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The coverage tasks of mobile robots are evolving towards large-scale and intelligent di- rections demanding urgent requirements for the coverage efficiency and environmental adaptabil- ity of coverage path planning. To address the inadequate adaptability of the traditional boustro- phedon cellular decomposition in complex maps and to improve coverage efficiency, a complete coverage path planning method is proposed. Firstly, based on the boustrophedon cellular decom- position method. a strategy of area-descending traversal and monotone polygon judgment was proposed to merge cells, reducing approximately half of the cell quantity. Finally, by establis- hing the mapping relationship between the starting and ending positions of cells, a genetic algo- rithm was employed to optimize the selection of cells' starting positions and the global access or- der. The research results show that: 1) when processing maps with dimensions of 1 300 pixels. the algorithm in this paper can obtain calculation results within 10 s, and compared with the boustrophedon method, neural network method, and contour line method, the growth rate of calculation time is smaller with the increase of map area: 2) compared with the boustrophedon method, contour line method, neural network method, and energy-optimal method, the total op- eration time of robots in this paper is reduced by 5.4% to 47.0%, and the ineffective operation time is reduced by 5.8% to 29.2%; 3) the average coverage rate of this algorithm on 1 800 test maps reaches 99,91%; 4) the tests further confirm the significant coverage efficiency advantages of the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

In order to further solve the optimization problem of reconnaissance constellation and strengthen the construction of space-based reconnaissance system, the current optimization methods of reconnaissance constellation are studied and summarized. The design and optimization of reconnaissance constellation has the characteristics of multi-parameter, multi-objective, nonlinear and discontinuous, which is a typical multi-objective optimization problem. Multi-objective evolutionary algorithm is a kind of biological intelligence algorithm based on population, which carries out probabilistic search in decision space according to predetermined heuristic rules. It does not need continuity, differentiability and other conditions of the problem, and can get a group of solution sets close to Pareto frontier within a limited number of search times. It can effectively solve the multi-objective optimization problem and has been widely used in the multi-objective optimization of reconnaissance constellation. This paper discusses the construction of the reconnaissance constellation optimization model and the classification, advantages and disadvantages of the multi-objective evolutionary algorithm, discusses the application of the multi-objective evolutionary algorithm in the reconnaissance constellation optimization, and points out the development direction of the reconnaissance constellation optimization based on the multi-objective evolutionary algorithm. [ABSTRACT FROM AUTHOR]

The compression strategy plays an important role in the performance of IoT time series data storage system. However, the current compression strategies can not adapt to the characteristics of NVM and IoT time series data. This paper proposes a polymorphic cooperative compression strategy for IoT time-series data based on NVM. Firstly, the overall structure of IoT time series data is given. Then, to address the consistent patterns in IoT time series data and the different granularity between user-space and kernel-space operations on NVM, a dual-compression strategy is devised. Initially, a lightweight compression method is applied directly as IoT time series data is received in user-space. This method efficiently reduces the volume of data for storage, while minimizing the impact on the timeliness of data storage. Moreover, a deep compression algorithm is designed for the kernel-space, primarily focusing on querying and analyzing anomalous time series data. Additionally, to address the competition for NVM bandwidth between deep compression and data storage, a dynamic adjustment algorithm that guarantees write bandwidth is proposed. Finally, a prototype of the polymorphic cooperative compression strategy is implemented and YCSB-TS is used to evaluate the results. The results show that the proposed method can effectively improve the write throughput of IoT time-series data by up to 161.3% and reduce the storage space by up to 14.6%, compared with InfluxDB, OpenTSDB, KairosDB and TVStore. [ABSTRACT FROM AUTHOR]

Aiming at the specific target search and controllable angle guidance of unmanned surface ships in uncertain waters, this paper explores the target detection method based on YOLO and the guidance control method based on side angle constraint. On the basis of analyzing the uncertain conditions such as sea fog, the paper designs the correlation filter with im- proved YOLOv5 algorithm to extract the target features, combines depth information to realize the detection and positioning of water surface targets, sliding mode variable structure guidance control to achieve the high maneuverability target side angle constraint guidance. TCP/IP combined with ROS is used to create a comer constrained terminal guidance system for information processing. Two-dimensional simulation experiment is completed with Autolabor platform, and the result of target guidance error is 1.54%, which proves the effectiveness of the method proposed. [ABSTRACT FROM AUTHOR]

