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Due to the long-term effect of the electric field and the heating from the conductor heating, many bubbles will precipitate on the surface of the oil-paper inside the bushing. When the bubbles move to the strong electric field, the partial discharge is caused, which seriously threatens the safe operation of the transformer hushing. Therefore, the conditions for bubble separation were analyzed based on the physical analysis of the liquid. The test platform for bubble observation inside the bushing oil-paper insulation was established, and lest samples with different water content were tested subsequently. The rapid heating method was employed to study the bubble generation over the oil-paper surface, and the variation of initial temperature of bubbles was obtained. The bubble initiation temperature between 0.5 °C/min and 5.5 °C/min was explored through experiments. The results show that the bubble initiation temperature differed by a maxi-mum of 30 °C at different temperature rise rated. When the rising rate is greater than 3 °C/min, the bubble initial temperature begin to decrease significantly; while when the temperature rising rale is less than 3 °C/min, the bubble generation initial temperature is more dispersive hut the result is normally distributed; When the moisture content increases, the initial temperature of bubble generation decreases significantly. When the moisture content increases from 2. 7% to 6. 8% , the initial temperature of the bubble drops by 44 °C. The research results have engineering application value for analyzing whether there are bubbles inside the bushing during operation, evaluating the internal insulation status of the transformer bushing, and ensuring the safe and stable operation of the power transformer. [ABSTRACT FROM AUTHOR]

Due to the problem of how to generate a paper which considers knowledge points and difficulty level in the auto-generation test system. A kind of generating mechanism is put forward called "volumes insideblock, chunk stratification", which divides a paper into several blocks according to the question types, and then divides each block into three layers to complete all of the correspording operation. In addition, a kind of algorithm is designed to balance the default level of the paper.The algorithm avoids the titles from the same knowledge points which makes the algorithm reach the application requirement in the control of the difficult level and the coverage of the knowledge points. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xi'an Polytechnic University is the property of Editorial Department of Journal of Xi'an Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Xi'an Polytechnic University is the property of Editorial Department of Journal of Xi'an Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Xi'an Polytechnic University is the property of Editorial Department of Journal of Xi'an Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, accelerated aging experiments under salt spray and vibration conditions were conducted on an auxiliary switch, and the surface corrosion, opening and closing state and spring length of the auxiliary switch will be selected as characteristic parameters to investigate the influence of environmental factors on its performance. The research results indicate that salt spray will make the surface wiring screw rust, when nickel layer is damaged, it can lead to a decrease in the content of nickel elements, the opposite of iron element. In addition to salt spray will also invade the internal, causing the opening and closing process jams, making the opening and closing torque increased by about 54%. Under the influence of vibration, the spring inside the auxiliary switch turns slack under the influence of external force, so that some of its wiring points cannot be opened and closed normally, and the vibration causes the internal structure to loosen, making the opening torque drop about 24.1%. The results of this paper have important reference significance for mastering the operation status of the auxiliary switch of the circuit breaker operating mechanism under complex working conditions, carrying out life prediction of the auxiliary switch of the circuit breaker operating mechanism, and preventing the fault occurrence of the circuit breaker operating mechanism. [ABSTRACT FROM AUTHOR]

Natural melting and shedding of the snow cover is an important approach to removing winter snow cover from photovoltaic (PV) modules, but there is a relative lack of research on the natural melting process of photovoltaic snow cover. To this end, the paper began with an analysis of the physical process of natural snow melting on photovoltaic modules and its heat balance. Subsequently, an experimental platform based on a walk-in microclimate laboratory was set up to test the effects of thermal radiation intensity, snow density, snow height and module tilt on the temperature and shedding time of the contact surface of PV modules. The experimental results show that under the experimental conditions of this paper, there is a certain correlation between the snow cover shedding and the contact surface temperature of the PV module, and the temperature at the time of shedding is generally in the range of -1～-0.1 ℃, but do not rise to the freezing point temperature. Besides the thermal radiation intensity, snow cover density, snow cover height and the tilt angle of the PV module all have a great influence on the contact surface temperature and shedding time of the PV module and the snow layer. Among the above influencing factors, the title angle has the most significant effect, compared with 23°, when the title angle reaches at 33° and 43°, PV module ice melting and snow shedding time are shortened by about 40% and 85% respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xi'an Polytechnic University is the property of Editorial Department of Journal of Xi'an Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Xi'an Polytechnic University is the property of Editorial Department of Journal of Xi'an Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Under time-varying loads, the stator current of cage motors shows non-stationary and non-periodic, which makes the existing rotor broken bar fault identification method under time- varying loads fail to diagnose due to the influence of the fundamental frequency modulation of the current. This paper proposes a time-varying load cage motor diagnostic method based on the Teager-Kaiser energy operator (TKEO), a new method for rotor broken bar fault diagnosis. The method used TKEO to extract the instantaneous frequency of the current signal to determine the severity of the broken bar fault of the motor. In order to verify the effectiveness and superiority of the method the current signals under different health states and different load conditions were ex- perimentally obtained and diagnosed. The analysis results were also compared with the traditional method. In multiple dimensions. The results show that this method is able to distinguish the health state of the motor more accurately under time-varying loads and has stronger robustness. to load disturbances; especially under light-load operation. the numerical difference of the relative change gradient of the method is 4~6 times higher in comparison with that of the traditional method. In addition, the extracted fluctuation range of the fault frequency is more concentrated in the range between 0~5 Hz. [ABSTRACT FROM AUTHOR]

