Artificial Intelligence Review最新文献_第5页

A novel fuzzy neural network approach with triangular fuzzy information for the selection of logistics service providers 基于三角模糊信息的模糊神经网络在物流服务商选择中的应用

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-22 DOI: 10.1007/s10462-025-11209-7

Lifang Wang, Saleem Abdullah, Ariana Abdul Rahimzai, Ihsan Ullah

{"title":"A novel fuzzy neural network approach with triangular fuzzy information for the selection of logistics service providers","authors":"Lifang Wang, Saleem Abdullah, Ariana Abdul Rahimzai, Ihsan Ullah","doi":"10.1007/s10462-025-11209-7","DOIUrl":"10.1007/s10462-025-11209-7","url":null,"abstract":"<div><p>In this article, we presents a novel fuzzy neural network approach designed to address multi criteria decision making (MCDM) problems, specifically for selecting logistics service providers. The proposed decision making model integrates triangular fuzzy numbers (TFNs) with a triangular fuzzy Einstein weighted averaging (TFEWA) aggregation operator to enhance the decision making process under uncertainty. Initially, we discussed the concept of triangular fuzzy numbers, which allows for the representation of uncertain and imprecise data typically presented in real-world decision making environments. The operational laws, score function, and Hamming distance measures for TFNs are presented to ensure accurate handling of the fuzzy input data. The TFEWA aggregation operator, which is based on Einstein norms and plays a crucial role in aggregating expert opinions in the evaluation process. In the decision making process, we collect expert opinions regarding logistics service providers, expressed as TFNs, which are then processed through the fuzzy neural network model. After that, we apply the proposed decision making model to select the best logistics service providers. The TFEWA operator computes values at the hidden and output layers, and activation functions are applied to produce final output values. These outputs provide a ranked list of logistics service providers based on their overall performance across multiple criteria. The effectiveness of this novel approach is validated through a comparative analysis with existing MCDM methods. The results demonstrate that the triangular fuzzy neural network approach outperforms traditional methods in terms of flexibility, accuracy, and its ability to handle uncertain, fuzzy data. Our method provides a robust decision support system, capable of managing complex decision making tasks in logistics and other fields.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11209-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review on 3D Gaussian splatting for sparse view reconstruction 用于稀疏视图重建的 3D 高斯拼接综述

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-22 DOI: 10.1007/s10462-025-11171-4

Haitian Liu, Binglin Liu, Qianchao Hu, Peilun Du, Jing Li, Yang Bao, Feng Wang

{"title":"A review on 3D Gaussian splatting for sparse view reconstruction","authors":"Haitian Liu, Binglin Liu, Qianchao Hu, Peilun Du, Jing Li, Yang Bao, Feng Wang","doi":"10.1007/s10462-025-11171-4","DOIUrl":"10.1007/s10462-025-11171-4","url":null,"abstract":"<div><p>Sparse view 3D reconstruction remains challenging due to inherent data scale limitations. Mainstream sparse view 3D reconstruction algorithms based on the NeRF framework struggle to balance generation quality and real-time performance. Recently, the advent of 3D Gaussian Splatting technology has demonstrated remarkable results, becoming increasingly prominent in 3D scene representation and reconstruction. Exploring the application of 3D Gaussian Splatting technology for sparse view 3D reconstruction represents a promising research avenue. Based on this, our paper provides a comprehensive review of current sparse view 3D reconstruction methods leveraging 3D Gaussian Splatting, with an emphasis on extracting effective reconstruction information from input images and utilizing these data to generate realistic scenes efficiently and reliably. We then provide a detailed discussion on how the algorithm addresses issues such as artifacts and scale ambiguous, which are common challenges in this field. In the subsequent sections, we present both quantitative and qualitative comparisons of various sparse-view 3D reconstruction methods, roughly demonstrating the advantages of sparse view 3D Gaussian splatting methods in terms of reconstruction quality and efficiency. Furthermore, we analyze the potential applications of sparse view 3D Gaussian splatting methods. Finally, we identify the challenges faced by sparse-view 3D Gaussian splatting reconstruction and suggest potential solutions. We hope that our analysis will provide valuable insights for future research efforts.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11171-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast machine learning for building management systems 楼宇管理系统的快速机器学习

