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For the linear discrete networked control system (NCS) which may suffer DoS attack on both sides of the controller, when the actuator has time-varying failure, the intelligent sensor unit uses wireless sensors to collect data. According to the large amount of data collected, the active fault tolerance/active passive capacity of linear discrete NCS under the discrete event-triggered communication mechanism (DETCS) is studied. The problem of cooperative controller design is discussed. Firstly, a linear discrete NCS model integrating DETCS, actuator fault, and network attack is established. Then, based on the idea of integral sliding mode control, an active fault-tolerant/attack active passive intrusion-tolerant cooperative controller is designed, and the actuator attack side network attack and sensor side network attack are extended to the state to obtain a new state vector. Then, an adaptive Kalman filter estimator (AKF) estimates the fault and attack information and then adjusts the initial fault-tolerant/intrusion-tolerant cooperative controller in real time according to the estimated information obtained by the adaptive Kalman filter estimator; finally, the MATLAB simulation example is used to verify the improvement of system performance by the designed control law and the saving of network resources by the introduction of DETCS.

This paper proposes a parallel computing analysis model HPM and analyzes the parallel architecture of CPU–GPU based on this model. On this basis, we study the parallel optimization of the ray-tracing algorithm on the CPU–GPU parallel architecture and give full play to the parallelism between nodes, the parallelism of the multi-core CPU inside the node, and the parallelism of the GPU, which improve the calculation speed of the ray-tracing algorithm. This paper uses the space division technology to divide the ground data, constructs the KD-tree organization structure, and improves the construction method of KD-tree to reduce the time complexity of the algorithm. The ground data is evenly distributed to each computing node, and the computing nodes use a combination of CPU–GPU for parallel optimization. This method dramatically improves the drawing speed while ensuring the image quality and provides an effective means for quickly generating photorealistic images.

Some image encryption algorithms are difficult to resist the chosen-plaintext attack against special images, in order to solve this problem and improve the security of the algorithm, this paper proposes a novel image encryption scheme based on the chaotic system, random number embedding and DNA-level self-adaptive permutation and diffusion. The architecture of preprocessing, permutation and diffusion is adopted. Firstly, an image preprocessing based on random number embedding (IPRNE) is presented, specifically, embed random numbers into the plain image, and then perform partition XOR operation on random numbers and their surrounding pixels to preprocess plain image. The random numbers are generated by a 4D memristive hyperchaotic system, and their embedding positions are controlled by the pixel sums of plain images. Secondly, the obtained image is encoded into a DNA matrix by use of a DNA encoding rule, and then a DNA-level self-adaptive permutation and diffusion processes are successively performed on it. Further, after decoding the diffused matrix, the cipher image is obtained. Besides, the feature information of DNA sequences of plain image is applied for disturbing the permutation and diffusion phases, which may be extracted automatically in the decryption process, and thus additional transmission and storage are avoided. Moreover, the plain image information and hyperchaotic system are integrated to design the DNA encoding /decoding rule for the plain image and mask matrix, and this can enhance the ability of the algorithm to resist chosen-plaintext attack. Experimental results and security analyses demonstrate that the proposed encryption is secure and effective, and it can be applied for image secure communication.

The behaviors of dairy cows, such as feeding, ruminating, running, resting (standing, lying), head-shaking, drinking, and walking, can indicate their health status. In this study, a multi-sensor was used to collect data of cow’s multi-behaviors for research on behavior recognition. Firstly, a collar style data acquisition system equipped with geomagnetic and acceleration sensors to collect the behavioral data of dairy cows during their daily activities was designed. Secondly, the dairy cow behavioral recognition fusion model based on K-Nearest-Neighbors (KNN) and Random Forest (RF) models were used for behavior classification. To verify the accuracy of the fusion model, the algorithms of KNN, RF, Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Learning Vector Quantization (LVQ) were introduced for comparative recognition experiments with different algorithms. The KNN-RF fusion model had the highest average recognition accuracy of 98.51%, followed by the KNN model with an average recognition accuracy of 95.37%, and the LVQ model had the lowest average recognition accuracy of 80.81%. For the recognition and verification of each behavior, the KNN-RF fusion model had the most obvious improvement in the recognition of dairy cow feeding behavior, with a recognition accuracy of 99.34%, followed by the KNN model with a recognition accuracy of 95.07%. All six models had the lowest recognition accuracy for cow head-shaking behavior: a recognition accuracy of 89.11% with the KNN-RF model followed by the RF model with a recognition accuracy of 85.14%. The system can quickly and continuously collect cow behavior information, accurately recognize individual behaviors, and provide a scientific basis for the optimal design and efficient management of digital facilities and equipment for dairy cows.