The adaptive motion algorithm based on predetermined motion trajectories is studied in this paper, addressing the problem of vehicle formation with multiple sensors. Firstly, optimal da- ta fusion estimation of sensor data from multiple sensors was obtained through processing using Kalman filtering acquiring the optimal position information of the vehicle formation. Then, the next step of vehicle formation motion planning was obtained through the exchange of position es- timation information between vehicles. Two types of vehicle formation motion were considered: fixed bidirectional communication cluster movement without a leader vehicle and fixed unidirec- tional communication cluster movement with a leader vehicle. The proposed method, unlike situa- tions where only the movement of formation vehicles towards the destination was controlled.ena- bled the vehicle formation to move towards the endpoint along the predetermined trajectory through the exchange of position information between adjacent vehicle nodes. To meet the practi- cal requirement of maintaining an ideal distance between vehicles, two virtual force models, name- ly traction force and topological force were introduced. Traction force primarily guides vehicles a- long the predetermined path to reach the destination, while topological force mainly maintains the connection topology among following vehicles. Simulation results indicate that the proposed method.compared to existing methods. effectively shortens the safe distance between vehicles and improves road utilization efficiency.demonstrating the good applicability. [ABSTRACT FROM AUTHOR]

To improve the stability of underactuated robotic arms in the sorting process of fragile parts, a fuzzy PID control algorithm is proposed to optimize their grasping performance by improving the particle swarm algorithm. Firstly, the characteristics of the underactuated robotic arm grasping force control system were analyzed and a specific strategy was proposed that combines particle swarm algorithm optimization algorithm with fuzzy PID grasping force control system. Secondly, methods such as dynamic inertia weights were introduced into particle swarm algorithm to improve its iteration speed and avoid falling into local optima. On this basis, the improved particle swarm optimization algorithm was used to optimize the relevant parameters of the fuzzy PID controller, achieving online self-tuning of fuzzy rule weights and quantization factors, and solving the problem of PID parameters unable to be dynamically adjusted. Finally, the method was simulated and analyzed. The results show that the control algorithm designed in this paper can achieve stable grasping force within 0.8 s. with a steady-state error of less than 0.2%, and a disturbance tuning time of 0. 262 s. The transient response speed, control accuracy, and stability of the system are significantly improved. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xi'an Polytechnic University is the property of Editorial Department of Journal of Xi'an Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Xi'an Polytechnic University is the property of Editorial Department of Journal of Xi'an Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To address the problem of missed detection and slow detection speed in safety belt wearing test for electric aloft work, this paper proposed a method for detecting the wearing of safety belts based on EPSA-YOLOv5 algorithm. This method was based on EPSANet backbone feature extraction network, which reduced the number of parameters in the network while maintaining good feature extraction performance, and speeding up the model recognition speed. By improving the spatial pyramid pooling structure, the model detection accuracy was improved; on this basis, an improved algorithm based on Soft-NMS was proposed to reduce the detection of targets. Experimental results show that the detection accuracy and speed of safety belt for aloft work based on EPSA-YOLOv5 network model are 2.34% higher than that of the original YOLOv5 model, which has practicality and efficiency. [ABSTRACT FROM AUTHOR]

The multi terminal flexible interconnection equipment with cascaded full bridge and dual active bridge structure could connect AC power grid and multiple DC power grid, and instantaneous power differences in different DC power grids and sub module faults could lead to unbalance grid current and DC capacitor voltage distribution. For the three-phase star interconnection structure, this paper adopted the control strategy of injecting zero sequence voltage to meet the different power requirements, derived the specific zero sequence voltage component expression, and quantitatively analyzed the limitation range of injected zero sequence voltage. Finally, a simulation experimental platform was built to verify the effectiveness of the method. The simulation results indicated that injecting zero sequence voltage on the basis of the normal control architecture can allocate the power between phases to ensure grid current balance, reduce imbalance from 6% to 1%,and maintain the even distribution of DC capacitor voltage. [ABSTRACT FROM AUTHOR]

