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The paper presents an information system for calculating the main paper-forming parameters of the pulp for given technological and design parameters of the grinding plant. The operation of the system is based on mathematical regression models of the processes that were obtained in the course of experimental studies of knife sets of various types. As input parameters in these models, the rotor speed, knife gap, concentration and degree of grinding of the mass are used. The output parameters are the grinding time, water retention capacity and average fiber length. The developed information system is a network web application with a modular structure. The modules are united by the main interface for the user. Each module implements a regression model for a specific type of headset. At the moment, the system has two modules for calculating the parameters of a percussion type headset and a headset with a curvilinear shape of knives. In the future, it is planned to add the ability to calculate indicators for headsets of other designs. It will also add the ability to solve optimization problems by finding the minimum or maximum value of the output parameters.

Paper artifacts are contaminated by external factors in the process of preservation such as foxing. For the problem of backward technology of rapid detection of foxing on paper artifacts, a method based on hyperspectral imaging technology is proposed to detect foxing spots on paper artifacts. After selecting the region of interest and obtaining the corresponding average reflectance, the difference in the average reflectance is found after comparing the healthy regions with the diseased regions. Using band operation and minimum noise fraction to observe the characteristics of foxing image, although there is overlap in different parts, the distribution distinction between moldy and healthy regions is obvious; K-nearest neighbor method and BP neural network are applied to establish the spectral discrimination model of paper artifacts with foxing spots, and the overall discrimination rate of the two methods is 73.3% and 85%, respectively. The results show that hyperspectral imaging can be used for the identification of foxing spots, but the distinction between different parts is not good, and the discrimination effect still needs to be improved.

This paper addresses the limitations of existing Solar Envelope (SE) methods to explore the trade-offs of solar radiation and urban shading, and to simultaneously account for several different Key Performance Indicators (KPIs). It offers an alternative parametric workflow - the Solar Block Generator (SBG) - which is based on an additive voxelization method by which multiple solar-driven massing alternatives are generated and evaluated for a given site, corresponding to a set of user-defined environmental KPIs. This method is tested here on an urban redevelopment case study in the Mediterranean (Tel Aviv). The results help achieve a more holistic approach for solar driven urban design.

The aim of this study is to compare and validate a building energy simulation model of a university building with monitored data. In addition, different building services engineering concepts are compared with each other regarding their ecological life cycle-based performance using Life Cycle Assessment (LCA) methods. Optimizing these performances regarding sustainability indicators plays an essential role in realizing a climate-neutral building stock. A university building from the 90s, which consists of almost 900 thermal zones, is used as a case study. Detailed heating consumption data are available for the inspected building and are compared to the simulation results. Different energy supply concepts are first subjected to conducting LCA and then to a detailed energy performance simulation. This paper presents a procedure that enables decision-makers to examine building services engineering issues and to derive conclusions in a time-saving manner regarding appropriate sustainable actions. The focus on sustainable energy supply systems is an essential milestone for the realization of climate-neutral building stocks. The scientific innovation is in the detailed reproduction of the existing building and the comparison between simulation data and real-time data as well as an innovative and experimental approach to building and Heating, ventilation, and air conditioning (HVAC) system simulation.

Deep learning models have been very successful in the application of machine learning methods, often out-performing classical statistical models such as linear regression models or generalized linear models. On the other hand, deep learning models are often criticized for not being explainable nor allowing for variable selection. There are two different ways of dealing with this problem, either we use post-hoc model interpretability methods or we design specific deep learning architectures that allow for (more) easy interpretation and explanation. This paper builds on our previous work on the LocalGLMnet architecture that gives an interpretable deep learning architecture. In the present paper, we show how group LASSO regularization (and other regularization schemes) can be implemented within the LocalGLMnet architecture so that we receive feature sparsity for variable selection. We benchmark our approach with the recently developed LassoNet of Lemhadri et al.

Light via our eyes influences visual performance, visual comfort and visual experience, but also affects several health related, non-image-forming (NIF) responses. New metrics have been developed to quantify the NIF effects of light. In order to incorporate these in lighting design practice, simulation tools are required that are able to process information about the spectral distribution of light sources and materials. However, most of the tools currently used for daylight and electric light simulations simplify the spectrum into RGB (Red, Green, Blue) colour values. This paper presents an overview of the currently used programs for simulating the NIF effects of light in building design and discusses the possibility of using existing spectral rendering software as an alternative. A review of literature shows that mostly Radiance or Radiance-based programs have been used so far, but new user-friendly tools could employ existing spectral rendering tools. As the NIF effects of light gain greater importance in lighting design, new simulation workflows are needed. This paper aims to support the development of future workflows by presenting the current state-of-the-art.

With the gradual improvement of the influence of intelligent robots in production and life, it has greatly facilitated people's production and life. Therefore, people's requirements for intelligent robots are also increasing, and are developing towards more humanization and intelligence. However, at present, there are still many imperfections in the field of intelligent robot technology in China. In order to solve the problems in work, we must further strengthen the research on artificial intelligence theory and robot technology. Only in this way can we realize the all-round development of intelligent robot system. So this paper will discuss the deep reinforcement learning in the theory of artificial intelligence, and explain its basic theory, research status, existing problems and future development direction. Moreover, under the background of the overall improvement of the current industrial development level, this paper will also talk about the manipulator widely used in the industrial field and the research status of manipulator control based on deep reinforcement learning, hoping to provide effective help for the development of related fields.