A good heat dissipating condition will help give full play to the performance and reliability of electronic components and devices. To strengthen the heat dissipating performance, a novel bionic heat sink of pin fins has been designed. The configuration of cooling pin fins in this heat sink is based on phyllotaxis theory in biology. The bionic configuration of the cooling pin fins has better homogeneity and complementarity, which can form a reasonable air flow channel to improve its cooling efficiency. For testing and analyzing the performance of the bionic heat sink and the effects of the phyllotactic parameters on the heat dissipating, some contrast experiments have been conducted using thermal infrared imager. The results show that the bionic heat sink has better heat dissipating performance, which can make the surface temperature of the heat block lower by 14.7%. This paper will supply a new and effective solution to heat dissipation problem and has a certain contribution for the development of electrical and electronic industry.

Conventional maintenance mode for the traction power supply system (TPSS) is to perform scheduled regular maintenance activities for power supply equipment, while such maintenance mode may result in undue maintenance tasks and low efficiency due to different degradation processes of different sorts of equipment. To address this problem, this paper introduces a preventive opportunistic maintenance (POM) method for TPSS based on equipment reliability. Firstly, a POM model is established by considering the equipment reliability degradation process based on Weibull distribution. Then, by considering the total power outage time in the planned operation cycle of TPSS as the optimization objective, the optimal maintenance scheme of TPSS is formulated by iterative method of maintenance strategies. The proposed method is verified by introducing practical maintenance strategies and fault record data of the traction transformer, circuit breaker and disconnector in an actual TPSS of a railway administration. Results show that the presented method can make full use of the existing fault data to develop a POM scheme for TPSS. It can improve maintenance efficiency and reduce power outage time, providing guidance to formulate scientific maintenance strategies for TPSS.

Time irreversibility is one of the fundamental properties of nonequilibrium complex brain activities and is relevant to various neurological conditions, e.g., epilepsy. However, the estimation of the joint probability distribution for quantitative time irreversibility (qTIR) is not trivial, and the application of qTIR in characterizing epileptic brain signals has received little attention. In this paper, we employ equal-value permutations instead of raw vectors to simplify qTIR, and we apply subtraction-based parameters to measure the probabilistic differences in order patterns for qTIR considering the forbidden permutations. We demonstrate that our simplified method, validated by chaotic and reversible model series and their surrogates, is equivalent to methods measuring the probabilistic difference between forward–backward vectors and the probabilistic difference between symmetric vectors. In characterizing epileptic brain electric activities, seizure electroencephalograms (EEGs) have the strongest qTIR due to the development of synchronous neuronal firing, and the qTIR of seizure-free EEGs lies between that of the healthy control and ictal EEGs. Overall, we conduct a comprehensive analysis of permutation-based qTIR for nonlinearity detection, and our findings regarding qTIR in epileptic EEGs improve our understanding of nonequilibrium epileptic brain electrical activity and might even contribute to predicting epileptic seizures.

Dynamic infrared scene projection system can provide infrared detectors such as infrared seeker with target infrared radiation characteristics equivalent to the real environment in the laboratory. It can produce dynamic infrared scene to simulate the infrared radiation characteristics of real objects and their environment. The system constitutes a hardware in the loop simulation test system together with multi axis motion platform, seeker system, control system and other related equipment. This paper presents a scheme of infrared scene projection system based on dual digital micro mirror device (DMD), describes the main functions and composition of the system, and focuses on the working mechanism of dual DMD to improve gray level and frame rate. The system consists of dual DMD infrared scene projector, synchronization control device and scene generation computer. Two DMDs are used to modulate the infrared radiation intensity of each frame image and the spatial distribution characteristics of the image, and jointly complete the modulation of the gray level and spatial distribution of the output image. At the same time, the synchronization control device works synchronously with the test equipment or seeker to ensure that the generated infrared field frame rate is synchronized with the image collected by the test system. At present, the high frame rate infrared scene projection system based on dual DMD can meet the index requirements of maximum frame rate of 100Hz and minimum integration time of 2ms, which can vary with the synchronization signal frequency, and meet the integration time and synchronization test requirements of a variety of infrared detection equipment.

