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Purpose This paper aims to discuss the precise altitude and velocity tracking control of a hypersonic vehicle, a global adaptive neural backstepping controller was studied based on a disturbance observer (DOB). Design/methodology/approach The DOB combined with a radial basis function (RBF) neural network (NN) was used to estimate the disturbance terms that are generated by the flexible modes of the hypersonic vehicle system. A global adaptive neural method was introduced to approximate the unknown system dynamics, with robust control terms pulling the system transient states back into the neural approximation domain externally. Findings The globally uniformly ultimately bounded for all signals of a closed-loop system can be guaranteed by the proposed control algorithm. Additionally, the command filtered backstepping methods can avoid the explosion of the complexity problem caused by the backstepping design process. In addition, the effectiveness of the proposed controller can be verified by the simulation used in this study. Research limitations/implications Normally lateral dynamics issue should be discussed in the process of control system designed, the lateral dynamics are not included in the nonlinear dynamic model of hypersonic vehicle used in this paper, merely the longitudinal flight dynamics are discussed in this paper. Originality/value The flexible states in rigid modes are considered as the disturbance of the system, which is estimated by structuring DOB with NN approximations. The compensating tracking error and prediction error are used in the update law of RBF NN weight. The differential explosions complexity derived from the backstepping procedure is dealt with by using command filters.

Probabilistic linguistic term set (PLTS), an efficient tool to describe decision information, can sufficiently express decision makers’ hesitation and preference. Probabilistic linguistic preference relation (PLPR) is based on PLTSs to describe the preference information of experts for paired alternatives. However, in practice, due to the complexity of the problem, the incompleteness of information and the lack of professional knowledge, the incomplete PLPR (InPLPR) with missing information often appears. Therefore, this paper proposes a decision-making method under InPLPR. Firstly, in order to fully consider the specific situation of missing values, missing linguistic term-InPLTS (MLT-InPLTS) is subdivided into missing single linguistic term-InPLTS (MSLT-InPLTS) and missing multiple linguistic terms-InPLTS (MMLT-InPLTS). Then, a two-stage mathematical optimization model of missing information estimation based on additive consistency, fuzzy entropy and hesitation entropy is established. Subsequently, aiming at the unacceptable consistency of complete PLPR (CPLPR) after filling in the missing values, a consistency improvement method based on the idea of gradient descent is proposed. Afterward, probabilistic linguistic weighted averaging (PLWA) operator is used to rank alternatives. Finally, medical supplier selection is taken as an example to verify the effectiveness of the proposed decision-making method, and the robustness and advantages of this method are illustrated by sensitivity analysis and comparison with other methods.

The ship safety domain plays a significant role in collision risk assessment. However, few studies take the practical considerations of implementing this method in the vicinity of bridge-waters into account. Therefore, historical automatic identification system data is utilised to construct and analyse ship domains considering ship–ship and ship–bridge collisions. A method for determining the closest boundary is proposed, and the boundary of the ship domain is fitted by the least squares method. The ship domains near bridge-waters are constructed as ellipse models, the characteristics of which are discussed. Novel fuzzy quaternion ship domain models are established respectively for inland ships and bridge piers, which would assist in the construction of a risk quantification model and the calculation of a grid ship collision index. A case study is carried out on the multi-bridge waterway of the Yangtze River in Wuhan, China. The results show that the size of the ship domain is highly correlated with the ship's speed and length, and analysis of collision risk can reflect the real situation near bridge-waters, which is helpful to demonstrate the application of the ship domain in quantifying the collision risk and to characterise the collision risk distribution near bridge-waters.

As the environment issue is put on the agenda, air pollution also concerns a lot. Nitrogen oxide (NOx) an is important factor which affects air pollution and is also the main gas emissions of the smoke and waste gas of FCC unit in petrochemical industry. It is important to accurately predict the NOx emission in advance for petrochemical industry to avoid air pollution incidents. In this paper, convolutional neural network (CNN) and long short-term memory (LSTM) are combined to predict the NOx emission in Fluid Catalytic Cracking unit (FCC unit). Convolutional-LSTM (CLSTM) is able to extract the spatial and temporal features which are essential information in the prediction of the NOx emission. The features in the factors of production which would affect the NOx emission are extracted by CNN which prepares time series data for LSTM. The LSTM layer is connected after CNN to model the irregular trends in time series. CNN, Multi-layer perception (MLP), rand forest (RF), support vector machine (SVM) and LSTM are implemented as baseline models. The results from the proposed CLSTM model showed better performance than all the baseline models. The mean absolute error and root mean square error for CLSTM were calculated with the values of 16.8267 and 23.7089 which are the lowest among all the models. The Pearson correlation coefficient and R2 for the proposed CLSTM model are calculated with the value of 0.9263, 0.8237 which are the highest among all the models. Furthermore, the residual graphs indicate the well matched performance between the observations and the predictions. The study provides a model reference for forecasting the NOx concentration emitted by FCC unit in petrochemical industry.

