generalized controlled objects, and the hydraulic servo system model is established. The exponential approaching rate is introduced to design the dynamics model and the trajectory tracking sliding controller of the hydraulic servo system model. By adjusting the upper and lower bounds of the external disturbance of the controller, the control rate is changed, the buffeting occurrence is reduced, and the response time is shortened, to realize the low error tracking of any trajectory of the robot. The experimental results show that the trajectory of the robot can be adjusted quickly and the desired trajectory is better tracked by the end.

The quality evaluation of innovation and entrepreneurship education is of vital importance. On the basis of analyzing the current research of the quality evaluation of innovation and entrepreneurship education, this paper constructs the efficient innovation and entrepreneurship education quality evaluation system of 4 dimensions: innovation and entrepreneurship curriculum and activities, innovation and entrepreneurship education conditions, innovation and entrepreneurship education channels, innovation and entrepreneurship education effectiveness and 16 sub indicators, establishes the extension priority-degree evaluating model, and makes empirical study for 3 universities in Ningbo City, so as to put forward the corresponding suggestions.

At present, ScanDisk is used to recover the data lost in network communication. But this method is limited in scope, and once the lost data is covered, it’s difficult or impossible to recover it, which results in low recovery degree. Accordingly, a recovery method for lost data in network communication based on RAID6 is proposed. Firstly, according to the mechanism of data loss in network communication, the missing data is divided into three categories: random loss, completely random loss and nonrandom loss, and then according to the results of classification, the recovery problem of the data loss in network communication is converted into the problem of matrix completion, finally, a low-rank decomposition model is proposed, according to the low rank characteristics of the matrix, the lost data in the matrix is recovered, thus the recovery of the lost data in network communication is finished. Experimental results show that the proposed method can easily recover the lost data in network communication with a simple operation, low computing complexity and strong applicability, and can be used as a universal recovery method for data lost in network communication.

Prior marketing literature highlights the customer value co-creation behavior on offline business. This paper focuses on investigating the effects of customer value co-creation behavior on the online purchase intention. Further, the hypothesis is tested via adopting the structural equation model method. The research shows that under the online shopping environment, the behaviors of customer participation and citizenship behavior have significant positive impacts on purchase intention and can be the direct antecedents of purchase intention. Compared with customer participation behavior, customer citizenship behavior has greater impacts on purchase intentions. The analysis outcome of the study has remarkable importance on improving the sales of online retailers.

Most of the existing sketch recognition algorithms are used to restrict the user’s drawing habits to achieve the stroke grouping and recognition. In order to solve the problem, a new sketch recognition algorithm based on Bayesian network and convolution neural network (CNN) is proposed. First, the input sketch is processed by Gaussian low-pass filter and a smoother stroke can be obtained. The stroke of continuous input is divided, then the Bayesian network and CNN are performed on stroke recognition respectively. The recognition result of Bayesian network is adopted when the reliability of stroke is larger than the threshold, otherwise recognition result of CNN will be adopted. The experiment result shows that the proposed algorithm is effective in circuit symbol recognition. The recognition rate was achieved 80.34% in the drawing process, and the final recognition rate was achieved 93.48%.

Policy text contains large amount of diversified data and strictly conforms to standards and specifications, but the traditional text clustering method cannot solve the problems of high dimensionality, sparse features, and similar meanings, so this paper proposes a weighted algorithm based on the LDA-Gibbs model to improve the accuracy of policy text clustering. Firstly, it provides realistic basis for the assumptions of the LDA-Gibbs topic model and the weighted algorithm; secondly, it pre-processes the existing policy text simulated data, establishes the LDA-Gibbs model, forms a weighted algorithm, and generates training data to determine the number of optimal topics in the LDA-Gibbs model and completes the final clustering of the policy text; finally, by summarizing, classifying and deducing the conclusions of the experimental data, this paper proves the objective validity and effects of this method. Hopefully the overall design of this method can be applied in the prospective study on the formulation of new policies in the future, the retrospective evaluation and testing of the existing policies and the formation of a two-way interactive mechanism.

