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Increasing Electric Vehicle (EV) integration to power grid has created additional technical challenges by requiring additional power/energy for charging EVs. With the introduction of Vehicle-to-Grid (V2G), EVs can strategically feed power stored in the batteries back to the power grid for grid supports, shifting their roles from traditional loads to distributed generations.Different than other types of storages in power grids, the availabilities and energy demands of EVs are subject to change. Traditional deterministic modeling and control methods for Demand Side Management (DSM) of stationary storages failed to capture the stochastic nature in EVs, thus making suboptimal control decisions. This dissertation proposes a stochastic optimization based DSM for energy management of V2G EVs a microgrid setting. In order to make the stochastic problem tractable, a model-free probability density estimation method is utilized together with sample average approximation. Real-life EV data is applied for demonstrating the correctness and merits of the proposed DSM.As EV penetration continues to grow, the energy management problem becomes oversized to be efficiently solved in a centralized manner. Towards this concern, a distributed DSM for V2G integration into distribution grids is proposed to dispatch the centralized computational burden to distributed nodes. The proposed DSM accounts for both nodal and networked operational cost. To accelerate the computational speed and guarantee convergence, the proposed DSM is tightly relaxed to a convex form using second-order cone programming. Real-life EV data is used to test the proposed DSM in an IEEE benchmark test system. Most of other existing researches on V2G integration has focused on numerical simulations. As an important area of research, this dissertation shows the design and implementation of two V2G prototypes which could be used as testbed for verification of various V2G applications. One platform is capable of both supplying DC loads and V2G, the other one is capable of bidirectional charging. The prototypes utilize a commercialized EV model with widely used CHAdeMO communication interface. Hardware and software design considerations are shown with test results showing the performances of the prototypes.

To meet the needs of energy storage system configuration with distributed power supply and its operation in the active distribution network (ADN), establish the dynamics of the all-vanadium redox flow battery energy storage system (BESS). On this basis, an energy storage operation of ADN strategy is proposed to stabilise the power fluctuation of the system. The energy storage configuration model with optimising objectives such as the fixed cost, operating cost, direct economic benefit and environmental benefit of the BESS in the life cycle of the energy is constructed, and the energy storage installation capacity, power and installation position are used as decision variables, which are solved by the dynamic programming algorithm. On the basis of case 33 and case 69 node examples and typical daily load and distributed generations output curve, the simulation analysis is carried out to obtain the optimal configuration result. At the same time, the system tie-line power and the dynamic characteristics of the energy storage system before and after the installation of the energy storage device are obtained. The simulation results show the validity of the model.

With the continuous development of deep sub-micron technology, the design scale of integrated circuits is getting larger and larger, and more and more interfaces between SOC and the outside, which poses more challenges to the physical design of integrated circuits. For large-scale SOC, it requires a lot of work to place and route the PADs by manual, what’s the worse, this work can always be with high error rate. In order to solve this problem, an original physical design method which uses scripts to place the PADs is created to realize the automatic arrangement of complex SOC PAD. This method is suitable for any SOC with separated IO and PAD. The form and parameters of the scripts can be adjusted according to the specific process, so as to realize the automatic PAD arrangement, shorten the physical design period, and speed up the time to get into the market. This method has been applied in many products of the company, such as PLC chips, ADC chips, MCU chips, RFID chips, etc.

Predicting trends in stock market prices has been the subject of research for a long time. With the development of technology and the exploration of new machine learning models, we have new options for analyzing stock market data. In this paper, the time-dependent stock is selected as the data source, and the multi-layer model of price prediction based on LSTM is applied to find out the correlation parameters that affect the training efficiency and prediction accuracy through the experimental comparison and analyze the relevant reasons. Finally, the feasibility of the LSTM model for predicting stock data is summarized and the improvement direction of a specific application is proposed.

This paper first introduces the influence of process parameter fluctuation on FPGA interconnect timing. Then, according to the characteristics of FPGA architecture and the implementation method, the reason of FPGA chip yield reduction caused by interconnect timing difference is analyzed. Finally, this paper proposes a technical scheme of embedded EFUSE timing calibration, and introduces the calibration circuit. Chip test results show that the scheme can effectively control interconnect timing deviation and improve FPGA chip yield when process fluctuation is serious.

