ABSTRACT Carbon emissions are a significant contributor to global warming. As one of the largest carbon emitters in the world, China is committed to establishing a carbon emission trading market to address the challenges posed by climate change. The carbon price is a fundamental component of the carbon financial market. Accurately predicting it can improve environmental quality, reduce energy demand, and promote economic growth. This study uses price data from the Guangdong carbon market as a case study and employs a hybrid model that integrates Convolutional Neural Networks (CNN) and Long Short‐Term Memory (LSTM) networks for carbon price forecasting. The findings indicate that: (1) the CNN–LSTM model exhibits optimal predictive performance when the sliding window is set to a size of 5 on the basis of previous carbon price data. (2) By incorporating significant indicator features from the Guangdong pilot carbon price dataset while maintaining a sliding window size of 5, the model achieves superior predictive accuracy, as evidenced by a Goodness of Fit (R2) of 0.8622 and a mean absolute error (MAE) of 0.0228, resulting in the most favorable comprehensive evaluation index. (3) The integration of one‐dimensional convolutional layers with LSTM layers in the CNN–LSTM model effectively leverages the strengths of CNNs for local feature extraction and the capabilities of LSTMs for modeling time series data. This approach leads to a substantial improvement in predictive performance compared with alternative models such as Support Vector Machine (SVM), Recurrent Neural Network (RNN), and LSTM.

ABSTRACT Robotic manipulators have become essential in demolition tasks involving hazardous, confined, or structurally unstable environments, where precise and responsive motion control is critical for safety and efficiency. Solving the inverse kinematics (IK) of demolition robot manipulators poses considerable challenges due to the inherent strong nonlinearity and coupling in their kinematic equations, along with potential singularities that can undermine real‐time computational efficiency and system robustness. To address these issues, this study introduces an enhanced Newton–Raphson (NR) approach, specifically optimized for inverse kinematic analysis of 6‐DOF manipulators equipped with a spherical wrist architecture frequently adopted in demolition robotic arms. The proposed method strategically partitions the manipulator into two three‐DOF segments and constructs NR iterative equations by utilizing the reconnecting constraints between these kinematic substructures. After solving a subset of joint variables iteratively, the remaining two joint angles are obtained analytically. The experimental results show that: Compared with the traditional Newton–Raphson method, the improved method has a faster convergence speed, higher accuracy, and better robustness, especially when dealing with singular points at the wrist. This makes the improved method applicable to the inverse kinematics control of dismantling robotic arms in complex environments.

sEMG(surface electromyography) signals have been widely used in rehabilitation medicine in the past decades because of their non-invasive, convenient and informative features, especially in human action recognition, which has developed rapidly. However, the research on sparse EMG in multi-view fusion has made less progress compared to high-density EMG signals, and for the problem of how to enrich sparse EMG feature information, a method that can effectively reduce the information loss of feature signals in the channel dimension is needed. In this article, a novel IMSE (Inception-MaxPooling-Squeeze- Excitation) network module is proposed to reduce the loss of feature information during deep learning. Then, multiple feature encoders are constructed to enrich the information of sparse sEMG feature maps based on the multi-core parallel processing method in multi-view fusion networks, while SwT (Swin Transformer) is used as the classification backbone network. By comparing the feature fusion effects of different decision layers of the multi-view fusion network, it is experimentally obtained that the fusion of decision layers can better improve the classification performance of the network. In NinaPro DB1, the proposed network achieves 93.96% average accuracy in gesture action classification with the feature maps obtained in 300ms time window, and the maximum variation range of action recognition rate of individuals is less than 11.2%. The results show that the proposed framework of multi-view learning plays a good role in reducing individuality differences and augmenting channel feature information, which provides a certain reference for non-dense biosignal pattern recognition.

Autonomous driving has gradually become a research hotspot in recent years. Visual Simultaneous Localization and Mapping (SLAM) technology can help unmanned vehicles accurately explore the environment at a lower cost, and the readability of the map can be improved by integrating target detection algorithms. However, the location and 3D shape of the object in the map were not obtained. The method of RGBD-SLAM based on object detection with two-stream YOLOv4-MobileNetv3 convolutional neural network is proposed in this paper. RGBD SLAM algorithm and target detection algorithm are combined to build an algorithm model that can generate the global sparse map and build target dense map quickly. The two-stream network is integrated to obtain 2D information about the target, and further combined with the camera pose after the front-end key frame detection of the SLAM algorithm in this paper, and the dense 3D point cloud of the target and the center point position of the object is obtained. Then, the sparse point cloud of the SLAM system and the dense point cloud of the target can be obtained. The experimental results show that the number of point clouds decreases by about 50% and the time for mapping accounts for about 60% of the global dense mapping time. The method of this paper can efficiently decrease the computational space and improve the speed of semantic mapping, which verifies its feasibility and superiority. It can be used to achieve large-area mapping and the ability to update maps during autonomous driving.