To effectively reduce the day-to-day fluctuations and differences in subjects’ brain electroencephalogram (EEG) signals and improve the accuracy and stability of EEG emotion classification, a new EEG feature extraction method based on common spatial pattern (CSP) and wavelet packet decomposition (WPD) is proposed. For the five-day emotion related EEG data of 12 subjects, the CSP algorithm is firstly used to project the raw EEG data into an optimal subspace to extract the discriminative features by maximizing the Kullback-Leibler (KL) divergences between the two categories of EEG data. Then the WPD algorithm is used to decompose the EEG signals into the related features in time-frequency domain. Finally, four state-of-the-art classifiers including Bagging tree, SVM, linear discriminant analysis and Bayesian linear discriminant analysis are used to make binary emotion classification. The experimental results show that with CSP spatial filtering, the emotion classification on the WPD features extracted with bior3.3 wavelet base gets the best accuracy of 0.862, which is 29.3% higher than that of the power spectral density (PSD) feature without CSP preprocessing, is 23% higher than that of the PSD feature with CSP preprocessing, is 1.9% higher than that of the WPD feature extracted with bior3.3 wavelet base without CSP preprocessing, and is 3.2% higher than that of the WPD feature extracted with the rbio6.8 wavelet base without CSP preprocessing. Our proposed method can effectively reduce the variance and non-stationary of the cross-day EEG signals, extract the emotion related features and improve the accuracy and stability of the cross-day EEG emotion classification. It is valuable for the development of robust emotional brain-computer interface applications.

The hybrid sensor network is mainly composed of static and dynamic sensor nodes. The dynamic node is the mobile robot with wireless sensor module installed. This paper proposes a robot navigation algorithm based on sensor technology and iterative maximum a posteriori estimation. It uses Kalman filter and least-squares fitting to improve RSSI measurement accuracy and the mobile robot only needs to use the received signal strength (RSSI) and odometer information to realize autonomous navigation in the sensing area. Moreover, static nodes are randomly deployed in the sensing area without a priori location information. Therefore, this algorithm has the advantages of low cost and ease of deployment. Both simulation and outdoor field experiments show the performance and effectiveness of the algorithm.

At present, the DC micro-grid power supply system based on new energy generation has become the primary developmental direction for improving the endurance of an unmanned surface vehicle (USV). In this study, an adaptive energy control strategy based on the moving average filtering algorithm is proposed to solve the severe impact of the pulsing load mutation on the hybrid energy storage system (HESS) in the DC micro-grid. The moving average filtering algorithm is used to filter the pulsating load power, and a battery slows the power change. Meanwhile, the super capacitor compensates for the instantaneous power mutation, optimizing the charge and discharge process of the battery. In addition, gain-varying adaptive control for the terminal voltage of the supercapacitor is adopted to stabilize it near the reference value, which solves the problem of voltage off-limit caused by the unequal output and absorption energy of the supercapacitor. The simulation results show that the proposed control strategy can effectively and quickly suppress the power fluctuation caused by the load mutation of the photovoltaic DC micro-grid system, improve the quality of the system output power, and enhance the reliability and stability of the system.

Green products are considered to be the only way for human beings to follow the strategy of sustainable development. They have become one of the hotspots of modern design, manufacture and consumption. A green design method of electromechanical products based on case-based reasoning is presented in this paper. This paper puts forward and uses a “EW&AHP fusion technology” to scientifically determine the index weight, and uses multi target decision-making method to design the index system, establish the evaluation optimization algorithm model of green electromechanical product design scheme, and comprehensively evaluate and optimize the green product design scheme from the aspects of economy, technology and green. Sort, provide decision support for production and operation of related enterprises. The results show that the algorithm can not only give full play to the role of the data itself, but also fully reflect the green requirements of the green electromechanical products, and also give consideration to the profit goal of the enterprise.

The constitution of regional logistics economic environment system has been defined. The concepts of system coupling, coupling development and coupling development degree have been explained. Nonlinear autoregressive model of BP Neural Network is selected as the prediction model for coupling development degree. Coupling development degree of RLEES from 2003 to 2020 has been calculated and predicted by using the data of the Northeastern Region. The programming implementation is in MATLAB. The simulation result shows the logistics delay effect is 2 years. The prediction results showed that the coupling development degree of Northeast LEES is gradually improving with the improvement of economic growth and structural development.