The elasticity of the distribution network indicates the ability of the system to restore its original state after interference, and the frequent occurrence of natural disasters requires that the distribution network can not only operate reliably under normal conditions, and but also maintain the necessary capabilities for extreme disasters. Accordingly, a reliability evaluation method of elastic distribution network based on fault incidence matrix(FIM)was proposed in this paper. With FIM, the explicit analytical calculation of the reliability, elasticity and power supply capacity of the distribution system can be achieved by using simplified matrix algebra and Boolean operation, which saves the repetitive search work of fault enumeration in the evaluation process, and the calculation time, and can be applied to the complex structure of the distribution network and easy to expand. The final results show that the calculation time of the proposed method is only about 0.055s, which is reduced by 87% and 75% respectively compared with that of the fault diffusion algorithm and the block algorithm. [ABSTRACT FROM AUTHOR]

Due to the fluctuation of PV power, it is difficult to keep the active power of the PV grid-connected inverter constant. To address the above problems, this paper proposed a new three-level integrated photovoltaic storage machine consisting of a T-type three-level DC/AC converter, a three-level Boost converter and a three-level bi-directional DC/DC converter and related control strategies. The three-level structure allowed for lower switching losses resulting in higher system efficiency and wider voltage range adaptability. In terms of control, frequency differentiation and deviation control were introduced in the control of the energy storage DC/DC converter to achieve frequency regulation of the transient/static support of the active power of the parallel network. The simulation and experiment show that when the frequency fluctuation range is 50.02～49.97 Hz（meeting the national standard）, the new optical storage all-in-one machine proposed in this paper can operate stably and be easily realized. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xi'an Polytechnic University is the property of Editorial Department of Journal of Xi'an Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Xi'an Polytechnic University is the property of Editorial Department of Journal of Xi'an Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Xi'an Polytechnic University is the property of Editorial Department of Journal of Xi'an Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Xi'an Polytechnic University is the property of Editorial Department of Journal of Xi'an Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Current point cloud semantic segmentation methods based on deep learning tend to overlook the boundary of objects in transition area, resulting in the problem of ambiguous features at the boundary. This article proposed a point cloud semantic segmentation method with boundary-aware and multi-feature fusion (BA-MFF). Firstly, the backbone network was optimized to make the extracted features more robust. Secondly, boundary-aware module(BAM)was designed to focus on the boundaries of objects in transition area. This module consisted of a boundary point prediction module(BPPM)and a feature aggregation module (FAM). The boundary point prediction module predicted the points belonging to boundaries by learning the features of neighboring points, and the feature aggregation module performed discriminative aggregation of point cloud features within the neighborhood. Finally, for more discriminative features, a multi-feature fusion module(MFFM)was introduced, which fused features between different channels. The experimental results show that the mean Intersection over Union (mIoU) of this method reaches 63.7% on the ScanNetV2 dataset. The Overall Accuracy (OA) and mIoU on the S3DIS are 88.2% and 62.3%, respectively. The method in this paper effectively focuses on the transition area and has some segmentation superiority. [ABSTRACT FROM AUTHOR]

In the DC microgrid, aimed at the power flow problem of dual active bridge (DAB) converters and their connected energy storage systems, this paper proposed an accurate direct power control(ADPC)method. The introduced control approach ensured constant power transmission on both sides of the DAB converter and was characterized by high accuracy, fast dynamic response, and excellent stability. It overcame the limitations of traditional direct power control(DPC)methods, which were highly dependent on system parameters. The effectiveness of the ADPC method was verified through simulations using MATLAB/Simulink and real-world experimental platforms. When compared to traditional power PI control methods, the proposed ADPC method, according to simulation and experimental results, significantly improves the dynamic performance of output power, especially under conditions of abrupt changes in power setpoints, load shifts, and input voltage fluctuations. Moreover, the method is independent of system parameters, enhancing the control method's compatibility and portability. The proposed method shows promising application prospects in the field of electric energy dispatch control within DC power supply networks. [ABSTRACT FROM AUTHOR]

In order to optimize the heat transfer characteristics of regenerative evaporative coolers under different climatic conditions and provide reference data for numerical simulation research. The dynamic coupling of heat and humidity parameters of the air flow in the regenerative evaporative cooler is the fundamental factor affecting the efficiency of evaporative heat transfer, and mastering its law can guide the structure optimization of the heat exchanger. In this paper, a simulation model is constructed based on the structure of regenerative evaporative cooler, and the variation of heat and humidity parameters in the airflow passage is studied by numerical simulation method with the application background of representative cities at home and abroad under different climate conditions. It is found that the relative humidity increases rapidly in the wet channel, and the length of 40 cm wet channel can meet the full evaporation demand under most working conditions. The evaporation cooling effect is greatly affected by the inlet air parameters, and the saturation degree of ambient air has a significant influence on the cooling effect. Keeping the latent heat exchange process in the high efficiency evaporation section and the sensible heat exchange process in the high temperature difference section of the channel can effectively improve the heat exchange efficiency of the evaporative cooler and shorten the length of the heat exchanger. [ABSTRACT FROM AUTHOR]

