最新刊期
卷 52 ， 期 9 ， 2024
- 摘要：A high-precision X-ray photon arrival time conversion model is crucial to the accuracy of X-ray Pulsar-based Navigation. Aiming at the current problem that the complete model is complex and the simplified model has limited accuracy, a fast simplified model with accuracy no less than the existing simplified model is proposed in this paper. Through the derivation of the existing complete model, the influence of each delay item on the accuracy of the model is theoretically analyzed, and it is pointed out that the Roemer delay is still the key to the accuracy of the simplified model. A fast simplified model was obtained by changing the expression of the Roemer delay and its second-order expansion, and considering the ease of access to physical quantities in practical application. The accuracy and computational efficiency of the proposed model are analyzed by using the complete model and the proposed simplified model to time-transform the measured photon data of NICER (Neutron star Interior Composition Explorer) and HXMT (Hard X-ray Modulation Telescope) satellites. Furthermore, the influence of orbital altitude and pulsar angular position measurement errors on the accuracy of the simplified model is analyzed by numerical simulation, and the accuracy and computational efficiency of the simplified model in the application of Earth orbit at different altitudes are discussed. The results show that the computational efficiency of the simplified model proposed in this paper is improved 50% than that of the Sheikh’s simplified model and 10% than the fei’s model, without causing a decrease in accuracy.关键词：X-ray pulsar;time conversion model;photon arrival time;high-precision simplified model15|16|0更新时间：2025-12-08
- 摘要：The task of document-level relation extraction aims to extract facts from multiple sentences of unstructured documents, which is a key step in the construction of domain knowledge graph and knowledge answering application. The task requires that the model not only capture the complex interactions between entities based on the structural features of documents, but also deal with the serious long-tail category distribution problem. Existing table-based relation extraction models try to solve this issue, but they mainly model documents in two-dimensional “entity/entity” space, and use multi-layer convolutional network or restricted self-attention mechanism to extract the interaction features between entities, which cannot avoid the influence of category overlap and capture the directional features of relationships, resulting in the lack of decoupled semantic information of interaction. For the above challenges, this paper proposes a new document-level relation extraction model, named DRE-3DC (Document-Level Relation Extraction with Three-Dimensional Representation Combination Modeling), in which the “entity/entity” modeling extend to the form of three-dimensional “entity/entities/relationship” modeling method. Based on the deformable convolution in triple attention mechanism, the model effectively distinguishes and integrates the interaction features under different semantic space and adaptively captures the document structural features. At the same time, we propose a multi-task learning method to enhance the perception of relation category combination of documents to alleviate the long-tail distribution problem. The experimental results reveal better score on DocRED and Revisit-DocRED dataset respectively. The effectiveness of the proposed method was verified by ablation experiment, comparative analysis and example analysis.关键词：document-level relation extraction;three-dimensional representation;triplet attention;deformable convolution;multi-task learning7|12|0更新时间：2025-12-08
- 摘要：With the increasing complexity of modern battlefield environment and the upgrading of aviation equipment technology, massive multi-source heterogeneous sensor data inevitably appear inconsistent and incomplete problems. Traditional multi-sensor fusion method ignores sensor features correlation, and forms a closed data-driven recognition system of sensors. Whereas expert cognition, domain experience, attribute rules and other knowledge can instruct model construction and inference recognition of comprehensive target recognition in the form of expert experience, rule constraints and so on, this paper presents a method of knowledge assisted integrated identification of aerial targets. First of all, a military combat knowledge map of typical aerial target features is constructed, and key feature parameters are extracted to establish a target identification framework model. Then data basic trust assignment and evidence conflict credibility are constructed at recognition and decision recognition level respectively. Besides, time-domain fusion rules for high-conflict evidence is formulated to adjust timing fusion weights by using historical data. Finally, type recognition of multi-sensor is hierarchically realized through static reasoning and dynamic fusion. This study recognition accuracy is better than the existing algorithms in typical aerial target recognition tasks, demonstrating the effectiveness of the proposed algorithm.关键词：target identification fusion;sequential fusion;aerial target;multiple knowledge;belief rule-based classification key word12|13|0更新时间：2025-12-08
- 摘要：Alzheimer's disease (AD) is a neurodegenerative disease that causes symptoms such as aphasia and decreased speech fluency. Researchers have used articulatory features, paralinguistic features such as fluency and pauses, or features extracted from transcribed text to detect Alzheimer's disease. However, traditional acoustic feature detection methods are difficult to obtain semantic information, while transcribing speech into text is time-consuming and laborious, and the quality of transcription is significantly degraded due to the effects of accent and disease in the elderly. In this paper, we propose a dVAE-BERT (discrete Variational Autoencoders-Bidirectional Encoder Representations from Transformers) model, which uses discrete Variational Autoencoders (dVAE) to convert speech into pseudo-phoneme sequences, and then uses the Bidirectional Encoder Representations from Transformers (BERT) model to model the connection relations of the pseudo-phoneme sequences to extract the representation of audio in the language dimension. The accuracy of the model on the ADReSSo (Alzheimer's Dementia Recognition through Spontaneous Speech only) dataset is 70.42%, which is 5.63% better than the baseline system, and its accuracy is 76.06% and 71.83% after fusion with Wav2vec2.0 and Hidden-unit BERT (HuBERT) models, respectively.关键词：Alzheimer's disease;speech detection;dVAE;BERT17|14|0更新时间：2025-12-08