Under the pressure of energy and environmental protection, we will promote the technological progress and demonstration of electric vehicles, and the construction of charging facilities will continue. Charging facilities planning and orderly charging, as two major research directions of electric vehicle infrastructure, are of great significance for the future development of electric vehicles. The optimal charging of electric vehicles can effectively improve the safe and economic operation ability of distribution network, which is of great significance to its safe operation. Therefore, this paper proposes the outsourcing test experiment and processing of urban electric vehicle public charging network based on 5G and big data. In this paper, through the analysis of the development status of urban electric vehicles, this paper proposes to optimize the charging mode of electric vehicles by combining the charging network forward and backward algorithm. In the outsourcing test experiment, the electrical safety test shows that when the current reaches 1.1-37.1kw: 5000A, when the power factor is 0.8 ∼ 0.9, when the short-circuit current impact is tolerated, the connection device will not affect the breaking operation by contact fusion welding, and the insulation protection will not be invalid. Through investigation and analysis, the satisfaction degree of electric vehicle optimization algorithm is increasing year by year. Through the analysis of the test results, the research in this paper has achieved ideal results and made a contribution to the research of urban electric vehicle public charging network.

Full power converter wind turbine is the main type of wind power, so the simulation calculation needs to establish accurate model parameters. This paper analyzes the model structure of PSASP program according to its low voltage ride through control and physical characteristics, and puts forward the parameter identification method of LVRT characteristics of full power converter wind turbine, and to use the LVRT data of 5. 5MW unit for parameter identification and simulation verification. This paper proposes that the electromechanical transient simulation can ignore the part of the generator model of the full power converter wind turbine, and simulates the grid side converter with the controlled current source. The main characteristics of LVRT are determined by the control system of the converter. In order to do the parameter identification, it is necessary to calculate and analyze the control characteristics of multiple measured data. First, determine the control mode, then determine the control parameters to complete the parameter identification. In this paper, the modeling conditions and model structure of the full power converter wind turbine are confirmed. The correlation between the parameters during the LVRT fault and the parameters during the LVRT recovery period and the LVRT characteristics is analyzed. In this paper, a parameter identification method is proposed to analyze the active current and reactive current during the LVRT fault, which has strong physical significance and operability. Based on the actual LVRT characteristics of 5. 5MW wind turbine, the parameter identification and simulation are carried out to verify the correctness of the method.

This paper deals with the new segmentation techniques for retinal blood vessels on fundus images. This technique aims at extracting thin vessels to reduce the intensity difference between thick and thin vessels. This paper proposes the modified UNet model by incorporating ResNet blocks into it which includes structured prediction. In this work we generate the visualization of blood vessels from retinal fundus image for two loss functions namely cross entropy loss and Dice loss where the network classifies several pixels simultaneously. The results shows higher accuracy by considering a much more expressive UNet algorithm and outperforms the past algorithms for Retinal Vessel Segmentation. The benefits of this approach will be demonstrated empirically.

As the core of the new monitoring system, the Internet of Things realizes the integration of wireless sensor networks and traditional communication networks, and provides a platform for remote management and monitoring of the underlying equipment. The intelligent transportation system framework built on this basis combines intelligent transportation technology and the organic combination of vehicle management technologies is conducive to the safety, speed and reliability of vehicle transportation, and plays an important role in further reducing transportation costs. Based on this, this article launched the research on the Internet of Things and intelligent transportation systems. This article first summarizes the concepts of the Internet of Things, intelligent transportation and wireless sensor network technology and the current research status at home and abroad. By analyzing and comparing the performance and characteristics of various communication methods, embedded core microprocessors and embedded operating systems, this paper proposes an overall design scheme of the Internet of Things transportation system based on embedded technology. This paper analyzes the path planning problems in the application of the transportation system, combining the shortest path algorithm simulation results and the actual characteristics of the transportation network, and proposes a simulation data fitting method based on two network parameters and Bellman-Ford, Dijkstra, and Floyd algorithms. The route optimization scheme, and the above-mentioned design scheme was implemented in the transportation system, and the scheme verification was carried out. Finally, this article describes in detail the overall debugging process and operating results of the transportation system, thereby fully verifying the feasibility and correctness of the design and implementation methods of the intelligent transportation system based on the Internet of Things. The research results show that when the INF-PROPORTION is small, the Dijkstra algorithm is better than the Bellman-Ford algorithm. When INF-PROPORTION=0.3, the two algorithms T overlap. Since then, the advantages of the Bellman-Ford algorithm gradually appear, but it is approaching in INF-PROPORTION. At 1 o’clock, the Dijkstra algorithm has a sharp decrease, which is again smaller than the Bellman-Ford algorithm. The second loop condition in the main loop of Dijkstra’s algorithm cannot be satisfied, resulting in a decrease in T.

