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The risk of human body electric shock has always been an important issue to be considered in the design of grounding systems. To protect the human body from the risk of electric shock and to facilitate the design of the grounding system. In this paper, we use the Partial-Element-Equivalent-Circuit (PEEC) method to give numerical simulations for a typical grounding system and electric power distribution system under fault conditions. The electric shock for a typical grounding system, i.g. TN system or TT system, under phase-to-earth or phase-to-neutral short-circuit faults is studied. It is found that the complicated grounding grid may have a low step voltage than a single electrode. The touch voltage under phase-to-earth or phase-to-neutral fault conditions is analyzed. The TT system generally has a higher touch voltage magnitude than the TN-C system.

Support vector machine (SVM) has good classification effect on small samples and data sets with errors. We propose to improve the Harris Hawk optimization algorithm (HHO) to optimize the SVM to further improve its classification performance. The parameters of the SVM affect its classification accuracy, but there are certain difficulties in the selection of parameters, so the HHO is used to select the optimal parameters of the SVM. The HHO is easy to fall into local optimum and the convergence accuracy is affected to some extent. Chaotic perturbation convergence and nonlinear time-varying updating strategy are introduced to maintain the randomness and diversity of populations in the iterative process of HHO, enhance the search ability, enable the algorithm to jump out of the local optimum, and improve the convergence performance. Through three comparative algorithms tested on six UCI datasets, it is demonstrated that the parameters selected by the improved HHO proposed can lead to better classification accuracy and stability of the SVM.

By means of knitting and weaving, the new yarn was woven into a new eco-friendly leatherette. A series of research methods and data are summarized through the optimization and improvement of the design process and the heat-setting treatment of the fabric after weaving, which will bring a breakthrough in the reform of the preparation, research and development of new environmentally-friendly materials and product application. The new environmentally-friendly imitation leather fabrics is superior to similar products and boasts advantages in product application, such fabric is comfortable, breathable, waterproof, windproof, folding-resistant, wear-resisting and biodegradable. The cost of its production is much lower than expensive leather products, in the meantime, the space for design and development in the aspect of fabric texture, pattern, and color can be expanded more compared with other leather and imitation leather products.

Imaging circuits of converting light signals to electrical signals is designed for different remote sensing satellites with different sensors in space applications. Based on a TDI CCD sensor, this paper presents the detailed design and implementation of imaging circuits which is more suitable for use on commercial satellites. This paper utilizes industrial-grade electronics to achieve imaging circuits, which becomes effective solutions to reduce the costs of remote sensing satellites. The signal processors receive analog output signals of TDI CCD to convert digital signals. The core processor, FPGA, receives and processes digital signals them from the signal processors and delivers to transceivers. The transceivers receive the image data from FPGA and deliver them to the host computer. Moreover, a schematic of the completed board of TDI CCD imaging circuits is presented. Finally, to verify the performance of the imaging circuit, the HCTF of TDI CCD was measured on this CCD imaging circuit. The measured result is consistent with the product manual, which shows that the imaging circuits perform well.

In-wheel motor (IWM), as an ideal power source of independent four-wheel drive electric vehicles, has been paid more and more attention due to its high-power density, low starting current, wide speed adjustment range, simple control system and robustness. However, the electromechanical issue is enlarged in both longitudinal and vertical because of in-wheel driven scheme. In this paper, the electromagnetic multi-field characteristic of IWM is investigated based on Fourier series method. The negative vibration coupling on vehicle dynamics is discussed by proposing a conjoint electromechanical FWD-EV model. Results shows that the motor incentive coupled with the vehicle system in multi-degree of freedom, caused the body and wheel resonance in the low speed, meanwhile deteriorated the anti-rollover capability of the IWM-EV in the high speed.