Aiming at the traffic safety problem caused by sudden agglomerate fog on the highway, this paper designs an early warning system of the robot for monitoring agglomerate fog on the highway based on Beidou short message and MQTT protocol. This system monitored environmental parameters such as visibility, location information, temperature and humidity in real time by the robot for monitoring agglomerate fog on the highway, and adopts the Beidou short message and MQTT protocol dual-channel communication mode to achieve two-way data communication with the cloud platform and server. The problem of data transmission loss caused by no mobile communication network is solved. Use the Internet of Things cloud platform and WEB application development technology to realize a system with the functions of monitoring data visualization, data monitoring and remote control of robot movement. The experimental results show that the communication that data interaction and remote control between the cloud platform and the robot is stable in the system, and the early warning of the level of fog is timely and effective, which improve the accuracy of monitoring agglomerate fog with broadly application foreground.

The structural similarity and numerical similarity in the similarity calculation of the traditional user-based collaborative filtering recommendation algorithm have their own shortcomings. Structural similarity only uses the structural information of user ratings without considering the numerical information for user similarity, and numerical similarity only uses the ratings to calculate the similarity without considering the structural information, which makes the similarity calculation lacks accuracy. Aiming at this problem, a method that considers both the structural similarity of numerical information and the numerical similarity of structural information is proposed. When improving the numerical similarity, the influence of item quality and item popularity on the numerical similarity is considered. Simultaneously, the commonly used numerical similarity calculation uses the improved structural similarity to optimize. The five-fold cross-validation method is tested on the Movielens100K public data set. Compared with the traditional user-based collaborative filtering recommendation algorithm, the experiment proves that the proposed method effectively improves the recommendation performance.

In response to the difficulty of detecting and classifying pests and vegetable and fruit leaves with pests and diseases, this study proposes a novel vegetable and fruit leaf pest detection method called deep block attention SSD (DBA_SSD) for the identification of pests and diseases and classification of the degree of pests and diseases of vegetable and fruit leaves. We propose three vegetable and fruit leaf pest detection methods, namely, squeeze-and excitation SSD (Se_SSD), DB_SSD, and DBA_SSD. Se_SSD fuses SSD feature extraction network and attention mechanism channel, DB_SSD improves VGG feature extraction network, and DBA_SSD fuses the improved VGG network and channel attention mechanism. To reduce the training time and accelerate the training process, the convolutional layers trained in the Image Net image dataset by the VGG model are migrated to this model, whereas the collected vegetable and fruit disease image dataset is randomly divided into training set, validation set, and test set in the ratio of 8:1:1. In addition, data enhancement methods, such as histogram equalization and horizontal flip were used to expand the image data. The performance of the three improved algorithms is compared and analyzed in the same environment and with the classical target detection algorithms YOLOv4, YOLOv3, Faster RCNN, and YOLOv4 tiny. Experiments show that DBA_SSD outperforms the two other improved algorithms, and its performance in comparative analysis is superior to other target detection algorithms.

Significant similarities exist between a spider web and wireless sensor network in terms of topology. Combining the unique advantages of the spider web in nature, such as invulnerability and robustness, with communication technology of a wireless sensor network presents high research value and broad development prospects. In this paper, a sort of a spiral artificial spider web based on 3D printing and its associated vibration testing device is proposed, which is used to study the transmission rule of vibration information of the spider web under given excitation conditions. It provides useful inspiration for establishment of an invulnerable communication rule of wireless sensor network. In order to investigate vibration transmission characteristics of the artificial spider web, vibration images are recorded and analyzed by a high-speed photography system, and vibration intensity is characterized by use of peak-to-peak value. Furthermore, vibration performance of the artificial spider web is studied under conditions of integrity and destruction, respectively. Our test observation reveals the vibration transmission rule of the unique structure of the spider web, providing a novel analysis method for improving invulnerability of the wireless sensor network.

