Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway *** such cases,train timetables need to be ***,timely and efficient train timetable rescheduling is still a ch...
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Unexpected delays in train operations can cause a cascade of negative consequences in a high-speed railway *** such cases,train timetables need to be ***,timely and efficient train timetable rescheduling is still a challenging problem due to its modeling difficulties and low optimization *** paper presents a Transformer-based macroscopic regulation approach which consists of two stages including Transformer-based modeling and policy-based ***,the relationship between various train schedules and operations is described by creating a macroscopic model with the Transformer,providing the better understanding of overall operation in the high-speed railway ***,a policy-based approach is used to solve a continuous decision problem after macro-modeling for fast *** experiments on various delay scenarios are *** results demonstrate the effectiveness of the proposed method in comparison to other popular methods.
The transition from cyber-physical-system-based (CPS-based) Industry 4.0 to cyber-physical-social-system-based (CPSS-based) Industry 5.0 brings new requirements and opportunities to current sensing approaches, especia...
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The transition from cyber-physical-system-based (CPS-based) Industry 4.0 to cyber-physical-social-system-based (CPSS-based) Industry 5.0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progress in large language models (LLMs) and retrieval augmented generation (RAG). Therefore, the advancement of parallel intelligence powered crowdsensing intelligence (CSI) is witnessed, which is currently advancing toward linguistic intelligence. In this article, we propose a novel sensing paradigm, namely conversational crowdsensing, for Industry 5.0 (especially for social manufacturing). It can alleviate workload and professional requirements of individuals and promote the organization and operation of diverse workforce, thereby facilitating faster response and wider popularization of crowdsensing systems. Specifically, we design the architecture of conversational crowdsensing to effectively organize three types of participants (biological, robotic, and digital) from diverse communities. Through three levels of effective conversation (i.e., interhuman, human-AI, and inter-AI), complex interactions and service functionalities of different workers can be achieved to accomplish various tasks across three sensing phases (i.e., requesting, scheduling, and executing). Moreover, we explore the foundational technologies for realizing conversational crowdsensing, encompassing LLM-based multiagent systems, scenarios engineering and conversational human-AI cooperation. Finally, we present potential applications of conversational crowdsensing and discuss its implications. We envision that conversations in natural language will become the primary communication channel during crowdsensing process, enabling richer information exchange and cooperative problem-solving among humans, robots, and AI.
BIG models or foundation models are rapidly emerging as a key force in advancing intelligent societies[1]–[3]Their significance stems not only from their exceptional ability to process complex data and simulate advan...
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BIG models or foundation models are rapidly emerging as a key force in advancing intelligent societies[1]–[3]Their significance stems not only from their exceptional ability to process complex data and simulate advanced cognitive functions,but also from their potential to drive innovation across various industries.
High-precision segmentation of polyps is crucial for timely screening and postoperative management of polyps. Although convolutional neural network (CNN)-based methods have made some progress in polyp segmentation res...
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High-precision segmentation of polyps is crucial for timely screening and postoperative management of polyps. Although convolutional neural network (CNN)-based methods have made some progress in polyp segmentation research, they still face several bottlenecks in complex pathological environments: 1) insufficient feature extraction capability hinders the identification of polyps under different pathological morphologies and 2) inadequate boundary refinement ability, due to low differentiation between foreground and background, leads to inaccurate edge extraction. To address these issues, this article introduces a double-branch boundary guidance network (DBG-Net). Specifically, a dual-encoding path equipped with a dual-branch adaptive fusion (DAF) module is built to facilitate effective complementary fusion of both global and local features. Subsequently, a boundary guidance mechanism (BGM), consisting of a boundary learning module (BLM) and a boundary embedding module (BEM), is introduced to refine the edges of polyp regions. Additionally, we propose a multipath attention enhancement module (MAEM) that captures rich global context through different paths to enrich feature representation. Finally, a cross-scale feature aggregation decoder (CFAD) is constructed to fully integrate the efficacious information derived from the encoding part, enabling accurate feature reconstruction. To validate the effectiveness of the proposed DBG-Net, a series of experiments were conducted on multiple datasets, from which maximum Dice and mean intersection over union (mIoU) values as high as 93.48% and 93.30% were obtained, effectively substantiating the superior performance of DBG-Net and its potential as a polyp segmentation tool in complex pathological scenarios, thus bringing hope to postoperative management in clinical practice.