Traditional maximum power point tracking(MPPT)control algorithms easily fall into local optima in local shaded environments, and the speed of intelligent optimization algorithms is slow. A composite IP＆O-SSA based on adaptive perturbation observation(IP＆O)and improved sparrow search algorithm(SSA)was proposed. This algorithm adds Tent sequence initialization to SSA and Levy flight strategy to early warning personnel, and further adaptive and filtering processing was performed on P＆O. The algorithm adopts a double-layer control structure, which first searches globally near the maximum power point through the improved SSA, and then slowly searches to track the maximum power point through the improved IP＆O. By simulating three scenarios in Simulink: standard environment, local shading, and sudden environmental changes, the simulation results show that in the standard environment, the algorithm first tracks the maximum power point, and the convergence time is shortened by 3 ms and 16 ms compared to the improved perturbation observation(P＆O) and SSA, with a tracking efficiency of 99.99%,and under local shading conditions, only the P＆O method falls into local optima and cannot effectively track the maximum power point of the system. Compared with the improved SSA, the average convergence time of the algorithm in this paper is shortened by 8 ms, and the tracking efficiency is as high as 99.68%, which is 0.09% higher. It is verified that the algorithm is applicable to most daily application scenarios, providing a theoretical control algorithm basis for improving the power generation efficiency of Photovoltaic system, and reducing unnecessary power loss for the subsequent grid connection of Photovoltaic system. [ABSTRACT FROM AUTHOR]

This paper focuses on an iterative learning fault-tolerant control strategy for discrete-time affine nonlinear systems where actuator faults arbitrarily occur. First of all, the actuator fault is decomposed into multiplicative model and additive one. Then, the behaviors of both the faults-corrupted control signals from the actuator to the systems and the faults-free ones from the iterative learning controller to the actuator are analyzed from the statistical point of view. Meanwhile, sufficient conditions of convergence are derived by resorting to norm. Last, in order to verify the effectiveness and reliability of the proposed results, numerical experiments are carried out. Both theoretical analysis and simulations illustrate that the developed strategy is satisfactory in maintaining decent tracking accuracy of the controlled systems, even though faults randomly occur in actuator [ABSTRACT FROM AUTHOR]

Aimed at the problems such as slow convergence speed, low accuracy and easy to fall into local optimality, an improved sparrow search algorithm based on multiple strategies（ISSAMS） is proposed in this paper. Circle chaotic mapping was introduced to initialize the population, increase the diversity of the population, and improve the global search ability. Using sine and cosine search strategy was used to update the location of the finder, select the best location, enhance the local search ability, and avoid falling into the global optimization. Firefly disturbance was added to update the optimal individual position, search for feasible solutions, and improve the local search ability and search speed. In order to verify the effectiveness of the improved algorithm, five reference functions were selected for simulation experiments, among which three functions were single-peak functions and two functions were multi-peak functions, and compared with genetic algorithm, grey wolf algorithm, particle swarm optimization algorithm and sparrow search algorithm. The simulation results show that the improved sparrow search algorithm based on multiple strategies has the ability to jump out of the local optimal solution, the convergence speed is faster, the accuracy is higher, and the overall performance is better than the other four algorithms. By applying this improvement to the threshold and weight of the optimized BP neural network, the error of the BP model optimized by no improved sparrow search algorithm is reduced by 14. 73%. Compared with the BP model optimized by the other three algorithms, the average absolute percentage error of the optimized model based on the multi-strategy improved sparrow search algorithm is the lowest and has the best effect. The effectiveness of the improved algorithm is further verified. [ABSTRACT FROM AUTHOR]

Aiming at the problems of time-consuming and high error rate in handwriting test scores statistics a handwriting test paper recognition method based on improved convolution neural network algorithm was proposed. In order to simplify the classification of the score» the score column of the handwritten test paper was divided to obtain ten types of numbers from 0 to 9. In order to improve the efficiency of handwritten score recognition, a classification and recognition algorithm of convolution neural network and Bayesian was proposed. The constructed convolution neural network model was used to extract the characteristics of handwritten digits. The PCA algorithm was used to reduce the dimensionality of the features.The hayes classifier was used to distinguish ten kinds of numbers from 0 to 9. the auuracy and efficiency of the algorithm were verified in the MNIST database.The paper score summation model was established and automatic summation is performed after recognition. The experimental results show that for the recognition uf the handwritten scores of 1 188 lest papers in 3 courses♦ the algorithm in this paper has a recognition rate of 98. 23% compared with other algorithms, the average recognition time per test paper is 7. 5 s, and verified its practicality. [ABSTRACT FROM AUTHOR]

