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The actuator controller is the key component of the active vibration control system. With the rapid development of microelectronics and power electronics in recent years, the control type actuator controller technology based on high-frequency pulse width modulation (PWM) pulse signal has become a research hotspot. At present, the actuator controller generally adopts the classic PID as the control strategy, but this classic PID control strategy is more suitable for controlling fixed loads and has poor real-time performance for dynamic load control.In response to this issue, this article improves the design of the control algorithm and proposes an actuator controller based on frequency feedforward parameter compensation PID control strategy. This strategy improves the real-time and dynamic load response capabilities of the control, with good current waveform following and low distortion. It can effectively improve and enhance the control effect, providing new ideas for the design of subsequent actuator controllers.

This paper presents a neural network-based feedback control method for enhancing the control precision and tracking speed of a permanent magnet brushless motor under command control. The proposed method involves real-time adjustment of the PID controller parameters using electromechanical output signals, enabling adaptive feedback control based on motor output. Experimental results demonstrate that this approach enhances real-time performance and dynamic load response capability, resulting in a current waveform with excellent tracking and low distortion. Overall, this method effectively improves and enhances control effectiveness. Furthermore, the developed control method is successfully applied to the development of tangible products.

In compressed sensing (CS), coherence is a simple and practical measure of the quality of a sensing matrix. The smaller the coherence of the sensing matrix, the better the reconstruction result. Most strategies for optimizing the coherence of a sensing matrix are only applicable to gray-value matrices and are not to binary matrices with fast computation speed and smaller storage space. Under a certain condition, the coherence can be improved by weighting the sensing matrix to make its condition number equal to 1, i.e., all non-zero singular values become the same. In this paper, we propose a method for reweighting CS models to reduce the condition number of the sensing matrix so as to improve its coherence, prove that the condition number decreases monotonically to 1 as the weighting times approaches infinity, and then obtain a coherence improvement model equivalent to the original CS model. Finally, we give the RwOMP recovery algorithm based on the proposed reweighted method and verify its superiority by using different binary sensing matrices for CS experiments.

Ensuring railway safety is a top priority, with a central focus on preventing accidents. By thoroughly analyzing data from railway accident investigations, we can pinpoint factors and patterns associated with different types of railway accidents. This proactive approach not only helps reduce the frequency of such incidents but also significantly boosts overall railway transportation safety. This paper investigates the impact of various risk factors on railway safety through the analysis of railway accidents by using data-driven Bayesian networks. First, key data representing the frequency of risk factors directly derived from railway accident reports are collected and analyzed. Then, the risk factors are incorporated into causal analysis for different types of railway accidents. Finally, a historical data-driven approach is utilized to model and gain new insights into the key risk factors causing different types of railway accidents. Meanwhile, a Tree-Augmented Naive Bayes (TAN) is employed to construct a model of interdependencies among risk factors, and the model is validated through sensitivity analysis and past accident records. The research findings demonstrate that the crucial risk factors for all types of accidents include undetected track damage, train operator skills, load, braking system conditions, train speed, traction system failures, level crossings, and bridge damage. Additionally, the research results highlight the differential impact of key factors on different types of accidents, providing a most probable explanation for observing the most likely configurations in the model for a specific scenario. This work contributes to accident prevention and safety decision-making.

With the rapid development of information technology, efficient multi label classification of massive data is one of the important tasks of big data systems. Semi supervised learning algorithm is an effective data classification method, currently mainly applied to the classification of single label data. This article proposes a multi label dynamic semi supervised learning algorithm based on dual selection criteria. The algorithm mainly establishes dual selection criteria for multi label pseudo labeled samples based on the COIN structure and K-nearest neighbor algorithm. A novel pseudo labeled sample selection method is designed, which improves the robustness and accuracy of the algorithm and effectively solves the problem of not considering sample correlation when selecting pseudo labeled samples. On this basis, by adding a performance evaluation mechanism to the model, the model can dynamically and adaptively extract pseudo labeled samples, improving the training speed and accuracy of the model. This article selected four convincing public test datasets for experiments, and the experimental results showed that the proposed semi supervised learning method has improved in multiple indicators such as robustness, accuracy, and training efficiency compared to current mainstream methods.

