International Journal of Pattern Recognition and Artificial Intelligence

Collaborative robots face challenges in achieving high accuracy using existing parameter identification methods, especially in applications like dragging, teaching, and collision detection. To address the issue of robot dynamics identification accuracy in scenarios with unknown parameters, this paper introduces a method for robot joint parameter identification based on the Cuckoo Search-backpropagation neural network (CS-BPNN). Initially, a kinetic model incorporating Coulomb viscous friction is presented. Subsequently, a BPNN is employed to approximate and fit the nonlinear function, while the CS algorithm is integrated into the BPNN model to optimize its parameters. Collected data undergo third-order Butterworth filtering, and a loss function compares predicted values with actual values to ascertain the accuracy of the proposed method. Experimental results demonstrate that the CS-BPNN approach proposed herein can converge the mean square error (MSE) to $9.79\times 10^{-4}$ N⋅m when robot dynamics parameters are unknown. This method boasts of a lower MSE and superior discrimination accuracy compared to traditional methods like least squares (LS), genetic algorithm (GA), and BPNN. Consequently, the method presented in this study not only enhances the accuracy of parameter identification but also offers a fresh perspective for the parameter identification of robot joint dynamics models.

Nanodrones are insect-sized drones that could fly in complex environments and confined spaces, and act as an emerging tool for covert surveillance and intelligence attacks, which would become a potential threat to national security. Radar has the advantage of wide range, all-day, and all-weather detection ability, making it a means of detecting such threat. First, this paper introduces a pertinent multiple-input multiple-output (MIMO) millimeter-wave (MMW) radar system, with the advantages of low cost and high accuracy. It is utilized to detect three targets: nanodrone, small helicopter, and mechanical bird, through which more detailed features can be obtained. Then, the echo data of the three targets are processed and analyzed, and their distinct micro-Doppler characteristics were obtained. Finally, the Radar Transformer target classification network is used to classify and identify the targets. It has been confirmed that desired results could be achieved through the above process.

The charger’s operation in the power grid will cause harmonic pollution, affecting the service life of the equipment and the quality of the power grid. The harmonic characteristics and detection during the charging process are studied. The simulation model of charger is established first using MATLAB, the results of the experiment show that odd harmonics are mainly generated during charging, and the distortion rate of the current harmonics remains between 0.231 and 0.443 during the charging process. A new wavelet neural network is proposed to detect the harmonic method. Experimental results indicate that, compared with previous detection methods, this network has better detection efficiency and accuracy for harmonics. But detection result of this network is unstable and does not even converge. A new adaptive bat algorithm with improved focusing distance is presented here to optimize the wavelet neural network, which effectively solves the defect of being sensitive to the node number of hidden layer, resulting in higher detection accuracy, faster detection speed, and stable convergence. The detection error range is reduced from −0.03 to 0.02, and the number of iterations is reduced to around 1000.

Emotion detection involves distinguishing the complex nature of emotions, cross-cultural variations, and ethical considerations. The proposed model addresses cross-cultural issues through diverse training and ensures ethical data usage through advanced machine-learning techniques to enhance accuracy. The research aims to develop a Dusky Canidae Optimization-based Deep Convolutional Neural Network model (DCO opt deep CNN) for emotion recognition in audio-visual data, benefiting various advancements. During preprocessing, audio and video are separated to enhance sound quality and eliminate data redundancy while retaining vital information. Features are then extracted and employed for training and testing using the automation and effectiveness of Deep CNNs. The concatenated features are optimized using a hybrid approach that combines Dusky Hawk and Canidae Wolf Optimization (CWO), offering simplicity and interpretability with optimal parameter settings. The proposed DCO model effectively classifies emotions within the data. Evaluating the database, the DCO model exhibits a performance enhancement of 1.28%, 2.91%, and 1.28% in accuracy, sensitivity, and specificity with respect to the existing learner memorizing optimization-based deep NN model.

Cleaning control based on changes in cleaning loss rate and impurity rate has emerged as a hot topic in the research on intelligent control for rice and wheat combined harvesters. However, numerous operation parameters can lead to deviations beyond the normal range of cleaning loss rate and impurity rate. The impact of cleaning control parameters in rice and wheat combined harvesters on cleaning loss rate and impurity rate often tends to be contradictory. How to combine different contradictory cleaning control strategies to get a more widely used and better cleaning control strategy is a hot issue in the current cleaning control research. In this paper, the granularity of quotient space is introduced into the cleaning control based on operation parameters, and a cleaning control model based on granularity synthesis theory is proposed. This method first constructs a knowledge base for intelligent control of cleaning tailored to the cleaning loss rate and impurity rate using production rule representation, and considers that these control strategies constitute different quotient spaces, and then organizes these quotient spaces according to granularity synthesis theory to get the cleaning control strategy. The experimental results verify that the validity of the cleaning control based on granular computing is better than fuzzy control.