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-17 DOI: 10.1007/s10462-025-11226-6

Mohammed Mshragi, Ioan Petri

{"title":"Fast machine learning for building management systems","authors":"Mohammed Mshragi, Ioan Petri","doi":"10.1007/s10462-025-11226-6","DOIUrl":"10.1007/s10462-025-11226-6","url":null,"abstract":"<div><p>Building management systems (BMSs) are increasingly integrating advanced machine learning (ML) and artificial intelligence (AI) capabilities to enhance operational efficiency and responsiveness. The transformation of BMSs involves a wide range of environmental, behavioural, economical and technical factors as well as optimum performance considerations in order to reach energy efficiency and for long term sustainability. Existing BMSs can only provide local adaptability by creating and managing information for a built asset lacking the capability to learn and adapt based on performance objectives. This research provides a comprehensive review of ML techniques in BMSs, with particular emphasis and demonstration of fast machine learning (FastML) techniques in a real-case study application. The study reviews optimization methods for ML algorithms, focusing on Long Short-Term Memory (LSTM) networks for energy consumption forecasting and exploring solutions that leverage hardware accelerators for low-latency and high-throughput processing. The High-Level Synthesis for Machine Learning (HLS4ML) framework facilitates deployment of fast machine learning models with BMSs, achieving substantial gains in hardware efficiency and inference speed in resource-constrained environments. Findings reveal that HLS4ML-optimized models maintain accuracy while offering computational efficiency through techniques like pruning and quantization, supporting real-time BMS applications. This research significantly contributes to the development of intelligent BMSs by integrating ML algorithms with advanced hardware solutions, ultimately improving energy management, occupant comfort, and safety in modern buildings.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11226-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scalable multi-modal representation learning networks 可扩展的多模态表示学习网络

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-17 DOI: 10.1007/s10462-025-11224-8

Zihan Fang, Ying Zou, Shiyang Lan, Shide Du, Yanchao Tan, Shiping Wang

{"title":"Scalable multi-modal representation learning networks","authors":"Zihan Fang, Ying Zou, Shiyang Lan, Shide Du, Yanchao Tan, Shiping Wang","doi":"10.1007/s10462-025-11224-8","DOIUrl":"10.1007/s10462-025-11224-8","url":null,"abstract":"<div><p>Multi-modal representation learning is recognized for its comprehensive interpretation across diverse modalities. Although existing approaches have yielded favorable results, they face challenges in high-order information preservation and out-of-sample data generalization. To tackle these issues, we propose a scalable multi-modal representation learning networks framework, which aims to learn optimal modality-specific projection matrices to project multi-modal features to a shared representation space. Specifically, weight guided modality-wise and row-sparsity driven feature-wise measures are considered to achieve adaptively hierarchical feature selection from the original data. Then, within the unified latent representation space, we employ hypergraph embedding to preserve the intricate high-order local geometric structures within the modality-specific high-dimensional spaces. Finally, we propose a proximal operator-inspired network architecture to resolve the optimization objectives, streamlining the process of feature auto-weighted selection and representation learning. The experimental results highlight the effectiveness and superiority of the proposed method, while online testing on out-of-sample data further demonstrates robust generalization. The code of the proposed method is publicly available at: https://github.com/ZihanFang11/SMMRL.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11224-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning semantic consistency for audio-visual zero-shot learning 学习语义一致性的视听零射击学习

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-17 DOI: 10.1007/s10462-025-11228-4

Xiaoyong Li, Jing Yang, Yuling Chen, Wei Zhang, Xiaoli Ruan, Chengjiang Li, Zhidong Su

