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Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Big Data Research (2096-0271) is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In view of the existing traffic signal detection algorithms, which require a large amount of computation and model, are difficult to deploy at the embedded end, and are difficult to detect long-distance traffic signals with a high miss rate, this paper designs a lightweight traffic signal detection algorithm for the embedded end, which aims at lightweight and real-time requirements. GhostNet network Ghost module and Ghost bottleneck layer structure are adopted to reduce the number of model parameters and improve the detection speed. To solve the feature similarity problem, the weighted bidirectional feature pyramid network is used to make the algorithm more sensitive to the target. The dense void space pyramid pool is used to optimize the extraction of global context information. Aiming at the problem of small target recognition, the information extraction of small target is enhanced through the improvement of multi-scale detection. Finally, the learning ability of the model is improved through knowledge distillation, and the detection performance is improved. Experimental results show that the recognition accuracy and recall rate of traffic lights by this detection algorithm reach 97.0% and 99% respectively, 2.7 and 3 percentage points higher than that of YOLOv5s. The model size is reduced to 8.06 MB, 58% of that of YOLOv5s, and the recognition rate is increased from 51 frames per second to 56 frames per second. Through testing in the embedded terminal, the improved algorithm can recognize the traffic signal in real time and accurately. [ABSTRACT FROM AUTHOR]

Symmetric non-negative matrix factorization (NMF) can naturally capture the embedded clustering structure in the graph representation. It is an important method for linear and nonlinear data clustering applications. However, it is sensitive to the initialization of variables, and the quality of the initialization matrix greatly affects the clustering performance. In semi-supervised clustering, it faces the challenge of learning a more discriminative representation from limited labeled data. This paper introduces a constrained propagation self-adaptive self-supervised non-negative matrix factorization clustering algorithm (CPS³NMF) to solve the above problems. The algorithm propagates finite constraints to unconstrained data points, constructing a similarity matrix imbued with constraint information. The resultant similarity matrix serves the role of a non-negative symmetric matrix decomposition in SNMF and functions as graph regularization for the assignment matrix, fully utilizing the limited constraint information to preserve the geometrical structure of data space. Concurrently, leveraging the sensitivity of initial features in SNMF, the algorithm employs adaptively learned weights to rank the quality of multiple initial matrices. By integrating results from multiple clustering attempts, it progressively enhances the performance of semi-supervised clustering. Experiments on 6 public datasets show that the proposed CPS³NMF algorithm outperforms other state-of-the-art algorithms, proving its effectiveness in semi-supervised clustering. [ABSTRACT FROM AUTHOR]

In order to improve the spatial resolution of brain magnetic resonance imaging (MRI) images, the super resolution (SR) reconstruction method based on deep convolutional neural network (CNN) has achieved remarkable results. Increasing network depth or width usually can expand the receptive field of the network to improve the quality of reconstruction, but it is difficult to train the network due to the large parameter quantity and huge computing requirements. Meanwhile, the network does not pay enough attention to the medium and high frequency features of the image, which affects the quality of reconstruction. To solve the above problems, this paper proposes a cascaded channels-space attention and encode-decode network for 3D-MRI image SR reconstruction. Firstly, feature extraction is carried out in the low resolution space, and the image features are extracted by the symmetric connected encoder-decoder with dilated convolution to alleviate the checkerboard artifacts. Secondly, a serial connected channel-space attention module is constructed to capture the interdependence between the feature channels by channel attention, and the spatial attention is increased to assign weight to information of different positions, effectively enhancing the network' s learning of medium and high frequency information. Finally, subpixel convolution is used to sample the feature map to the target image size and meanwhile reduce memory consumption. Experimental results on public Kirby21 and BraTS datasets show that the proposed method is superior in both quantitative peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and subjective visual quality when compared with traditional SR algorithms and mainstream CNN-based SR algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The existing NAS methods can not directly show the relationship between network models and candidate modules and the accuracy of model recognition. At the same time, many NAS methods have poor scalability and cannot extend their search strategies to arbitrary search space. In response to the above challenges, this paper proposes a visual module contribution neural architecture search method. In this paper, the concept of module contribution is first proposed, and the unified sampling principle in arbitrary search space is given by analyzing the dilemma of the contribution calculation process. Finally, the neural network architecture is generated through a dynamic network programming algorithm for specific constraints. Extensive experimental results demonstrate the effectiveness of the proposed algorithm. Using the CIFAR-10, CIFAR-100, and ImageNet16-120 datasets, the average accuracy on the NAS- Bench- 201 benchmark is 93.33%, 71.07%, and 42.69%, respectively. [ABSTRACT FROM AUTHOR]