With the widespread application of image recognition technology, the commercial application value of image denoising is gradually increasing. To optimize the performance of non local mean image denoising algorithms, the similarity of image blocks in this algorithm is now integrated into the evaluation calculation steps of image block direction similarity and geometric information similarity. A neural network structure is used to optimize the mapping function between image blocks, assisting the NLM algorithm in finding the similarity of image blocks. The denoising experimental results for the improved algorithm show that when the noise variance is 25, the normalized peak signal-to-noise ratios of the improved algorithm are 0.58, 0.62, and 0.43, respectively. The improved non local mean image denoising algorithm outperforms other comparative algorithms in terms of information entropy, peak signal-to-noise ratio, and average gradient, which represent the denoising effect. When the noise variance is 45, the normalized structural similarity, mutual information, root mean square error, and absolute error of the algorithm are 0.88, 0.78, 8.2, and 9.7, respectively. The improved non local mean image denoising algorithm performs better than other algorithms in terms of evaluation indicators such as structural similarity, mutual information, root mean square error, and absolute error in preserving original information. It can be seen that the improved algorithm designed in this study has better denoising performance than common methods, and can retain the core information of the original image. It can provide higher quality image denoising services for meteorological prediction, agricultural planting, and other fields.

The aiming accuracy of the Unmanned Aerial Vehicles (UAV) Steadicam head can be affected by many factors, such as the state of the UAV during the actual flight and the installation error of the system related hardware. In order to eliminate the influence of objective factors on the UAV Steadicam, a Kalman filter aiming algorithm based on the coordinate transformation method is proposed to eliminate the attitude error of the UAV Steadicam and improve the accuracy of the system. The algorithm uses coordinate transformation to eliminate mounting errors and combines coordinate transformation and Kalman filtering methods to eliminate objective errors of the UAV in flight. The experimental simulation results show that our method can accurately give the amount of azimuth and pitch angle error compensation during the flight of the UAV, improving the accuracy of the UAV Steadicam head. Ultimately, the method is applied to the development of a real product.

Focusing on the issues of the effects of the high failure rate and poor maintenance on the ZPW-2000A track circuit during in-field operations, this paper proposes a multi-objective maintenance decision optimization method by considering the number of maintenance personnel. The developed method introduces not only the service age regression, increased failure rate, system's average reliability, maintenance costs, but also the availability calculation model in the case of multiple maintenance personnel. We solve the model using the integer coded BB-MOPSO algorithm, and obtain the Pareto front solution set, and then the optimal maintenance plan of the system is further established. In addition, the number of optimal maintenance personnel is analyzed in detail. Compared to the results of traditional maintenance strategies, the results obtained in this paper show that the proposed model can reduce the maintenance costs, under the same working conditions, by 13.29%, moreover, it can more comprehensively reflect the impact of maintenance activities on the system reliability, maintenance costs, and availability. Furthermore, the method can meet the needs of decision-makers for diversified solutions and can provide an effective decision support while on site.

In compressed sensing, a small enough restricted isometry constant (RIC) of the sensing matrix satisfying the restricted isometry property (RIP) is the powerful guarantee on the precise reconstruction of a sparse discrete signal. Under a certain condition, the RIC can be improved by weighting the sensing matrix so that its all nonzero singular values become the same, i.e., the condition number of the new sensing matrix is 1. In this article, by multiplying the linear system ATAx = ATy by a matrix related to ATA and repeating the process many times, we propose a method of reweighting the sensing matrix to improve the condition number of sensing matrix so as to improve its RIC, prove that the condition number tends to 1 as the weighting times approaches infinity monotonically, and then obtain an RIC improvement model equivalent to the original CS model. For the improvement model, we use the algorithms of orthogonal matching pursuit, iterative hard thresholding and L1/2-regularisation to recover sparse signals, and verify the superiority of the proposed algorithms by using different existing sensing matrices for CS experiments.

