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Transient simulation in power engineering is crucial as it models the dynamic behavior of power systems during sudden events like faults or short circuits. Electromagnetic transient simulations involve multiple coordinated tasks. Traditional simulations are centralized and struggle to meet scalability requirements. To achieve these goals, distributed electromagnetic transient simulation has emerged as a new trend. Nevertheless, the distributed electromagnetic transient simulation introduces network communication. Achieving real-time simulation across distributed nodes poses the challenge of minimizing communication costs. In this paper, our proposal focuses on optimizing the task orchestration to reduce communication costs. Specifically, in the electromagnetic transient simulation, these tasks has certain communication pattern where the communicated objects of each task are pre-defined. We represent the pattern as a graph, with tasks represented as nodes and communications as edges. Furthermore, we propose to use graph partition with the objective of minimal communication costs and fine tune the partitions with the resource requirements of each distributed node. The experimental results demonstrate that our proposal has strength in achieving high-performance electromagnetic transient simulation.

This paper studies the distributed bipartite output formation control problem of heterogeneous discrete-time linear multi-agent systems (MASs) via cooperative output regulation theory. To construct the bipartite formation, under the structurally-balanced augmented directed signed graph, the followers of two antagonistic subgroups are supposed to respectively keep the desired relative positions with the leader, also called as the exosystem, in the same magnitude but the opposite sign. Since the information of exosystem can not be directly obtained by all followers, the distributed exosystem observer based on the discrete-time algebraic Riccati equality (ARE) is presented and the distributed state feedback controller is further designed. Moreover, in the case where the states of followers are not available, the distributed output feedback controller is proposed by introducing the state estimator. Finally, two numerical examples are given to demonstrate the effectiveness of analytic results.

Retinal disease detection and diagnosis relying solely on artificial retinal diseases will put great pressure on doctors and increase the rate of misdiagnosis. Therefore, the development of computer vision technology has brought the possibility for ophthalmologists to use computer-aided diagnosis. In recent years, most models for retinal disease recognition have been based on deep learning, which has the disadvantage of requiring large amounts of data and training time. It is also partly based on broad learning and its disadvantages are that feature extraction ability is limited and poor scalability. To overcome these limitations, we propose a novel artificial intelligence-based approach for the automatic assessment of retinal disease images called a multi-view deep-broad learning network (MDBL-Net), which absorbs the advantages of deep learning and broad learning. MDBL-Net comprises a Multi-view and Multi-scale Feature Extraction (MMFE) module and a Multi-scale Aggregation (MA) block. The MMFE module extracts features of different scales by learning feature representations from multiple views, while the MA block fully aggregates multi-scale deep-broad features from low-level to high-level representations. Experiments were conducted on two public datasets, UCSD and OCT2017. Experiments were conducted on two public datasets, UCSD and OCT2017, and results demonstrate that MDBL-Net achieves high accuracy even with limited training data (only 1%) and significantly reduces training time compared to traditional deep learning models. Specifically, MDBL-Net achieved an accuracy of 96.93% on the UCSD dataset and 99.90% on the OCT2017 dataset, outperforming state-of-the-art models in both cases. These findings suggest that the proposed MDBL-Net approach holds great promise for the task of retinal disease screening and recognition.

Owing to the strong protection of data privacy, federated learning (FL) has become a key method to achieve intelligent decision making in smart homes. However, under realistic conditions, such as differentiated requirements and heterogeneous service environments, FL in smart homes faces the problems of non-independent and identically distributed (non-IID) data and uneven computing power, which lead to the poor adaptability of global models. To address this issue, this study proposes a cluster FL architecture based on edge-cloud collaboration. Firstly, a Gaussian mixture model-based cluster FL is proposed to improve the model accuracy by clustering features on the FL dataset and ensuring an independent identical distribution of the data. Subsequently, a model training strategy based on edge-cloud collaboration is proposed to achieve the sharing of edge-cloud computing power by split learning, which provides sufficient computing power for model training. The simulation results show that the proposed architecture improves the accuracy of global models, meanwhile ensures the provision of normal network service.

Substation equipment defects are important factors affecting the safe operation of power grids. However, many non-rigid defects have low detection accuracy and poor robustness,due to boundary ambiguity, irregular shape and tiny size. To address these problems,we propose a swin-transformer with recursive gated convolution framework for substation equipment non-rigid defect. Firstly, in order to effectively detect non-rigid defect objects to improve the discriminability of image features, we design the Swin-Transformer with Recursive Gated Convolution(Swin-RGC) framework to extract the interaction features between spaces in the deep model. Secondly, to avoid the loss of object location information, the Task-aligned One-stage Object Detection(TOOD) head is improved by fusing Coordinate Attention modules. Finally, a substation equipment defect detection dataset is established to provide a baseline for detecting non-rigid defects in substation power equipment. Experiment results on our dataset demonstrate that our proposed method achieves the performance of 69.9% Mean Average Precision (mAP) in the substation equipment non-rigid defect detection, which outweighs the state-of-the-art approaches.

This paper addresses the distributed secure consensus problem for linear multi-agent systems (MAS) with unmeasurable states and Denial-of-Service (DoS) attacks. The observer is designed to be used in estimating unmeasurable states of agents. Then, an observer-based distributed event-triggered control protocol is proposed to save network resources. By using the piecewise Lyapunov function approach, several sufficient conditions for ensuring secure consensus are obtained. Further, a rigorous theoretical analysis shows that the given event-triggering scheme exhibits no Zeno behavior. Finally, the effectiveness of the proposed method is verified by numerical simulation.

Studying the changes of vegetation coverage and carbon storage in a terrestrial ecosystem will provide important information for ecosystem optimization. The Kubuqi Desert is a typical ecological fragile area, and its ecological construction is not only to build an ecological barrier to protect northern China but also to be a case of similar regions in the world. Engebei ecological demonstration area, located on the northern margin of the central Kubuqi Desert, was selected as the study area. The objective is to establish a carbon inversion model through the LASSO regression model in this area which can promote its ecological development. Taking Landsat, Sentinel-2A remote sensing data, and field sample data as data sources. The vegetation coverage was calculated through the remote sensing data. The carbon reserves in plots were calculated through the biomass equation. Then, the appropriate carbon storage estimation model was found, which was established by the LASSO regression model and the Sentinel-2A remote sensing images. Finally, the carbon reserves over the years were calculated by this estimation model. Research shows that: first, the overall vegetation coverage of the study area was not high, which was around 27%–37%; second, the proportion of lower (I) level vegetation coverage in the study area decreased overall, and the proportion of higher (V) level vegetation coverage generally stabilized; third, carbon reserves remain roughly above 71752.64t, up by 20,209.61t in 2021 compared to 2020, and up by 14,867.02t in 2021 compared to 1991.

