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The speed regulation technology for energy conservation of pump and fan systems occupies an important position in industrial field. The paper focuses on the rapid development and research significance of speed regulation technology for energy conservation based on different permanent magnet adjustable speed drives (PMASDs). The development history and speed regulation scheme for energy conservation are introduced step by step. The features of theoretical model based on magnetic vector potential and equivalent magnetic circuit methods are presented and studied in the paper, which is benefit to select a suitable method for a specific drive. Several optimization methods of design parameters of PMASDs are given in the paper. The structure and operating principle of some new speed regulation scheme are described, and their advantages, disadvantages and feasibility of engineering application are analyzed. Finally, the proposed speed regulation technologies for energy conservation are summarized and classified from the aspects of magnetic circuit structure, permanent magnet rotor structure, conductor rotor structure, and the slip power conversion scheme. In a conclusion, the power-fed PMASD will be the future of energy conservation technology for the high-efficiency and low-cost large pump and fan systems.

The main challenges in the field of fault diagnosis of robot arms lie in the difficulties of acquiring fault data and ensuring model applicability. For a fault robot arm, the trained models typically only perform well on test data and cannot be effectively applied in practical scenarios. As a result, the time cost is very much to construct adequate fault datasets. The paper proposes a dynamic simulation method for obtaining fault data to address these issues. The motion feature of the arm with joint faults is replicated by the simulation software, thereby obtaining vibration signals in the fault mode as samples. Additionally, under the main framework of Deep Learning (DL) with an end-to-end feature extraction capability, a Stacked Continuous Wavelet Transform (SCWT) method is designed to visualize timing signals graphically based on the traditional wavelet transform. Furthermore, to enhance traditional DL performance, a dual-channel architecture for data fusion within DL is designed to enrich the feature space and improve fault-distinguishing ability. Lastly, a Domain Discriminator $G_{d}$ is designed to identify the upper bounds for differences between spatial distributions of simulated and actual fault data. By the domain discriminator, the feature distribution of target and source data is aligned, facilitating the transfer application of the simulation-trained diagnostic model on the actual fault. The proposed method is tested and evaluated using a self-constructed experimental data set. The results substantiate its effectiveness and superiority.

Knowledge graphs have emerged as a useful resource and tool for representing real-world entities and their relations, which gained increasing importance in the fields of deep learning and machine learning. This research aims to investigate the academic publications of knowledge graphs between 2013 and 2022 based on the core collection of the Web of Science and examine hot topics and the latest developments in this subject. Thus, the present research adopted a bibliometric analysis to explore the indicators, which mainly focus on different variables from the diachronic productivity of scientific publications to the most prolific countries and the leading publication journals. By means of VOSviewer software, the most productive authors and the frequency of author keywords were further analyzed. The results manifest that dramatic growth has been identified in the past five years due to the output of publications regarding this subject, and the frequently explored themes were mainly conducted from six dimensions, focusing on ontology modelling, knowledge extraction, knowledge graph embedding, graph-based knowledge representation, multi-modal knowledge graphs and knowledge-aware applications. The findings could help researchers gain a thorough understanding of knowledge graph research, optimize research topic choices, and detect new directions for future studies.

Incremental learning aims to alleviate the catastrophic forgetting problem of deep neural networks during learning sequential data stream. This problem is even more challenging when old data is unavailable, since learning system can only be trained under the supervision of current data. To address this problem, we proposed a prototype sampling mechanism based on K-means clustering method. On the one hand, we proposed to use K-means clustering to pick out class-representative prototypes for each old class. During incremental stages, prototypes and deep features from current data are trained together to maintain the distinction and balance between old and new classes. On the other hand, we proposed to attach a mask to the loss function based on the cosine similarity between the prototypes and the current data. Which further enhances the discrimination between old and new classes compared to naive knowledge distillation schemes. Extensive experiments conducted on three benchmark datasets including CIFAR100, Tiny-ImageNet and vggface2 verified the effectiveness and advantages of our proposed method. Specifically, we improved class incremental performance by 1.6%, 1.2% and 1.7% on three datasets respectively.

Because the traditional zero-sequence overcurrent protection method is not effective in detecting single-phase-to-ground faults (SPGF) in a low-resistance grounded system (LRGS), this paper analyzes the fault characteristics of a 10-kV LRGS in detail. From the perspective of the time domain, the relationship between zero-sequence current and zero-sequence voltage is deduced, and the characteristics corresponding to faulty lines and nonfaulty lines are analyzed. The analysis reveals that the ratio fluctuation coefficients of the zero-sequence current to the differential zero-sequence voltage corresponding to faulty lines and nonfaulty lines have notably different characteristics; consequently, a high-sensitivity SPGF detection method is proposed. This method considers the existence of unbalanced loads and asymmetric parameters in the distribution network, can effectively identify high-impedance faults as high as 5000 $\Omega $ and nonlinear arc grounding faults, and can resist noise interference with a signal-to-noise ratio of 20 dB. Finally, many simulations and comparisons based on PSCAD/EMTDC verify that the proposed detection method has better applicability than the existing methods in detecting high-impedance SPGFs.

Deep Reinforcement Learning (DRL) methods are inefficient in the initial strategy exploration process due to the huge state space and action space in large-scale complex scenarios. This is becoming one of the bottlenecks in their application to large-scale game adversarial scenarios. This paper proposes a Safe reinforcement learning combined with Imitation learning for Task Assignment (SITA) method for a representative red-blue game confrontation scenario. Aiming at the problem of difficult sampling of Imitation Learning (IL), this paper combines human knowledge with adversarial rules to build a knowledge rule base; We propose the Imitation Learning with the Decoupled Network (ILDN) pre-training method to solve the problem of excessive initial invalid exploration; In order to reduce invalid exploration and improve the stability in the later stages of training, we incorporate Safe Reinforcement Learning (Safe RL) method after pre-training. Finally, we verified in the digital battlefield that the SITA method has higher training efficiency and strong generalization ability in large-scale complex scenarios.