- 摘要：Constellation shaping, one of the key techniques for the communication systems, can provide shaping gain. However, recently proposed constant composition distribution matching (CCDM) probabilistic amplitude shaping (PAS) scheme is only suitable for square constellation modulation but not for general structured 2D constellation. This paper presents a generalized CCDM shaping, which can be directly applied to any 2D constellation with a symmetric structure. Furthermore, taking into account the 5G low-density parity-check (LDPC) standard (especially for the puncture structure), the presented CCDM shaping is combined with the 5G LDPC codes, resulting in a 5G LDPC coded shaping modulation scheme. The numerical results show that the performance of the proposed scheme is consistent with that of the conventional PAS scheme. The simulation results also show that the proposed 5G LDPC coded shaping modulation scheme can achieve a shaping gain of about 0.6 dB and a puncturing gain of about 0.5 dB (compared with the non-puncturing design).关键词：coded modulation;LDPC codes;probabilistic shaping;shaping gain;5G10|14|1更新时间：2025-12-08
- 摘要：With the rapid development of the industrial Internet, industrial production needs to satisfy personalized user requirements. Due to the wide variety of personalized product specifications, an efficient and intelligent scheduling method is particularly important for manufacturing enterprises. From the perspective of deployment mode, existing intelligent scheduling systems can be divided into two categories: enterprise on-premises deployment and cloud scheduling services. The computing and storage resources of the local scheduling system are relatively limited, making it difficult to meet the needs of accurate scheduling algorithms. In contrast, cloud scheduling systems require the support of a large amount of industrial core scheduling data and charge on demand. The overhead of computing, storage, and network transmission makes scheduling service costs high. Additionally, uploading core industrial data to the cloud may carry the risk of data leakage. To address these issues, this paper takes the hot rolling production of iron and steel as an example, introduces edge computing technology into intelligent production scheduling, and proposes a cloud-edge collaborative industrial internet production scheduling framework (PSECC). The framework preprocesses the original industrial data at the edge to ensure that core production data is kept at the enterprise end, while the algorithm is solved in the cloud. The framework is also extended by deploying a general-purpose algorithm. Based on the PSECC framework, we designed and realized a cloud-edge decomposition method for hot rolling production scheduling tasks in steel. Experiments show that the performance of the cloud-edge collaborative production scheduling method proposed in this paper is similar to that of the conventional solver, but it can avoid uploading industrial core data to the cloud, and the choice of cloud solver is more flexible. In terms of performance, the total scheduling time of cloud scheduling is 1.4 to 3.7 times that of PSECC, and the network transmission time is 10 to 15 times..关键词：cloud-edge collaboration;industrial Internet;hot steel rolling;job shop scheduling;personalized customization;intelligent production scheduling7|18|0更新时间：2025-12-08
- 摘要：Si/SiC cascaded H-bridge inverters enable a combination of different devices to ensure low output current total harmonic distortion (THD) and high device efficiency. However, this also presents the challenge of switching and assigning Si/SiC cells. In this paper, a model predictive control (MPC) with variable weight is designed to select the total switch state and assign the cell switch combination. In this method, a variable weight based on the switching loss of the device is introduced into the cost function of selecting the total switching state of the inverter and the switching combination of Si/SiC cells, to improve the efficiency and output current harmonic distortion rate of the inverter. The effectiveness of variable-weight MPC is verified on the five-level Si/SiC cascaded H-bridge inverter device, and the output current THD is reduced by up to 2.05% and the device loss is reduced by up to 4.53% compared with the fixed-weight MPC.关键词：silicon carbide MOSFET;silicon IGBT;cascaded H-bridge inverter;switching loss;model predictive control9|16|0更新时间：2025-12-08
- 摘要：Feature extraction is a key operation for hyperspectral image (HSI) classification. For current classification approaches, they usually ignore the information preservation and spatial distribution in feature extraction, which may export features with low information utilization and disordered distribution, generating unsatisfactory prediction results. To remedy such deficiencies, a novel method based on structure-wise feature reconstruction is proposed for the HSI classification. This method can reduce the information loss and improve the information preservation during the process of feature extraction. In addition, the distribution is also fully considered to enhance the discriminability and separability. In this proposed method, considering the reconstruction idea and the self-expression theory, a structure-wise feature reconstruction model is constructed to extract the features of the HSI, which can improve the information utilization of original information from the HSI and describe the structure reflecting the well-ordered distribution. Here, an optimization with alternative updating is presented to solve the above constructed model. The support vector machine is finally used to classify the extracted features and predict the labels of the HSI. The Salinas, Pavia Center, Botswana, and Houston datasets are used for experimental validation. Results show that the proposed method achieves the better classification performance compared with some state-of-the-art approaches, which is averagely higher 2.6%, 3.9%, 3.3% at OA (Overall Accuracy), AA (Average Accuracy), and Kappa indexes.关键词：hyperspectral image classification;information preservation;structure-wise feature reconstruction;feature distribution;self-representation;model optimization;support vector machine10|16|0更新时间：2025-12-08