With the environmental pollution and the shortage of oil resources becoming more and more serious, the development and application of new energy vehicles have attracted more and more attention. Engine is an important part of new energy vehicles, and its performance has a great impact on the vehicle. Compared with traditional industrial motors, new energy vehicle engines have higher requirements on power density, and the improvement of power density poses new challenges to the design of motor cooling system. The purpose of this paper is to study the engine cooling device of new energy vehicles and improve the overall performance of the vehicle. The main research content of this paper is to lay a foundation for the theoretical basis of the engine cooling device, elaborate the working principle of the motor cooling system and the loss of the motor in operation. Then, the heat dissipation system of permanent magnet synchronous motor based on heat pipe is studied experimentally. Aiming at the problem of only considering the temperature rise and ignoring the pressure loss in the flow channel design, a flow channel design method considering the motor temperature rise and the flow channel pressure loss is proposed, and the motor flow channel is optimized. The test results show that the maximum temperature rise at the end is close to 16.56 °C, which is in good agreement with the simulation results. It shows that the heat pipe based heat dissipation system can effectively reduce the temperature rise of motor winding, which provides a new idea for the heat dissipation design of permanent magnet synchronous motor

Fire detection technology based on video images is an emerging technology that has its own unique advantages in many aspects. With the rapid development of deep learning technology, Convolutional Neural Networks based on deep learning theory show unique advantages in many image recognition fields. This paper uses Convolutional Neural Networks to try to identify fire in video surveillance images. This paper introduces the main processing flow of Convolutional Neural Networks when completing image recognition tasks, and elaborates the basic principles and ideas of each stage of image recognition in detail. The Pytorch deep learning framework is used to build a Convolutional Neural Network for training, verification and testing for fire recognition. In view of the lack of a standard and authoritative fire recognition training set, we have conducted experiments on fires with various interference sources under various environmental conditions using a variety of fuels in the laboratory, and recorded videos. Finally, the Convolutional Neural Network was trained, verified and tested by using experimental videos, fire videos on the Internet as well as other interference source videos that may be misjudged as fires.

Comparators play a significant role in the semiconductor industry and have become indispensable in the design of ADC. The delay and energy consumption are two important indicators of the comparator. Many designs have been made to reduce the delay and energy consumption, such as separated gata-biasing cross-coupled transistors for a new latching stage, and pMOS is used to replace nMOS in comparators. This paper analyzes the working principle of the proposed comparators designed for different needs reported on different papers. It compares their simulation results about key data such as delay and energy consumption.

For industrial-grade manipulators, the study of trajectory tracking control issues provides an important guarantee for accurate and safe work. Therefore, the trajectory control input driving torque can meet the requirements of the robot arm to accurately track a given target trajectory, and the process of building a decision tree is a process of dividing the feature space. For a given training data set, a set of if-then is summarized the rule of. Based on this, this paper launches the research of multi-degree-of-freedom manipulator joint trajectory tracking control method based on decision tree. Based on the established kinematics and dynamics model of the manipulator, this paper uses a proportional-integral-derivative (PID) sliding mode controller based on the sliding mode surface of the manipulator to perform the trajectory tracking control of the end of the manipulator, and the simulation results of the improved sliding mode control are compared with the simulation results of the improved sliding mode control. The simulation results of the PID controller and the traditional sliding mode controller are compared. This paper finally verifies the effectiveness of the proposed new sliding mode controller based on the expanded state observer through the experimental platform. The speed and chattering problems of the trajectory tracking at the end of the manipulator are better than those of the controller on the experimental platform. Finally, this paper adopts the sliding mode variable structure control strategy combining the double-power reaching law and the improved terminal sliding mode surface to study the trajectory tracking control of the planar two-degree-of-freedom manipulator.

Sleep staging is an important process for detecting sleep quality and diagnosing sleep disorders. However, traditional sleep staging is a labor-intensive task, and it is prone to subjective errors. Therefore, this paper innovatively proposes an automatic sleep staging model based on single-channel EOG—CRNN-HMM. The CRNN-HMM in this paper combines Convolutional recursive neural networks(CRNN) and hidden Markov model(HMM). The main idea of this model is to use CRNN to automatically extract features from EOG, and send the feature signals to a variant of RNN, Bi-directional Long Short-Term Memory(BiLSTM), to mine the dependencies between sleep stages and realize automatic staging of sleep data. Finally, a Hidden Markov Model is used to convert the prior information of the sleep phase of the adjacent EOG cycle in order to improve the classification performance of S1, thereby improving the classification performance of CRNN. The simulation results show that the overall accuracy of the model on the CAP-Sleep data set reaches 95.0%, which proves that the model can provide a way for the evaluation of sleep quality.

Facial recognition is one of the main research directions in the field of artificial intelligence and image processing. It has been widely used in identity authentication, video surveillance and biological detection. Because it is non-contact, natural, convenient and reliable, facial recognition has become a popular choice for biometric systems. The accuracy of facial recognition still needs to be improved, the main goal of this paper is to improve the accuracy of face recognition. Based on the support vector machine method, the focus is on the feature extraction and feature matching of face images. In view of the particularity of face images, the pre-processing of face images is studied. In this paper, grayscale normalization and geometric normalization pre-processing methods are used. In order to reduce the interference factors of the image as much as possible, the features are high-lighted, and the non-featured parts are weakened, this paper adopts the Histogram of Oriented Gradient feature extraction method. Then we proposed a new method based on SVM, which uses a one-to-many method to construct multiple SVM classifiers, selects the optimal parameters through repeated experiments, and selects ORL face database for testing. The recognition rate can reach about 98.5%.

