Engineering Applications of Artificial Intelligence最新文献_第10页

Efficient one-step multi-trial electroencephalograph spectral clustering via unsupervised covariance-based representations 通过基于协方差的无监督表示进行高效的一步式多试次脑电图频谱聚类

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-21 DOI: 10.1016/j.engappai.2024.109502

{"title":"Efficient one-step multi-trial electroencephalograph spectral clustering via unsupervised covariance-based representations","authors":"","doi":"10.1016/j.engappai.2024.109502","DOIUrl":"10.1016/j.engappai.2024.109502","url":null,"abstract":"<div><div>As an important research branch of artificial intelligence, decoding motor imagery electroencephalograph (MI-EEG) is notoriously famous in engineering of constructing noninvasive brain-computer interfaces (BCIs). Clustering becomes a crucial manner in decoding MI-EEG due to lack of effective labels. However, recently clustering methods for EEG rely on modeling time-series characteristics with high dimensions, as well as classical clustering frameworks, which requires a large iterative consumption. To address these challenges, we proposed a novel <strong>E</strong>fficient <strong>o</strong>ne-<strong>s</strong>tep <strong>EEG s</strong>pectral <strong>c</strong>lustering (EosEEGsc) method for multi-trial scenarios. Firstly, two forms of covariance-base representations are constructed for the multi-trial MI-EEG samples using unsupervised manner. Subsequently, the similarity graphs are constructed according to such representation, and a weighting strategy between similarity graphs and spectral embedding is progressively iterated using a one-step spectral clustering manner. Comparative experiments were conducted on ten MI-EEG datasets from BCI Competitions. The EosEEGsc achieved better clustering performance with lower time complexity quickly converged to local optima during the one-step framework. Ablation studies have demonstrated the necessity of two key components of EosEEGsc, and parameter sensitivities have validated the robustness. Our method offers a novel option for online MI-BCIs. When labels for MI tasks cannot be quickly annotated, employing the EosEEGsc method enables rapid cluster acquisition, thereby guiding precise control instructions for MI-BCIs output.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534588","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

Three-stage unsupervised learning approach fusing novel pseudo-label diffusion and math-physics translating for real-time structural damage detection 融合新型伪标签扩散和数学物理转换的三阶段无监督学习法，用于实时结构损伤检测

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-21 DOI: 10.1016/j.engappai.2024.109438

{"title":"Three-stage unsupervised learning approach fusing novel pseudo-label diffusion and math-physics translating for real-time structural damage detection","authors":"","doi":"10.1016/j.engappai.2024.109438","DOIUrl":"10.1016/j.engappai.2024.109438","url":null,"abstract":"<div><div>Unsupervised learning techniques have been asserted as pivotal approaches in vibration-based structural damage detection due to scarce labeled data, whereas unrealistic premise on sufficient data from health status and inexplicit reflection on math-physics in various algorithms degrade their applicability in practical engineering. Therefore, this study devised a three-stage unsupervised learning approach for real-time structural damage detection. It incorporates novel pseudo-label diffusion based on different damage-sensitive-features extraction strategies, and math-physics translating using adaptative fuzzy clustering algorithm optimization. Comprehensive validations upon a well-known numerical benchmark model and full-scale laboratory shaking table tests are conducted, the results of which confirm the effectiveness and superiority of the proposed method. Integrated with novel pseudo-label diffusion and explicit math-physics translating mechanisms, the sophisticated framework is expected to fully automatically discriminate unequivocal structural damage in a strictly unsupervised schema, providing an explicit interpretation avenue from non-physical clustering to end-to-end decision-making.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534589","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

Small object detection by Edge-aware Neural Network 利用边缘感知神经网络检测小物体

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-21 DOI: 10.1016/j.engappai.2024.109406

