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Cables are mainly composed of wires and are used to transmit signals or electrical energy. It can be seen everywhere in real life. However, due to the changeable environment in which it is located, it often leads to the problem of insulation aging. Therefore, it is particularly important to study the law of cable insulation aging and to evaluate its life. Humpback whale hunting behavior is simulated by WOA, a swarming intelligence algorithm. It can intelligently identify the relationship between various data. SVR is a linear regression model with a special computational loss. The aim of this paper is to investigate a WOA-SVR model to evaluate the aging law and life of cable insulation. This paper analyzes a variety of detection models. Finally, the partial discharge detection model and the depolarization detection model are selected for comparative testing with the WOA-SVR model studied in this paper. The test method is to count the aging of cable insulation under different temperature, humidity, and electric field strength environments. The test results show that the evaluation accuracy of the WOA-SVR model in this paper is 92%, 97%, and 98%, respectively, under different temperatures, humidity, and electric field strengths. The average accuracy of its evaluation is higher than the other two models. Therefore, the WOA-SVR model is more accurate and reliable for the cable insulation aging law and life evaluation.

Due to the complex working environment of marine cables, the prediction of residual insulation life of power cables has become a concern. This study mainly discusses the power cable insulation residual life prediction method considering the uncertainty of influencing factors. The aging time points were 0 h, 72 h, 144 h, 264 h, 432 h, 600 h, 844 h and 1152 h. Finally, the elongation at break, the dielectric loss angle of frequency domain dielectric spectrum and space charge distribution are explored. At the beginning of aging, the decline trend of elongation at break curves was gentle, from 510% to 499.6%, 480.6%, and 470.2%. The results show that the difference value of the life prediction method designed in this study is small, which can be effectively used to predict the residual life of power cable insulation.

The adaptability plays a significant role in moving detection. The diverse scenarios in real world still challenge this problem. Therefore, in this paper, we proposed an adaptive moving detection method, namely Adaptive Random-based Self-Organizing back- ground subtraction (ABSOBS) method. This method can adaptively extract the moving objects in various conditions and eliminate the “ghost” pixels simultaneously. Therefore, a robust initialization strategy is proposed to remove the noise pixels caused by the initialized frames. The proposed method uses a random- based scheme which allows the foreground pixels to up- date the neural network with a small probability. This strategy allows our algorithm to efficiently handle scene changes. Moreover, a foreground filter based on random rule is designed to eliminate the “ghost” pixel. More importantly, ABSOBS adopts a regulator to control the updating rate in different conditions. It makes our method easy-to-used and need not to set the parameters manually. The experiment results on various scenarios show that our method improves the detection accuracy for the SOBS and outperforms other state-of- the-art methods.

The six-degree-of-freedom flexible joint manipulator is a complex system that suffers from the problem that the trajectory planning results are inconsistent with the control results. To keep the planned trajectory within the control range of the manipulator, a hierarchical structure control strategy is designed, which consists of a trajectory planning layer, a model predictive control layer, and a bottom control layer. Specifically, first, the target joint angles are obtained by a time-optimal trajectory planning algorithm based on a genetic algorithm in the trajectory planning layer. Second, in the model predictive control layer, considering the system physical constraints, the model predictive controller is adopted to provide the set points for the Proportion-Differentiation (PD) controllers. Finally, in the bottom control layer, the manipulator moves along the target trajectory under the PD controllers with the feedback control law. The simulation results show that, compared with the PD control strategy, the hierarchical structure control strategy can achieve better control performance and reduce the tracking error of the terminal trajectory by 33.70%.

The current dynamic gesture contour feature extraction method has the problems that the recognition rate of dynamic gesture contour feature and the recognition accuracy of dynamic gesture type are low, the recognition time is long, and comprehensive is poor. Therefore, we propose a dynamic gesture contour feature extraction method using residual network transfer learning. Sensors are used to integrate dynamic gesture information. The distance between the dynamic gesture and the acquisition device is detected by transfer learning, the dynamic gesture image is segmented, and the characteristic contour image is initialized. The residual network method is used to accurately identify the contour and texture features of dynamic gestures. Fusion processing weights are used to trace the contour features of dynamic gestures frame by frame, and the contour area of dynamic gestures is processed by gray and binarization to realize the extraction of contour features of dynamic gestures. The results show that the dynamic gesture contour feature recognition rate of the proposed method is 91%, the recognition time is 11.6 s, and the dynamic gesture type recognition accuracy rate is 92%. Therefore, this method can effectively improve the recognition rate and type recognition accuracy of dynamic gesture contour features and shorten the time for dynamic gesture contour feature recognition, and the F value is 0.92, with good comprehensive performance.