Hyperspectral image (HSI) classification has always drawn great attention in the field of remote sensing. Various deep learning models are in the ascendant and gradually applied to HSI classification. Nevertheless, limited-labeled and class-imbalanced datasets largely make the classifier prone to overfitting. To address the above problem, this article proposes a two-branch generative adversarial network with multiscale connections (TBGAN), which includes two generators to produce the spectral and spatial samples, respectively. Thereinto, the spectral generator is imbued with the self-attention mechanism to maximumly capture the long-term dependencies across the spectral bands. And meanwhile, an elaborated discriminator with two branches is devised in TBGAN for extracting the joint spectral-spatial features. Besides, the multiscale connections are placed between the discriminator and two generators to alleviate the instability problems caused by the inherently backward propagation of gradients in GAN. Furthermore, a feature-matching term is added to the loss function to prevent the generators from overtraining upon the current discriminator, thereby further improving the stability of the network. Experiments upon three benchmark datasets demonstrate that TBGAN achieves an extremely competitive classification accuracy and exerts lower sensitivity to the training sample size compared with several state-of-the-art methods.

By integrating communications in different domains, integrated radio and optical networks can serve a wider range of applications and services. Integrated radio and optical network scenarios will involve more weak-computation-ability network nodes, such as small-cell base stations. To pursue efficient integrated radio and optical networks, more efficient ways to conduct transmission under the demand of edge and cloud collaboration are required. The lack of forward-looking resource allocation may easily lead to a waste of network resources without an expected return. Therefore, an efficient resource allocation scheme needs to consider certain issues: 1) a comprehensive perspective of traffic prediction; 2) a release of pressure on the transmission pipeline during the prediction process; and 3) a reduction of loss of edge nodes due to the computation. In this paper, benefiting from machine learning, we propose a resource allocation with edge-cloud collaborative traffic prediction (TP-ECC) in integrated radio and optical networks, where an efficient resource allocation scheme (ERAS) is designed based on the prediction results with the gated recurrent unit model. We maximize the utilization of limited resources to improve the awareness of network status. We present three evaluation indicators and build a network architecture to evaluate our resource allocation scheme. Through edge-cloud collaboration, our proposal can improve traffic prediction accuracy by 9.5% compared with single-point traffic prediction, and resource utilization is also improved by edge-cloud collaborative traffic prediction.

Human activity recognition (HAR) is gaining interest with many important applications including ubiquitous computing, health-care services and detection of diseases. Smartphone sensors have high acceptance and adherence in daily life and they provide an alternative and economic way for activity recognition. To improve the performance of smartphone sensor-based HAR, a novel smartphone sensor-based HAR method (hybrid diversity enhancement with selective ensemble learning, HDESEN) that utilizes selective ensemble learning with differentiated extreme learning machines (ELMs) is proposed, where hybrid diversity enhancement is proposed to boost the diversity of base models and an improved binary glowworm swarm optimization (IBGSO) is employed to effectively enhance the learning process by choosing a superior subset for ensemble instead of all. Firstly, statistical features in the time domain and frequency domain are extracted and integrated from smartphone sensors and then three filter-based feature selection methods are utilized for desirable base models. Secondly, to enhance the diversity of the base models, three types of diversities are introduced to construct different base models, respectively. Among them, Bootstrap is introduced to design distinctive training data subsets for differential base models, random subspace and optimized subspace are proposed to obtain different feature spaces for constructing base models. Thirdly, a pruning method based on glowworm swarm optimization (GSO) is proposed to find the optimal sub-ensemble from the pool of models from all diverse types to implement selective ensemble learning. The experimental results on tow publicly available datasets (UCI-HAR and WISDM) demonstrate the proposed HDESEN can reliably improve the performance of HAR and outperforms the relevant state-of-the-art approaches.

Outlier detection in vibration signals can play an important role in addressing the issue of structural or environmental changes during vibration testing. In this study, a transformer-based model for outlier detection is proposed. Unlike previous statistical and regression outlier detection methods, the proposed model can identify the outlier location in a high dimensional observation space using the self-attention mechanism. The location of outliers within the vibration observation is marked by a combination of a spatial label and a temporal label. The outlier detection performance of the model is verified by a numerical study of the plane wave and an experimental study of the vibrating plate. These two studies show that the proposed model has good label prediction accuracies (all above 85%) toward the outlier location within the plane wave and vibrating plate observations.

Founded on bounded rationality and limited information, evolutionary game theory has been preliminarily applied in many fields, such as electricity market (EM). To address the complex behavioral decision-making issues in the more-common three-population multi-strategy evolutionary game (3PmSEG) scenarios in EM. This paper explores the long-term evolutionarily stable equilibrium (ESE) characteristics of general 3PmSEG systems with the aim of systematically investigating the evolution process of long-term on-grid bidding of a generation-side EM based on these features. First, the long-term ESE characteristics of general three-population two-strategy and three-strategy evolutionary games are thoroughly investigated. Complete relative net payoff (RNP) parameters are defined for these games. Then, the modeling idea of general 3PmSEGs is elaborated. Research shows that the game can be guided to evolve toward an expected long-term ESE point by properly adjusting its RNP parameters. To verify this, finally, the long-term on-grid bidding of power generation is investigated for a tripartite generation-side EM. The case study reveals that effective government supervision can effectively promote new energy accommodation of the market. Overall, the models developed in this paper are relatively universal and practical, which can provide some theoretical and methodological references for complex evolutionary game issues in related fields.

Aiming to solving the nonlinear coordinated trajectory tracking control problem of multi-USVs under the influence of slowly changing and uncertain environmental disturbances, reducing energy consumption and ensuring tracking performance and stability, a distributed event-triggered adaptive coordinated trajectory tracking controller (DET-ACTTC) for multiple unmanned surface vessels (multi-USVs) is proposed based on the undirected communication topology. By introducing a virtual leader, a leader-follower formation is used to generate the coordinated tracking errors. Based on the expected formation and states, the aggregate tracking errors are defined, and event-triggered measurement errors are constructed to design the event-triggered conditions. An adaptive term is used to compensate for external environment disturbances, and the DET-ACTTC is deduced for each USV in the group. The tracking errors of the event-triggered coordinated trajectory tracking control (ETCTTC) system gradually converge to zero, and Zeno behavior is avoided. The theoretical results are verified by simulations.

