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Blade tip-timing is one of the most effective methods for blade vibration parameters identification of turbomachinery. Once-per revolution (OPR) sensor is usually used to determine the rotating speed and as a time reference. However, the OPR sensor leads to a large measurement error or even failure, or it is difficult to install. A new improved two-parameter method without the OPR sensor is proposed to identify the synchronous vibration parameters of variable rotating speed blades. Three eddy current sensors are required to identify the excitation order, vibration amplitude, resonance rotating speed frequency, resonance frequency, and the initial phase of the blades. Numerical simulation of blade synchronous vibration parameters identification is presented, and the identification error of the method is investigated. The simulation results show that the identification accuracy of this method is better than that of the traditional two-parameter method and the improved method in reference, especially in the identification of the vibration initial phase. Experiments are conducted based on the blade tip-timing vibration measurement system. The results indicate that the standard errors of vibration parameter identification results between the new method and the method in reference are smaller, except for the initial phase. It is consistent with the results of the simulation identification. The synchronous vibration parameter identification of variable rotating speed blades without the OPR sensor is achieved based on the new improved two-parameter method.

The fault characteristics of the rolling bearings of wind turbine gearboxes are unstable under actual operating conditions. Problems such as inadequate fault sample data, imbalanced data types, and noise labels (error labels) in historical data occur. Consequently, the accuracy of wind turbine gearbox bearing fault diagnosis under actual operating conditions is insufficient. Hence, a new method for the fault diagnosis of wind turbine gearbox bearings under actual operating conditions is proposed. It uses an improved label-noise robust auxiliary classifier generative adversarial network (rAC-GAN) driven by the limited data. The improved rAC-GAN realizes a batch comparison between the generated and real data to ensure the quality of the generated data and improve the generalization capability of the model in scenarios of actual operating conditions. It can be used to generate a large number of multitype fault data that satisfy the characteristics of the probability distribution of real samples and display higher robustness to label noises. Experiments indicate that, compared with other methods, the new method exhibits a higher accuracy in the multistate classification of rolling bearings under actual operating conditions when driven by the limited data with noise labels.

The goal of trajectory planning is to shorten the flight distance as much as possible on the premise of ensuring the safety of UAV in flight. Therefore, the research of trajectory planning has broad prospects and great significance. As the key technology of trajectory planning, optimization algorithm has increasingly become one of the focuses of scholars at home and abroad. The dynamic programming algorithm is characterized by high computational efficiency and global optimization in trajectory planning. In 3D trajectory planning, as the spatial search space expands, the number of grid points increases faster, and time complexity of the dynamic programming algorithm is ${O}({n}^{3}) $ . It often leads to a “Curse of Dimension” phenomenon, which lowers its computational efficiency drastically. To solve this problem, this paper divides the entire planning space into stages based on Bellman’s optimality principle. A dynamic programming-genetic algorithm(DPGA) is proposed by using genetic algorithm(GA) in each stage for optimization, while using dynamic programming algorithm(DP) in global planning. The global optimization ability of the algorithm is verified through convergence analysis. Moreover, based on a series of simulation experiments, it shows that the improved algorithm proposed in this paper is more efficient than the dynamic programming algorithm and genetic algorithm alone in global optimization.

In this paper, a weakly supervised framework is proposed for Abnormal Behavior Detection and Localization (ABDL) in the scenes. First, the objects in the scene such as pedestrians, vehicles, etc. are detected using the Faster Regional Conventional Neural Network (Faster R-CNN); then, the object behavior is described by a Histogram of Large Scale Optical Flow (HLSOF) descriptor; finally, the Multiple Instance Support Vector Machine (MISVM) is trained and then used to identify the testing behaviors as normal or abnormal. Summarily, the proposed approach has three main advantages: (1) Benefit from the Faster R-CNN, our approach can analyze the behavior at object-wise, which makes our approach has good generality and high computational efficiency; (2) The HLSOF descriptor can characterize the object behavior efficiently, and is insensitive to the variations of the size of objects; (3) As a weakly supervised learning framework, the MISVM only requires the labels at the bag level rather than instance level, which makes our approach has high accuracy as the supervised approaches but not requires completely labeled training samples, only the frame-level label is required. Experimental results analysis on different datasets validates the effectiveness of our approach.

Ambient backscatter communication (AmBC) has great application prospects in the green Internet of Things due to its shared nature of energy and spectrum. In this paper, we have done research on the signal detection problem in the AmBC system and proposed to use the k-means clustering analysis algorithm to detect the received backscattered signal. At the same time, in order to obtain a more obvious clustering center, this paper introduces a multi-antenna receiver channel detection mechanism. The proposed method can compare and analyze the energy received by the receiver antennas in an extremely short time slots. The channel state information between the reader and the backscatter tag is obtained, and the optimal communication channel is selected, thereby effectively improving the clustering effect. Finally, a large number of experimental simulations are provided to compare and analyze the corresponding BER performance to confirm our theoretical research.