Script event prediction aims to infer subsequent events given an incomplete script. It requires a deep understanding of events, and can provide support for a variety of tasks. Existing models rarely consider the relat...
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Script event prediction aims to infer subsequent events given an incomplete script. It requires a deep understanding of events, and can provide support for a variety of tasks. Existing models rarely consider the relational knowledge between events, they regard scripts as sequences or graphs, which cannot capture the relational information between events and the semantic information of script sequences jointly. To address this issue, we propose a new script form, relational event chain, that combines event chains and relational graphs. We also introduce a new model, relational-transformer, to learn embeddings based on this new script form. In particular, we first extract the relationship between events from an event knowledge graph to formalize scripts as relational event chains, then use the relational-transformer to calculate the likelihood of different candidate events, where the model learns event embeddings that encode both semantic and relational knowledge by combining transformers and graph neural networks (GNNs). Experimental results on both one-step inference and multistep inference tasks show that our model can outperform existing baselines, indicating the validity of encoding relational knowledge into event embeddings. The influence of using different model structures and different types of relational knowledge is analyzed as well.
CHATGPT,one of the leading Large Language Models(LLMs),has acquired linguistic capabilities such as text comprehension and logical reasoning,enabling it to engage in natural conversations with humans.
CHATGPT,one of the leading Large Language Models(LLMs),has acquired linguistic capabilities such as text comprehension and logical reasoning,enabling it to engage in natural conversations with humans.
In order to tackle the intricate challenges of train dispatching and command within China's extensive high-speed railways (HSRs) network, this paper presents a novel parallel dispatching approach founded on the AC...
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In order to tackle the intricate challenges of train dispatching and command within China's extensive high-speed railways (HSRs) network, this paper presents a novel parallel dispatching approach founded on the ACP (Artificial systems, Computational experiments, and Parallel execution) methodology. We provide a comprehensive overview of the framework and technical architecture of the parallel dispatching system (PDS), offering detailed insights into each fundamental module. It initiates by establishing the PDS for HSRs, emphasizing the deep integration of the actual and artificial systems. The methods for train operation situation deduction, rapid dispatching strategy generation, and comprehensive evaluation of parallel dispatching are then expounded, each tailored to different scales. Subsequently, the paper proposes the creation of a parallel dispatching platform for HSRs, utilizing a real line's dispatching system as a prototype. Two typical scenarios are considered to validate the effectiveness of the PDS. The computational experiments are designed and executed in the artificial dispatching system to facilitate accurate deduction of operational scenarios and swift generation of dispatching solutions within defined computational constraints. The adjustment effects are assessed and iteratively optimized through parallel execution. The research outcomes of this paper can serve as a theoretical foundation and technical resource for the design, implementation, and validation of PDS in China's high-speed railway network.
Given the complex nature of cyber-physical-social systems (CPSSs), understanding their mechanism is essential for analyzing and controlling their actions while minimizing potential harm. However, studying CPSS in the ...
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Given the complex nature of cyber-physical-social systems (CPSSs), understanding their mechanism is essential for analyzing and controlling their actions while minimizing potential harm. However, studying CPSS in the real world is costly and constrained by legal and institutional factors. Computational experiments have emerged as a new method for quantitative analysis, and this article proposes a method of using computational experiments for analyzing CPSS, which consists of model docking, experiment design, and experiment analysis. The cloud manufacturing service ecosystem (CMSE) is used as a typical case study to verify the effectiveness of the proposed method by simulating different operation strategies. The results show that the computational experiments method is effective in providing new means and ideas for analyzing CPSS.
Dear Editor,Scene understanding is an essential task in computer *** ultimate objective of scene understanding is to instruct computers to understand and reason about the scenes as humans *** vision is a research fram...
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Dear Editor,Scene understanding is an essential task in computer *** ultimate objective of scene understanding is to instruct computers to understand and reason about the scenes as humans *** vision is a research framework that unifies the explanation and perception of dynamic and complex scenes.
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