Cabled seafloor observatories play an important role in ocean exploration for its long-term, real-time, and in-situ observation characteristics. In establishing a permanent, reliable, and robust seafloor observatory, a highly reliable cable switching and fault isolation method is essential. After reviewing the advantages and disadvantages of existing switching methods, we propose a novel active switching method for network configuration. Without additional communication path requirements, the switching method provides a way to communicate with a shore station through an existing power transmission path. A coded voltage signal with a distinct sequence is employed as the communication medium to transmit commands. The analysis of the maximum bit frequency of the voltage signals guarantees the accuracy of command recognition. A prototype based on the switching method is built and tested in a laboratory environment, which validated the functionality and reliability of the method.

Aims Metagenomic next-generation sequencing (mNGS) has been utilized for diagnosing infectious diseases. It is a culture-free and hypothesis-free nucleic acid test for diagnosing all pathogens with known genomic sequences, including bacteria, fungi, viruses and parasites. While this technique greatly expands the clinical capacity of pathogen detection, it is a second-line choice due to lengthy procedures and microbial contaminations introduced from wet-lab processes. As a result, we aimed to reduce the hands-on time and exogenous contaminations in mNGS. Methods and results We developed a device (NGSmaster) that automates the wet-lab workflow, including nucleic acid extraction, PCR-free library preparation and purification. It shortens the sample-to-results time to 16 and 18·5 h for DNA and RNA sequencing respectively. We used it to test cultured bacteria for validation of the workflow and bioinformatic pipeline. We also compared PCR-free with PCR-based library prep and discovered no differences in microbial reads. Moreover we analysed results by automation and manual testing and found that automation can significantly reduce microbial contaminations. Finally, we tested artificial and clinical samples and showed mNGS results were concordant with traditional culture. Conclusion NGSmaster can fulfil the microbiological diagnostic needs in a variety of sample types. Significance and impact of the study This study opens up an opportunity of performing in-house mNGS to reduce turnaround time and workload, instead of transferring potentially contagious specimen to a third-party laboratory.

Aiming at solving the problem of poor performance of airborne freestyle skiing balance, this paper presents the research of airborne freestyle skiing balance control based on ant colony algorithm. On the basis of defining the trajectory division of the airborne balance of freestyle skiing and the track of the center of gravity of the human body, a sensor is used to collect the data of the airborne balance of freestyle skiing, and the moving average, denoising, and normalizing processes are done. The training label of the ant colony algorithm is made by the analog signal matrix, the implementation foreground of the key posture frame of freestyle skiing is extracted, the disturbed area in the key posture frame is removed by the clustering algorithm, and the key posture area of freestyle skiing is obtained. The incremental clustering of the data of the key posture area of freestyle skiing is conducted, the incremental posture data mining model of freestyle skiing is established, the mining parameters are input into the mining model, and the incremental data mining is realized by the ant colony algorithm to complete the research on the control of the airborne balance of freestyle skiing. The results show that the proposed method has good reliability, good convergence, and strong response ability.

With the increasing popularity of electric vehicles, the disordered charging of large-scale electric vehicles will have a great impact on the safe operation of regional distribution network. In order to solve the security problems that may occur in the power grid, this paper uses the time-sharing pricing time division method for EV charging to meet the needs of EV users. Based on this method, a multi-objective optimization model is established, which takes the electric vehicle charging capacity and power as the constraints, and based on the minimum user charging cost and the minimum load curve variance. Then, the model is solved by non-dominated sorting genetic algorithm (NSGA -Ⅱ), and the optimal compromise solution is extracted by using fuzzy set theory. Finally, the correctness of the proposed model is verified by the example.