The mining method optimization in subsea deep gold mines was studied. First, an index system for subsea mining method selection was established based on technical feasibility, security status, economic benefit, and management complexity. Next, an evaluation matrix containing crisp numbers and triangular fuzzy numbers (TFNs) was constructed to describe quantitative and qualitative information simultaneously. Then, a hybrid model combining fuzzy theory and the Tomada de Decisao Interativa Multicriterio (TODIM) method was proposed. Finally, the feasibility of the proposed approach was validated by an illustrative example of selecting the optimal mining method in the Sanshandao Gold Mine (China). The robustness of this approach was demonstrated through a sensitivity analysis. The results show that the proposed hybrid TODIM method is reliable and stable for choosing the optimal mining method in subsea deep gold mines and provides references for mining method optimization in other similar undersea mines.

At the end of the battlefield, the efficient networking among combat resources to ensure the real-time and reliable transmission of information is one of the keys to forming information superiority. Network resources will become scarcer in contested environments. According to the mission characteristics and differentiated needs, making full use of the limited network resource to guarantee task is the focus of network attention. Therefore how to optimize the use of limited network resources based on the characteristics of tasks becomes a very important issue. According to the research results in tactical data link and civil mobile communication many researchers have contributed, we compare the representative mission-driven networking technologies and analyze their integral development.

In order to improve the performance rating ability of university patent achievements transformation, a performance rating model of university patent achievements transformation is proposed based on AHP. Establish a big data analysis model of university patent achievement transformation performance, analyze the constraint parameters of university patent achievement transformation performance rating by using quantitative recursive analysis method, extract the fuzzy association rule feature quantity of university patent achievement transformation performance big data, and make optimization decision and comprehensive evaluation of university patent achievement transformation performance rating by using the methods of performance reliability transformation model comprehensive evaluation and fuzzy decision. The expert system analysis model of university patent achievement transformation performance rating is established, and the analytic hierarchy process (AHP) is used to adaptively optimize the university patent achievement transformation performance rating. The maximum likelihood estimation and fuzzy multi-parameter joint feature analysis are used to realize the optimization design of the university patent achievement transformation performance rating model. Combined with embedded B/S framework, the performance rating system of university patent achievement transformation is designed, and the information processing module of university patent achievement transformation performance rating system is designed by ISA/EISA framework. The simulation results show that this method has higher accuracy and better convergence, and improves the performance rating efficiency of university patent achievements transformation.

Whether a roadway support scheme is reasonable or not significantly affects the safety of workers and the mining efficiency of mines. This paper concentrates on the optimization of roadway support schemes with group decision making methods. At first, in order to convey the decision makers’ preference more fully and aptly, assessment information for schemes is transformed into LNNs (linguistic neutrosophic numbers). For the purpose of overcoming the weakness of the extant comparison method, the likelihood of LNNs is advised. After that, an extended MABAC (multi-attributive border approximation area comparison) method based on likelihood is presented. Three stages are contained in this modified approach. Stage 1 aims to obtain the normalized decision making matrix; Stage 2 builds an inclusive criteria weight model to compute the weighted decision making matrix; Stage 3 gets the ranking order of alternatives by defining three approximation areas. Subsequently, a case study is given to confirm the practicability of the proposed method. At last, sensitive analysis and comparative analysis are conducted, followed by some discussions and conclusions.

The modular multilevel converter (MMC) is now the most attractive topology for medium and high voltage power conversion applications with several advantages over the traditional voltage source converter (VSC). However, due to a large number of sub-modules (SMs) in the MMC, system reliability is a big challenge in its practical application, where each SM may be considered as a potential point of failure. In this paper, a reliability evaluation based on the Markov model is proposed for the MMC. The failure rates of the power electronic devices and SMs are firstly analyzed. Then, the Markov model and the state transition equation of the system are built in detail. A general reliability evaluation function is established, in which the mean time to failure and reliability evaluation of the MMC with redundant SMs are also discussed. Finally, a practical direct current (DC) distribution example for reliability evaluation is analyzed, and the results verify that the reliability evaluation based on the Markov model could provide a useful reference for project design.