Facial micro-expressions can reveal a person’s actual mental state and emotions. Therefore, it has crucial applications in many fields, such as lie detection, clinical medicine, and defense security. However, conventional methods have extracted features on designed facial regions to recognize micro-expressions, failing to effectively hit the micro-expression critical regions since micro-expressions are localized and asymmetric. Consequently, we propose the Haphazard Cuboids ( $HC$ ) feature extraction method, which generates target regions by haphazard sampling technique and then extracts micro-expression spatio-temporal features. $HC$ consists of two modules: spatial patches generation ( $SPG$ ) and temporal segments generation ( $TSG$ ). $SPG$ is assigned to generate localized facial regions, and $TSG$ is dedicated to generating temporal intervals. Through extensive experiments, we demonstrate the superiority of the proposed method. Afterward, we analyze two modules with conventional and deep-learning methods and find that they can significantly improve the performance of micro-expression recognition, respectively. Thereinto, we embed the $SPG$ module into deep learning and experimentally demonstrate the effectiveness and superiority of our proposed sampling method in comparison with state-of-the-art methods. Furthermore, we analyze the $TSG$ module with the maximum overlapping interval ( $MOI$ ) method and find its coherence with the maximum interval of the apex frame distribution in CASME II and SAMM. Therefore, analogous to the human face’s region of interest (ROI), micro-expressions also inherit similar ROI in the temporal dimension, whose positions are highly relevant to the intensive moment, i.e., the apex frame.

High-data-rate impulse radio ultra-wideband (IR-UWB) wireless communication system suffers from serious intersymbol interference (ISI) issues in an indoor multipath environment. This paper proposes an auto-step least mean square time-domain equalization algorithm based on quadratic function (QA-LMS), which outplays the overall convergence speed, steady-state error, signal-to-noise ratio (SNR) threshold and robustness compared to the state of the art relevant to the adaptive step-size algorithms. The proposed algorithm does not need to preset the parameters according to channel conditions. The algorithm converges absolutely fast in the mean square error (MSE) learning curve. To meet the bit error rate (BER) of forward error correction (FEC) code, the algorithm improves the SNR threshold of traditional auto-step LMS (A-LMS) by 4.4 dB in CM1 and 3.6 dB in CM3 of 802.15.3a channel model respectively. The proposed algorithm is stable and robust in nonstationary environment with the advantage of vectorial step-size that based on the gradient of estimated error and high step detection.

A novel design method for compact planar lowpass filter (LPF) with prominent roll-off rate and wide stopband suppression is proposed by using fragment-type structures (FTS) with multi-bit scheme. The proposed multi-bit FTS design has the potential to achieve higher LPF performance at higher optimization efficiency, without any prerequisite knowledge about the pivotal LPF structures needed in conventional LPF designs. The definition of multi-bit FTS and the implementation of LPF design with multi-bit FTS scheme are presented in detail. Compared to the LPF design with 1-bit FTS, the 2-bit FTS design may use smaller decision space, take fewer iterations in multi-objective optimization searching, and yet achieve sharper roll-off rate, higher stopband suppression and a wider stopband. For demonstration, planar LPFs on PCB using the 1-bit FTS and 2-bit FTS are designed and tested. The 2-bit FTS design yields a sharp roll-off rate of 123.3 dB/GHz, a relative stopband bandwidth of 165% referred to stopband suppression of 20 dB, and a high figure-of-merit of 21060. Effects of the geometry parameters of the 2-bit elements are further discussed to give some guide rule on using the 2-bit FTS design.

A novel adaptive tracking control strategy without singularity is proposed for quadrotor systems subject to uncertain dynamic parameters. Based on the design technique of bounded control, a new differentiable saturated function is proposed and subsequently adopted to ensure that the developed translational thrust remains within the preassigned range in the position control loop. A technical lemma is established to analyze the relationship between the convergence of the attitude tracking error and the position tracking error in the presence of thrust saturation. Then, by the compensation term in the form of an attitude error function, a singularity-free torque controller, which can avoid analytic calculations involving the desired attitude derivative, is presented in the attitude control loop. Finally, we prove that the proposed method can achieve asymptotic tracking without singularity. Simulation results illustrating the effectiveness of the proposed strategy are exhibited.

Traditional aerial target tactical intention recognition is based on a single moment of reasoning, while actual battlefield target tactical intention is realized by a series of actions, so the target state reflects dynamic and temporal variation. To solve this problem, bidirectional propagation and attention mechanisms are introduced based on a gated recurrent unit (GRU) network, and bidirectional gated recurrent units with attention mechanism (BiGRU-Attention) air target tactical intention recognition model is proposed. We use a hierarchical approach to construct the air combat intention characteristic set, encode it into temporal characteristics, encapsulate the decision-maker’s experience into labels, learn the deep-level information in the air combat intention characteristic vector through a BiGRU neural network, and use the attention mechanism to adaptively assign network weights, and then place air combat characteristic information with different weights in a softmax function layer for intention recognition. Comparison with a traditional air tactical target intention recognition model and analysis of ablation experiments show that the proposed model effectively improves the tactical intention recognition of air targets.

The identification of low-density parity-check (LDPC) codes with higher rate over a candidate set is more difficult than that of LDPC encoders with lower rate. For the given soft information on a received sequence, we present a method to compute the average Taylor polynomial of the syndrome posterior probability according to the parity-checks of the candidates. Simulation results show that the proposed method performs as well as the existing works when dealing with lower rate encoders. Meanwhile, our method outperforms the existing works in higher rate encoders identification, with the gain being about 0.5 dB.

Deep learning has become a mainstream method in marine data processing field. However, the raw marine data, characterized by fluctuations, outliers and noise, is a serious obstacle to its performance improvement. To address this problem, a hybrid deep computing model is developed for marine sensor data prediction, combining smoothing and deep belief echo state network (DBEN). The proposed structure adopts a two-stage data processing mode to deal with the complex characteristics of marine time series. In the preprocessing stage, four smoothing methods are considered for fluctuation reduction and outlier handling, so that purer data conducive to achieving a higher prediction accuracy can be gained. In the prediction stage, DBEN equipped with efficient feature learning serves as a nonlinear approximator. The effectiveness of the constructed hybrid model is tested on real-world marine time series of different sources and traits, and its superiority in prediction accuracy is demonstrated by readout comparisons and statistical measures.

