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The marine internet of things (MIoT), an increasingly important foundation for ocean development and protection, consists of a variety of marine distributed sensors under water. These sensors of the MIoT have always been highly dependent on batteries. To realize in situ power supply, a flexible seaweed-like triboelectric nanogenerator (S-TENG) capable of harvesting wave energy is proposed in this study. The flexible structure, designed with inspiration from the seaweed structure, processes extensive marine application scenarios. The bending and recovering of the S-TENG structure under wave excitations are converted to electricity. As the output performance increases with the number of parallel connected S-TENG units, an S-TENG system with multiple units could serve for floating buoys, coastal power stations, and even submerged devices. Through the demonstration experiments performed in this study, the flexible, low-cost S-TENG could become an effective approach to achieve a battery independent MIoT.

Power flow controllers and circuit breakers are important devices in a DC grid, and combining them and expanding their ports can save many components. This paper proposes an integrated multiport DC power flow controller with a DC circuit breaker that can achieve power flow control and fault removal on multiple DC lines during steady and fault states, respectively. First, the device's topology based on modular multilevel converters is proposed, and the principles of power flow control and self-power balance based on bridge arm voltage are introduced. Second, it is improved by adding a fault removal capability. The equivalent circuit and fault currents in different time sequences are analyzed, and the component parameter selection method is designed. Third, in the PSCAD/EMTDC, the power flow control and fault removal capabilities are verified using three conditions: flow control, line fault, and bus fault. Finally, the number of components and steady-state losses are analyzed. Overall, the proposed device has power flow control and fault removal functions as well as a certain limiting effect on the fault current, which greatly reduces the system construction cost.

For valve-controlled hydraulic servo system, the suppression of mechanical dynamics uncertainties and the analysis of internal pressure dynamics stability (i.e. the pressure into the two chambers of the actuator) are still the widespread concern issues. This study presents an easy-to-implement observer-based output feedback control scheme for hydraulic valve-controlled single-rod actuator system with addressing mismatched mechanical dynamic uncertainties and proving internal pressure dynamics stability analysis. The singular value perturbation approach is used to simplify the system model, and then a practical output feedback controller augmented with extended-state observer is developed through back-stepping design approach. The main contribution of this study is two-fold. First, a practical reduced-order model-based back-stepping controller is developed. Second, the stability issue of internal pressure dynamics is analysed. Besides giving theoretical analysis, extensive comparative co-simulations and experiments are carried out to illustrate the effectiveness of the proposed method. The present method can guarantee prescribed tracking transient performance in the presence of uncertain disturbances. Also, the stability of internal dynamics is proved.

In the power operation scenario, user information, power equipment, environment, tasks, time and other data are collected through sensors, mobile terminals, wearable devices, GPS/Beidou/UWB positioning system, and computer input. The information space model is constructed from five dimensions: people, equipment, environment, task, time, etc., to realize the logical correspondence between physical space and information space. In the power operation scenario, the user wears the wearable device to automatically perceive the power operation scene information, and the information space model provides target positioning, path planning, navigation, electronic fence, audio and video conference, intelligent interaction, etc. as needed to realize the power field operation process precise guidance.

User equipment (UE) is required to comply with the relevant radio frequency (RF) electromagnetic field (EMF) exposure limits, which are of relevance to establish the maximum permissible transmitted power and the maximum equivalent isotropically radiated power (EIRP). Recently, international RF EMF exposure guidelines, such as those published by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) as well as by the IEEE, have been updated. In this paper, the implications of the revised incident power density limits are investigated in terms of maximum permissible transmitted power and the maximum EIRP for devices operating in close proximity of the user. A similar analysis is conducted according to the US Federal Communications Commission (FCC) regulation on RF exposure. EMF compliance of UE is studied by means of numerical modelling of patch antenna arrays of different array sizes taking into consideration of possible beam-steering operations, at frequencies ranging from 10 GHz to 100 GHz. The results are compared with the 3rd Generation Partnership Project (3GPP) requirements on the total radiated power (TRP) and EIRP levels. The present implications of the incident power density limits for 5G millimeter-wave UE will give valuable insights to mobile equipment manufacturers, network operators, and standardization bodies.