We propose a novel seven-level pulse amplitude modulation (PAM-7) combined with trellis coded modulation (TCM) for data center applications. Four symbols are combined in the 4D space to encode ten information bits. The novel scheme called 4D PAM-7 TCM is experimentally demonstrated with commercially available devices at 1550 nm at rates of 210/225 Gbit/s. Excellent performance is achieved by employing advanced digital signal processing algorithms both at transmitter and receiver. The TCM scheme is compared with the PAM-6 modulation format which has the same spectral efficiency. The 4D PAM-7 TCM format improves the sensitivity by 5.5 dB compared to the best PAM-6 results assuming that the system works with the KP4 FEC. The TCM solution may transport the next Ethernet data rate of 800G using $4\times 200\text{G}$ transceivers.

Mine development scheme selection is an important project in mine construction, as the benefit of a mine is directly affected by the quality of the scheme. In this paper, we consider the selection problem of mine development scheme under fuzzy environment. Firstly, linguistic neutrosophic numbers (LNNs) are chosen to fully describe people’s linguistic evaluation information. To advance the following study, a new similarity measure of LNNs based on consistency degree is defined, and some important properties are also proved. Then, a new method based on similarity measure is proposed to cope with linguistic neutrosophic decision-making issues. It contains two weight determination models. One model takes the advantages of majority rule to calculate the experts’ weights objectively. Another model uses the idea of the technique for order preference by similarity to ideal solution to obtain the weights of criteria. Lastly, when the evaluation index system of mine development scheme selection is constructed, the feasibility and strengths of our approach are indicated through an illustration and some related comparisons.

Software-defined networking (SDN) has emerged as a new network paradigm that promises control/data plane separation and centralized network control. While these features simplify network management and enable innovative networking, they give rise to persistent concerns about reliability . The new paradigm suffers from the disadvantage that various network faults may consistently undermine the reliability of such a network, and such faults are often new and difficult to resolve with existing solutions. To ensure SDN reliability, fault management , which is concerned with detecting, localizing, correcting and preventing faults, has become a key component in SDN networks. Although many SDN fault management solutions have been proposed, we find that they often resolve SDN faults from an incomplete perspective which may result in side effects. More critically, as the SDN paradigm evolves, additional fault types are being exposed. Therefore, comprehensive reviews and constant improvements are required to remain on the leading edge of SDN fault management. In this paper, we present the first comprehensive and systematic survey of SDN faults and related management solutions identified through advancements in both the research community and industry. We apply a systematic classification of SDN faults, compare and analyze existing SDN fault management solutions in the literature, and conduct a gap analysis between solutions developed in an academic research context and practical deployments. The current challenges and emerging trends are also noted as potential future research directions. This paper aims to provide academic researchers and industrial engineers with a comprehensive survey with the hope of advancing SDN and inspiring new solutions.

A gridless sparse reconstruction technology is applied to monostatic MIMO radar DOA estimation. Since this technology is only applicable to a uniform linear array, the received signal of MIMO radar is first subjected to reduced-dimension transformation to make it equivalent to a uniform linear array signal model. In order to reduce the computational complexity, the received signal is further subjected to singular value decomposition (SVD). Then use the ANM method to reconstruct the Toeplitz matrix of the model, and finally perform Vandermonde decomposition on the reconstructed Toeplitz matrix to estimate the target orientation. Compared with traditional subspace methods such as MUSIC, ESPRIT, etc. This method has higher resolution and can resolve coherent sources. The simulation results verify the excellent performance of this method.

With increased urbanization, accidents related to slope instability are frequently encountered in construction sites. The deformation and failure mechanism of a landslide is a complex dynamic process, which seriously threatens people&rsquo, s lives and property. Currently, prediction and early warning of a landslide can be effectively performed by using Internet of Things (IoT) technology to monitor the landslide deformation in real time and an artificial intelligence algorithm to predict the deformation trend. However, if a slope failure occurs during the construction period, the builders and decision-makers find it challenging to effectively apply IoT technology to monitor the emergency and assist in proposing treatment measures. Moreover, for projects during operation (e.g., a motorway in a mountainous area), no recognized artificial intelligence algorithm exists that can forecast the deformation of steep slopes using the huge data obtained from monitoring devices. In this context, this paper introduces a real-time wireless monitoring system with multiple sensors for retrieving high-frequency overall data that can describe the deformation feature of steep slopes. The system was installed in the Qili connecting line of a motorway in Zhejiang Province, China, to provide a technical support for the design and implementation of safety solutions for the steep slopes. Most of the devices were retained to monitor the slopes even after construction. The machine learning Probabilistic Forecasting with Autoregressive Recurrent Networks (DeepAR) model based on time series and probabilistic forecasting was introduced into the project to predict the slope displacement. The predictive accuracy of the DeepAR model was verified by the mean absolute error, the root mean square error and the goodness of fit. This study demonstrates that the presented monitoring system and the introduced predictive model had good safety control ability during construction and good prediction accuracy during operation. The proposed approach will be helpful to assess the safety of excavated slopes before constructing new infrastructures.