The simple and efficient network structure of U-Net is widely used in medical image segmentation, and many scholars have made various researches on the U-Net structure. This paper elucidates in the following: firstly, the paper summarizes the key challenges of the U-Net network in the field of medical image segmentation; next, it elaborates the formats and characteristics of medical image datasets that are commonly used in the U-Net network; then, it summarizes the six improvement mechanism of U-Net: skip connection mechanism, generative adversarial network, residual connection mechanism, 3D-UNet, Transformer mechanism, and dense connecting mechanism. Finally, the paper discusses the relationship between these improvement mechanisms and commonly used medical data formats, and points out the ideas and directions for future improvement, so as to stimulate the unlimited potential of U-Net in medical image segmentation. [ABSTRACT FROM AUTHOR]

It is very important for autonomous driving vehicles to accurately detect objects such as vehicles and pedestrians on the road in real time. Aiming at the problems of missed detection and poor detection effect of small targets in the autonomous driving scene, this paper proposes an automatic driving target detection algorithm that improves the YOLOv7 algorithm. Firstly, it modifies the modules in the network to expand the receptive field, reduces the size of the receptive field module, and improves the speed of the model and enhances the ability to extract image feature information. Secondly, the paper introduces the BRA attention mechanism at the output of the backbone network to improve the model's ability to small target objects. Finally, it replaces the original CIOU loss function of the algorithm with the EIOU loss function to minimize the difference between the height and width of the predicted frame and the real frame, and speeds up the convergence of the model while achieving better positioning results. The experimental results show that: on the KITTI dataset, when the improved YOLOv7 algorithm performs target detection, its mAP reaches 94.7%, which is 3.1 percentage points higher than the original YOLOv7 algorithm, and it has achieved higher detection accuracy in small target object detection. It effectively improves the model's detection effect on small targets. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems of the practical Byzantine fault-tolerant algorithm (PBFT), such as insufficient system dynamics, low consensus efficiency and poor system robustness caused by the random selection of master nodes, a consensus algorithm of CRPBFT based on consistent hash and random selection is proposed. Firstly, the nodes are grouped by consistent hash, and the dynamic change mechanism of nodes is added to provide a dynamic network structure for the system. Secondly, the reputation value of the node is dynamically calculated according to the performance of the node in the consensus. At the same time, this paper defines three node reputation levels, namely, the candidate list of primary nodes, common nodes and malicious nodes. The primary node that is reliable and whose identity is difficult to be maliciously predicted, is selected through the verifiable random function, and the nodes that satisfied the reputation value requirements are selected to form a relatively stable consensus cluster. Experimental results show that CRPBFT algorithm is more reliable than consensus node cluster in PBFT algorithm, and its performance in consensus delay, throughput and system robustness is better than PBFT algorithm. [ABSTRACT FROM AUTHOR]