The random sample consensus (RANSAC) based algorithm is widely used in estimating the two-view geometry from image point correspondences. However, it often becomes extremely slow when the data is contaminated by a large percentage of incorrect matches. To address this problem, the paper proposes a new modification of RANSAC called LP-RANSAC that is robust to varying inlier ratios and achieves large computational savings without deterioration in accuracy. LP-RANSAC integrates the locality preserving constraint into the universal RANSAC framework, which prunes most of the unreliable correspondences before the hypothesize-and-verify loop and guides non-uniform sampling to generate and verify promising models earlier. Unlike other guided sampling strategies, the proposed method is simple to implement and does not require any prior information. Extensive experiments performed on the publicly available datasets reveal that LP-RANSAC can achieve more accurate and stable solutions at much lower computational cost (in milliseconds on standard CPU) than state-of-the-art methods, particularly when handling problems with low inlier ratios.

The integrated energy system (IES) with various energy demands and distributed energy resources has been a significant approach to improve the efficiency of energy utilization. Considering the uncertainties of renewable energy sources and loads, the energy dispatch optimization for multiarea IESs is studied in this paper. Different from the most current studies, not only the electrical power and heat distribution in each area is optimized, but also the coordination power dispatch between areas. A hierarchical learning method is proposed inhere to improve the operation performance of the multiarea IESs. The proposed method with data-driven way is a model-free method which has no requirement for the accurate mathematical model. With the hierarchical structure, the electrical power dispatched between areas is optimized in upper layer, together with the dispatching optimization in each area at lower layer, to decrease the operation cost for the system and power demand from the power grid. Finite horizon discrete dynamic process model is adopted to simulate the data for learning. The simulation results show the effectiveness of the optimization policy can achieve an economic and stable operation for the multiarea IES.

Automated guided vehicles (AGVs) are widely used for material handling in warehouses and automated production lines due to their high efficiency and low cost. However, AGVs usually interact with each other because of the restricted capacity of the layout. Although many algorithms have been proposed to address the problem, most of them are inefficient for collision and deadlock avoidance in dynamic environments. This paper proposes a dynamic resource reservation (DRR) based method supporting time-efficient scheduling and collision avoidance of multiple AGVs. In this method, the layout is divided into square blocks with the same size that are abstracted as points in the undirected graph. In order to solve the collision and deadlock problem dynamically, the shared resource points of each vehicle are extracted from their guide paths in real time. Unlike the traditional approaches most of which adopt a static point occupation policy, DRR exploits dynamical reservations of shared resource points to change AGV movement states for avoiding collisions and deadlocks, resulting in better time efficiency. We jointly implement the algorithm on both central and local controllers. Extensive simulation results demonstrate the feasibility and efficiency of the proposed collision and deadlock prevention method.

A biopsy needle is a tool employed to fetch a portion of tissue at a point in the volume of diseased tissue, i.e., a Single Sample with a Single Spoon (SS-SS). Using this tool, the pathological status of the unhealthy tissue is generally represented by that of the specimen. However, for the tumor growing heterogeneously, like brainstem glioma, its inner constituent is heterogeneous. Therefore, the histological status of the specimen cannot precisely represent that of other points except the sampled point itself. The common principle of the existing solutions to this problem is to design the needle that fulfills biopsy operation by obtaining Multiple Samples with a Single Spoon at different points (MS-SS). The drawback of the designs following this principle is that during one biopsy operation, the latter samples are contaminated by their formers. The reason for the shortcoming is that the same spoon grabs all samples. To solve this problem, we design a novel biopsy tool that implements a single insertion to obtain Multiple Samples in multiple desired points with Multiple Spoons (MS-MS). The detailed system structure design, the motion procedure of the needle spoon, and the critical stress on the crucial mechanical parts are explicitly presented. Experiments using a built prototype is carried out on agar phantom and ex vivo porcine tissue, respectively. The results demonstrate the feasibility of the novel position-adjustable multi-point synchronizing biopsy tool for investigating intratumoral heterogeneity.

Prostate cancer is the second-deadliest cancer in men in the United States, seriously affecting people's life and health. The Gleason grading system is one of the most reliable methods to quantify the invasiveness of prostate cancer, which is of great significance for risk assessment and treatment planning for patients. However, the task of automating Gleason grading is difficult because of the complexity of pathological images of prostate cancer. This paper presents an automated Gleason grading and Gleason pattern region segmentation method based on deep learning for pathological images of prostate cancer. An architecture combining the atrous spatial pyramid pooling and the multiscale standard convolution is proposed for the segmentation of the Gleason pattern region to get accurate Gleason grading. In addition, the postprocessing procedure based on conditional random fields is applied to the prediction. The quantitative experiments on 1211 prostate cancer tissue microarrays demonstrate that our results have a high correlation with the manual segmentations. The mean intersection over union and the overall pixel accuracy for the Gleason pattern region are 77.29% and 89.51%, respectively. Furthermore, the results of the automatic Gleason grading were comparable to the results of experienced pathologists. The inter-annotator agreements between the model and the pathologists, quantified via Cohen's quadratic kappa statistic, was 0.77 on average. Our study shows that the method of combining different deep neural network architectures is suitable for more objective and reproducible Gleason grading of prostate cancer.

Dynamic area coverage is widely used in military and civil fields. Improving coverage efficiency is an important research direction for multi-agent dynamic area coverage. In this paper, we focus on the non-optimal coverage problem of free dynamic area coverage algorithms. We propose a distributed dynamic area coverage algorithm based on reinforcement learning and a γ-information map. The γ-information map can transform the continuous dynamic coverage process into a discrete γ point traversal process, while ensuring no-hole coverage. When agent communication covers the whole target area, agents can obtain the global optimal coverage strategy by learning the whole dynamic coverage process. In the event that communication does not cover the whole target area, agents can obtain a local optimal coverage strategy; in addition, agents can use the proposed algorithm to obtain a global optimal coverage path through off-line planning. Simulation results demonstrate that the required time for area coverage with the proposed algorithm is close to the optimal value, and the performance of the proposed algorithm is significantly better than the distributed anti-flocking Algorithms for dynamic area coverage.

