339 research outputs found
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
7-{4-[(1,3-Benzodioxol-5-yl)methyl]piperazin-1-yl}-1-cyclopropyl-6-fluoro-4-oxo-1,4-dihydroquinoline-3-carboxylic acid
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