Image-to-image translation methods have advanced from focusing on image-level info to incorporating pixel-level and instance-level details. However, with feature-level constraint, deviation occurs when the network overemphasizes convolutional features, neglecting traditional image feature extraction. To address this, we proposed the multimodal image translation algorithm MASSE based on a Singular Squeeze-and-Excitation Network, combining GANs and SENet. It utilizes SVD features to assist the SENet in managing the scaling degree. The SENet employs SVD to extract features and enhance the Excitation operation to obtain new channel attention weights and form attention feature maps. Then, image content features are refined by combining convolutional and attention feature maps, and style features are obtained by the style generator. Finally, content and style features are combined to generate new style images. Ablation experiments showed the optimal SVD parameter is 128, producing the best translation results. According to FID, MASSE outperforms current methods in generating diverse images.

Traditional solid screw rotors suffer from excessive weight, structural redundancy, low material utilization, and high energy consumption, conflicting with the growing demand for efficient, sustainable manufacturing. To address these challenges, this study proposes a lightweight design method for hollow, internally supported male screw rotors that simultaneously enhances stiffness and static–dynamic performance. A parameterized structural model with four key design variables was established, and multi-physics simulations integrating fluid flow, heat transfer, and structural mechanics were conducted to obtain mass, maximum deformation, and first-order natural frequency. Based on these simulation results, a surrogate-assisted multi-objective evolutionary optimization framework was employed: an enhanced Newton–Raphson-based optimizer (SNRBO) was used to tune the extreme gradient boosting surrogate (XGBoost 1.5.2), and the tuned surrogate then guided the Nondominated Sorting Genetic Algorithm III (NSGA-III) to perform multi-objective search and construct the Pareto front. Compared with a conventional solid rotor, the optimized design reduces mass by 64.43%, decreases maximum deformation by 4.41%, and increases the first-order natural frequency by 82.14%. These findings indicate that the proposed method provides an effective pathway to balance lightweight design with structural safety and dynamic stability, offering strong potential for green manufacturing and high-performance applications in energy, aerospace, and industrial compressor systems, and providing robust support for further advances in this field.

The focus of this paper is on achieving a win-win situation regarding the economic, environmental, and social impacts of the cold chain logistics terminal distribution system. This paper proposes three multi-objective models to investigate the above effects by incorporating soft time windows, heterogeneous fleets, and path flexibility, with defining the objectives of examining logistics costs, fuel consumption, carbon emissions, quality damage to perishable commodities, and customer satisfaction using six evaluation functions. To solve the proposed models, an efficient optimization framework is developed by combining domain operators with versatile multi-objective evolutionary algorithms (MOEA) to obtain Pareto solutions. Extensive experiments are conducted to test the validity of the concerned model and algorithms. The results demonstrate that: (1) the proposed algorithm is effective in solving the proposed model; (2) the proposed multi-path strategy can effectively improve the performance of cold-chain logistics systems compared to single-path strategies; (3) evaluation functions that assess customer satisfaction greatly affect the performance of cold-chain logistics systems; and (4) the trade-off relationship between the objectives should be investigated to define the model. The paper also provides valuable managerial insights for improving the efficiency and sustainability of cold-chain logistics operations.

Unpaired image translation with feature-level constraints presents significant challenges, including unstable network training and low diversity in generated tasks. This limitation is typically attributed to the following situations: 1. The generated images are overly simplistic, which fails to stimulate the network’s capacity for generating diverse and imaginative outputs. 2. The images produced are distorted, a direct consequence of unstable training conditions. To address this limitation, the unpaired image-to-image translation with diffusion adversarial network (UNDAN) is proposed. Specifically, our model consists of two modules: (1) Feature fusion module: In this module, one-dimensional SVD features are transformed into two-dimensional SVD features using the convolutional two-dimensionalization method, enhancing the diversity of the images generated by the network. (2) Network convergence module: In this module, the generator transitions from the U-net model to a superior diffusion model. This shift leverages the stability of the diffusion model to mitigate the mode collapse issues commonly associated with adversarial network training. In summary, the CycleGAN framework is utilized to achieve unpaired image translation through the application of cycle-consistent loss. Finally, the proposed network was verified from both qualitative and quantitative aspects. The experiments show that the method proposed can generate more realistic converted images.

Augmented Reality (AR) for interactive entertainment is exploring the potential of open space and multiplayer synchronization to expand user experience on mobile devices. However, complex interaction problems have hindered the development of related products. Based on a hybrid research methodology, this study proposes a set of design strategies including design elements, design framework, and functional attributes with priority ranking. To verify the effectiveness of the strategies, we developed ParallelWorld as an application example. It incorporates three basic modes of multiplayer-synchronized AR interaction: player to player, multiplayer to one virtual element, and multiplayer to physical space. Usability test and user interview suggest that ParallelWorld delivers a qualified experience for synchronized multiplayer in open space.