- 摘要：Micro-grid is a distributed small-scale power generation and distribution system, which has realized the circular flow of electricity through adjacent energy trading according to the different needs of prosumers. In order to develop optimal price and transaction strategies in energy trading of micro-grid, we proposed a double sealed bid (DSB) auction scheme according to the characteristics of consortium blockchain. Except met key economic properties (individual rationality, budget balance, and so on), this scheme would determine the final winner based on the users' offers, bids, volumes, average price and other factors. In the meanwhile, in order to protect the personal privacy of users in the auction process, we proposed the blockchain-based differential privacy (BDP) algorithm based on the differential privacy theory and the characteristics of the DSB auction scheme, which was satisfied with differential privacy demands and mean validity through privacy analysis and data validity analysis. Finally, we applied the BDP algorithm to the DSB auction scheme and realized a safe and efficient double energy auction privacy-preserving scheme—differential privacy-based double auction on blockchain (DPDAB), which not only developed the optimal price and transaction strategy but also protected the users' privacy in the process of auction. In addition, we analyzed the influence of the BDP algorithm on auction data and the data computation time overhead on the auction scheme through experiments, and proved the validity of the DPDAB scheme in terms of average benefit, user satisfaction and social welfare through comparative experiments.关键词：double sealed bid auction;differential privacy;consortium blockchain;micro-grid;energy trading7|10|0更新时间：2025-12-08
- 摘要：Heterogeneous memory systems composed of traditional dynamic random access memory (DRAM) and new non-volatile memory (NVM) can be organized in a horizontal architecture or a hierarchical architecture. The horizontal DRAM/NVM architecture often requires page migration technologies to improve memory access performance. However, hot page monitoring and migration implemented in operating systems would cause significant software performance overhead. The hardware-supported hierarchical architecture even increases the memory access latency for big data applications with poor data locality due to the deeper memory hierarchy. To this end, this paper proposes a reconfigurable heterogeneous memory architecture that can be converted between horizontal and hierarchical architectures at runtime to dynamically adapt the memory access characteristics of different applications. We design a DRAM/NVM heterogeneous memory controller (HMC) based on the new instruction set architecture RISC-V (Reduced Instruction Set Computing-V). The HMC uses a few hardware counters for memory access monitoring and analyzing, and achieves dynamic address mapping and efficient page migration between DRAM and NVM pages. Experimental results show that the DRAM/NVM hybrid memory controller can improve application performance by 43%.关键词：non-volatile memory;heterogeneous memory systems;heterogeneous memory controller;memory access monitoring;page migration7|17|0更新时间：2025-12-08
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Robust Identification of Nonlinear State-Space System Based on Dual Heavy-Tailed Noise Distributions
摘要：The state space model is a common and important model structure for automation and control. In this paper, the robust identification of nonlinear state-space model corrupted by outliers is investigated. The outliers imposed on both the state transition process and the output measurement process are considered and a more comprehensive and robust identification algorithm is proposed. To ensure the robustness of the proposed algorithm, two independent heavy-tailed Student's t-distributions are used to describe the state noise and the output noise, respectively. Then the particle smoothing method is applied to estimate the posterior distribution of the unknown states. Finally, the expectation maximization algorithm is used to realize the parameter estimation problem. The mathematical decomposition of the Student's t-distribution is employed in the identification process which brings two main advantages: (1) facilitating the derivation and implementation of the proposed algorithm; (2) providing a more clearer explanation of the robustness of the algorithm. The usefulness of the proposed algorithm is demonstrated via the numerical and mechanical examples.关键词：nonlinear state-space model;robust system identification;Student's t-distribution;particle smoother;expectation maximization algorithm10|13|0更新时间：2025-12-08 - 摘要：The linear property of lightweight cipher ACE and SPIX was researched. The linear property of ring AND-gate combination was described accurately with mixed-integer linear programming. The nonlinear operation of ACE and SPIX was transformed into ring AND-gate combination. Based on this, the linear models of ACE permutation and SLISCP permutation were constructed with mixed-integer linear programming. The models returned the optimal linear characteristics of 2-step to 4-step ACE permutation and 2-step to 5-step SLISCP permutation. It was proved that 7-step and 12-step ACE permutation achieved the 128-bit security and 320-bit security respectively, and 7-step and 13-step SLISCP permutation achieved the 128-bit security and 256-bit security respectively. For the ACE permutation and SLISCP permutation with any number of steps, authenticated encryption algorithm ACE-AE-128 and SPIX can resist the linear distinguish attack of plaintext processing stage.关键词：mixed-integer linear programming;constraint problem solving;lightweight cipher;linear analysis16|11|1更新时间：2025-12-08