Abstract Embedding thread lift rhytidectomy, also known as “thread lifting” in China, with the natures of simple operation, less trauma and quick recovery, is progressively used in clinical practice as a new technology of face lifting. Herewith, a brief introduction of the previous advances of thread lifting techniques and materials in the facial beauty industry, combined with the discussion on various types of sutures, common complications, and the site of actions were provided. The main limitations of present thread lifting material include: (1) the use of non-absorbable sutures is liable to cause allergies and a series of complications; (2) the absorbable sutures are easily degradation, and people need to reshape in a relatively short period. Therefore, the high biocompatible spider silk was proposed as a novel material of thread lifting suture and related devices, the advantages and preliminary achievements on spider silk were also addressed. Graphic Abstract

The multiobjective optimization problems in project management have always been the issues on which project managers focus. Currently, few investigators consider the multiobjective optimiza...

It is very important to demonstrate the reliability of software system, but the existing technical ability is seriously insufficient. In this paper, a simulation method of software reliability index demonstration is studied. With time as the driving force, Monte Carlo simulation is carried out on the mission profile, and the execution process and possible failure situation of the mission are simulated. According to the failure data obtained in the simulation process, the reliability index of the software is calculated. At the same time, a software system reliability demonstration tool based on simulation is developed, and the reliability demonstration of the application object “state monitoring software” has been verified.

In the construction industry, it is of great importance for project managers (PM) to consider the resource allocation arrangement problem based on different perspectives. In this situation, the management of resources in construction becomes a challenge. Generally speaking, there are many objectives that need to be optimized in construction that are in conflict with each other, including time, cost, and energy consumption (EC). This paper proposed a multi-objective optimization framework based on the quantum genetic algorithm (QGA) to obtain the best trade-off relationship among these goals. The construction resources allocated in each construction activity would eventually determine its execution time, cost, and EC, and a complexed time-cost-energy consumption trade-off framework of the project is finally generated due to correlations between construction activities. QGA was performed to find the best combination among time, cost, and EC and the optimal scheme of resource arrangement under this state. The construction process is simulated in BIM to check the rationality of this resource allocation mode. An industrial plant office building in China is presented as an example to illustrate the implementation of the proposed model. The results show that the presented method could effectively reduce 7% of cost, 17% of time, and 21% of energy consumption. This developed model is expected to help PMs to solve the problem of multi-objective optimization with limited resource allocation.

In order to improve the safety of the distribution network, the use of electric operating mechanism for ring closing and power exchange has gradually become a necessary function of the new intelligent distribution ring main unit. However, most of the traditional ring main unit has not been equipped with electric operating mechanism due to the difficulty of transformation, and many opening and closing actions still need to be manually operated by the staff, so the personal safety of the staff can not be guaranteed. The purpose of this paper is to develop a portable universal remote intelligent control device for ring main unit, which is used to replace manual operation to complete the switching operation, so as to reduce the possibility of safety accidents. The device adopts portable and modular design, so it is easy to install and disassemble. Modular adapter design can be applied to different specifications of ring main unit in power system to ensure the operation reliability of traditional ring main unit. When the technology is mature, the device can be widely used in various types of remote control electric opening and closing operation of old ring main unit. It can improve work efficiency, avoid personnel safety risk caused by manual operation in short distance on site, and can also be used as the emergency operation device of existing new intelligent distribution ring main unit, which has a wide application prospect.

Container technology represented by LXC and Docker provides an efficient and lightweight virtualization technology for the edge computing paradigm, which improves the resource utilization and isolation methods of edge nodes. With the widespread use of container technology in the edge computing paradigm, the security issues it faces have become a hot topic of current research. Aiming at the security of container image in the edge computing environment, the behavioral strategies among malicious image containers, secure image containers, and intrusion detection systems, are analyzed and established by a dynamic game model for edge computing container intrusion detection. Based on the perfect Bayesian equilibrium theory, the malicious image container attack and the main factors affecting the performance of the intrusion detection system are analyzed. Through detailed analysis of the respective complex decision-making processes of the container and the intrusion detection system, an analysis framework is provided for the intrusion detection system to formulate the optimal defense strategy. Simulation experiments show that the container security dynamic game model proposed can effectively reduce the system overhead of the intrusion detection system while protecting the security of the container data.