Event detection is an important foundation of non-intrusive load monitoring algorithm. In this paper, the common household appliance load events are classified, and a new triple-threshold event detection algorithm is proposed aimed at solving the problems of false detection and missing detection in the practical application. Firstly, a low power threshold is used to realize high-sensitive detection of the load events, and secondly the detected events are spliced according to the time threshold to get the complete events. Thirdly, the high threshold is used to discriminate the complete event set to filter out the disturbance caused by load fluctuation. Finally, the results are modified with a correction logic. The test results carried with static data show that, the algorithm proposed in this paper is more accurate for positioning the time of putting into and cutting off load, which is conducive to improve the accuracy of transient interval interception of load events, and has advantages in detecting slow rising load events. In addition, the algorithm proposed in this paper has a small amount of calculation, which can meet the requirements of application in the hardware of smart meter.

Objective and reasonable evaluation is the basis of power-generating unit optimal operation, but evaluation method based on single weight can’t adapt to the high accuracy requirement of comprehensive evaluation. In order to achieve the accurate evaluation of power-generating unit operating reliability, this paper firstly calculates subjective weight and objective weight of target attribute by analytic hierarchy process method and entropy weight method. It calculates the associated weight of target attribute by grey relational degree method, and it obtains the linear combined weight by relaxation factor. Then, the power-generating unit operating reliability is evaluated by improved TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), and the evaluation result is obtained. Finally, the relaxation factor is continuously evaluated from 0 to 1 to perform sensitivity analysis. This paper evaluates the operation reliability of nine wind turbines in a wind farm, and the evaluation result is closer to actual operation state of wind turbines. This method has high reliability and practical value, and it provides a new technical means for making a reasonable maintenance plan of wind turbines.

The paper reported the temporal feature analysis method describing for automata models of computation systems. The method is based on from source algorithm to modified algorithm expanding makes it possible to take characteristics of work process related to execution time measurement. The paper considers the transition method from source algorithm representation to modification a final state machine with additional states providing registration of temporal features. The paper demonstrates method usage example on computer system authorization algorithm, approves an algorithm complexity for features registration O(n) for n-states automata algorithm. The method can be used for a large class algorithm, but is recommended to apply it to an algorithm, separated on procedure thus mean of commands amount should be more than 500 for more time measurement accuracy.

As a value-added service that improves the efficiency of online customer service, customer service robots have been well received by sellers in recent years. Because the robot strives to free the customer service staff from the heavy consulting services in the past, thereby reducing the seller’s operating costs and improving the quality of online services. The purpose of this article is to study the intelligent customer service robot scene understanding technology based on deep learning. It mainly introduces some commonly used models and training methods of deep learning and the application fields of deep learning. Analyzed the problems of the traditional Encoder-Decoder framework, and introduced the chat model designed in this paper based on these problems, that is, the intelligent chat robot model (T-DLLModel) obtained by combining the neural network topic model and the deep learning language model. Conduct an independent question understanding experiment based on question retelling and a question understanding experiment combined with contextual information on the dialogue between online shopping customer service and customers. The experimental results show that when the similarity threshold is 0.4, the method achieves better results, and an F value of 0.5 is achieved. The semantic similarity calculation method proposed in this paper is better than the traditional method based on keywords and semantic information, especially when the similarity threshold increases, the recall rate of this paper is significantly better than the traditional method. The method in this article has a slightly better answer sorting effect on the real customer service dialogue data than the method based on LDA.

Rotating machinery often produces continuous impact during operation due to the change of load and speed, which shows the characteristics of unsteady state and time-varying. Its working state can not be comprehensively judged by a single vibration state parameter. Therefore, this paper proposes to use acoustic sensors to collect the fault noise signal of rotating machinery, and use the whole column of sensors to detect the fault noise signal. Based on the microphone array, this paper studies the adaptive beamforming algorithm (MVDR) to locate the fault source of rotating machinery in space. The effect of fault source location is verified by simulation and equipment measurement experiments. The acoustic sensor does not in contact with the equipment, which will not damage the generator set, but also provide more effective information for fault source location and fault diagnosis and analysis.

Since pseudo-code phase modulation and carrier frequency modulation composite reconnaissance signals have both the good anti-interference capability and the low probability of interception, they have been widely used in electronic reconnaissance systems. This paper proposes a multi-parameter estimation method for pseudo-random binary phase code (PRBC) and linear frequency modulation (LFM) composite modulation signal in a complex electromagnetic environment. Firstly, this paper uses the square operation to remove the code phase information; secondly, we use the delay correlation and two times frequency estimation to estimate the starting frequency and the frequency modulation rate; finally, the wavelet transform algorithm is utilized to estimate the code rate parameter. Through the simulation analysis and full comparison with other methods, we can prove the performance superiority of this method under low signal-to-noise ratios (SNRs).

In recent years, multiple measurement systems fusion technologies have developed rapidly. Ultrasonic ranging is paid much attention because it has the advantages of directly measuring short-distance targets, high longitudinal resolution, and a wide application range. However, when the ultrasonic distance measurement is carried out by assembling the circuit board, it is found that the switch is triggered multiple times, and the measurement data is inaccurate. This paper proposes a new method that adding debounce to improve the accuracy of measurement. Besides, Some suggestions are provided to decrease the operation difficulty in the actual measurement process. Based on the experimental result, the accuracy and efficiency of ultrasonic distance measurement are greatly improved via this method proposed in this paper. By the way, the single-chip computer simulation technology simulates the ranging phenomenon in the experiment.