Aiming at the problem of scale transformation or occlusion in the process of visual object tracking, an anti-occlusion fast correlation filtering object tracking algorithm is proposed in this paper, which is mainly composed of three parts: position filter, fast scale filter and Kalman filter. Firstly, the position filter and fast scale filter were initialized. Secondly, during tracking, the average-peak to correlation energy（APCE） occlusion criterion was used to judge whether the target was occluded. If there was no occlusion, the target position was estimated by position, scale filter and feature. When the target was occluded, the current position of target occlusion was estimated by Kalman filter instead of the estimated value of position filter. Finally, the correlation tracking results were calculated. The experimental results show that when tested on the CVPR 2013 benchmark public data set, the algorithm is compared with the mainstream algorithm, the accuracy of video sequences dealing with occlusion and scale transformation attributes is improved by 0.047, the success rate is improved by 0.067, the center position error is improved by 21.128 pixels, and the distance accuracy is improved by 0.153. It can better solve the problems of occlusion and scale change, and has good tracking speed. [ABSTRACT FROM AUTHOR]

In order to improve the positioning accuracy of the automatic dropping cloth vehicle, the problem of increasing error and decreasing positioning accuracy in the strongly nonlinear system is solved by the extended Kalman filter simultaneous localization and mapping（SLAM） algorithm. In this paper, an integrated muti-innovation extended Kalman filter simultaneous localization and mapping（IMI-EKF-SLAM） algorithm was proposed, which enabled the automatic dropping cloth vehicle localization system to change from a single update to multiple iterations in the process of state update, and explored the iteration value n and the new interest dimension p. The simulation and experimental results show that the improved algorithm reduces the state estimation error and the IMI-EKF-SLAM algorithm locates best when the iteration value n is 2 and the new interest dimension p is 3. [ABSTRACT FROM AUTHOR]

In order to study the effects of different sintering temperatures and heat treatment on the microstructure and properties of copper alloys, TiH2/Ti-doped Cu-Y2O3 composite powders were prepared by high-energy ball milling in this paper. After pressing-sintering-heat treatment, copper matrix composites were obtained.The microstructure of the composite was characterized by optical microscope（OM） and scanning electron microscope（SEM）, and Vickers hardness tester was used to test its hardness and eddy current conductivity tester. The results show that after high-energy ball milling and mixed powder, elements Y, Ti and O are evenly distributed on the surface of the composite powder. The sintering temperature and heat treatment has an important influence on the microstructure and properties of TiH2/Ti-doped Y2O3 reinforced copper-based composites. The TiH2/Ti-doped Y2O3 reinforced copper matrix composites achieved the excellent properties after sintering at 850 ℃ for 2 h and aging at 450 ℃ for 3 h. The electrical conductivity and hardness of TiH2-doped Y2O3 reinforced copper-based composites are 86.9% IACS and 70.1 HV respectively, and the electrical conductivity and hardness of Ti-doped Y2O3 reinforced copper-based composites are 76.8% IACS and 73.6 HV. [ABSTRACT FROM AUTHOR]

In order to solve the problem of poor consistency between the traditional super-resolution image quality assessment (SRIQA） index and human subjective perception, this paper presents a reference free super-resolution image quality evaluation method by using multi-layer perceptron(MLP). This method used the pre trained VGG16 network to extract the perceptual quality features of the super-resolution image, and established the regression model between the perceptual quality features of the super-resolution image and the corresponding subjective quality score through MLP. Experimental results show that the Pearson linear correlation coefficient (PLCC) and Spearman rank order correlation coefficient (SROCC) of the proposed algorithm on the public super-resolution image data set exceed 0.95, which is significantly superior to other existing image quality evaluation methods, and has higher consistency with human subjective perception. [ABSTRACT FROM AUTHOR]