The crack image on the rail surface has a small target, many background interference information and a high resolution, making its target detection effect not ideal. In this regard, the paper proposes an improved target detection algorithm of YOLOV5s to improve the detection effect of rail cracks. The VOV-GSCSP module is introduced in the neck network, and the ordinary convolution is replaced by the lighter convolution method GSconv to reduce the computational effort of the network while retaining more detailed information. The feature pyramid structure is improved and a multipath cross-layer fusion structure is proposed to incorporate the information of the backbone network across layers in the process of feature pyramid downsampling to retain more original feature information and improve the accuracy of target detection. Meanwhile, the CA attention module and Transformer structure are introduced to further enhance the information extraction of higher-order semantics. The experimental results show that the improved YOLOV5s algorithm achieves an average mean accuracy (mAP) of 62.4%, which is 6.2 percentage points better than the original YOLOV5s algorithm; the recall rate (Recall) is 92.2%, which is 4.4 percentage points better. [ABSTRACT FROM AUTHOR]

The density peaks clustering (DPC) algorithm has several issues such as difficulties in accurately finding cluster centers of manifold data, and easily produce the joint errors in the allocation of residual samples. To address these issues, this paper proposes a density peaks clustering algorithm based on shared neighbor degree and probability assignment (SP-DPC). Firstly, using K-nearest neighbor and shared K-nearest neighbor, the shared neighbor degree between sample points is defined, and the local density of sample points is redefined using this information to identify the correct cluster centers. Next, the transfer probability assignment strategy and the evidence probability assignment strategy are proposed to optimize the allocation of the residual sample together based on their K-nearest neighbors, thus avoiding the joint allocation errors. Finally, SP-DPC algorithm is compared to DPC, SKM-DPC, DPC-NN, DBSCAN, and K-means algorithms using 17 synthetic datasets and 12 UCI datasets. The experimental results show that the SP-DPC algorithm achieves relative optimal values in the three evaluation indexes of AMI, ARI and FMI as a whole and outperforms other algorithms in terms of clustering efficiency. [ABSTRACT FROM AUTHOR]

Spatial Modulation(SM) and Orthogonal Frequency Division Multiplexing(OFDM) Multi-Mode Index Modulation (MM-OFDM-IM) techniques have significant advantages in Energy Efficiency(EE) and Spectral Efficiency(SE), respectively. The combination of multi-dimensional index modulation technology can apply multiple physical resources to meet the high demand for data transmission and system capacity in wireless communication systems. Therefore, SM and MM-OFDM-IM systems can be flexibly combined to construct an OFDM Multiple Mode Space Frequency Composite Index Modulation (MM-OFDM-SFCIM) based on space frequency combination. This system can retain the silent subcarriers in traditional subcarrier index modulation technique, expanding new composite coefficient dimensions and improving its SE and EE. Aiming at the complex characteristics of this system, this paper proposes a joint detection algorithm EL-REML (REML based on symbolic Energy and LLR) based on symbolic energy and log-likelihood. The simulation results show that the MM-OFDM-SFCIM system achieves about 30% improvement in SE compared to traditional space-frequency combination systems, and the EL-REML algorithm proposed in this system achieves a 3-4 dB improvement in BER (Bit Error Rate) compared to the LLR algorithm. Compared with traditional ML algorithm, this algorithm has lower computational complexity. [ABSTRACT FROM AUTHOR]

Articulation disorder, as a medical difficulty, currently mainstream speech recognition technologies are not well adapted to the needs of this field. At the same time, speech recognition technology for dysarthria utilizes a combination of pre training and personalized training to further improve algorithm performance and reduce recognition word error rate through data-driven methods. However, currently, speech recognition technology for dysarthria still has a certain distance from practical commercial use, and its development is limited by data scale and technology. So far, there have been no comprehensive articles on speech recognition for dysarthria. It is urgent to compare and analyze the construction methods and advanced technologies of various datasets in this field, in order to facilitate researchers entering the field to quickly acquire knowledge in this field. This paper conducts a survey on existing datasets, mainstream algorithms, and evaluation methods, and summarizes the scale, form, and characteristics of mainstream speech impairment datasets at home and abroad. It analyzes the mainstream algorithms for speech recognition with dysarthria, and provides the performance and characteristics of different algorithms. Finally, the performance evaluation indicators of the algorithm model based on the severity level of patients with dysarthria are studied, and future research directions are discussed, in order to provide help for the researchers engaged in speech recognition with dysarthria and assist in the rapid development of this field. [ABSTRACT FROM AUTHOR]