Aiming at the problem of tracking skip-glide targets using radar, this paper proposes an Unscented Kalman Filter (UKF) tracking algorithm based on the improved Jerk model. Aiming at the estimation error introduced by the artificial preset acceleration variance and Jerk frequency of the conventional Jerk algorithm, this method calculates the acceleration variance adaptively through the current position estimation value and the current position one-step prediction value, and correlates the jerk frequency with the acceleration variance, realizing the adaptive adjustment of the model parameters while estimating the target motion state. At the same time, the improved Jerk model is combined with UKF algorithm, and the overall algorithm flow is given, and simulation experiments are carried out. The simulation results show that compared with the conventional Jerk model algorithm, the method proposed in this paper realizes the adaptive adjustment of model parameters, making the tracking process more adaptable to the maneuvering characteristics of the target. [ABSTRACT FROM AUTHOR]

In the evolution of the Internet from Web 1.0 to Web 3.0, algorithms have played an irreplaceable role, enabling information technology to functionally realize the transmission of one-way content to decentralized interaction. Algorithms have a subtle impact on people's lives, work, and learning, and this impact is so great that it has produced the idea of algorithmic society and algorithmic era. Algorithms belong to the basic category of digital law and are an important tool for digital rule of law. In existing research, scholars are more concerned about how to regulate algorithms, and less concerned about the legal nature of algorithms. There has been a brief discussion on this issue. However, how the legal nature of algorithms should be defined is still inconclusive. This article aims to answer this question. To this end, this paper sorts out the existing theoretical schemes of the legal nature of algorithms and their interrelationships, including speech, due process, patents, and trade secrets, and analyzes the sources and arguments of these theoretical schemes. There are internal and external reasons why the legal nature of algorithms has formed many disputes. These reasons are also difficult to define the legal nature of algorithms, including: the algorithm ontology and carrier are easy to confuse, the algorithm and the program are indiscriminate, the algorithm and data, the result mix, and the limitation of governance rules. When seeking justification for various theoretical claims, one logical premise is easily overlooked, that is, the criteria for determining the legal nature of algorithms. This paper puts forward these standards, namely the principle of legality, the principle of respecting the fruits of labor, the principle of maintaining public safety, the principle of promoting innovation, and the principle of maximizing social benefits. On this basis, this paper examines various theoretical schemes, and finally proves that the legal nature of the algorithm is a trade secret, not something else. In terms of methodology, this paper uses the method of identification analysis to discuss various scenarios one by one. Compared with previous literature, this paper makes contributions in the following aspects: first, this paper sorts out the existing debates on the legal nature of algorithms in detail, grasps the arguments of these theoretical schemes, and specifically examines them. Second, this paper analyzes the reasons for many disputes over the legal nature of algorithms. The widespread use of algorithms has also caused heated debates about algorithms. Algorithms have been in the field of legal science for a long time, but there are frequent misunderstandings about how their legal nature should be defined, and the reasons behind them deserve careful analysis. Third, this paper clarifies the criteria for defining the legal nature of algorithms, and argues that algorithms are trade secrets on this basis. Algorithms cannot be called speech, nor are they due process. The argument that algorithms are patents is also untenable, and although algorithms have the possibility of patentability in the current legal system, algorithm patenting is not a good solution. [ABSTRACT FROM AUTHOR]

The existing YOLO target detection algorithm, based on one-stage concept, aims for multi-objective detection. But this algorithm performs not well for dual classification detection and add more performance overhead while detection. In order to improve the detection efficiency of dual classification mask wearing during the period of COVID-19, this paper proposes a real-time detection method based on YOLO for detecting the condition of bi-objective mask wearing. The optimization is based on the latest YOLO version 4: firstly, the feedforward input layer of the model is improved to optimize the data augment part and the adaptive image scaling is added to improve the efficiency of the detection for both dual classification and small objectives; secondly, the adaptive anchoring frame is added to replace the activation function so as to reduce the computation and thus improve its efficiency; thirdly, the optimization of Neck and the addition of Focus structure improve the capability of feature fusion and reduce the amount of parameters to raise the efficiency. The experimental results showed that, compared with the existing YOLO version 4, using this method in this paper improves F1 by 0.33% and mAp by 0.71% in the data set. Also, the detection efficiency is also improved significantly under the same experimental condition. [ABSTRACT FROM AUTHOR]