In order to sail safely in a marine environment with neighboring obstacles, a local collision avoidance algorithm based on a steering maneuver considering International Regulations for Preventing Collisions at Sea (COLREGs) is proposed for unmanned surface vehicles (USVs). In this paper, the algorithm consists of three parts: collision risk assessment, steering occasion determination, and navigation waypoint update. The collision risk assessment is used to judge a collision risk based on an analytical feasible angle interval by the closest point of approach method. The steering occasion determination provides a safe distance interval from an obstacle by analyzing a marginal steering angular velocity. The navigation waypoint update generates a temporary waypoint to navigate the USV and considers the overtaking, head-on, and crossing rules of COLREGs. The temporary waypoint is calculated based on a real-time optimal orientation angle, which is determined by an optimization objective function for angle deviation. The three parts for local collision avoidance are determined by a finite state machine. In simulations, a USV with actual dynamic constraints runs successfully in a static and dynamic multiobstacle environment, and the results indicate the feasibility and validity of the proposed algorithm.

The reinforcement learning framework for multi-hop relational paths is one of the effective methods for solving knowledge graph incompletion. However, these models are associated with limited performances attributed to delayed rewards and far-fetched search trajectories. To overcome these challenges, we propose the searching window and multi-policy agent. The searching window provides a large action space, so that the agent can backtrack based on the newly obtained information and establish a local graph instead of a path chain. Based on the searching window, a double long short-term memory (DBL-LSTM) policy network is introduced to encode the local graph and relation sequence, after which the encoding information is used by the agent to select a correct entity to grow the local graph. Furthermore, multi-policy agent separately infers the local graph through three different policy networks, then, all local graphs are integrated into an information-rich local graph. Experiments using the WN18RR dataset revealed that local graph reasoning with searching window had greater rewards than path reasoning, the proposed DBL-LSTM policy network improved all HITS@N(N = 1,3,5,10) compared to prior works, and that the multi-policy agent achieved higher hit rates than single-policy agent.

Accurate propagation characteristics are essential for future indoor millimeter-wave (mmWave) small cell network planning. This paper presents propagation measurements at 26 GHz and 38 GHz which are important candidate bands for fifth generation mmWave communication. Measurements are conducted in an indoor corridor, as well as a stairwell whose mmWave channel is seldom investigated before. In these measurements, an omnidirectional biconical antenna is used as transmitter and a steerable directional horn antenna is used as receiver. The directional and omnidirectional path loss exponents, shadow factors, cross-polarization discrimination ratios and root-mean-square delay spreads are analyzed for both line-of-sight and non-line-of-sight scenarios in both co-polarization and cross-polarization, and these characteristics are compared for different frequencies and environments. It is found obvious depolarization phenomenon in non-line-of-sight scenario for higher frequency. Compared to the corridor, the stairwell has larger path loss exponents and root-mean-square delay spreads, and the depolarization is also more evident in stairwell. The results in this paper are beneficial to building efficient and robust indoor mmWave communication systems.

Quantum-key-distribution (QKD) enabled optical networks can provide secure keys for different kinds of applications to ensure the high security of communication processes. As the number of wavelength channels in a single-core fiber is limited, space division multiplexing (SDM) has been considered to be a valuable technique to provide more wavelength resources, which solves the problem of insufficient channel capacity. Due to the rapid expansion of data volume, the SDM-based QKD network has been regarded as an important paradigm. At the same time, resource allocation in multi-core optical networks is a problem worthy of studying. In this work, four routing, core, and wavelength allocation (RCWA) algorithms are proposed in the multi-core optical network for quantum service requests and classical service requests, which are referred to as RCWA without perception (RCWA-WP), RCWA with crosstalk perception (RCWA-XTP), RCWA with spectrum perception (RCWA-SP), and RCWA with core perception (RCWA-CP). Simulations are carried out to evaluate the network performance in terms of blocking probability, key utilization, and average crosstalk intensity. The comparative results indicate that the RCWA-SP algorithm can improve network performance and reduce the blocking probability, while the RCWA-XTP algorithm can reduce inter-core crosstalk during transmission.

The human microbiome is comprised of thousands of microbial species. These species will substantially influence the normal physiology of humans and cause numerous diseases. Microbiome data can be measured by sequencing, microarray, or other technologies. With the fast development of these technologies, downstream analysis methods should also be designed to effectively and accurately discover the valuable information that is hidden in the data. Many methods have been designed for the count data of microbiome. However, to our knowledge, there are only a few methods developed for the continuous microbiome data. Many microbiome data have an over-dispersed and zero-inflated data structure. Traditional methods rarely characterize this data structure and only focus on the differences in the abundance between different samples. In this study, we introduce a novel method, the zero-inflated gamma (ZIG) omnibus test, to specifically test the continuous and zero-inflated microbiome data. In this test, abundance will be tested along with zero prevalence and dispersion. We compared this method with five other popular methods. We found that ZIG omnibus test has significantly higher power and a similar or lower false-positive rate than the competing methods in the tests of simulated data. It also found more proved microbiomes in the real data with tonsil cancer. So, we conclude that ZIG omnibus test is a robust method across various biological conditions in the differential expression test of microbiome data.

In recent years, sensor-based human activity recognition (HAR) has become a hot topic due to the advancement of sensing technologies, wireless communication technologies and nano-technologies. Since the sensor signals are usually non-stationary and quite noisy, both selecting the discriminant feature representations and finding out the optimal parameters for recognition algorithm play an important role for the enhanced performance and robustness of an HAR system. However, most of the previous research focused on one of them ignoring their interactions. Very few studies focused on these two aspects simultaneously. Considering the two factors separately may lead to inferior HAR performance. This paper presents a novel HAR framework which can optimize the feature set and the parameters of recognition algorithm synchronously for robust and optimal system performance. A new hybrid feature selection methodology using game-theory based feature selection (GTFS) and binary firefly algorithm (BFA), called GTFS-BFA, is proposed. GTFS-BFA is a hybrid methodology combining evidence from both filter and wrapper feature selection methods. It consists of two phases, namely pre-selection phase and re-selection phase. Pre-selection phase relies on game-theory-based filter method, while the re-selection phase uses binary firefly algorithm (BFA) as a wrapper method. The popular and efficient algorithm kernel extreme learning machine (KELM) is utilized as a classifier. The experimental results indicate that the proposed method can obtain better comprehensive performance in terms of four performance measures through a comparison to other existing methods on daily activity dataset from five body positions.

