Withthe economic development, people pay more attention to their own health, health has become one of the key topics of social concern, healthinformation management system has become the main means of observing the ...
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ISBN:
(纸本)9798350379860;9798350379877
Withthe economic development, people pay more attention to their own health, health has become one of the key topics of social concern, healthinformation management system has become the main means of observing the health status of the population in a certain region, but the integration of the health data of the monitored people withtheir life data is not in place. In this paper, we use UAV tilt photography and monolithic modeling technology to establish a fine-grained realistic three-dimensional model of the community and community population health data to achieve the construction of a community model and a hybrid model of population health, and design and develop a community population health management system based on the Cesium platform, which provides a fine-grained management of the key populations in the community, and timely treatment for special populations, reflecting the concept of the supremacy of life, and also creating a new healthinformation management system for people who are monitored. It also creates a new model of real-life 3D technical support.
Long text summarization aims to extract key information from lengthy texts and generate concise and accurate summaries. However, due to the complexity of long text information and the limitation of text length, existi...
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ISBN:
(纸本)9798350379860;9798350379877
Long text summarization aims to extract key information from lengthy texts and generate concise and accurate summaries. However, due to the complexity of long text information and the limitation of text length, existing summary methods encounter hurdles like semantic degradation and redundant information. To tackle these problems, this article proposes a two-stage long text summarization method based on extraction-generation. this method jointly trains an extractor and a generator, effectively combining the strengths of extractive and abstractive methods. the extractor is utilized to extract key information to tackle the challenge of handling long inputs, while the generator allows the use of dynamic sentence-level attention weights during decoding. this enables the generator to dynamically adjust the importance of sentences based on contextual information, leading to more precise summary generation. Extensive experiments conducted on the arXiv dataset demonstrate the superior performance of our proposed model in the task of long text summarization.
Withthe dense deployment of satellite constellations and the rapid expansion of ground wireless communications, satellite-to-ground links are facing increasingly severe interference from aerial nodes and ground base ...
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ISBN:
(纸本)9798350379860;9798350379877
Withthe dense deployment of satellite constellations and the rapid expansion of ground wireless communications, satellite-to-ground links are facing increasingly severe interference from aerial nodes and ground base stations. In addition, as the demand for satellite communications in services such as text, voice, and video continues to grow, the requirement for high information rates is also increasing. therefore, satellite communication systems are now in urgent need of solving the anti-interference problem and improving the information transmission rate to meet the increasingly complex communication needs. Inspired by the good performance of bit-interleaved coded modulation (BICM) over wireless fading channel, we propose a novel bit-interleaved Turbo-Hadamard code (thC) aided high-order quadrature amplitude modulation (QAM) scheme for anti-jamming satellite-terrestrial systems, namely the BIthCM scheme. the scheme proposed in this paper takes into account the anti-interference performance and transmission rate of the system, and has strong interference resistance while ensuring high information rate. through simulation, it is verified that the proposed BIthCM outperforms the thC-QAM system over boththe additive white Gaussian noise (AWGN) channel and the Partial Band Noise Jamming (PBNJ) channel.
Knowledge graph is a handy tool to show how things are related to each other. To facilitate computation and reasoning on the knowledge graph, embedding techniques are needed to map the knowledge to a lower dimensional...
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ISBN:
(纸本)9798350379860;9798350379877
Knowledge graph is a handy tool to show how things are related to each other. To facilitate computation and reasoning on the knowledge graph, embedding techniques are needed to map the knowledge to a lower dimensional space. However, the importance of entity types and information about the different locations of entities is often underestimated in knowledge graph embedding techniques. To address the shortcomings of current embedding methods, we propose an embedding framework for knowledge graph entity completion based on entity types and relational compositions, noted as JETRC. Our framework integrates type informationthrough an entity type transition matrix and the use of relational composition representations to learn the latent information of entities at different locations in triple, thus better capturing the semantic relationships and logical dependencies among entities. An attention mechanism-based encoder is used to update embeddings of entities and relations. Experimental results indicate that JETRC significantly improves accuracy in the task of knowledge graph entity completion. It outperforms the comparison model in MRR metric, Hits@1 metric and Hits@10 metric.
Clustering is an unsupervised machine learning method, which aims to group data points according to the similarity of data. As a mature clustering algorithm, K-means has been widely used in the location problem. this ...
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ISBN:
(纸本)9798350379860;9798350379877
Clustering is an unsupervised machine learning method, which aims to group data points according to the similarity of data. As a mature clustering algorithm, K-means has been widely used in the location problem. this paper discusses its application in an express service location problem, especially through the improved K-means algorithm to optimize the location decision. the advantage of the algorithm in this paper is that it considers the different influence of the receiving volume and the delivering volume on the cost of the express logistics field, the self-defined weighted distance is introduced to combine the express transportation distance withthe express delivery and receipt index, which more accurately reflects the economic cost and benefit of the express delivery enterprise. On this basis of the weighted distance, the algorithm is customized and improved through a capacity constraint in order to satisfy the location demand of the express delivery enterprise. Finally, a numeric experiment shows the effectiveness and practicality of our algorithm on the express service location problem.