Based on cloud model and matter element theory, and combining the uncertain reasoning characteristics of the cloud model and qualitative and quantitative analysis can be carried out at the same time by matter element theory, a power transformer fault diagnosis method is proposed, which effectively solves the problem of fewer data samples, especially fewer fault data samples. Taking the actual data as an example, the improved matter-element theory model and correlation calculation data are compared, and the results show that the improved matter element theory model has higher diagnostic accuracy than traditional methods. Example analysis verifies the correctness and effectiveness of the method.

With the continuous growth of China's power grid scale and load, more and more attention is paid to the security and stability of regional power grid. Since the proportion of power generation, transmission and substation planning in some areas is not coordinated, grid structure is not reasonable, this will lead to the frequent fault of safety and stability of the power grid, so some measures must be taken to optimize the transformation. In this paper, it use operation of the power grid in northern Chenggu area as the research object, which belong to Hanzhong, Shaanxi Province. Based on the analysis of the general methods and the steps of regional power network calculation, combined with the characteristics of the grid structure in the area, this paper analyses the typical operation mode of the regional power grid, based on the analysis results, the reactive power optimization is optimized. Aiming at the stability problem of regional power network, and according to different operation mode, this paper puts forward the corresponding measures to improve the level of power grid stability.

SIFT matching algorithm is used to carry out the binocular three-dimensional imaging. Active projection is introduced to solve the problem of low feature quantity and poor matching results in the matching process. By means of projection random speckle, the matching feature is increased, and the matching quality is greatly improved. According to the train running part of the three-dimensional imaging experiment, achieved a good imaging result. Compared with the Fourier profilometry in the active three-dimensional imaging technology. The experimental results show that the structured light projection binocular three-dimensional imaging has a better effect.

A fault diagnosis method that based on Rough Sets (RS) and Support Vector Machine (SVM) is proposed, because of the diversity and redundancy of fault data for the mine hoist motor. RS theory is used to analyze the stator current fault data of mine hoist machine in order to exclude uncertain, duplicate information. For getting the optimal decision table, the equivalence relationship of positive domains of between decision attributes and different condition attributes is analyzed in the decision tables to simplify condition attributes. The optimal decision table is as the SVM input samples to establish the SVM training model. And the mapping model which reflects the relation of the characteristics between condition attribute and decision attribute is obtained by SVM training model in order to realize the fault diagnosis of the mine hoist machine. The simulation results show that the fault diagnosis method based on RS and SVM ca accuracy of fault diagnosis.

Scraper conveyor is one of main electrical and mechanical equipment in the mining face, and its reliability has an important significance to production safety. In scraper conveyor reliability life analysis, the linear parameter method is often used to estimate reliability life parameters in traditional Weibull distribution model. But the error of prediction results is larger due to the nonlinear characteristics of fault data. To solve this problem, a simulated annealing algorithm is proposed to optimize the model parameters for the scraper conveyor reliability life prediction. Research results show that the simulated annealing algorithm has small life prediction error, and raises the accuracy of the scraper conveyor reliability life prediction.

The topology of Three-Phase PWM Voltage Source Rectifier (VSR) is introduced in this paper. The mathematical model in three-phase static and two-phase rotary coordinate system is built. According to the component of reference voltage vector in two-phase static coordinate, the sector and the action time can be calculated directly. Then, a three phase PWM rectifier model is established. The simulation results indicated that the dc-side can output stable voltage, verified the correctness and feasibility of the theory.

Support vector machine is a machine learning algorithm developed by Vapnik from the statistical learning theory for data classification via study from a small sample of fault data. For fault data it can isolate the fault categories accurately even though only has the small sample of data. In the present work, support vector machine's classification mechanism and its application in mechanical fault diagnosis are introduced. Therefore, give an instance the support vector machine makes fault classification for the coal mine scraper conveyor's faults. Last but not the least, put forward some of the shortcomings of the support vector machine and look forward to the direction of development of the support vector machine fault diagnosis in the future.