- 摘要：The data of nodes in industrial Internet have characteristics of high dimensionality, redundancy and mass and traditional malicious behaviors detection model cannot make a fast and accurate judgment on the malicious behaviors of industrial Internet. A real-time detection method of malicious behaviors in industrial Internet based on feature combination optimization is proposed. The feature combination of industrial Internet malicious behaviors sample data are optimized by improved fast correlation filtering algorithm and principal component analysis algorithm based on singular value decomposition. Based on symmetric uncertainty information measurement index and approximate Markov blanket criterion, feature correlation calculation, redundant feature identification and exclusion are performed. Several candidate feature combinations are obtained from different configurations of feature dimensions; Use decision tree evaluator to select the feature combination with the highest accuracy; To acquire the optimal feature combination of lower dimension and higher valuable information, the principal component analysis of singular value decomposition is used for further reduce dimension of feature; To classify malicious behaviors samples in industrial Internet through combing extreme gradient boosting algorithm and the optimized feature combination. The proposed method is verified based on Mississippi State University's multi-class power system attack sample data; The experiment demonstrate that training time of the feature combination optimization detection model can be reduced by 57.53%, and the average detection time of a single sample is 0.002 ms, which can be reduced by 23.99%. The accuracy, recall and F1 value of the detection model based on feature combination optimization are improved by 1.11%, 1.25% and 1.01%, respectively compared with those before feature optimization. The outstanding advantage of method in this paper is that it can significantly reduce model detection time while improving model detection effect, and can better adapt to the real-time requirements of industrial Internet.关键词：industrial Internet;improved fast correlation filtering algorithm;principal component analysis algorithm based on singular value decomposition;feature combination optimization;extreme gradient boosting;real-time detection of malicious behaviors7|12|1更新时间：2025-12-08
- 摘要：This paper proposes a multi-stage scheduling framework to realize multi-strategy scheduling of sparrow populations in different stages of initial location, foraging, detection, and anti-predation. Halton sequence and Tent mapping are used to improve the quality of the population individuals and the distribution uniformity of initial position. In the foraging stage, aiming at the deterioration of the population quality caused by the position competition between the finder and the joiner, the best fit ratio is designed to control the quantitative relationship between the two, and the collision rebound operator is used to change the optimal trajectory of the joiner beyond the fit ratio. After the adaptation ratio is met, judge whether there is a natural enemy through investigation, and if there is, enter the anti-predation stage, and use Levy flight and combine exponential distribution to design a random migration mechanism to generate a potential global optimal solution area; when no natural enemy is found for many times in a row in order to prevent the population from falling into local extremum, an early warning mechanism is established and the locust algorithm is used for multi-path development to avoid a single optimization direction. The alternate operation and coordinated scheduling of different strategies and mechanisms balance the diversity and convergence of the algorithm. Experimental results show that, compared with the latest sparrow variant algorithm and meta-heuristic improved algorithm, the algorithm is significantly better than the comparison methods in terms of optimization efficiency and convergence accuracy.关键词：sparrow search algorithm;multi-stage scheduling framework;best adaptation mechanism;simulated early warning mechanism;random migration mechanism13|17|0更新时间：2025-12-08
- 摘要：The local encryption technique for multi-layer grids based on the lattice Boltzmann method computes the flow characteristics at different levels through multi-layer grids, which avoids the inefficiency and waste of computational resources in single-layer uniform Cartesian grids. But there is still an undesirable effect on the parallel performance. The load-balancing effect in parallel computing is considered in this paper. Starting from a single-layer grid, we study the load-balancing-based grid partitioning method by considering the computational characteristics of multi-layer grids. At the same time, the grid partitioning is separated from the program implementation, and parallel computation with arbitrary grid partitioning is achieved in both single-layer and multi-layer grids. The relationship between load partitioning and the respective time overheads of the different processes is investigated in a single-layer grid with different parallel strategies for 2D vascular flow. The characteristics of multiscale grids with respect to the order of operations is first discussed for multi-layer grids. Second, three different multi-layer grids are used to verify the computational results of the two-dimensional aerofoils. Finally, the relationship between load balancing and time overhead is further investigated by using three different meshing methods in each grid. Parallel performance tests on a 128-core HPC (High Performance Computing) platform show that the strong scalability can reach up to 60%, and the weak scalability can reach 82.78%. This high scalability result shows the significant improvement of the parallel performance in multi-layer grid computing by improving the load balancing performance.关键词：lattice Boltzmann method;multilayer grids;massive parallel computing;load balancing5|10|0更新时间：2025-12-08
