Modern neural networks (NNs), while effective at learning representations from given samples and handling downstream pattern recognition tasks, typically contain tens to hundreds of millions of parameters. The growth in NN size motivates ongoing research on effective network compression with the purpose of reducing the computational burden without significantly sacrificing the model performance. It is especially critical when deploying NNs on resource-constrained devices where the computation and storage efficiency are highly concerned. A promising and currently popular solution to model compression is to replace the NN weight matrix with its low-rank tensor approximation, i.e., implementing an efficient latent factorization of tensors (LFT) process on the NNs parameters. Based on thorough investigations into the state-of-the-art LFT-based model compression methods, this survey 1) provides a comprehensive review on the latest research progress of LFT-based model compression methods on various NNs (e.g., Convolutional NNs, Recurrent NNs, and Transformers); 2) summarizes a number of widely-used LFT toolboxes; 3) evaluates LFT methods for model compression on a variety of main-stream NN backbones; and 4) discusses the development trend of LFT-based model compression technique. This survey aims to provide a systematic and comprehensive survey concerning the LFT-based model compression methods to artificial intelligence researchers and engineers, thereby promoting further research development in this crucial field.

Pedestrian detection plays a critical role in intelligent perception systems in autonomous vehicles, which directly influences the reliability and safety of the overall system. Advanced in-vehicle sensor technology has enabled the continuous evolution of pedestrian detection systems by leveraging heterogeneous multimodal inputs such as RGB, infrared, depth, Light Detection And Ranging, and event data. Nevertheless, establishing a robust pedestrian detection system that is capable of integrating and processing such heterogeneous multimodal data effectively remains a significant challenge. At the same time, growing concerns about data privacy among automobile manufacturers have hindered further advances in detection model performance by restricting the sharing of private data within the industry. In this paper, a novel personalised federated learning framework, Kolmogorov-Arnold network-based Dual Expert Transformer Heterogeneous Personalized Federated Learning (KDET-HPFL), is proposed for multimodal pedestrian detection. To be specific, the KDET pedestrian detector is developed based on an expert feature selection module (which is designed to adaptively choose essential features from multimodal data) and a Group-Rational Kolmogorov-Arnold Network module, which enhances the feature extraction capabilities and improves the detection performance effectively. The HPFL framework is proposed for data privacy protection on heterogeneous multimodal data, where a cross-client aggregation (CCA) method is put forward by integrating different aggregation methods for certain layers in the KDET detector. With CCA, the HPFL framework achieves personalised feature retention of multimodal data pairs on multiple clients and improved model aggregation effect for each client. Experimental findings reveal that the proposed KDET-HPFL framework outperforms some existing personalised federated learning frameworks for pedestrian detection on four public datasets (i.e., LLVIP, STCrowd, InOutDoor, and EventPed) with mAP scores of 73.74%, 75.39%, 66.14%, and 79.57%, respectively.

The reliable operation of industrial systems requires not only the prompt detection of faults but also their accurate classification into the appropriate categories. At present, numerous data-driven industrial fault detection and diagnosis models, which have been developed based on historical fault data, frequently neglect the issue of label noise. When labels are corrupted by noise, a significant degradation in the performance of industrial fault detection models can be observed. In this article, alabel-noise-resistanttime-seriesclassification (LNRTSC) method based on consistency-driven label correction is proposed. First, an attention-based temporal correlation-enhanced encoder is introduced to extract low-dimensional representations of industrial time series. Then, label confidence, which is assessed based on local label consistency, is utilized to correct noisy labels during training. In addition, a two-stage self-supervised enhancement strategy is designed to guarantee the reliability of the corrected labels. Specifically, a reconstruction loss term is introduced to assist feature extraction in the warming-up stage, and a newly designed contrastive loss term is added to the loss function for the LNL training stage, which mitigates the effect of false negatives. Finally, the effectiveness of the LNRTSC method is validated on the Tennessee Eastman process and the SEU-gearbox datasets. When compared to peer methods, the LNRTSC approach demonstrates substantial improvements in fault classification performance on corrupted data.

