Few existing studies cover the state-of-the-art multi-objective particle swarm optimization (MOPSO) algorithms. To fill the gap in this area, the research background of multi-objective optimization problems (MOPs) was introduced, and the fundamental theories of MOPSO were described. The MOPSO algorithms were divided into three categories according to their features: Pareto-dominated-based MOPSO, decomposition-based MOPSO, and indicator-based MOPSO, and a detailed description of their existing classical algorithms was also developed. Next, relevant evaluation indicators were described, and seven representative algorithms were selected for performance analysis. The experimental results demonstrated the strengths and weaknesses of each of the traditional MOPSO and three categories of improved MOPSO algorithms. Among them, the indicator-based MOPSO performed better in terms of convergence and diversity. Then, the applications of MOPSO algorithms in production scheduling, image processing, and power systems were briefly introduced. Finally, the limitations and future research directions of the MOPSO algorithm for solving complex optimization problems were discussed.

A spatial-temporal multi-graph convolution traffic flow prediction model by integrating static and dynamic knowledge graphs was proposed, as current traffic flow prediction methods focus on the spatial-temporal correlation of traffic information and fail to fully take into account the influence of external factors on traffic. An urban traffic knowledge graph and four road network topological graphs with distinct semantics were systematically constructed, drawing upon the road traffic information and the external factors. The urban traffic knowledge graph was inputted into the relational evolution graph convolutional neural network to realize the knowledge embedding. The traffic flow matrix and the knowledge embedding were integrated using the knowledge fusion module. The four road network topology graphs and the traffic flow matrix with fused knowledge were fed into the spatial-temporal multi-graph convolution module to extract spatiotemporal features, and the traffic flow prediction value was outputted through the fully connected layer. The model performance was evaluated on a Hangzhou traffic data set. Compared with the advanced baseline, the performance of the proposed model improved by 5.76%-10.71%. Robustness experiment results show that the proposed model has a strong ability to resist interference.

A comprehensive evaluation and categorization of blockchain-based mobile crowdsensing (MCS) data processing was conducted, in order to address the wide participation of users, the flexible mobility of collection devices, and the complexity of communication environment in mobile crowdsensing data processing. Firstly, the developments of MCS and blockchain were reviewed, and the challenges of MCS data processing and the characteristics of blockchain were introduced. Secondly, a blockchain-based mobile crowdsensing architecture (BMCA) was designed to achieve decentralized data management, data security assurance, precise data quality evaluation, and enhanced credibility of incentives. Then, existing data processing techniques were sorted from privacy-preserving, data quality evaluation, and incentive mechanism. Finally, the current problems and challenges in resource consumption control, precise data analysis, full-cycle and differentiated privacy-preserving, and integrated mode application of blockchain-based MCS data processing research were discussed, and the potential future research direction was pointed out.

An unmanned aerial vehicle (UAV) small target detection algorithm based on YOLOv5, termed FDB-YOLO, was proposed to address the significant issue of misidentification and omissions in traditional target detection algorithms when applied to UAV aerial photography of small targets. Initially, a small target detection layer was added on the basis of YOLOv5, and the feature fusion network was optimized to fully leverage the fine-grained information of small targets in shallow layers, thereby enhancing the network’s perceptual capabilities. Subsequently, a novel loss function, FPIoU, was introduced, which capitalized on the geometric properties of anchor boxes and utilized a four-point positional bias constraint function to optimize the anchor box positioning and accelerate the convergence speed of the loss function. Furthermore, a dynamic target detection head (DyHead) incorporating attention mechanism was employed to enhance the algorithm’s detection capabilities through increased awareness of scale, space, and task. Finally, a bi-level routing attention mechanism (BRA) was integrated into the feature extraction phase, selectively computing relevant areas to filter out irrelevant regions, thereby improving the model’s detection accuracy. Experimental validation conducted on the VisDrone2019 dataset demonstrated that the proposed algorithm outperformed the YOLOv5s baseline in terms of Precision by an increase of 3.7 percentage points, Recall by an increase of 5.1 percentage points, mAP₅₀ by an increase of 5.8 percentage points, and mAP_50:95 by an increase of 3.4 percentage points, showcasing superior performance compared to current mainstream algorithms.