{"title":"Small object detection by Edge-aware Neural Network","authors":"","doi":"10.1016/j.engappai.2024.109406","DOIUrl":"10.1016/j.engappai.2024.109406","url":null,"abstract":"<div><div>The object detection method is widely applied in industrial inspections. However, many detectors face challenges in accurately capturing the blurred edge details of small objects, resulting in inaccurate bounding box predictions. To address this, we propose an Edge-aware Neural Network (EANN) for small object detection. Firstly, we introduce a Channel and Spatial Attention Fusion Module (CSAFM) to enhance the edge features of small objects, enabling the network to extract more discriminative information. Next, we propose a Multiple Aggregation Feature Pyramid (MAFP) to integrate multi-scale deep features into shallow features. This fusion enriches the shallow features with abundant semantic information, thereby aiding in the detection of small objects. Additionally, we propose a Side and Center Point Aligned Intersection over Union loss (SCPAIoULoss) to enhance the bounding box regression when there is minimal overlap between predicted and ground truth boxes. SCPAIoULoss combines Side Ratio (SR) loss, Center Point Distance (CPD) loss, and Intersection over Union (IoU) loss. The utilization of SR Loss directly constrains the width and height regression of bounding boxes, while CPD loss introduces stricter constraints to facilitate bounding box regression. Furthermore, IoU loss promotes the overall regression of predicted boxes. We extensively experiment on Tiny CityPersons, WiderFace, and our proposed dataset of base station data centers to validate the effectiveness of our method. The results indicate that our method surpasses several State-of-The-Art (SOTA) approaches in small object detection and can be effectively applied to the task of inspecting base station data centers.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534590","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

Brain tumor classification using weighted least square twin support vector machine with fuzzy hyperplane 使用带模糊超平面的加权最小平方孪生支持向量机进行脑肿瘤分类

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-21 DOI: 10.1016/j.engappai.2024.109450

{"title":"Brain tumor classification using weighted least square twin support vector machine with fuzzy hyperplane","authors":"","doi":"10.1016/j.engappai.2024.109450","DOIUrl":"10.1016/j.engappai.2024.109450","url":null,"abstract":"<div><div>Brain tumor is an aberrant growth of cells in the brain and represents one of the most lethal cancers around the world. The advanced machine learning models, like twin support vector machine have effectively addressed brain tumor classification tasks with promising results. However, despite its success, it lacks efficient learning as it solves a pair of quadratic programming problems and struggles to distinguish between support vectors and noises. To address these challenges, a novel multi-class classification model based on least square twin support vector machine and fuzzy concepts is formulated. It uses both membership and non-membership weights and integrates local neighborhood information among data points according to their importance. Moreover, to capture the uncertainty in the dataset, the proposed method computes a fuzzy hyperplane, taking all the parameters as fuzzy variables. Further, the model’s efficiency is enhanced by solving a system of linear equations only rather than solving a quadratic programming problem. To show the effectiveness of the proposed algorithm, the numerical experiments on 15 benchmark datasets in terms of average accuracy, <span><math><mi>F</mi></math></span>-measure, and training time are illustrated. The findings shows that the proposed technique outperforms other baseline models by achieving average accuracy of 88.79% with a linear kernel and 91.71% with a non-linear kernel. The proposed method is also applied to classify brain tumors into four different classes, achieving an average accuracy of 93.45%, which proves its outstanding performance. Moreover, the Friedman and Wilcoxon signed-rank statistical tests are used to confirm the method’s robustness and generalization capability.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535245","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

Robust autoregressive bidirectional gated recurrent units model for short-term power forecasting 用于短期电力预测的鲁棒自回归双向门控递归单元模型

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-20 DOI: 10.1016/j.engappai.2024.109453

{"title":"Robust autoregressive bidirectional gated recurrent units model for short-term power forecasting","authors":"","doi":"10.1016/j.engappai.2024.109453","DOIUrl":"10.1016/j.engappai.2024.109453","url":null,"abstract":"<div><div>Accurate short-term power forecasting (STPF) provides reliable support for the stable operation of power systems. However, due to the randomness of consumer behavior and energy properties, outliers inevitably exist in power series. Considering its negative influence, effectively extracting features from the power series with outliers has become a significant challenge in STPF. This paper develops a robust hybrid model to handle this issue. The proposed model utilizes the robust regression technique to handle outliers. An adaptive rescaled Huber loss is developed to approximate the complex distribution of the actual power series. Moreover, the proposed model applies autoregressive and bidirectional gated recurrent units to extract linear and nonlinear features of power series, respectively. Meanwhile, the attention mechanism extracts the temporal feature through the attention representation, which considers the correlations between different moments. The proposed model obtains the optimal coefficients of determination between predictions and observations on the wind power series as 0.9629 and power load series as 0.978, which indicates that the proposed model performs competitive robustness and generalization on the daily operation of renewable energy systems.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535244","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