Moving target detection is widely used in intelligent video surveillance, multimedia applications, and other fields. Compared with traditional moving target detection, moving target detection of UAV video images has the same ideas, but there are many interference factors. Aiming at the problem of moving target detection of UAV,a moving target detection algorithm based on accurate motion vector compensation is proposed. In this algorithm, SURF algorithm is used to obtain the matching pair of motion vectors, and RANSAC algorithm is used to delete the matching features to obtain the accurate motion vectors.At the same time, the influence of the target motion vector on the global motion estimation is eliminated by the adaptive background filtering method. By establishing the affine transformation model of the background motion vector, the background motion vector is substituted into the model for parameter estimation.After the motion compensation image is calculated, the moving target detection results are obtained by inter-frame difference.Experimental results show that the algorithm is robust and can accurately detect moving targets in the background of UAV shooting.

Small wind turbines are key devices for micro generation in particular, with a notable contribution to the global wind energy sector. Darrieus turbines, despite being highly efficient among various types of vertical axis turbines, received much less attention due to their starting characteristics and poor performance in low wind speeds. Radically different concepts are proposed as a potential solution to enhance the performance of Darrieus turbine in the weak wind flows, all along the course of Darrieus turbine development. This paper presents a comprehensive review of proposed concepts with the focus set on the low wind speed performance and critically assessing their applicability based on economics, reliability, complexity, and commercialization aspects. The study is first of its kind to consolidate and compare various approaches studied on the Darrieus turbine with the objective of increasing performance at low wind. Most of the evaluated solutions demonstrate better performance only in the limited tip speed ratio, though they improve the low wind speed performance. Several recommendations have been developed based on the evaluated concepts, and we concluded that further critical research is required for a viable solution in making the Darrieus turbine a low speed device.

Pseudo-random spread spectrum photon counting (PSSPC) is a well-established technique for three-dimensional (3D) imaging. Based on the pseudo-random spread spectrum photon counting system, a fast imaging technique that is able to accurately recover multiple depths at individual pixels is presented. Firstly, a pre-filtering algorithm is used to denoise the original data. Then the accelerated Richardson-Lucy iterative deconvolution algorithm is introduced. The method is based on the principles of vector extrapolation and does not require the minimization of a cost function. For multi-depth estimation in the presence of moderate background light, we experimentally demonstrate that our imaging technique outperforms the existing method. We have successfully improved the range resolution from 21cm to 8cm, thus breaking the Full Width at Half-Maximum (FWHM) resolution limit. The separation Root Mean Square Error (RMSE) has been reduced to 3.82cm by the proposed method for the surface-to-surface separation of 8cm. This is a factor of 4 improvement over the conventional method for multi-depth recovery. Also, our imager has achieved 0.5cm lateral resolution by distinguishing two squares closely placed 0.5cm apart from each other.

Limited by the bandwidth of IEEE 802.11p protocol, a large-size data block commonly cannot be transmitted within one communication link in a vehicular ad hoc network (VANET). When transmitting large-size data block in VANETs, the traditional routing-layer-based solutions usually employed a large number of routing paths to extend the data link duration through continuously changing routing path, which commonly leads to frequent communication link disconnection and re-connection. Another shortcoming of the routing-layer solutions in transmitting large-size data block is that dense relay node is required to obtain sufficient stable routing paths for maintaining the long data transmission duration. In this paper, we present a new scheme for efficiently transmitting large-size data block in VANETs, which dedicate to improve the data transmission efficiency through shortening the total duration of the data transmission process at the network architecture and physical connection levels. Based on the assistance of the dual-communication mode and edge computing technique, a heterogeneous network architecture is proposed to introduce multiband orthogonal frequency division multiplexing (MB-OFDM) based ultra-wideband (UWB) technique into the VANETs for transmitting large-size data block. The key challenges of the scheme and alternative solutions for the challenges are analyzed, in which several mechanisms for quick parameter pre-configuration, the quick MB-OFDM UWB link establishment and highly efficient multi-user access to the OFDM-UWB network are presented. The key obstacles that limit the effectiveness of the scheme are also evaluated, of which the results show the presented scheme is feasible.