Due to the distributed and dynamic characteristics of the Internet of Vehicles (IoV) and the continuous growth in the number of devices, content-centric decentralized vehicular named data networking (VNDN) has become more suitable for content-oriented applications in IoV. However, the existing centralized architecture is prone to the failure of single points, which results in trust problems in key verification between cross-domain nodes and consuming more power and reducing the lifetime. Focusing on secure key management and power-efficient routing, this article proposes a blockchain-based key management and green routing scheme for VNDN. A blockchain-based key management scheme is presented to achieve secure and efficient distribution and verification of keys. Specifically, all trusted agencies (TAs) form a consortium blockchain for storing public key hashes to ensure the authenticity of users’ public keys. A green global routing scheme based on node relaying pressure (GGNRP) is proposed to save power consumption and reduce the forwarding delay. A new node relay pressure metric is introduced to assist with routing decisions. Detailed experiments and analysis show that, compared with the existing scheme, the proposed scheme can achieve secure key management and GGNRP can decrease the power consumption and average delay by 15.8% and 63.2%, respectively.

Precision instrument, machinery manufacturing equipment needs to use a monitoring system to monitor production details in real time during its production and processing. However, the traditional remote monitoring system has a weak ability to process monitoring images, resulting in a low peak signal-to-noise ratio of the system. The surveillance image is blurry. Therefore, based on digital image processing, a new remote monitoring system for machinery manufacturing equipment is designed. In hardware, the front-end of voltage signal acquisition is redesigned, and the communication circuit is optimized. In software, digital image processing technology is used to sharpen the image fuzzy contour, correct the image texture, and enhance the fuzzy details of the monitoring image; the remote positioning and monitoring function of the system is set to realize the remote monitoring of mechanical manufacturing equipment. Experimental results: Compared with the monitoring system under the traditional design, the peak signal-to-noise ratio of the remote monitoring system designed this time is much higher than that of the traditional monitoring system. Visible digital image processing technology can help the surveillance system to enhance the fuzzy details of the image.

Aiming at the problem of inaccurate fault feature identification in traditional fault feature identification methods of intelligent manufacturing equipment, a virtual instrument-based fault feature identification method of intelligent manufacturing equipment is proposed. Using virtual instrument technology, a data acquisition card for intelligent manufacturing equipment fault data is designed to achieve data acquisition. On this basis, the equipment fault features are extracted by gray level co-occurrence matrix, and the extracted fault features are taken as sample data to construct the feature matrix to achieve the purpose of identifying fault matrix. The experimental results show that the designed fault feature identification method for intelligent manufacturing equipment based on virtual instrument is more complete and accurate, and the sum of squared errors of the identification is lower than other methods, indicating that the reliability of the identification method has been improved.

Space rays can cause serious damage to the vital organs of astronauts. To assess the radiation safety of astronauts in the space environment, the most direct and effective way is to send radiation equivalent phantoms equipped with various radiation sensors into space through the spacecraft, and collect relevant radiation data. The phantom will be subjected to a large impact load during the lift-off of spacecraft, leading the various layers of the phantom to be dislocated or crushed, which will affect the collection of radiation data. Based on the Chengdu Dosimetric Phantom (CDP) and according to the distribution and installation requirements of radiation sensors, the phantom is sliced in multiple layers and connected by a center rod to obtain a model for space radiation dose collection, called Radiation Dosimetric Model (RDM). According to the mechanical parameters of the spacecraft during lift-off, we use Ansys Workbench Software to conduct Statics, Modal and Response Spectrum analysis of the RDM. Under a static load of 6.5g, we calculate the stress and deformation of the layers and the center rod of the RDM, as well as their size and distribution, carry out modal analysis, and calculate the maximum response of the RDM under a given acceleration spectrum with a load peak value of 6.5g. The result shows that under the dynamic and static load of 6.5g, the layers and the center rod will not be dislocated, crushed, etc. This ensures the safety of Radiation Dosimetric Model during the lift-off process, and has important theoretical significance and practical value.

The detection of product defects is essential in quality control in manufacturing. This study surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects of products, such as electronic components, pipes, welded parts, and textile materials, into categories. Second, recent mainstream techniques and deep-learning methods for defects are reviewed with their characteristics, strengths, and shortcomings described. Third, we summarize and analyze the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies used for defect detection, by focusing on three aspects, namely method and experimental results. To further understand the difficulties in the field of defect detection, we investigate the functions and characteristics of existing equipment used for defect detection. The core ideas and codes of studies related to high precision, high positioning, rapid detection, small object, complex background, occluded object detection and object association, are summarized. Lastly, we outline the current achievements and limitations of the existing methods, along with the current research challenges, to assist the research community on defect detection in setting a further agenda for future studies.