Lane detection via disentangled representation network with slope consistency loss 通过具有斜率一致性损失的分解表示网络进行车道检测

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-20 DOI: 10.1016/j.engappai.2024.109449

{"title":"Lane detection via disentangled representation network with slope consistency loss","authors":"","doi":"10.1016/j.engappai.2024.109449","DOIUrl":"10.1016/j.engappai.2024.109449","url":null,"abstract":"<div><div>Existing works in lane detection focus on learning the general robust representation across different scenarios to overcome the impact of the lack of visual cues. However, factors leading to the absence of visual cues vary across different scenarios and the training data from challenging conditions is relatively small compared to common conditions. These problems result in the inability of existing methods to maintain robust lane detection in different scenarios for practical applications. To address these problems, this work presents a novel Disentangled Representation Network called DRNet, which disentangles the lane feature representations using a disentangled representation network to efficiently learn the lane representations corresponding to the specific condition. Meanwhile, DRNet also mitigates the adverse effects of data imbalance. Specifically, we disentangle lane representation via five branches, respectively to the common scenes, crowded objects, low light, dazzle light and other conditions. Due to the separated model of different conditions, each branch can be represented using a small number of parameters, which can be sufficiently learned using corresponding training subset. Moreover, existing works perform lane classification or regression using pixel-level losses, which neglect the important shape information. To this end, we design a novel slope consistency loss to take both global and local slope consistencies between prediction and ground truth into account for lane detection, which can adaptively adjust the lane shape and location. Extensive experiments on the CULane and TuSimple datasets show that our DRNet outperforms state-of-the-art methods, as it can reach 81.07% <em>F1</em> on CULane and 97.97% on TuSimple.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535243","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

Development of physics-guided neural network framework for acid-base treatment prediction using carbon dioxide-based tubular reactor 利用基于二氧化碳的管式反应器开发用于酸碱处理预测的物理引导神经网络框架

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-20 DOI: 10.1016/j.engappai.2024.109500

{"title":"Development of physics-guided neural network framework for acid-base treatment prediction using carbon dioxide-based tubular reactor","authors":"","doi":"10.1016/j.engappai.2024.109500","DOIUrl":"10.1016/j.engappai.2024.109500","url":null,"abstract":"<div><div>Accurate acid-base treatment prediction is necessary to achieve the required yield, given the inherent complexity, high nonlinearity, and restricted availability of data samples; to address this challenge, a data-driven approach was developed. However, the technique is constrained by the need for sufficient data to construct an accurate model and lacks both process insight and physical consistency. Therefore, this study introduces a physics-guided neural network model for acid-base treatment prediction in a dynamic tubular reactor using the fundamental physical intermediate variables obtained through the derivation process of the reaction schematic. By integrating batch experimental data, which provides key intermediate variables such as residence time and hydroxide ion concentration, the model addresses the challenge of high nonlinearity and limited data availability. The result shows that the physics-guided potential of a hydrogen predictor had outstanding performance in terms of prediction accuracy (greatest coefficient of determination value of 0.9381). The proposed model demonstrated an average improvement of 24.92% in pH prediction accuracy compared to traditional models without physical guidance, with a maximum improvement of up to 64.95% under limited data conditions. Moreover, downsampling tests revealed that the proposed model maintained robust performance with minimal accuracy reduction even when data was limited without overfitting implication.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534591","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

Perturbation defense ultra high-speed weak target recognition 扰动防御超高速弱目标识别

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-20 DOI: 10.1016/j.engappai.2024.109420