Traditional control instruments and DCS control systems are limited by shortcomings such as single control function, independent controlled elements, inability to close-loop control on site, and ineffective communication. Aiming at these problems, this paper proposes a kind of industrial furnace edge controller which has the functions of acquisition, calculation, control, storage, output and communication in one. The edge controller structure, edge computing and the control method applied to edge computing are designed. The fuzzy PID control method is compared with the conventional PID control method by taking the temperature control of resistance furnace as an example. The simulation shows that the control effect of fuzzy PID on resistance furnace temperature is obviously better than that of conventional PID. The test results also show that the performance indices for the fuzzy PID control are better, which again proves that the fuzzy PID control outperforms. The research results of this paper are of reference value for engineering applications.

In recent years, with the improvement of Internet of Things technology and the continuous improvement of economic level, people’s consumption consciousness has also changed, especially for the packaging of networked products put forward higher requirements. This paper mainly studies the visual expression of networked product based on Internet of Things technology. Starting from the increasing influence of Internet electronic consumption in China’s economic consumption system, this paper deeply discusses the systematic product logistics packaging design under the Internet consumption form. This paper analyzes and discusses the differences between Internet consumer commodity packaging and physical consumer commodity packaging in functional orientation, added value of packaging, systematic visual design and other aspects.

With the development of virtual reality technology and its application in various fields, how to realize the natural and efficient interaction between human and virtual environment has always been a hot research issue. This paper mainly studies the realistic analysis of the relationship between augmented reality and virtual reality under the background of VR virtual reality. Starting from the research on virtual reality, this paper combines the features of virtual reality with the elements of visual presentation to conduct research on visual presentation in the sensory experience of virtual reality, analyze and find out the types and quantitative methods of visual presentation. This paper is supported by advance research, which is more in line with people’s perceptual needs for visual presentation. This study expands the research content of virtual reality visual presentation, provides guidance for virtual reality design practitioners, and has certain value and practical application prospects.

Based on the demand for smart grid 5G network, this paper studies 5G mobile communication technology and the industry's 5G private network construction mode. Combined with power grid requirements, this paper focuses on analyzing the uninterrupted 5G private network solution, the principal of the solution is also explained. Taking a 5G test network deployed at a converter station as an example, the construction, isolation, operation, and maintenance plans of the uninterrupted 5G private network solution in the power communication network is proposed, the actual tests verify the feasibility of the continuous 5G private network. This paper will provide reference for the promotion and application of 5G private network technology in the power grid.

Satellite identification is a form of space quantum information, which refers to the use of artificial earth satellites as the transmitting station between earth communication stations to perform satellite identification to construct a space quantum information system. However, at this stage, a single form of identification cannot meet the needs of various aspects such as the development of the information society. And mobile broadband services. Based on this, this paper has carried out research on the construction of space quantum information system based on satellite recognition. This paper firstly introduces the research significance of space quantum information transmission based on satellite identification, and then analyzes the research status of space information system based on satellite identification and the research status of space quantum information system based on satellite identification at home and abroad. Aiming at the problem of asymmetrical transmission of space information, this paper proposes, a spatial asymmetric transmission scheme based on layered modulation and network coding, and in view of the low communication quality caused by the influence of weather and surrounding environment on the space-based communication channel, this paper proposes an adaptive transmission scheme of spatial information based on satellite cooperation. The survey results show that under the MNC strategy and the traditional zero-padded strategy, the end-to-end bit error rate performance of S2 is not much different. As the signal-to-noise ratio continues to increase to 30dB, the two are basically the same. In the whole range of k value, the access probability of the MNC strategy is equal to a constant 0.67 and remains unchanged. When k=1, under the OUS scheme, it shows that the two source nodes have the same probability of accessing the channel, and the access probability is 50%, which is 16.7% lower than that of the MNC scheme.

Aiming at the series fault arc phenomenon in landscape lighting and the hidden dangers of electrical fires, in this paper, a landscape power supply series fault arc model is constructed and its model is simulated. The simulation results show that when a fault occurs, the arc current becomes smaller (almost zero) due to the increase in the impedance of the lighting circuit; this phenomenon is called the “current zero off” phenomenon of the fault arc current. The current zero off phenomenon of the fault arc current is the main fault feature in the landscape lighting circuit. In this paper, the wavelet algorithm is used to detect the fault current waveform. According to the fault characteristics, by judging whether the modulus maximum value of the wavelet coefficient has periodic characteristics with an interval of 100±15 sampling points, it is analyzed whether a series-type arc fault occurs. The built physical model verifies the feasibility and correctness of the arc detection algorithm. The research results of this paper have certain reference value for the detection and application of fault arc.

In this paper, an innovative design of balancing mechanism is completed after solving the problem of collision between balancing weight and barrel by TRIZ. For the original design of adding a balancing weight, the Functional Model Analysis is set and the key point of the collision problem is found. For the sake of improving the original design, this paper find out the key point of collision problem and adequate analysis by TRIZ theory, and proposed an innovative solution at last.