When large-scale photovoltaic is connected to the interconnected power grid, the problem of voltage and frequency stability is prominent due to the shortage of photovoltaic active power output during low voltage fault. Firstly, this paper established the frequency response model of the combination of hydropower and photovoltaic, and theoretically analyzed the correlation between the system frequency and the key parameters of photovoltaic active power of LVRT. Then, through the normalized sensitivity index of photovoltaic parameters, it quantitatively analyzed the impact of key parameters of photovoltaic LVRT active power on transient frequency. Thirdly, based on the above normalized sensitivity, the optimization model of photovoltaic active power parameters meeting multi-scenario transient stability constraints was established with the objective of minimizing frequency deviation. Finally, the adaptability of the optimized parameters was verified through an example. With the AC line N-2 fault as an example, the minimum transient frequency is 49.5 Hz in forward mode, which increases by 1.5 Hz. Meanwhile, the maximum transient frequency is 50.4 Hz in outgoing mode. Both scenarios meet the requirements of frequency stability, which verifies that the proposed method can improve the stability of system frequency. [ABSTRACT FROM AUTHOR]

A new multi-region image segmentation model based on improved OSV image decomposition was proposed for the coexistence of image structure and texture components. The model preserved the divergence-free vector field of texture image, combined multi-region image segmentation model, which made the decomposed texture and structure image more accurate and improved the accuracy of segmentation. In order to solve the proposed model in this paper, the alternating direction multiplier method was designed. The proposed method could accomplish both image decomposition and image segmentation. The experimental results show that compared with the related classical models, the proposed model can significantly improve the subjective visual effect of image segmentation, and the precision, recall and Jaccard similarity coefficient are at least 3.42%, 0.77% and 4.89% higher respectively. [ABSTRACT FROM AUTHOR]

In order to improve the energy efficiency and cooling performance of evaporative cooling air conditioning system, the problems existing in the thermal process of traditional evaporative cooling air conditioning system were analyzed in this paper, and a dew point evaporative cooling air conditioning system based on cross flow dew point indirect evaporative cooler was proposed. An analytical model of air treatment process of dew point evaporative cooling air conditioning system is established. Thermodynamic calculation and analysis are carried out under typical working conditions, and the calculation results are compared with the traditional evaporative cooling system. The results showed that the air supply temperature of dew point evaporative cooling system is 1.7 ℃ lower than that of traditional evaporative cooling system, and the moisture content is 0.9 g/kg lower. At the same time, when the exergy efficiency is increased by about 2%,the effective output of exergy is increased by about 16.9%.It shows that the dew point evaporative cooling system has better thermodynamic performance than the traditional evaporative cooling system. [ABSTRACT FROM AUTHOR]

Based on the analysis of the present situation of intelligent clothing in the cross field of intelligent equipment and clothing industry at home and abroad, the paper discusses the classification and development trend of functional materials in the field of intelligent clothing, the implantation of frontier science and technology into intelligent clothing, including modern electronic information technology and communication sensing technology, and the progress of flexible wearable technology in the fields of medical treatment, health care and sports. Promoting the innovation, maintainability and comfort of domestic smart clothing, integrating art and technology are important factors to promote the development of intelligent clothing industry. The paper puts forward that paying attention to sustainable design, breaking the bottleneck of environmental protection, paying attention to functional diversification system, meeting the needs of minimalist life in design, and promoting the commercial transformation of design products are the future development trends. [ABSTRACT FROM AUTHOR]

The Taiwan Bunun traditional clothing art characteristics and cultural connotations are scrutinized to promote the inheritance and development of minority costume culture, with reference to the elements of national costume. Taking the clothing form of Bunun ethnic minorities in Taiwan as the research object, this paper conducts a research based on the theoretical research methods of folk culture. Using the inductive analysis method and the typical pictures of Bunun costumes, the shape characteristics of Bunun men’s and women’s clothing are obtained. The influencing factors of garment shape formation are analyzed from five aspects:natural environment, climate, Han clothing element, conservative thought and other national influence. This paper expounds the practical functions, adult signs, aesthetic consciousness, worship beliefs, worship of nature, tribal identity and other cultural connotations of Bunun clothing. [ABSTRACT FROM AUTHOR]

Combined with the biological nitrogen removal technology of printing and dyeing wastewater at home and abroad, this paper briefly describes the difficulties of nitrogen removal from printing and dyeing wastewater and the main nitrogen circulation modes in the process of biological nitrogen removal. Taking ammoniation, nitrification and denitrification as an example in the mainstream nitrogen removal process, the process mechanism and the main microorganisms involved are discussed, and the advantages and disadvantages of the current treatment effects of nitrification and denitrification in printing and dyeing wastewater are analyzed. The results show that there are a lot of organic nitrogen and inorganic nitrogen in the comprehensive printing and dyeing wastewater, and biological nitrogen removal is still the mainstream nitrogen removal process in printing and dyeing wastewater. In addition, to strengthening the ammoniation, nitrification and denitrification processes involved in the nitrogen removal process of printing and dyeing wastewater, sulfur autotrophic denitrification(SAD)and anaerobic ammonia oxidation(Anammox)are the most promising development directions biological nitrogen removal technology of printing and dyeing wastewater. In the future, it is necessary to further carry out research on the mechanism and application of SAD and Anammox based on the water quality characteristics of printing and dyeing wastewater to prove the feasibility of its engineering application. [ABSTRACT FROM AUTHOR]