Bordering on the Mainland of China, Macao is a city with diverse population and is also a world-famous gambling city. The residents living in Macao are not only localresidents, but a large-scale of residents are from the Mainland of China. There are diversified living habits among different resident groups. Some of them live and work in Macao most of the time, while others cross the border between Macao and the Mainland of China frequently. These diversities result in different demands for mobile phone contract services. Machine learning and data mining are powerful tools used by telecom companies to monitor the behavior of their customers. This paper aims to build a contract service recommendation model suitable for Macao telecom companies, which can accurately recommend the best fit contract services according to call data records (CDR). Based on data mining algorithm and large number of comparative experiments, this study conducts a study on variety of factors that have effect on the contract recommendation for obtaining a composition structure of factors that have the greatest impact on contract recommendation accuracy so as to improve the operation efficiency of the model. In addition, this study makes comparisons among five classification algorithms, including Bayesian, logistic, Random trees, Decision tree (C5.0), and KNN (k-nearest neighbor). The results are analyzed by different metrics such as Gain, Lift, ROI (region of interest), Response, and Profile. Experimental results demonstrate that the best classifier is decision trees (C5.0), and the contract service obtained by this method can achieve a recommendation accuracy close to 90%, which successfully reaches the expected goal.

Quadcopter is an important way for the human to explore the physical world. The brain-computer interface (BCI) technology is used to control the quadcopter flight in order to help disabled persons communicate with the external world freely. In this study, a quadcopter control system using a hybrid BCI based on off-line optimization and enhanced human-machine interaction was designed to control the quadcopter flight in 3D physical space. The proposed system implemented the control of quadcopter moving up/down, forward/backward, left/right by six different SSVEP, and turning left/right by left-hand and right-hand motor imagery. Meanwhile, the optimization of the control system and the human-machine interaction enhancement improved practicability in real-time use. Five subjects participated in an on-line experiment to control the quadcopter flight in real-time. The average classification accuracy of EEG-based commands in the on-line experiment was 87.09±2.82% and information transfer rate (ITR) was 0.857±0.085 bits/min. The results demonstrated the feasibility of multidirectional control of quadcopter flight in 3D space by using hybrid BCI technology and revealed the practicality and operability of the hybrid BCI control system based on off-line optimization and human-machine interaction enhancement.

To achieve better control performance, a comprehensive control parameter design method that considers economics, stability and dynamic performance is essential for hybrid multi-terminal HVDC systems. In this paper, a hierarchical model for the control system of Hybrid-MTDC is constructed and the parameters are optimized on two layers. On the system layer, using the normalization processing method, a system-layer objective function that considers both the minimum network loss and the voltage offset is formed for Hybrid-MTDC systems and solved with the proposed penalty interior point method. On the converter layer, using the state-space matrix method, a generic small-signal stability range of each control parameter can be obtained by the traversal calculation of the eigenvalues. Then, to identify the stricter stable operating region for the DC voltage controller under severe conditions, an additional stability criterion based on the mixed-potential theory is deduced, and the design-oriented boundaries are generated and added to the feasible region. Finally, within the design-oriented boundaries, a general dynamic performance evaluation function is constructed to determine the optimal control parameters. Utilizing the proposed method, a typical Hybrid-MTDC system is investigated, and experimental verifications are provided to validate the effectiveness and accuracy.

Transient electromagnetic (TEM) techniques have been proven to be efficient for nondestructive testing (NDT) operations due to their pulsed eddy-current properties. However, in the field of downhole measurements, harsh environments may significantly influence the NDT performance in downhole casings. In this paper, an empirical mode decomposition (EMD) method based on borehole TEM array signal denoising and baseline wander (BW) correction is proposed to compensate for the bad measurement conditions that affect downhole NDT. Based on the borehole TEM signal model, we investigated the principle of the EMD approach for the borehole TEM response, where the background magnetic noise and temperature drift effects were analyzed by considering the motion measurement and effective permeability. It was found out that although the BW can be effectively removed with the EMD approach, the performance of the signal denoising is closely related to the measurement speed of the downhole NDT sensors. To solve this problem, we proposed an array-based ensemble EMD method to improve the denoising performance of the borehole TEM signals by formulating a three-dimensional borehole TEM data structure, where the generation of the noise-aided data can be more efficient by employing the borehole TEM array. The performance of the proposed method was verified by applying it to a borehole TEM system for the NDTs of oil-well casings. In addition, field experiments were conducted, and the results demonstrated the effectiveness of the proposed method.

Objective: Laryngopharyngeal Reflux Disease (LPRD) is prevalent and has a range of symptoms. However, diagnosing LPRD is difficult because of the lack of specific symptoms or clinical gold standard. An objective and reliable test, which does not rely on a perfect clinical gold standard, is required in clinical practice. Methods: 60 normal volunteers and 74 confirmed Laryngopharyngeal Reflux (LPR) patients were labelled based on the combined consideration of the reflux symptom index, reflux finding score, 24h oropharyngeal pH monitoring and results of anti-reflux treatment. 72 candidate features were extracted from pH recordings and the most efficient feature combination was detected using a stepwise wrapper method. The labelled data were combined with 1552 unlabelled data for feature selection and model training using semi-supervised learning. A latent class model method was used to assess the proposed model based on 64 additional validation data and an imperfect clinical reference test. Results: A new score (named W score), which significantly improved the sensitivity of the LPRD test (82.67% vs. 24.09%) and has a relatively high specificity (80.19%), was proposed. W score concurs with the complicated clinical test. Conclusion: W score which significantly improves LPRD diagnostic efficiency, can aid the clinical diagnosis of LPRD. W score provides an objective, efficient, and reliable indicator for the application of anti-reflux treatment in clinical practice. Significance: More LPRD patients can benefit from anti-reflux treatment in clinical practice and fewer patients may suffer from, for example, the side effects of unnecessary long-term Proton Pump Inhibitors (PPI) treatments.

With rapid development of smart grid, terminal power load is paid more and more attention, especially that of air conditioning system, which constitute an integral part of summer peak electricity load. In this paper, through controlling indoor air temperature deduced by equivalent thermal parameters-model, daily energy consumption and energy consumption during peak hours can be adjusted and reduced to realize demand side management. To reduce peak load of grid and ensure stable and reliable operation of grid, four temperature control strategies, including constant setting temperature control strategy, pre-cooling control strategy, curtailment control strategy and improved curtailment control strategy were proposed. Meanwhile, three operation schemes of the chiller were also employed. Furthermore, considering the power consumption and curtailment, five indicators including hourly average load, hourly average load ratio, energy saving ratio, load-shedding ratio during peak hours and coefficient of performance (COP) have been proposed to evaluate the performance. Results showed that the operation scheme of operating numbers control obtained better effect. Energy saving ratio was highest of 32.5% with curtailment control strategy, while load-shedding ratio during peak hours was optimal with improved curtailment control strategy. However, COP was maximum at 3.66 when pre-cooling control strategy was employed. This study can guide the operation of water-cooled air conditioning system through energy saving ratio and COP, also it has more benefits on economy of power system by hourly average load and load-shedding ratio.