Abstract With the rapid development of sensor technology and artificial intelligence, the video gesture recognition technology under the background of big data makes human‐computer interaction more natural and flexible, bringing richer interactive experience to teaching, on‐board control, electronic games, etc. In order to perform robust recognition under the conditions of illumination change, background clutter, rapid movement, partial occlusion, an algorithm based on multi‐level feature fusion of two‐stream convolutional neural network is proposed, which includes three main steps. Firstly, the Kinect sensor obtains RGB‐D images to establish a gesture database. At the same time, data enhancement is performed on training and test sets. Then, a model of multi‐level feature fusion of two‐stream convolutional neural network is established and trained. Experiments result show that the proposed network model can robustly track and recognize gestures, and compared with the single‐channel model, the average detection accuracy is improved by 1.08%, and mean average precision (mAP) is improved by 3.56%. The average recognition rate of gestures under occlusion and different light intensity was 93.98%. Finally, in the ASL dataset, LaRED dataset, and 1‐miohand dataset, recognition accuracy shows satisfactory performances compared to the other method.

Abstract In sEMG (surface electromyography) pattern recognition, most of the research focuses on the static pattern recognition of different limbs, ignoring the importance of changing load intensity, and joint angle movement information. Traditional static qualitative pattern recognition cannot adjust the motion amplitude and load intensity, so it is of great significance to study the continuous prediction of wrist angle under different load intensities. Based on the correlation between the surface EMG signal and the joint angle signal, the article is based on the neural network to identify and predict the wrist angle under different loads continuously quantitatively. The sEMG signal in this article was collected with the approval and review of the Ethics Committee and the people's informed consent. Since qualitative pattern recognition cannot adjust the wrist movement range and the different load training intensity, the article establishes an angle prediction model based on a genetic algorithm to optimize the extreme learning machine (ELM). In addition, the article analyzes the influence of different loads on the continuous prediction accuracy of the wrist angle, realizes the continuous quantitative angle of the precise wrist prediction. Experimental analysis shows that the wrist joint angle predicted by the ELM optimized based on genetic algorithm is close to the actual angle, and the average error is about 5.96 degrees.

The purpose of this paper is to present a novel curved Gaussian Mixture Model (CGMM) and to study the application of it in motion skill encoding. Primarily, Gaussian mixture model (GMM) has been widely applied on many occasions when a probability density function is needed to approximate a complex probability distribution. However, GMM cannot efficiently approach highly non-linear distributions. Thus, the proposed novel CGMM, as a weighted mixture of curved Gaussian models (CGM), is structured with non-linear transfers, which reshapes the flat GMM into a geo-metrically curved one. As a consequence, CGMM has more freedoms and flexibilities than the flat GMM so a CGMM requires fewer number of components in fitting highly non-linear motion trajectories. Moreover, we derive a dedicated iterative parameter estimation algorithm for the CGMM based on maximum likelihood estimation (MLE) theory. To evaluate the performance of the CGMM and its parameter estimation algorithm, a series of quantitative experiments are carried out. We first test the model performance in the data fitting task with the generated synthetic data. Then a motion skill encoding test is carried out on a human motion trajectory dataset built by a Virtual Reality (VR) based motion tracking system. The empirical results support that CGMM outperforms state-of-the-arts in the model performance test. Meanwhile, CGMM has a significant improvement in encoding high dimensional non-linear trajectory data compared to the GMM in motion skill encoding test with its dedicated parameter estimation algorithm.

Summary For the problem of surface electromyography (sEMG) gesture recognition, considering the fact that the traditional machine learning model is susceptible to the sEMG feature extraction method, it is difficult to distinguish the subtle differences between similar gestures. The NinaPro DB1 dataset is used as the research object, and the sEMG feature image and the Convolutional Neural Network (CNN) are combined to recognize 52 gesture movements. The CNN model effectively solves the limitations of traditional machine learning in sEMG gesture recognition, and combines 1‐dim convolution kernel to extract deep abstract features to improve the recognition effect. Finally, the simulation experiment shows that compared with the accuracy of the raw‐sEMG images based on the CNN and the sEMG‐feature‐images based on the CNN and sEMG based on the traditional machine learning, the multi‐sEMG‐features image based on the CNN is the highest, which coming up to 82.54%.

Summary Gesture recognition has always been a research hotspot in the field of human‐computer interaction. Its purpose is to realize the natural interaction with the machine by recognizing the semantics expressed by gesture. In the process of gesture recognition, the occlusion of gesture is an inevitable problem. In the process of gesture recognition, some or even all of the gesture features will be lost due to the occlusion of the gesture, resulting in the wrong recognition or even unrecognizability of the gesture. Therefore, it is of great significance to study gesture recognition under occlusion. The single shot multibox detector (SSD) algorithm is analyzed, and the front‐end network is compared. Mobilenets is selected as the front‐end network, and the Mobilenets‐SSD network is improved. In tensorflow environment, based on the improved network model, the self‐occlusion gesture and object occluding gesture are trained in color map, depth map, and color and depth fusion respectively. The recognition models of self‐occlusion gestures and object‐occlusion gestures in color map, depth map, and color and depth fusion are obtained. And compare and analyze the learning rate, loss function, and average accuracy of various models obtained for occlusion gesture recognition.