Up to now, COVID-19 has been diagnosed in the world as many as about sixty million, which has caused tremendous pressure and burden to hospitals and medical systems. The number of chest X-rays required to be reviewed by doctors of relevant specialties is more than ever before. Besides, in the process of recognition, there are many difficulties for human identification of chest X-ray images, such as the naked eye is difficult to find tiny abnormalities, the chest X-ray images researchers have is limited and doctors is unfamiliar with this new disease. In this case, it is very necessary to use deep learning to help doctors diagnose pneumonia and solve some urgent difficulties. In the field of deep learning, transfer learning focuses on saving solution model of previous problems, and takes advantage of it on other different but related problems. Therefore, we can use the existed Imagenet model to help us train the dataset of COVID-19 and pneumonia. Moreover, deep learning depends on a large amount of data, but when the data is few, data augmentation is able to increase data by some methods, so it is a effective way to overcome the lack of train data. In terms of the good function of these two ways, we use them to improve performance and help doctors improve diagnosis so as to relieve the current tense medical situation.

In order to convey some useful information, people often design some virtual characters to complete the task of information transmission. Animation is a kind of video technology for people to educate children. Cartoon characters are virtual character models established by designers. Designers create different kinds of character models. The behavior of these character models is similar to that of human beings. Virtual characters can help us accomplish many tasks [1]. The use of computer-aided system image design has been recognized by many designers. This paper briefly outlines the concept of image design. This paper puts forward the types of professional CAD software. This paper also completed the application-oriented research of the auxiliary character image design system.

As an important part of modern machinery, gears are safe and reliable directly related to the normal operation of the mechanical system, and when the gears are abnormal, it is very important to carry out fault diagnosis in time and effectively. Wavelet transform is effective in signal time-frequency analysis and fault diagnosis. Based on the wavelet analysis method, this paper first filters and de-noises the vibration signal data of a certain type of gear, and then performs fault analysis. Time-frequency analysis of time-domain signals has a better effect on gear fault diagnosis.

This paper presents a novel fixed-time adaptive tracking control scheme for the hypersonic flight vehicles (HFVs) subject to asymmetric time-varying constraints, uncertain dynamics and unknown external disturbances. By incorporating the back-stepping technique and radial basis function neural networks (RBFNNs), the uncertain dynamics of HFVs are estimated. Note that most existing results only achieve practical fixed-time stability but not fixed-time stability, or require specific knowledge of all the dynamics of HFVs. To remove such restrictions, a fixed-time controller is newly constructed by means of a tuning functions and a projection operator-based adaptation mechanism. In consequence, the tracking errors can asymptotically converge to the preassigned compact set within fixed-time and the asymmetric time-varying constraints of HFVs never are violated. Finally, the effectiveness and superiority of the proposed control strategy is demonstrated by numerical simulations.

It is difficult to characterize and monitor the quality fluctuation caused by multi-correlation parameters in manufacturing process. Motivated by the powerful ability of digital images to characterize process states, this paper presents a quality spectra fluctuation modeling method based on deep transfer learning. Firstly, through the multi-parameter correlation of spectra pixels, the quality spectra is constructed to characterize quality fluctuation. Then, a deep residual network transfer learning model is used to identify the types of quality fluctuation. Finally, the effectiveness analysis of proposed model is demonstrated by the Tennessee Eastman process.

Welding seams identification is a challenging task in robotic welding process. In this paper, we present a method for the automatic identification and location of welding seams for robotic welding using handy 3D scanner. The method uses point cloud based local feature matching and continuous feature search algorithm, which aim at achieving an adaptive seam identification process that can work on similar work pieces with different sizes. The results together with the visual verification demonstrate that the proposed algorithms have ability to identify the welding seam from point cloud acquired by handy laser scanner.

Mobile manipulators (MMs) performing the tasks of autonomous navigation, object grasp and transportation are widely used in industrial production and other domains. This paper presents a control system with multiple functions and good extendibility for a commonly configured MM, which controls the multi-degrees of freedom (DOF) movement of the MM while navigating, positioning, pose estimating and object grasp. This system has shown desirable control performance on an experimental platform.

With the increasingly cutting-edge market competition, it is difficult to form long-term advantages for enterprises by solely relying on low cost. Middle and high-end quality customers prefer product quality in decisions for .purchasing. Enterprises can establish a good brand image, obtain high returns through the dependence on quality. Through quality management, enterprises obtain continuous improvement and economic benefits will be continuously increased. Six Sigma management is widely used in quality management in enterprises. It is a management method proposed by MOTOROLA, which can effectively improve work efficiency and product quality. Six Sigma management designs and supervises each production process and business process to achieve maximum customer satisfaction with minimum losses and improve corporate profits. This paper discusses the application strategy of Six Sigma management in enterprise supply chain quality management.