Common gait deviations in cerebral palsy can be divided into spastic hemiplegia and spastic diplegia gait patterns. These gait deviations require the use of a large amount of data obtained by clinical gait analysis through video, kinematic, kinetic, electromyographic sensors, and plantar pressure data to assess patient gait characteristics. In this paper, we use predictive forward dynamics to investigate the effects of the biarticular hamstrings (HAMS), gastrocnemius (GAS) and biceps femoris short head (BFSH) on the knee joint in gait cycle. We attempted to explore the characteristics for reproducing the patient’s gait by modifying the parameters of model muscles. We applied mild, moderate and severe muscle weakness or contracture to the HAMS, GAS and BFSH muscle groups, respectively, and trained the model to walk at a self-selected speed, showing that with the more severe contracture, the non-swinging phase presented more severe knee hyperflexion and stronger knee torque, and the sensitivity for change is ranked by GAS>BFSH>HAMS. In swing phase, HAMS and GAS contractures aggravate the knee angle, whereas contractures of BFSH have a weak effect on knee angle. Mild HAMS muscle weakness accelerated walking speed, while moderate and severe HAMS muscle weakness hindered walking speed instead. BFSH muscle weakness is more sensitive to knee joint torque. Finally, a variable parameter impedance controller for the lower limb exoskeleton rehabilitation robot is developed. We apply the knee joint angle and torque parameters optimized by predictive forward dynamics simulation as the expected values for the robot to achieve customized tuning of the motion trajectory for the exoskeleton rehabilitation robot and meet the different rehabilitation stages.

This paper presents a novel one-dimensional (1D) frequency scanning dual-slot-waveguide array antenna with versatile advantages such as large scan volume, high frequency sensitivity, low cross-polarization and low sidelobe. Electromagnetic waves radiate through the leaky-wave dual slots, machined on the side chambers of the single-ridge serpentine waveguide. By properly designing the serpentine waveguide, which plays the role of delay line, such 1D frequency scanning array antenna can achieve 39° scanning over a frequency range from 9.7 GHz to 10.3 GHz, and high frequency sensitivity of 65°/GHz. The dual slots with all the adjacent monomers inclined in same direction are designed to acquire low cross-polarization and avoid high-order mode radiation. The cross-polarization is 45 dB lower than the corresponding co-polarization over the whole working band. Taylor aperture distribution is employed to achieve a low sidelobe (-21 dB). The nonresonance VSWR is below 1.2, and meanwhile the resonance VSWR remains a low level, around 2.5. Furthermore, our proposed 1D array as a building block can compose a novel 2D electric scanning array, with frequency scanning in one dimension and phase scanning in the orthogonal dimension. A 2D array consisting of three waveguide elements is simulated to predict the phase scanning performance, and the results indicate that a 120° scanning performance can be obtained. At last, a 1D serpentine dual-slot-waveguide array antenna is fabricated, whose measurements show good agreement with simulations.

This study aims to investigate the incentive mechanism of different availability payment methods applied in Public Private Partnership (PPP) contracts. We present a basic model in a multitask environment in which a risk-averse private contractor chooses two types of noncontractible efforts: one is unproductive, in the sense that it saves building cost but sacrifices social benefits; the other one is productive, as it reduces operating cost without social loss. We find that the PPP contract using separate charges for availability and performance is more desirable than using single unitary charge if the government can detect the social loss cause by unproductive effort. However, the latter brings about more social welfare over the former if the social loss is not observable to the government and the observable value of benefit-cost ratio of the unproductive effort is relatively larger than the benefit-cost ratio of the productive effort.

For search and rescue (SAR), the dynamic connectivity issue caused by mobility of aerial node will severely influence the actual performance in aerial backbone network (ABN). To capture true aerial mobility feature, some application-oriented aerial mobility model (AMM) has been developed. However, existing 3D AMM cannot satisfy some specific demands for in SAR, especially smooth 3D trajectory and rapid connectivity coverage. In this paper, we propose authentic 3D mobility model based on spiral line (3D SLMM), such as Z-Or-SLMM and Z-Non-SLMM. Firstly, we review the basic concept of 2D SLMM. Then, we develop authentic 3D SLMM based on 2D SLMM. Meanwhile, we validate synthetic trajectory of 3D SLMM through the real traces. In the further, we investigate some mathematical features of 3D SLMM, such as mathematical expression and the distribution of spatial nodes. Finally, we evaluate some key mobility property of 3D SLMM. The result shows that Z-Or-SLMM and Z-Non-SLMM all have specific mathematical expression and uniform distribution of spatial nodes at steady status. Besides, the Z-Or-SLMM and Z-Non-SLMM all possess authentic and smooth 3D trajectory. Specially, the aerial node in Z-Or-SLMM merely takes 1.5 minutes to cover 100% target region of 200m*200m. We believe that the 3D SLMM can be directly deployed in the actual scene, and provide many helpful and credible guidelines in the design and analysis of realistic ABN.