The position sensors are necessary for the permanent magnet synchronous motors(PMSM) in order to obtain the position feedback information, while also increasing the integrated system cost and the spatial occupancy, therefore, the research work for new type of position sensorless PMSM become a mainstream. The key technology of the new type motor is to obtain the position information without the position sensor. This paper proposes an improved sliding mode observer(SMO) based control strategy for PMSM. By using the improved SMO, the position and speed information can be estimated and used to form a complete closed-loop control system. Real time simulation of the proposed scheme is performed through a semi-physical simulation platform to verify the effectiveness of the proposed control strategy.

For open circuit fault of permanent magnet brushless DC motor(BLDCM) with trapezoidal back EMF, this paper proposes a half-bridge strategy based fault-tolerant control(FTC) method: by utilizing the current sensors to detect and locate the one-phase open circuit fault, adding a half bridge to the traditional full bridge drive circuit, and connecting it to the neutral point of the motor, so that the BLDCM can realize the fault-tolerant function under open circuit fault condition. The concrete content of this paper are as follows: First, the halfbridge based FTC schematic and theoretical analysis is introduced, then the BLDCM control model under normal and fault conditions are established in Simulink module for the analysis and comparison work, finally experiment work are performed to verify the effectiveness of the proposed control method under open circuit fault condition.

The accuracy of PV output power is essential for the performance improvement of PV electricity station. Facing the fact that the original dataset of PV output power contains a lot number of abnormal data, an identification method is proposed based on kernel density estimation. First, the main factors that can influence the output power of PV system are recognized, i.e. solar radiation and module temperature. And then, the kernel density estimation method is applied to derive the joint distribution function of PV output power, solar irradiance and module temperature. Finally, according to the narrowest interval principal, the rational range of PV output power is given. With the real operational data from a PV power station in Qionghai, Hainan, the superiority of the proposed method identifying the abnormal PV output power in terms of precision, effectiveness and practicability is demonstrated by comparing it with the traditional 3-$\sigma$ method.

This paper designs and provides an internal and external network data interactive access gateway for dual-network isolation architecture. Based on asynchronous communication and advanced queue technology, the external mobile applications can efficiently access the intranet business service data by transforming and reconstructing the transmission content of the entire internal and external network interaction link. It can not only reuse the existing intranet service data, but also ensure data security and reduce the difficulty of development and deployment. The response time is shortened by request concurrent monitoring and advanced queue technology, which can meet the control requirements of the production environment for response time and server pressure during high concurrency. The interaction of TCP long connections between internal and external networks is realized through the sorting and assembly of data packets and the one-to-one matching of internal and external network sessions. At the same time, it also provides security control functions such as service registration and identity authentication to ensure the safety and credibility of the transmitted data.

Based on the periodic maneuvering conditions in the near space, this paper makes an in-depth study of Sine Wave model. When the preset maneuvering angular rate of Sine Wave model is consistent with the real maneuvering angular rate of the target, the tracking accuracy of the target is the highest. But the maneuvering angular rate in the real situation is often unknown or even changing. In this paper, a maneuver detection method based on the framework of Kalman filter is proposed for the variable maneuvering angular rate situation of near space hypersonic vehicles during the cruise segment. At the same time, an adaptive adjustment method of filter parameters and model parameters is given. Finally, the simulation proves the effectiveness of the improve model.

Considering the low accuracy of existing salient object detection algorithms in addressing boundary objects problem, this paper proposes a novel algorithm which detects salient object through Sparse Reconstruction on Robust Dictionary. Firstly, the prior information of the salient objects connected with image boundary is statistically analyzed based on public datasets. Secondly, the evaluation and optimization of dictionary elements are performed to construct robust dictionary via removing low background boundary superpixels and adding high background internal superpixels. saliency maps are generated according to the sparse reconstruction errors on the new dictionary. Finally, integration of multi-level saliency maps is achieved by means of linear weighted fusion strategy with learned weight coefficients. Extensive experimental results demonstrate that the proposed algorithm is superior to the state-of-the-art models on benchmark datasets.