{"title":"Learning semantic consistency for audio-visual zero-shot learning","authors":"Xiaoyong Li, Jing Yang, Yuling Chen, Wei Zhang, Xiaoli Ruan, Chengjiang Li, Zhidong Su","doi":"10.1007/s10462-025-11228-4","DOIUrl":"10.1007/s10462-025-11228-4","url":null,"abstract":"<div><p>Audio-visual zero-shot learning requires an understanding of the relationship between audio and visual information to determine unseen classes. Despite many efforts and significant progress in the field, many existing methods tend to focus on learning strong representations, neglecting the semantic consistency between audio and video as well as the inherent hierarchical structure of the data. To address these issues, we propose Learning Semantic Consistency for Audio-Visual Zero-shot Learning. Specifically, we employ an attention mechanism to enhance cross-modal information interactions, aiming to capture the semantic consistency between audio and visual data. Meanwhile, we introduce a hyperbolic space to model the hierarchical structure of the data itself. Moreover, the proposed approach includes a novel loss function that considers the relationships between input modalities, reducing the distance between features of different modalities. To evaluate the proposed method, we test it on three benchmark datasets <span>(hbox {VGGSound-GZS}{{textrm{L}}^{cls}})</span>, <span>(hbox {UCF-GZS}{{textrm{L}}^{cls}})</span>, and <span>(hbox {ActivityNet-GZS}{{textrm{L}}^{cls}})</span>. Extensive experimental results show that the proposed method achieves state-of-the-art performance on all three datasets. For example, on the <span>(hbox {UCF-GZS}{{textrm{L}}^{cls}})</span> dataset, the harmonic mean is improved by 5.7%. Code and data available at https://github.com/ybyangjing/LSC-AVZSL.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11228-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gene selection for single cell RNA-seq data via fuzzy rough iterative computation model 基于模糊粗糙迭代计算模型的单细胞RNA-seq数据基因选择

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-17 DOI: 10.1007/s10462-025-11213-x

Zhaowen Li, Jie Zhang, Yuxian Wang, Fang Liu, Ching-Feng Wen

{"title":"Gene selection for single cell RNA-seq data via fuzzy rough iterative computation model","authors":"Zhaowen Li, Jie Zhang, Yuxian Wang, Fang Liu, Ching-Feng Wen","doi":"10.1007/s10462-025-11213-x","DOIUrl":"10.1007/s10462-025-11213-x","url":null,"abstract":"<div><p>Single cell RNA-seq data have the characteristics of small samples, high dimension and noise. Due to these characteristics, gene selection must be carried out before clustering and classifying. This study explores gene selection in a single cell gene decision space (<i>scgd</i>-space) via fuzzy rough iterative computation model (<i>FRIC</i>-model). First, in order to overcome the strictness of the equality between gene expression values, the equality between gene expression values is replaced by the distance between gene expression values, and the fuzzy symmetric relation on the cell set of a <i>scgd</i>-space is defined. In this fuzzy symmetric relation, two variable parameters are introduced: one controls gene subsets, the other dominates the distance between gene expression values. Then, <i>FRIC</i>-model in a <i>scgd</i>-space is established, which overcomes the deficiencies of classical rough set model and fuzzy rough set model. This model applies the iterative computation strategy to define some evaluation functions. These functions include fuzzy rough approximations and dependency functions. Next, a gene selection algorithm based on <i>FRIC</i>-model is designed. At last, the designed algorithm is testified in several publicly open single cell RNA-seq datasets to estimate its performance. The experimental results show that the designed algorithm is more effective than some existing algorithms, is fast and does not occupy too much memory.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11213-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative analysis of methodologies and approaches in recommender systems utilizing large language models 使用大型语言模型的推荐系统的方法和方法的比较分析

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-17 DOI: 10.1007/s10462-025-11189-8