This paper proposes a YOLO-based efficient lightweight network (YOLO-ELWNet) for onboard object detection based on the YOLOv3. A channel split and shuffle with coordinate attention module is developed in the backbone block, which effectively reduces the size of model parameters and computational cost while maintaining the detection accuracy. A new feature fusion network is proposed in the neck block, where a cross-stage partial with efficient bottleneck module is put forward to improve the feature extraction ability and reduce the computational cost. The Scylla intersection over union-based loss function is utilized in the head block, which accelerates the convergence speed of the YOLO-ELWNet. The effectiveness of the proposed YOLO-ELWNet is validated on the open source KITTI vision benchmark. The performance of YOLO-ELWNet is superior to some mainstream lightweight object detection models in terms of detection accuracy and computational cost, which demonstrates its applicability for resource-constrained onboard object detection.

In practical applications, the simultaneous optimization of numerous design parameters in time-consuming multi-objective optimization experiments is recognized as a significant bottleneck across various scientific and engineering disciplines. A prominent example is the optimization of machining parameters for achieving efficient and precise robotic belt grinding (RBG). This paper presents a closed-loop machining parameter optimization approach, which comprises two key stages: forward multi-task prediction and backward multi-objective parameter optimization. In the first stage, a physics-informed neural network (PINN) method is introduced, which integrates the multi-gate mixture-of-experts multi-task learning method with an RBG mechanism model to simultaneously predict material removal depth and averaged surface roughness. In the second stage, a powered multi-objective particle swarm optimization (MOPSO) method is developed, which combines a standard MOPSO method with a non-linear Powerball technique, to efficiently optimize the RBG machining parameters with a limited number of training iterations based on the learned PINN model. Two optimal machining parameter solutions are generated and recommended for the RBG machining process. The effectiveness and superiority of the proposed closed-loop parameter optimization method are validated through comparative experiments, which demonstrate its advantages in both coprediction accuracy and optimization efficiency.

Accurate prediction of material removal depth and averaged surface roughness is crucial for evaluating the performance of robotic belt grinding (RBG). Nevertheless, the machining parameters of RBG across different spaces exhibit various data distributions, which often results in prediction shifts on unseen machining parameters when using conventional approaches. In this article, we introduce a pairwise domain adaptation-assisted dual-task learning (PW-DA-DTL) method for copredicting material removal depth and averaged surface roughness with regard to new RBG machining parameter spaces. The multigate mixture-of-experts method is employed as the foundational framework for dual-task learning, effectively capturing and modeling the relationships between material removal depth and average surface roughness by leveraging their inherent task interdependencies. The pairwise domain adaptation strategy is put forward to simultaneously enhance sample diversity and mitigate cross-domain data distribution discrepancy between the existing and new RBG machining parameter spaces. Comparative experiments are presented to demonstrate the effectiveness and superiority of the proposed PW-DA-DTL method.

Multivariate time series forecasting (MTSF) is of significant importance in the enhancement and optimization of real-world applications. The task of MTSF poses substantial challenges due to the unpredictability of temporal patterns and the complexity in modeling the influence of all nonpredictive sequences on the target sequence at different time stages. Recent research has demonstrated the potential held by the Transformer algorithm to augment long-term forecasting capability. However, certain obstacles considerably obstruct the direct application of the Transformer to MTSF, such as an unsuitable embedding method, inadequate consideration of intervariable associations, and the intrinsic restriction of the point-wise objective function. To overcome these challenges, the Fusionformer, an effective Transformer-based forecasting model, is put forth in this article, which is characterized by three distinctive features: 1) the introduction of a segment-wise sequence embedding (SWSE) method allows for the conversion of the input sequence into multiple informative segments; 2) the implementation of a fusion attention mechanism (FAM), designed to capture predominant features across the time dimension and to model intricate intervariable dependencies; and 3) the development of an adversarial learning method, equipped with an auxiliary discriminator, facilitates the learning of data distribution, instead of progressively correcting the prediction error, thus substantially enhancing the MTSF's accuracy. Furthermore, a Fusionformer-based risk assessment (FRA) method is structured for open-pit mine slope failure early warning issue (SFEW), which aims to prevent potential disasters by accurately predicting future slope movement trends and assessing the probabilities of landslide occurrences. Experimental outcomes validate that Fusionformer outperforms existing forecasting methods, while the FRA framework provides valuable insights and practical guidance for real-world applications.