Sulfur hexafluoride (SF6) gas leakage in populous urban areas, once occurring, can cause death from suffocation if its concentration largely exceeds 1000ppm and oxygen concentration is low than 19 vol-%. Leakage that cannot be detected and responded to with prompt and effective measures can even lead to large death tolls. Presently, few systematic technical approaches to monitoring, early-warning, consequence prediction, and emergency response of SF6 leakage have been reported. In this paper, a method for constructing the early-warning and emergency response system for SF6 leakage in substations is proposed. Firstly, the concentration distribution of leaked SF6 gas at different leakage points within the substation space is analyzed using CFD simulation to determine the coordinates of sensitive areas where the exceeding of the threshold value of SF6 concentrations is first detected and thus to ascertain sensor monitoring points. By altering leakage locations and leakage orifice diameters within the substation space, the data concerning the coupling relationship between leakage time, leakage orifice diameter, and concentration are obtained, and a prediction model of diffusion concentration of SF6 leakage in substations is established through regression. Based on the prediction model, an emergency response system for SF6 leakage in substations is constructed; additionally, in combination with safety management data of substations, the files required for emergency responses to SF6 leakage can be identified immediately after occurrence, which provide a guidance for on-field personnel to take emergency responses and safety prevention measures. In this paper, a case study of a substation leakage event is presented to describe the method to construct an early-warning and emergency response system for leakage in substations, as well as the application of the method. The results of this research can provide a theoretical basis for early-warning and emergency response to SF6 leakage, thereby improving the inherent safety levels of substations.

This paper investigates the impact of institutes and papers over time based on the heterogeneous institution-citation network. A new model, IPRank, is introduced to measure the impact of institution and paper simultaneously. This model utilises the heterogeneous structural measure method to unveil the impact of institution and paper, reflecting the effects of citation, institution, and structural measure. To evaluate the performance, the model first constructs a heterogeneous institution-citation network based on the American Physical Society (APS) dataset. Subsequently, PageRank is used to quantify the impact of institution and paper. Finally, impacts of same institution are merged, and the ranking of institutions and papers is calculated. Experimental results show that the IPRank model better identifies universities that host Nobel Prize laureates, demonstrating that the proposed technique well reflects impactful research.

Clustering is an efficient method for improving the communication performance of Vehicular Ad hoc NETworks (VANETs) that adopt Vehicle to Vehicle (V2V) communications. However, how to maximize the cluster stability while accounting for the high mobility of vehicles remains a challenging problem. In this paper, we first reconstruct the similarity function of the Affinity Propagation (AP) clustering algorithm by introducing communication-related parameters, so the vehicles with low relative mobility and good communication performance can easily be selected as cluster heads. Then, by formally defining three scaling functions, a weighted mechanism is designed to quantitatively assess the effect on the cluster stability when a vehicle joins it. Base on them, from the perspective of global balance, a new AP clustering algorithm for the whole clustering process is proposed. To ensure the validity of simulations, we use the vehicular mobility data generated on the realistic map of Cologne, Germany, and perform a series of simulations for eleven metrics commonly adopted in similar works. The results show that our proposed algorithm performs better than other algorithms in terms of the cluster stability, and it also effectively improves throughput and reduces packet loss rate of VANETs over the classical APROVE algorithm and the NMDP-APC algorithm.