{"title":"Perturbation defense ultra high-speed weak target recognition","authors":"","doi":"10.1016/j.engappai.2024.109420","DOIUrl":"10.1016/j.engappai.2024.109420","url":null,"abstract":"<div><div>Ultra high-speed target recognition in complex electromagnetic environments is a critical and fundamental machine perception issue. It is difficult to ensure privacy protection and intelligent confrontation with centralized training in many practical situations. In this paper, an efficient ultra high-speed target recognition approach, named InVision, for robot systems is proposed using deep trustworthy federated learning (DTFL). InVision is accurate, safe, fast, and robust. Particularly, geometric component transformer (GCT) is presented to significantly promote neural element's complex representation description ability. And an ambiguity-aware cooperative learning (AACL) scheme is developed to relieve the noisy label problem. Moreover, decentralized federated training (DFT) is designed to mitigate the intractable privacy protection problem, to efficiently search for similarities and reduce the representation redundancy. Furthermore, to promote the running speed of the system in real-world environments, a lightweight deep architecture, called Mobile-XB, is developed. Extensive quantitative and qualitative experiments are carried out, and the results demonstrate that InVision greatly outperforms the outstanding comparison methods, establishing efficient connections and extraction, and providing security guarantees.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535242","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

Reconstructing causal networks from data for the analysis, prediction, and optimization of complex industrial processes 从数据中重建因果网络，用于分析、预测和优化复杂的工业流程

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-19 DOI: 10.1016/j.engappai.2024.109494

{"title":"Reconstructing causal networks from data for the analysis, prediction, and optimization of complex industrial processes","authors":"","doi":"10.1016/j.engappai.2024.109494","DOIUrl":"10.1016/j.engappai.2024.109494","url":null,"abstract":"<div><div>Lacking the understanding of the first principles leads to the apparent black box attributes of complex industrial processes. How to understand complex industrial processes from data and guiding industrial decision-making has become an urgent problem to solve. However, the existing data-driven models are also black boxes, focusing only on the correlation relationships between data without reflecting causal relationships. Therefore, this study addresses the challenge of double black boxes in complex industrial decision-making, proposing a research framework of \"causal analysis → performance prediction → process optimization\". Firstly, nonparametric copula entropy, network deconvolution, and information geometric causal inference are integrated to construct the causal relations network. Also, the observability and controllability of complex industrial processes are analyzed to provide valuable insights for improving the dataset. Then, drawing inspiration from the transformational machine learning idea, an explainable predictive model is constructed for predicting key performance indicators. Lastly, taking this predictive model as the process surrogate model, the optimal process parameters are solved using the particle swarm optimization algorithm. Moreover, the dataset of 16600 samples from a real-world injection molding process is used for application validation. The research results show that by reconstructing the causal relations network from data, the proposed framework can support the analysis, prediction, and optimization of complex industrial processes, achieving the decision-making goals of safety, robustness, improving quality and efficiency.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534332","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

CNCAN: Contrast and normal channel attention network for super-resolution image reconstruction of crops and weeds CNCAN：用于农作物和杂草超分辨率图像重建的对比度和正常通道注意力网络

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-10-19 DOI: 10.1016/j.engappai.2024.109487

{"title":"CNCAN: Contrast and normal channel attention network for super-resolution image reconstruction of crops and weeds","authors":"","doi":"10.1016/j.engappai.2024.109487","DOIUrl":"10.1016/j.engappai.2024.109487","url":null,"abstract":"<div><div>Numerous studies have been performed to apply camera vision technologies in robot-based agriculture and smart farms. In particular, to obtain high accuracy, it is essential to procure high-resolution (HR) images, which requires a high-performance camera. However, due to high costs it is difficult to widely apply the camera in agricultural robots. To overcome this limitation, we propose contrast and normal channel attention network (CNCAN) for super-resolution reconstruction (SR), which is the first research for the accurate semantic segmentation of crops and weeds even with low-resolution (LR) images captured by low-cost and LR camera. Attention block and activation function that considers high frequency and contrast information of images are used in CNCAN, and the residual connection method is applied to improve the learning stability.</div><div>As a result of experimenting with three open datasets, namely, Bonirob, rice seedling and weed, and crop/weed field image (CWFID) datasets, the mean intersection of union (MIOU) results of semantic segmentation for crops and weeds with SR images through CNCAN were 0.7685, 0.6346, and 0.6931 in the Bonirob, rice seedling and weed, and CWFID datasets, respectively, confirming higher accuracy than other state-of-the-art methods for SR.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535247","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