This article first introduces the implementation of FPGA dynamic partial reconfiguration, combined with the space radiation environment’s demand for FPGA configuration refresh, and analyzes the advantages and disadvantages of different refresh control systems. According to the understanding of the FPGA bitstream structure, this paper proposes a Self-Refresh bitstream design scheme based on FPGA dynamic partial reconfiguration and designs the corresponding bitstream. The simulation results show that the bitstream design scheme is easy to implement in FPGA and significantly reduces the refresh time.

WeChat has become an essential social media platform in China. This research investigates the importance of the WeChat Red packet as a motivator at achieving user’s satisfaction and loyalty in China. To investigate its impact, and factors that attribute to its popularity and acceptability, we extended technology acceptance model (TAM), in addition to the main model factors, “perceived usefulness and perceived ease of use” our proposed model include, perceived trust, perceived security, and perceived entertainment. The questionnaire was designed, and SPSS was used for the analysis. The research results provide insight into how WeChat Red Packet can motivate users and build their satisfaction to improve their loyalty which in turn increases WeChat users’. These results have future implication and practice for research.

The design of an MCU chip Bootloader program is introduced, and a UVM-based verification environment for interactive communication between the UART VIP and the Bootloader is built. The Bootloader design is simple and convenient, and has a strong security. The designed Bootloader can realize the import of interactive applications to the MCU chip through the interconnection of the UART interface and the computer RS232 interface, which is convenient for upgrading and maintaining the embedded programs in practical applications. Before the tape-out process, the UART VIP is used to simulate the host computer operation to pre-test whether the Bootloader meets the design expectations, thus reducing the risk of tape-out failure. The application of the UVM verification methodology makes the Bootloader’s verification environment more portable, and make it more convenient to construct complex verification scenarios.

In substations, a localization system based on a wireless sensor network (WSN) is a challenge, because the propagation of the measured signal could be blocked by various devices. In other words, non-line-of-sight (NLOS) propagation, where the signal propagation path is occluded, will affect measurement accuracy. A novel localization method based on a two-step weighted least squares and a probability distribution function is proposed to reduce the influence of NLOS error on the localization result. In this method, the initial multi-group localization result is obtained by the two-step weight weighted least-squares method, and the probability distribution function of the target is constructed by using the initial localization results, which can effectively reduce the influence of the NLOS error on the localization result. The simulation and test results show that the proposed method can keep the coordinate error within 30 cm in the substation. Compared with the localization result of two-step weighted least-squares (TSWLS) method, the average localization error is reduced by more than 1 m. Compared with the other two similar algorithms, the localization accuracy is improved by more than 50%. The tested results show that the localization performance of the method is robustness in the NLOS environment of the substation. While ensuring stability, the proposed algorithm is less efficient than some existing ones. However, under the calculation conditions of ordinary computers, the average single-point calculation time is less than 0.1 s, which can meet the needs of applications in substations.

The serial peripheral interface (SPI) is an important module for realizing communication between the APB bus in the SOC chip and peripheral SPI devices. Therefore, efficient and sufficient verification of the function of the SPI module is very important for the design and manufacture of the SOC chip. In this article, a verification environment for SPI module is built based on universal verification methodology (UVM). By introducing the register abstraction layer (RAL), the efficiency of register attribute checking and configuration is greatly improved. In addition, the use of constrained random excitation and automatic result comparison function has realized the full verification of the SPI function, with a coverage rate of 100%. Finally, the verification environment of the SPI module has been successfully migrated to the SOC verification environment.