In recent years, robot path planning has gained high attention. The traditional adaptive Monte Carlo localization (AMCL) has such problems as limitations in global localization, and incomplete path and time-consuming problem in path planning due to too much calculation of meaningless nodes by the jump point search (JPS) algorithm. In view of the above problems, this paper proposed a method for vision-based initial localization of automated guided vehicle (AGV) and path planning with (pruning optimization) PO-JPS algorithm. The core contents include: vision-based AMCL localization module and improved JPS algorithm based on pruning optimization. Firstly, Oriented FAST and Rotated BRIEF (ORB) features are extracted from the images collected by vision, and coordinates are localized with the features, coupled with the initial map by laser SLAM, to construct a bag-of-words (BoW) library of features. The key frame most similar to the current one is obtained by comparing the similarity between the current and historical frames in the BoW library. The Euler transformation between these two frames is calculated, to carry out pose estimation. This pose, as an initial value, is provided to the AMCL for particle iteration. Secondly, in the path planning stage, an improved JPS algorithm based on pruning optimization is proposed, and a strategy that the repeated intermediate inflection points in the complemented path after pathfinding are deleted is designed. Therefore, while a complete path is obtained, the calculation workload and memory consumption for meaningless nodes during node extension are reduced successfully, and the efficiency of the pathfinding algorithm is raised. Finally, verification of the method proposed in this paper is completed through a large number of simulations and physical experiments, which saved 17.7% of the time compared to the original JPS algorithm and 279.6% to the A* algorithm.

The six-dimensional (6D) pose object estimation is a key task in robotic manipulation and grasping scenes. Many existing two-stage solutions with a slow inference speed require extra refinement to handle the challenges of variations in lighting, sensor noise, object occlusion, and truncation. To address these challenges, this work proposes a decoupled one-stage network (DON6D) model for 6D pose estimation that improves inference speed on the premise of maintaining accuracy. Particularly, since the RGB images are aligned with the RGB-D images, the proposed DON6D first uses a two-dimensional detection network to locate the interested objects in RGB-D images. Then, a module of feature extraction and fusion is used to extract color and geometric features fully. Further, dual data augmentation is performed to enhance the generalization ability of the proposed model. Finally, the features are fused, and an attention residual encoder-decoder, which can improve the pose estimation performance to obtain an accurate 6D pose, is introduced. The proposed DON6D model is evaluated on the LINEMOD and YCB-Video datasets. The results demonstrate that the proposed DON6D is superior to several state-of-the-art methods regarding the ADD(-S) and ADD(-S) AUC metrics.

The increasing urban logistics transportation activities hugely impact the economy and environment. This paper investigates the combined impact of ambient temperature, path flexibility, and hybrid fleet on the economy and environment in urban logistics. The proposed problem is a variant of the location-routing problem (LRP) named dual-mode energy-conserving LRP (DMECLRP) that handles the combination of three types of decisions: the location of depots, the design of the distribution routes, and the location of charging stations (CSs) and battery swapping stations (BSSs). The objective is to minimize the total cost, including the daily fixed costs of operating depots, the cost of renting vehicles and depreciation, driver compensation, and the routing costs, where the latter can be defined concerning the cost of the consumed energy and CO2 emissions. Due to the NP-hardness of the problem, this paper presents a Q-learning-based hyper-heuristic (QLHH) algorithm to address the DMECLRP. The QLHH employs a Q-learning approach to select appropriate heuristics through its search process and simulated annealing to determine the acceptance of solutions. Results show that the proposed algorithm is effective, providing competitive results for LRP benchmark instances within reasonable computing time, and the proposed model can effectively reduce logistics costs, energy consumption, and CO2 emissions. Extensive analyses are carried out to empirically assess the effect of ambient temperature, path flexibility, and hybrid fleet on key performance indicators, including energy consumption, carbon emissions, and operational costs. Several managerial insights are provided.

The goal of the multi-objective optimization algorithm is to quickly and accurately find a set of trade-off solutions. This paper develops a clustering-based competitive multi-objective particle swarm optimizer using the enhanced grid for solving multi-objective optimization problems, named EGC-CMOPSO. The enhanced grid mechanism involved in EGC-CMOPSO is designed to locate superior Pareto optimal solutions. Subsequently, a hierarchical-based clustering is established on the grid for improving the accuracy rate of the grid selection. Due to the adaptive division of clustering centers, EGC-CMOPSO is applicable for solving MOPs with various Pareto front (PF) shapes. Particularly, the inferior solutions are discarded and the leading particles are identified by the comprehensive ranking of particles in each cluster. Finally, the selected leading particles compete against each other, and the winner guides the update of the current particle. The proposed EGC-CMOPSO and the eight latest multi-objective optimization algorithms are performed on 21 test problems. The experimental results validate that the proposed EGC-CMOPSO is capable of handling multi-objective optimization problems (MOPs) and obtaining superior performance on both convergence and diversity.