- 摘要：A pseudo-random number generator for image encryption has been developed, utilizing a spatiotemporal chaotic system with multilayer elementary cellular automata. To solve the existing problems of limited parameter space and local chaotic behavior based on coupled image lattice system, a dynamic random coupled map lattices (DRCML) system based on a multilayer elementary cellular automaton (MECA) is proposed. The MECA is designed on the basis of the elementary cellular automaton (ECA), and DRCML system is iterating with the MECA simultaneously, and the DRCML system of each lattice in the coupled system and the pseudo-random perturbation method are obtained through the iterative output of the MECA. The DRCML system is compared and analyzed by bifurcation diagram, Kolmogorov Sinai entropy and output sequence uniformity , and the correlation between the randomness of the generated sequence of the system and any two lattices is analyzed. The theoretical analysis and experimental results show that the DRCML system has better chaotic properties and wider parameter space than other coupled map lattices systems, and the generated sequences have better ergodicity, uniformity and randomness. The results show that the DRCML system has a promising application in the field of cryptography.关键词：coupled map lattices;multilayer elementary cellular automata;chaotic system;dynamic coupling;cryptosystem4|18|0更新时间：2025-12-08
- 摘要：In response to the long optimization time and high energy consumption faced by traditional particle swarm optimization algorithm in global path planning for autonomous underwater vehicle, this paper proposes an improved T-distribution fireworks-particle swarm optimization algorithm (TFWA-PSO), this algorithm integrates the efficient global search capability of the fireworks algorithm with the rapid local optimization characteristics of the particle swarm optimization algorithm. In the mutation stage, an adaptive T-distribution mutation is proposed to expand the search range, and it is theoretically demonstrated that this explosive mutation approach enables individuals to enhance their search ability near the local optimal solution. In the selection stage, a fitness selection strategy is proposed to eliminate individuals with poor fitness, solving the problem of the traditional fireworks algorithm's tendency to lose excellent individuals, and comparing the convergence speed between the improved T-distribution fireworks algorithm and the traditional fireworks algorithm. The improved algorithm's explosion, mutation operations, and selection strategy are integrated into the particle swarm algorithm. The velocity update formula of the particle swarm algorithm is improved, while the convergence proof of the improved algorithm is proved theoretically. The simulation results indicate that the TFWA-PSO can effectively plan the shortest path. Compared to the given intelligent optimization algorithms, TFWA-PSO on average reduces the time to find the optimal path by 24.72%, lowers energy consumption by 17.33%, and decreases the average path length by 16.96%.关键词：autonomous underwater vehicle;global path planning;fireworks algorithm;particle swarm optimization;adaptive T-distribution mutation;convergence proof21|12|0更新时间：2025-12-08
- 摘要：To address the problem of extended target tracking (ETT) with irregular shape, this paper proposes a random hypersurface model-adaptive progressive bayesian filter (RHM-APBF). First, the local cumulative distribution of the continuous state prior probability density of extended target is randomly sampled, and the optimal position of the sampling point is obtained by minimizing the modified Cramer-Von Mises distance between the local cumulative distribution of the continuous probability density and the Dirac mixture probability density. Then, the sampled particles are migrated to the posterior dense area to obtain a more accurate posterior probability density approximation by progressive update with adaptive variable step size. Furthermore, the random hypersurface model is used to represent the measurement source distribution of arbitrary star-convex extended targets, and an adaptive progressive filter for tracking star-convex irregular shape extended target is proposed, which effectively recurses the multi-feature probability density of irregular shape extended targets. Finally, the effectiveness of the proposed method is verified by the tracking simulation experiments of the extended target (ET) and group target (GT) at different noise level and complex random environment.关键词：extended target tracking;local cumulative distribution;progressive update;random hypersurface model;irregular shape14|15|0更新时间：2025-12-08
- 摘要：Global Positioning System (GPS) L5, Beidou B2 and Galilea E5 are important components of the global navigation satellite system (GNSS), providing life safety related applications services for civil aviation. The L5, B2 and E5 signal are working in the protected aeronautical radio navigation service (ARNS) band (962~1213 MHz). At the same time, distance measuring equipment (DME), the civil aviation navigation system, is also working in this frequency band. The high-power pulse signal emitted by DME will interfere with satellite navigation signals such as L5/B2/E5, leading to abnormal acquisition of satellite signals by the receiver or loss of lock in the tracking loop. Traditional interference sparse domain suppression, such as DME interference zeroing method in time domain and time-frequency hybrid domain, can completely eliminate the satellite signal overlapping with interference while suppressing interference. In order to reduce the loss of satellite signals while mitigating the DME interference, this paper proposes a DME interference suppression method based on local robust preprocessing using robust statistical theory. According to the sparse domain characteristics of the DME interference, the robust statistical theory of non-gaussian distribution is applied to the extraction of the data samples, which can reduce the effect of the satellite signal while inhibiting the interference. The experimental results show that the performance of DME interference suppression method based on the local robust preprocessing is superior to the corresponding traditional sparse domain method, and the output acquisition factor increases the 1~2 dB by the traditional sparse domain method.关键词：global navigation satellite system;interference of distance measuring equipment;interference suppression;sparse domain;robust statistical theory5|13|0更新时间：2025-12-08