Foundational models in natural language processing, computer vision and multimodal learning have achieved significant breakthroughs in recent years, showcasing the potential of general artificial intelligence. However, these models still fall short of human or animal intelligence in areas such as causal reasoning and understanding physical commonsense. This is because these models primarily rely on vast amounts of data and computational power, lacking direct interaction with and experiential learning from the real world. Many researchers are beginning to question whether merely scaling up model size is sufficient to address these fundamental issues. This has led the academic community to reevaluate the nature of intelligence, suggesting that intelligence arises not just from enhanced computational capabilities but from interactions with the environment. Embodied intelligence is gaining attention as it emphasizes that intelligent agents learn and adapt through direct interactions with the physical world, exhibiting characteristics closer to biological intelligence. A comprehensive survey of embodied artificial intelligence was provided in the context of foundational models. The underlying technical ideas, benchmarks, and applications of current embodied agents were discussed. A forward-looking analysis of future trends and challenges in embodied AI was offered.

A 3D target inversion algorithm based on multi-feature reconstruction was proposed in order to solve the problems of large memory occupation and time-consuming training in deep learning-based three-dimensional inversion methods. Four types of features, horizontal area, center depth, vertical thickness and residual density of the target were extracted by feature decomposition to realize the compression of the three-dimensional model and reduce the memory occupation. The multi-feature reconstruction of inversion network (MRNet) was designed to realize the prediction of the four types of target features by different Decoder, and the four types of features were used to reconstruct the three-dimensional model to realize the inversion of the 3D target. The gradient union was introduced at the input of the network to realize the enhancement of target boundary information. The CA attention mechanism was introduced at the cross-layer connection to realize the differentiation of Decoder’s prediction function and optimize the inversion effect. The simulation results showed that the local relative accuracy of MRNet was improved by more than 30% compared with 3D U-Net, reaching 88.91%, and the training time per round was only 1/13 of 3D U-Net. MRNet was applied to Vinton Salt Mound, and the distribution of caprocks was obtained more accurately, which verified that MRNet had certain generalizability.

Existing research on the retrieval task of similar cases ignores the legal logic that the model should imply, and cannot adapt to the requirements of case similarity criteria in practical applications. Few datasets in Chinese for case retrieval tasks are difficult to meet the research needs. A similar case retrieval model was proposed based on legal logic and strong interpretability, and a case event logic graph was constructed based on predicate verbs. The statutory knowledge corresponding to various crimes was integrated into the proposed model, and the extracted elements were input to a neural network-based scorer to realize the task of case retrieval accurately and efficiently. A Confusing-LeCaRD dataset was built for the case retrieval task with a confusing group of charges as the main retrieval causes. Experiments show that the normalized discounted cumulative gain of the proposed model on the LeCaRD dataset and Confusing-LeCaRD dataset was 90.95% and 94.64%, and the model was superior to TF-IDF, BM25 and BERT-PLI in all indicators.

A road pavement distress situation forecasting method employing graph convolutional networks was introduced, addressing the prediction problem of road pavement distress generation and deterioration. Firstly, a topological network was established through clustering algorithms, selecting the main influencing factors of the target pavement distress during its evolution. Subsequently, to enhance the expressive capability of the graph neural network for distress information, a graph topology enhancement method was employed, constructing views related to distress information from both static and dynamic aspects. Finally, an enhanced graph neural network (GNN) architecture was applied, by incorporating attention mechanisms in the view dimension to adjust the influence of different views and utilizing Transformer and GRU modules in the temporal dimension to enhance the predictive performance of the model for pavement distress states over extended time sequences. The internal calibration tests of the model, including ablation studies, multi-sample testing, and hyperparameter control group validation, demonstrated the applicability and stability of the proposed model. For the large and sparse pavement disease dataset, the mean absolute error of this model converged within 4.0, which was better than the results of the traditional prediction algorithms in terms of comprehensive performance.