As the fifth-generation (5G) mobile communicationis utilizing millimeter-wave (mmWave) frequency bands, electromagnetic field (EMF) exposure emitted from a 5G mmWave mobile handset should be evaluated and compliant with the relevant EMF exposure limits in terms of peak spatial-average incident power density. In this work, a fast power density (PD) assessment method for a 5G mmWave mobile handset using the equivalent currents (EQC) method is proposed. The EQC method utilizes the intermediate-field (IF) data collected by a spherical measurement system to reconstruct the EQCs over a reconstruction surface, and then computes the PD inclose proximity of the mobile handset with acceptable accuracy. The performance of the proposed method is evaluated using a mmWave mobile handset mock-up equipped with four quasi-Yagi antennas. The assessed PD results are compared with those computed using full-wave simulations and also those measured with a planar near-field (NF) scanning system. In addition, three influencing factors related to the accuracy of the EQC method, namely, the angular resolution, the phase error, and the handsetposition in the IF measurements, are also analyzed. The proposed method is a good candidate for fast PD assessment of EMF exposure compliance testing in the mmWave frequency range.

Microhaplotype marker is a new type of forensic genetic marker, and it is becoming more and more important with the development of massively parallel sequencing technology. Therefore, a unified and standardized nomenclature is required. Kenneth K. Kidd has proposed and published a nomenclature for microhaplotype markers. The nomenclature involves a root “MH” which is followed by chromosome numbers and unique characters established by the authors in their initial publication. Mh01kk001 is an example following this nomenclature system. Almost at the same time, our team proposed another similar nomenclature. After comparing it with Kidd’ nomenclature, our team improved our nomenclature and proposed a supplement for the nomenclature of microhaplotype markers. It involves five parts: the root “MH”, the chromosome number, the name of lab which published the loci initially, the individual numbers of the loci and the version number that defined by the cluster of SNPs, for example, mh01kk001A.

Power flow control and fault current limitation are two major problems that require an urgent solution in a direct current (dc) grid. This study proposes a novel interline dc power flow controller having a fault current limitation capability (FCL–PFC). The proposed controller is expected to realise overall control of the internal power flow in a dc grid and to limit the fault current under fault conditions. FCL–PFC exhibits small capacity and low loss and does not require energy from an alternating current (ac) system. The topological structure of FCL–PFC is first introduced. Then, the principles of power flow control and self-power balance using the dc voltage and ac circulating current, respectively, is discussed. The control strategy under stable conditions is further designed. Based on the proposed FCL–PFC action sequence under fault conditions, this study describes the calculation and analysis of the dc fault current in different periods for maximising the fault current limitation ability of FCL–PFC. Finally, a simulation model is used in PSCAD/EMTDC to verify the power flow control and fault current limitation capabilities of the proposed FCL–PFC. The economy of combining FCL–PFC with direct current circuit breakers is analysed.

This paper describes a novel transformable land/air robot that is capable of terrestrial locomotion and aerial locomotion. What is unusual about the robot is that it can transform between the two modes of locomotion at will through the transformable mechanism, allowing the robot to overcome large obstacles in their mission environment. The wheel mechanism of the robot is shared by both terrestrial and aerial locomotion, instead of simply adding a quadrotor to a wheeled mobile robot. The objective of this paper is to design the robot and establish the kinematic and dynamic models for the transformable process. Herein, we focus on the design of the driving wheels and transformable mechanism. A series of experiments about the energy analysis and the transformation from aerial locomotion mode to terrestrial locomotion mode were performed with the physical prototype; the experiment results confirmed the validity of our design and the theoretical analysis that are helpful to optimize the key parameters in our design. Moreover, our work can provide a reference for the study of the flying car.