Time-Wavelength Division Multiplexing Passive Optical Network (TWDM-PON) is considered as a promising solution of next generation PON (NG-PON). The integration of Edge Computing (EC) and TWDM-PON can satisfy the QoS requirements of delay-sensitive applications by providing storage, processing and caching capabilities at the network edge. However, with limited resource capacity, edge nodes in TWDM-PON are vulnerable to network attacks, e.g., Distributed Denial of Service (DDoS) attacks. Resource exhaustion in the attacked nodes easily leads to QoS degradation and even service blocking. This paper investigates how to effectively schedule traffic to mitigate the DDoS attack in EC-enabled TWDM-PON. Based on the collaboration of edge nodes, an adaptive traffic scheduling algorithm is designed to minimize the impact of DDoS attacks on delay sensitive services. The performance of the proposed algorithm is evaluated in simulation, where direct and indirect DDoS attacks are simulated. Besides, the attack duration and the number of attacked nodes are considered in the evaluation. Simulation results show that the proposed algorithm can effectively mitigate DDoS attacks in terms of reducing the QoS degradation rate and blocking rate of delay-sensitive services.

A critical challenge in image restoration is the presence of various types of noise. Meanwhile, noise detection is a crucial step in mixed noise removal. This paper tackles the challenge of restoring images corrupted by a mixture of additive Gaussian and multiplicative Gamma noise. In the proposed method, we integrate the noise detection process into a variational model using a dual formulation of a maximum a posteriori (MAP) estimator. The variational model consists of a novel adaptive fidelity term and a plugin-and-play regularization term. The fidelity term contains an adaptive weight that can automatically detect the noise types, levels, and pollution ways for each pixel. There is flexibility in choosing a plugin-and-play regularization term. For example, we can use a model-based regularizer or a deep learning-based regularizer. In addition, we present a splitting algorithm to minimize the proposed cost functional. This splitting technique enables us to transfer a mixed noise removing problem to several subproblems, including noise removal and detection. The noise detection process can be iteratively estimated by the proposed algorithm itself. Therefore, in the numerical experiments, the proposed model outperforms the existing Rudin-Osher-Fatemi (ROF), Aubert-Aujol (AA), BM3D, and deep learning-based single type denoiser. Experimental results show that the proposed model can remove noise more efficiently and better preserve details in images. Compared to the existing best-performing single type denoiser, on average, the improvements of PSNR values range from 0.33 dB to 0.81 dB under noise mixture ratios $\alpha =0.4, 0.6$ .

Small cells (SCs) are expected to be ultra-densely deployed in or close to the traffic hot-spots in the fifth generation (5G) mobile networks to provide wireless capacity cost-effectively. Traffic hot-spots change over time, which means SCs cannot be deployed in a one-off manner as macrocells normally do, rather they should be constructed in a staged process. Hence, mathematical models that capture the time-varying staged-construction process, are urgently needed for operators to effectively predict the construction period, but are currently lacking. In this paper, inspired by the foam bursting process-a natural phenomenon that can be observed in daily life such as hand-washing, we first propose a novel model that can predict the time-varying expectation and logarithmic variance of SC coverage areas. Then, we verify the model by real network deployment cases. Additionally, in order to extract parameters from historical base station deployment data, a parameter estimation algorithm is designed and verified. The findings of the paper reveal that mobile operators should construct ultra-dense SC networks in a staged manner like how larger foams split into smaller ones.

In the fifth-generation (5G) mobile networks, the traffic is estimated to have a fast-changing and imbalance spatial-temporal distribution. It is challenging for a system-level optimisation to deal with while empirically maintaining quality of service. The 5G load balancing aims to address this problem by transferring the extra traffic from a high-load cell to its neighbouring idle cells. In recent literature, controller and machine learning algorithms are applied to assist the self-optimising and proactive schemes in drawing load balancing decisions. However, these algorithms lack the ability of forecasting upcoming high traffic demands, especially during popular events. This shortage leads to cold-start problems because of reacting to the changes in the heterogeneous dense deployment. Notably, the hotspots corresponding with skew load distribution will result in low convergence speed. To address these problems, this paper contributes to three aspects. Firstly, urban event detection is proposed to forecast the changes in cellular hotspots based on Twitter data for enabling context-awareness. Secondly, a proactive 5G load balancing strategy is simulated considering the prediction of the skewed-distributed hotspots in urban areas. Finally, we optimise this context-aware proactive load balancing strategy by forecasting the best activation time. This paper represents one of the first works to couple the real-world urban event detection with proactive load balancing.

Network slicing is a key enabling technology to realize the provisioning of customized services in 5G paradigm. Due to logical isolation instead of physical isolation, network slicing is facing a series of security issues. Side Channel Attack (SCA) is a typical attack for slices that share resources in the same hardware. Considering the risk of SCA among slices, this paper investigates how to effectively allocate heterogeneous resources for the slices under their different security requirements. Then, a SCA-aware Resource Allocation (SCA-RA) algorithm is proposed for Ultra-reliable and Low-latency Communications (URLLC) and Enhanced Mobile Broadband (eMBB) slices in 5G RAN. The objective is to maximize the number of slices accommodated in 5G RAN. With dynamic slice requests, simulation is conducted to evaluate the performance of the proposed algorithm in two different network scenarios. Simulation results indicate that compared with benchmark, SCA-RA algorithm can effectively reduce blocking probability of slice requests. In addition, the usage of IT and transport resources is also optimized.

Stock return forecasting typically requires a large number of factors and these factors usually exhibit nonlinear relations with each other. Conventional methods of stock return forecasting mainly fall into two categories: Technical Analysis and Fundamental Analysis. Technical Analysis focuses on time-series data, while Fundamental Analysis explores low-frequency fundamental variables. Although there are substantial works on either time-series analysis or fundamental analysis, few studies have enriched the time-series forecasting with fundamental variables, as the features are characterized by different frequencies, scales and types. In this paper, we propose a Long Short-Term Memory and Deep Neural Network (LSTM-DNN) hybrid model to integrate the fundamental information into time-series forecasting tasks. We demonstrate how investors can benefit from the superior performance of LSTM-DNN by constructing a long-short portfolio that takes long positions in stocks with the highest forecasting returns and short positions in stocks that are expected to decline. Extensive experimental results on real data show that our novel framework could improve the profitability of long-short portfolio strategies compared to the state-of-the-art approaches. We also find evidence indicating that the outperformance of LSTM-DNN model comes from its enhanced ability to extract information from the nonlinear relations among various features, rather than bearing more market risks. Besides the novel framework, we propose a cross-section normalization method, which benefits the framework by providing enriched cross-section signals.

