Engineering Applications of Artificial Intelligence最新文献_第2页

An improved reinforcement learning-based differential evolution algorithm for combined economic and emission dispatch problems

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-29 DOI: 10.1016/j.engappai.2024.109709

Yuan Wang , Xiaobing Yu , Wen Zhang

{"title":"An improved reinforcement learning-based differential evolution algorithm for combined economic and emission dispatch problems","authors":"Yuan Wang , Xiaobing Yu , Wen Zhang","doi":"10.1016/j.engappai.2024.109709","DOIUrl":"10.1016/j.engappai.2024.109709","url":null,"abstract":"<div><div>To overcome challenges posed by escalating environmental pollution and climate change, the combined economic and emission dispatch problem is proposed to balance economic efficiency with emission cost. The primary objective of the problem is to ensure that emissions are minimized while optimal economic costs are achieved simultaneously. However, due to the nonlinear and nonconvex characteristics of the model, the optimization is confronted with many difficulties. Hence, an innovative improved reinforcement learning-based differential evolution algorithm is proposed in this article, with reinforcement learning seamlessly integrated into the differential evolution algorithm. Q-learning from reinforcement learning technique is utilized to dynamically adjust parameter settings and select appropriate mutation strategies, thereby boosting the algorithm's adaptability and overall performance. The effectiveness of the proposed algorithm is tested on thirty testing functions and combined economic and emission dispatch problems in comparison with the other five algorithms. According to the experimental results of testing functions, superior performance is consistently achieved by the proposed algorithm, with the highest adaptability exhibited and an average ranking of 1.4167. Its superiority is further demonstrated through Wilcoxon tests on results of testing functions and combined economic and emission dispatch problems with the proportion of 100%, and the proposed algorithm is significantly better than other algorithms at a 0.05 significance level. The superiority of the proposed algorithm in optimizing combined economic and emission dispatch problems demonstrates that the proposed algorithm is shown to be adaptable to complex optimization environments, which proves useful for industrial applications and artificial intelligence.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109709"},"PeriodicalIF":7.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Low-cost language models: Survey and performance evaluation on Python code generation

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-29 DOI: 10.1016/j.engappai.2024.109490

Jessica López Espejel, Mahaman Sanoussi Yahaya Alassan, Merieme Bouhandi, Walid Dahhane, El Hassane Ettifouri

{"title":"Low-cost language models: Survey and performance evaluation on Python code generation","authors":"Jessica López Espejel, Mahaman Sanoussi Yahaya Alassan, Merieme Bouhandi, Walid Dahhane, El Hassane Ettifouri","doi":"10.1016/j.engappai.2024.109490","DOIUrl":"10.1016/j.engappai.2024.109490","url":null,"abstract":"<div><div>Large Language Models (LLMs) have become a popular choice for many Natural Language Processing (NLP) tasks due to their versatility and ability to produce high-quality results. Specifically, they are increasingly used for automatic code generation to help developers tackle repetitive coding tasks. However, LLMs’ substantial computational and memory requirements often make them inaccessible to users with limited resources. This paper focuses on very low-cost models which offer a more accessible alternative to resource-intensive LLMs. We notably: (1) propose a thorough semi-manual evaluation of their performance in generating Python code, (2) introduce a Chain-of-Thought (CoT) prompting strategy to improve model reasoning and code quality, and (3) propose a new dataset of 60 programming problems, with varied difficulty levels, designed to extend existing benchmarks like HumanEval and EvalPlus. Our findings show that some low-cost compatible models achieve competitive results compared to larger models like ChatGPT despite using significantly fewer resources. We will make our dataset and prompts publicly available to support further research.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109490"},"PeriodicalIF":7.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Supporting multi-criteria decision-making processes with unknown criteria weights

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-29 DOI: 10.1016/j.engappai.2024.109699