A multi-modal augmented model for click through rate (MMa4CTR) tailored for micro-videos recommendation was proposed. Multi-modal data derived from user interactions with micro-videos were effectively leveraged to construct embedded user representations and capture diverse user interests across multi-modal. The aim was to reveal the latent semantic commonalities, by combining and crossing features across modalities. The overall recommendation performance was boosted via two training strategies, automatic learning rate adjustment and validation interruption. A computationally efficient multi-layer perceptron architecture was employed, in order to address the computational demands brought on by the vast amount of multi-modal data. Performance comparison experiments and sensitivity analyses of hyperparameter on WeChat Video Channel and TikTok datasets demonstrated that MMa4CTR outperformed baseline models, delivering superior recommendation results with minimal computational resources. Additionally, ablation studies performed on both datasets further validated the significance and efficacy of the micro-video modality cross module, the user multi-modal embedding layer, and the strategies for automatic learning rate adjustment and validation interruption in enhancing recommendation performance.

A new arc additive manufacturing process—droplet+arc additive manufacturing (DAAM) technology was applied to manufacture aluminum alloy samples in order to improve the quality and the efficiency of aluminum alloy. A new droplet generation system (DGS) was applied instead of the conventional wire feeding system, which makes the material addition and arc energy independent of each other. The formed material is 2219 aluminum alloy, and a trace amount of Mg element was added through the DGS. A thin-walled structure was deposited using the DAAM system at a significantly higher deposition rate (160 $ {\mathrm{m}\mathrm{m}}^{3}/\mathrm{s} $) than conventional wire and arc additive manufacturing techniques. The microstructure of the cross section of the thin-walled structure was observed and analyzed. Results showed that the grain morphology of the thin-walled structure was dominated by columnar crystals and exhibited a periodic distribution of inner-layer columnar crystals and inter-layer equiaxed crystals. The average tensile strengths in the horizontal and vertical directions were 455.4 MPa and 417.0 MPa after T6 heat treatment, while the yield strengths were 342.2 MPa and 316.4 MPa, respectively. The comparison results with the previous studies show that the addition of Mg element increases the yield strength of 2219 aluminum alloy, but leads to a corresponding decrease in elongation.

An improved non-dominated sorting genetic algorithm II (NSGA-II) was proposed in order to address the challenges of time-consuming manual airspace sectorization and the difficulty in comparing the quality of different sectorization schemes. A three-dimensional multi-objective optimization model for sectorization was established by using a grid-region-sector hierarchy in order to balance controllers’ workload within sectors and reduce workload differences between sectors. A fitness evaluation operator, a probability-adaptive combination crossover operator and a dynamic mutation operator were incorporated in the NSGA-II algorithm in order to enhance the number of feasible solutions, solution diversity and computational efficiency. A simulation was conducted for the automatic 3D sectorization of Xi'an high-altitude airspace. Results showed that the optimized scheme improved workload balance within sectors by 37% and reduced inter-sector workload by 24% compared with the current sectorization configuration. The proposed improved NSGA-II provided a broader range of options for decision-makers with varying preferences compared with traditional weighted multi-objective optimization algorithms.

The large-scale point clouds are sparse, the traditional point cloud methods are insufficient in extracting rich contextual semantic features, and the semantic segmentation results have the problem of fuzzy object boundaries. A 3D point cloud semantic segmentation algorithm based on boundary point estimation and sparse convolution neural network was proposed, mainly including the voxel branch and the point branch. For the voxel branch, the original point cloud was voxelized, and then the contextual semantic features were obtained by sparse convolution. The initial semantic label of each point was obtained by voxelization. Finally, it was input into the boundary point estimation module to get the possible boundary points. For the point branch, the improved dynamic graph convolution module was first used to extract the local geometric features of the point cloud. Then, the local features were enhanced through the spatial attention module and the channel attention module in turn. Finally, the local geometric features obtained from the point branch and the contextual features obtained from the voxel branch were fused to enhance the richness of point cloud features. The semantic segmentation accuracy values of this algorithm on the S3DIS dataset and SemanticKITTI dataset were 69.5% and 62.7%, respectively. Experimental results show that the proposed algorithm can extract richer features of point clouds, accurately segment object boundary regions, and has good semantic segmentation ability for 3D point clouds.