With the continuous growth of vehicle ownership and the popularization of Internet of Vehicles applications， vehicle terminals generate large amounts of data messages that need to be processed in real time. In the scene of high-speed vehicle movement， the traditional Internet of Vehicles navigation system has a transmission delay due to vehicle differential positioning data， which results in a certain deviation in vehicle positioning results and cannot obtain high-precision positioning results in time. Based on this， this paper proposes a technology solution for car networking navigation based on BDS positioning and edge computing. It uses an improved genetic algorithm to allocate resources for terminal positioning requests， effectively reduces the service delay of the entire edge network， and uses optimize the lossless Kalman filter algorithm based on edge node to improve the positioning accuracy of the car network node. Experiments show that the method proposed in this paper can provide real-time accurate， low-latency and high-precision positioning services for large-scale Internet of Vehicles terminals， and has high practical application value. [ABSTRACT FROM AUTHOR]

The complexity of modern war is increasing，and the rapid operational mission planning is of great importance to improve the efficiency of command and control. This paper presents a joint operational Task Matrix （TM） model，which is a theoretical method for mission planning; A belief network model is put forward to describe the relationship among the elements in TM model ;A naive bayesian learning method for belief network is designed; A mechanism of imagination is put forward to speed up the learning process ; A search algorithm named Deep Minimum Threat Generation Tree（DMTGT） is proposed, which can efficiently calculate task priority by balancing search error and search speed. Finally, the validity of above models and algorithms is verified by simulation experiments. [ABSTRACT FROM AUTHOR]

The materials supply for sea islands is the basis for ensuring survival needs of persons on sea islands. Considering factors such as transportation cost，storage cost，recycling demand and transportation capacity, it is a vehicle path planning problem with time window and capacity limit to use many transportation tools to supply materials for sea islands within a time cycle. The intelligent algorithm provides a preferable solution to solve this complex irregular optimization problem. The paper establishes a mathematical model of mission planning of materials supply for sea islands, explores the solving process of genetic algorithm， and uses an example to calculate it. The optimization result shows that the scheme can save transportation costs and storage costs， and also meet the need of materials supply cycle，which can provide a reference for scientific mission planning of materials supply for sea islands. [ABSTRACT FROM AUTHOR]

Mission Planning of Border Control operations refers to the process in which the relevant forces of border control make use of the Mission Planning system to plan and design relevant actions in order to achieve a specific action destination, so as to form a relatively scientific and reasonable action plan. In this paper, strategic analysis is carried out from the subject object, system composition and hierarchy of border control action task planning, and based on the transmission algorithm, the basic process of border control action task planning is discussed. [ABSTRACT FROM AUTHOR]

In the era of the digitaleconomic, computing power has become an important indicator to measure the degree of national economic development. With analyzing the two national strategies of "Channel Computing Resources from the East to the West" progect and "Construction of Chengdu-Chongqing Economic Circle", and based on the construction of Chengdu-Chongqing Hub Node, the reasons that why Chengdu-Chongqing hub node has been laid out as one of the eastern hub nodes were analyzed. It due to four aspects, namely, strategic location advantage of computing, data integration system guarantee, computing infrastructure compaction, and maturity of computing application scenarios. Further, the operations in the advanced data center of Western (Chongqing) Science City demonstrate that the nationalcomputing hub built in Chengdu-Chongqing economic circle boosts the project that channels computing resources from the east to the west. Then the paper discussed the coordinated development between Tianfu data center cluster and Chongqing data center cluster, as well as the coordinated development between national computing hub built in Chengdu-Chongqing economic circle and national computing hub built in Guizhou province. It suggested that Tianfu data center cluster and Chongqing data center cluster should build coordination mechanisms from six aspects, including the network, data, computing power, industry, algorithm and energy. The neighboring national computing hubs can implement coordination based on the allocation of computing power business, the cross-domain circulation of data elements, and new business formats for digital cooperation. [ABSTRACT FROM AUTHOR]