Jakub Więckowski , Wojciech Sałabun

{"title":"Supporting multi-criteria decision-making processes with unknown criteria weights","authors":"Jakub Więckowski , Wojciech Sałabun","doi":"10.1016/j.engappai.2024.109699","DOIUrl":"10.1016/j.engappai.2024.109699","url":null,"abstract":"<div><div>Decision support systems are crucial in today’s tech-driven world, assisting decision-makers with complex choices. Determining criteria weights is a paramount aspect, significantly influencing outcomes. Traditionally, criteria weights are derived from objective measures, subjective expert knowledge, or a combination of both, each with its own strengths and limitations. This paper presents a novel approach for addressing unknown criteria relevance by systematically generating weight vectors, thus exploring a broader decision problem space. The proposed methodology is adaptable to various multi-criteria methods, enhancing its applicability across different scenarios. Its effectiveness is empirically validated through two practical examples: Glomerular Filtration Rate (GFR) evaluation and bridge construction method selection, demonstrating its broad applicability. Comparative analysis with existing objective weighting techniques reveals the limitations of current approaches and highlights the improved decision-making capabilities enabled by the proposed method. This research addresses a critical gap in the reliability and robustness of existing methods, particularly in situations with unknown criteria weights. Key contributions include a new decision-making methodology and an innovative ranking formulation using fuzzy sets, with empirical verification strengthening the utility of the approach. This paper offers a promising solution for advancing multi-criteria decision analysis, especially in complex scenarios with uncertain criteria relevance.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109699"},"PeriodicalIF":7.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantum-inspired semantic matching based on neural networks with the duality of density matrices

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-29 DOI: 10.1016/j.engappai.2024.109667

Chenchen Zhang , Qiuchi Li , Dawei Song , Prayag Tiwari

{"title":"Quantum-inspired semantic matching based on neural networks with the duality of density matrices","authors":"Chenchen Zhang , Qiuchi Li , Dawei Song , Prayag Tiwari","doi":"10.1016/j.engappai.2024.109667","DOIUrl":"10.1016/j.engappai.2024.109667","url":null,"abstract":"<div><div>Social media text can be semantically matched in different ways, viz paraphrase identification, answer selection, community question answering, and so on. The performance of the above semantic matching tasks depends largely on the ability of language modeling. Neural network based language models and probabilistic language models are two main streams of language modeling approaches. However, few prior work has managed to unify them in a single framework on the premise of preserving probabilistic features during the neural network learning process. Motivated by recent advances of quantum-inspired neural networks for text representation learning, we fill the gap by resorting to density matrices, a key concept describing a quantum state as well as a quantum probability distribution. The state and probability views of density matrices are mapped respectively to the neural and probabilistic aspects of language models. Concretizing this state-probability duality to the semantic matching task, we build a unified neural-probabilistic language model through a quantum-inspired neural network. Specifically, we take the state view to construct a density matrix representation of sentence, and exploit its probabilistic nature by extracting its main semantics, which form the basis of a legitimate quantum measurement. When matching two sentences, each sentence is measured against the main semantics of the other. Such a process is implemented in a neural structure, facilitating an end-to-end learning of parameters. The learned density matrix representation reflects an authentic probability distribution over the semantic space throughout the training process. Experiments show that our model significantly outperforms a wide range of prominent classical and quantum-inspired baselines.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109667"},"PeriodicalIF":7.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliable federated learning based on delayed gradient aggregation for intelligent connected vehicles

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-29 DOI: 10.1016/j.engappai.2024.109719

Zhigang Yang, Cheng Cheng, Zixuan Li, Ruyan Wang, Xuhua Zhang

{"title":"Reliable federated learning based on delayed gradient aggregation for intelligent connected vehicles","authors":"Zhigang Yang, Cheng Cheng, Zixuan Li, Ruyan Wang, Xuhua Zhang","doi":"10.1016/j.engappai.2024.109719","DOIUrl":"10.1016/j.engappai.2024.109719","url":null,"abstract":"<div><div>As an organic combination of the Internet of Vehicles and intelligent vehicles, Intelligent Connected Vehicles (ICVs) have very high research and application value. Traditional data application methods require the local aggregation of sensitive user data, which poses a threat to user data privacy. Federated learning (FL) is a promising machine learning method that leverages distributed, personalized datasets to enhance performance while preserving user privacy. However, in mobile environments, unreliable client data can degrade the global model, reducing accuracy. Additionally, the mobility of ICVs can destabilize the training process, prolonging model updates and diminishing aggregation accuracy. To address these challenges, this paper proposes a dynamic asynchronous aggregation method that improves both reliability and training efficiency in FL for mobile networks. Therefore, it becomes crucial to find reliable aggregation of mobile device participation in FL tasks. To this end, we propose a reliable FL scheme, which only selects reliable mobile devices to participate in model aggregation to improve the generalization ability of the model. In addition, we design a dynamic asynchronous aggregation method based on reputation scores without affecting the model. Reduce model training time without compromising performance. Through experimental analysis, it is proved that this method can improve the reliability and effectiveness of FL tasks in mobile networks.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109719"},"PeriodicalIF":7.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance evaluation of uranium enrichment cascades using fuzzy based harmony search algorithm