Salma S. Elmoghazy, Marwa A. Shouman, Hamdy K. Elminir, Gamal Eldin I. Selim

{"title":"Comparative analysis of methodologies and approaches in recommender systems utilizing large language models","authors":"Salma S. Elmoghazy, Marwa A. Shouman, Hamdy K. Elminir, Gamal Eldin I. Selim","doi":"10.1007/s10462-025-11189-8","DOIUrl":"10.1007/s10462-025-11189-8","url":null,"abstract":"<div><p>Recommendation systems are indispensable technologies nowadays, as they enable analysis of the huge amount of information available on the internet, helping consumers to make decisions effectively. Ongoing efforts are essential to further develop and align them with the evolving demands of the modern era. In the last few years, large language models (LLMs) have made a huge leap in natural language processing. This advancement has directed researchers’ efforts towards employing these models in various fields, including recommender systems, to leverage the vast amount of data they were trained on. This paper presents a comparative study of a set of recent methodologies that adapt LLMs to recommendations. Throughout the discussed research work, we come up with the insight that LLMs offer significant benefits due to the amount of knowledge they possess and their powerful ability to represent textual data effectively, making them useful in common recommendation issues like cold-start. Also, the variety of fine-tuning and in-context learning techniques enables adaptation of LLMs to a wide range of recommendation tasks. We discussed issues addressed in the reviewed research work and the solutions proposed to enhance recommendation systems. To provide a clearer understanding, we propose taxonomies to categorize the reviewed work based on underlying techniques, involving the role of LLMs in recommendations, learning paradigms, and system structures. We explore datasets, recommendation- and language-related metrics commonly used in this domain. Finally, we analyzed findings in related work, highlighting possible strengths and limitations of using LLMs in recommender systems.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11189-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence in chemical exchange saturation transfer magnetic resonance imaging 人工智能在化学交换饱和转移磁共振成像中的应用

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-17 DOI: 10.1007/s10462-025-11227-5

Swee Qi Pan, Yan Chai Hum, Khin Wee Lai, Wun-She Yap, Yi Zhang, Hye-Young Heo, Yee Kai Tee

{"title":"Artificial intelligence in chemical exchange saturation transfer magnetic resonance imaging","authors":"Swee Qi Pan, Yan Chai Hum, Khin Wee Lai, Wun-She Yap, Yi Zhang, Hye-Young Heo, Yee Kai Tee","doi":"10.1007/s10462-025-11227-5","DOIUrl":"10.1007/s10462-025-11227-5","url":null,"abstract":"<div><p>This review delves into the transformative role of Artificial Intelligence (AI) in advancing Chemical Exchange Saturation Transfer (CEST) Magnetic Resonance Imaging (MRI), a cutting-edge imaging method for non-invasive biochemical mapping. CEST MRI faces many technical challenges that hinder its clinical adoption. AI-driven approaches have emerged as one of the promising solutions to address some of these limitations. The evolution of AI in CEST MRI is traced from its inception, with pioneering studies in AI-driven image analysis, to current trends reflecting a marked increase in AI-related CEST publications. This review highlights AI’s impact on various stages of the CEST MRI pipeline, including accelerated imaging acquisition and reconstruction, improved pre-processing and denoising methods, and advanced quantification techniques. Furthermore, AI has demonstrated potential in clinical applications, such as disease diagnosis, molecular subtyping, and treatment monitoring, underscoring its growing relevance in the field. This review also examines the challenges in AI applications and future directions in CEST MRI, including the use of synthetic data, the explainability and interpretability of AI models, and their implications for clinical adoption. Overall, this review provides a comprehensive understanding of the current state of AI applications in CEST MRI and will inspire further research to unlock the full potential of this powerful molecular imaging technique.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11227-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing decentralized energy storage investments with artificial intelligence-driven decision models 用人工智能驱动的决策模型加强分散式储能投资

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-16 DOI: 10.1007/s10462-025-11204-y