Recent deep learning-based watermarking methods have achieved impressive results. However, they struggle with unknown distortions and often suffer from poor generalization, slow convergence, unstable training, and degraded visual quality in watermarked images. To address the above problems, this paper proposes a two-stage separable adversarial distortion (TSAD)-based robust watermarking algorithm for diffusion tensor imaging (DTI). The algorithm uses a noise-free end-to-end network in the first stage for learning and training DTI images. In the second stage, it fixes the watermark embedding network trained in the first stage, interacts the noise distortion network with the watermark extraction network to perform adversarial training for improving robustness. Experimental results show that our method achieves comparable or better robustness to seen distortions and better robustness to unseen distortions, along with enhanced stability, faster convergence, and improved visual quality in watermarked DTI images.

At present, footprint image retrieval based on deep learning mainly focuses on complete footprint, but many of the footprints obtained in the field of public safety and criminal investigation are incomplete forms, Therefore, the feature analysis of incomplete footprint has important practical significance. Based on multiple scale features fusion, we proposed a method to solve incomplete footprints retrieval. On the basis of extracting the global feature of footprint, this method simultaneously extracts multiple local features from different stages of the backbone network to supplement the footprint feature information. The multi-scale feature orthogonal fusion module is used to reduce redundant features, improve the expression ability of footprint features, and solve the problem of incomplete footprint retrieval to a certain extent. The experiment shows that our method has certain effectiveness in retrieving problems on incomplete footprint, with a Top 1 accuracy of 87.08%, expanding the scope of footprint research.

In recent years, with the rapid development of information technology, the volume of image data has grown exponentially. However, these datasets typically contain a large amount of redundant information. To extract effective features and reduce redundancy from images, a representation learning method based on the Vision Transformer (ViT) has been proposed, and to our best knowledge, Transformer was first applied to zero-shot learning (ZSL). The method adopts a symmetric encoder–decoder structure, where the encoder incorporates Multi-Head Self-Attention (MSA) mechanism of ViT to reduce the dimensionality of image features, eliminate redundant information, and decrease computational burden. Consequently, it effectively extracts features, and the decoder is utilized for reconstructing image data. We evaluated the representation learning capability of the proposed method in various tasks, including data visualization, image reconstruction, face recognition, and ZSL. By comparing with state-of-the-art representation learning methods, the outstanding results obtained validate the effectiveness of this method in the field of representation learning.

Accurate prediction is of great significance to the construction of a smart city. However, current models only focus on mining the relationship among sequences and ignore the influence of the predicted sequences on future predictions, so we propose a Dynamic Attention Neural Network (DANN) based on encoder-decoder, which combines encoder context vectors and newly generated decoder context vectors to jointly dynamically representation learning, then generates corresponding predicted values. DANN processes data via the Bi-directional Long Short-Term Memory (Bi-LSTM) network as the fundamental structure of the network between encoder and decoder. What’s more, in order to produce a new feature representation with low redundancy, gate mechanism network module is used to adaptively learn the interdependence of multivariate feature data. The relevant experiments show that compared with baseline models, DANN has the most stable long-term prediction performance, which reduces the problem of error accumulation to a certain degree.

CNN-based methods have made great progress in single-image rain removal. Most recent methods improve performance by increasing the depth of the network. To fully extract local and global features while reducing inference time, we propose a top-to-down attribute-insensitive multiscale hourglass network for rain streak and raindrop removal. For the rain removal task, we expect that the constructed network can accurately identify the various attributes of the rain information characteristics of the small target. Considering the difference in the size, shape, direction and density of rain streak and raindrop, inspired by the performance of hourglass architecture to capture multiscale features in human pose estimation, we introduce an attribute-insensitive hourglass module to recognize the attributes of rain streak and raindrop in a unified framework. This feature extraction module could capture the characteristics of rain streak and raindrop with different attributes. This stacked hourglass blocks down-sample features and then up-samples them back to the original resolution based on discrete wavelet transform and inverse discrete wavelet transform. We perform extensive experiments on five synthetic and real-world de-raining datasets to validate the effectiveness of our proposed network on rain streak and raindrop removal. The qualitative and quantitative results show that our method is suitable for removing rain streak and raindrop in a unified framework. We present the results of generalization and ablation study for key components, we also report the accuracy of semantic segmentation after preprocessing with all rain removal methods. Our source code will be available on the GitHub: https://github.com/Ruini94/AIMHNet.