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-29 DOI: 10.1016/j.engappai.2024.109710

S. Dadashzadeh, M. Aghaie

{"title":"Performance evaluation of uranium enrichment cascades using fuzzy based harmony search algorithm","authors":"S. Dadashzadeh, M. Aghaie","doi":"10.1016/j.engappai.2024.109710","DOIUrl":"10.1016/j.engappai.2024.109710","url":null,"abstract":"<div><div>The production of energy in nuclear reactors needs enrichment of fuels. There is some interest in taking the fuel enrichment level to 3–5% by cascades. Optimization of the isotopic cascades is essential to make this process economic. This study presents a Fuzzy-based Harmony Search (FHS) algorithm aimed at dynamic parameter adaptation as well as establishing a balance between exploration and exploitation, which significantly increases the convergence speed of the algorithm. Accelerating the convergence of the algorithm is demonstrated in the Sphere, Schwefel, Ackley, and Drop-Waves benchmarks at first. This approach also enhances performance in several test cases of optimum cascade problems, with results validated through comparisons with conventional methods. According to the results, the total number of centrifuges using FHS reached 6306 in test case 1, which was reduced 44 pieces compared to the method used by Palkin, and 55 pieces compared to the real coded genetic algorithm. The total number of centrifuges using FHS reached 2808 in test case 4 with a different type of gas centrifuge, which decreased 27 pieces compared to the direct search method. Similar results were obtained in other test cases, indicating the effectiveness of the FHS algorithm in minimizing the total number of centrifuges and total flow rates.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109710"},"PeriodicalIF":7.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semantic segmentation model based on edge information for rock structural surface traces detection

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-29 DOI: 10.1016/j.engappai.2024.109706

Xiaofeng Yuan , Dun Wu , Yalin Wang , Chunhua Yang , Weihua Gui , Shuqiao Cheng , Lingjian Ye , Feifan Shen

{"title":"Semantic segmentation model based on edge information for rock structural surface traces detection","authors":"Xiaofeng Yuan , Dun Wu , Yalin Wang , Chunhua Yang , Weihua Gui , Shuqiao Cheng , Lingjian Ye , Feifan Shen","doi":"10.1016/j.engappai.2024.109706","DOIUrl":"10.1016/j.engappai.2024.109706","url":null,"abstract":"<div><div>Fast and accurate detection of rock structural surface traces is crucial for geology and engineering fields. In recent years, deep learning techniques like U-Net (UNet) have been applied to rock structural surface traces detection by virtue of its high accuracy and strong robustness. However, the loss of important information during the downsampling process may hinder the model performance for rock structural surface traces detection. To alleviate this problem, this paper proposes a semantic segmentation model based on edge information (Edge-UNet) for rock structural surface traces detection. In Edge-UNet, an edge pooling method is designed, which can retain more trace features rich in edge information in the downsampling process, so as to enhance the learning of the model for traces. Then, an edge semantic enhancement structure based on edge pooling is designed to strengthen the edge information in Edge-UNet's encoder. In addition, a channel space attention gate based on edge information is incorporated in Edge-UNet's decoder, which facilitates the model to capture fine trace features. These designs clarify the retention and utilization of edge information in principle which enhances the interpretability of the model. Finally, Convolutional neural network -based and Transformer-based semantic segmentation models were selected for comparison experiments with Edge-UNet, respectively. From the experimental results, Edge-UNet outperforms the other models in three performance metrics, which verifies the superior performance of Edge-UNet in rock structural surface trace detection task.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109706"},"PeriodicalIF":7.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semantic graph neural network with multi-measure learning for semi-supervised classification

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-29 DOI: 10.1016/j.engappai.2024.109647