- 摘要：Currently, more and more medical image segmentation models are using Transformer as their basic structure. However, the computational complexity of the Transformer model is quadratic with respect to the input sequence, and it requires a large amount of data for pre-training in order to achieve good results. In situations where there is insufficient data, the Transformer's advantages cannot be fully realized. Additionally, the Transformer often fails to effectively extract local information from images. In contrast, convolutional neural networks can effectively avoid these two problems. In order to fully leverage the strengths of both convolutional neural networks and Transformers and further explore the potential of convolutional neural networks, this paper proposes a multi-scale convolution modulation network (MSCMNet) model. This model incorporates the design methodology of visual Transformer models into traditional convolutional networks. By using convolution modulation and multi-scale feature extraction strategies, a feature extraction module based on multi-scale convolution modulation (MSCM) is constructed. Efficient patch combination and patch decomposition strategies are also proposed for downsampling and upsampling of feature maps, respectively, further enhancing the model's representation ability. The mDice scores obtained on four different types and sizes of medical image segmentation datasets - multiple organs in the abdomen, heart, skin cancer, and nucleus - are 0.805 7, 0.923 3, 0.923 9 and 0.854 8, respectively. With lower computational complexity and parameter count, MSCMNet achieves the best segmentation performance, providing a novel and efficient model structure design paradigm for convolutional neural networks and Transformers in the field of medical image segmentation.关键词：medical image segmentation;multi-scale;convolutional modulation;Transformer16|16|0更新时间：2025-12-08
- 摘要：In the case that several controllable events (control commands) are allowed to execute simultaneously, the supervisor in the framework of discrete event systems (DESs) selects one randomly. However, in practical applications, such as traffic scheduling and robot path planning, the problems of directed control and numerical optimization should be considered. This paper introduces an optimization mechanism to quantify the control cost and combines supervisory control theory (SCT) with reinforcement learning. A systematic procedure is proposed to synthesize the optimal directed supervisor of a DES based on reinforcement learning, which makes the controlled system achieve the following three goals: (1) the control specifications relevant to security and liveness are not violated; (2) at most one controllable event can be executed at each state; (3) the cumulative cost of event execution from the initial state to a mark state is minimal. First, given the automaton models of the plant and specifications, the target automaton model is obtained by the synchronous operation of these two models; a cost function is defined and assigns the execution cost for each event in the target model. Second, the non-blocking and maximally permissive supervisor is synthesized by SCT. Finally, the supervisor is transformed into a Markov decision process and then the Q-learning algorithm is utilized to compute the optimal directed supervisor. Two applications are used to verify the effectiveness and correctness of the proposed method. The simulation results show that the proposed method can realize the directed control of the system, and the numerical cost of the directed supervisor is minimized.关键词：discrete-event system;directed supervisor;reinforcement learning;optimal control;numerical optimization;traffic systems6|15|0更新时间：2025-12-08
- 摘要：A quadrotor unmanned aerial vehicle (UAV) system is full of parameter uncertainties and strong couplings, and the performance of a quadrotor UAV is easily degraded by external disturbances.To ensure the flight stability of the quadrotor UAV, a fuzzy linear active disturbance rejection control based on an improved linear extended state observer(LESO) is proposed in this paper.Parameters of the linear active disturbance rejection control are adaptively adjusted by a fuzzy algorithm, and the second-order differential signal of position and attitude angle of the quadrotor UAV is extracted by a levant tracking differentiator, and then the total disturbance of the quadrotor UAV is extracted, the fuzzy controller takes the total disturbance deviation and its differential as input, thus optimizing the estimation accuracy of the LESO for the total disturbance.The convergence of the LESO and the stability of the closed-loop system are analyzed.Finally, the proposed control strategy is verified from the control signals, dynamic responses and of the robustness of the system.关键词：a quadrotor UAV;linear extended state observer;fuzzy control;levant tracking differentiator27|17|1更新时间：2025-12-08
- 摘要：Facing the increasingly severe traffic congestion problem, the intelligent transportation system has been rapidly developed and widely used, and the traffic speed prediction, a cornerstone task, has attracted much attention. In recent years, deep learning has been widely used in the research of traffic speed prediction, and the research direction has also shifted from modeling time correlation to considering complex spatiotemporal correlation. The graph neural network fits the graph structure data of the traffic network and has become the mainstream method for modeling spatial correlation. To date, most research works have noted the importance of modeling dynamic spatial correlations in the task of traffic speed prediction. However, predefined or adaptive matrices for spatial feature learning are essentially static, and are not sufficient to match the complex and dynamic characteristics of spatial correlations. Moreover, through the analysis of multiple real traffic speed datasets, we find that the local fluctuations of inter-node dependencies are more dynamic than the global influence of the traffic network, which indicates that the spatial correlation can be derived from the global and local angles. Therefore, we propose an end-to-end global and local aware dynamic graph neural network model for traffic speed prediction. The traffic speed flow is first decomposed into static components and dynamic components by the self-decomposition layer, and then the dynamic graph generation module constructs a real-time dynamic graph for the dynamic components to match their dynamics. With the constructed dynamic graph and the input predefined graph, we model higher-order representations of these two classes of spatial correlations through graph convolution operations. Besides, we use causal convolution in the temporal module to capture temporal correlations in traffic data. Finally, residual connections are used to aggregate spatiotemporal correlations and feed to the output layer for final speed prediction. Experimental results on two highway datasets and one urban road dataset show that our proposed model outperforms state-of-the art models in terms of MAE and RMSE.关键词：traffic speed prediction;spatial-temporal correlations;dynamic graph neural network;graph generation9|10|0更新时间：2025-12-08