The integrated thermal management of advanced fighter jets directly affects the combat performance of the aircraft. This paper develops a set of multi-cycle integrated thermal management control system, based on the stepper motor drive servo valve. The stepper motor control system is integrated in the aircraft Remote Execution Unit (REU). The controlled information, such as flow, pressure, temperature, is collected and combined through the Remote Interface Unit (RIU). Through the IEEE-1394 bus, feedback achieves deterministic transmission between control-action-response, and through inverse time protection, the high reliable power drive can be achieved. The open-loop vector micro-stepping driving strategy of the stepper motor is designed, simplifying software and hardware designs. The experimental results show that the strategy developed in this paper can better realize the characteristics of two-phase current sine wave, have better acceleration performance, improve the controllability of the opening and closing angle of the stepping motor servo valve, and satisfy the comprehensive thermal management of advanced fighters.

The paper discusses hydrokinetic systems low head hydro power as an element renewable resource. In the paper a review of the existing and upcoming orthogonal and axial turbines schemes is outlined. Based on a morphological approach comprehensive survey of various schemes and qualitative comparison, is presented. The proposed engineering solutions reduce the structure weight and the processability increases. These factors lead to a decrease in the cost. The engineering solutions under consideration are designed to operate in low-pressure flows, regardless of their direction. Thanks to these features, the scope of their use expands. They can be used both in high tide and in the use of the sea currents kinetic energy.

With advances in the mobile communications, many service-related tasks can be made quickly and easily. Mobile banking is one such service that has eliminated the need for a consumer to go to a branch to carry out many common transactions. In a country like India, where the last mile reach through brick-and-mortar banking facilities, mobile phones can complement the reach. This paper describes how mobile cloud architecture can be employed for banking and services to customers to enhance their banking experience as well as ensuring information security. This paper focuses on cloud-based risk architecture for banking solutions to address various issues related to mobile banking such as processing speed and storage capacity. Improved random forecast algorithm is used for the evaluation of the system. This proposed system achieves 99% of the system.

Aiming at the defects of DC energy storage elements, complex structure and large volume of unified power flow controller (UPFC), the paper combines flying capacitor multilevel technology and voltage vector synthesis technology with unified direct power flow controller. A three-level direct power flow controller (3L-DPFC) and a new control strategy are proposed. By obtaining the power parameters of the receiving end and substituting them into the derived calculation formula, the compensation voltage can be deduced to adjust the phase and amplitude, and the change relationship between the compensation voltage and the power parameters of the receiving end can be calculated through multiple groups of data, so as to adjust the active power flow and reactive power flow in the power grid. Compared with the unified direct power flow controller, it has the advantages of no DC energy storage element, small volume, simple structure, and the voltage stress of each switch is reduced by half, which reduces the failure rate and economic cost of the controller. In the paper, the topology and control strategy of three-level synthetic DPFC are described in detail, and its buck AC conversion theoretical analysis is verified by simulation.

The implementation of training complexes based on virtual reality for the training of specialists in machine-building, chemical, mining and other industries is associated with the need to solve a number of tasks. These include simulating various physical processes (temperature, humidity, and air pressure), loads (speed of movement and angle of inclination of the surface), and other external factors that cannot be reliably simulated in virtual reality. Therefore, an urgent task is to integrate various simulation systems into the software and hardware platform of virtual training complexes, which will provide a realistic immersion in the subject area due to virtual reality, as well as simulate the necessary external influences on the student. The paper considers this process by the example of integration of a simulation system of physical loads into virtual training complexes. The basis of the simulation system under consideration is a controlled treadmill. The use of treadmills allows students to develop muscle memory, perform physical training and increase the degree of immersion in virtual reality, which positively affects the effectiveness of their training. However, their integration requires the solution of a number of practical tasks for the transfer of information between the individual subsystems of the virtual training complex. The paper considers algorithms and software for solving these problems. The described approaches can be used to integrate various simulation systems into the software and hardware platform of virtual training complexes for the organization of comprehensive training of specialists.

This paper takes the physical model of Radar Cat’s Eye as the research object, based on geomagicdesign software’s powerful reverse modeling function, the reverse modeling and design process of Radar Cat’s Eye is explained in detail. The detailed steps and main precautions of scanner calibration in the process of Radar Cat’s Eye reverse modeling are given. At the same time, the main contents of model scanning, data processing, reverse modeling, model comparative analysis and so on are also introduced and explained in detail. In short, the relevant contents of this paper provide reference and help for the reverse modeling and design of other models.

This paper discusses structuring of hardware and software for an autonomous social robot. It shows that the real-world social robot operations face the challenge of environmental variability and uncertainty of the objective parameters. Thus, a social robot must be capable of situation analysis for better autonomy. We propose a modular distributed structure of the control system. Separate modules monitor the status and control the subsystems of the robot. General coordination of subsystems is provided by the Supervisor module. For the robot to function autonomously, the Supervisor must be capable of situation analysis and its key functions: objective retrieval and analysis, situation description, configuring and strategizing the solution. The robot’s sensory inputs help acquire the objective and its parameters to describe the situation. Description relies on the database of a priori knowledge of the environment and its objects. Analysis is linked to a reduction in the uncertainty of the objective parameters and situation description. For a case study, the paper demonstrates a maze-solving strategy as affected by the situation.