In the literature, most of the consensus methods require the control directions of all agents to be known. This paper deals with the distributed consensus problem without such requirements for networked Lagrangian systems subjected to uncertain dynamics. Unknown control directions are nonidentical in the sense that the control direction related to each control input in an individual Lagrangian system can be different and unknown as well as that the control directions for different Lagrangian systems are allowed to be distinct. Regarding the communication topology, the only requirement is to contain a fixed directed spanning tree. Based on the estimated consensus value, novel auxiliary sliding mode variables are constructed and applied to develop the distributed adaptive consensus control scheme. It is proved by adopting the special properties of the Laplacian matrix on directed graphs and matrix theory that global uniform boundedness of all closed-loop signals and the asymptotic consensus can be achieved. Simulations on networked two-link revolute joint arms are provided to verify the validity of the proposed scheme.

As the grid-connected AC-DC converter is the key equipment that links the low voltage direct current (LVDC) microgrids and the utility grid, the safe and stable operation of the converter is of great significance to improve the reliability of the LVDC microgirds. Since the converter's reliability is affected by the converter's topological structure and modulation strategy, a civil LVDC microgrid case study is analyzed, and the power losses, thermal stresses, lifetimes and reliability of components in the AC-DC converters with different topologies and modulation strategies are evaluated in this paper. The influence of these two factors on the reliability and efficiency of the converter is discussed, and the normalized weighted total harmonic distortion (NWTHD) with different modulation strategies is analytically determined and compared. The results show that when the converter operates with unity power factor in this application scenario, among the two-level converter, flying capacitor converter, neutral point clamped (NPC) converter and T-type converter, the T-type converter has the highest reliability. The effects of the converter's topology and modulation strategy on the capacitor reliability are relatively small. In view of reliability and power quality, third harmonic injection pulse width modulation (THIPWM) or space vector pulse width modulation (SVPWM) is more suitable for the converter in this microgrid.

Subspace codes are widely used in error corrections of random network coding. In this article, subspace codes based on partial injective maps of vector spaces over finite fields are considered. Several bounds of the subspace codes (n, M, 2b, e)q based on e-partial injective maps of F(n)q are presented. The anticode bound and Ahlswede-Aydinian bound of the subspace codes (n, M, 2b, e)q are obtained by using the EKR theorem for e-partial injective maps of F(n)q . Finally, we show that the (n, M, 2b, e)q subspace codes based on e-partial injective maps of F(n)q reach the Wang-Xing-Safavi-Naini bound if and only if they are certain Steiner structures in Ine.

Given the important challenges associated with the processing of brain signals obtained from neuroimaging modalities, cognitive systems have been proposed as useful and effective frameworks for the modeling and understanding of brain activity patterns. They also enable direct communication pathways between the brain and external devices (brain–computer/machine interfaces). However, most of the research so far has focused on lab-based applications in constrained scenarios, which cannot be extrapolated to realistic field contexts. Considering the decoding of brain activity, biomedical engineers provide excellent tools to overcome the challenges of learning from brain activity patterns that are very likely to be affected by nonstationary behaviors and high uncertainty. The application of health and neural engineering to learning and modeling has recently demonstrated its remarkable usefulness for coping with the effects of extremely noisy environments, as well as the variability and dynamicity of brain signals. In addition, neurobiological studies have suggested that the behavior of neural cells exhibits functional patterns that resemble the properties of computational neuroscience to encode logical perception.

Currently, most consensus control approaches need each agent to access all its neighbors' states directly. The distributed consensus issue of second-order multi-agent systems subject to position constraints is studied without such requirements. The only condition for the communication topology is to include a directed spanning tree. An innovative reference position is provided to deal with the position constraints while eliminating the need for neighbors' velocity variables. An adaptive control method is designed by constructing a sliding-mode-esque variable so that each agent's transformed position can converge towards the reference position. This new method can guarantee uniform boundedness of each closed-loop signal as well as asymptotic consensus, and the requirements to meet the position constraints are satisfied at all times. Numerical simulation verifies the correctness of the theoretical results.

In this paper, a novel spatial domain color image watermarking technique is proposed to rapidly and effectively protect the copyright of the color image. First, the direct current (DC) coefficient of 2D-DFT obtained in the spatial domain is discussed, and the relationship between the change of each pixel in the spatial domain and the change of the DC coefficient in the Fourier transform is proved. Then, the DC coefficient is used to embed and extract watermark in the spatial domain by the proposed quantization technique. The novelties of this paper include three points: 1) the DC coefficient of 2D-DFT is obtained in the spatial domain without of the true 2D-DFT; 2) the relationship between the change of each pixel in the image block and the change of the DC coefficient of 2D-DFT is found, and; 3) the proposed method has the short running time and strong robustness. The experimental results on two publicly available image databases (CVG-UGR and USC-SIPI) have shown that the proposed method not only has satisfied the needs of invisibility but also has better performance in terms of robustness and real-time feature, which show the proposed method has both advantages of spatial domain and frequency domain.