Recommendation of point of interest (POI) is one of the most popular topics in location-based social network (LBSN). The existing recommendation methods do not fully consider the deep influence of the spatial and temporal intervals between sequences of check-in data on the recommended sequences. They ignore the global information in the checkin data and focus on the local preferences in the recent check-in sequence of a single user. To address these problems, this paper proposes a global feature fusion based spatiotemporal network (GSTN) interest point recommendation algorithm. The method uses graph neural networks to obtain global features of the heterogeneous network graph of check- in data, and incorporates spatiotemporal gating into the traditional gating structure, fuses global features to model users'mobile behavior, and then introduces a self-attentive mechanism to learn user preference vector representation. Finally, the experiments are carried out on two real datasets. The experiments show that the proposesd approach outperforms similar algorithms in terms of recommendation performance and verifies the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

A multi-objective vehicle routing optimization model is established to minimize total cost and equilibrium degree for multi-objective vehicle routing problem with time windows, and a hybrid multi-objective grey wolf algorithm is proposed to solve the problem. Mainly design three strategies: (1) A new encoding and decoding method is designed to achieve the conversion of continuous grey wolf position vectors to discrete customer sequences. (2) Convergence and distribution indicators are used to select decision individuals. (3) Multiple deletion and insertion operators have been designed to implement local routing optimization. To demonstrate the effectiveness of the algorithm, some numerical examples in Solomon are used as examples to experimentally compare the proposed algorithm with MOIGA and improved ACO algorithms. Experimental results show that the hybrid multi-objective grey wolf algorithm proposed in this paper can find a better Pareto solution, and its performance is better than other evolutionary algorithm. [ABSTRACT FROM AUTHOR]

The intelligent classroom is a popular application scenario in the field of artificial intelligence. This paper proposes a face detection algorithm based on improved YOLOv5, named YOLOv5-SASA, to address the issues of missed or false detection caused by small or occluded faces in images captured by cameras located far away or at an angle. The algorithm consists of three parts. Firstly, the CSPDarknet53 network is utilized in the backbone layer, and the BasicRFB module is used in the final spatial pooling layer to enhance the network's feature extraction ability. Secondly, the NWD loss function is employed to improve the model's robustness in detecting small targets. Thirdly, the independent self- attention mechanism module SASA is introduced in the head layer to address the issue of face occlusion and reduce the model's parameter count. Finally, the improved YOLOv5 network is optimized by reducing the number of neurons in the middle layer channels and adjusting the learning rate to avoid overfitting. Experimental results demonstrate that the proposed method outperforms the original network in the easy, medium, and hard levels of the WiderFace validation set, achieving accuracies of 97.5%, 96.3%, and 86.5%, respectively, which effectively improves the accuracy of face detection in classroom scenarios. [ABSTRACT FROM AUTHOR]

In order to counter the limitations of the original YOLOv5 human fall detection task, a highly accurate fall detection algorithm, called C2D-YOLO, is proposed in this paper. The original task struggles to effectively handle complex detail capture, deformation handling, target adaptation to different scales, and occlusion detection. To overcome these challenges, several improvements are made to the YOLOv5 model. Firstly, a new feature extraction module called C2D is introduced, which improves feature characterisation, captures complex details, and handles deformations by combining deformable convolution, standard convolution, and channel-space hybrid attention mechanisms. Secondly, in the neck network, Swin Transformer block replaces the bottleneck layer of the C3 module to retain more feature information, thereby improving target detection accuracy at different scales and enhancing performance under occlusion. Finally, the head module of YOLOv5 is enhanced based on the decoupled structure of YOLOX borrowed from YOLOv5 to optimise classification and regression performance. Experimental results show that this method achieves a 3.2 percentage points improvement in mAP0.5 and a 6.5 percentage points improvement in mAP0.5: 0.95 compared to existing YOLOv5s. These improvements significantly increase detection accuracy and reduce false alarm rates. [ABSTRACT FROM AUTHOR]