High voltage switches are indispensable in power system which will control and protect the high voltage lines. According to the research of high voltage switch equipment accident rate in the international conference on power grid for according, the high voltage switch equipment mechanical failure accidents accounted for more than 70%. Thus, it is meaningful to research the switch monitoring technology and develop an integration device with easy installation and reliable data transmission. In this paper, a new type of non-contact current sensor is used to develop the online monitoring system for the mechanical characteristics of HV switches, which will monitor and analyze the action characteristics of the opening coil, closing coil and energy storage motor.

Identifying fake news on the media has been an important issue. This is especially true considering the wide spread of rumors on the popular social networks such as Twitter. Various kinds of techniques have been proposed for automatic rumor detection. In this work, we study the application of graph neural networks for rumor classification at a lower level, instead of applying existing neural network architectures to detect rumors. The responses to true rumors and false rumors display distinct characteristics. This suggests that it is essential to capture such interactions in an effective manner for a deep learning network to achieve better rumor detection performance. To this end we present a simplified aggregation graph neural network architecture. Experiments on publicly available Twitter datasets demonstrate that the proposed network has performance on a par with or even better than that of state-of-the-art graph convolutional networks, while significantly reducing the computational complexity.

In view of the existing researches that do not fully consider the role of cold/heat load of micro-energy grid(MEG) in large-scale PV absorption, and the problem that the source/charge interaction is not fully carried out in the dispatching of distribution network with MEG, a day-ahead optimization dispatching method of active distribution network(ADN) with MEG access is proposed, which takes demand response(DR) into account. First, a two-tier dispatch framework for ADN is established, and a DR mechanism is introduced into the framework to build an interactive relationship between ADN and MEG; Second, a two-layer optimal dispatching mathematical model of ADN with DR is introduced, in which the MEG layer is based on DR subsidies, and the system operation plan is formulated with the lowest operating cost as the goal; the ADN layer optimizes the load demand at each time period by adjusting the DR subsidies, with the goal of maximizing revenue. Constraints include distribution network power flow constraints, branch current constraints, distributed power constraints, and MEG equipment model constraints; Third, the chaotic particle swarm algorithm based on particle dimensional entropy and greedy strategy is used to solve the model. Finally, the results of calculation examples show that the method proposed in this paper can reduce the comprehensive loss of the ADN in multiple scenarios.

In order to realize the undifferentiated browsing of the cloud terminal for the large power grid dispatching system, this paper proposes a framework design of the cloud terminal to support the local/remote undifferentiated browsing. First, a standardized storage specification for interactive interfaces is proposed, which expands the existing CIM/G specifications to achieve cross-system and cross-software analysis and display of interactive interfaces; Then, it provides a graphical development tool for applications (APP), which can be used to achieve rapid and fine-grained development of applications that comply with cloud terminal specifications, and explores the use of cloud terminals for full life cycle management of application services; Finally, the standard human-computer interaction specification is discussed, and the undifferentiated invocation of data and services is realized by standardizing the structure of interactive messages. The developed human-machine cloud terminal can break the limitation of the existing localization of the dispatching system, realize the undifferentiated display of the dispatching interface and the undifferentiated call of the cloud platform application service.

In recent years, as an important technology in deep learning, target detection has been widely used in all aspects of life. Aiming at the problem of occlusions in face recognition, this paper adopts SSD (Single Shot MultiBox Detector) deep learning target detection algorithm to classify and locate face occlusions. The average precision of all categories (mAP) reached 95.46% through the self-built data set of 7 types of common face occlusion. Experiments show that this method can effectively detect the face occlusion, which provides a new idea for automatic intelligent face recognition and has a broad application prospect.

Vision-based pedestrian detection technology is widely used in security surveillance, such as transportation, prisons, and important places. Security monitoring requires pedestrian detection not only to be accurate and reliable, but also to meet the needs of real-time monitoring, and the false alarm rate is low. The existing pedestrian learning method based on deep learning has high reliability, but the timeliness cannot meet the monitoring requirements. Traditional pedestrian detection methods are time-sensitive, but false alarm rates tend to be higher. This paper proposes a self-learning pedestrian detection method, combined with motion detection, HOG feature extraction, and SVM classification for preliminary pedestrian detection. The false alarms caused during the pedestrian detection process are manually confirmed, and then the system automatically detects the false alarm image at a specific time. Self-learning, retraining the classifier, so as to continuously reduce the pedestrian detection false alarm rate and improve the pedestrian detection performance index through self-learning during the system operation, and the timeliness can meet the needs of video surveillance. Experiments show that the false alarm rate of this method is low, timeliness is high, and the recognition rate is also high.