When surrounded by the heterogeneous networks environment, mobile terminals are often expected to select an optimal network quickly from the detected alternatives to ensure the required or better quality of service. How to reduce the probability of ping-pong effect of the process as much as possible is one of the most prominent challenges. From the two viewpoints of attribute weight and utility value, a flexible hybrid multiple attribute decision making heterogeneous network selection algorithm consisting of FAHP, standard deviation, and GRA is proposed. In this algorithm, comprehensive evaluation value of each candidate network is calculated by integrating attribute weight and utility value. Only the network whose comprehensive evaluation value is larger than that of current network and is at least the preassigned threshold will be chosen as the target network and start the handover process. Otherwise the terminal will remain on the current network. Simulation results show that among the four traffic classes this algorithm can quickly select the most suitable network and significantly reduce the number of vertical handovers and the number of ping-pong effects, compared with other four existing baseline algorithms.

Today, an increasing amount of trades would be improving these new skills of internet and smart technology as the main tool to promote their products and services. Increasing the skill and ability both to attract the new consumers of young and elder in smart business context is an important challenge for trade managers and has described their considerable attention from the academic groups. More recently, driven by advance developments in Information and Communications Technologies (ICTs) and Mixed-Reality (MR) technologies, Virtual-Reality (VR), and Augmented-Reality (AR), embodied devices, sensors, mobile terminal device, microchip design, power efficiency and more broadly the Internet of Things (IoT), the chance to remotely connect to physical and virtual objects, mobile devices and products would have given rise to the appearance of smart technologies and smart services. Therefore, these businesses directors imminently want to realize the connections with consumer motivation, trust, security risk and the behavioral intention of consumer in requirement of user's utilizing smart technologies, in order to capture a crucial chance from now on smart trade proliferation to find those possible users, The primary concern in this article would be to understand the need and inspect these influences of sensing on perceived value, consumer's reputation, trust and to employ the smart technology of user behavioral intention. Next, the questionnaire would be made complete by online investigation proof ( $N=92$ ) collected from those active users who would have attended in using smart technologies. Thus, analyzed results with confirmatory factor analysis, and structural equation modeling would be also used, applying the SmartPLS 2.0, to certify if these questionnaire proofs would get proper to research model. Lastly, these outcomes would apply the evidence that perceived value, consumer's reputation and trust all could affect behavioral intention to employ smart technologies. All these consequences of the article with significances for practice and theory could be more cared enough to keep in mind, too.

Due to high cost of the high-resolution polarimetric synthetic aperture radar (PolSAR) image, there are few literature on the automatic target recognition (ATR) of small target using polarimetric features. With the limitation of high-resolution POLSAR dataset, SAR image simulation can play an important role in the investigation of polarimetric features for man-made target. This paper presents a SAR image simulator and several polarimetric features extraction methods for small complex targets. The proposed SAR simulator is composed of a SAR echo simulator and an image formation algorithm. In order to generate the SAR echo efficiently and accurately, the shooting and bouncing rays (SBR) and physical optics (PO) hybrid method is employed for high frequency electromagnetic scattering prediction of complex objects. Based on the parameters of the moving and stationary target acquisition and recognition (MSTAR) dataset, the PolSAR images of military vehicles are created. In addition, the multi-reflection analysis can be done by decomposing the simulated SAR image into the images corresponding to the first, second, and third order reflections since SBR is a ray tracing based approach. After the simulated SAR image is generated, we employ target decomposition theorems to obtain polarimetric features for target interpretation. Using polarimetric decomposition, the scattering signals of the vehicle are decomposed into a weighted combination of various simple scattering mechanisms, which can be used to form a set of images representing different scattering features. In addition, a polarimetric feature map can be obtained by coloring the scattering centers according to their scattering mechanisms. The testing results show that the polarimetric features fit the multi-reflection analysis well for the simulated SAR image.

At present, there are some problems in database technology, such as low sharing of data, poor security of data, and frequent data leakage. Data bottlenecks are also a big constraint. In order to satisfy the user's needs to edit, manage and store the data, it is necessary to replicate the data to improve the accessibility of data. This paper expounds the design and implementation of related technologies, and hopes to offer some help to perfect these technologies.

