Emergent Incident Response for Unmanned Warehouses with Multi-agent Systems*

Unmanned warehouses are an important part of logistics, and improving their operational efficiency can effectively enhance service efficiency. However, due to the complexity of unmanned warehouse systems and their susceptibility to errors, incidents may occur during their operation, most often in inbound and outbound operations, which can decrease operational efficiency. Hence it is crucial to to improve the response to such incidents. This paper proposes a collaborative optimization algorithm for emergent incident response based on Safe-MADDPG. To meet safety requirements during emergent incident response, we investigated the intrinsic hidden relationships between various factors. By obtaining constraint information of agents during the emergent incident response process and of the dynamic environment of unmanned warehouses on agents, the algorithm reduces safety risks and avoids the occurrence of chain accidents; this enables an unmanned system to complete emergent incident response tasks and achieve its optimization objectives: (1) minimizing the losses caused by emergent incidents; and (2) maximizing the operational efficiency of inbound and outbound operations during the response process. A series of experiments conducted in a simulated unmanned warehouse scenario demonstrate the effectiveness of the proposed method.Comment: 13 pages, 7 figure

An Emergency Disposal Decision-making Method with Human--Machine Collaboration

Rapid developments in artificial intelligence technology have led to unmanned systems replacing human beings in many fields requiring high-precision predictions and decisions. In modern operational environments, all job plans are affected by emergency events such as equipment failures and resource shortages, making a quick resolution critical. The use of unmanned systems to assist decision-making can improve resolution efficiency, but their decision-making is not interpretable and may make the wrong decisions. Current unmanned systems require human supervision and control. Based on this, we propose a collaborative human--machine method for resolving unplanned events using two phases: task filtering and task scheduling. In the task filtering phase, we propose a human--machine collaborative decision-making algorithm for dynamic tasks. The GACRNN model is used to predict the state of the job nodes, locate the key nodes, and generate a machine-predicted resolution task list. A human decision-maker supervises the list in real time and modifies and confirms the machine-predicted list through the human--machine interface. In the task scheduling phase, we propose a scheduling algorithm that integrates human experience constraints. The steps to resolve an event are inserted into the normal job sequence to schedule the resolution. We propose several human--machine collaboration methods in each phase to generate steps to resolve an unplanned event while minimizing the impact on the original job plan.Comment: 15 pages, 16 figure

Changes in respiratory structure and function after traumatic cervical spinal cord injury: observations from spinal cord and brain

Respiratory difficulties and mortality following severe cervical spinal cord injury (CSCI) result primarily from malfunctions of respiratory pathways and the paralyzed diaphragm. Nonetheless, individuals with CSCI can experience partial recovery of respiratory function through respiratory neuroplasticity. For decades, researchers have revealed the potential mechanism of respiratory nerve plasticity after CSCI, and have made progress in tissue healing and functional recovery. While most existing studies on respiratory plasticity after spinal cord injuries have focused on the cervical spinal cord, there is a paucity of research on respiratory-related brain structures following such injuries. Given the interconnectedness of the spinal cord and the brain, traumatic changes to the former can also impact the latter. Consequently, are there other potential therapeutic targets to consider? This review introduces the anatomy and physiology of typical respiratory centers, explores alterations in respiratory function following spinal cord injuries, and delves into the structural foundations of modified respiratory function in patients with CSCI. Additionally, we propose that magnetic resonance neuroimaging holds promise in the study of respiratory function post-CSCI. By studying respiratory plasticity in the brain and spinal cord after CSCI, we hope to guide future clinical work

4-Ethoxy­imino-N′-methoxy­pyrrolidin-1-ium-3-carboximidamidium dichloride

The title compound, C8H18N4O2 2+·2Cl−, contains two oxime groups. The methyl oxime group has a Z configuration, and the ethyl oxime group is disordered, with both Z and E configurations in occupancies of 0.840 (8) and 0.160 (8), respectively. In the crystal structure, there are a number of N—H⋯Cl hydrogen bonds

tert-Butyl 3-[N-(tert-butoxy­carbonyl)methyl­amino]-4-methoxy­imino-3-methyl­piperidine-1-carboxyl­ate

The title compound, C18H33N3O5, was prepared from N-tert-butoxy­carbonyl-4-piperidone using a nine-step reaction, including condensation, methyl­ation, oximation, hydrolysis, esterification, ammonolysis, Hoffmann degradation, tert-butoxy­carbonyl protection and methyl­ation. The E configuration of the methyl­oxime geometry of the compound is confirmed

CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation

Surface defect inspection is of great importance for industrial manufacture and production. Though defect inspection methods based on deep learning have made significant progress, there are still some challenges for these methods, such as indistinguishable weak defects and defect-like interference in the background. To address these issues, we propose a transformer network with multi-stage CNN (Convolutional Neural Network) feature injection for surface defect segmentation, which is a UNet-like structure named CINFormer. CINFormer presents a simple yet effective feature integration mechanism that injects the multi-level CNN features of the input image into different stages of the transformer network in the encoder. This can maintain the merit of CNN capturing detailed features and that of transformer depressing noises in the background, which facilitates accurate defect detection. In addition, CINFormer presents a Top-K self-attention module to focus on tokens with more important information about the defects, so as to further reduce the impact of the redundant background. Extensive experiments conducted on the surface defect datasets DAGM 2007, Magnetic tile, and NEU show that the proposed CINFormer achieves state-of-the-art performance in defect detection