When it comes to walking robots, foot trajectory is a crucial element that can significantly influence the efficiency of the walking robot. This paper analyses the various foot return trajectories, which can provide higher step length while consuming less power. It is done through mathematical analysis and verified using simulations in software such as MSC Adams and Solidworks. This paper also discusses the kinematic and dynamic analysis of the two degrees of freedom leg using theoretical approaches in MATLAB and verifies the results using the simulation in MSC Adams.

With the arrival of the era of big data, people have gradually realized the importance of data. Data is not just a resource, it is an asset. This paper mainly studies the realization of Web data mining technology based on Python. This paper analyzes the overall architecture design of distributed web crawler system, and then analyzes in detail the principles of crawler’s URL function module, crawler’s web crawl function module, crawler’s web page parsing function module, crawler’s data storage function module and so on. Each function module of the crawler system was tested on the experimental computer, and the data information was summarized for comparative analysis. The main significance of this paper lies in the design and implementation of a distributed web crawler system, which, to a certain extent, solves the problems of slow speed, low efficiency and poor scalability of traditional single computer web crawler, and improves the speed and efficiency of web crawler in grasping information and web page data.

The development of music is a tortuous process, and the network relationship between each genre and each artist is intricate. In order to have a better understanding of the history of music, this paper tells the stories hidden in the history of music by means of data processing. Firstly, this paper establishes a model to evaluate the similarity between music by using ISOMAP algorithm. At the same time, the forest evolution model was established to mark the most revolutionary musical characters. Finally, using the Page-Rank algorithm, we get the founders of several music genres. It turns out that the figures who led the development of music don’t coincide with the figures who revolutionized music. Through the analysis of this paper, we can more clearly understand the development of music and the evolution of genres.

A signature is a mark or name that represents the identity of the people and the Signature Verification System (SVS) is used to validate the identity of people. The signature verification system is mostly used for bank cheques, vouchers, intelligence agencies and others. There are two types of SVS which are online and offline signature verification systems. The paper deals with an offline signature verification system. The proposed system consists of four main stages, (i) image acquisition, (ii) image pre-processing, (iii) feature extraction and (iv) classification. The image pre-processing steps involved binarization, noise removal using Gaussian filter and image resizing and thinning. In the feature extraction stage, Bag-of-Features with the Speeded Up Robust Features (SURF) extractor was utilized. In the third stage, the Support Vector Machine (SVM) classifier is used. Lastly, the confusion matrix and the verification rate were used to evaluate the performance of the classifier. In this paper, we implement and compare the performance of the signature verification system without entering the user ID and the signature verification system entering the user ID. For the ratio of 75% and 25% of the training and testing, respectively, the average accuracy for the signature verification system without entering the user ID is 71.36%, whereas the average accuracy for the signature verification system entering the user ID is 79.55%.

This paper presents an adaptive frequency Phase locked loop (PLL) based approach for grid connected multifunctional inverter is presented for residential applications. The proposed control structure deals with non-linear loads and also non ideal grid voltages like magnitude variation, frequency variation, unbalance harmonics, etc. and can effectively address the DC offset in a voltage signal which in turn improves the power quality. A third order generalized integrator based PLL with adaptive frequency and DC offset elimination blocks will effectively deal with the power grid voltage fluctuations, frequency variations and eliminate the phase difference between PLL output. This approach is implemented as control algorithm for single-stage single-phase grid connected multifunctional inverter topology for PV applications which feeds energy to the grid. The Maximum Power Point Tracking is obtained by Perturb & Observe method for extracting maximum power from a Photovoltaic system. A detailed analysis with the proposed control technique is presented in this paper. Experimental tests are conducted at various operating conditions to describe and verify the performance of the proposed control using MATLAB / Simulink.

Doppler Velocity Log (DVL) aided Strapdown Inertial Navigation System (SINS) is commonly used for the applications of Autonomous Underwater Vehicles (AUVs). In lack of other aiding sensors, how to maintain precise integrated navigation is still a challenge issue. In this paper, the structure and basic principles of the SINS is presented. Alignment calibration of SINS and DVL, fast in-motion alignment and data fusion are key factors which has great influence on the navigation accuracy. Research efforts taken in these fields in recent years are surveyed. This paper may provide a firm foundation for the researchers in related areas.

With the rapid development of computer technology, Internet technology and artificial intelligence technology, the amount of global data has exploded. However, the single-machine serial mode of traditional data mining cannot be directly transplanted to the cloud platform. Only by parallelizing and improving many classic data mining algorithms can the cloud computing platform and data mining be effectively combined. Therefore, it is of great significance to the research and implementation of parallel algorithm technology for time series data mining. The purpose of this paper is to study the research and implementation of parallel algorithm technology for time series data mining. This paper adopts the method of literature data, mathematical statistics, logic analysis and other research methods to study the parallel algorithm technology research and realization of time series data mining, mainly to make useful explorations of time series data mining and visualization technology. It embodies the design ideas of big data analysis tools, and finally reflects the power and market value of data analysis tools through the display of the platform. Research shows that running in the same data set and the same experimental environment, the improved parallel collaborative filtering algorithm ACF in this paper has higher time running efficiency than the parallel algorithm MCF based on the cooccurrence matrix, and in the case of larger data sets, the more obvious the time difference.

