In a mixed-product assembly line, material supply is very complicated, dynamically changing and error-prone due to large material flow. These uncertainties often cause some imperceptible Abnormal Logistics States (ALSs), which seriously hinder accurate and efficient delivery of materials to the assembly lines. There is no relevant theoretical model and efficient information collection technology so far. This paper aims to detect those imperceptible abnormal logistics states in mixed-product assembly lines through a novel RFID-based multi-deviation detection approach. Some ALSs' parameters are defined from different perspectives such as time, location, number, sequence and path. A calculation model of ALSs has been built by processing RFID data, then a RFID-based multi-deviation model is presented to quantify the abnormal degree of ALSs. Based on the developed models, a judging algorithm for ALSs of material supply is proposed. Compared to conventional eKanban monitor system, the proposed approach can detect the ALSs of material supply efficiently and accurately, including those that are difficult to detect in eKanban monitor system.

Climate change makes it necessary to implement precise carbon dioxide reduction measures, and establishing a port carbon dioxide emission inventory has become a key step in port management.However, the establishment of a port carbon emission inventory, while crucial for the execution of environmental policies, currently lacks effective monitoring and path planning schemes to support this process. To address these issues, we propose a path planning scheme for port-based carbon emission monitoring. The scheme analyzes the factors affecting the effectiveness of the scheme and establishes a hierarchy of these factors to optimize the monitoring path. Furthermore, a multi-objective path planning mathematical model is established in this article, introducing the optimal goals of economic and environmental costs to achieve the overall path planning objectives. Lastly, this paper focuses on the initial path planning problem of drone monitoring and proposes an improved A* algorithm (IEHA). The algorithm improves the search method of child nodes by eliminating nodes that collide with obstacles, thereby reducing the threat of path collisions. At the same time, the evaluation function is improved by introducing the average value and Gaussian distribution probability function into the heuristic function, enhancing the reliability of the evaluation function, reducing the length of the optimal path, and improving operational efficiency. In the simulation experiment results, the proposed algorithm outperforms A*, ACO, and IEA algorithms in optimizing path length, reducing collision points to ensure path safety, and robustness. This indicates that the proposed algorithm can effectively improve the path planning performance of drones in port carbon dioxide emission monitoring, providing an effective technical means for port carbon dioxide emission monitoring.

Ultra-high-frequency radio-frequency identification (UHF RFID) is widely applied in different industries. The Frame Slotted ALOHA in EPC C1G2 suffers severe collisions that limit the efficiency of tag recognition. An efficient full-duplex anti-collision scheme is proposed to reduce the rate of collision by coordinating the transmitting process of CDMA UWB uplink and UHF downlink. The relevant mathematical models are built to analyze the performance of the proposed scheme. Through simulation, some important findings are gained. The maximum number of identified tags in one slot is g/e (g is the number of PN codes and e is Euler's constant) when the number of tags is equal to mg (m is the number of slots). Unlike the Frame Slotted ALOHA, even if the frame size is small and the number of tags is large, there aren't too many collisions if the number of PN codes is large enough. Our approach with 7-bit Gold codes, 15-bit Gold codes, or 31-bit Gold codes operates 1.4 times, 1.7 times, or 3 times faster than the CDMA Slotted ALOHA, respectively, and 14.5 times, 16.2 times, or 18.5 times faster than the EPC C1 G2 system, respectively. More than 2,000 tags can be processed within 300 ms in our approach.

Wheeled inverted-pendulum robots with movable upper structures and variable payloads exhibit configuration-dependent equilibrium drift and payload-dependent dynamic variation, which complicate balancing control. This paper proposes a gain-scheduled controller–observer framework for payload-adaptive balancing of such a robot. First, the multi-body system is reduced to a control-oriented equivalent inverted-pendulum model through center-of-mass lumping, from which a parameter-varying linearized model is established. On this basis, an H∞ state-feedback controller with input constraints is synthesized in a linear matrix inequality (LMI) framework, and an augmented-state observer is designed to estimate the residual equilibrium offset induced by payload variation. To improve robustness over the operating range, the frozen-point design is extended to a sampled-model multi-model synthesis framework, and gain scheduling is implemented with respect to the measurable arm angle. Nonlinear Simscape simulations show that the proposed method can recover balance at representative fixed operating points, compensate effectively for load-induced equilibrium drifts, and preserve stable balancing performance under slow arm-angle variation. Quantitative comparisons with an LQR baseline further support the effectiveness of the proposed framework for payload-adaptive balancing control.

The multi-agent cooperation is an important task in a distributed control system. Its key part is how to generate coalition group of agents. The ant algorithm is used often to solve this problem in MAS control system. But it has some deficiencies, such as partial optimization, slow constringency and low precision. With the respective virtues of genetic algorithm and ant algorithm, a new ant algorithm was presented in this work. It included two phase. In the first phase, it produced the information elements through genetic algorithm, and obtained optimization coalition through ant algorithm in the second phase. The simulation experiments were done and the results show that both the optimizing performance and time performance of new ant algorithm are improved enormously. The new algorithm not only quickens the constringency process and advances the precision of calculation result, but also avoids partial optimization problem.