Recently outstanding object detection results are achieved by the faster region-based convolutional network (faster R-CNN). Particularly, high-quality detection proposals are obtained by the region proposal network (RPN). Nevertheless, part of the parameters in RPN is assigned by prior knowledge. Therefore the underfitting problem is likely to appear on the training model of RPN. In other words, the generalization ability of RPN is not enough. Increasing parameters is an effective solution to this problem. Thereupon a strengthened RPN (SRPN) is designed to expand the exploring space of RPN. Acquiring the optimal parameter values of SRPN is a non-deterministic polynomial-time hard problem, which can be solved by swarm intelligence algorithms. Thereafter a particle swarm optimization (PSO) and bacterial foraging optimization (BFO)-based learning strategy (PBLS) is introduced to optimize the classifier and loss function of SRPN. In SRPN, a novel multi-level extracting network is created to improve the feature sampling ability. Moreover, the mathematical model of the smooth L1 loss function is improved to boost the fitting ability. Additionally, support vector machine (SVM) method is applied to enhance the classifier learning capability. PBLS is applied to SRPN (PBLS_SRPN). The parameters of SVM and the improved loss function are optimized by the BFO and PSO methods, respectively. Then, the performance of SRPN is further promoted. The excellent results are obtained by our proposed methods on PASCAL VOC 2007, 2012, MS COCO, and KITTI data sets. Consequently, PBLS_SRPN is effective for object detection in autonomous driving.

Network slicing has been viewed as a key enabler for the next-generation software-defined and cloud-based network (e.g., 5G and beyond) to accommodate diverse services in a flexible and cost-efficient fashion. Network slicing allows a network slice provider (NSP) to operate on a common network infrastructure to create customized isolated logical networks (i.e., network slices) for network slice customers (NSCs), (i.e., service providers). NSP and NSCs are independent operators who pursue profit maximization, while in the literature, only network cost optimization is intensively investigated in terms of service function chain embedding, i.e., virtual network function (VNF) placement and flow routing. Therefore, slices should be dimensioned (i.e., resources allocated to slices) according to the resource availability and the economic mechanism in the network, so as to optimize the resource utilization and improve the profit of NSP/NSCs. In this paper, we study elastic slice dimensioning with resource pricing as a Stackelberg pricing game, in which the NSP sells slices by pricing resources and NSCs adjust their slice's resource demand on VNF capacity and bandwidth, while both are trying to maximize their profit. Then, we formulate optimization problems for the pricing game and find that a closed form solution of the optimal price cannot be obtained for a non-trivial network. Hence, we propose a resource pricing algorithm that aims to maximize the NSP's profit and the network's social welfare. Compared with existing usage-based pricing method and two heuristic methods, our proposed pricing algorithm for slice dimensioning strikes a trade-off between maximizing NSP's profit and other metrics, including the resource utilization. Hence, it will helpfully exploiting the benefits of network slicing.

Pyrene, composed of four fused benzene rings, is a polycyclic aromatic hydrocarbon (PAH) that has served as a model compound for evaluating the toxic effects of PAHs. In this paper, 114 male rats were dosed daily by oral gavage with either vehicle (corn oil) or pyrene (1500 mg/kg/day) for four days. A method based on the gray wolf optimization-enhanced machine learning approach was then developed to identify pyrene poisoning in rats using the indices from blood analysis. The results showed that there were significant differences in blood analysis indices between the control and the pyrene groups (p

Mobility is a key factor that influences the spatial and temporal fluctuation of wireless traffic, which is highly coupled with the user experience. Previous researches barely explore the properties of the traffic when the mobility is considered. In this paper, by combining stochastic geometry and queueing theory, we model the spatio-temporal properties of the traffic with mobility. We propose an analytical framework to evaluate the relationship between the perplex traffic and the network performance for both the fast and the partial mobility cases. We derive and bound the success probability and the mean packet throughput for different mobility models, which capture the effect of traffic and mobility on the network performance. Then, the mathematical definitions of the quality of experience (QoE) and the system cost are proposed and evaluated both analytically and numerically. Our results reveal that the QoE does not always increase as the system cost increases, but begins to stabilize when the system cost increases to a certain extent. We also find that mobility in wireless networks may be useful to reduce the delay and improve the QoE. Our work provides a useful reference for the design of wireless networks when the spatio-temporal fluctuation of traffic with mobility is considered.

Feature selection enhances classification accuracy by removing irrelevant and redundant feature. Feature selection plays an important role in data mining and pattern recognition. In this paper, we propose a hybrid feature subset selection algorithm called the maximum Pearson maximum distance improved whale optimization algorithm (MPMDIWOA). First, based on Pearson's correlation coefficient and correlation distance, a filter algorithm is proposed named maximum Pearson maximum distance (MPMD). Two parameters are proposed in MPMD to adjust the weights of the relevance and redundancy. Second, the modified whale optimization algorithm can act as a wrapper algorithm. After introducing the maximum value without change (MVWC) and the threshold, the filter algorithm and the wrapper algorithm are combined to form an algorithm called MPMDIWOA. In MPMDIWOA, the filter algorithm and wrapper algorithm are called different times according to changes in MVWC and threshold. Finally, the optimal classification accuracy was found. The proposed method is tested on 10 benchmark datasets from UCI machine learning databases. The experimental results show that the classification accuracy of the proposed algorithm is significantly higher than that of the other three wrapper algorithms and one hybrid algorithm.

In this paper, an analysis method is proposed to determine capture tolerance capability of an orthogonal distributed satellite capture device that exhibits characteristics, including modularization, miniaturization, extensibility, and peripheral layout. The verifiable limit poses of the satellite are obtained when the pose error of the satellite reaches the boundary of the tolerance domain of the capture device. A dynamic model of a body combined with a satellite and robotic arm, and a contact dynamic model between the combined body and the capture device are established to lay the foundation for the capture determination of satellites in limit poses. A pose-iteration method is used to predict the pose trajectory of the satellite during the capture process, and the results of the satellite capture in the limit poses are obtained. The tolerance capability of the capture device is obtained on verifying the success or failure of the capturing satellite in limit poses. The tolerance capability of an example is analyzed by utilizing the proposed analysis method and is tested via simulation software. With respect to the four typical limit poses in the tolerance domain, the simulation results indicate that the capture device successfully captures the satellite. The results reveal the accuracy of the obtained tolerance and also validate the effectiveness of the proposed tolerance analysis method. The contact dynamics model is imported into the method of tolerance capability analysis to realize the accurate calculation of the time-varying dynamic tolerance capability of the capture device.