The basic foundation for the development of the image processing is image segments. Primary analysis, such as analysis of images and visualization of images, begins with segmentation. Image segmentation is one of the important parts of digital image processing. Depending on the accuracy and accuracy of the segmentation, the results of the image analysis, including the size of the object, the size of the object, and so on. In the first section of this study, briefly describe the types of image segments. Also use Mathlab language's powerful modern programming tools to explore the image segmentation methods and compare the results. As a result of the experiment, it is more accurate to accurately measure the trajectory of the image segmentation of the image as a result of the Otsu-based method of B space. This will apply to further research. Өнгөний мэдээлэлд суурилсан дүрс сегментчлэх аргын судалгаа Хураангуй: Дүрс боловсруулах судалгааны ажлын үндсэн суурь нь дүрс сегментчлэл юм. Дүрсэнд анализ хийх, дүрсийг ойлгох зэрэг анхан шатны боловсруулалт нь дүрс сегментчлэхээс эхэлдэг. Дүрс сегментчлэл нь дижитал дүрс боловсруулалтын чухал хэсгүүдийн нэг юм. Сегментчлэлийг хэр зэрэг үнэн зөв, нарийвчлал сайтай хийснээс шалтгаалан, дараагийн дүрс таних, обьектын хэмжээ зэрэг дүрс шинжлэлийн алхамын үр дүн ихээхэн хамаардаг. Энэхүү судалгааны ажлын эхний хэсэгт дүрс сегментчлэх арга төрлүүдийн талаар товч танилцуулна. Мөн орчин үеийн програмчлалын хүчтэй хэрэгсэл болох Mathlab хэлний функцуудыг ашиглан дүрс сегментчилж гарсан үр дүнгийн харьцуулалтыг танилцууллаа. Туршилтын үр дүнд RGB өнгөний орон зайн B бүрэлдэхүүнд суурилсан Otsu-ийн аргийг ашиглан дүрсийг сементчилэх нь уламжлалт дүрс сегментчилэх аргаас нэн сайн үр дүнтай илүү нарийвчлалтай байна. Үүнийг цаашдын судалгааны ажилдаа хэрэглэх болно. Түлхүүр үг: RGB дүрс, босго (Threshold) утга, гистограм, Otsu-ийн арга, дүрс боловсруулалт

The DC power flow controller (DCPFC) is a hotspot in DC power flow analysis and control field, which can effectively improve the power flow control degree of DC power grid. The DCPFC is usually installed at the DC bus of the hub converter station. Its power flow control function is an effective complement to the power flow control of the converter station. Therefore, it is necessary to coordinate the DCPFC and the converter station. This paper adds the network loss, static voltage stability index and voltage offset into the objective function of the optimal power flow. The optimisation model of multi-objective power flow of AC–DC hybrid system with DCPFC is established. The converter station and the DCPFC cooperate with each other to control the AC–DC system flow, and realise the coordinated control of the converter station and the DCPFC to achieve minimal loss of network, static voltage stability margin maximisation, and the use of coordinated control of the control system with economic benefits, easier scheduling and so on. Using simulation software to optimise the power flow of the hybrid system, a five-terminal hybrid system is taken as an example to verify the effectiveness of the proposed method.

In this paper, aiming at the problem that the on-site rescue personnel can not quickly prepare to perceive the operation damage of the equipment in the disaster damage environment, the invention integrates the machine vision intelligent recognition and wireless sensor acquisition technology and innovatively designs the on-site environment and the equipment perception acquisition method. Firstly, the multi-band point cloud environment map is created by using machine vision method, and the damage state of power equipment is identified from the perspective of emergency disposal to realize the accurate perception and information association of the site environment; secondly, the power field equipment identification based on mobile terminal is studied to realize the identification of the site power grid equipment and related components; finally, the wireless communication modules such as ZigBee are used To realize the fast networking and data acquisition of power equipment sensors. By using augmented reality technology, the real-time operation monitoring data collected by the equipment and the physical equipment are integrated and displayed, and emergency workers can quickly obtain the operation status of the corresponding parts and assist the on-site workers to more accurately and comprehensively view and judge the operation of the whole equipment and parts.

Research and development of multi-information intelligent acquisition module of power grid disaster loss, power grid disaster loss information fusion analysis and prediction module, and multi-person information real-time interaction module. Based on these three modules, a prototype system of power grid disaster intelligent perception and emergency command is developed for the application scenarios of emergency drill, training, and command operation. Develop the integrated interface of the prototype system, realize the software and hardware integration of intelligent mobile terminal, background equipment monitoring system and electric power emergency command platform, and realize the field application verification in the company headquarters and typical electric power emergency drill, rescue and command site of provincial company, realize the intelligent perception of power grid disaster and auxiliary decision-making and visual command under the condition of missing information in the emergency site, so as to realize the real-time monitoring of power grid disaster. It can effectively improve the efficiency of emergency command in disaster judgment, operation command, and material dispatching.