The backgrounds are cluttered, the spot sizes of apple leaf disease are varying in complex environments, and the existing models have the problems of multiple parameters and a large amount of calculation. Thus, an apple leaf disease recognition network, ConvNext network based on attention and multiscale feature fusion (MA-ConvNext), was proposed. A multiscale spatial reconstruction and channel reconstruction block (MSCB) and a feature extraction block with triplet attention fusion (TAFB) were utilized to effectively extract the features at different scales and enhance the focus on leaf disease spots. Additionally, a stepwise relational knowledge distillation method was employed to fuse the "teacher" network (MA-ConvNext) with an "intermediate" network (DenseNet121) to guide the training of the "student" network (EfficientNet-B0) and achieve the model lightweighting. Experimental results showed that MA-ConvNext achieved a recognition accuracy of 99.38%, improving by 3.98 percentage points, 7.55 percentage points and 4.27 percentage points compared to ResNet50, MobileNet-V3, and EfficientNet-V2 networks, respectively. After the stepwise relational knowledge distillation, the recognition accuracy further improved by 1.76 percentage points, with a smaller network size and parameters of 1.56×10⁷ and 5.29×10⁶. respectively. The proposed method offers new insights and technical support for the precise detection of pests and diseases in agriculture.

A power amplifier operating at 2.4-2.5 GHz was designed based on GaAs HBT technology to meet the requirement of high linearity and high transmission power for the Wi-Fi 6 RF front-end module. The amplifier achieved high linear output power using adaptive bias, second harmonic impedance control, and multistage amplifier distortion complementing. Output matching network insertion loss was reduced by taking advantage of bonding-wire inductance with high-quality factor, and DC and RF power detector was integrated. Test results showed that the gain of the amplifier was 30.6-30.7 dB, the input and output return loss were less than ?10 dB, the output 1 dB compression power was 29.2 dBm, and the corresponding power added efficiency was 26.4%. Under the test signal of 802.11ax standard, MCS7 modulation strategy, and 40 MHz bandwidth, the maximum output power of the amplifier was 24.1 dBm when the error vector magnitude was less than ?30 dB. Under the MCS9 modulation strategy, the maximum output power of the amplifier was 23.6 dBm when the error vector magnitude was less than ?35 dB. Under the MCS11 modulation strategy, the maximum output power of the amplifier was 22.4 dBm when the error vector magnitude was less than ?40 dB, and the corresponding maximum power added efficiency was 10.2%.

A method based on dynamic position encoding and multi-domain attention feature enhancement was proposed to fully exploit the positional information between the template and search region and harness the feature representation capabilities. Firstly, a position encoding module with convolutional operations was embedded within the attention module. Position encoding was updated with attention calculations to enhance the utilization of spatial structural information. Next, a multi-domain attention enhancement module was introduced. Sampling was conducted in the spatial dimension using parallel convolutions with different dilation rates and strides to cope with targets of different sizes and aggregate the enhanced channel attention features. Finally, a spatial domain attention enhancement module was incorporated into the decoder to provide accurate classification and regression features for the prediction head. The proposed algorithm achieved an average overlap (AO) of 73.9% on the GOT-10K dataset. It attained area under the curve (AUC) scores of 82.7%, 69.3%, and 70.9% on the TrackingNet, UAV123, and OTB100 datasets, respectively. Comparative results with state-of-the-art algorithms demonstrated that the tracking model, which integrated dynamic position encoding as well as channel and spatial attention enhancement, effectively enhanced the interaction of information between the template and search region, leading to improved tracking accuracy.

State-of-the-art data-driven intelligent computations (DDICs) were comprehensively reviewed in order to effectively solve the increasingly complex and expensive optimization problems (EOPs) emerging in real-world applications, which can effectively reduce computing costs and improve solutions. The latest research achievements of DDICs were outlined from both algorithm and application perspectives. Various technical points in generalized DDICs and adaptive DDICs were summarized and categorized. The challenges and opportunities faced by DDICs in solving EOPs were analyzed. Future research potential trends were proposed, such as conducting deeper theoretical analyses, exploring novel learning paradigms, applying these methods in various practical fields, and so on. This aims to provide targeted references and directions for researchers, stimulating innovative ideas to more effectively address the complex EOPs encountered in real-world applications.