In this paper, a novel outlier detection method is proposed for industrial data analysis based on the fuzzy C-means (FCM) algorithm. An adaptive switching randomly perturbed particle swarm optimization algorithm (ASRPPSO) is put forward to optimize the initial cluster centroids of the FCM algorithm. The superiority of the proposed ASRPPSO is demonstrated over five existing PSO algorithms on a series of benchmark functions. To illustrate its application potential, the proposed ASRPPSO-based FCM algorithm is exploited in the outlier detection problem for analyzing the real-world industrial data collected from a wire arc additive manufacturing pilot line in Sweden. Experimental results demonstrate that the proposed ASRPPSO-based FCM algorithm outperforms the standard FCM algorithm in detecting outliers of real-world industrial data.Note to Practitioners—Electric arc (which is governed by the current and arc voltage) plays a significant role in monitoring the operating status of the wire arc additive manufacturing (WAAM) process. The nominal periodic current and voltage may occasionally change abruptly due to anomalies (such as arc instability, unstable metal transfer, geometrical deviations, and surface contaminations), which would affect the quality of the fabricated component. This paper focuses on detecting possible anomalies by analyzing the current and voltage during the WAAM process. A novel clustering-based outlier detection method is proposed for anomaly detection where abnormal and normal instances are categorized into two separate clusters. A new particle swarm optimization algorithm is put forward to optimize the initial cluster centroid so as to improve the detection accuracy. The proposed outlier detection method is applied to real-world data collected from a WAAM pilot line for detecting abnormal instances. Experimental results demonstrate the effectiveness of the proposed outlier detection method. The proposed outlier detection method can be applied to other industrial applications including electrical engineering, mechanical engineering and medical engineering. In the future, we aim to develop an online outlier detection system based on the proposed method for real-time for anomaly detection and defect prediction.

In this paper, an explainable artificial intelligence (AI) technique is employed to analyze the match style and gameplay of the national basketball association (NBA). A descriptive analysis on the evolution of the NBA gameplay is conducted by using clustering and principal component analysis. Supervised-learning based AI models (including the random forest and the feed-forward neural network) are applied to produce accurate predictions on NBA outcomes at a season-by-season and a month-by-month basis. To evaluate the interpretability of the established AI models, an explainable AI algorithm is utilized to deduce and assess the precise reasoning behind the model prediction based on the local interpretable model-agnostic explanation method. To illustrate its application potential, the method is applied to the open-source NBA data from 1980 to 2019. Experimental results demonstrate the effectiveness of the introduced explainable AI algorithm on predicting NBA outcomes with interpretation.

Due to the ever-increasing amount of various information provided by the internet, recommendation systems are now used in a large number of fields as efficient tools to get rid of information overload. The content-based, collaborative-based and hybrid methods are the three classical recommendation techniques, whereas not all real-world problems (e.g. the food recommendation problem) can be best addressed by such classical recommendation techniques. This paper is devoted to solving the food recommendation problem based on many-objective optimization (MaOO). A novel recommendation approach is proposed by transforming the original recommendation problem into an MaOO one that contains four different objectives, i.e., the user preferences, nutritional values, dietary diversity, and user diet patterns. The experimental results demonstrate that the designed recommendation approach provides a more balanced way of recommending food than the classical recommendation methods that only consider individuals’ food preferences.

In this article, a dynamic-neighborhood-based switching PSO (DNSPSO) algorithm is proposed, where a new velocity updating mechanism is designed to adjust the personal best position and the global best position according to a distance-based dynamic neighborhood to make full use of the population evolution information among the entire swarm. In addition, a novel switching learning strategy is introduced to adaptively select the acceleration coefficients and update the velocity model according to the searching state at each iteration, thereby contributing to a thorough search of the problem space. Furthermore, the differential evolution algorithm is successfully hybridized with the particle swarm optimization (PSO) algorithm to alleviate premature convergence. A series of commonly used benchmark functions (including unimodal, multimodal, and rotated multimodal cases) is utilized to comprehensively evaluate the performance of the DNSPSO algorithm. The experimental results demonstrate that the developed DNSPSO algorithm outperforms a number of existing PSO algorithms in terms of the solution accuracy and convergence performance, especially for complicated multimodal optimization problems.