In view of the inherent defects of traditional network protocols, unreasonable video content distribution process, content scheduling and routing mechanism need to be optimized in the current edge computing (EC) network. A centralized control EC network architecture of named data networking (NDN) is proposed in this paper. EC nodes are deployed on the edge of the network to provide caching and computing capabilities, and distribute video streams. So that transmission delay is reduced, bandwidth cost is saved, and the quality of experience (QoE) is improved. Firstly, the communication mechanism of NDN is introduced to distribute content and improve the efficiency of content distribution. In the scheme, the content distribution mechanism of video stream is improved in a distributed way. Different bit rate versions of video clips are distributed and buffered on the distributed EC nodes, so as to avoid redundant cache and save storage resources. In addition, the user interest content model is constructed. That is, video is divided into different categories according to content and user content preference model. Finally, with the limited cache and computing resources of EC nodes, the software defined network (SDN) mechanism is introduced for centralized management and control. And it provides a solution for content scheduling problem under resource constraints. In the scheme, the content distribution mode is decided by the effective arrangement of network resources, and the user's request is processed and responded cooperatively to avoid redundant cache and transmission. Based on the selection of forwarding response mode, the distributed characteristics of EC nodes and the routing and forwarding mechanism of NDN are fully utilized to provide users with response. It is shown by the experimental results that the proposed scheme optimizes the load on the network side and the experience of the client to a certain extent.

In view of the demand for flexible drive and large load of the soft robot in the practical application, a novel type of flexible-actuated multi-degree-of-freedom (multi-DOF) parallel soft robot is designed. The proposed robot in parallel combination of three groups of flexible-actuated elements (FAEs) realizes large load by increasing the bearing area at the connection between flexible-actuated units (FAUs). In order to improve the driving flexibility, the multi-layer FAU is used to drive independently in parallel so as to realize omnidirectional bending movement by pneumatic drive. With the coupled analysis in terms of motion and force, the mapping model of kinematic attitude parameters and the external load force with output air pressure value is established. Finally, an experimental prototype is developed and an experimental test platform is built. Then, the comparison among the experimental data, simulation results and theoretical results verifies the capability of multi-DOF omnidirectional movement and flexible-actuated large load.

Over the past decade, a large number of software defect prediction approaches have been proposed to identify the defect-prone modules by mining software repositories. Recently, a novel scenario called Cross-Version Defect Prediction (CVDP) begins to draw increasing research interests, as it is more reasonable and applicable in practice to adopt the labeled defect data of previous versions to predict defects in the current version of the same project. As a software project often has multiple previous versions, CVDP on this kind of projects will face the following two critical but seldom reported issues, namely, data distribution difference and class overlapping. In this paper, we address these two issues by solving a version selection problem via a Cross-version model with Data Selection (CDS). The proposed CDS is a novel framework which treats the defect prediction of existing and new files in different ways. For the existing files, we propose a novel Clustering-based Multi-Version Classifier (CMVC), which can automatically select the training data from the most relevant and noise-free versions by assigning them higher weights than the others. We proposed a Weighted Sampling Model (WSM) for the new files which have never appeared in previous version by incorporating the outputs of CMVC. We evaluate the proposed CDS model on 28 versions across 8 software projects, and the experimental results demonstrate that CDS outperforms three baseline methods and a state-of-the-art approach in terms of three prevalent performance indicators.

The ternary optical computer (TOC) has attracted increasing attention from its providers and potential customers because of the advantages of its optical processor, such as low power consumption, numerous trits, parallelism, dynamical reconfigurability and bitwise allocability. The analysis of its performance has become an urgent problem to be solved in recent years. This paper builds a four-stage TOC service model by introducing synchronous multi-vacations and tandem queueing. Here, a vacation refers specifically to an optical processor vacation. Additionally, we propose a processor-divided-equally(PDE) strategy and a task scheduling and optical processor allocation algorithm under this strategy. Assuming that the intervals of task arrival follow a homogeneous Poisson process, we obtain some important performance indicators, such as the mean response time, mean number of tasks, and optical processor utilization under the PDE strategy, which are based on the M/M/1 and M/M/n queueing systems with exhaustive service and synchronous multi-vacations. The numerical results illustrate that the number of small optical processors has an important effect on the performance of TOC and that high vacation rate can improve the system performance to some extent.

During mobile edge computing, due to the movement of nodes and the exhaustion of node energy, link failure occurs thus reducing the network lifetime in the mobile ad-hoc network. When the route fails, because the single-path protocols need to restart the route discovery process, the delay of the network is greatly increased. Therefore, the multi-path routing protocol is proposed, saving the cost of route discovery. In this paper, we propose an ad hoc on-demand multi-path distance vector (AOMDV) routing protocol based on link lifetime and energy consumption prediction (named LLECP-AOMDV) for mobile edge computing. In the route discovery phase, the energy grading strategy is adopted. When the node energy is lower than the threshold, it no longer participates in the route discovery. In the routing selected phase, the path is selected based on the lifetime of the route link and the minimum energy consumption of the route. According to energy consumption, packet delivery rate, end-to-end delay performance indicators, we evaluate the comparison results. The result shows that under most network performance indicators and parameters, the proposed LLECP-AOMDV is superior to the other three protocols, which improves the network lifetime, reduces the node's energy consumption and the average end-to-end delay. The protocol is very useful for mobile edge computing.

State of health (SOH) estimation is always an important factor in ensuring the reliability and safety of lithium-ion batteries. In view of the shortcomings of the existing SOH estimation methods, such as non-universal, the estimation of different batteries is limited, and the accuracy is insufficient. A fusion estimation method that depends on an empirical degradation model and a data-driven method is proposed. First, we construct an empirical degradation model of lithium-ion battery SOH with charge-discharge cycles. Four working condition characteristics are extracted from the actual charging and discharging process of batteries. Then, with these features as inputs, the prediction error of the empirical degradation model is taken as the output, and training the error compensation model becomes dependent on the data-driven method. The actual working condition characteristics of the tested lithium ion battery are substituted into the training error compensation model, and the model output is fed back to the prediction results of the empirical degradation model. A high-precision estimation of lithium-ion battery SOH is thereby achieved. Finally, the proposed method is verified based on the NASA lithium-ion battery data set. The results show that the fusion method is applicable to different lithium-ion batteries of the same type, and the mean absolute percentage error of SOH estimation is approximately 2%, indicating that the proposed method exhibits good estimation performance and applicability.