With the convenience and flexibility, the UAV has received more attention in the search and rescue. Due to inherent mobility of UAV, the aerial backbone network maybe suffers some problems of dynamics of connectivity, such as uncertain delay jitter or occasional connection interruption. However, the existing mobility model could not accurately capture aerial mobility without the smooth trajectory and the capacity of rapid coverage. In this paper, we propose a novel mobility model based on spiral line (SLMM) for aerial backbone network. Firstly, we describe the background and related work. Then, we propose the basic concept and advantages of SLMM. In addition, we investigate the mathematical feature of SLMM. Besides, we propose some novel mobility metrics and analyze quantitatively the mobility feature of SLMM through simulation. Finally, we evaluate the network performance under the SLMM. The result show that the SLMM has the smooth trajectory and the uniform distribution of spatial nodes at steady state. In addition, covering the zone of area of 16pi the aerial node merely take almost 27s under the SLMM. Meanwhile, the convergence time could be adjusted by changing the internal parameters. Moreover, the aerial backbone network could approach the throughput of 0.43Mb/s, the end-to-end delay of 4.42ms and the packet loss rate of nearly 0.00%° under the SLMM, and the best choice of routing strategy is the AODV protocol. We believe that the SLMM as a significant supplement of mobility model can provide the helpful guideline in the design and analysis of aerial backbone network.

In recent years, the high-speed urbanization process and human activities have led to the fragmentation and the connectivity reduction of natural landscape patches, resulting in the degradation of urban ecological services and biodiversity. The construction of ecological network and the optimization of landscape pattern are significantly important to improve the urban ecological environment and urban ecological security. In this paper, a case study of Haikou, an island city of China is performed, the selection of ecological source areas is optimized by granularity reverse method and principal component analysis. The minimum cumulative resistance model (MCRM) is used to construct the ecological resistance surface, and ecological corridors and ecological nodes are obtained, so as to optimize the urban ecological network and the connectivity of landscape patches. The results show that the 1400m granularity landscape component is the optimal landscape component structure for Haikou. There are 38 ecological source areas in Haikou, and 14 ecological landscape patches need to be added. The distribution of ecological source areas is mainly affected by topography and geomorphology. The northwest has huge and scattered ecological source areas, while the southeast has small and concentrated ecological source areas. In the areas around Meilan airport and Hongcheng lake, there is an ecological trap with significant difference between dominant and recessive ecological resistance separately. Haikou ecological network consists of 81 ecological corridors and 76 ecological nodes. Affected by the main urban area in the north, Haikou ecological network has the high density in the middle and southern areas. The construction of ecological network has significantly improved the overall connectivity of the ecosystem in the study region. This research provides a scientific basis for the future urban ecological environment planning of Haikou.

The access to and sharing of medical image data is essential to accelerate the research progress of complex diseases and sudden disease outbreaks. Multicenter image data is collected from different medical institutions, and the contrast and brightness of the images are significantly different, making it difficult to use the images directly. Herein, we introduce a standardized method based on magnetic resonance imaging, referred to as Histogram specification-grid search (HS-GS), which is mainly used to eliminate differences in image contrast and brightness. A Gaussian probability density function with adjustable parameters is used to generate the cumulative distribution function, and the transfer function required for the HS mapping is constructed to obtain standardized image sets based on the controllable parameters. The image sets are used to perform the GS task of radiomics classification to find the optimal controllable parameter combination and classification results, and then obtain the optimal standardized image sets. We used HS-GS to test and verify the predictive ability of the standardized mixed image sets for glioma grading, and compared it with existing methods. The experiments indicate that the standardized image sets generated by the HS-GS algorithm retain excellent stability after mixing and also show excellent classification performance. This novel image set standardization technique has proven to be a promising solution for integration into medical expert systems.

The seasonal effect on land cover classification has been widely recognized. It is important to use the imagery acquired at key points of vegetation phenological development to obtain a higher classification accuracy for land cover. This study compared the effect of seasons on landscape classification and the quasi-circular vegetation patches (QVPs) detection from four fused Gaofen 1 images acquired in the different seasons by using the pixel-based random forest (RF) and object-based support vector machine (SVM) methods over the Yellow River Delta, China. The results from this study demonstrated that the seasonal effect on classifying landscapes and detecting the QVPs is significant, especially for the pixel-based RF method. The object-based SVM method was more appropriate for classifying landscape from the non-growing season images, while the pixel-based RF approach was more suitable for classifying the growing-season images. The spring data (April imagery; overall accuracy = 99.8%) and the winter data (February imagery; F measure = 65.9%) yielded the best results for landscape classification and QVP detection, respectively, by using the object-based SVM approach. Therefore, in practice, we recommend the use of February to April imagery with the object-based SVM approach to map the QVPs in the future.

