SCARA robots are a common type of industrial robots, which widely used in tasks such as assembly, processing, and handling. Their design incorporates horizontal rotary joints and translational joints, resulting in high repeatability of positioning accuracy and speed, making them particularly suitable for scenarios requiring rapid and precise operations. However, to achieve precise control of SCARA robots, it is essential to establish an accurate mathematical model. An accurate mathematical model can precisely describe the kinematic and dynamic characteristics of a SCARA robot. Through this model, we can gain insights into the robot's motion patterns under various working conditions, including changes in key parameters such as position, velocity, and acceleration. In this paper, we first use the Modified Denavit-Hartenberg method to model the kinematics of the SCARA robot, derive its kinematic equations, and verify them through simulations in MATLAB's Robotics Toolbox. Subsequently, we conduct dynamic modeling of the SCARA robot based on the Lagrangian method. Finally, we successfully construct a three-dimensional model of the SCARA robot using SOLIDWORKS software. To further verify the accuracy of the established dynamic model, we import it into the Adams simulation environment, laying a solid foundation for subsequent in-depth simulation research on control strategies.

Hemodialysis filters are widely used in the treatment of kidney diseases. In order to reduce the occurrence of medical accidents, they need to go through a strict inspection process before being put into use to avoid defective products from entering the market, so it is crucial to locate and classify hemodialysis filters defects. In this paper, a deep learning method based on machine vision is proposed to solve the problem that the background of the inspected products is very similar to the defects and difficult to distinguish them. First, image denoising is performed, then the center of the product is located and the circle is expanded into a rectangle, the edge is extracted using a firstorder difference operator to distinguish the background from the defects, and finally the image after the extended channel is provided to the lightweight U-Net for training by online sample sampling. The test results show that the proposed method have achieved 0.78 mean IoU, 4.67% false detection rate and 0.67% missing rate on the hemodialysis filter dataset, which demonstrates the effectiveness of the proposed method.

Integrating large-scale sensors into the network has become a research hotspot for its promising flexibility in monitoring vitally critical wild areas. However, the existing Internet of Things (IoT) systems are limited due to the lack of a stable power supply, which seriously affects the system’s sustainability. The combination of sensors equipped with cordless power batteries and long-distance power transmission has ushered in a new era. Using the unmanned aerial vehicles (UAVs) to charge the battery ensures the flexibility and sustainability of the sensor in environmental detection. In this work, we aim to provide a solution for maintaining the sustainability of the sensors while optimizing UAV trajectory to minimize the overall energy consumption of UAV. Since deep reinforcement learning successfully solves the NP-hard combinatorial optimization problem, deep reinforcement learning is introduced in this work to obtain a feasible solution. We formulate the trajectory planning of UAV as a Markov decision problem and employ a deep reinforcement learning (DRL) model based on an attention mechanism to find the optimal policy efficiently, named the optimal trajectory planning algorithm based on DRL (OTPDRL). The experimental results suggest the OTPDRL obtains a good trade-off between performance gain and computational time.

Surface defect detection is important in the industrial field. Most factories use the difference method to solve the problem of defect detection. However, difference method can’t solve misjudgments caused by shooting angle and location. In this paper, using the information entropy to solve the problem of the misjudgment for qualified products caused by product position and camera Angle. At the same time, 30 experiments were carried out to determine the threshold, and then 100 experiments were carried out to compare the accuracy of information entropy and difference methods, including the images of qualified products and unqualified products. Finally, the information entropy method is better than the difference method, and its detection accuracy is 97%.

SaaS is widely used in the fields of operation management, business process outsourcing, data analysis, and information security. However, with the improvement of the degree of information, the generalized SaaS platform is unable to satisfy the requirements of enterprise personalization. SaaS products face great challenges in personalized customization technology due to the application of multi-tenant architecture. The challenges include multi-tenant customization in SaaS model, data isolation during customization, and mapping of multi-tenant virtual warehouse locations to actual locations. Therefore, we decompose personalization technology into metadata driver, cloud data placement and mapping mechanism for research. We decompose the personalized technologies into metadata drive, cloud data placement, and mapping mechanism. In order to solve the problem that traditional personalization customization is unable to be applied in the SaaS field, we propose a multi-tenant personalized warehousing mode architecture, designed a personalization warehouse management platform based on SaaS model, and realized SaaS-based data isolation, interface customization, rapid positioning, and on-demand customization services. Experimental results show that the proposed multi-tenant personalized warehouse model can achieve data isolation and customization, and reflect the advantages of highly automated warehousing, shared storage resources, and on-demand customization in terms of warehouse management, data security, and user experience.

With the continuous development of industrialization, warehouse storage is becoming more and more intelligent. The storage state of goods in the warehouse is one of the very important components of the warehouse management system. In order to improve the utilization rate of the warehouse and the efficiency of warehouse storage, this paper proposes a warehouse management system based on state machine to deal with the state of goods in different situations, and the feasibility of this method is verified by an example. The warehouse management system using this method is more efficient and convenient than the traditional one.

Infectious disease transmission research is usually suffered from complex interactions and large number of calculations. As one of main means of this research, simulation can be efficient by adopting distributed architectures. However, most existing distributed simulation architectures rely on gateway or backbone to achieve efficient global management, which inherently generate a communication bottleneck when scale expanded. This paper proposes a pure distributed architecture for these simulations by a shared memory system, reflective memory network. Based on reflective memory and local memory, this paper designed a distributed dynamic memory management method. And Data Management, Federation Management, and Simulation Management three essential functions of the simulation are all settled well by this method.