In order to study the waveform selection of radar seeker in the terminal guidance and tracking phase, this paper proposes a waveform selection algorithm in the guidance and tracking process of cognitive radar seeker. Firstly, we establish the processing flow of radar seeker waveform selection by combining the seeker guidance and observation filtering models; Secondly, we use the interactive multiple model unscented Kalman filter (IMMUKF) algorithm to solve the nonlinear tracking problem. We minimize tracking error by searching for waveform parameters based on the maximum mutual information criterion. The simulation results show that the waveform selection algorithm can effectively improve the seeker tracking accuracy and provide stable guidance overload. [ABSTRACT FROM AUTHOR]

Starlink is the only giant constellation in low Earth orbit currently being rapidly deployed. This paper analyzes the advantages and characteristics of low orbit satellite communication and summarizes Starlink construction and system architecture. The Satellite Toolkit (STK) is used to model and simulate 1 584 satellites of the first stage " Group 1". Then, according to the networking mode of Starlink satellite equipped with laser communication payload, Dijkstra algorithm is used to solve the shortest inter-satellite communication link distance between any two Starlink satellites. Thus, the theoretical shortest communication delay is calculated. The average communication delay from Hawaii Camp Smith to the Yokota. Air Base is 42. 700 7492 ms. Based on the simulation results, the military application of Starlink in communication is predicted. [ABSTRACT FROM AUTHOR]

Aiming at the low accuracy and time-consuming problems of AKAZE algorithm in image matching of glass-encapsulated electrical connectors, an improved image matching algorithm based on LK optical flow and grid motion statistics is proposed in this paper. Firstly, the AKAZE algorithm is used to extract the feature points of the glass-encapsulated electrical connector, and the M-LDB descriptor is used for the feature description. Then, LK optical flow method is used to calculate the matching area for conditional constraints, so as to obtain matching points, and the FLANN algorithm is used for feature matching. Finally, the image of the glass-encapsulated electrical connector is divided into multiple grids, and the number of correct matching points in the neighborhood of the well-matched feature points by FLANN is calculated and the threshold value is used to eliminate the incorrect matching points. The experiments in this paper use public data sets and glass-encapsulated electrical connector data to verify and analyze the performance of the algorithm in terms of real-time and accuracy. The results show that the improved algorithm has a matching accuracy of more than 93% when dealing with image pairs of glass-encapsulated electrical connectors with changes in blur, brightness and rotation, and takes less than 0.4 s, which proves the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Aiming at many problems caused by manual acceptance in decoration projects, this paper proposes an improved SSD algorithm and applies it to supervision work to replace manual acceptance and promote the realization of intelligent supervision. Since the SSD algorithm has problems such as re-examination of the same target and poor detection of small targets, the DPN network is used to replace the basic feature extraction network VGG16 in this paper. DPN combines the advantages of Resnet and Densenet and has better feature extraction ability. The feature maps are fused by weighted FPN to highlight the contributions of feature maps of different layers and enrich the semantics of feature maps for prediction. The use of depthwise separable convolution reduces the number of parameters of the model and improves the inference speed of the algorithm. The experimental comparison shows that the average accuracy of the improved model is increased by 3.47%, and the average accuracy of small number detection is improved by up to 15%, which proves that the new model has a good effect in the task of supervision target detection. [ABSTRACT FROM AUTHOR]

In maritime operations, the use of firepower is one of the important issues studied in the decision-making stage of command and control. The core issue of the use of firepower in maritime operations is to reasonably formulate the matching scheme between maritime combat forces, weapons and enemy targets within the effective time limit, so as to optimize the ground preparation efficiency of maritime formation and minimize our losses. This paper analyzes and summarizes the current situation of the development of firepower application methods in maritime operations, studies the current main firepower application methods and puts forward some enlightenment and suggestions on the firepower application methods in future maritime operations. [ABSTRACT FROM AUTHOR]

