Engineering Applications of Artificial Intelligence最新文献_第7页

A hybrid genetic-fuzzy ant colony optimization algorithm for automatic K-means clustering in urban global positioning system 城市全球定位系统中 K 均值自动聚类的遗传-模糊蚁群混合优化算法

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-06 DOI: 10.1016/j.engappai.2024.109237

{"title":"A hybrid genetic-fuzzy ant colony optimization algorithm for automatic K-means clustering in urban global positioning system","authors":"","doi":"10.1016/j.engappai.2024.109237","DOIUrl":"10.1016/j.engappai.2024.109237","url":null,"abstract":"<div><p>This paper introduces an innovative automatic K-means clustering algorithm, namely HGA-FACO, which seamlessly integrates the noise algorithm, Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Adaptive Fuzzy System (AFS). The rationale behind the HGA-FACO algorithm is to mitigate the shortcomings of traditional K-means, particularly the reliance on pre-determined cluster centers and the need for specifying the number of clusters in advance. By optimizing the search strategy, HGA-FACO efficiently circumvents local optima and effectively explores the global optimal solution, resulting in more accurate and stable clustering outcomes. To validate the superiority of the HGA-FACO over conventional K-Means Clustering (KMeans) and other intelligent clustering approaches such as ACO-KMeans, GA-KMeans (GAK), particle swarm optimization KMeans (PSOK), and ACO-GAK, we conducted comprehensive experiments on taxi Global Positioning System (GPS) datasets sourced from four distinct cities. Employing rigorous evaluation metrics including Silhouette Coefficient (SC), Partition Coefficient (PBM), Davies-Bouldin Index (DBI), and Sum of Squared Errors (SSE), the experimental results convincingly demonstrate that the HGA-FACO significantly outperforms its counterparts across all metrics, highlighting its exceptional performance in clustering effectiveness and compactness. While the HGA-FACO faces challenges related to computational complexity and the necessity for initial parameter tuning, its performance limitations on small-sized or unevenly distributed datasets are acknowledged. Nevertheless, the algorithm's advancements in the field of clustering algorithms are undeniable and hold immense potential for practical applications, notably in city hotspot identification.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151364","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

Dynamic instance-aware layer-bit-select network on human activity recognition using wearable sensors 利用可穿戴传感器识别人类活动的动态实例感知层位选择网络

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-06 DOI: 10.1016/j.engappai.2024.109260

{"title":"Dynamic instance-aware layer-bit-select network on human activity recognition using wearable sensors","authors":"","doi":"10.1016/j.engappai.2024.109260","DOIUrl":"10.1016/j.engappai.2024.109260","url":null,"abstract":"<div><p>During recent years, deep convolutional neural networks have achieved remarkable success in a wide range of sensor-based human activity recognition (HAR) applications, which often require high computational cost and memory footprint, hence hindering practical HAR deployment on resource-limited mobile and wearable devices. Quantization has provided an effective solution to compress models and accelerate activity inference in real-world situations. However, most previous quantization schemes are static, which always utilize the same bit-width for all activity samples in a given layer. Intuitively, since activity samples are highly diverse according to their difficulty level, it is rather unrealistic to maintain a consistent bit-width quantization configuration for different activity samples. Based on dynamic quantization strategy, this paper introduces a novel Layer-Bit-Select Network named LBSNet to adaptively determine the optimal bit-widths of each convolutional layer according to the difficulty level of recognized activities. To achieve this goal, we design a lightweight Bit-selector, which is jointly optimized with a given main network. In such a way, easy activities such as sitting may be allocated to lower bit-widths, while high bit-widths may handle more complicated or hard activities like falls. Extensive experiments are conducted on several mainstream HAR benchmarks including WISDM, UCI-HAR, UniMiB-SHAR, and PAMAP2 to validate the effectiveness of our proposed approach. For instance, it can achieve round 5.3<span><math><mo>×</mo></math></span> speedup and 6.2<span><math><mo>×</mo></math></span> model size compression, with merely 0.6% accuracy drop on WISDM dataset, compared to full-precision model. This approach has great potential to yield more computation-efficient and faster activity inference on mobile embedded platforms.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151366","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