Recently serverless cloud computing which was proposed by Amazon in 2015 is getting more favored by developers because of its charging model of pay-by-usage, applicability for fine-grained services and transparency of the servers to developers. Although a lot of study has proved that the performance and cost of serverless platform is more ideal than the traditional cloud computing service, the new metering model of serverless which depends on execution time, usage count and memory footprint is still unfamiliar to most developers and researchers. In this paper, we firstly make an introduction to the new metering model and the advantages of serverless over traditional cloud services. Then according to the cost affecting factors, we make a comprehensive survey of some constraints that mainly impact the cost of serverless and related methods to reduce cost in three levels—function level, container level and cloud platform level. Based on the analysis, we also propose some directions worthy of in-depth study in the future.

Flexible rolling is suitable to manufacture small batch of 3D curvature parts with various shapes. In this paper, the influence of end effect on saddle curved part was studied firstly. The finite element model was designed based on the flexible rolling forming apparatus. The areas characteristics of forming saddle curved part were analysed, and the influence of end effect on the saddle curved part with different forming size was studied. The results demonstrate that: the forming saddle curved part consists of three forming areas by characteristics, and the forming mechanism of each forming area is analyzed deeply; under the end effect action, with the sheet length increases, the plastic strain and minimum forming thickness of forming curved part are stable; when the sheet length is 200mm, 300mm, 400mm and 500mm, the percent of the stable forming area of forming curved part is 52%, 72%, 79% and 83% respectively, and the percent of stable forming area increases by 20%, 27%, 31% respectively, therefore the utilization rate is obviously improved. With the increasing sheet width, the plastic strain is increasing and the minimum forming thickness is decreased; when the sheet width is 85mm, 90mm, 95mm and 100mm, the percent of stable forming area of forming curved part is 67%, 61%, 59%, 58% respectively, and the percent of stable forming area decreases by 6%, 8%, 9% respectively, therefore the utilization rate is decreasing.

The maintenance of power transmission system appeals automatic inspection system to replace manual inspection. Inspection based on Unmanned Aerial Vehicle (UAV) generates a huge number of photos to be processed. These aerial photos have several challenging features: high density, small, scale variation. In order to ameliorate the performance on detection of important objects as insulators and pin bolts, an efficient multi-scale training algorithm called SNIPER is introduced. SNIPER enhanced Faster RCNN was trained on a powerline dataset for object detection. SNIPER provides a good average precision and average recall on large objects like insulators and medium objects with adequate annotated instances like pin bolts and dampers. However, SNIPER fails to locate missing pin or displaced pin, possibly due to their similarity to pin bolts. Future development of a SNIPER-based cascaded detection scheme could help detect defected small objects.

Power cable is one of the most widely used electrical equipment in distribution system. Due to some historical reasons, the accidents such as cut or damage cables during construction often occur. In this paper, the calculation model of power cable power frequency magnetic field under different laying conditions is established. Based on the analysis of magnetic flux density around power cable, a positioning method of underground cable based on electromagnetic method is proposed. Three horizontal magnetic field sensors are used to locate the path of the cable,two vertical magnetic field sensors are used to locate the depth of the cable, so as to realize the three-dimensional positioning of the power cable. The simulation results of MATLAB show that the average absolute error of the method is about 0.03 m when the sensor array is 0.35 m around the cable in path positioning, and it can accurately locate the location of underground cable in depth location.

Three full-scale specimens of prefabricated frame tongue-and-groove beams connected by high-strength bolts were tested under low-cycle reciprocating loads. The failure patterns, hysteresis characteristics, skeleton curves, ductility of specimens, stiffness degradation and energy dissipation capacity of the assembled specimens were studied and compared with the full-scale ordinary concrete specimens. The test results show that the new form of connection is basically in line with the mechanical behavior of the ordinary concrete specimen, and it is feasible to apply it to the prefabricated frame structure. The test explores the influence of the thickness of the connecting steel plates on the mechanical performances of the prefabricated specimens, and find that the both sides connecting steel plate are more beneficial to the underside plates. In addition, in the design, reasonable selection of bolt and aperture size, the control of the machining error of the specimens, the effective connection of the steel bars jointied of laminated layer can further improve the installation efficiency and seismic performance.