- 摘要：Biomedical events, as an important part of biomedical text mining, play an important role in biomedical research and disease prevention. Trigger identification is the key and prerequisite step of biomedical event extraction, which aims to extract the key words describing event types. Traditional trigger identification methods rely too much on natural language processing tools in the process of feature extraction, consuming a lot of manual cost. In addition, due to the particularity of biomedical literature—there are many long text sentences, the problem of long-distance dependence is obvious. To solve these problems, we propose a hybrid structure, which is composed of residual convolution neural network and bidirectional long short term memory, hybrid neural network and multi head attention mechanism. The proposed model uses residual convolution neural network to extract vocabulary-level features and bidirectional long short term memory to obtain contextual semantic information. Furthermore, spatial domain sliding windows divide long sentences into equal-length short sentences without damaging context information, which can avoid long-distance dependency without destroying the context information. The experimental results show that our method outperforms the state-of-the-art methods on the commonly used multi-level event extraction (MLEE) corpus, achieving 81.15% F-score.关键词：biomedical event extraction;trigger detection;ReCNN-BiLSTM;spatial domain sliding window;MUH-Attention;mixed neural network5|14|0更新时间：2025-12-08
- 摘要：Due to the relationship of one-to-many between images and depth maps in monocular depth estimation, there is a problem of scale ambiguity in monocular depth estimation itself. In order to improve the inherent ambiguity problem in geometric modeling of monocular depth estimation, this paper introduces a monocular multi-frame depth estimation method based on multi-view stereo (MVS) to construct moving depth and dig the scale clues. The traditional monocular depth estimation and MVS depth estimation are organically combined to improve the inherent ambiguity problem in the geometric modeling of monocular depth estimation. On this basis, two channel attention modules are designed to improve the network's ability to perceive scene structures and process local information, so as to more fully integrate features of different scales and produce more accurate and clearer depth maps.In the test results of the KITTI dataset, the average relative error and square relative error of this paper have been improved by 4.7% and 8.0% respectively compared to the baseline network, with all error and accuracy indicators surpassing other mainstream unsupervised monocular depth estimation methods.关键词：monocular depth estimation;unsupervised learning;deep learning;multi-scale;channel attention10|14|0更新时间：2025-12-08
- 摘要：Concept drift is an important performance factor in stream data mining, mainly handled by incremental updating or retraining models, but not fully utilizing existing knowledge. This paper proposed an concept drift adaptive prediction method based on dynamic sample selection, starting from the comprehensive use of all samples. The method performs local consistency based drift detection when new samples arrive, removes noisy samples in the region when drift is detected, and reuses historically similar concepts when new concepts are detected. Finally, multi-representative point summarization is performed for different categories of samples in the region, and the prediction model is updated simultaneously. In this paper, the denoising effect is verified on synthetic datasets containing different drift types, and the prediction task is performed on the real dataset. The experimental results show that the method can effectively remove the drift noise due to conceptual drift, which effectively improves the performance of the prediction model. The prediction outperforms the popular concept drift adaptive model.关键词：concept drift;local drift detection;stream data;sample selection;sample denoisy;adaptive forecast5|13|0更新时间：2025-12-08
- 摘要：Attacks, disturbances, and uncertainty often exist in microgrids and endanger the safe operation of the system. To solve these problems, a voltage controller with attack compensation is designed to reduce or offset the impact of attack on system stability. The attack observer is designed to observe the attack in the microgrid. Using multi-agent consensus protocol and considering the problem of full state constraints, tangent barrier Lyapunov function is used to constrain the designed state variables, so that the system states are constraint within the preset range, and the reactive power sharing is realized. The adaptive fuzzy system is used to estimate the changes of some parameters in the system to improve the adaptive ability of the controller. The effectiveness of the controller is verified by simulation.关键词：microgrid;multi-agent system;attack;barrier Lyapunov function;adaptive fuzzy system25|16|0更新时间：2025-12-08