Abstract Under the background of economic globalization, enterprises face with more severe and uncertain environmental dynamism, and its lean production system redesign strategy is more critical. Firstly, given the dynamic environment that enterprises are facing, the redesign of the lean production system based on the environmental dynamism is proposed. Secondly, environmental dynamism is divided into two dimensions: market dynamics and technology dynamics, which is calculated by the objective method. Thirdly, by establishing the redesigned model of the lean production system based on environmental dynamism, the relationship between environmental dynamism and lean production level is analyzed. Fourth, the data of 251 listed companies from different industries in 2014 to 2017 were analyzed to verify the specific impact of environmental dynamism on the enterprise's lean production level. It was found that the relationship between environmental dynamism and the enterprise lean production level is presented as “U,” “S,” and other more complex relationships. At last, some suggestions are put forward to optimal the redesign of lean production systems under different environmental dynamism.

Summary The ant colony labor division model can be used to solve the dynamic and variable traffic signal timing problem because of its adaptive and self‐adjusting characteristics. Based on the basic model, this paper proposes a new extended ant colony labor division model for traffic signal timing. This model is combined with the vehicle characteristics to modify the calculation method of environmental stimulus values. Using the vehicle delay in the unit period, the original fixed response threshold is modified to the dynamic response threshold, and the state transition equation is reconstructed. Based on the mixed traffic flow model of two‐phase single‐point intersection cellular automata, through comparative experiments and discussion and analysis, it is found that the extended ant colony labor division model can effectively improve road traffic capacity according to road conditions and reasonable traffic signal timing.

Abstract The eye center localization is a crucial requirement for various human-computer interaction applications such as eye gaze estimation and eye tracking. However, although significant progress has been made in the field of eye center localization in recent years, it is still very challenging for tasks under the significant variability situations caused by different illumination, shape, color and viewing angles. In this paper, we propose a hybrid regression and isophote curvature for accurate eye center localization under low resolution. The proposed method first applies the regression method, which is called Supervised Descent Method (SDM), to obtain the rough location of eye region and eye centers. SDM is robust against the appearance variations in the eye region. To make the center points more accurate, isophote curvature method is employed on the obtained eye region to obtain several candidate points of eye center. Finally, the proposed method selects several estimated eye center locations from the isophote curvature method and SDM as our candidates and a SDM-based means of gradient method further refine the candidate points. Therefore, we combine regression and isophote curvature method to achieve robustness and accuracy. In the experiment, we have extensively evaluated the proposed method on the two public databases which are very challenging and realistic for eye center localization and compared our method with existing state-of-the-art methods. The results of the experiment confirm that the proposed method outperforms the state-of-the-art methods with a significant improvement in accuracy and robustness and has less computational complexity.

This paper proposes a dynamic 3D reconstruction method for recovering a surface shape from a set of images that are captured by a hand-held camera. A light source is attached to the camera as a photometric constraint. Thus, we can effectively calculate photometric stereo using the relative moving camera. The key contributions of our work are a robust pixel matching method to build effective correspondences between images for normal estimation, and an optimization method to correct the deviation in the recovered surface shape that is caused by the nonideal illumination in a close-range lighting condition. Specially we correct the recovered shape by adding an interpolation surface that is estimated using sparse control points from the structure from motion. The effectiveness of our method is verified on real datasets with a digital camera and a smart phone.

Vision-based gesture recognition accords with natural communication habits of human and can carry out long-distance and non-contact interactions. So it has become a hot direction in human-computer interaction research whose recognition effect largely depends on the performance of image preprocessing and recognition algorithms. In this paper, a gesture recognition method using color image and depth image combined is designed. For the influence of the angle on the same gesture, the skeleton algorithm is optimized based on the layer-by-layer stripping concept. The fast refinement algorithm improves the process of repeated scanning, extracts the key node information in the skeleton map of the hand, and establishes the spatial axis of the hand to determine the gesture direction. The gesture recognition experiment was performed based on convolutional neural network. The results showed the recognition accuracy rate was 96.01%, and the robustness and accuracy of the proposed recognition method were verified.

Aiming at the phenomenon of low lean level of Chinese enterprise and the over lean level of foreign enterprise, it is a significant to build an evaluation tool of the enterprise lean degree to guide the sustainablility of the enterprises' lean improvement. Combined with the current research results of lean, a design scheme of knowledge base system based on lean evaluation model with 5 layers structure is put forward. With the existing model representation method, a method of model knowledge representation are combined with the object-oriented and framework, and which makes the model knowledgeable. An UML technology is used to modelling the cases of the system, and the dynamic process and static class of the system are studied. Finally, a simulation example is given to verify the effectiveness of the system.