Community detection has been one of the key research directions in network analysis. Most of the current network community detection algorithms mainly use the structural information of the network to adopt a greedy algorithm to maximize a certain indicator, which cannot fully consider the node feature information, edge weight, and network community relationship asymmetry. To address this situation, this paper proposes an autoencoder-like modularity nonnegative matrix factorization (AMNMF) community detection algorithm. The algorithm expands the depth of non-negative matrix factorization by using an encoder-like structure, and introduces modularity and graph regularizer into the objective function optimization process of non-negative matrix factorization to fully mine the node and community structure information in the network. The problem of community relationship imbalance is solved by adding orthogonal constraints to the middle layer of the encoder. Experiments on multiple real networks show that: AMNMF is an effective NMF extension algorithm that uses node feature information and network structure information. Compared with the best results of baseline algorithms, it achieves an improvement of about 15% to 122%, and can accurately and effectively complete the community detection task. [ABSTRACT FROM AUTHOR]

Synthetic minority oversampling technique (SMOTE) has become one of the mainstream methods for dealing with unbalanced data due to its ability to effectively deal with minority samples, and many SMOTE improvement algorithms have been proposed, but very little research existing considers popular algorithmic-level improvement methods. Therefore a more comprehensive analysis of existing SMOTE class algorithms is provided. Firstly, the basic principles of the SMOTE method are elaborated in detail, and then the SMOTE class algorithms are systematically analyzed mainly from the two levels of data level and algorithmic level, and the new ideas of the hybrid improvement of data level and algorithmic level are introduced. Data-level improvement is to balance the data distribution by deleting or adding data through different operations during preprocessing; algorithmic-level improvement will not change the data distribution, and mainly strengthens the focus on minority samples by modifying or creating algorithms. Comparison between these two kinds of methods shows that, data- level methods are less restricted in their application, and algorithmic-level improvements generally have higher algorithmic robustness. In order to provide more comprehensive basic research material on SMOTE class algorithms, this paper finally lists the commonly used datasets, evaluation metrics, and gives ideas of research in the future to better cope with unbalanced data problem. [ABSTRACT FROM AUTHOR]

The combination of Non-Orthogonal Multiple Access (NOMA) technology and Device-to-Device (D2D) communication technology has outstanding advantages in achieving efficient spectrum utilization and large-scale access. Aiming at the problem of single channel allocation mode and the difficulty in obtaining optimal power allocation within D2D group in existing NOMA-D2D systems, this paper constructs a joint resource allocation algorithm with D2D group and rate as the optimization target. Firstly, in terms of sub-channel allocation, this problem is converted into a bilateral matching problem, and a D2D group channel allocation algorithm based on many-to-one scenarios is proposed, Then, the difference of two convex functions (DC) programming method based on successive convex approximation is used to find the power allocation value close to the optimal one. The simulation results show that the proposed channel matching algorithm in the many-to-one scenario is superior to the one in the one-to-one scenario in terms of sum rate, and the proposed power allocation algorithm is closer to the optimal power allocation compared to the dual iterative algorithm. [ABSTRACT FROM AUTHOR]

With the popularization of smart devices and the continuous development of wireless communication technology, a large amount of trajectory data is recorded in wireless networks, and frequent item mining for trajectory data has become a research hotspot. In order to update the frequent trajectory set when new data is added to the trajectory database and the minimum support is changed, we propose the dynamic minimum support incremental updating PrefixSpan algorithm (DMSIU-PrefixSpan). A method is proposed in the algorithm to estimate the support of a sequence, in which the confidence of its suffix subsequence is approximated to replace its own confidence when estimating the support of a sequence. With this method, the sequence support can be calculated without scanning the database. DMSIU-PrefixSpan algorithm can make full use of the information of frequent trajectory set. When incremental trajectory data is added to the original trajectory database and the minimum support changes, the candidate sequence is generated by connecting frequent trajectory sequence and frequent 1 sequence, and the candidate sequence support is estimated using the support estimation method to discover the frequent sequences. The algorithm no longer needs to save the original trajectory data and achieves fast update of the frequent trajectory set. The accuracy of the support degree estimation and the effectiveness of the algorithm of this paper are verified by the mining experiments of the trajectory database. [ABSTRACT FROM AUTHOR]