State grid puts forward the development strategy of “three types and two networks,” fully applies modern information technologies such as “big, cloud, things, mobile, and intelligence,” and strives to create a ubiquitous power Internet of things with comprehensive status perception, efficient information processing, and convenient and flexible applications, intelligent analysis and calculation of big data based on advanced sensing is one of the key technologies for the construction of ubiquitous power Internet of things. The research of this project can greatly improve the coverage of the power grid equipment status perception dimension, improves the intelligent analysis ability of multi-spectral time series data, effectively enhances the real-time performance of massive data input and effective feedback of the computing system, and improves the efficiency of power grid equipment status perception and fault warning. Benefits, forming a monitoring and early warning system for power grid equipment is based on intelligent analysis of multi-spectral data [1].

As an important part of the life support system for high performance fighter pilots, the aviation anti-G regulator can protect pilots from overload during maneuvering flight. Based on the design library of mechanical and pneumatic components in AMESim, one-dimensional flow simulation model and mechanical motion model of the system were established according to the structural principle of the aviation anti-G regulator. By simulating the overlord environment, the working curve of the anti-G regulator under different overloads were obtained. By changing the structure parameters, effects of several key parameters on the performance were discussed. The simulation results can play a guiding role in the structural optimization of the aviation anti-G regulator.

In this paper, an amphibious robot compounded with wheel and fin is proposed which integrated two propulsive motions with land walking and underwater swimming. The robot also has the ability of self-adaptive climbing in the complex environment, i.e. the robot moves forward on land with the paddles wheels on the ground and turns the rockers upward for climbing the obstacle. The structure of the robot is designed and the principle of the self-adaptive climbing mechanism is described. The performance of obstacle crossing is analyzed. The process of self-adaptive climbing of the amphibious robot is simulated by using the ADAMS. Finally ˈ the experimental prototype is developed and tested.

At present domestic emergency information interaction lack of flexibility, after a natural disaster, on-site rescue workers with the rescue headquarters, emergency command center, sharing of information between were studied based on the scene of the electric power emergency mobile information real-time interactive methods, design a kind of power at the scene of the emergency mobile friendly each other, are at the scene of the emergency and emergency command center cluster information real-time interactive technology, gives the power emergency field information real-time interactive system prototype and power at the scene of the emergency information real-time interactive software and hardware integration solutions, to build the efficient method of multi-type information submitted by emergency disposal across departments and information interaction ability.

To evaluate the impact of the randomness and correlation of photovoltaic (PV) and load on AC/DC systems with a multiport current flow controller (M-CFC), this paper proposes a probabilistic optimal power flow calculation for AC/DC systems based on a nonparametric kernel density estimation. First, according to the M-CFC model, the DC power flow calculation method with M-CFC was deduced, and its influence on line loss was analyzed. Second, a nonparametric kernel density estimation with an adaptive bandwidth was used to accurately describe the probability distribution of the PV and load, and correlation samples of the PV and load were obtained by the mixed copula function. Then an optimization model that considers system loss and static security was established, and a fast nondominated sorting genetic algorithm based on the elite strategy (NSGA-II) was used to calculate the multi-objective probability optimal power flow of the AC/DC system. Finally, a case study was performed on the modified IEEE39 bus system using measured PV and load data. We verified the nonparametric kernel density estimation with an adaptive bandwidth can better adapt to random component uncertainty, and M-CFC can improve the static security of the system.

The precision of roller kiln temperature control is an important factor to control the lead content in the glaze of color ceramic. This paper studies the temperature control of the glaze firing process in the roller kiln and investigates the application of Fuzzy-PID control technology in the glaze firing process control system of roller kiln for bone china to achieve the “green ceramic” process requirements. Taking the single-chip microcomputer as the control core, based on fuzzy control, the temperature of the electric heating roller kiln is precisely controlled. At the same time, the GPRS network is used to realize real-time data monitoring. Finally, the fuzzy-PID improvement strategy is proposed. Hope that this research can promote the progress of China’s color-burning process development.