In light of the millions of households that have adopted intelligent assistant powered devices, multi-turn dialogue has become an important field of inquiry. Most current methods identify the underlying intent in the dialogue using opaque classification techniques that fail to provide any interpretable basis for the classification. To address this, we propose a scheme to interpret the intent in multi-turn dialogue based on specific characteristics of the dialogue text. We rely on policy-guided reinforcement learning to identify paths in a graph to confirm concrete paths of inference that serve as interpretable explanations. The graph is induced based on the multi-turn dialogue user utterances, the intents, i.e., standard queries of the dialogues, and the sub-intents associated with the dialogues. Our reinforcement learning method then discerns the characteristics of the dialogue in chronological order as the basis for multi-turn dialogue path selection. Finally, we consider a wide range of recently proposed knowledge graph-based recommender systems as baselines, mostly based on deep reinforcement learning and our method performs best.

In the fifth-generation (5G) mobile networks, proactive network optimisation plays an important role in meeting the exponential traffic growth, more stringent service requirements, and to reduce capital and operational expenditure. Proactive network optimisation is widely acknowledged as one of the most promising ways to transform the 5G network based on big data analysis and cloud-fog-edge computing, but there are many challenges. Proactive algorithms will require accurate forecasting of highly contextualised traffic demand and quantifying the uncertainty to drive decision making with performance guarantees. Context in Cyber-Physical-Social Systems (CPSS) is often challenging to uncover, unfolds over time, and even more difficult to quantify and integrate into decision making. The first part of the review focuses on mining and inferring CPSS context from heterogeneous data sources, such as online user-generated-content. It will examine the state-of-the-art methods currently employed to infer location, social behaviour, and traffic demand through a cloud-edge computing framework; combining them to form the input to proactive algorithms. The second part of the review focuses on exploiting and integrating the demand knowledge for a range of proactive optimisation techniques, including the key aspects of load balancing, mobile edge caching, and interference management. In both parts, appropriate state-of-the-art machine learning techniques (including probabilistic uncertainty cascades in proactive optimisation), complexity-performance trade-offs, and demonstrative examples are presented to inspire readers. This survey couples the potential of online big data analytics, cloud-edge computing, statistical machine learning, and proactive network optimisation in a common cross-layer wireless framework. The wider impact of this survey includes better cross-fertilising the academic fields of data analytics, mobile edge computing, AI, CPSS, and wireless communications, as well as informing the industry of the promising potentials in this area.

Normal estimation is an essential task for scanned point clouds in various CAD/CAM applications. The method (GLSRNE) based on guided least squares representation (GLSR) balances speed with quality well among state-of-the-art methods. First, it segments each neighborhood into multiple sub-neighborhoods. For some neighborhoods, the segmentation is obtained by GLSR which is an efficient subspace segmentation model and widely applied in other applications. The segmentation of the rest neighborhoods is inferred via the subspace structure propagation (SSP) algorithm. Then, each sub-neighborhood is fitted by a plane. The plane achieving the minimum distance with the current point is selected for the final normal estimation. We make improvements for effectiveness and efficiency in the following three aspects. First, to improve the speed of GLSR, we propose a novel iterative algorithm to reduce the computation complexity from $O(n^{3})$ to $O(n^{2})$ with its convergence guaranteed theoretically, where $n$ represents the number of the data points. Moreover, this proposed algorithm will also be useful for other applications. Second, we add a normal constraint for SSP to improve accuracy. Third, when selecting one plane to estimate the final normal, we consider the match between the plane and all neighbors, whereas GLSRNE only considers the match between the plane and the current point. The experiments exhibit that our method is faster than GLSRNE and more effective than GLSRNE and other state-of-the-art methods.

Burden surface distribution plays a key role in achieving an energy-efficient status of blast furnace (BF). However, actual adjustment of burden surface usually depends on the operator's experience when the production status changes. Meanwhile, due to the characteristics of high dimension, strong coupling, and distributed parameters, it is difficult to establish the accurate mechanism model for BF ironmaking process. Considering the aforementioned issues, this paper proposes an integrated multi-objective optimization framework for optimizing burden surface distribution based on the analysis of BF operation characteristics. Firstly, data-driven models are constructed for two objectives, i.e., gas utilization ratio (GUR) and coke ratio (CR), and two constraints using adaptive particle swarm optimization (APSO) based extreme learning machine (ELM), named APSO-ELM. Multi-objective optimization is subsequently carried out between GUR and CR using the multi-objective differential evolution algorithm (MODE) to generate the Pareto optimal solutions. Finally, TOPSIS is applied to select a best compromise solution among the Pareto optimal solutions for this optimization problem. Comprehensive experiments are presented to illustrate the performance of the proposed integrated multi-objective optimization framework. The experimental results demonstrate that the proposed framework can give a reasonable burden surface profile according to the production status changes to guarantee the BF operation more efficient and stable.

Energy Efficient Hierarchical Clustering (EEHC) method is a distributed random clustering algorithm for residential wireless sensor networks (RWSNs) with the goal of maximizing network lifetime. However, during selection of the cluster head stage, a node declares itself as a cluster head to neighboring nodes with probability p, which does not consider well the topological characteristics of the network. The new energy-efficient hierarchical clustering approach based on neighbor rotation (EEHCN) for RWSN has been proposed in this paper, the new idea of which determines whether a node of RWSN can become a cluster head node by comparing the comprehensive weighting of the degree of the sensor node and the distance between the sensor node and the center of the subarea. When the cluster head is replaced, a member node in the set of neighbor nodes of the current cluster head becomes the new cluster head for a random time slice, and the merged information is transmitted to the nearest sink node in a multi-hop manner according to the routing information. The experimental results demonstrate that the proposed method is more effective in saving network energy, improving data transmission efficiency, and maximizing network lifetime of RWSN. The innovative research work is very useful for the relative application of the RWSN, such as smart home or smart community.

Physical layer authentication is an important way to ensure the security of optical communication network. We hereby propose a scheme to realize it by measuring the variation of the bit error rate (BER) on both sides of communication. In this scheme, the legitimacy of the receiver is identified by analyzing the BER variation of the optical fiber loop based on the short-term correlation of the channels. We then simulate a 16 PSK optical transmission system with intensity modulation direct detection-orthogonal frequency division multiplexing (IMDD-OFDM). The authentication effect is analyzed in the case of disturbance and beam split, as well as replacement of optical fiber channels caused by eavesdropper (Eve). The results show that this scheme is sensitive to the three kinds of attacks. A high probability of detection (PD) and a low false alarm rate (FAR) can be obtained. The experimental results show that with the increase of the frequency test, PD and FAR tend to be stable, and the authentication effect is better with the accuracy rate 100%.