Unmanned aerial vehicles (UAVs) are the focus of development and Cultivation in China, especially in artificial intelligence. Current UAV control mainly carries out related flight tasks through a certain professional training of pilots'hand-held remote controllers, which is undoubtedly not conducive to the popularity of UAVs. In recent years, the rapid development of artificial intelligence, especially in image recognition, has achieved good results. In many practical applications, its identification ability surpasses human beings and brings opportunities to the development of all walks of life. In order to popularize UAV better, this paper studies the introduction of gesture recognition control in UAV, and puts forward a scheme of camera mounted in UAV and human gesture shooting and recognition, which simplifies the cumbersome control of UAV.

Smoke detection technology based on computer vision is a popular research direction in fire detection. This technology is widely used in outdoor fire detection fields (e.g., forest fire detection). Smoke detection is often based on features such as color, shape, texture, and motion to distinguish between smoke and non-smoke objects. However, the salience and robustness of these features are insufficiently strong, resulting in low smoke detection performance under complex environment. Deep learning technology has improved smoke detection performance to a certain degree, but extracting smoke detail features is difficult when the number of network layers is small. With no effective use of smoke motion characteristics, indicators such as false alarm rate are high in video smoke detection. To enhance the detection performance of smoke objects in videos, this paper proposes a concept of change-cumulative image by converting the YUV color space of multi-frame video images into a change-cumulative image, which can represent the motion and color-change characteristics of smoke. Then, a fusion deep network is designed, which increases the depth of the VGG16 network by arranging two convolutional layers after each of its convolutional layer. The VGG16 and Resnet50 (Deep residual network) network models are also arranged using the fusion deep network to improve feature expression ability while increasing the depth of the whole network. Doing so can help extract additional discriminating characteristics of smoke. Experimental results show that by using the change-cumulative image as the input image of the deep network model, smoke detection performance is superior to the classic RGB input image, the smoke detection performance of the fusion deep network model is better than that of the single VGG16 and Resnet50 network models, the smoke detection accuracy, false positive rate, and false alarm rate of this method are better than those of the current popular methods of video smoke detection.

The proposed Circular Electron-Positron Collider (CEPC) presents new challenges for pixel detectors in terms of cell size and functionality. A high data rate digital design and readout architecture of a Monolithic Active Pixel Sensor (MAPS) prototype for the CEPC vertex detector is presented. The MAPS prototype, together with the column drain based readout architecture, benefiting from the ALPIDE (ALice PIxel DEtector) and FE-I3 approaches, has been implemented to achieve high spatial resolution, fast readout, and low power consumption. The simulation results indicate the readout logic works properly with input data rates of 120MHz; while both analog front-end and in-pixel readout logic meet the 25ns bunch spacing constraint.

Mining method selection (MMS) is one of the core contents in mining design. As the influencing factors of MMS are in general of ambiguity or uncertainty, MMS can therefore be deemed as a complex fuzzy multi-criteria decision making problem. To resolve this problem, an extended multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) approach is studied in this paper. Three major contributions of this study are included. At first, comparative importance is crisp number in most stepwise weight assessment ratio analysis (SWARA) method, while linguistic terms are utilized to determine the criteria weights in the improved SWARA. Then, several Heronian mean (HM) operators related to linguistic neutrosophic numbers (LNNs) are defined to cope with evaluating indicators’ interrelations. Special cases are discussed, and primary properties are proved as well. At last, in combination with improved SWARA and HM operators, a novel decision making method, extended MULTIMOORA approach based on LNNs, is proposed. Applying this new approach to selecting the optimal mining method through an example, the feasibility is indicated. In addition, analyses of the strengths of the presented method are performed in comparison with other existing approaches.

The installation error of USBL acoustic transceiver array is one of the important error sources for the SINS/USBL integrated navigation system. The installation error of USBL transceiver array can be divided into coordinate shift error and angle rotation error. The angle rotation error (i.e., installation error angle) has a great influence on the positioning accuracy of USBL. In this paper, the traditional surround sailing method is used to study the relationship between the installation error angle and the USBL positioning accuracy. The influence on USBL positioning is analyzed from the component of heading, pitch, and roll of installation error angle, and the simulation results show that the horizontal positioning errors of USBL are mainly affected by the heading and rolling installation errors. The horizontal positioning error of USBL caused by heading or rolling installation error of 1° will result in 17.45 m, under the circumstance that the depth of transponder is 100 m and the radius of surround sailing is 1000 m, without considering the influence of other positioning errors. 1.75 m horizontal positioning error of USBL caused by pitching positioning error of 1° pitching positioning error and the horizontal positioning error of USBL caused by heading and pitching positioning error is also related to the horizontal distance between the transmitter and transponder. A dynamic calibration algorithm of installation error angle based on incremental iteration is proposed in this paper, which could realize the dynamic on-line estimation of installation error angle of USBL transceiver array. The installation error angle increment module can accurately estimate the installation angle error by iterating more than 10 times. In the case of ignoring the other error terms, the horizontal positioning error caused by the installation error angle can be reduced from 8 to 2 m in the positioning range of 1000 m.