To address the situation of insufficient reserve of resources in the initial stage of emergency rescue, a multi-objective location-allocation model for emergency supplies with timeliness and fairness being considered concurrently is proposed in this paper. The first objective is defined as time cost, including transport time cost and waiting time cost. The second objective is defined as the number of short supplies. The allocation of supplies is taken into account together with the urgency degree in a disaster area (DA), and the DAs with different urgency degrees are given different minimum allocation quantities of supplies to achieve fairness to a maximum extent. To solve this complex problem, a multi-objective hyper-heuristic (MOHH) optimization framework based on an evolutionary algorithm is proposed in this paper. In the framework, twelve low-level heuristics (LLHs) are designed with the actual information in the problem field being taken into account, and an online learning-based choice strategy is designed to choose high-quality and efficient LLHs. In addition, three acceptance criteria (AC) based on the D matrix are put forward to improve the performance of the MOHH framework. It is verified through comparative analysis experiments that the LLHs and the model are effective and the performance of the proposed multi-objective algorithm is better than that of NSGA-III and MOPSO.

A robotic grasp detection algorithm based on multiscale features is proposed for autonomous robotic grasping in an unstructured environment. The grasp detection model borrowed the YOLOv3 object detection algorithm and retained the original idea of multiscale detection to improve the perception ability of the grasp rectangle on different scales. Squeeze and excitation blocks were embedded into the Residual Networks (ResNet) structure of the original model, with deformable convolution (DC) introduced, so that the model attained stronger feature extraction ability to cope with more complex grasp detection tasks. Meanwhile, the prediction of the direction angle was transformed into a combination of classification and regression, achieving the prediction of the direction angle of the grabbing frame under different postures. The model was simulated on the Cornell grasp dataset. The results demonstrate that the algorithm in this study can effectively balance the accuracy and efficiency of detection and can migrate the prediction of the grasp rectangle to new objects. The results of online grasp experiments on a Baxter robot show that the average grasp success rate of 93% is achieved for 10 different objects, demonstrating the practical feasibility of the algorithm.

The sEMG signal-based recognition is broadly used in the field of human-computer interaction. To improve the signal classification accuracy, this paper proposes a two-dimensional transformation pruning capsule network (TDPCAPS) to recognize different hand gestures. To apply deep learning methods to signal classification, a two-dimensional transformation method is proposed, which converts feature vectors into two-dimensional feature data. Moreover, using the capsule network to explore the characteristics of the sEMG signal, as this model overcomes the defect that the convolution neural network fails to capture the correlation among features. However, the capsule network requires a lot of computing resources, so this paper adopts a pruning mechanism to reduce the number of coupling coefficients and speed up the calculating process. In the experiments of electrode displacement and several subjects, the recognition accuracy of TDPCAPS reaches 84.92% and 80.31%, respectively. Meanwhile, the classification time for a window is reduced by 11.39%. The experimental results show that the proposed method can ensure recognition accuracy and improve computational efficiency at the same time.

多配送中心车辆路径规划(multi-depot vehicle routing problem,MDVRP)是现阶段供应链应用较为广泛的问题模型,现有算法多采用启发式方法,其求解速度慢且无法保证解的质量,因此研究快速且有效的求解算法具有重要的学术意义和应用价值.以最小化总车辆路径距离为目标,提出一种基于多智能体深度强化学习的求解模型.首先,定义多配送中心车辆路径问题的多智能体强化学习形式,包括状态、动作、回报以及状态转移函数,使模型能够利用多智能体强化学习训练;然后通过对MDVRP的节点邻居及遮掩机制的定义,基于注意力机制设计由多个智能体网络构成的策略网络模型,并利用策略梯度算法进行训练以获得能够快速求解的模型;接着,利用2-opt局部搜索策略和采样搜索策略改进解的质量;最后,通过对不同规模问题仿真实验以及与其他算法进行对比,验证所提出的多智能体深度强化学习模型及其与搜索策略的结合能够快速获得高质量的解.

A method of contradictory problems collaboratively modeling for extension design based on the knowledge model of requirement-function-behavior-effect-structure (RFBES) is proposed. The RFBES design process model contains three mappings: RFB-based requirement mapping, FES-based product system level mapping, and behavior-based evaluation mapping. The modeling process of design contradictions is the analysis process of RFBES, that is, requirements analysis determines design goals, system analysis determines design objects, and behavior evaluation determines contradictions. The extension set method is used to classify the actual product behavior, the product design contradiction problem model is established according to the extension classification results. Thus, the hierarchical design contradiction model for structure, effect, function could be established in a collaborative mode, which helps designers solve design contradictions efficiently. The proposed method is applied to construct the design contradiction model in cutting machine innovative design, and the feasibility of the method is verified.