To promote the speed of the supersonic air-to-air missile response when the target moves rapidly, a serial compliant mechanism based on the traditional double-slider mechanism actuated by shape memory alloy (SMA) wire is proposed to adjust the protruding height of the flow effector (FE) from the projectile. The aim is to achieve the active disturbance control of the projectile flight attitude. The actuation displacement of the SMA wire is amplified in the output direction, and the FE actuator structure is more lightweight and compact. Based on strain energy, Castigliano's second displacement theorem, and the coordinate transformation method, a free-end displacement mathematical model for a single corner filleted flexure hinge with a single load is developed. For the serial compliant mechanism under single or multiple loads, displacements of the focus points on different rigid links are investigated under varying boundary conditions, such as one fixed end and the other free, double fixed ends, one fixed end and the other a guide end, and double guide ends. The input displacement, output displacement, and displacement amplification of the proposed FE actuator are provided based on the above-mentioned compliance model and verified by finite element analysis and experiments.

Data compression and decompression have been widely used in modern communication and data transmission. But how to decompress the corrupted lossless compressed files remains a challenge. Aiming at the Lempel-Ziv-Storer-Szymanski (LZSS), a lossless data compression algorithm widely used in the field of general coding, this paper proposes an effective method to repair the errors and decompress and restore the corrupted LZSS files, and provides the theoretical basis for the method. By using the residual redundancy left by the LZSS encoder to carry the check information, the method can repair the errors in LZSS compressed data without any loss of compression performance. The proposed method neither requires additional bits nor changes coding rules or data formats. It is fully compatible with standard algorithms. That is, the data compressed by LZSS with error repair capability can still be decompressed by the standard LZSS decoder. The experimental results verify the validity and practicability of the proposed method.

We have entered an era where copious amounts of sensitive data are being stored in the cloud. To meet the rising privacy, reliability, and verifiability needs, we propose Gecko, a multi-cloud dispersal scheme where: (a) the key used to encrypt the data file is the secret in a Latin-square-autotopism secret-sharing scheme, (b) data files and encryption keys are dispersed separately to multiple clouds, and (c) a blockchain-based integrity-check protocol is devised to pinpoint faulty data. Gecko enables fast and thorough key renewal: when a portion of the key (the secret) is leaked, we replace all shares of the partially-leaked secret without replacing the secret itself; this immediately resists targeted attack to certain file without re-encrypting the data file itself. Key renewal is further accelerated by the blockchain-based integrity check. We evaluate Gecko theoretically and experimentally against the traditional AONT-RS dispersal scheme, drawing two conclusions: 1) Gecko admits powerful key renewal and identification of damaged data, with a minor transfer overhead; and 2) Gecko performs key renewal three to five times faster than AONT-RS hybrid-slice renewal (the closest thing AONT-RS has to key renewal).

In this paper, a multiuser multi-antenna relay system with wireless power transfer is investigated, where the relay node harvests energy from the radio frequency signal sent from the source node and utilizes the harvested energy to forward the information signal to users. Under the transmit power constraints at all nodes, the optimization problem is formulated which maximizes the achievable sum rate of the relay system by optimizing the time switching (TS) factor and the source and the relay precoding matrixes. We divide the complex non-convex objective problem into several subproblems which are solved by the proposed alternating optimization (AO) scheme. We also propose a novel power allocation scheme across subchannels to maximize the sum rate. Since the objective function to optimize the relay power allocation is not convex, difference of convex programming is adopted to transform the original non-convex problems into convex optimization problems. Simulation results are provided to verify the analysis and demonstrate the efficiency of the proposed AO scheme.

To overcome the disadvantage of clinical manual sleep staging, a convenient, economical, and efficient multi-class automatic sleep staging method is proposed based on long short-term memory network (LSTM) using single-lead electrocardiogram signals. From electrocardiogram signals, heart rate variability and respiratory signals were calculated, and, then, totally 25 features were extracted. Four different classifiers, including the two-class classifier to distinguish between wake and sleep, the three-class classifier to distinguish wake, non-rapid eye movement sleep, and rapid eye movement, the four-class classifier to distinguish wake, light sleep, slow wave sleep, and rapid eye movement, and the five-class classifier to distinguish wake, sleep stage N1, sleep stage N2, sleep stage N3, and rapid eye movement, were constructed using the LSTM. The single-lead electrocardiogram data from 238 patients with full sleep stages during sleep were used for the training set and the data from other 60 patients were regarded as a validation set. The rest of 75 patients have left aside for testing set. The accuracy of two-class, three-class, four-class, and five-class sleep staging was 89.84%, 84.07%, 77.76% and 71.16% and the Cohen's kappa statistic k was 0.52, 0.58, 0.55, and 0.52, respectively, which realized the moderate agreement with clinical analysis. When expanding the dataset to extra 1068 patients with missing sleep stages, the accuracy has no obvious reduction but the Cohen's kappa statistic k dropped to 0.51, 0.52, 0.48 and 0.43, respectively. The proposed method, in this paper, is promising for low-cost, efficient, and convenient sleep staging in home care monitoring.

In hybrid device-to-device (D2D) and cellular networks, the cross interference between different communication systems severely limits the system performance. Fortunately, non-orthogonal multiple access (NOMA)-based coordinated direct and relay transmission (CDRT) can fully exploit the inherent property of NOMA to minimize such interference, which provides a potential strategy for hybrid networks to enhance the spectral efficiency (SE) and extend the cell coverage. Based on this idea and the superior SE requirements in future wireless networks, a D2D communication assisted uplink CDRT using NOMA is proposed, in which a base station (BS) directly communicates with a cell-center user (CCU) while communicating with a cell-edge user (CEU) with the aid of a relay. Particularly, uplink transmission of CCU and D2D communication from CCU to CEU are jointly designed according to NOMA principle in the cooperative phase. To verify the system performance of the proposed scheme, closed-form expressions of the system ergodic sum rate (SR), outage performance and outage throughput are derived, respectively. Numerical results illustrate that the proposed scheme achieves superior system ergodic SR as well as outage throughput with slight outage performance loss of uplink transmission, compared with conventional NOMA-based uplink CDRT.