Fluid Catalytic Cracking (FCC), a key unit for secondary processing of heavy oil, is one of the main pollutant emissions of NOx in refineries which can be harmful for the human health. Owing to its complex behaviour in reaction, product separation, and regeneration, it is difficult to accurately predict NOx emission during FCC process. In this paper, a novel deep learning architecture formed by integrating Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) for nitrogen oxide emission prediction is proposed and validated. CNN is used to extract features among multidimensional data. LSTM is employed to identify the relationships between different time steps. The data from the Distributed Control System (DCS) in one refinery was used to evaluate the performance of the proposed architecture. The results indicate the effectiveness of CNN-LSTM in handling multidimensional time series datasets with the RMSE of 23.7098, and the R2 of 0.8237. Compared with previous methods (CNN and LSTM), CNN-LSTM overcomes the limitation of high-quality feature dependence and handles large amounts of high-dimensional data with better efficiency and accuracy. The proposed CNN-LSTM scheme would be a beneficial contribution to the accurate and stable prediction of irregular trends for NOx emission from refining industry, providing more reliable information for NOx risk assessment and management.

The strap-on launch vehicle is widely adopted in order to increase the launching capability of the launch vehicle, especially in the unmanned system. The connect and separation equipment between the core and the booster is one of the most key techniques in the aerospace unmanned system. In this paper, the mechanical property of the astronautic separation equipment is researched based on the different axial stiffness in tension and compression characteristics, which could be simulated as the bi-linear spring model. With the single degree of freedom analytical schematic model, every design parameter that affects the separation equipment's stiffness is researched, which can optimize the astronautic separation equipment's mechanical property. This method can provide a reference and a basis for the aerospace unmanned system design.

Mobile terminals are often in the dynamic environment of heterogeneous networks. For some reasons, they need to switch between different networks, namely vertical handover. At this moment, it is a very crucial issue for the mobile terminal to select the best suitable one rapidly from all detected alternative networks on condition that the selection result can avoid the ping-pong effect as much as possible. This paper proposes a heterogeneous network selection algorithm based on the combination of network attribute and user preference. Taking full account of user preferences for each candidate network and the actual situation of heterogeneous networks, the algorithm combines three typical MADM methods, namely FAHP, Entropy and TOPSIS. We first use FAHP to calculate the subjective weights of network attributes and the subjective utility values of all alternatives for four typical traffic classes, and then use Entropy and TOPSIS to respectively get the objective weights of network attributes and the objective utility values of all alternatives. Finally, according to the comprehensive utility value of every candidate network and a threshold, the most appropriate network, whose comprehensive utility value is maximum and greater than the corresponding value of the current network of the mobile terminal, is selected to access. The proposed algorithm not only avoids the one-sided nature of a single algorithm, but also dynamically adjusts the proportion of each algorithm in the final result according to the actual requirements. Simulation results indicate that the proposed algorithm can accurately select the optimal access network, significantly reduce the number of vertical handovers and provide the required QoS and QoE in terms of the quantified benefit from vertical handoff, compared with three existing hybrid algorithms.

Data publish-subscribe scheme is a valuable approach to share and retrieve data from cloud platforms selectively. However, the common practice of standard encryption hinders the effective keyword-based data subscription over outsourced data. Attribute-based keyword search (ABKS), which fulfills the functionality of attribute-based encryption and searchable encryption, is a promising cryptographic primitive to construct secure data publish-subscribe scheme. In this paper, for the first time, we propose a decentralized and expressive CP-ABKS scheme with computation outsourcing and collusion resistance. Then we construct a novel data publish-subscribe scheme based on the proposed CP-ABKS to realize secure and flexible data sharing among multiple users. Both the access and subscription policies are expressed with the Linear Secret Sharing Scheme structure that can be derived from any monotonic Boolean formula and any threshold gate. To protect the keyword privacy, the subscription policy is partially publicized, and a keyword localization method is designed to enable the server to reconstruct the subscription policy and execute the search protocol in linear time. Additionally, the proposed scheme adopts online/offline encryption and outsourced decryption in a fully decentralized setting. Security analysis and performance evaluation show that our scheme achieves a good balance between security goals and practical efficiency.

The gate system is used to estimate the direction of RFID tags carriers when they are going through the gate. Normally, it is difficult to achieve and keep a high accuracy in estimating motion dire...

At present, image change detection technology as an important image processing technology has been widely used, and a novel synthetic aperture radar (SAR) image change detection algorithm based on hybrid genetic FCM and image registration is proposed in this paper. First of all, the algorithm performs registration with two photographs taken in the same region images at different time using Harris operator and Sift operator, then, the ratio method and logarithmic method is combined to extract the initial differences image, and then the Principal Component Analysis (PCA) method is used to reduce the dimension of the difference image. Finally the hybrid genetic fuzzy C-means (FCM) algorithm is used to determine the classification of feature vector space, and the classification results are compared with the reference image, to obtain the change information. The purpose of using hybrid genetic FCM is to divide the initial difference image clustering into the changed type and the unchanged type, so as to get the final segmentation result. The FCM algorithm improved by genetic algorithm can effectively avoid that the FCM algorithm will fall into local minimum when the initial cluster center selection is not appropriate. As the genetic algorithm is a global optimization search algorithm, it can improve the segmentation effect of FCM. The experimental results show that the proposed algorithm has the highest global correct rate of 98.10% and 99.74%.