Recent application of deep learning in medical image achieves expert-level accuracy. However, the accuracy often degrades greatly on unseen data, for example data from different device designs and population distributions. In this work, we consider a realistic problem of domain generalization in fundus image analysis: when a model is trained on a certain domain but tested on unseen domains. Here, the known domain data is taken from a single fundus camera manufacture, i.e. Canon. The unseen data are the image from different demographic population and with distinct photography styles. Specifically, the unseen images are taken from Topcom, Syseye and Crystalvue cameras. The model performance is evaluated by two objectives: age regression and diabetic retinopathy (DR) classification. We found that the model performance on unseen domain could decrease significantly. For example, the mean absolute error (MAE) of age prediction could increase by 57.7 %. To remedy this problem, we introduce an easy-to-use method, named enhanced domain transformation (EDT), to improve the performance on both seen and unseen data. The goal of EDT is to achieve domain adaptation without using labeling and training on unseen images. We evaluate our method comprehensively on seen and unseen data sets considering the factors of demographic distribution, image style and prediction task. All the results demonstrate that EDT improves the performance on seen and unseen data in the tasks of age prediction and DR classification. Equipped with EDT, the \(\mathrm{R}^2\) (coefficient of determination) of age prediction could be greatly improved from 0.599 to 0.765 (n = 29,577) on Crystalvue images, and AUC (area under curve) of DR classification increases from 0.875 to 0.922 (n = 1,015).

At present, the converter station is generally equipped with visible light, infrared, and ultraviolet monitoring systems, and some of them have alarm functions, but the automation is insufficient, the sensitivity is not high, and it is easy to be falsely reported. At the same time, a large amount of data is collected for the operation and maintenance personnel to judge and increase the operation and maintenance workload. At the same time, it is impossible to help the operation and maintenance personnel to detect potential accidents early, curb the spread of accidents in advance, and help to prevent the occurrence of fires at the converter station. Therefore, this paper studies the online state evaluation method of the UHV (Ultra High Voltage) converter valve tower based on infrared/ultraviolet image recognition. Through artificial intelligence image recognition technology, it can accurately diagnose and identify abnormalities such as open flame, heat, discharge and water seepage in valve hall equipment.

In a distribution network system with multiple power quality controllers, the control effect of each power quality controller is to ensure that the power quality of the access node can reach the expected goal, but due to the interaction effect between the power quality controllers, power quality controllers adopted in the local control of calculated to adjust instruction, unable to realize multiple nodes distribution system power quality at the same time to reach the expected aim. In this paper, for IEEE 33-node standard distribution network system, the nodes contraction method is firstly adopted to eliminate irrelevant nodes, the expression of interaction effect mechanism between power quality controllers is derived, and the relative gain system matrix (RGA) is introduced to quantitatively analyze the degree of interaction effect between controllers Then USES the master-slave control structure with central controller, selection of multi-objective particle swarm optimization (MOPSO) applied to the central controller of the coordinated control calculation, through multi-objective particle swarm optimization algorithm to get a group can make the distribution network system in the assessment of more nodes at the same time to achieve the desired effect of optimal solution set, and then by the central controller according to the optimal solution set distribution adjust instruction to the assessment of each node power quality controller is carried out. PSCAD simulation results show that the coordinated control effect is good.

Due to the open nature of the UAV network, its external environment is complex, potentially risky, and vulnerable to a variety of attacks. The attacker will destroy the connection between the drones, intercept the information transmitted on the communication link, and interfere with the mission messages between the drones, thus requiring us to encrypt the data between the UAV nodes. The current mainstream UAV network communication uses a symmetric encryption algorithm, which is faster but less robust against network packet loss problems. The innovation of this paper is to propose an encryption communication scheme based on SM4 algorithm, and improve the stream encryption mode (CTR) of SM4 algorithm. Compared with the traditional SM4 CTR algorithm, the encryption and decryption speed is improved by 7.7%, and ChaCha20 flow Encryption algorithms are more tolerant of packet loss.

This project is about design and implementation of Augmented Reality cloud recognition power platform for 3D Entity Objects. Typical power operations include equipment inspection, equipment maintenance, emergency repair, reverse brake operation, line operation and so on. Combining with the characteristics of electric power operation site, to improve the on-site operation mode, through the interactive collaboration such as terminal multidimensional exhibition, backstage data fusion, and custom services, the clouds recognition system will provide grid on-site an automatic and intelligent new solution based on AR.