Aimed at the problems such as slow convergence speed, low accuracy and easy to fall into local optimality, an improved sparrow search algorithm based on multiple strategies（ISSAMS） is proposed in this paper. Circle chaotic mapping was introduced to initialize the population, increase the diversity of the population, and improve the global search ability. Using sine and cosine search strategy was used to update the location of the finder, select the best location, enhance the local search ability, and avoid falling into the global optimization. Firefly disturbance was added to update the optimal individual position, search for feasible solutions, and improve the local search ability and search speed. In order to verify the effectiveness of the improved algorithm, five reference functions were selected for simulation experiments, among which three functions were single-peak functions and two functions were multi-peak functions, and compared with genetic algorithm, grey wolf algorithm, particle swarm optimization algorithm and sparrow search algorithm. The simulation results show that the improved sparrow search algorithm based on multiple strategies has the ability to jump out of the local optimal solution, the convergence speed is faster, the accuracy is higher, and the overall performance is better than the other four algorithms. By applying this improvement to the threshold and weight of the optimized BP neural network, the error of the BP model optimized by no improved sparrow search algorithm is reduced by 14. 73%. Compared with the BP model optimized by the other three algorithms, the average absolute percentage error of the optimized model based on the multi-strategy improved sparrow search algorithm is the lowest and has the best effect. The effectiveness of the improved algorithm is further verified. [ABSTRACT FROM AUTHOR]

For the scheduling algorithm applied in CAN FD network, when the earliest deadline algorithm of average partition coding method performs non-preemptively scheduling on messages, it will have the problems of limited encoding ability for a large range of deadlines, high probability of priority reversal of messages and high bus load. By analyzing various causes that cause of message transmission delay and combined with the shortcomings of previous partition scheduling algorithms, this paper proposes an improved method of message scheduling based on the earliest deadline first algorithm based on exponential – power function partition, that is, on the basis of exponential partition of message deadline, power function partition subdivision is further adopted. At the same time, the schedulability of the algorithm is analyzed and verified by simulation with CANoe. Experiments show that compared with the existing average partition scheduling algorithm, the improved algorithm expands the representation range of deadline, reduces bus load, optimizes priority inversion problem, and achieves better scheduling effect. [ABSTRACT FROM AUTHOR]

In view of the problems that node clock drift， network link delay and poor synchronization ability in Ethernet， this paper constructs a clock synchronization system model based on the communication principle of RTEthernet protocol. By studying the composition and working principle of RTEthernet， three factors affecting the time synchronization precision of real-time Ethernet-drift rate， network transmission delay and clock Byzantine fault are considered. Based on this， the original FTA clock synchronization algorithm is analyzed， and it is found that its fault tolerance performance is obviously reduced when Byzantine faults increase. The idea of "median fault tolerance" is introduced to improve it and RTE-FTM algorithm is proposed. Through the CANoe simulation platform， two Byzantine faults and no Byzantine faults in the system （seven nodes） are compared and analyzed， and the precision loss rate of the system is reduced by 3.1%， which verifies the convergence and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Aiming at low accuracy of recognition in complex leakage images of subway tunnels, this paper studied the application of image recognition algorithms in tunnel water-leakage disease, was using two kinds of neural networks: U-Net and PSP-Net. Based on the subway tunnel images, tunnel leakage disease sample data set was constructed, where 5000 subway tunnel disease image data and 4000 labeled images were included. This data set could be used for deep neural convolution network training. U-Net and PSP-Net deep learning frameworks were established, and the neural networks were trained by self-made data set. The accuracy of the prediction results from U-Net and PSP-Net was evaluated, and the performance of the two networks in the application of leakage disease identification was compared. U-Net had stronger coverage for the disease and quick detection, while PSP-net had higher accuracy fit to fine detection. [ABSTRACT FROM AUTHOR]

Aiming at the problem of scale transformation or occlusion in the process of visual object tracking, an anti-occlusion fast correlation filtering object tracking algorithm is proposed in this paper, which is mainly composed of three parts: position filter, fast scale filter and Kalman filter. Firstly, the position filter and fast scale filter were initialized. Secondly, during tracking, the average-peak to correlation energy（APCE） occlusion criterion was used to judge whether the target was occluded. If there was no occlusion, the target position was estimated by position, scale filter and feature. When the target was occluded, the current position of target occlusion was estimated by Kalman filter instead of the estimated value of position filter. Finally, the correlation tracking results were calculated. The experimental results show that when tested on the CVPR 2013 benchmark public data set, the algorithm is compared with the mainstream algorithm, the accuracy of video sequences dealing with occlusion and scale transformation attributes is improved by 0.047, the success rate is improved by 0.067, the center position error is improved by 21.128 pixels, and the distance accuracy is improved by 0.153. It can better solve the problems of occlusion and scale change, and has good tracking speed. [ABSTRACT FROM AUTHOR]