Antimony pollution is a new challenge facing the pollution control in the textile printing and dyeing industry in recent years. Its emission characteristics and targeted treatment technologies need to be summarized. In this paper, the antimony pollutant emission pathways, discharge concentrations, and important influencing factors in polyester textile printing and dyeing production were systematically discussed. Meanwhile, the treatment process of antimony containing wastewater was induced from the aspects of treatment technology principle, application and characteristics. It could be found that the antimony pollution was mainly produced from the alkali de-weighting process, refining processes, and the high-temperature and high-pressure dyeing process. The hydrolysis of polyester fiber under strong alkali and the violent movement of molecular chain under high temperature and high pressure are the main reasons for antimony precipitation. The iron salt-based coagulation and precipitation technology was the most widely used in practice, but it was constrained by the valence of antimony contaminants and pH of wastewater. The sludge reduction also needs further breakthrough. Therefore, it is urgent to develop antimony treatment technology with high selectivity and improve the green and environment-friendly cleaner production mode of textile printing and dyeing industry. [ABSTRACT FROM AUTHOR]

In view of the problems of data sparsity and cold-start in traditional recommender model, the introduction of knowledge graph as side-information can address the above problems and be interpretable. However, knowledge graph is more biased towards propagation of knowledge than user preferences and difficult to capture high-order relations. To solve these problems, collaborative factor module was introduced into propagation-based method in this paper to capture high-order relations and discover latent patterns. In addition, a density gate composed of three co-occurrence matrix density parameters was designed, so that the collaborative factor module could dynamically control the output by the sparsity of the co-occurrence matrix. Contrast experiments were carried out on public film, book and music data sets. The experimental results demonstrate that the model performs well in the click-through-rate scenario, and the indicators are significantly improved on the data sets whose relations of knowledge graph are difficult to explain user preferences. [ABSTRACT FROM AUTHOR]

The traditional public opinion data collection methods of power network have some problems, such as low recall rate, low calculation accuracy and being time-consuming. Therefore, the topic crawler technology was used to improve the data collection method. Firstly, the topic crawler technology was used to build the data collection framework, and based on the framework, the topic vector of network public opinion was constructed. Secondly, we defined the topic and keyword of network public opinion, and calculated the similarity between keyword vector and power web page by using the similarity model, which was added to the web crawler queue. Finally, used the best first search strategy, setted the web page with the highest similarity as the first priority, downloaded and stored network public opinion related data, completed data crawling and realized data collection. The experimental results show that the average recall rate of the method in this paper is as high as 92%, the accuracy of web page similarity calculation is higher than 90%, and the average time of data acquisition is 36 minutes, which is better than the comparison method. [ABSTRACT FROM AUTHOR]

Manual, brush or ion blowing methods are adopted in the clearing system of detachable electric energy meter, resulting in low accuracy of stain identification and poor cleaning effect. Therefore, the methods of binocular vision and machine learning were used to improve it. The system takes the control center as the core, the identification module was connected with the cleaning service subsystem through the communication protocol, and the system framework was constructed. A binocular vision system was established. After the binocular camera was calibrated by neural network, the image information of disassembled electric energy was collected, and the stain location was completed through coordinate conversion and parallax calculation. The positioning results were input into the convolution neural network (CNN), and the dirt identification of the disassembled watt hour meter was completed through grid division, feature mapping and other operations. The cleaning device was designed in the cleaning service subsystem, and the electric energy meter was removed according to the stain identification results to complete the design of the cleaning system. Taking cleaning 500 electric energy meters as an example, the experimental results show that the method in this paper takes 22 minutes, the recognition error is about 10%, and the verification effect of the cleaned electric energy meter is improved from 70% to 97%. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low completion rate and large remaining proportion of collection tasks in traditional information collection methods, the Markov prediction model of multi sensing location is used to dynamically collect the real-time information of mobile group intelligence sensing network logs. The trajectory sequence generation algorithm was used to extract the trajectory sequence of mobile users. Using the multi perception complementary control method, the learning transition probability matrix was obtained. Adopting improved methods could effectively improve the proportion of task completion. By describing the problem, the Markov prediction model of multi sensing location was introduced, the detailed prediction steps were given, and the information was collected dynamically. The experimental results show that the task completion rate of the improved method in this paper is about 93.24%, and the highest remaining proportion is 96.84%. [ABSTRACT FROM AUTHOR]