An embedded system is a set of hardware and software that is often designed for a single task. It is a computing device that directly involves various devices or applications for disease analysis, patient management and telemedicine biomedical applications. It is a disease that women are at risk of uterine dysfunction born prematurely. It has complex causes and physical and mental health to treat it hard, bringing great physical and psychological pain. Although some scholars have made achievements in the mechanism of disease TCM treatment, therapy and results, etc., they do not constantly limit the suffering of patients. Affecting factors include disease, pathogenesis and disease progression. In traditional Chinese medicine, these properties are still one of the main problems in the previous system. Premature Ovarian Failure (POF) is a gynecological disease that causes infertility, infertility, and menopause and kidney system to introduce the proposed Deep Learning system analysis. Hormone Replacement Therapy (HRT) is currently the most popular choice for women with ovarian dysfunction who can get rid of menopausal syndrome. However, due to its need for Chinese traditional medicine, Chinese Medicine (CM) is shining new, and doctors can treat ovarian disorder with Mino herbs and comprehensive treatments. It emphasizes acupuncture as individual physical therapy for POF. Although CM's ability to be supported by clinical trials and evidence from a meta-analysis in many preclinical trials, there are still adverse events related to traditional Chinese medicine treatment, the safety of which cannot be guaranteed. Updated information CM is running the cosmic disorder.

LoRa (Long Range) technology is a long-distance, low-power wireless transmission technology based on Chirp (linear frequency modulation) technology, which has been widely regarded as the most mature low-power IOT (Internet of things) standard at present. The statistical characteristics of preamble symbol of LoRa system under AWGN channel were analyzed in this paper. When the preamble symbol detection algorithm with various SF (spreading factor), accumulative length, the number of output and detection times was used, the results of detection probability, false detection probability and missed detection probability were derived under a sort of signal-to-noise ratio and detection threshold. The simulation results were consistent with the theoretical analysis. It provides a theoretical basis for analyzing the synchronization and networking performance of LoRa system.

The quality of underwater imaging can be severely degraded by particles in the turbid water due to absorption and backscatter effects. This study proposes an underwater image recovery method based on hyperspectral polarization imaging. The proposed method is based on a physical degradation model, and combines polarization recovery and spectral fusion methods. Thus, the optimal spectral fusion is realized according to the spectral selectivity of the polarization recovery method, leading to underwater imaging with improved clarity as well as effective colour information restoration. The experimental results show that, compared with other methods, the comprehensive restorative abilities of the method proposed in this study are better with respect to subjective vision and various image quality evaluation indexes. The proposed method is effective for enhancing the visibility and contrast of underwater images for objects comprising different materials and at different imaging distances. Consequently, the proposed method has potential application value in many underwater complex environments.

HPLC (high speed power line carrier) is widely used in electricity information collection, but because of the large variation of attenuation of power line channel, in some environment, it needs wireless communication as supplement router, the current wireless technology, such as LORA(long range radio), NB-IOT(narrow band internet of thing) are for narrowband which can not be used to form a hybrid network with HPLC. So wireless broadband communication in electric system need to be studied. In this paper, we evaluate the performance of networking under wireless broadband communication with the reference of HPLC standard because there is not an approved standard about wireless broadband communication for electric system currently.

With the increasing mutual interaction between smart grid and users, intelligent acquisition terminals and mobile operation terminals are widely used and accessed. The types of data transmitted in the network are more complex, and the requirements for information security protection are higher. Currently, the general security protocols for power systems do not meet the new requirements. This paper studies ISO/IEC 9798 series of standards, combines key agreement, entity authentication and hybrid algorithm technology, designs a secure channel protocol for high-security data transmission, and analyzes its complexity and time performance. Finally, it is completed based on security chip. Data transmission test shows that it has the characteristics of high security and flexible protocol, and can effectively meet the security and real-time requirements of data communication.

Since the performance of pyramid Lucas-Kanade (LK) based optical flow algorithm would be influenced by different environments, this paper evaluates the simulation accuracy of the pyramid LK algorithm in cities, plains and mountains, respectively. The simulation results show that the LK algorithm can maintain high precision under the large motion, and the flight strategy of UAV under different environment is given according to the algorithm’s accuracy, In order to ensure the high accuracy of the optical flow sensor in different environments. For example, in the urban environment, when the UAV’s velocity is 12m/s-l5m/s, the best altitude is 80m-l50m. Simulation verification experiments are carried out in different environments. The simulation results show that the proposed UAV flight strategy is generally correct.