A frequency-based modeling approach has been developed for the vehicle fitted with Hydraulically Interconnected Suspension (HIS) system. This frequency-based model has fewer degrees of freedom (DOF) than the reference time-based model. Several physical parameters of HIS system are selected to analytically investigate their effects on several indicators of vehicle roll, pitch and bounce modes, such as roll and pitch angular acceleration, vertical acceleration, and tire ground force. The HIS system parameters are also coordinately tuned and optimized to meet the ride comfort requirement. The full vehicle drop test is conducted for the experimental validation. The analytical results of the proposed model have a good agreement with the measurements.

The online monitoring of pointing error (PE) caused by the main reflector (MR) deformation under wind disturbance has become a knotty problem for large reflector antennas. This paper presents a real-time estimation algorithm for monitoring wind-induced PE by optimized acceleration measurement points. First, based on an unbiased minimum-variance input-state estimation theory via accelerations measurement, we set up the PE observation model which illustrates the relation between the structural vibration state and PE. Moreover, compared with the frequency domain integration approach, the non-integral observation model without introducing a low-frequency-cut filter can sense pseudo-static and low-frequency PE. Second, a sensor placement method considering sensor constraint spacing (SPMCSCS) is proposed to achieve a high-quality monitoring effect with low information redundancy. In the method, the constraint spacing between sensors is determined by a novel scan recognition algorithm in which regional division, and regional correlation analysis of the modal shape are carried out successively. Finally, the simulation implementation of a 7.3-m antenna under a wind disturbance is used to illustrate the algorithm.

As a position servo system to isolate disturbance from its carrier, stabilized platform requires high-precision and high-adaptability control. However, conventional integer-order PID (IOPID) control fails to meet that requirement. In this paper, a new controller design scheme is proposed for stabilized platform based on fractional calculus. The designed controller is called fractional-order PID (FOPID) controller, which has two extra parameters compared to conventional PID controller. On one hand, it provides more degrees of freedom to design FOPID controller. On the other hand, its differential order and integral order provides more flexibility to tune the controller performance. Therefore, a design method of FOPID controller based on dynamic software modeling is presented. To obtain the ideal controller's parameters, the particle swarm optimization (PSO) bionic algorithm is used to optimize an objective function. In addition, software simulation platform and hardware experiment system are built to design and test the FOPID controller. Finally, the results of simulation and experiment are included to show the effectiveness of the new control method.

Wafer acceptance test (WAT) is a key process of semiconductor manufacturing. The collected testing parameters can be used in identification of wafer defects, improvement of product yield, and control of production costs. However, WAT parameters regularly have characteristics such as high dimensions and strong redundancy, which prevent the wafer yield from accurate prediction and effective improvement. To overcome these shortcomings, a hybrid feature selection method is proposed to identify key WAT parameters influencing wafer yields. This method is composed of two stages, i.e. filter selection and wrapper selection. In filter selection, the minimum Redundancy Maximum Relevance (mRMR) filtering parameter pre-screening criterion based on mutual information (MI) is proposed. The relevance between each parameter and the wafer yield value is calculated by MI. At the same time, the criterion of MI is used to measure the redundancy between each parameter to select the minimum redundancy parameters, and reduce feature size for further searches. In wrapper selection, a wrapped key parameter identification model based on genetic algorithm (GA) and deep belief network (DBN) is designed. The coding and optimization of candidate input parameters are realized by GA. The wafer yield prediction error value of the DBN and the weight of the selected features are solved as the fitness function to realize the selection process of the combined parameters. In experiment, both testing data sets and industrial data are used to demonstrate the efficiency of this proposed method.

Clean renewable energy is given high priority to generate power in power system dispatching. Unfortunately, the objective to minimize wind power curtailment may results in additional costs, since the inverse peak regulation characteristic of wind power probably result in redundant power qualities curtailed to ensure the balance of the electric supply and demand in real time. In this paper, considering the cost of deep peak regulation of thermal unit and wind power curtailment, unit commitment comprehensive optimal model is proposed to minimize total cost by searching the optimal wind power curtailment. In order to achieve an economic strategy among the wind power curtailment and the deep peak regulation, two indexes including the amount of hourly optimal wind power curtailment and the critical value of comprehensive wind power curtailment are developed. Particle swarm optimization algorithm is employed for solving the proposed model. Afterwards, the relationship between the degree of deep peak regulation and the amount of wind power curtailment is researched, the derived conclusions can provide valuable information for system operators.

Quantum key distribution (QKD) has attracted much attention on secure communications across global networks. QKD over satellite networks can overcome the limitations of terrestrial optical networks, such as large attenuation over long distance fiber channel and difficulty of intercontinental domain communications. Different QKD networks (around the world) can intercommunicate through quantum satellites, leading to a global quantum network in near future. This raises a new resource allocation and management problem of QKD involving multiple satellite layers and distributed ground stations. Using existing schemes, a single satellite cannot perform QKD for ground stations for the whole day. Moreover, the research problem is more challenging due to limitations of satellite coverage: limited cover time of low earth orbit (LEO) satellite, high channel losses of geostationary earth orbit (GEO) satellite, etc. To overcome these limitations, our study proposes a double-layer quantum satellite network (QSN) implemented quantum key pool (QKP) to relay keys for ground stations. We propose a new architecture of trusted-repeater-based double-layer quantum satellite networks, comprising GEO and LEO satellites. We also address the routing and key allocation (RKA) problem for key-relay services over QSNs. We propose a novel joint GEO-LEO routing and key allocation (JGL-RKA) algorithm to solve the RKA problem. Simulative results show that the proposed scheme can increase success probability of key-relay services significantly. We also present the impact of different route selections mechanisms, number of satellite links, satellite node capability, and service granularity on network performance.

The fast and diversified development of Internet services leads to numerous services with differentiated delay tolerance of optical connections set-up are converging in the optical network. Consequently, the average set-up delay of optical connection is increased, which will inevitably bring pressures to optical networks. To solve this problem, a delay aware routing spectrum assignment (RSA) algorithm based on services scheduling in priority queues with dynamic virtual topology construction (QVT-RSA) is proposed. First, the QVT-RSA algorithm reasonably identifies various different services and arranges them in order according to their priority, waiting time and delay tolerance requirements. Then, software defined networks (SDN) controller with multi-threads technology is adopted to innovatively parallel serve the ordered service requests to reduce the average set-up delay of optical connections, which also offset the induced delay caused by services scheduling. The network resource is accurately shared among multi-threads using the proposed mutual exclusive resource utilization model realized by dynamic virtual topology construction. The continuously coloring and usable virtual topology linkage reconfiguration can provide topology resources for service requests and adapt the dynamic transformation among serving, waiting and new arriving service requests. The shortest-path routing and first fit spectrum assignment (SPFF-RSA) algorithm is used as the baseline algorithm. Furthermore, by combining the SPFF-RSA algorithm and partial strategy of our strategy, the QSPFF-RSA and V-RSA algorithms are proposed as baseline algorithms as described in V-B. The simulation experiment results demonstrate that the average optical connections set-up delay of QVT-RSA algorithm is reduced compared with SPFF-RSA and QSPFF-RSA algorithms, and the blocking probability of QVT-RSA algorithm is reduced compared with SPFF-RSA, QSPFF-RSA and VRSA algorithms. The optical connections set-up success probability within delay tolerance about high, medium and low priority services of QVT-RSA algorithm is higher than that of the SPFF-RSA, QSPFF-RSA and VRSA algorithms.