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In order to effectively monitor and warn the temperature of electrical contact and maintain the smooth operation of cable equipment, DS18 B20 temperature sensor was introduced into the monitor module to improve the connection efficiency of temperature information of each contact, collect the temperature information of electrical connectors in real time, and reduce the time of temperature analysis and processing resources. Then an internal communication conversion mechanism was constructed by configuring the marking controller with different transmission channels to weaken the differences between data, normalize the data format, remove noise and invalid data, and effectively shorten the time of data transmission. Based on the above process, the change of indicator color and voice reminder were used to realize the over temperature warning, and the specific monitoring and warning process was designed. The experimental results show that the temperature data collection time of this system is always less than 6 s, which is much lower than the 10 s and 9 s of the comparison system, and the minimum false alarm rate of the system in this paper is only 4.27%. Even though the highest false alarm rate is only 6.02%, it is lower than 11.18% and 16.62% of the comparison system, only one-half of the comparison system, proving that the system can effectively achieve temperature monitoring and early warning. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the features in the process of strip surface defect identification can not be automatically and accurately extracted. In this paper, a new approach to strip surface defect recognition based on deep learning is proposed. This paper first analyzes the basic theory of deep learning, on the basis of this, the basic model of strip surface recognition is established. Then the basic model parameters are trained by the prepared training images. The model extracts image features layer by layer through hidden layers,thus automatically acquire the essential characteristics of the target. Identification and classification are performed. Finally, the validity of the model is verified by testing image. The experimental results show that the accuracy of strip surface defect identification can reach more than 98%, it meets the requirements of strip steel identification. [ABSTRACT FROM AUTHOR]

In many high-power AC drives, with the increase of the switching frequency of the converter, the generated harmonic and electromagnetic interference(EMI)noise are getting higher and higher. Aiming at the serious problems of harmonics and EMI noise of three-level neutral-point-clamped(NPC)high-frequency converters, based on the space vector pulse width modulation(SVPWM)strategy, a random SVPWM method based on Markov chain was proposed. In this method, the random number generated by Markov chain could be applied to SVPWM strategy, so that the actual frequency change could be more randomly and evenly distributed on both sides of the theoretical switching frequency, with the output voltage harmonic noise of the actual system with also dispersed in a wide frequency band, and the EMI noise of the converter was also reduced. The simulation results show that the modulation strategy proposed in this paper has a significant improvement in the harmonic distribution compared with the traditional SVPWM strategy, and the conducted EMI noise is also reduced from 140 dB/μV to 120 dB/μV. [ABSTRACT FROM AUTHOR]

The traditional curvature calculation method is sensitive to local changes and noise in the discrete domain, which makes the corner detection accuracy and location deviate. In order to solve the above problems, a multi-scale discrete curvature is proposed to calculate the curvature and detect corners. Firstly, Canny edge detector was used to process the input image to obtain a binary edge image, then the edge contour was extracted from the edge image, the gap was filled along the contour, and T-shaped corners were detected and marked. Secondly, under three different smoothing scales, Gaussian function was used to smooth each plane curve, and then a new curvature measurement method was used to calculate the curvature of each point on each contour after smoothing, and the local maximum point of absolute curvature was taken as the candidate corner point of each scale. Finally, the curvature value of candidate corners was compared with the curvature threshold. The points greater than the curvature threshold were defined as candidate real corners, and the candidate corners that could be detected at three scales were regarded as real corners. The experimental results show that when the algorithm is used to detect the standard corner data laboratory image, the correct corner number is 159, the wrong corner number is 90, the missing corner number is 16, and the localization error is 1.257 2,and that when the standard corner data block image is tested, the detected correct corner number is 46, the wrong corner number is 13, the missing corner number is 1, and the localization error is 1.013 2.Compared with the existing three classical corner detection algorithms, the detection method in this paper has better corner detection performance. [ABSTRACT FROM AUTHOR]

Due to the optical transmission characteristics, underwater images are degraded to different degrees than natural images, and there are many problems such as color deviation, poor visibility and fuzzy details.Based on the generation of general adverse networks (GAN),an algorithm of underwater image enhancement dense fusion GAN (DFGAN) algorithm was proposed. The algorithm improved the existing GAN and corrected the blue and green bias of the image. Meanwhile, this paper constructed a dense feature fusion mechanism in the generator, which made up for the loss of detail information in the process of image correction by making full use of the feature information in the image.The experimental results show that the enhanced image of this algorithm has been improved in the numerical value of four general objective quality evaluation indexes, such as structural similarity, peak signal-to-noise ratio,information entropy and underwater color image quality evaluation. It is proved that the algorithm can effectively correct the color of underwater low-quality image, recover texture characteristics and improve visual quality. [ABSTRACT FROM AUTHOR]