At present, cloud computing, as a strategic emerging industry in China, has greatly promoted the development of informatization. Although State Grid Corporation of China’s regulation cloud construction is steadily advancing, there is no effective security protection in the face of illegal access, virtual machine escaping, abnormal operation traceability, etc. In this paper, a security monitoring system based on the cloud platform is designed to achieve the comprehensive real-time monitoring of cloud platform security events of the hosts, virtual machines and cloud management software, timely identify of security threats with early warnings, and guarantee the operation of the cloud platform in security, through the investigating and in-depth research on the security monitoring technology of key components of the cloud platform.

When the robot grasps the aerospace structural parts, the structural parts are in a scattered and layered scene. It is difficult for the traditional robot to grasp the position of the structural parts to meet the welding positioning requirements. This paper proposes a multi-stage robotic grasping method, which pre-processes, initializes and accurately registers the point cloud acquired by the vision system to obtain the posture of the structural parts; finds the initial posture of the structural parts through the feature plane, using an undirected graph to build a posture matching library, and finally get the grab collection of the structural parts, so that the structural parts in any posture can be adjusted to satisfy the final positioning posture. The experimental results show that the method effectively solves the positioning and posture transformation requirements of aerospace structural parts and can meet the adjustment of different postures.

Alarm messages for grid monitoring are an important way to supervise the operation of power grids. Since the use of alarm messages is increasing exponentially due to the continuous expansion of the scale of power grids, a processing method for alarm messages based on statistics is proposed in this study. Entropy theory in information theory is introduced into the calculation of information value in power-grid alarming. By means of multiple entropy definitions, an evaluation index system for information value is constructed. Based on the analytic hierarchy process (AHP), various alarm-message entropies are used as indices to comprehensively assess the information value and level of each alarm message. Finally, an example is given to illustrate the effectiveness and practicality of the proposed method. This study provides a new idea for the intelligent classification of alarm messages.

Shared office space is on the rise in China, and at the same time, it is facing the challenge of improving service capacities. The traditional researches are mainly based on the logic of things in space, which emphasizes the rational configuration of the properties of “things”. This paper put forward a design model of Shared office space based on users’ logic of behavior. In the end, this paper got the types and demands of users and the characteristics of the behavior in sharing office space through analysis. Determined the requirements of which equipment would be used in Shared office space; Got the user’s logic of behavior and carried on the space plan of the equipment; Digital human model and equipment model were built and simulated. The simulation results of Shared office space design were evaluated comprehensively and the conclusion was drawn.

The 500kV unified power flow controller (UPFC) has been built in Southern Suzhou, which improves the power supply capacity of the grid. However, due to the UPFC access, the impedance value of the conventional distance protection measurement could not truly reflect the fault location. To solve it, the impact of the UPFC access on the transmission line protection is studied in this paper. Firstly, the model of unified power flow controller based on modular multilevel converter (MMC-UPFC) was established. The control strategies for both the series and parallel side converters were designed. Then, by the fault sequence network diagram, the equivalent circuit model is given, and the influence of the UPFC access transmission line on the traditional distance protection is analyzed in details. Finally, simulations were carried out in PSCAD/EMTDC to verify the different mode effect on the traditional distance protection, when a single-phase ground fault by the metal occurs. The results show that in STATCOM mode, the apparent impedance is affected by the reactive power injected or absorbed, and it is more severe in UPFC mode.

Because SiC MOSFET has excellent performance, this paper designs an isolated bidirectional DC/DC converter based on SiC MOSFET. Firstly, the paper analyzes the driving circuit of SiC MOSFET used in this paper, and tests the SiC MOSFET and its driving with double pulse. Then introduces the topology of the converter, analyzes the working principle, and introduces the control strategy adopted by the converter. The simulation model was built in MATLAB to verify the feasibility of the system. Finally, it is verified by experiments that the converter can run normally and realize related functions.