Most transformer substations in power supply facilities rely on sulfur hexafluoride electrical equipment. A sulfur hexafluoride gas leak can cause serious health concerns if effective measures are not adopted in time. Therefore, in this study, a sulfur hexafluoride gas leakage monitoring, early-warning, and emergency disposal model was established. First, taking the main transformer chamber of an underground transformer substation as the research object, a 3D-model was built, and a numerical simulation was performed. Second, the simulation results were utilized to determine the dispersion and concentration distribution of the sulfur hexafluoride gas, identify concentration-sensitive areas, and arrange sensors based on the simulation results, to ensure early-warning in case of leaks. Then, a sulfur hexafluoride gas leakage monitoring and early-warning model was built based on the data collected using sensors at the monitoring points; thereafter, a construction method was developed for a sulfur hexafluoride gas leakage emergency disposal model, which can be referenced to establish a leakage gas recycling system. This paper also provides some recommendations regarding the determination of the optimal conditions for this emergency recycling device, which can be utilized to maintain the concentration of sulfur hexafluoride gas below a specified value and to construct a recycling time prediction model. The results of the study can provide a theoretical basis for sulfur hexafluoride gas leakage early-warning and emergency disposal, which will contribute to the prevention of suffocation-related accidents.

Energy Internet (EI) is assumed to be the advanced stage of smart grids (SG), which is aiming to facilitate the utilization and sharing of energy through a highly flexible and efficient grid by leveraging renewable energy and Internet technology. To ensure coordinated operations of subsystems in EI, it requires the assistance of the high-performance system protection communication network (SPCN). As a promising networking technology, Software-defined networking (SDN) is applied to the communication architecture construction of SPCN due to the benefits of a global view of the SDN controller along with the programmability. At the same time, in light of the significance of control services to EI and to ensure continuous and reliable transmission, dual routes are urgent to be preplanned so that the SDN controller freely switch control services from the primary routes to the alternate routes in the case of failures. However, the existing constructed routing strategies confront the conflict of network risk concentration as well as the requirement of service latency. Moreover, there are few studies considering both network operation risk and the latency requirement during routes planning. Hence, the problem of simultaneously minimizing the effect of network risk and potential delay of routes is formulated as a multi-objective optimization problem, which is NP-hard. We propose a novel risk-aware routes planning mechanism (RSRM) based on an evolutional algorithm. Extensive emulation results demonstrate that the proposed approach shows a better performance in different network topologies. At the same time, the metric of balancing risk of the network with RSRM achieves the minimum in comparison with other schemes while guarantees the near minimal service latency.

The passive localization using multiple sensors is considered for emitter transmitting pulse signals with unknown start transmission time and period. Time of arrival (TOA) of a pulse is estimated at every sensor, and it is difficult for sensors to intercept all pulses during the observation time in practice, as there maybe obstacle on the line of sight or the signal is too weak to be detected, namely, incomplete measurements. In this paper, an algorithm based on a combination of a new second difference of coherent time delays (SDCTD) measurement and traditional time difference of arrival (TDOA) is proposed to improve localization performance in the presence of two kinds of TOA errors, including the independent estimation errors and common errors among multiple sensors. The Cramer-Rao lower bound for the proposed algorithm is derived, and the simulations validate that the performance of the proposed algorithm significantly outperforms existing algorithms, especially in the case of pulse loss.

A kinematic equation of profiling float is nonlinear and has time-varying parameters. Traditional PD controllers not only demonstrate an inconsistent response to different depth controls but also face problems of overshooting and high power consumption. To realize the goal of depth control of profiling buoy under low power consumption, an improved double PD control method was proposed in this paper. The real-time prediction of position and low-power running of the sensor were realized through sparse sampling and depth prediction. The combination control over position, speed, and flow was realized by introducing the speed and flow expectation function. Then, a MATLAB/Simulink simulation model was constructed, and the proposed controller was compared with a single PD controller and an improved single PD controller. Among ten depth control tests, the proposed method was superior given its short response time, small overshooting, small steady-state error, and low power consumption. Moreover, it achieved a consistent control effect on different target depths. The simulation results demonstrated that a nonlinear and time-varying floating system controlled by the proposed method has favorable robustness and stability. This system will consume minimal power simultaneously.