Based on the two-dimensional (2D) tan-sin-cos-coupling (2D-TSCC), a new image protection method is designed, this method includes steganography and encryption. First, a 2D-TSCC system is designed. The 2D-TSCC has a large parameter space in a hyperchaotic state. The chaotic trajectory fills the entire window. The chaotic sequence generated by the 2D-TSCC has a good pseudorandomness, so it can be used in steganography and encryption. Then, the amount of information contained in each bit of the cover image is analyzed, and the three bits which carry the least amount of information are selected. The secret image is hidden in these three bits base on the 2D-TSCC. Finally, the carrier image is scrambled and diffused by the 2D-TSCC. The ciphertext is generated in this way. Send the ciphertext to the recipient through channel transmission, and the recipient obtains the secret image by decrypting twice.

At present, the traditional grid 3D model is huge, and the running software and hardware environment is complex. It is difficult to apply across computer platforms, the model rendering efficiency is low, and the mobile terminal is difficult to load and run. The core of 3D engine is the real-time high-speed rendering technology of 3D model. The traditional 3D model needs a lot of post-rendering after modeling. It needs to calculate and process the material, texture, and illumination of the model, which consumes a lot of computing resources and labor cost. This paper innovatively adopts the rendering algorithm based on grid business characteristics and realizes the high speed of 3D power equipment model on mobile terminal equipment through the application of modules and algorithms such as material management, particle system, bulletin board, HDR environment, and illumination. Rendering solves the problem of high cost of post-production rendering of traditional 3D models.

On the background of process automation for slaughtering enterprises, a robot system for porcine abdomen cutting based on binocular vision is established. The system is composed of industrial robot, end effector, binocular camera, IPC and hanging bracket, etc. The study firstly builds transforming relations among coordinate systems by robot calibration. Then, carcass image is taken by using binocular camera, depth information of image is obtained, central line of carcass is recognized and coordinate values of path points are acquired in the end. Finally, moving trajectory of robot can be planned for guiding robot to complete abdomen cutting operation. The experiment shows that the designed system can achieve the scheduled task well and obtain comparatively ideal cutting effect.

This paper designs a BRAM test circuit with scan-chain structure based on CLB resources of FPGA, and implements it on XC7K325T. Post-PAR timing report show that the test speed of this design exceeds 300Mhz. Moreover, the design adopts modular structure, has good universality, and is convenient to transplant on different types of FPGA.

A resource scheduling scheme based on combined bidirectional auction is proposed for resource scheduling across data centers. Based on the definition of user priority and task urgency, the scheme estimates the SLA(service level agreement) violation occurred in each work during the auction process and calculates the income of the cloud provider. Simulation results show that this algorithm ensures the success rate of auction, and compared with the traditional combined bidirectional auction scheme in which a single auction is completed, the energy consumption reduction of this scheme in the auction period and the profit of the auction cloud provider are greatly optimized.

DC voltage control is one of the key technologies of DC power grid operating control. Here, the local voltage measurement is replaced by the pilot voltage in traditional droop control, and the droop control based on the pilot voltage is applied to the DC power grid. When the power disturbance appears in the DC power grid, the coordination of multiple converter stations can be better realised and the voltage fluctuation of the DC power grid can be reduced. In addition, this paper also applies the concept of pilot voltage to the analysis and control of interconnected DC power grid, and analyses important significance of pilot voltage to DC power grid. The transmission of the power in DC interconnected system can be guided by the change of the pilot voltage. The simulation results verify the effectiveness of the proposed method.

Urban residents work indoors for a long time, and indoor air quality is becoming an important indicator that residents care about in their work and life. Based on the Internet of Things, this paper designs an indoor air monitoring system, which can monitor and collect various parameters of indoor air in real time, upload them to the data center for analysis, and push them to the user end for display. Meanwhile, a prediction model is established to predict the air quality indicator in the next few hours by taking CO2 as an example, and construct a complete air quality evaluation to provide to users, so that other intelligent terminals or users can adjust the doors and windows, to make ensure the excellent indoor air quality.