Artificial intelligence-based blade identification in operational wind turbines through similarity analysis aided drone inspection 通过相似性分析辅助无人机检测，基于人工智能识别运行中风力涡轮机的叶片

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-06 DOI: 10.1016/j.engappai.2024.109234

{"title":"Artificial intelligence-based blade identification in operational wind turbines through similarity analysis aided drone inspection","authors":"","doi":"10.1016/j.engappai.2024.109234","DOIUrl":"10.1016/j.engappai.2024.109234","url":null,"abstract":"<div><p>Tracking changes in wind turbine blade surface features over time, particularly during operation, is imperative for the early detection of potential damages. Advances in drone technology and Artificial Intelligence (AI) enable capturing and analysing numerous high-resolution blade images. It is essential to identify individual blades from inspection images captured at different times, despite potential changes in their surface features. Traditional AI-based classification algorithms could not link images of the same blades without retraining the system, hindering the identification process. In this study, we converted a classification problem into a similarity learning problem using Siamese Convolution Neural Networks (S-CNN) to automatically identify and retrieve corresponding blade images based on their unique visual surface features in response to a single query blade image, thereby eliminating the need to retrain the entire network. An advanced deep learning segmentation method is employed to segment the blade images as a preprocessing step to eliminate the influence of the image background on the identification task. The performance of the proposed model is verified using drone images of wind turbine blades, demonstrating near human-level precision in identifying images depicting the same individual blades.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151231","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

Novel method of performance-optimized metastructure design for electromagnetic wave absorption in specific band using deep learning 利用深度学习优化特定波段电磁波吸收性能的新型结构设计方法

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-06 DOI: 10.1016/j.engappai.2024.109274

{"title":"Novel method of performance-optimized metastructure design for electromagnetic wave absorption in specific band using deep learning","authors":"","doi":"10.1016/j.engappai.2024.109274","DOIUrl":"10.1016/j.engappai.2024.109274","url":null,"abstract":"<div><p>In this paper, we propose a new method that utilizes deep learning techniques to design a metastructure for an electromagnetic absorber. This method enables the effective design of a metastructure with the desired performance (spectrum of S11<−10 dB) in the frequency band specified by the designer, within a wideband range from 2 to 40 GHz. The proposed absorber consists of two dielectric layers with varied conductive patterns and a back reflector. Critical to the absorber's microwave performance is the binary pattern configuration, organized in a 20-pixel square, along with the sheet resistance and layer thickness of each layer, contributing to a significant design freedom exceeding <span><math><mrow><msup><mn>10</mn><mn>37</mn></msup></mrow></math></span> degrees of freedom. Our model for performance-optimized design involves three steps: Initially, with limited data from 26,000 sets, a Variational Autoencoder (VAE) was trained to map S11 spectra and arrange a latent space linked to metastructure. Subsequently, we developed a spectrum prediction network to correlate patterns with S11 spectra, leveraging a pre-trained decoder from the auxiliary VAE in the first step. The final step trains a network for designing a metastructure with broadband absorption. To verify the performance of a metastructure designed by the developed method, we compared their performances with those obtained through Finite Difference Time Domain (FDTD) simulation and the developed network. And also to further validate our approach experimentally, the designed metastructures were fabricated by silkscreen printing using carbon paste ink, and some bands (1–18 GHz, 26.5–40 GHz) were measured to compare with the performance predicted by the VAE network.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151461","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

Time mesh independent framework for learning materials constitutive relationships 独立于时间网格的学习材料构成关系框架

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-06 DOI: 10.1016/j.engappai.2024.109165