In places where people are concentrated, such as scenic spots, the statistical accuracy of existing crowd statistics algorithms is not enough. In order to solve this problem, a crowd counting algorithm based on adaptive convolution neural network (A-CNN) is proposed, which is based on video monitoring technology. The process of its pooling is dynamically adjusted according to different feature graphs. Then the pooled weights are adjusted adaptively according to the contents of each pooled domain. Therefore, CNN can extract more accurate features when processing different pooled domains under different iteration times, so as to achieve adaptive effect finally. The experimental results show that the proposed A-CNN algorithm has improved the recognition accuracy.

Logistics routing problem is a typical NP hard problem, which is very difficult to solve accurately. On the basis of establishing logistics path optimization model, an immune clone algorithm is proposed. To improve the accuracy of search algorithms, the clonal selection and high frequency variations in the immune algorithm method are introduced. Then the antibody encoding virtual distribution point algorithm is designed to improve search efficiency. The benchmark problem of logistics delivery path optimization is simulated and analyzed. Experimental results show that the proposed immune cloning algorithm expands the range of population search and it have obvious advantages in solving large-scale complex physical distribution optimization problems. Also, the proposed algorithm can solve the optimal distribution of logistics effectively.

Joint sparse representation is not ideal for the processing of outliers in image, so a weighted joint sparse representation model for image denoising is proposed. This model introduces a weighted matrix of common information shared by data samples and reduces the influence of outliers. The greedy algorithm based on weighted simultaneous orthogonal matching pursuit is used to approximate the global optimal solution of the model effectively. The weighted noisy image block is used to remove the mixed noise of the image by jointly coding the nonlocal similar image blocks. By combining global priori knowledge and sparse errors into one unified framework, the denoising performance is further improved. Experimental results show that the denoising performance of this method is better than the existing hybrid denoising methods.

It is critical in a multi-sectoral company to unify the understanding of lean management, and it is conducive to solidly promoting the improvement and innovation of the company’s overall management. The parameter of the lean rate can reflect the lean situation of the enterprise and the company’s departments in a more intuitive method. At the same time, this parameter allows all departments of the company to measure their own management which is based on this, and implement a reasonable lean management.

Data correlation evaluation is the basis for data analysis, and the academic community has proposed many indicators to evaluate it, such as the Euclidean distance, and angle cosine, and so on. However, it is difficult for these indicators to effectively express the correlation degree of complex objects. Using traffic intersections as an example, this article proposes an effective method to evaluate the correlation between complex objects. First, based on a large quantity of basic data, a standard data format describing traffic intersection attributes was proposed. Then, experienced engineers were asked to grade the correlation between intersections. Finally, the two intersection standard format datasets were used as model inputs, the engineer correlation rating as the output of the model, and the support vector machine model was employed for training. The results of this data experiment demonstrate that the trained model can effectively express the correlation degree between traffic intersections, and therefore proves the validity of the method.

Sharpening for dynamic images of remote network video is helpful to improve the dynamic images quality of remote network video and facilitate the subsequent use. Currently, most of remote network video dynamic images are completed based on the DSP chip, the cost of processing is high. In this paper, we propose a method to sharpen remote network video dynamic images based on the physical model. Firstly, image enhancement is carried out. Then, the dark channel priority method and the transmittance estimation method are analyzed to complete the sharpening. Experiments show that the proposed method can effectively improve the efficiency of image sharpening, and the sharpness of image is high and the practicability is strong.

In order to use intelligent robot to realize industrial automation, it is necessary to study the obstacle avoidance method of intelligent robot in cloud computing environment. The traditional obstacle avoidance method mainly uses fuzzy controller to realize the obstacle avoidance of intelligent robot. The problem of low recognition accuracy exists. In this paper, a design method of laser rangefinder for obstacle avoidance of intelligent robot in cloud computing environment is proposed. Firstly, the location problem of intelligent robot by laser rangefinder is modeled. Then, the obstacles are made feature extraction. Finally, the wavelet neural network classifier is used to identify obstacles. Experimental results show that the proposed method can realize the effective obstacle avoidance of intelligent robot.