In order to improve the positioning accuracy of the automatic dropping cloth vehicle, the problem of increasing error and decreasing positioning accuracy in the strongly nonlinear system is solved by the extended Kalman filter simultaneous localization and mapping（SLAM） algorithm. In this paper, an integrated muti-innovation extended Kalman filter simultaneous localization and mapping（IMI-EKF-SLAM） algorithm was proposed, which enabled the automatic dropping cloth vehicle localization system to change from a single update to multiple iterations in the process of state update, and explored the iteration value n and the new interest dimension p. The simulation and experimental results show that the improved algorithm reduces the state estimation error and the IMI-EKF-SLAM algorithm locates best when the iteration value n is 2 and the new interest dimension p is 3. [ABSTRACT FROM AUTHOR]

The optimization performance of the PSO algorithm is affected by the speed update formula. Too fast convergence speed may cause the algorithm to miss the global optimal solution; too slow convergence speed may cause the algorithm to fall into the local optimal solution. Aiming at this problem, this paper proposes a PSO optimization algorithm based on improved compression factor, namely FPSO. By introducing the compression factor equation, the speed iteration formula is improved, and the influence on the algorithm caused by the improper setting of the learning factor is reduced. The new adjustment mechanism not only ensures the convergence performance of the PSO algorithm, but also weakens the influence of the speed boundary on the algorithm. Finally, five classical functions are selected to test the performance of the algorithm. The test results show that, compared with the traditional PSO algorithm, the proposed algorithm improves the global convergence ability and shortens the convergence time. [ABSTRACT FROM AUTHOR]

The well inclination and azimuth are the main measurement parameters in the calculation of the wellbore trajectory. However, compared with the azimuth error of the conventional well inclination, the azimuth angle measurement error of the inclinometer under the small angle inclination is larger. In order to improve the azimuth angle measurement accuracy of the inclinometer under the small-angle well inclination, the azimuth angle measurement under the 5°～10° small-angle well inclination was compensated based on the BP neural network algorithm. In this paper, a two-dimensional vector composed of standard well inclination and measured azimuth is used as input, and a one-dimensional vector composed of standard azimuth is used as output, and a network model of double input and single output is established. The learning samples are divided into training set and test set by random selection, so that the network has better generalization ability. The simulation test results show that the BP neural network error correction model runs stably and the compensation accuracy reaches 10 -6, which can improve the measurement accuracy of the azimuth angle under the small-angle well inclination from ±5.3° to within ±1.7°. [ABSTRACT FROM AUTHOR]

The accurate passive moving target localization is one of the key technologies for the safety and rescue of the people in a complex underground space, such as tunnels, and mine roadways. In this paper, the method based on TOA/ DOA parameter estimation for high resolution passive mobile target localization is proposed. Because of the echo-multipath of still object and the serious fading in tunnel, the strong minimum norm interference suppression method is proposed based on the zero limit of eigenvalue decomposition under the condition of low SNR. This suppresses the direct wave and the echo-multipath interference of still object and detects the reflected wave of moving targets. Meanwhile, the RMCHS (Root Min-norm based on Cross High-order Spectra) algorithm is proposed to overcome nanosecond dense multipath and background noise in the tunnel. The RMCHS algorithm can be used to estimate the TOA/ DOA parameters with high resolution at low SNR, so the high resolution passive moving target localization based on TOA/ DOA parameter estimation is achieved. Simulation results show that the proposed strong interference suppression method and the RMCHS algorithm have strong robustness in tunnel. The proposed method is significantly better in comparison with the traditional algorithm. The influence of noise and nanosecond dense multipath on the performance of the algorithm is reduced. [ABSTRACT FROM AUTHOR]