The development of information display interaction from planar 2D to stereoscopic 3D has become increasingly mainstream. In the future, there will be more and more 3D content creators and consumers. The core model engine is standardized, lightweight, cross-platform and 3D-based. The interactive content library has become a hot topic in the IT industry. This paper studies the mainstream model file conversion compression technology based on the characteristics of the power grid business, and realizes the compression and weight reduction of the three-dimensional model. In this paper, compressive sensing-based 3D model compression method is used to achieve rapid compression of server-side models, which can greatly reduce the model space and increase the loading speed of the model. Compressed sensing compresses data at the beginning of data acquisition. When sampling, it does not follow the frequency twice as high as the maximum cut-off frequency of the initial signal as described by Shannon Nyquist Theorem, which greatly reduces the amount of data sampled, and does not require subsequent data recompression, which can greatly increase the model’s compression efficiency and quality. First, get the original model and split the model data into material data, animation data, grid data, node tree data, and map data. According to the model data classification, compression-sensing algorithm based on the business characteristics of power grid services was used to compress the model for sampling. Get a discrete representation of the model after sampling. Obtain the compressed model, and combine the material data, animation data, grid data, node tree data, and map data according to the characteristics of the power business and associate it with the unique identifier of the business model. The compression of mainstream model files based on the characteristics of power grid services can effectively improve the operational efficiency of 3D models. The research results lay a solid foundation for the engine technology in the future development of interactive business systems for displaying 3D power equipment based on 3D model engines. The development of a prototype of a 3D model engine can provide basic support for the construction of a unified power 3D model library for the future grid and 3D interaction of power services.

This paper aims at the human-machine interaction requirements of the new generation of dispatching systems, and based on the new human-computer interaction technology trends, studies and proposes a new human-computer interaction solution for the new generation of dispatching systems. Starting from the on-the-spot requirements analysis, the research foundation and basic principles are described. The implementation route is given. Apply mature and applicable new human-computer interaction technology to support the new grid control architecture with “physical distribution and logical unity”.

The microgrid is a weaker inertial system than the traditional large grid, which contains many power electronic converters and is a highly nonlinear environment. In view of the above problems, this paper adopts an improved power control method, so that the micro-source output voltage has no static difference and reactive output. Independent of the line connection impedance. For the low-voltage micro-grid, the problem of low power distribution accuracy and nonlinear load between the traditional micro-sources of the droop controller is adopted. By designing the virtual impedance, the equivalent line impedance is designed to be resistive near the power frequency. The line impedance characteristics of the low-voltage micro-grid reduce the sensitivity of the power-average control of the micro-source inverter to the impedance of the output line, and ensure a smooth transition of the grid-connected process.

Artificial intelligence (AI) + education has become an indispensable part of China’s national strategy with regard to AI. This paper redefines relevant concepts of AI + education, studies the core technology of AI + education, and proposes a four-level systematic framework of AI + education. Besides, this paper probes into the application scenarios, typical models and main technologies of AI + education.

For the nonlinear characteristics of turbine flow sensor and the influence of medium temperature, the traditional compensation method is difficult to apply. This paper uses BP neural network based method to improve measurement accuracy. Firstly, the reason of nonlinearity and the influence of temperature on flow measurement are analyzed theoretically. Then the nonlinear correction and temperature compensation scheme based on neural network is proposed, and different optimization algorithms are used for training. After simulation experiments and analysis, the results show that neural network the maximum reference error of the sensor after compensation is 0.683%. Compared with the traditional least square fitting, the accuracy is greatly improved, which can effectively solve the influence of nonlinearity and temperature change on flow measurement, expand the measurement range of turbine flow meter, and improve the measurement accuracy so as to meet the requirements of industrial use.

In the process of industrial automation production and daily life, the flow is an important parameter that often needs to be measured and controlled. In order to ensure the accurate measurement of flow meter, the flowmeter needs to be calibrated before production, during use and after maintenance. However, most of the existing calibration methods are complicated in the calibration process, and there are many human interventions. The functions of the intelligent flowmeter are not fully utilized, and the unified management requirements for equipment during large-scale deployment cannot be met. This paper provides a method for automatic calibration of intelligent turbine flowmeter, which can be applied to a variety of intelligent instruments. In the calibration phase, the flowmeter automatically completes the calibration data collection at each calibration point set by the user. After all calibrations are completed, the calibration data will be stored in the flowmeter inside and it also will be uploaded to the host computer through the wireless network. At the same time, the configuration information can be received remotely. In the measurement phase, according to the internal calibration data and the K value fitting method selected by the user, the flowmeter can realize the non-linear correction of flow measurement and improve the accuracy of measurement results. User also can self-configure the instrument parameters. Compared with the existing calibration and correction methods of flowmeter, it simplifies the calibration process, has a high degree of automation, improves the accuracy of measurement results, and gives full play to the functions of intelligent instruments.