水库防洪调度问题(RFCO)是复杂的多目标问题(MOPs),具有众多复杂的约束条件,相互依存的决策变量,以及相互冲突的优化目标,传统研究多停留在将多目标问题转换为单目标问题解决,在实际应用中存在一定限制.鉴于此,提出一种针对水库防洪调度的多目标优化方法——文化鲸鱼算法(MOCWOA).MOCWOA以文化算法(CA)为框架,在种群空间采用鲸鱼优化算法(WOA),在信度空间定义了3种知识结构以提高算法所得结果的多样性和收敛精度.MOCWOA先应用于典型测试函数的优化,之后进一步应用于实际的水库防洪调度问题,并与几种优秀的多目标优化算法进行对比,结果表明,无论是在典型测试函数上,还是在实际RFCO问题上,MOCWOA都具有一定的优势.

Aiming at the design contradiction between the requirements and the structure in the case adaptation, an extension integration model of contradictory problems is established. And the corresponding solution method is given. Firstly, the extension integration model is constructed by analyzing the incompatible problems and antithetical problems in the case adaptation and separating the extension set of customer requirements, and the extension transformation theory is introduced into the case adaptation. Secondly, the extension integration model is solved by combining the extension strategy generation method and the transforming bridge method. And the obtained scheme is evaluated and selected based on the extension superiority evaluation; Then, store the adapted case into the case library. At the same time, the extension knowledge involved in the process of solving the contradictory problem is extracted and stored in the design knowledge base as a reference for subsequent design. Finally, taking the SK-15 vacuum pump as an example, the feasibility and effectiveness of the above method are verified.

Location-Based AR(Augmented Reality) experiences have increasingly appeared in a variety of products in recent years, and many studies have provided initial evidence of the effectiveness of this form of interaction in facilitating user experience and social communication User-generated content (UGC) is a user-centred method of developing and managing online knowledge bases, which includes significant social characteristics. Although few studies have thoroughly examined this mode of engagement, the usage of AR technology for UGC offers enormous potential to improve user experience and social ties among users. This study combines AR with UGC to create an augmented reality multiplayer real-time interactive application for usage in the community, promoting communication among residents, fostering close neighbourhood ties and shared values enhancing residents’ lives. The main function of the product is to use props to individually or collaboratively produce UGC in order to "transform" the community area in a personalized way. The product also has a complete asset system and friend system to promote a balanced and social experience.

由于设计参数之间的复杂关联性,产品开发过程中变更传播难以避免.为降低变更传播带来的开发成本增加、开发周期延长等风险,提出基于产品参数基元网络的变更传播路径优化方法.在参数关联关系与变更传播模式分析的基础上,结合可拓基元理论对设计参数进行形式化表示,并构建产品参数基元网络模型取代传统的模块或零部件关联网络模型;分析变更影响传播特性,研究变更代价评估模型;设计了基于时序差分的变更传播路径优化算法,高效搜索获取最优的变更传播方案.最后,以汽车离合器为案例,验证所提方法的有效性.

This paper proposes a feature fusion-based improved capsule network (FFiCAPS) to improve the performance of surface electromyogram (sEMG) signal recognition with the purpose of distinguishing hand gestures. Current deep learning models, especially convolution neural networks (CNNs), only take into account the existence of certain features and ignore the correlation among features. To overcome this problem, FFiCAPS adopts the capsule network with a feature fusion method. In order to provide rich information, sEMG signal information and feature data are incorporated together to form new features as input. Improvements made on capsule network are multilayer convolution layer and e-Squash function. The former aggregates feature maps learned by different layers and kernel sizes to extract information in a multiscale and multiangle manner, while the latter grows faster at later stages to strengthen the sensitivity of this model to capsule length changes. Finally, simulation experiments show that the proposed method exceeds other eight methods in overall accuracy under the condition of electrode displacement (86.58%) and among subjects (82.12%), with a notable improvement in recognizing hand open and radial flexion, respectively.

With the increasing proportion of the logistics industry in the economy, the study of the vehicle routing problem has practical significance for economic development. Based on the vehicle routing problem (VRP), the customer presence probability data are introduced as an uncertain random parameter, and the VRP model of uncertain customers is established. By optimizing the robust uncertainty model, combined with a data-driven kernel density estimation method, the distribution feature set of historical data samples can then be fitted, and finally, a distributed robust vehicle routing model for uncertain customers is established. The Q-learning algorithm in reinforcement learning is introduced into the high-level selection strategy using the hyper-heuristic algorithm, and a hyper-heuristic algorithm based on the Q-learning algorithm is designed to solve the problem. Compared with the certain method, the distributed robust model can effectively reduce the total cost and the robust conservatism while ensuring customer satisfaction. The improved algorithm also has good performance.