The safe running of substations is an important part of power system security, as well as an important work of the power companies. The intrusion of kites, plastic trash, small animals and other objects will affect the safe running of substations. With the development of video monitoring technology, intelligent recognition technology of images and videos, video monitoring has gradually become the main monitoring means of unattended substations. This paper firstly analyses the research status and main technologies of video object recognition. To achieve the detection of foreign objects in substations, moving object detection and video object detection technologies are studied. And two outlines are proposed: using background subtraction and image classification, using YOLOv3 to detect objects in videos. In the end of the paper, YOLOv3 was tested by training a detector of small animals. The results show that the value of mAP equals to 0.7169 and the detection speed fulfills the request of real-time detection. This method could serve as reference for detecting and tracking intrusion objects in substations.

Mechanical fault is a common fault of gas insulated switchgear (GIS). If not found in time, it will cause major safety hazards such as opening and closing fault. In this paper, a diagnosis method based on improved Mel cepstrum coefficient for GIS mechanical fault on-line monitoring is proposed. Firstly, the Mel frequency cepstral coefficients (MFCC) are extracted from the preprocessed sound signals; in order to adapt to the characteristics of smooth sound energy change in GIS operation, the MFCC is optimized to obtain improved features; Support Vector Machine (SVM) is introduced to build a GIS mechanical fault diagnosis model based on acoustics and uses bagging algorithm to integrate the SVM model. In this study, mechanical fault is simulated on real GIS to obtain real fault sound signals for training and testing. The experimental results show that compared with the traditional MFCC, the improved MFCC has a higher recognition accuracy in GIS fault sound recognition system. The F1-score is generally over 80%, and the F1-score of specific mechanisms can even reach 99%.

After the software is published, its users are mainly operators. Although they can find problems with the software, they cannot identify and reproduce software failures. When the software fails, there must be a lot of information related to the failure in the running data of the software to be tested. Therefore, on the basis of obtaining the running data of the software under test, a test case generation method based on user operation is proposed. Based on the generation of software failure, the operation related to the failure is filtered out from the user operation. By taking these operations as the values of the system parameters to be tested, test cases that will inevitably cause software failure are obtained. Finally, the validity of the method is verified by an example, and the test cases obtained are specific to the faults of the software.

Feature is an important connection point of hierarchical structure. Large-scale network feature detection becomes an important way to analyse hierarchical structure. In this paper, we use tensor decomposition based large-scale network feature detection to study hidden relationships among different users and infer its potential feature structure. Data set experiment is used to verify the effectiveness of the algorithm from two aspects of accuracy and efficiency. At the same time, the two algorithms of tensor decomposition and Fast Newman and SLPA are compared to find that the hierarchical structure feature detection algorithm based on tensor decomposition is more efficient and more accurate.

This paper proposed a power allocation mechanism and mode switching strategy for the series-parallel hybrid electric vehicles. A test-data based simulation platform for series-parallel plug-in hybrid electric vehicles is established and developed for theoretical verification. The results indicate that the proposed control strategy realized a more reasonable power distribution in the combined power system under various working modes, which means a remarkable improvement in vehicle fuel economy.

Battery management system (BMS), as the key component in electric vehicles (EVs), takes the responsibility of sate-monitoring and safety-protection for the battery pack. State of charge (SOC) and state of health (SOH) are highly correlated with the safe and efficient operation of EVs, thus BMS must realize accurate estimation of them. This paper presents a combined SOC and SOH estimation for lithium-ion battery pack with passive balance control over the battery’s cycle lifespan. Considering the slow variation of SOH and fast variation of SOC, a dual time scale combined SOC and SOH estimation method is proposed. The battery cell with minimum capacity is located offline and then SOC is estimated and updated online, which lightens the computational burden for BMS on EVs. Results show that the proposed method can realize accurate SOC and SOH estimation. The SOH estimation error can be beneath 3% and the SOC estimation accuracy can improve 7%.