Understanding human movement patterns is of fundamental importance in transportation planning and management. We propose to examine complex public transit travel patterns over a large-scale transit network, which is challenging since it involves thousands of transit passengers and massive data from heterogeneous sources. Additionally, efficient representation and visualization of discovered travel patterns is difficult given a large number of transit trips. To address these challenges, this study leverages advanced machine learning methods to identify time-varying mobility patterns based on smart card data and other urban data. The proposed approach delivers a comprehensive solution to pre-process, analyze, and visualize complex public transit travel patterns. This approach first fuses smart card data with other urban data to reconstruct original transit trips. We use two machine learning methods, including a clustering algorithm to extract transit corridors to represent primary mobility connections between different regions and a graph-embedding algorithm to discover hierarchical mobility community structures. We also devise compact and effective multi-scale visualization forms to represent the discovered travel behavior dynamics. An interactive web-based mapping prototype is developed to integrate advanced machine learning methods with specific visualizations to characterize transit travel behavior patterns and to enable visual exploration of transit mobility patterns at different scales and resolutions over space and time. The proposed approach is evaluated using multi-source big transit data (e.g., smart card data, transit network data, and bus trajectory data) collected in Shenzhen City, China. Evaluation of our prototype demonstrates that the proposed visual analytics approach offers a scalable and effective solution for discovering meaningful travel patterns across large metropolitan areas.

A large number of high-power single-phase electric heating loads, represented by air conditioners entering into domestic consumption is easy to cause asymmetric distribution of three-phase loads in three-phase four-wire power supply system. It is difficult to popularize traditional post-treatment methods for three-phase unbalance problems because of their complicated control algorithm, lack of algorithm stability, and high cost of manufacturing or land occupation. In this paper, a three-phase unbalance allocation strategy is proposed, which combines hierarchical clustering method with traversal algorithm to divide users’ air conditioners into three phases and reduce three-phase unbalance in the power grid, to reduce the difficulty of post-treatment of the three-phase unbalance problem. The allocation strategy for 50 air conditioner users is simulated and its effectiveness is given by comparison result.

With the development of demand response technology, it is possible to reduce power shortages caused by loads participating in power grid dispatching. Based on the equivalent thermal parameter model, and taking full account of the virtual energy storage characteristics presented during electro-thermal conversion, a virtual energy storage model suitable for electric heating loads with different electrical and thermal parameters is proposed in this paper. To avoid communication congestion and simplify calculations, the model is processed by discretization and linearization. To simplify the model, a control strategy for electric heating load, based on the virtual state ofcharge priority list, is proposed. This paper simulates and analyzes a control example, explores the relevant theoretical basis affecting the control effect, and puts forward an optimization scheme for the control strategy. The simulation example proved that the proposed method in this paper can reduce power storage in the grid over a long period of time and can realize a power response in the grid.

Previous studies on parallel mechanisms have been limited to symmetrical and semi-symmetrical parallel robots. However, there is relatively little research on asymmetrical parallel robot, especially on asymmetrical parallel robots with 4 degrees of freedom. Considering of disadvantages of modern technology, a new type of asymmetric parallel manipulator is proposed based on the theory and method of position and orientation characteristic equations in this article. First, the topology theory of parallel mechanism is expounded, the robot structure is described and the position and orientation characteristic set is calculated to obtain the degrees of freedom and the coupling degree of the parallel manipulator. Then the structure coupling–reducing theory is used to realize the structure decoupling of the robot. Second, the forward kinematics solution of the parallel robot is analyzed by ordered single-open-chain method, the inverse kinematics solution is solved by the algebraic method, and a numerical example is provided to confirm the correctness of the solution procedure. Finally, the workspace of the robot is analyzed by search method. Generally, the above theoretical research provides an important reference value for the industrial design and application of the parallel robot.