The traditional hepaticcell carcinoma (HCC) pathological grading depends on biopsy, which will cause damage to the patient's body and is not suitable for everyone's pathological grading diagnosis. The purpose of this paper is to study the pathological grading of liver tumors on MRI images by using deep learning algorithm, so as to further improve the accuracy of HCC pathological grading. An improved network model based on SE-DenseNet is proposed. The nonlinear mapping relationship between feature channels is modeled and recalibrated using attention mechanism, and rich deep-seated features are extracted, so as to improve the feature expression ability of the network. The method proposed in this paper is verified on the data set including 197 patients, including 130 training sets and 67 test sets. The experimental results are evaluated by receiver operating characteristic (ROC) and area under the ROC curve (AUC). The improved SE-Densenet network achieves good results, and AUC 0.802 is obtained on the test set. The experimental results show that the method proposed in this paper can well predict the pathological grade of HCC.

With the rapid development of our country’s economy and industry, atmospheric environmental issues have gradually attracted more and more people’s attention. In recent years, the vigorous development of the Internet of Things technology has made it possible to “Internet of Everything”. At present, the common traditional atmospheric environment monitoring systems on the market generally have shortcomings such as single function, large system measurement error, inability to collect data for a long time, large system power consumption, and inability to view data after being far away from the device. As Bayesian network theory is more and more applied to the modeling of monitoring events, more and more learning optimization methods about Bayesian network have been proposed and become a research hotspot. The purpose of this paper is to study the design of atmospheric environmental health monitoring system based on spatial Bayesian network. This paper studies the intelligent anomaly detection technology based on Bayesian network, the purpose is to use the intelligent anomaly detection technology to predict unknown behaviors that affect the health of the atmospheric environment, improve the learning ability of the detection system, and strengthen the active defense capability of the atmospheric environmental health monitoring system; Secondly, this article proposes an improved spatial Bayesian network model based on the shortcomings of the spatial Bayesian network. Finally, in view of some of the shortcomings of the detection system, this paper proposes a system model that combines anomaly detection with other detection mechanisms, which can significantly improve the accuracy of detection and reduce the false alarm rate of the atmospheric environmental health monitoring system. This paper aims at the above shortcomings and at the same time integrates the current situation of the atmospheric environment monitoring system, analyzes the possible trends of the atmospheric environment monitoring system, and combines the advantages of the spatial Bayesian network to address temperature, humidity, carbon monoxide concentration, and suspended particle concentration. For the main atmospheric environment indicators, a multi-functional, low measurement error, low power consumption, and low-cost atmospheric environment monitoring system is designed. Experimental research results show that: in terms of concentration, nitric oxide and nitrogen dioxide are the highest, at 58% and 68%, respectively, and they are also the largest in the air, accounting for a total of 70%. All in all, the monitoring system needs to monitor even more types of gases listed above, so that the feedback of the atmospheric environment health status is more accurate.

with the rise and rapid development of mobile communication, intelligent terminal and data system, we are entering the era of mobile Internet. In recent years, more and more data need to be processed and transmitted in daily life, and structured data is becoming more and more important. Among them, multi-mode matching technology can search data in a wider range. Matching for multiple patterns at a time avoids unnecessary matching, accelerates the matching process, and helps to find longer matching information and obtain higher accuracy. This paper mainly introduces the high camouflage intrusion detection method of structured database based on multi-mode matching. This paper uses the high disguised intrusion detection method of structured database based on multi-mode matching, collects sensitive information of wireless access points and stations through the communication of WLAN in multimodal matching, then intercepts and forges data packets to initiate replay attack. Replay attack is characterized by abnormal traffic in the network, which can be detected by statistical analysis. The experimental results show that the high camouflage intrusion detection method based on multi-mode matching makes the camouflage intrusion detection rate increase by 23%. The limitations of the design and research of camouflage intrusion detection are analyzed, discussed and summarized, so as to enrich the academic research results.

In this paper, a MATLAB Simulink model of a Quadruped Robot is presented alongside its remote, control and monitor user interface that has been developed by using the fundamentals of Internet of Things on a Node-Red Flow and the FRED-Cloud Server. Robotics and Automation over the recent years have developed exponentially and hence have been a key factor in the rise of Industry 4.0 which has usurped manual supervision and operation in industrial and manufacturing processes around the globe. The design and creation of technologically advanced robots integrated with computer-based software for their automation has not only successfully made the tasks facile to manage within short spans of time, but also has increased the efficiency notably. The stability and mobility of quadruped robots is considered to be ideal on differing terrains with minimal subtle changes, thereby making it an asset. Internet of Things on the other hand, has paved its way over the control of robots as well, with its unparalleled benefits. This paper is focused on the design and execution of the Quadiuped model which includes the observation of the various significant graphs achieved post simulation with respect to electrical values such as power and current consumption, and a visual animation of the robot running in the workspace. Furthermore, a single platform is developed and displayed that allows a user to log in for security puiposes and thereby, operate and monitor the functions and conditions of the bot easily, ranging from remote visual support, directional integrity, damage control and more, without the need of multiple platforms to carry out varying tasks with respect to control.