- 摘要：With the rapid development of intelligent animal husbandry, cattle facial recognition has become a key aspect of intelligent farming in cattle ranches. However, due to the complexity of the ranching environment and the limited autonomy of animals, the process of collecting and identifying cattle facial data is severely affected by environmental factors such as blurriness, occlusion, and lighting. To address this issue, a complex scene-adaptive dual-branch efficient cattle facial recognition algorithm is proposed. This algorithm first designs a data augmentation strategy based on pixel fusion. By calculating fusion coefficients using the beta distribution, the left and right facial images of cattle are integrated at the pixel level, enriching the sample's feature information. Simultaneously, the algorithm enhances the network's ability to learn cattle facial features under blurriness and occlusion, improving its generalization ability to complex scenes. Furthermore, a novel attention mechanism called composite dual-branch adaptive attention (CDAA) is introduced into the main feature extraction network. This mechanism adaptively strengthens the weights of the channel and spatial attention branches as scene information changes, enhancing the network's feature selection ability in complex scenarios. Next, a dual-branch feature extraction structure combining FaceNet and U-LBP (Uniform Local Binary Patterns) is designed. The extracted feature vectors are adaptively weighted and fused to increase the network's robustness in overly bright or dark environments. Finally, an improved cross-entropy loss (Focal Loss) is incorporated into the loss function. Weight coefficients are dynamically adjusted based on the complexity of the scene information to autonomously control the classification of difficult and easy samples. To evaluate the effectiveness and real-time performance of the algorithm, ablation experiments are conducted on a specific dataset, comparing it with various typical recognition algorithms. The experimental results indicate that the proposed algorithm effectively meets real-time requirements, achieving an accuracy of 87.53% on the open test set with a recognition speed of 108 frames per second. Moreover, in complex scenarios, the recognition performance of the proposed algorithm surpasses that of the comparative networks.关键词：complex scenarios;image fusion;dual-branch structure;cattle face recognition;scene adaption8|11|2更新时间：2025-12-08
- 摘要：To address the problem that Lamb wave defect probability imaging requires obtaining a reference signal from a non-destructive structures with the same material characteristics, a probability imaging method based on reference signal theoretical prediction was proposed to achieve Lamb wave defect localization. This method combines the initial signal with the dispersion characteristics of Lamb waves, and calculates the reference signal on the probability imaging path based on the fitted amplitude attenuation. The defect characterization index was defined based on the Hilbert spectrum of the calculated reference signal and the corresponding response signal on the path. Combining it with the Lamb wave elliptical probability imaging method achieved defect localization. The experimental results of this method for single- and double-defect imaging localization show that the method can obtain probability images of defects, and the absolute error for defect localization is less than 7.07 mm. This method solves the problem of measuring Lamb wave reference signals and can accurately locate defects, with good practicality.关键词：probability imaging;Lamb wave;nondestructive testing;defect;damage;dispersion;Hilbert spectrum11|15|1更新时间：2025-12-08
- 摘要：With the development of Internet application, more and more secret traffics with secret requirements face security challenge in terms of being attacked or eavesdropped, when they transmitted in space division multiplexing elastic optical network (SDM-EON). To ensure the transmission security of secret traffic and reduce the waste of spectrum resources, a network coding-based multipath optical transmission for security traffic (NC-MOTS) is proposed for SDM-EON. In the routing stage, we design a path cost function which can evaluate the probability of link eavesdropping, the number of path hops and the idle spectrum resources on the path. So, the NC-MOTS selects two paths or three paths transmission strategy with the minimal route cost. In fiber core selection, we design an evaluation value to evaluate the path’s spectrum fragmentation and load imbalance. Then, we select the fiber core with low spectrum fragmentation and load balancing which meets the bandwidth requirements of secret traffic. In the stage of spectrum allocation, a spectrum fragmentation and load-sensing spectrum allocation method is designed to meet the network coding conditions. Through the sliding spectrum window, the spectrum block that can be network encoded and causes the low spectrum fragmentation and makes load balance between fiber cores is found. Compared with other routing spectrum allocation algorithms using network coding, the simulation results show that the proposed NC-MOTS can effectively reduce the secret traffic’s blocking probability and improve spectrum resource utilization when the network load is greater than 200 Erlang.关键词：SDM-EON;network coding;multipath;eavesdropping;secret traffic;blocking probability11|13|0更新时间：2025-12-08
- 摘要：In order to better cope with the environmental changes in dynamic multi-objective optimization, an evolutionary algorithm with angular correction of difference vectors and hierarchical multi-population co-evolution (ACHMP) is proposed. According to the historical information, use the unscented Kalman filter model to predict the population centroids, generate different difference vectors through different centroids at different times, and then use the unscented Kalman filter to correct the angle of the difference vectors. A multi-population coevolution model is proposed, which divides the population into three parts to evolve in different directions. The sub-population supervises the evolution of the master population, which not only improves the performance of the algorithm, but also ensures the diversity of the population. Experimental results with 10 comparison algorithms on different test problems show that the ACHMP algorithm performs better than the other algorithms in general, which proves that the angle correction and hierarchical multi-population method proposed in this paper has strong competitiveness in dealing with dynamic multi-objective optimization problems.关键词：dynamic multi-objective optimization;difference vector angle correction;hierarchical multi-population;unscented Kalman filter;prediction strategy5|13|0更新时间：2025-12-08
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