In order to solve the problem of low efficiency in software operation, we need to research the defect prediction of monitoring configuration software. The current method has the low efficiency in the defect prediction of software. Therefore, this paper proposed the software defect prediction method based on genetic optimization support vector machines. This method carried out feature selection for the measure of complexity of software, and built software defect prediction model of genetic optimized support vector machine, and completed the research on the efficient prediction method of software defects. Experimental results show that the proposed method improves the quality of software effectively.

The robot’s arm trajectory can be used to improve the quality of the robot work. The current method utilizes the searching ability of the Alopex algorithm to perform a rough search of the robot’s path of action to achieve the trajectory planning of its arm, but the planning efficiency is low. To this end, this paper uses chaos control to plan the robot arm trajectory. Based on the description of the robot arm trajectory planning, the robotic arm trajectory planning is completed by using the obstacle to the intersection time, probability prediction, motion planning strategy and real-time obstacle avoidance. Experiments show that this method can carry on the efficient planning to the robot arm trajectory.

Traditional image contour tracking algorithm has low tracking accuracy. To solve this problem, an object contour tracking algorithm based on local significant edge features in complex background is proposed. In the algorithm, the projective invariant is firstly introduced, to construct the geometric information descriptor between the edge positions of the infrared image, and set up the histogram of the feature number of each target contour. The geometric similarity between the features is measured by the pasteurized coefficient, the edge features of the neighbourhood around the object contour are established, and the object contour with significant features in the edge of the image is searched. Combining Shape-context operator with edge feature, the feature description vector can be formed, and Euclidean distance is defined to track measurement function. Using this function, the selected object contour is tracked preliminarily. The random consistency checking algorithm is used to eliminate the false tracking feature points and obtain the best tracking value of the object contour, thus the infrared image’s object contour tracking is carried out in the complex background. Experimental simulation shows that the proposed algorithm has high tracking accuracy and effectively improves the quality of infrared image analysis.

In the context of abnormal network environment, cloud computing needs to rationally schedule resources in order to meet users’ needs. In this paper, an improved trust-driven load balancing scheduling model based on hybrid genetic ant colony is proposed to optimize resources allocation. Each subtask is assigned to a virtual resource. After the task is classified, the initial solution of the resource is calculated using genetic theory, and the optimal solution is obtained by using the ant colony theory, and the optimal resource node is acquired. The benefit function is utilized to calculate the trust requirements of the task for resources, and reasonable resources are obtained by mapping according to different trust values. The average trust benefit of the task on the resource pool is calculated, and the task-resource pairs larger than the average benefit are counted and filtered. According to the matching degree of benefit value of the resource and task, the task is scheduled to the resource with the lowest resource load, and the optimization of load balancing scheduling process is implemented. Experimental results show that using the improved model in this paper can achieve the purpose of resource load balancing.

The massive data of Web text has the characteristics of high dimension and sparse spatial distribution, which makes the problems of low mining precision and long time consuming in the process of mining mass data of Web text by using the current data mining algorithms. To solve these problems, a massive data mining algorithm of Web text based on clustering algorithm is proposed. By using chi square test, the feature words of massive data are extracted and the set of characteristic words is gotten. Hierarchical clustering of feature sets is made, TF-IDF values of each word in clustering set are calculated, and vector space model is constructed. By introducing fair operation and clone operation on bee colony algorithm, the diversity of vector space models can be improved. For the result of the clustering center, K-means is introduced to extract the local centroid and improve the quality of data mining. Experimental results show that the proposed algorithm can effectively improve data mining accuracy and time consuming.

In order to solve the problem of low recognition accuracy in recognition of 3D face images collected by traditional sensors, a face recognition algorithm for 3D point cloud collected by mixed image sensors is proposed. The algorithm first uses the 3D wheelbase to expand the face image edge. According to the 3D wheelbase, the noise of extended image is detected, and median filtering is used to eliminate the detected noise. Secondly, the priority of the boundary pixels to recognize the face image in the denoising image recognition process is determined, and the key parts such as the illuminance line are analyzed, so that the recognition of the 3D point cloud face image is completed. Experiments show that the proposed algorithm improves the recognition accuracy of 3D face images, which recognition time is lower than that of the traditional algorithm by about 4 times, and the recognition efficiency is high.