Cross-domain text classification has broad application prospects in the field of data mining. Since transfer learning can help target domain data to achieve the sharing and transfer of semantic information with the help of existing knowledge domains, transfer learning are generally used to achieve cross-domain text processing. Based on this, we propose a cross-domain text classification algorithm -MTrA. The algorithm is based on TrAdaBoost, taking into account the distribution differences between the source domain and the target domain. It uses the Maximum Mean Discrepancy(MMD) as the initial weight parameter of the two domain. MTrA adds a weight backfill factor that considers the accuracy of the source domain classification and balances the weight update method of the source domain data. Through the verification in the dataset 20 Newsgroups, Compared with the traditional TrAdaBoost algorithm, it improves the classification accuracy by 9.4% on average. it proves the effectiveness and advantages of the algorithm.

Rolling technique process needs powerful technical support and control level of rolling has been turning point at present. The key to some questions has not been solved thoroughly by the limitation based on conventionalcontrol theory. It is very necessary to realize forward development in control performance by introducing new control theory and method. It is always primary object to improve gauge control accuracy in hot strip mills. Limitation based on conventional PID is emerged when the controlled system is complex, nonlinear and time-variety. A new control strategy based on RBF-PID is introduced and has been applied in high nonlinear, time - varying hydraulic MN- AGC of 1700ASP The practical results have shown that the controller can improve the precision of the product gauge and has good performance.

In recent years, the expressway traffic accidents caused by mass of fog have occurred from time to time, which have severely affected lives, property and traffic. This article proposes a mass of fog monitoring robot that can run on the highway guardrail, including its mechanical structure design and control system design, to realize the monitoring, traffic warning and traffic control of the fog on the expressway, thereby reducing the occurrence of such traffic accidents. The robot can be directly suspended on the guardrail. The limit wheel, driving wheel and driven wheel are close to the upper trough of the guardrail, the directional guide wheel is close to the lower crest of the guardrail, and a retractable HD camera is installed on the top. Using ESP32 microcontroller as the controller, the control system includes graphic processing module, Beidou communication module, motor drive module, power supply module and obstacle avoidance module. The test prototype based on LEGO components has good operating conditions, the robot runs at a speed of 5km / h, and the power storage mileage can reach 30 km.

A large number of distributed generators (DG) has been integrated into the distribution network, which has changed its flow and brought challenges to the existing dispatch methods. To cope with the fluctuating power caused by these DG in the distribution network, a multi-time scale active and reactive power coordinated dispatch method is proposed in this paper. Firstly, based on the prediction of renewable energy output in the next 24 hours, the operational output of DGs and energy storage (ES) is optimized in the day-ahead stage. Then, combining the latest prediction data and the reference value from the day-ahead stage, the intra-day stage is conducted every 4 hours. Thus, the operational cost is minimized, and the power fluctuation caused by DG to the main grid is reduced. Finally, simulation results verified the effectiveness of this method.

Identifying malicious code families is a prerequisite for malicious code prevention which has become a research hotspot. Aiming at the confusion of malicious code family, a malicious code detection and classification method based on Kalman filtering is proposed. The classification accuracy after salinization reached 93.5%, experimental proof that the algorithm has high accuracy and robustness.

The coordination of the spatial distribution of residential areas and service industries has positive significance for optimizing urban functional zoning and facilitating urban residents’ work and life. Based on the POI data of Jinan City in 2018, this paper uses the GIS spatial analysis method to comprehensively analyze the current layout and associated characteristics of residential areas and different types of service industries. The results show that the high-kernel density areas of various service industries in Jinan are mainly distributed in the central urban area, including Lixia District, Shizhong District, Tianqiao District and Huaiyin District. The kernel density values of service industries in surrounding counties are low; The distribution of various service industries in Jinan has a strong positive spatial correlation, and the clustering results in the central urban area show high values and high values are adjacent; The distribution of residential areas and service industries in the downtown area of Jinan has a strong spatial correlation. The distribution of residential areas and service industries in surrounding counties is relatively discrete, and the spatial correlation is significantly lower than in the central urban area.

Based on the current situation of digital map resource interconnection and mutual inspection, this paper studies three modes of unified retrieval of traditional guided data sources, and proposes a digital map resource retrieval model based on Web Services. This paper also designs digital map resources unified retrieval result fusion algorithm and metadata update algorithm based on Web Services in detail, which can be used for the development of digital map resource unified retrieval system.