Successive interference cancellation (SIC) and mobile base stations (MBSs) have been separately exploited to effectively collect data in wireless networks. This paper aims to jointly optimize SIC and MBSs movement to minimize MBS's data collection time for given data. First, we design a DPS algorithm for finding some dominating points (good locations for an MBS) in a two-dimensional area. Based on these dominating points, we build a suitable trajectory for MBS's traveling. Then, we divide the whole traveling path into many path parts and then allocate one or several time slots for each path part. In each time slot, we design a farthest-node-allocation (FNA) algorithm for scheduling node transmissions and determining the MBS's moving speed. The simulation results show that the proposed algorithm can significantly reduce the MBS's data collection time than the scheme without using the SIC.

When communicating between node machines in different locations in the network virtual lab system, the network layer shields the differences of the lower layer networks and cannot provide uniform data transmission of connectionless packets. Aiming at this problem, a self-correcting and optimal scheduling technique for communication networks based on deep machine learning algorithm is proposed. The branching algorithm of the algorithm mainly involves enhancing data learning. By using TCP/IP protocol for communication, a method and program implementation of communication using Socket mechanism under TCP/IP protocol are proposed. System users access the virtual lab primarily through the appropriate browser. The core part of the network virtual lab is server-side communication technology and experimental design. In a virtual lab system, a large number of nodes must be relied on for real-time communication, and the TCP/IP protocol must be followed. The Socket mechanism should be used to implement TCP/IP-based communication. Two breakthroughs were realized, resource scheduling decision and optimization of indirect system parameter selection. The experimental results show that the technical requirements of high throughput and ultra-low latency of the current virtual experimental system are realized.

Routing technology is an indispensable part of wireless sensor networks (WSNs) in contemporary studies. Considerable attention is being given to the energy problem and the delay of data transmission. However, a few studies have explored routing algorithms in wireless rechargeable sensor networks (WRSNs) using mobile charging equipment. In this paper, we propose a clustering hierarchical routing algorithm for WRSNs based on K-means method (K-CHRA), which takes the energy distribution and data transmission into consideration under the influence of mobile equipment (ME) while combining mobile sink and mobile charger in WRSNs. The mobile sink is responsible for data collection and mobile charger replenishes energy for sensor nodes. The simulations demonstrate that K-CHRA can reduce energy consumption and the data transmission delay. Thus, K-CHRA has excellent equilibrium characteristics with balanced energy dissipation throughout all sensor nodes in the networks. Finally, a large number of experiments demonstrate that our proposed algorithm K-CHRA suits for many kinds of scenarios of networks.

In the field of social network, fast detection of the burst topic plays a decisive role in emergency response and disposal. However, social data are noisy and sparse, which evolves with time going on and space changing make it difficult to catch the instant semantics with traditional methods. Instead of passively waiting for an emergency topic, we try to detect the latent burst topic in its budding stage. In this paper, we propose a fast burst topic detect method, namely, FBTD, which aligns data prediction with characteristic calculation to detect burst term from the real-time spatial-temporal data stream and integrates local topic detection with global topic detection to find the spatial-temporal burst topic. Our method controls the delay within a 0.1 s level while preserving the topic quality. The experiments show that preferable effects are procured, and our method outperforms the state-of-the-art approaches in terms of effectiveness.

Wireless backhaul networks provide vital infrastructure support for large public and private networks, and their efficient design is essential for smoothly handling the rapid growth of the Internet traffic. For its efficient capacity utilization, a well-planned topology would be crucial for such networks. Existing studies on planning for wireless backhaul network topologies have mostly focused on a single parameter or on a few performance aspects, e.g., to minimize the network cost or to maximize the network reliability. It would, however, be more realistic to consider multiple performance aspects jointly, subject to a variety of system constraints for a microwave-based wireless backhaul network, as proposed in this paper. For the optimization, we formulate a general cost that incorporates the various performance aspects considered, based on different weight factors. We develop an integer linear programming (ILP) optimization model and also propose an efficient heuristic algorithm to plan cost-minimized tree topologies for both single stage and multi-stage design scenarios. This paper shows that the proposed heuristic algorithm is efficient to optimize multiple system objectives jointly and performs close to the ILP model. The performances of the topologies planned with the periodic constraint and the single stage scenario are also close to each other further confirming the efficiency of the proposed heuristic algorithm.