In this article, we examine the system control design of a flexible hypersonic vehicle with an unknown direction control. Prescribed performance controls, backstepping controls, and radial basis function neural network are used to design the controllers. Different from a traditional radial basis function neural network, the fully tuned dynamic radial basis function neural network has a better approximation ability; and the weight vector, respective centers, and width of the Gaussian function of neural network are regulated by adaptive laws designed in the controller. The proof and analysis of stability are taken in this article for the fully tuned dynamic neural network introduced to control the system. Furthermore, prescribed performance control can guarantee the tracking errors satisfy the specified conditions. The unknown control direction is solved with the Nussbaum function in the controller. Finally, the simulations demonstrate the effectiveness and corrective measures of the control strategy.

As the power grid continues to grow, the number of components on buses is increasing. It requires bus differential protection to integrate reliability and security. This paper has introduced the inspection and handling process of a malfunction accident of 220kV microcomputer bus differential protection. The model bus differential protection was the product after six unified. The basic principle of busbar differential backup protection has been introduced. The causes of misoperation caused by microcomputer bus differential protection are analyzed from three aspects: criterion of operation bus, generation of differential current, and rationality of operation bus voltage blocking. Preventive measures and solutions for preventing malfunction of microcomputer bus differential protection has been given in this paper. Through the suggestion of changing the logic of the criterion of the running bus and the opening of the busbar voltage, the reliability of the protector has been greatly improved and the safe and stable operation of the power grid has been ensured.

A new frequency-hopping communication system is proposed which has excellent anti-follower jamming properties. Traditional frequency-hopping communication is designed by switching the channel it used quickly in a pseudo random way to avoid being detected as well as escaping the jamming, and the frequency-hopping sequence is only a one-dimension pseudo-random vector, while the novel one which is called the multiple parallel frequency-hopping communication system has a high-dimension pseudo-random frequency-hopping pattern, so it is impossible to be cracked in the communication span. Furthermore, the tradition frequency-hopping communication will be failed once a follower jamming tracking and disturbing successfully, while the proposed multiple parallel frequency-hopping system is almost completely unaffected even one of the high-dimension frequency-hopping sequences is blocked completely. Finally, combined with the variable frequence-hopping idea, a 4D frequency-hopping pattern is recommended in order to achieve a better balance and higher cost-benefits.

In order to improve the uniformity and the utilization rate of the feed liquid in the process of wet leaf feeding and reduce the reduction value of the brightness of the tobacco slice. Under the condition of no circulating hot air, the liquid macromolecular mass was cut into small molecular mass using the small molecular processor of permanent strong magnetic material liquid and the influence of tobacco slice feeding on the uniformity of feeding, the change of the lightness value of the tobacco slice and the sensory quality is analyzed. We can draw the conclusion by semi-supervised clustering analysis: under the condition of no circulating hot air, through the process of the small molecules of material liquid and when the radial Angle of the nozzle is 87.2°, the axial Angle of the nozzle is 91.6°, storage time is 4 h, the uniformity of feeding increased to 62.95%, the decreased value of the brightness of the tobacco slice reduced 2.28 and the sensory evaluation value increased 3.94.

A new type of baggage diversion lifting mechanism is proposed which is driven by an electronic cylinder and designed to realize lifting and resetting function. The mechanism we designed can meet the requirements of lifting stroke in small working space and has the feature of simple structure. The parameter design of the mechanism is done, and the dynamics and kinematics analysis of the mechanism are carried out. In order to verify the feasibility of the lifting mechanism, the driving force and the joint forces are obtained, which can also provide the basis for the selection of the driving element and the optimization of the mechanism.

Power cable is one of the most important components in energy internet and energy system integration. To provide a decision support for maintenance of power cable, this paper proposes a method for comprehensive operation condition assessment of power cable based on fuzzy analytic hierarchy process and set pair analysis approach. An evaluation indicator system of cable condition was established, and the operation state was divided into four ranks, including normal, attention, abnormal and serious state. Then the fuzzy analytic hierarchy process and set pair analysis approach were introduced for the condition assessment of power cable. Operation condition assessment of power cable was conducted with the proposed method through an actual example, and the result corresponds well with the actual situation.

Reasonable sales forecast is very important for enterprises. The short-term and long-term sales changes of a product are helpful for enterprises to make marketing strategies and sales decisions. On the basis of in-depth analysis of the characteristics of a certain algorithm model and long and short memory neural network, and according to the data set provided by a supermarket chain in kaggle competition, a XGBoost-LSTM neural network combination model for sales forecasting and a classical time series prediction model are constructed to compare the experimental results. The experimental results show that the XGBoost-LSTM neural network prediction model has higher accuracy than the time series prediction model, which can provide an important scientific basis for the supermarket chain to make sales forecast.