A tunnel lining surface defect detection algorithm TDD-YOLO was proposed, for the problems of insufficient global information extraction and low detection accuracy of existing object detection algorithms in tunnel lining defect detection. The algorithm was based on the YOLOv7 framework. Firstly, MobileViT was used as the backbone feature extraction network to improve the global and local information extraction capability of the network. Secondly, Coordinate attention (CA) module was added after the upsampling and downsampling of the feature pyramid network to highlight the feature information of defects and remove the interference of background information. Finally, a convolutional module called TP Block was proposed to further improve the feature extraction ability of the network with less computation. Five algorithms, SSD, Faster-RCNN, EfficientDet, YOLOv5 and YOLOv7, were selected for comparison and analysis, in order to verify the effectiveness of the proposed algorithm. Results showed that the F1 of TDD-YOLO algorithm was 77.43%, which had an improvement of 15.58%, 17.36%, 12.19%, 6.32%, and 6.14%, respectively, compared with the above five contrast algorithms. The mAP was 77.52%, which had an improvement of 15.20%, 14.24%, 9.44%, 7.44%, and 6.39%, respectively. The TDD-YOLO algorithm has the highest defect recognition accuracy and the best overall performance, which is suitable for defect detection task of actual tunnel projects.

A time-aware combination (TAC) method for temporal knowledge graph completion was proposed aiming at the problem that the existing temporal knowledge graph embedding methods only consider the relationship of temporal information or encode independent temporal vectors and the completion performance of these methods is not high enough. The effectiveness of temporal information on knowledge graph completion methods was analyzed by modeling dimensional features. Different learning methods have different effects on the representation learning ability after considering the embedding of temporal information through the embedding method of combining the embedded and independent temporal information. Long short-term memory (LSTM) network was utilized to encode temporal information, learn more accurate temporal dimension features and help to improve the performance of temporal graph. Experiments on ICEWS14, ICEWS05-15 and GDELT datasets verified the effectiveness of the time-aware combination method. The related research performance metrics were compared. Results show that the proposed method performs better in link prediction.

The robot’s structure and transmission principles were analyzed given the characteristics of long gear transmission chains and high reverse frequency in fully gear-coupled robots. Theoretical modeling of gear backlash and a three-dimensional tolerance simulation analysis method were proposed to reduce the impact of backlash on the transmission accuracy of robots. The theoretical modeling and the tolerance simulation analysis of gear backlash were conducted for the cylindrical gear mechanism at the drive end. The two results were consistent. An equivalent backlash modeling method that equivalents the bevel gear pair as a hypothetical cylindrical gear pair was proposed for the bevel gear mechanism in joints, and the theoretical modeling analysis result accorded with the tolerance simulation analysis results. The end error of the robot was calculated by taking the robot shoulder joint swing transmission chain as the analysis object. The calculation method of bevel gear backlash and the motor angle compensation method were proposed to reduce gear backlash and the return error of the transmission chain caused by the gear backlash. The effectiveness of this compensation method was validated through experiment.

A dead band compensation strategy for CTPS control was proposed aiming at the problem that the occurrence of return power as well as the failure of soft switching was caused after the addition of bridge arm dead band to the triple phase-shift cooperative control (CTPS) of dual active bridge (DAB) converter. The coupling relationships between different mode shift ratios and the power transfer model and the switching conditions of CTPS control modes were corrected based on the principle of return power generation by analyzing the changes of transformer primary and secondary side voltages and leakage currents caused by the dead band of the bridge arm in different modes of CTPS control. Then the effective control of the impact of dead band on CTPS control was realized. The proposed compensation scheme suppressed the return power caused by the dead band, restored the soft-switching performance of the CTPS control, and had better current stress than before compensation. Experiments before and after dead band compensation were conducted separately to verify the analysis of the dead band effects and the proposed compensation strategy.