In order to avoid losses, which is caused by electromechanical failure in the coal large transport equipments, this article introduced a model that integrated by breakdown extraction, failure diagnosis and fault analysis in the large-scale scraper chain conveyor breakdown. The weak signal of mechanical vibration detection by ANFIS is adopted. And it is also used for Troubleshooting effectively and accurately. Lastly, an effective diagnosis suggestion is given through the instrumentation KS-2000. And by this way, we not only proved the feasibility and superiority of this plan, but also achieved predictive maintenance in the true sense.

This paper studies the Fractional Kalman Filter and its applications to the extraction of image texture in electronic image processing. Firstly, with the introduction of fractional calculus, Fractional recursive equation is presented. Next, a state space model of a given image and, based on this, the Fractional Kalman Filter algorithm are proposed. Finally, an example of electronic image processing is given to demonstrate the effectiveness of proposed algorithm and the simulation result shows that the details of the image are enhanced, while the background noise of the image is attenuated efficiently.

A novel intelligent instrument design for curing tinnitus is introduced in this paper. According to different extent tinnitus, the many different testing sounds such as square wave, sine wave and natural sounds can be produced and the signal frequency and sound intensity level can be adjusted in order to cure tinnitus disease by this new intelligent instrument. The system is constructed in the upper computer and lower microcontroller C8051F410 as the master chip, the data communication is completed with CAN. Based on the C++ Builder, the interface which can display the data and receive the order is designed so the some information of the tinnitus suffers can be achieved in the upper computer for doctor management. This system has been put into practice and has great application value. Keywordsintelligent instrument, curing tinnitus, C++ Builder, C8051F410

As a new research field on controlling science and engineering, fractional-order control gets more and more concerning. This article provides an overview of fractional-order control of background and basic knowledge of mathematics, giving different definitions and numerical algorithms of fractional calculus, finally establishing a mathematical model of fractional-order systems which used for analyzing and researching image signal processing of fractional-order systems.

Based on multi-sensor information fusion theory, a Gas fire-extinguishing control panel is introduced in this paper. The intelligent algorithm of neural network and fuzzy consequence is used to get the fire probability. According this principle, we designed an information fusion module which would send the probability to the main control panel to carry out the alarm and linkage. The practical application results indicate that the proposed method can effectively decrease the mistake rate of the fire detector and fire extinguisher.

According to main distribution regions of coal resource and the characteristic of gangues, a novel image process method of separating gangues from coal is proposed with wavelet analysis in this article. Under the different frequency ranges, a series of image processing approaches are exerted on coal gangue images with wavelet transform. Images of gangues and coal are first collected by means of the measurement system, and then are denoised. The image edges are detected by fast multi-scale edge detection. Images are segmented by algorithm of self-adaptive threshold. Using the multi-resolution merit of wavelet analysis, gangues images decomposition are especially stressed on, which provides the important data for selecting gangues from the coal. The coal gangues automatic separation system is realized by means of a newly ARM microprocessor, which has characteristics of high-performance, low power dissipation and low-cost exactly. The experiments result reveals that the proposed methods are efficient and practicable compared to traditional methods. And the suggested system is satisfied with accuracy requirements and real-time performance.

The weight and volume of power supply devices can be further reduced, and noise can be decreased by using a three-phase converter in combination with a high-frequency AC link. A new control method of pulse density modulation (PDM) is used in the converters. The operation principle of the circuit is explained in detail. The expressions of the phase voltage and current and harmonics analysis are described separately. The simulation results prove the analysis to be true.

A novel neural network speed identification scheme for the speed sensorless direct torque control (DTC) of an induction motor drive system is presented in the paper. The system uses current and voltage sensors for rotor speed and rotor flux estimation with a digital signal processor (DSP) TMS320F240 in a closed loop control system. Rotor speed identification is based on the model reference adaptive control (MRAC) theory with a neural network using the backpropagation (BP) algorithm. The suggested speed identification method has been validated by a simulation study.