In this paper, a novel particle swarm optimization (PSO) algorithm is put forward where a sigmoid-function-based weighting strategy is developed to adaptively adjust the acceleration coefficients. The newly proposed adaptive weighting strategy takes into account both the distances from the particle to the global best position and from the particle to its personal best position, thereby having the distinguishing feature of enhancing the convergence rate. Inspired by the activation function of neural networks, the new strategy is employed to update the acceleration coefficients by using the sigmoid function. The search capability of the developed adaptive weighting PSO (AWPSO) algorithm is comprehensively evaluated via eight well-known benchmark functions including both the unimodal and multimodal cases. The experimental results demonstrate that the designed AWPSO algorithm substantially improves the convergence rate of the particle swarm optimizer and also outperforms some currently popular PSO algorithms.

Optimization is an important research field, especially in engineering, physical sciences, and economics. The main purpose of optimization is to maximize the profit and minimize the cost of production as well as the loss of the system. Evolutionary computation algorithms, such as the genetic algorithm and the particle swarm optimization (PSO) algorithm have been successfully employed in solving various optimization problems. Owing to its application potential and promising performance in discovering the optimal solution, the PSO algorithm has been recognized as a powerful optimization technique and attracted an ever-increasing interest in the evolutionary computation community. In this article, a novel$N$-state Markovian jumping PSO (NS-MJPSO) algorithm is presented where the velocity updating equation is adjusted based on the state evolution governed by a Markov chain. The performance of the proposed NS-MJPSO algorithm is evaluated via some widely used mathematical benchmark functions. The experimental results demonstrate that the developed NS-MJPSO algorithm outperforms some currently popular PSO algorithms on the widely used benchmark functions.

In this paper, a novel particle swarm optimization (PSO) algorithm is proposed in order to improve the accuracy of traditional clustering approaches with applications in analyzing real-time patient attendance data from an accident & emergency (A&E) department in a local U.K. hospital. In the proposed randomly occurring distributedly delayed PSO (RODDPSO) algorithm, the evolutionary state is determined by evaluating the evolutionary factor in each iteration, based on whether the velocity updating model switches from one mode to another. With the purpose of reducing the possibility of getting trapped in the local optima and also expanding the search space, randomly occurring time-delays that reflect the history of previous personal best and global best particles are introduced in the velocity updating model in a distributed manner. Eight well-known benchmark functions are employed to evaluate the proposed RODDPSO algorithm which is shown via extensive comparisons to outperform some currently popular PSO algorithms. To further illustrate the application potential, the RODDPSO algorithm is successfully exploited in the patient clustering problem for data analysis with respect to a local A&E department in West London. Experiment results demonstrate that the RODDPSO-based clustering method is superior over two other well-known clustering algorithms.

In this paper, the state estimation problem is investigated for a class of continuous-time complex networks with bounded distributed delay. For the network under consideration, only the outputs from a fraction of nodes are available and this put forward the new challenge, that is, the so-called partial-nodes-based state estimation problem. In order to reduce the usage of the communication resources, a general event-triggering rule is considered in the design of the estimator. A novel estimator is constructed and, by constructing novel Lyapunov-Krasovskii functionals, some easy-to-check conditions are derived such that the error dynamics is exponentially ultimately bounded. Furthermore, it is shown that the Zeno behavior can be excluded from the event-triggering rules. Numerical simulations are presented to further illustrate the effectiveness of the theoretical results.

This paper presents the design of a sun tracker based on an Arduino microcontroller. The main parts of the solar tracker are a photovoltaic panel, a stepper motor and a GPS module. The basic functions and calculations are described, together with demonstration tests of the stepper motor and the GPS module.