{"title":"Time mesh independent framework for learning materials constitutive relationships","authors":"","doi":"10.1016/j.engappai.2024.109165","DOIUrl":"10.1016/j.engappai.2024.109165","url":null,"abstract":"<div><p>Real-world datasets are rarely populated by evenly distributed entries; unevenness may be caused by sensor malfunctions or randomized sampling due to the process nature. Modeling the constitutive relationship (CR) of materials in scenarios where the temporal data available are uneven is a serious challenge for black box approaches such as artificial neural networks. This work presents a general framework capable of modeling uneven sampled data, which is composed of an Encoder–Decoder (ED) structure. In our framework, the Encoder can process an uneven input sequence, thanks to an approximation of the Ordinary Differential Equations (ODE), and project it into a lower dimensional latent space; the Decoder, on the other hand, can map the compressed information into the output of interest, the material stress response in this work. In the proposed temporal mesh independent framework, the Encoder is a multi-layer structure, with each layer consisting of a Long-Short Term Memory (LSTM) layer, a Closed form Continuous Time (CfC) layer, and a Self Multi-Head Attention Layer (MHAL) layer connected in series. The Decoder can be one Fully Connected Network (FCN) or two FCNs in parallel; in the latter case, the Decoder is capable of giving the mean and the variance of the output. The presented mesh-independent framework demonstrates good accuracy despite both the unevenness and the noise of the training data, specially when its results are compared to the standard ones; thus extending the applicability of neural-network-based black box models in real world applications.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151365","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

Selection of smartphone-based mobile applications for obesity management using an interval neutrosophic vague decision-making framework 利用区间中性模糊决策框架选择基于智能手机的肥胖管理移动应用程序

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-05 DOI: 10.1016/j.engappai.2024.109191

{"title":"Selection of smartphone-based mobile applications for obesity management using an interval neutrosophic vague decision-making framework","authors":"","doi":"10.1016/j.engappai.2024.109191","DOIUrl":"10.1016/j.engappai.2024.109191","url":null,"abstract":"<div><p>The selection of mobile applications for managing obesity poses a complex multicriteria decision-making (MCDM) challenge. This complexity arises from the diverse criteria of the apps, their respective values, and the need to determine the relative importance of these criteria. Therefore, this study contributes to the body of knowledge by evaluating smartphone-based mobile applications for obesity management through the development of a novel MCDM selection framework. The decision matrix formulates the quality assessment criteria and identifies smartphone applications for diagnosing obesity. In the research methodology, the MCDM solution is presented by integrating two methods: the interval neutrosophic vague-based fuzzy-weighted zero-consistency (INV-FWZIC) method for weighting the quality assessment criteria and the interval neutrosophic vague-based fuzzy decision by opinion score method (INV-FDOSM) for selecting smartphone applications for obesity. The results indicate that the ‘technology-enhanced features’ and ‘usability’ criteria received the highest equal weight score (<em>0.2183</em>), while the criterion of ‘behavior change techniques’ received the lowest weight (<em>0.1783</em>). The group decision-making results show that Application <em>A</em><sub><em>1</em></sub> (<em>Noom Weight Loss Coach</em>) is the best, with a score of <em>0.6869</em>, while Application <em>A</em><sub><em>7</em></sub> (<em>Cronometer</em>) is the worst, with the lowest score of <em>0.6165</em>. Various assessment approaches, including systematic ranking, reliability and validity analyses, sensitivity analysis, and comparison analysis, are employed to evaluate and validate the proposed framework.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151230","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

Towards collaborative fair federated distillation 实现协作式公平联合蒸馏

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-05 DOI: 10.1016/j.engappai.2024.109216