Confronting the dilemma of resource shortage and environmental pollution, the idea of circular economy (CE) has attracted widespread attentions. To address these problems, several industrial companies have incorporated the CE in their operation or design. In this sense, the phosphorus chemical firms (PCFs) are promoting CE to achieve sustainable development goals since phosphorus is a non-renewable resource and one of the nutrient elements essential for crop growth. Thus, this paper aims to find a suitable way to assess the performance of CE for PCFs. Firstly, the evaluation index system of CE is designed according to the characteristics of CE for PCFs. Then, a new framework based on the qualitative flexible (QUALIFLEX) and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods is established to pick out the best enterprise. The linguistic neutrosophic number (LNN) is advised to describe decision making information with linguistic true, hesitant and false membership degree. Moreover, an entropy-based approach is used to determine the index weights, and the VIKOR method is presented to determine the concordance index. After that, all possible permutations are itemized, and the ranking result is obtained according to the general concordance index values of each permutation. Finally, an example of assessing the performance of CE for PCFs in China is given to illustrate the proposed method, and its feasibility is demonstrated. The robustness and advantage of this approach are also indicated with sensitivity and comparison analysis. The results show that the proposed VIKOR-QUALIFLEX method is reliable and stable for assessing the performance of CE for PCFs, and provides references for the construction and management of CE for PCFs.

When the photovoltaic and energy-storage microgrid(PV-ESM) operate under a conventional droop or virtual synchronous generator(VSG) control, the system frequency will be unstable if the load is disturbed violently. To solve the problem, the feedback loop control strategy of the ESPS with a first order differential control links of high-pass filter is proposed from the aspect of optimizing energy storage power station(ESPS) dynamic power response, and the adaptive selection basis of damping coefficient and inertia time constant is given. An improved VSG frequency adjustment strategy based on feedforward power is proposed in order to support power frequency actively by EPSP power adjustment. Taking a typical off-grid PV-ESM scenario in an industrial park as an example, the simulation results show that the proposed strategy could support frequency recovery rapidly when frequency instability induced by load mutation and ensure the quality of power supply.

As the main force of new energy generation, photovoltaic power generation is widely used in microgrid. Considering the coordinated operation of photovoltaic and energy storage system (ESS) in the island operation mode, the control strategy of voltage stability and power balance was studied for the low-voltage DC microgrid composed of the distributed generations. When the loads of DC microgrid system change abruptly, the DC bus power will be excessive or deficient, which will affect the stability of bus voltage. In this paper, a parallel structure of ESS and photovoltaic cell was used, and a bidirectional DC/DC converter was used to connect the energy storage battery with the DC bus. Based on RBF neural network tuning PID control, an improved bidirectional DC/DC converter operation mode control algorithm was proposed. When the system load changed suddenly, the voltage stability and power balance of DC bus were realized simultaneously. The effectiveness of the proposed algorithm was verified by simulation.

The traditional communication technology between electric vehicles (EVs) and DC charging piles often causes problems such as low reliability, high cost of communication line and high maintenance cost. The power line communication (PLC) technology is a mature technology and has the advantages of simplifying charging cable, reducing cable cost, which has certain practicability and feasibility. This paper deeply analyzes the channel characteristics, noise characteristics and uses OFDM as transmission method for the PLC signal between EVs and DC charging piles. The bit error rate (BER) is a measure index in this paper, this paper calculates the BER under various working conditions in order to determine the performance of OFDM technology in the power line channel. The simulation results indicate that PLC technology based on OFDM could meet the communication demand between EVs and DC charging piles, which have some certain utilities.

The single, repetitive and aimless approach to the driving of birds in the existing airport field caused the birds to quickly "develop" the inertial indication, which was almost "immune" to the equipment, and put heavy pressure on the work of the airport. Nowadays, with the increasing number of flights, how to design and develop an intelligent system for driving birds, so as to make driving birds more scientific, intelligent and effective has become the research focus of the current work on driving birds. The intelligent bird drive system studied in the project links the motion of the bird drive equipment with the bird situation information, and combines the current popular Internet of things technology to effectively avoid the blind startup of the bird drive equipment caused by human wrong commands. It not only responds to the adaptability of birds to the drive equipment, but also improves the effect of the drive equipment. This paper describes how to design the gas gun under the intelligent bird-driving system, and finally tests the feasibility of this scheme.

Plug and play (PNP) technology of Battery Energy Storage Power Station (BESPS) based on the emergency support and scale application background of battery energy storage is presented in this paper, and the key to the PNP technology is fully studied. Firstly, PNP technology of BESPS is divided into two dimensions: energy flow level to achieve ‘plug’ goal and information flow level to achieve ‘play’ goal. Then, advanced applications in energy flow level are given, which include conversion control between grid-connected and off-grid, active voltage and frequency support, load peak shifting. The basic principles of establishing BESPS grid-connected communication model and communication mechanism based on information flow are first proposed. The communication interaction method with the example of between Control Center of Power Station (PSCC) and Power Dispatching Center (PDC) is analyzed. the compatibility of BESPS can be improved by researching the basic ways of device interface interoperability, which includes two parts: battery to Energy Storage Unit (ESU), ESU to BESPS. Finally, the BESPS operating between various operation modes are verified by simulation. The research work in this paper has a very positive guiding significance and reference value for the development of enhanced BESPS and solving the problems of large-scale and flexible grid connection of BESPS.