To improve user service satisfaction level (SSL), this paper investigates a resource allocation method for multi-cell semantic communication systems with multi-service hybrid transmission mode. For the coexistence of ...
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ISBN:
(纸本)9798350379860;9798350379877
To improve user service satisfaction level (SSL), this paper investigates a resource allocation method for multi-cell semantic communication systems with multi-service hybrid transmission mode. For the coexistence of textual semantic communication service and ultra-high reliability and low latency (URLLC) service, we utilize a deep reinforcement learning (DRL) method to solve dynamic sub-channel assignment and power allocation optimization problem. then, we design an event triggering control mechanism (ETC) and a centralized training and distributed execution (CTDE) method for the multi-agent DRL-based resource allocation network, to reduce transmission delay of the multi-cell semantic communication system. Simulation results illustrate that the proposed algorithm can achieve better service satisfaction level performance with lower time cost compared with existing methods.
In order to solve the problems of low efficiency and difficult practical application of the centralized method in the coordinated control of urban road network signals, this paper proposes a game-based multi-intersect...
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ISBN:
(纸本)9798350379860;9798350379877
In order to solve the problems of low efficiency and difficult practical application of the centralized method in the coordinated control of urban road network signals, this paper proposes a game-based multi-intersection cooperative control method. By constructing a distributed game model, the signal control between intersections is regarded as a game relationship, and the Nash equilibrium solution is solved by using a mixed strategy game, and the signal control strategies of each intersection are obtained. At the same time, combined withthe multi-agent reinforcement learning framework, Nash Q-learning is used to update the benefit matrix to realize the learning strategy of the agent. Simulation results show that the proposed method can significantly reduce the average delay and waiting time under low traffic demand[1], and outperforms single-agent control under high traffic demand, effectively avoiding traffic congestion and reducing network delay.
the Russia-Ukraine conflict has demonstrated that urban combat capability will become a core element determining the outcome of local wars. Withthe intelligent transformation of urban combat systems, their highly rel...
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ISBN:
(纸本)9798350379860;9798350379877
the Russia-Ukraine conflict has demonstrated that urban combat capability will become a core element determining the outcome of local wars. Withthe intelligent transformation of urban combat systems, their highly reliable, anti-interference, and robust communication networks have become the premise for instant "seamless communication" connecting various equipment platforms on the battlefield. Due to the characteristics of wireless self-organizing networks, such as decentralized networking, multi-hop long-distance transmission, and support for node mobility, they are widely used in the construction of terminal subnets for urban combat systems, enabling significant enhancements in human-computer interaction and collaborative reconnaissance and strike capabilities. this paper proposes an elastic networking topology control mechanism based on the Grey Wolf Optimizer algorithm for the network layer of self-organizing networks, and conducts simulation and comparative analysis. the results indicate that this method enhances the networking flexibility and network lifespan of self-organizing networks, ensuring reliable communication for urban combat.
Event detection plays a significant role in tennis match analysis. Current methods for event detection mostly rely on cues from audience sounds and players' positions and are confined to videos captured by profess...
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ISBN:
(纸本)9798350379860;9798350379877
Event detection plays a significant role in tennis match analysis. Current methods for event detection mostly rely on cues from audience sounds and players' positions and are confined to videos captured by professional facilities. the performance of these methods suffers from audio interference and the obscuration of players. In this paper, we propose a new event detection method based on ball trajectories for tennis matches. Our method learns event-related features from the trajectory, displacement, and flight direction of a tennis ball in successive frames and uses the contextual information of the ball trajectory to mitigate quality degradation of the trajectory under certain conditions. We also manually annotate each frame of video with a label of events in the dataset of GridTrackNet, which encompasses tennis videos captured from both professional and amateur footage, and designate it as GridTrackNet-A. Experiment results show the proposed method obtained superior performance with macro F1-score of 91.65% and MAE of 0.63 on the GridTrackNet-A dataset.
Novel Class Discovery (NCD) has emerged as a vital area of research in machine learning and computer vision, aiming to identify novel classes in unlabeled datasets by leveraging knowledge from labeled datasets. Recent...
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ISBN:
(纸本)9798350379860;9798350379877
Novel Class Discovery (NCD) has emerged as a vital area of research in machine learning and computer vision, aiming to identify novel classes in unlabeled datasets by leveraging knowledge from labeled datasets. Recent advances in NCD, especially after 2023, have focused on addressing key challenges such as imbalanced data, catastrophic forgetting, and improving the generalization capabilities of models. We examine how recent works integrate incremental learning, self-supervised techniques, and uncertainty quantification to enhance the discovery of novel classes. the role of generative models and transfer learning is also highlighted, particularly in domain-specific applications such as remote sensing, biomedical data, and synthetic aperture radar (SAR) imagery. Our review provides insights into the strengths, limitations, and future directions of NCD research, focusing on scalability, interpretability, and real-world applicability.
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