This paper designs a reliability test device for Fujitsu 4Mbit SPI interface ReRAM memory. The device uses FPGA as the lower computer controller to realize the basic data writing and reading operation of the SPI interface ReRAM memory. The upper computer software supports the programmable instrument control, the data writing and reading function test of the ReRAM memory, the SEU detection under the continuous reading operation, the continuous writing and reading function test of the specified address. It can be used for the reliability test of SEE, data retention and endurance of ReRAM memory. In the SEE sensitivity test, it is found that the device is prone to SEFI under heavy ion irradiation. After stopping the beam and restarting the system, the data of the ReRAM storage unit has not changed, which proves that the SEU of the data storage unit array has not occurred.

Computer fast scan modeling for converter valves starts with 3D modeling technology based on computer geometry modeling, visual modeling technology based on depth of field sensors, automatic layered reconstruction technology of internal and external parts models of electrical equipment, developing the rapid layering construction technology research of a panoramic model for electrical equipment. Taking the converter valve as the object, the traditional 3D modeling, fast scanning modeling and model layering are determined, and a computer-identifiable model is created using fast depth-of-field scanning technology, and a refined 3D model of the traditional design stage is hierarchically displayed. Realize the all-round display of the interior and exterior parts of the power equipment model on the job site.

This paper mainly studies the optimization of search path planning in the process of multi-uav relay mission. The purpose of search path planning is to determine the specific location of unknown ground mobile personnel and network them so that the uav can carry out relay tasks. For uav relay communication interrupt, distortion and other issues that may occur, analysis of the communication network information transmission route selection, selection of communication time delay as the performance index, by changing the path choice of uav to improve the performance of communication network. The simulation results show the effectiveness of improving the performance of communication relay and communication network.

The Nash equilibrium method is studied in this paper, and a new motion prediction algorithm based on Nash equilibrium is proposed. This method includes 3 main contents: 1. Analyze and abstract the influencing factors of object motion. 2. Analyze the effects of each factor and list the Nash equilibrium equation. 3. Find the optimal solution of the system of equations. Nash equilibrium method is used to attain the purpose of overall consideration of all influencing factors of motion prediction. This algorithm can solve the problem that the current motion prediction algorithm can’t take all the influence factors into account. To verify this method, this method is introduced to the ocean drift motion prediction and prove the accuracy of the method through three sets of comparison experiments.

Controlling a crowd using multi-touch devices appeals to the computer games and animation industries, as such devices provide a high-dimensional control signal that can effectively define the crowd formation and movement. However, existing works relying on pre-defined control schemes require the users to learn a scheme that may not be intuitive. We propose a data-driven gesture-based crowd control system, in which the control scheme is learned from example gestures provided by different users. In particular, we build a database with pairwise samples of gestures and crowd motions. To effectively generalize the gesture style of different users, such as the use of different numbers of fingers, we propose a set of gesture features for representing a set of hand gesture trajectories. Similarly, to represent crowd motion trajectories of different numbers of characters over time, we propose a set of crowd motion features that are extracted from a Gaussian mixture model. Given a run-time gesture, our system extracts the K nearest gestures from the database and interpolates the corresponding crowd motions in order to generate the run-time control. Our system is accurate and efficient, making it suitable for real-time applications such as real-time strategy games and interactive animation controls.

In this paper, a Spark-based big data cluster is built to analyze the large-scale multi-source log data and to detect computer and network abnormal behaviors. By developing and assembling data mining, machine learning, and statistical analysis technologies, the large-scale log data can be quickly analyzed, and the abnormal attack behaviors can be accurately recognized.

This paper analyzes the designing of the existing SRAM single event upset (SEU) detection system and points out that the upset statistical method based on PC software is the system performance bottleneck. The paper aims at the bottleneck and presents a single event multiple-bit upset (MBU) detection system based on FPGA logic circuit, which has high detection speed, high data integrity, etc.