The dispatching and control cloud (referred to as dCloud) with virtualization technology, which has the characteristics of resource sharing and dynamic deployment, leads to serious challenges for the security protection system of the current power monitoring system. The mandatory access control model selected by the security protection system is not suitable for the situation that user data hosted in the cloud. This paper studies the attribute-based encryption technology, and proposes an optimization method that uses the characteristics of the dCloud itself, to improve encryption efficiency and attribute revocation, and then implements an access control system based on attribute encryption model which suitable for dCloud. The experimental results show that the access control system designed in this paper solves the security problems introduced by super-privilege and resource sharing in the dCloud, which can effectively protect the user data and enhance the security to dCloud.

This paper proposes a novel method to achieve the automatic registration of optical images and Light Detection and Ranging (LiDAR) points in urban areas. The whole procedure, which adopts a coarse-to-precise registration strategy, can be summarized as follows: Coarse registration is performed through a conventional point-feature-based method. The points needed can be extracted from both datasets through a matured point extractor, such as the Forster operator, followed by the extraction of straight lines. Considering that lines are mainly from building roof edges in urban scenes, and being aware of their inaccuracy when extracted from an irregularly spaced point cloud, an "infinitesimal feature analysis method" fully utilizing LiDAR scanning characteristics is proposed to refine edge lines. Points which are matched between the image and LiDAR data are then applied as guidance to search for matched lines via the line-point similarity invariant. Finally, a transformation function based on extended collinearity equations is applied to achieve precise registration. The experimental results show that the proposed method outperforms the conventional ones in terms of the registration accuracy and automation level.

Aiming to increase production capacity and efficiency, also avoid production imbalance, bogie of Electric Multiple Unit (EMU) maintenance production line was optimized. The 5th level wheel set maintenance line was analyzed and its process flow chart was drawn. Then use Plant-simulation software to model and simulate. The results showed that there are five bottlenecks and three blocks in the production line currently. The reasons were analyzed combination with the industrial engineering theories. The process layout adjustment was carried out on eight procedures respectively. The working hour of single train reduces by 13.3% after optimization. In order to further improve the production efficiency, the longest CRH380A wheel set maintenance line was simulated with mixed-flow production scheduling. Two scheduling methods were conducted, one was the motor trucks (D) and tow trucks (T), and the other was the 3rd, 4th and 5th level of maintenance. When the quantity ratio of the three levels of maintenance is 1:1:1, the optimal scheduling order is five-four-three. Considering single 5th level maintenance, the best order of trains is TDDDTDDD, the working hour of the maintenance decreases by 3.8%. The research accomplishments above provide technical support for process layout optimization, mixed-flow scheduling of EMU bogie maintenance and production controlling.

In view of the power supply damage caused by overheating of power devices, this paper proposes a switching power supply whose output power can be adjusted according to the operating temperature. The power supply adds a temperature and power monitoring unit and an output current control unit to the conventional switching power supply technology, and uses the monitoring units to test the operating temperature and input-output power of each power device in the system, and analyzes the relationship between temperature rise and loss. The relationship realizes the diagnosis of the state of the switching power supply, and reduces the temperature by reducing the output current and reducing the loss of the power source with hidden troubles, thereby achieving the purpose of extending the life of the power supply and improving the reliability of the power supply network.

Objective: The new concept of consumption requires that products not only meet the technical requirements such as the use of functions and structures, but also satisfy people’s aesthetic needs through novel designs, reasonable layouts, beautiful lines, and detailed handling; Methods: The combination of industrial design modeling theory and Computer Aided Industrial Design technology has created computer-aided modeling methods based on computer platforms, enabling product modeling to quickly and intuitively perform 3D rendering, virtual assembly, animation demonstration and simulated working state; Results: Before the product is put into production, a complete three-dimensional three-dimensional digital model can be presented in the computer for the designer to compare and analyze. The designer can arbitrarily modify and perfect the industrial product material, color and other modeling elements, so as to reduce the complex design process, shorten product development cycle, significantly increase product performance and market competitiveness; Conclusion: Digital models using computer-aided software design can output data directly for rapid prototyping and manufacturing, So, as an important application technology of form design, computer-aided design (CAD) technology should meet the new requirements of form design.