Gang Kou, Hasan Dinçer, Edanur Ergün, Serkan Eti, Serhat Yüksel, Ümit Hacıoğlu

{"title":"Enhancing decentralized energy storage investments with artificial intelligence-driven decision models","authors":"Gang Kou, Hasan Dinçer, Edanur Ergün, Serkan Eti, Serhat Yüksel, Ümit Hacıoğlu","doi":"10.1007/s10462-025-11204-y","DOIUrl":"10.1007/s10462-025-11204-y","url":null,"abstract":"<div><p>Decentralized energy storage investments play a crucial role in enhancing energy efficiency and promoting renewable energy integration. However, the complexity of these projects and the limited resources of the companies make it necessary to determine strategic priorities. This paper tries to define effective investment strategies for the improvements of the decentralized energy storage projects. In the first stage, the selection of mass experts is made via information gain-based mass expert selection. Next, the assessments of the experts are balanced based on the opinion of the best expert by using q-learning algorithm. Moreover, determinants of decentralized energy storage investments are examined with molecular fuzzy (MF) cognitive maps. Finally, strategy alternatives for decentralized energy storage investments are ranked with MF multi-objective particle swarm optimization (MOPSO). The main contribution of this study is the identification of the most effective decentralized energy storage investment alternatives by establishing a novel model. The main novelty of the proposed model is that considering information gain-based mass expert selection technique allows for higher consistency and decision efficiency. Owing to this issue, the decision-making process is accelerated, and the applicability of the results increases. The findings indicate that customer expectations (weight: 0.2577) and financial issues (weight: 0.2513) are the most essential criteria in improving the performance of decentralized energy storage investments. Furthermore, hydrogen-based energy storage (average value: 0.1878) and distributed battery swapping stations (average value: 0.1877) are the most important decentralized energy storage investment alternatives.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11204-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling and effect analysis of machining parameters for surface roughness and specific energy consumption during TC18 machining using deep reinforcement learning and neural networks 基于深度强化学习和神经网络的TC18加工过程中表面粗糙度和比能耗的加工参数建模及影响分析

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-11 DOI: 10.1007/s10462-025-11178-x

Juan Lu, Huailong Mu, Haibin Ouyang, Zhenkun Zhang, Weiping Ding

{"title":"Modeling and effect analysis of machining parameters for surface roughness and specific energy consumption during TC18 machining using deep reinforcement learning and neural networks","authors":"Juan Lu, Huailong Mu, Haibin Ouyang, Zhenkun Zhang, Weiping Ding","doi":"10.1007/s10462-025-11178-x","DOIUrl":"10.1007/s10462-025-11178-x","url":null,"abstract":"<div><p>Under the impetus of green manufacturing and a low-carbon economy, the critical challenge lies in reducing energy consumption while maintaining machining quality. Against this background, this paper presents the method of modeling and effect analysis for surface roughness and specific energy consumption during TC18 machining using Deep Reinforcement Learning and Neural Networks. In this method, to reduce the experiment cost, multilayer-layer design (MLD) for computer simulation is applied to design a physical experiment, and to improve modeling accuracy, backpropagation neural network (BPNN) optimized by Double deep Q network algorithm (DDQN) is utilized to develop the prediction models of surface roughness (<i>Ra</i>) and specific energy consumption of cutting (<i>E</i><sub><i>sec</i></sub>). Finaly, the synergistic influence of cutting parameters on <i>Ra</i> and <i>E</i><sub><i>sec</i></sub> is analyzed based on the prediction models of <i>Ra</i> and <i>E</i><sub><i>sec</i></sub> built by MLD and DDQN-BPNN. The effectiveness and low cost of MLD and the excellent prediction performance of DDQN-BPNN are verified by comparisons of optimized BPNNs using common heuristic optimization algorithms through the milling experiment of TC18. These technologies provide effective solutions for modeling and factor impact of target features in machining field, and research results provides an effective guidance for the selection of milling parameters of TC18 to reduce the specific energy consumption of cutting under ensuring or improving machining quality.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11178-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0