In earth observation systems, especially in the detection of small and weak targets, the detection and recognition of long-distance infrared targets plays a vital role in the military and civil fields. However, there are a large number of high radiation areas on the earth’s surface, in which cirrus clouds, as high radiation areas or abnormal objects, will interfere with the military early warning system. In order to improve the performance of the system and the accuracy of small target detection, the method proposed in this paper uses the suppression of the cirrus cloud as an auxiliary means of small target detection. An infrared image was modeled and decomposed into thin parts such as the cirrus cloud, noise and clutter, and low-order background parts. In order to describe the cirrus cloud more accurately, robust principal component analysis (RPCA) was used to get the sparse components of the cirrus cloud, and only the sparse components of infrared image were studied. The texture of the cirrus cloud was found to have fractal characteristics, and a random fractal based infrared image signal component dictionary was constructed. The k-cluster singular value decomposition (KSVD) dictionary was used to train the sparse representation of sparse components to detect cirrus clouds. Through the simulation test, it was found that the algorithm proposed in this paper performed better on the the receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve, had higher accuracy rate under the same recall rate, and its F-measure value and Intersection-over-Union (IOU) value were greater than other algorithms, which shows that it has better detection effect.

Chinese word segmentation (CWS) is an important basic task for NLP. However, the word segmentation model trained by the generic domain corpus has a significant decline in performance in the word segmentation task oriented to the specific domain. Aiming at the features of domain segmentation, this paper using domain corpus as the training samples, and proposed combined with the terminology dictionary, new word detection and Bi-LSTM-CRF segmentation method to improve the problem of out-of-vocabulary (OOV). The word segmentation experiment was carried out on the corpus of the automotive domain. The results show that the precision and recall of the word segmentation have reached 0.95, and the value of F1 also achieved 0.95, and they are better than state-of-the-art method. This method can also be combined with N-gram and chi-square statistic to further improve the recognition accuracy of OOV.

Due to the volatility of natural resources, the power fluctuations of photovoltaic (PV) generation have serious negative impacts on the power quality. This paper reports a novel approach to resolve this problem in a combined cascade hydro-PV-pumped storage (CH-PV-PS) generation system through appropriate power distribution on a small time scale. According to the characteristics of power generation systems and multi-constraints, how to obtain the optimal smoothing effects with the small cost is a key challenge. For this purpose, wavelet packet decomposition is modified by constructing cost function and ideal power trajectory sequences to obtain a new adaptive power distribution method for the CH-PV-PS generation system considering the boundary conditions of the system in this paper. At the same time, to meet the real-time control requirements of actual systems, an additive function is presented to improve the optimization speed of the adaptive power distribution method. In the meantime, a fuzzy controller is designed to optimize the inevitable delay time of the power scheduling system, and the starting threshold is set to avoid the frequently mode conversion of pumped storage. Finally, the performance of the method is evaluated based on the PV station data in Xiaojin County, Sichuan Province, China. Simulation results based on Matlab-simulink indicate that the proposed method can effectively suppress PV power fluctuations and ensure normal operation of the CH-PV-PS generation system within the multiple constraints.

Vehicle tracking using automotive radar is an essential part of autonomous driving. With the resolution of radar increasing, a vehicle generates multiple measurements per time and should be considered as an extended target. By using multiple radar measurements, the tracking algorithm can estimate the kinematic state and extension of vehicle. In this paper, we propose a method to track the vehicle in clutter environment by using extended vehicle model and probabilistic data association with Markov chain Monte Carlo technique. Simulation results show the effectiveness and performance our approach for vehicle tracking under clutter environment.

In this paper, we design a V-BLAST MIMO-OFDM communication system with high capacity and high spectrum utilization rate. We exploit a non-feedback pipelined architecture for a 2×2 V-BLAST MIMO-OFDM and implement our system only by Analogs AD9361 RF Agile Transceiver and Xilinxs ZC706 FPGA development kits, which makes our system more compact and efficient. The experiment results illustrates a 150Mbps data transmission with 64QAM and 40MHz bandwidth by using our system.