With the development of the electricity industry in our country, the kinds and the quantities of electrical equipment are increasing quickly. When the electrical equipment is checked and evaluated, the recognition of equipment is very important. In this paper, based on gray level co-occurrence matrix to extract the texture of equipment, we proposed a way to identify the electrical equipment. First it uses the gray level co-occurrence matrix texture matching recognition of electrical equipment, and then adopts the method of fuzzy logic according to the result of the match on the classified recognition of electric equipment. By the experiments, the correctness and feasibility of identification method to the electrical equipment are proved.

With the rapid development of electric power industry and the expansion of power grid scale, more and more attentions have been paid to the safe operation of electrical equipment. The target recognition method we used now couldn’t recognize power transformer well, which is based on gray information. In this paper, a transformer identification algorithm based on improved Hu moment invariants is proposed. The transformer identification algorithm proposed in this paper has the advantages of good recognition effect and high recognition accuracy. The correctness and feasibility of the proposed algorithm have been verified by experiments.

With the comprehensive construction of smart grid, Internet of Things technology has been widely applied to the power generation, transmission, scheduling and many other aspects. First, a framework for the information communication system of power Internet of things is proposed Second a method based on fuzzy logic is given to judge the sensitive information. Third, the way to deal with different information is shown according to the sensitivity of the data. The feasibility and correctness of the proposed framework and method are verified by tests.

Power equipment is of great importance to the power of things, the operation of equipment directly impact on the safety and stability of the power of things. Therefore, it is important to ensure secure and stable operation of the power grid by prejudging monitoring, observing and fault diagnosis of power equipment. A fault diagnosis method of power equipment based on the technology of power internet of things and KNN algorithm is proposed in this paper. Through the real-time sensing and data acquisition of the basic equipment status in power network operation, the equipment status is analyzed and prejudged, which can provide alarm signals of the equipment to avoid greater security risk.

This paper presents a method that the algorithm of calculating the electric field combining with vector electric field multi-electrode gradient information is optimized by using the compression-aware greedy reconstruction algorithm, which can effectively identify the electric field gradient information and improve the accuracy rate of the voltage level detection judgment, through the research on the calculation method of the electric field gradient information of the electrical equipment in the substation. This method can accurately calculate the electric field distribution and voltage level around the high voltage conductor by analyzing the electric field information around the charged body. The distribution of the field strength at one interval of the charged carrier is obtained by tested in complex power frequency electric field environment of the 110 kV substation equipment. The results show that the method can effectively improve the detection capability of the voltage level of the charged carrier and the response speed of the electric field around the charged body, and improve the safety of the staff working in the complex electric field.

In order to solve the problem of the inaccuracy of the traditional online operation risk assessment model based on a physical mechanism and the inability to adapt to the actual operation of massive online operation monitoring data, this paper proposes an online operation risk assessment of the wind power system of the convolution neural network (CNN) considering multiple random factors. This paper analyzes multiple random factors of the wind power system, including uncertain wind power output, load fluctuations, frequent changes in operation patterns, and the electrical equipment failure rate, and generates the sample data based on multi-random factors. It uses the CNN algorithm network, offline training to obtain the risk assessment model, and online application to obtain the real-time online operation risk state of the wind power system. Finally, the online operation risk assessment model is verified by simulation using the standard network of 39 nodes of 10 machines New England system. The results prove that the risk assessment model presented in this paper is more rapid and suitable for online application.

An knowledge-based system (KBS) on aircraft assembly drilling should be develop because that assembly drilling requires much experience about process parameter, Tools, Equipments and fixtures to drill precisely and efficiently and the knowledge may update rapidly to suit the improvement of process. This paper lists the influence factor of aircraft assembly drilling which includes fastener, structure type and material. A Hierarchical case representation model presents to resolve the problem level by level in order to simplify the complexity. After discussing the Knowledge base (repository), Architecture of KBS and inference method, the KBS implement is introduced to explain its use.