Automatic optimization algorithms are crucial for vehicle body lightweight design;however, existing methods remain inefficient leading to excessive iterations that increase both time and costs. Current interactive opt...
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This research aims to apply artificial intelligence technology to a manufacturing industry, specifically, to forecast temperature and insulation values of motors from the CNC machine. dataset from motor sensors are co...
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This research aims to apply artificial intelligence technology to a manufacturing industry, specifically, to forecast temperature and insulation values of motors from the CNC machine. dataset from motor sensors are collected and forecasting models are trained using four deep learning models, namely, multilayer perceptron (MLP), long-short term memory (LSTM), LSTM autoencoder, and bidirectional LSTM (Bi-LSTM). Models are evaluated by measuring the deviation of forecasting values from the real values. Two measures, root mean square error (RMSE) and mean absolute error (MAE), are used to assess model’s performance. Experiments are conducted and found that the Bi-LSTM yielded the lowest RMSE and MAE numbers, hence, the best model to be selected. Further development has been implemented by integrating Bi-LSTM and genetic algorithm (GA) in order to optimize the model performance. Instead of searching the huge hyperparameter space of the neural network, the integrate GA-LSTM model using RMSE as a fitness function to reduce the search space and obtain the optimal or near optimal hyperparameters. The empirically best model is found which yields a lower RMSE value of 0.041 comparing to 0.18 when not optimized.
When natural or man-made disasters occur at sea, a maritime unmanned rescue system-of-systems (MURSoSs), as an important guarantee for the safety of people's lives and property, has a rapid response to emergency r...
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ISBN:
(数字)9798350384185
ISBN:
(纸本)9798350384192
When natural or man-made disasters occur at sea, a maritime unmanned rescue system-of-systems (MURSoSs), as an important guarantee for the safety of people's lives and property, has a rapid response to emergency rescue services. In the design process of MURSoSs, it is often faced with problems such as unexplainable mechanisms and inaccurate modeling due to the characteristics of multi-level coupling and irregular emergence. A design method of MURSoSs based on an attention Transformer is proposed. First, this paper decomposes the MURSoSs into a multi-level organizational structure composed of individual performance, group structure, and overall effectiveness. Second, the maritime unmanned rescue simulation environment is constructed, to obtain the multi-level evolution data of group structure and overall effectiveness under different individual performance, and the attention Transformer is used to mine the functional correlation relationship between levels. Finally, the experimental simulation prediction is carried out by using this function relationship. Taking the design of MURSoSs as an example, the results show that the constructed model accurately quantifies the correlation relationship between levels and effectively reveal the internal logic of the emergence process of maritime rescue.
This study aims to clarify the contours of artificial intelligence (AI) and present how it has evolved in different publication venues in the different stages of its development. Based on the noun phrases extracted fr...
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Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication...
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Few-shot relation extraction with none-of-the-above (FsRE with NOTA) aims at predicting labels in few-shot scenarios with unknown classes. FsRE with NOTA is more challenging than the conventional few-shot relation ext...
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The rapid development of the low-altitude economy (LAE) has significantly increased the utilization of autonomous aerial vehicles (AAVs) in various applications, necessitating efficient and secure communication method...
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The rapid development of the low-altitude economy (LAE) has significantly increased the utilization of autonomous aerial vehicles (AAVs) in various applications, necessitating efficient and secure communication methods among AAV swarms. In this work, we aim to introduce distributed collaborative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two AAV swarms and construct these swarms as two AAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of AAVs when constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives by formulating a multi-objective optimization problem, which is NP-hard and with a large number of decision variables. Accordingly, we design a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead, which contains a conditional variational autoencoder (CVAE)-based generative method and a proposed powerful swarm intelligence algorithm. In this framework, CVAE can collect expert solutions obtained by the swarm intelligence algorithm in other environment states to explore characteristics and patterns, thereby directly generating high-quality initial solutions in new environment factors for the swarm intelligence algorithm to search solution space efficiently. Simulation results show that the proposed swarm intelligence algorithm outperforms other state-of-the-art baseline algorithms, and the GenSI can achieve similar optimization results by using far fewer iterations than the ordinary swarm intelligence algorithm. Experimental tests demonstrate that introducing the CVAE mechanism ach
knowledge Graph (KG) is a directed heterogeneous information network that contains a large number of entities and relations, which is widely used as effective side information in rec-ommender systems. Moreover, in rec...
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ISBN:
(纸本)9781665438599
knowledge Graph (KG) is a directed heterogeneous information network that contains a large number of entities and relations, which is widely used as effective side information in rec-ommender systems. Moreover, in recommender systems, the Graph Convolutional Network (GCN) model is introduced to mine the relatedness between entities in a KG because of its efficiency in extracting spatial features on topological graphs. The knowledge Graph Convolutional Network (KGCN) model up-dates the embedding of a currently positioned entity by aggregating the information of adjacent entities selected randomly. Never-theless, it has two limititations: 1) the information of neighbors se-lected randomly cannot accurately represent the current entity in the KG; 2) the model is hard to converge as graph features (i.e. The spatial relation features and semantic information features of en-tities in the KG) grow. To solve these limitations, in this paper, a meta-path (i.e., a sequence of artificially constructed relationships) is introduced into the selection of neighbors in the KGCN model to enhance the representation of each entity. Furthermore, two construction methods of the meta-path - constructing a meta-path based on the same relation (KGCN-SP) and the characteris-tics of KG (KGCN-MP) -are proposed. The experiments based on three real-world datasets demonstrate that the neighbor selection based on the meta-path is able to collect more accurate infor-mation from a KG and improve the recommendation performance effectively.
knowledge graph alignment aims to link equivalent entities across different knowledge graphs. To utilize both the graph structures and the side information such as name, description and attributes, most of the works p...
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This paper is concerned with improving the computational efficiency of the matrix-based Rényi's α-order entropy, which enables directly being accessed from the given data without knowing the probability dens...
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