The traditional neighborhood rough set model has low noise immunity and computational efficiency for mixed data. This paper establishes a local variable-precision neighborhood rough set model with dynamic change of attributes based on matrix theory. Firstly, on the basis of the updating law of local diagonal matrix and transition matrix, the dynamic updating mechanism of approximation under the local variable-precision neighborhood rough set of mixed information system is constructed; secondly, according to this dynamic updating mechanism, a new dynamic algorithm of local variable-precision neighborhood rough set with attribute change is proposed; finally, through the experimental analysis, it can be seen that the proposed dynamic algorithm has a high computational efficiency and a good robustness. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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[Objective] Trajectory planning is critical for the motion control of a manipulator. The motion time and joint vibration of the manipulator can be reduced by providing a fast and stable reference trajectory to the controller. The single objective trajectory optimization algorithm remains insufficient in meeting the increasing production needs; therefore, it is often necessary to use a multiobjective heuristic algorithm to optimize the motion trajectory of the manipulator while satisfying its kinematic constraints to optimize the motion time and reduce the joint impact or energy loss, improving the production efficiency of the manipulator and ensuring its stability during motion. An improved adaptive multiobjective particle swarm optimization (IAMOPSO) method is studied for the time- and impact-based optimal trajectory planning of a multidegree-of-freedom manipulator. [Methods] First, to obtain physically continuous motion trajectories, including position, velocity, and acceleration, we use a quintic B-spline curve to interpolate the joint path points based on the local controllability of the curve. The trajectory optimization process is a step-by-step approach so that the interpolated solution optimally satisfies the kinematic constraints of the manipulator, and the B-spline curve passes through all interpolation points, rendering the motion trajectory of the manipulator more adaptable. Second, a specific objective function improves the motion efficiency of the manipulator. Under certain kinematic constraints, an expression for the joint impact of the manipulator is designed to improve the trajectory tracking accuracy and ensure the stability of the manipulator during operation tasks. Finally, to increase the diversity of the nondominated solution set while avoiding the local extremum, the multi-objective particle swarm optimization (MOPSO) algorithm is improved through a hybrid strategy of mutation operators, adaptive weights, and dynamic learning factors; consequently, the objective function is optimally solved, and the average optimal solution is selected using a normalization function. A nonlinear mutation operator is adopted to encourage particles to comprehensively explore the decision space, ensuring population diversity in the initial iteration of the algorithm, and an adaptive weight strategy and dynamic learning factor are adopted to balance the exploration and development of the MOPSO algorithm, making it easy for the algorithm to determine the global optimal solution. [Results] 1 The quintic B-spline curve accurately interpolates the path points and ensures the continuity of the acceleration and jerk curves, thus meeting the requirements of smoothness and continuity of the joint trajectory of the manipulator; 2 the optimal trajectory planning method based on the IAMOPSO algorithm can yield a Pareto front with good convergence, select the time- and impact-based optimal trajectory according to the average optimal criterion, and achieve continuity of joint angles and their derivatives. [Conclusions] The optimal trajectory planning method for the manipulator based on IAMOPSO proposed in this paper can improve the motion efficiency and tracking accuracy of the manipulator, obtain Pareto front surfaces with good convergence, ensure smooth and continuous joint curves of the manipulator, and improve the operational efficiency and stability of the manipulator. [ABSTRACT FROM AUTHOR]