To solve the current security problems of the microservice architecture, the security mechanism of the API gateway platform using the microservice platform is designed and implemented. To solve the corresponding security problems, a new API platform security mechanism is designed. In the data transmission, the hybrid encryption algorithm hybridized by encryption algorithm of Rivest Shamir Adleman (RSA) and encryption algorithm of Advanced Encryption Standard (AES) is used for data encryption. An API gateway between the client and the microservice is introduced as the only entrance for users to access the microservice, avoiding the complexity of the client logic caused by the direct communication between the client and the server. After the introduction of the API gateway, the API-level service operation authority can be controlled; the security of the front-end and back-end transmission data is improved without affecting the transmission performance. The algorithm designed takes longer time than the AES encryption algorithm, but shorter than the RSA encryption algorithm, which indicates that the time-consuming of the algorithm designed is acceptable, showing the feasibility of the algorithm. The content can provide an important theoretical basis for the security of API gateway platform of microservice architecture.

This paper systematically analyzes the quality supervision data system of water conservancy projects. Then, according to the characteristics of water conservancy project quality supervision text, the Word2vec algorithm and TFIDF algorithm are combined to construct a feature extraction system of water conservancy project quality supervision text suitable for short length and few samples. Finally, a semi-supervisory model system consisting of logical regression, simple Bayes, and SVM is introduced to solve the problem of incomplete quality supervision risk data for water conservancy projects. To sum up, on the basis of the three parts, i.e. data system, feature extraction, and semi-supervisory text classification, we build a water conservancy project quality risk assessment framework and provide a data processing tool for machine learning of hydraulic engineering quality risks.

Geomagnetic observation data has unique academic research significance, and it is also an important national basic strategic resource. Based on the correlation and temporal characteristics of surrounding stations in geomagnetic observation data, this paper proposes the use of LSTM neural network method to predict the missing data of the target station based on the data of the reference station, and it is compared with the traditional BP neural and CNN neural network method. The results show that the LSTM neural network is superior to the BP neural network and CNN neural network in geomagnetic data reconstruction, and the predicted geomagnetic data has higher accuracy.

With increasing penetration intermittent renewable energy and the interactions between different power sources in an interconnected system, low frequency oscillations may occur and potentially threaten the security of power systems if the grid cannot support adequate damping. Locating sources of low frequency oscillations is of great importance, which needs finding the mechanism of the damping of low frequency oscillations for interpretation. The difficulty of this problem lies in the fact that the parameters associated with the power system model can range from slightly uncertain to entirely unknown. Therefore, we firstly focus on identifying the network-reduced model to characterize low frequency oscillation, and then utilizing Hamilton analysis to reveal mechanism of oscillation. Accordingly, the problem of locating low frequency oscillation is equivalent to identify equivalent negative damping coefficient. In this paper, we propose a novel data-driven method that estimates equivalent damping coefficients and topological parameters of the network-reduced model simultaneously. More specifically, the proposed method utilizes the sparse representation to select the most dominant nonlinear terms from a set of dictionary functions, which finally balances the data fitness and achieves dynamics learning. We validate and evaluate the proposed method on IEEE 9-bus test system and IEEE 39-bus test system. The results demonstrate the effectiveness of the proposed method in achieving dynamics learning and locating sources of low frequency oscillations from measurement data.

Firstly, this paper designs the process of personalized recommendation method based on knowledge graph, and constructs user interest model. Second, the traditional personalized recommendation algorithms are studied and their advantages and disadvantages are analyzed. Finally, this paper focuses on the combination of knowledge graph and collaborative filtering recommendation algorithm. They are effective to solve the problem where [Formula: see text] value is difficult to be determined in the clustering process of traditional collaborative filtering recommendation algorithm as well as data sparsity and cold start, utilizing the ample semantic relation in knowledge graph. If we use RDF data, which is distributed by the E and P (Exploration and Development) database based on the petroleum E and P, to verify the validity of the algorithm, the result shows that collaborative filtering algorithm based on knowledge graph can build the users’ potential intentions by knowledge graph. It is enlightening to query the information of users. In this way, it expands the mind of users to accomplish the goal of recommendation. In this paper, a collaborative filtering algorithm based on domain knowledge atlas is proposed. By using knowledge graph to effectively classify and describe domain knowledge, the problems are solved including clustering and the cold start in traditional collaborative filtering recommendation algorithm. The better recommendation effect has been achieved.