With the increasing bandwidth requirements for people and the development of urbanization, the movement of the population in the city (especially the supercity) has an increasing influence on the traffic distribution in both space and time dimensions. The imbalanced distribution results in the regional blocking of different areas in different time periods and reduces the spectrum resource utilization in elastic optical networks. To resolve this problem, this paper proposes a tidal traffic model to formulate a kind of tidal traffic phenomenon firstly. Based on the analysis for this model, the area-aware routing and spectrum allocation algorithm that focuses on the traffic adjustment in specific functional areas is proposed. And two benchmark algorithms named min-hop k-shortest path routing algorithm and occupied-slots-as-weight k-shortest path routing algorithm are introduced. The evaluation results show that, compared to the benchmark algorithms, the proposed area-aware algorithm could reduce the blocking probability efficiently from 2% to 47% with low time complexity.

Target tracking is a key task for maintenance of maritime rights and interests in the coastal countries, which mainly depends on the cruises of large official ships, and their deficiencies are high cost and less stealth. USVs (Unmanned Surface Vehicles) have been gradually employed in maritime ISR(Intelligence, Surveillance and Reconnaissance) due to autonomy, safety and flexibility. In this paper, a target tracking algorithm for USV's vision is proposed, which considers motion features of the USV, such as velocity, pitch and heading. Firstly, a CM(Centroid Matching) method for the target detection is proposed based on FT(Frequency-Tuned) saliency method, which can remove environment disturbances. Secondly, a fDSST(fast Discriminative Scale Space Tracker) is improved for target tracking by considering motion information of the USV from aboard GPS and IMU(Inertial Measurement Unit), where the learning rates of the correlation filters are adjusted adaptively according to variety of the velocity, pitch and heading of the USV. Lastly, the modified target detection and tracking are integrated to acquire a robust tracking result. Based on our USV called as `Jiuhang 490', sea tracking trials for rubber boat in four encountering scenes are executed at Nanjiang port in Qingdao city, China in July 2018. The favorable performance of the proposed algorithm is quantitatively validated comparing to the original fDSST algorithm in the accuracy of position, scale of the target and the tracking speed.

This article studies the problem of cooperative fault-tolerant output regulation of leader-follower multi-agent systems with sensor faults. To compensate for the faults existing in the followers, distributed observers based on relative output estimation errors are firstly designed. Then an adaptive fault-tolerant output regulation framework is built by solving the regulator equation. It is shown that stability of the closed-loop system can be ensured and that all tracking errors will converge to zero under the designed fault-tolerant controller. Finally, simulation results demonstrate the effectiveness of the proposed control law.

Moving target detection is of vital importance to maritime security and maritime resource protection. However, the detection of slow or weak targets is difficult based on traditional methods. A new detection method is proposed by using the different motion variations of radar moving target and sea clutter in the range-Doppler spectrum sequence. The first step in implementing this method is the separation of moving target and sea clutter by the low-rank representation, in which the target and clutter are modeled as foreground and background components. Subsequently, a sea clutter discriminator is constructed within the sea clutter bandwidth to further remove the sea clutter (false alarms) that exists in the foreground. The proposed method can reduce the sea clutter power while maintaining the target power and improve the detection rate of moving targets, especially slow or weak targets. Data collected with airborne maritime surveillance radar in maritime moving target indication (MMTI) mode are used to validate the performance of the proposed method. The experimental results demonstrate that the improvement in the signal-to-clutter ratio (SCR) obtained with the proposed method is better than that obtained with space-time adaptive processing (STAP, including 1DT-STAP, 3DT-STAP and sparse-STAP) and principal component pursuit (PCP) methods; additionally, the figure of merit (FOM) of the proposed method is higher than that of the constant false alarm rate (CFAR) and PCP method. Furthermore, the tracks of ships are obtained by applying a location constraint to the foreground sequence.

College students are the most active, most sensitive, and most prone group with respect to various psychological problems in contemporary society. In recent years, with the intensification of social competition, including various pressures such as studies, examinations, economic loss, emotional loss, and employment, the incidence of anxiety, depression and suicide rates has increased. To effectively pay attention to the psychological development of college students and to strengthen mental health education, this research proposes a method to automatically identify the anxiety of college students using a Takagi-Sugeno-Kang (TSK) fuzzy system and deep features. First, preprocess the collected EEG of college students. Secondly, use convolutional neural network (CNN) to extract deep features from the input data. Finally, TSK fuzzy system is used to classify features to obtain the final recognition result. Through experiments on standard data sets and self-made data sets, the experimental results verify the superiority of the anxiety identification method used in this study. The experimental results further demonstrate that the depth features have richer information than traditional features. The noise immunity of TSK fuzzy system makes it show good classification performance and generalization. The recognition results can quickly locate students with anxiety disorders and narrow the scope of investigation for students with psychological problems. The automatic recognition of college students' anxiety can improve the efficiency of schools and teachers in investigating students' psychological problems. This research has very good practical application value.

The purpose of this article is to identify football and related environmental variables through its VR images under the current situation where the vision system has become the only way for football robots to obtain the external environment, so as to improve the chances of winning the game. First, this article uses color filters to enhance the VR football data to distinguish between shadow games and aliens. The best environment for enhancing the image is automatically determined by the Ostu method, so that the image is not affected by shadows as much as possible, and the outline of the image can be sealed. At the same time, using the humanoid medium-sized football game machine system as the platform, the relevant processing algorithms of the humanoid football robot front-view system are studied to realize the work of color image segmentation, edge extraction, straight line extraction, cross-line recognition and target post-recognition. PA-SIFT algorithm is used to quickly identify the graphics. Data verification results show that the recognition rate of the PA-SIFT algorithm can reach 96%, ensuring the real-time and feasibility of the algorithm. In addition, the divide-and-conquer algorithm and the related processing algorithm of the vision system are combined to determine the central area of the image, so that the algorithm is not affected by the external environment, and the algorithm is robust and can improve actual competition.