Since ancient times, there have been precedents of planting trees and flowers around siheyuan.Based on the intervention of aesthetics, the landscape modularity is combined with the residential landscape architecture examples in various places.Based on computer aided technology, this paper takes residential area as an example and expounds the different forms of plant landscape configuration in various buildings.Combined with computer aided technology, it can quickly and effectively extract the plant landscape module and apply it to the plant landscape design, providing reference for the plant landscape design of other residential buildings.

Two axles four wheel drive electric vehicle is an important development tendency of electric vehicle. However, how to coordinate the torque distribution between front and rear axles thus ensuring vehicle stability while maximizing motor’s efficiency still confronts great challenges. In this paper, we propose a three-layer hierarchical control method, which not only incorporates prior engineering knowledge into the control policy so as to relieve computational burden for real-time usage, but also properly deals with the priority between safety and system efficiency. Simulations are conducted to verify the effectiveness of the proposed method. Results show that the derived control policy can ensure high system efficiency when safety is guarenteed. Simultaneously, it can actively decrease the motor torque to improve vehicle stability when wheel slip occurs.

This paper proposes a Markov decision process based optimal energy management strategy for plug-in hybrid electric vehicle with a hybrid energy storage system, which mainly consists of an ultracapacitor, a power battery pack and a DC/DC converter. Firstly, the mathematical model is built for the overall system. Secondly, based on modified worldwide harmonized light duty test cycle, the power transition probability matrix is constructed to describe the Markov property of driver’s power demand in natural driving. Then by combining the stochastic property of driver’s power demand and deterministic state transition of the hybrid powertrain system, the energy management optimization problem is formulated into a typical Markov decision process. Finally, the derived optimization problem is solved by policy iteration and the corresponding optimal control map is generated. The optimality of the solution is investigated and discussed in detail.

Camera calibration, image feature detection, matching and other aspects have become barriers that traditional 3D reconstruction methods are difficult to break through. The important role of deep learning in data detection and classification has a deep impact on the real world, and has become a research hotspot at home and abroad to deal with this problem. In this paper, a 3D reconstruction method of image sequence based on depth learning is proposed. Firstly, the principle and method of 3D reconstruction are introduced. Then, the new method of 3D reconstruction based on image is studied and discussed in combination with deep learning theory. Finally, the conclusion and prospect are given.

VHF data exchange system (VDES) is an enhanced and updated system of AIS (Automatic Identification System) focused on maritime mobile communication, and VDE (VHF Data Exchange) is a main function of VDES. In this paper, we analyze the original adaptive modulation and coding (AMC) scheme of terrestrial VDE (VDE-TER). Because the original AMC scheme reserves several modulation and coding schemes for future use, we present an improved AMC scheme with more MCSs to increase system throughput. Simulation results have shown that the throughput performance of the proposed AMC scheme is improved.

In the low-carbon background, China must develop green manufacturing actively, and take a new road to industrialization. Government subsidies can promote the promotion of low-energy products, and thus control the total carbon emissions. Aiming at production decision of a kind of manufacturing business, A 0-1 Nonlinear decision model was built with consumer surplus as the optimization objective. This model provided guidance for the government policy decision and the firm production decision. A genetic algorithm was put forward to solve the 0-1 nonlinear decision model. A case study of refrigerator was reported to demonstrate the feasibility and rationality of this approach.

With the construction of information infrastructure and the smart grid development, big data platform become an important component to support the construction of smart grid, as a consequence, the quality of the data contain in the big data platform become an important part of the process of big data platform construction. In this paper, a discussion about the current data quality analysis model was presented and also the typical data quality problem appears in the process of information infrastructure construction of the State Grid Corporation is analyzed. On the basis of this, the data quality assessment for the information system in the whole life cycle of the State Grid Corporation is designed. As a result, data quality can be checked by the analysis model in order to ensure the data quality level in the whole life cycle of the information system, finally a case was carried out for the preliminary analysis by using the mentioned framework, the result show that this framework can be effective in embedded into the workflow and can be helpful for the data quality assessment.