{"title":"Towards collaborative fair federated distillation","authors":"","doi":"10.1016/j.engappai.2024.109216","DOIUrl":"10.1016/j.engappai.2024.109216","url":null,"abstract":"<div><p>Federated Learning (FL), despite its success as a privacy-preserving distributed machine learning framework, faces significant bottlenecks, including high communication costs, heterogeneity issues, and unfairness, throughout various phases of the training process. Federated Distillation (FD) has recently emerged as a promising solution to tackle heterogeneity and enhance communication efficiency in FL. In addition, significant effort has been put forth in recent years to support various notions of fairness associated with the FL ecosystem, such as Collaborative Fairness, which seeks to ensure the fair distribution of rewards among participants based on their level of contribution. Although several works have been done to promote collaborative fairness in FL, they are mostly well-suited for FL algorithms based on model updates or gradient sharing during the training procedure. Guaranteeing collaborative fairness in FD methods is still completely unexplored where it can have potential applications in communication engineering, healthcare, banking, finance, and social networks in large-scale software, etc., as most Knowledge Distillation (KD) based FL algorithms promote either identical global logits or identical global model updates sharing among the clients after the distillation process. This is unfair because severely underperforming participants can gain access to the knowledge of all high-performing participants while contributing almost nothing to the learning process. In this paper, we propose a novel Collaborative Fair Federated Distillation (CFD) algorithm with a view to exploring collaborative fairness in KD-based Federated Learning strategies. We leverage the reputation mechanism to rank the participants in order of their contributions and appropriately distribute logits among them while maintaining competitive performance. Extensive experiments on benchmark datasets validate the efficacy of our proposed method as well as the practicality of the proposed logit-based reward scheme.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151460","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

Corrigendum to “Short-term high-speed rail passenger flow prediction by integrating ensemble empirical mode decomposition with multivariate grey support vector machine” [Eng. Appl. Art. Intellig. 136PB (2024) 109005] 对 "集合经验模式分解与多元灰色支持向量机相结合的短期高铁客流预测 "的更正 [Eng. Appl. Art. Intellig. 136PB (2024) 109005]

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-05 DOI: 10.1016/j.engappai.2024.109080

引用次数: 0

An evolutionary computational approach for the identification of distribution networks models 识别配送网络模型的进化计算方法

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-05 DOI: 10.1016/j.engappai.2024.109184

引用次数: 0

An adaptive spatial–temporal prediction model for landslide displacement based on decomposition architecture 基于分解结构的自适应滑坡位移时空预测模型

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-09-05 DOI: 10.1016/j.engappai.2024.109215

{"title":"An adaptive spatial–temporal prediction model for landslide displacement based on decomposition architecture","authors":"","doi":"10.1016/j.engappai.2024.109215","DOIUrl":"10.1016/j.engappai.2024.109215","url":null,"abstract":"<div><p>Landslide displacement forecasting is a core issue in geohazard research, it is particularly challenging for accumulation-type landslides with complex geological patterns. Traditional landslide displacement prediction methods use single-point modeling and often fail to consider the spatial correlation characteristics of each deformation point on the surface of a landslide. On the other hand, they have difficulty in learning the changes caused by rainfall and reservoir water level. To tackle these obstacles, we introduce an adaptive spatial–temporal landslide displacement prediction model based on a decomposition architecture, named Self-Adaptive Unet with Decomposed Temporal Attention Encoder(SAU-DTAE). To effectively separate the features of different scales in time series changes and model them separately, we employ a progressive decomposition architecture based on a Lightweight Temporal Attention Encoder(LTAE). Furthermore, we design a gating mechanism with Sample Entropy (SampEn) to adaptively extract global and local spatial features at multiple scales. By quantifying the spatial complexity, we can achieve adaptive extraction of spatial correlation features. Relevant experiments were conducted with the 2016-2023 Interferometry Synthetic Aperture Radar (InSAR) landslide displacement dataset of the Three Gorges area. The new proposed algorithm was compared and validated against several classical time-series prediction models: Back Propagation(BP) neural network, Long Short Term Memory(LSTM) neural network, Gated Recurrent Unit(GRU), Convolutional LSTM(ConvLSTM), Informer, and Autoformer. The findings from the experiment indicated that our model surpassed the benchmark models, achieving superior prediction results on the test set. The Mean Absolute Error (MAE) was 5.516 millimeters(mm), the Root Mean Square Error (RMSE) was 3.856 mm, and the R-Square(<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>) was 0.896.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151362","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