Junchao Lin , Yuan Wan , Jingwen Xu , Xingchen Qi

{"title":"Semantic graph neural network with multi-measure learning for semi-supervised classification","authors":"Junchao Lin , Yuan Wan , Jingwen Xu , Xingchen Qi","doi":"10.1016/j.engappai.2024.109647","DOIUrl":"10.1016/j.engappai.2024.109647","url":null,"abstract":"<div><div>Graph Neural Networks (GNNs) have attracted increasing attention in recent years and have achieved excellent performance in semi-supervised node classification tasks. The success of most GNNs is attributed to the availability of the original graph structure. However, recent studies have shown that GNNs are vulnerable to the complex underlying structure of the graph, making it necessary to learn comprehensive and robust graph structures for downstream tasks, rather than relying only on the raw graph structure. In light of this, we seek to learn optimal graph structures for downstream tasks and propose a novel framework for semi-supervised classification. Specifically, based on the structural context information of graph and node representations, we encode the complex interactions in semantics and generate semantic graphs to preserve the global structure. Moreover, we develop a novel multi-measure attention layer to optimize the similarity rather than prescribing it a priori, so that the similarity can be adaptively evaluated by integrating measures. These graphs are fused and optimized together with GNN towards semi-supervised classification objective. Extensive experiments and ablation studies on six real-world datasets clearly demonstrate the effectiveness of our proposed model and the contribution of each component. The proposed model not only addresses the inherent vulnerabilities of GNNs to complex graph structures, but also introduces a pioneering approach to learning comprehensive and robust graph representations for semi-supervised classification tasks.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109647"},"PeriodicalIF":7.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predefined time convergence guaranteed performance control for uncertain systems based on reinforcement learning

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-29 DOI: 10.1016/j.engappai.2024.109734

Chun-Wu Yin

{"title":"Predefined time convergence guaranteed performance control for uncertain systems based on reinforcement learning","authors":"Chun-Wu Yin","doi":"10.1016/j.engappai.2024.109734","DOIUrl":"10.1016/j.engappai.2024.109734","url":null,"abstract":"<div><div>The prescribed performance control method (PPCM) is commonly employed to ensure the guaranteed performance control of non-linear systems. However, traditional approaches suffer from certain drawbacks, such as the dependence of parameter settings for the performance constraint function on the initial tracking error value and the inability to specify the convergence time of tracking error according to engineering requirements. This paper focuses on designing a fault tolerant control strategy with prescribed convergence time and prescribed transient performance for uncertain systems, considering parameter perturbance, actuator faults, and unknown initial states. Firstly, we introduce an error conversion function that transforms the tracking error with any initial value into a new error variable starting from zero. This resolves the issue of depending on the initial value of tracking error in setting parameters for the performance constraint function in prescribed performance control methods. Subsequently, we derive a novel Lyapunov stability criterion for predefined time (PDT) convergence and design a fault-tolerant control strategy using backstepping control method while ensuring prescribed convergence time and prescribed performance. In this approach, we propose a new online reinforcement learning intelligent algorithm to estimate compound interference caused by actuator faults, control saturation constraint increment, system parameter perturbation, and external interference. The theoretical proof establishes predefined time convergence of the closed-loop system. Finally, numerical simulations are conducted on industrial robots with actuator faults to validate the effectiveness of our designed control strategy.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109734"},"PeriodicalIF":7.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Benchmarking neural radiance fields for autonomous robots: An overview

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-29 DOI: 10.1016/j.engappai.2024.109685

Yuhang Ming , Xingrui Yang , Weihan Wang , Zheng Chen , Jinglun Feng , Yifan Xing , Guofeng Zhang

{"title":"Benchmarking neural radiance fields for autonomous robots: An overview","authors":"Yuhang Ming , Xingrui Yang , Weihan Wang , Zheng Chen , Jinglun Feng , Yifan Xing , Guofeng Zhang","doi":"10.1016/j.engappai.2024.109685","DOIUrl":"10.1016/j.engappai.2024.109685","url":null,"abstract":"<div><div>Neural Radiance Field (NeRF) has emerged as a powerful paradigm for scene representation, offering high-fidelity renderings and reconstructions from a set of sparse and unstructured sensor data. In the context of autonomous robotics, where perception and understanding of the environment are pivotal, NeRF holds immense promise for improving performance. However, few survey has discussed such a potential. To fill this gap, we have collected over 200 papers since the publication of original NeRF in 2020 and present a thorough analysis of how NeRF can be used to enhance the capabilities of autonomous robots. We especially focus on the perception, localization and navigation, and decision-making modules of autonomous robots and delve into tasks crucial for autonomous operation, including 3-dimensional reconstruction, segmentation, pose estimation, simultaneous localization and mapping, navigation and planning, and interaction. Our survey meticulously benchmarks existing NeRF-based methods, comparing their reported performance, and providing insights into their strengths and limitations. Moreover, we target the existing challenges of applying NeRF in autonomous robots, including real-time processing, sparse input views, and explore promising avenues for future research and development in this domain. We especially discuss potential of integrating advanced deep learning techniques like 3-dimensional Gaussian splatting, large language models, and generative artificial intelligence. This survey serves as a roadmap for researchers seeking to leverage NeRF to empower autonomous robots, paving the way for innovative solutions that can navigate and interact seamlessly in complex environments.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"140 ","pages":"Article 109685"},"PeriodicalIF":7.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0