Big data applications have been applied in various domain. With the development of big data in electric power, the application of smart grid is developing towards a more intelligent and coordinated direction. However, except for the functionality realization, the non-functional requirement such as data quality become an more and more important issues. For the reason that it relate to the validation of final results of function in big data area. The criterial of quality evaluation is crucial to the evaluation for data quality, and also the standardizations the scientific management of big data in enterprises can be used as the guide the construction of new information infrastructure. This paper investigates the existing data quality evaluation dimensions, model and application domain, and combines with the practice in industry, analyses the standards, dimensions and methods and then gives suggestions on promoting the data quality evaluation in specific domain.

To ensure the assembly performance of precision assembly system, a high-precision assembly system for gear-piece and gear-shaft is developed in this paper, which consists of a computer, two manipulators, a coaxial alignment module(CAM), a selective compliance assembly robot arm (SCARA), an adjusting platform and a force sensor. The CAM is used to calculate the position and angular deviation of gear-piece and gear-shaft. The force sensor is equipped to detect the contact force. In the stage of adjusting alignment, position-based visual servoing (PBVS) is adopted to ensure high precision alignment of parts. In the insertion stage, an impedance control method based on position is proposed to ensure the contact force. Experiments are carried out to demonstrate the validation of the proposed system and methods. Automated assembly of parts with a 5μm gap is achieved.

In this paper, the friction characteristics and the drilling force theory model have been studied in friction drilling austenitic stainless steel AISI 321 for solving the drilling difficult problem of thin sheet workpiece. Firstly, a equivalent test platform of friction drilling friction characteristics is established based on CA6140 lathe, and the friction characteristics of AISI 321 are studied under the different conditions of contact pressure and speed for revealing the effect of pressure and speed on the friction coefficient between tool and workpiece. Then, the empirical formula model of friction coefficient is established based on the experimental results. Finally, the improved drilling force theory model of friction drilling process is developed based on the unique geometrical shape of friction drilling tool and the equivalent friction model. Results show that the predicted force model is more accurate, which considered the actual friction coefficient model.

A series of carbon-rich hydrogenated amorphous silicon carbide (a-Si1–xCx:H) films were prepared at different radio frequency (RF) powers from silane-ethylene-hydrogen plasma. The effect of the RF power on the bonding configurations and microstructures has been investigated. The grown films were characterized by a collection of techniques including Scanning Electron Microscope, Fourier transform infrared(FTIR) spectroscopy, Raman scattering and photoluminescence spectroscopy. The deposition rate increases upon RF power due to the enhancement of chemical reactivity of plasma. The carbon to silicon ratio increases, for more C2H4 molecules decompose with the enhancement of RF power and more carbon atoms are bonded into the films. Raman G peak position shifts to a higher wavenumber, which indicates that the size and concentration of sp2 carbon clusters increase as the RF power becomes stronger. Blue-green photoluminescence is detected at room temperature. The PL band can be attributed to the existence of the amorphous carbon clusters in films with high carbon concentrations.

Carbon-rich hydrogenated amorphous silicon carbide (a-Si1–xCx:H) films were deposited by plasma enhanced chemical vapor deposition (PECVD) using silane, ethylene and hydrogen as gas sources. The effect of relatively low hydrogen dilution on the properties of as-deposited samples was investigated. A variety of techniques including Scanning Electron Microscope (SEM), Fourier transform infrared spectroscopy (FTIR), Raman scattering (RS), UV-VIS spectrophotometer and photoluminescence (PL) spectroscopy were used to characterize the grown films. The deposition rate decreases with hydrogen dilution. The silicon to carbon ratio increases slightly with the addition of hydrogen. The phenomenon can be attributed to the dissipation of power density caused by hydrogen dilution. Raman G peak position shifting to a lower wave number indicates that hydrogen dilution reduces the size and concentration of sp2 carbon clusters, which is caused by the etching effect by atomic hydrogen. The optical band gap, which is controlled by the sp2 carbon clusters and Si/C ratio, changes unmonotonously. The as-deposited samples exhibited a blue-green room-temperature (RT) PL well visible to the naked eye with UV excitation. The PL band can be attributed to the radiative recombination of electron-hole pairs within small sp2 clusters containing C=C and C-H units in a sp3 amorphous matrix.