The transmission of malicious information is often accompanied with normal information flows in complex networks. For the intrusion of unknown malicious information, if there is no effective defensive method to suppress its diffusion, the network may be confronted with incredible catastrophe. In this paper, to enhance the security of information diffusion in complex networks, a node trust-value management mechanism (NTVMM) is proposed. Basically, node trust value and threshold are assigned with node importance at the beginning. According to node benefit and loss in information diffusion, the two algorithms, node trust-value updated algorithm (NTVUA) and node trust-threshold updated algorithm (NTTUA), are devised to dynamically update node trust value and trust threshold, respectively. Compared with the existing node quarantine and edge blockage schemes, NTVMM only relies on the trust relationship of nodes to suppress malicious information diffusion with no need for knowing the global infection information of the network. Besides, to further make a trade-off between information security and information loss, NTVMM is associated with the classical information diffusion model to obtain the most appropriate trust threshold. Finally, we devise the network profit function to evaluate the profits brought by different methods. The simulations are respectively carried out in two synthetic complex networks and a real-world complex network. The results of simulation demonstrate that when malicious information invades the network, NTVMM can reduce the number of infected nodes in the network and enhance the security of information diffusion. Moreover, compared with node quarantine and edge blockage schemes, the proposed scheme can obtain a better network profit, and make a trade-off between information security and information loss.

In this paper, a cache-enabled wireless network with circuit power consumption is considered, and the strategy of joint transmission and local caching is studied. The optimization problem of energy consumption minimization is formulated, where the optimized variables include the transmission rate and the local caching rate. The constraint conditions contain the maximum data departure curve, the minimum data departure curve, and the range of the local caching rate. The original problem is intractable and is transformed into a two-layer optimization one by variable substitution. Under given caching policy, the sufficient and necessary conditions for the optimal offline transmission strategy are deduced by using the transmission characteristics. Then, an energy-efficient offline transmission strategy is proposed, and its optimality is proved. The simulation results are provided to verify the performance of the proposed strategy. The simulation results reveal the impact of the cache capacity and the file popularity on energy consumption. Under the same conditions, the performance of the proposed strategy is better than that of the other known algorithms. The proposed strategy to energy-efficient communication is helpful for future green communication.

The quaternion gradient plays an important role in quaternion signal processing, and has undergone several modifications. Recently, three methods for obtaining the quaternion gradient have been proposed based on generalized HR calculus, the quaternion product, and quaternion involutions, respectively. The first method introduces the quaternion rotation, which is difficult to calculate and often relies on lookup tables; the second is cumbersome because it depends on all the real and imaginary parts of the variables and parameters; the third transforms the gradient from the quaternion domain to the real domain, and uses real derivatives and the real chain rule. In this paper, we generalize the quaternion involutions method to calculate the quaternion gradient of the quaternion matrix function. Several examples are presented in which the proposed gradient is applied to the adaptive filter, Kalman filter, and kernel filters to solve the associated optimization problems.

A fractional-step docking strategy based on adaptive sensing is proposed for an orthogonally distributed container docking device that exhibits various characteristics, including modularization, miniaturization, extensibility, and peripheral layout. A dynamic model of a body combined with a space station container and robotic arm and a contact dynamic model between the combined body and the docking device are established to lay the foundation for the docking determination of the container in limit poses. Taking the structural and dynamic characteristics of the orthogonally distributed docking device into consideration, the actions of a diagonal double-docking hook were matched under limited working conditions. A 6-D force sensor placed at the end of the robotic arm was used to perceive the contact force of the container and the position of the docking hook, providing a basis for selecting a docking strategy. The tolerance of the docking device was analyzed when applying the docking strategy, the results of which demonstrated a much larger tolerance than that found in the synchronous docking. An orthogonally distributed docking device and an experimental platform were developed, and the tolerance of the device when using the adaptive sensing docking strategy was tested and verified. A comparison between the experiment and simulation proves the effectiveness and feasibility of the proposed adaptive sensing docking strategy.

Splitting and combination are two important events of group target motion. However, the existing tracking approaches for group target splitting and combination events suffer the problems of high-computational cost and low accuracy. Under the random finite set framework, with target extent modeled by random matrix, the algorithms for group target splitting and combination tracking based on δ-generalized labeled multi-Bernoulli filter are researched. Three classical splitting modes of group target are discussed. With appropriate splitting criteria developed, e.g., the setting of the splitting gate, the chosen of the splitting dimension, the compensation of the subgroup's centroid position, and so on. According to the characteristics of each mode, the efficiency and the accuracy of the algorithm for group target splitting event are improved. The group combination approach is derived, where the representation of labels under the tack complicatedly changed condition, e.g., the group splitting and combination events jointly exist are given. With the velocity combination criterion established according to the target motion trend, a decreased sensitivity of the algorithm for target splitting event is avoided. The results show that the proposed algorithms have improved the tracking performance for group target splitting and combination events.

In this paper, a new generalized Nash bargaining framework is proposed for joint channel and power allocation in device-to-device (D2D) communication underlying cellular networks. In the considered system, cellular users (CUs) must jointly determine to share radio resources with D2D users (DUs) and to charge the DUs accordingly. The proposed framework aims to maximize the overall throughput of the communication system by guaranteeing the minimum rate of each CU and proportional fairness and efficient power allocation among the CUs and DUs. To make this NP-hard problem more tractable, it is decomposed into two sub-problems: channel assignment and power allocation. First, an optimal channel assignment strategy is derived using a max-weighted max-flow algorithm. Then, the optimal power allocation strategy for both DUs and CUs is analyzed using the Lagrangian multiplier method. Numerical results are presented to show the throughput performance characteristics of different resource allocation solutions. Comparisons between the proposed and traditional policies show the significant effect of fairness on the transmission performance.

In this paper, the secure energy-efficient transmission problem for an artificial-noise (AN)-aided simultaneous wireless information and power transfer (SWIPT) and vehicle-to-infrastructure (V2I) communication system with the imperfect channel state information (CSI) is studied. The secure energy efficiency problem is formulated with the imperfect CSI, where the constraints contain the minimum secrecy rate request of the legitimate vehicle (LV), the minimum energy harvesting (EH) request, the maximum transmit power, and the power splitting (PS) value. By exploiting the maximum ratio transmission (MRT) scheme and the norm-bounded matrix theory, we convert the original intractable problem to a fractional optimization one with three variables. Then, a two-level optimization algorithm is presented for the nonconvex fractional optimization problem. By introducing an auxiliary slack variable, the first level problem can be solved via one-dimensional optimization algorithm. The second level fractional optimization problem was furhther converted into an integer optimization one by the Dinkelbach method, and the closed-form solutions of the transmit power and the AN power are provided. Simulation results are given to verify the performance of our presented algorithm.