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Multimodal joint prediction of traffic spatial-temporal data with graph sparse attention mechanism and bidirectional temporal convolutional network Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-18 Dongran Zhang, Jiangnan Yan, Kemal Polat, Adi Alhudhaif, Jun Li
Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, existing multimodal traffic joint modeling methods often lack flexibility in spatial–temporal
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Cyber-Physical Internet (CPI)-enabled logistics infrastructure integration framework in the greater bay area Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-18 Hang Wu, Linhao Huang, Ming Li, George Q. Huang
The emerging digital economy has facilitated the digital transformation of the logistics industry toward a standard and collaborative state. The logistics infrastructure has accordingly attracted much attention as the basis for supporting digital economy implementation. Moreover, the initiatives of regional collaborative development, such as in the Greater Bay Area (GBA) of China, have accelerated
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ACAT-transformer: Adaptive classifier with attention-wise transformation for few-sample surface defect recognition Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-17 Zhaofu Li, Liang Gao, Xinyu Li, Yiping Gao
Deep learning-based methods demonstrate acceptable performance on few-sample surface defect recognition, which is a pivotal instrument for quality control in intelligent manufacturing systems. However, deep learning models often experience overfitting to the limited training data and struggle with generalizing to unseen test data due to the discrepancy between the feature distributions. Moreover, the
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Column generation based hybrid optimization method for last-mile delivery service with autonomous vehicles Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-16 Hongjian Hu, Hu Qin, Gangyan Xu, Nan Huang, Peiyang He
The research addresses the Last-Mile Delivery Service with Autonomous Vehicles (LMS-AV), which focuses on the multiple trips made by autonomous vehicles (AVs). This feature, widely utilized in real-world applications, has the potential to not only reduce the number of required vehicles and drivers but also lower operating costs. Recognizing these benefits as opportunities, we aim to enhance these advantages
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DPDGAD: A Dual-Process Dynamic Graph-based Anomaly Detection for multivariate time series analysis in cyber-physical systems Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-15 Junxuan Liao, Jing Li, Yu Chen, Rongbin Gu, Ying Zhu, Weizhou Peng
With the rapid development of cyber–physical systems, an increasing amount of data is stored in the form of multivariate time series. Detecting anomalies within multivariate time series has become a crucial means to ensure the proper functioning of cyber–physical systems. Traditional methods for anomaly detection in multivariate time series often categorize features into temporal and spatial features
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Cross-Supervised multisource prototypical network: A novel domain adaptation method for multi-source few-shot fault diagnosis Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-15 Xiao Zhang, Weiguo Huang, Chuancang Ding, Jun Wang, Changqing Shen, Juanjuan Shi
Multi-source domain adaptation (MSDA) has demonstrated superior performance in intelligent fault diagnosis (IFD) compared to single-source domain adaptation (SSDA), as it can provide more comprehensive and diverse information from multiple fully-labeled source domains. However, in many real industrial scenarios, acquiring multiple fully-labeled source domains is challenging because labeling all the
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Product innovation design approach driven by implicit relationship completion via patent knowledge graph Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-15 Shaofei Jiang, Jingwei Yang, Jing Xie, Xuesong Xu, Yubo Dou, Liting Jing
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Lane changing maneuver prediction by using driver’s spatio-temporal gaze attention inputs for naturalistic driving Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-15 Jingyuan Li, Titong Jiang, He Liu, Yingbo Sun, Chen Lv, Qingkun Li, Guodong Yin, Yahui Liu
Driver’s lane changing maneuver prediction holds significant importance in enhancing the functionality of the Advanced Driver Assistance System (ADAS) and ensuring driving safety. This study introduces an innovative approach to model the driver's spatio-temporal gaze attention to the traffic environment, and realizes high-precision lane changing prediction by integrating multi-source inputs. Firstly
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Developing a fast and accurate collision detection strategy for crane-lift path planning in high-rise modular integrated construction Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-15 Aimin Zhu, Zhiqian Zhang, Wei Pan
Crane-lift path planning (CLPP) ensures the safe and efficient installation of hefty modules in high-rise modular integrated construction (MiC). The implementation of CLPP requires effective collision detection strategies. However, existing collision detection strategies suffer from limitations in terms of computational intensity or insufficient accuracy. This paper aims to develop a fast and accurate
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Explainable highway performance degradation prediction model based on LSTM Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-14 Xin Sun, Honglei Wang, Shilong Mei
With the dense and huge highway network in China, the highway maintenance management system has become the most concerned issue for Chinese highway managers in recent years. Therefore, based on the highway network in Guizhou Province of China, this paper established the semi-rigid asphalt pavement performance multi-output Long Short-Term Memory (LSTM) prediction model with regional applicability, including
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AHFormer: Hypergraph embedding coding transformer and adaptive aggregation network for intelligent fault diagnosis under noise interference Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-13 Fangyuan Lei, Ziwei Chen, Xiangmin Luo, Long Xu, Te Xue, Jianjian Jiang
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Towards multi-scene learning: A novel cross-domain adaptation model based on sparse filter for traction motor bearing fault diagnosis in high-speed EMU Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-13 Feiyu Lu, Qingbin Tong, Jianjun Xu, Ziwei Feng, Xin Wang, Jingyi Huo, Qingzhu Wan
Fault diagnosis of traction motor bearing is of great significance to improve the reliability and safety of high-speed electric multiple units (EMU). While the fault diagnosis method based on cross-domain adaptation has been successful in scenarios involving speed or load fluctuations, existing methods ignore the independence and diversity of features, resulting in unsatisfactory diagnostic results
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Ensemble learning based hierarchical surrogate model for multi-fidelity information fusion Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-13 Yitang Wang, Yong Pang, Tianhang Xue, Shuai Zhang, Xueguan Song
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Onto-SAGCN: Ontology modeling and spatial attention-based graph convolution networks for aircraft assembly quality prediction Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-13 Qiang Zhang, Qun Luo, Anan Zhao, Cijun Yu, Qing Wang, Yinglin Ke
Aircraft assembly is an essential stage in the aircraft manufacturing industry, and the increasing complexity of aircraft functionality has put higher requirements for assembly quality. Various deep learning methods based on image or structural data have been used to predict assembly quality. However, these methods focus more on the structural relationships and interactions between assembled products
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Data Collection, data mining and transfer of learning based on customer temperament-centered complaint handling system and one-of-a-kind complaint handling dataset Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-13 Ching-Hung Lee, Xuejiao Zhao
One of the most significant sources of information from customers is customer complaints. Successful and effective complaint management can end complaint crises and ensure client loyalty, which is a sign of great service performance. In this paper, we proposed a novel customer temperament-centered and e-CCH system-based data collection and data mining method titled “3D” model for customer complaint
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Automated detailing of exterior walls using NADIA: Natural-language-based architectural detailing through interaction with AI Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-12 Suhyung Jang, Ghang Lee, Jiseok Oh, Junghun Lee, Bonsang Koo
Learning building information modeling (BIM) systems has always been a challenge for BIM adoption. Although groundbreaking performances of large language models (LLMs) have inspired many researchers to consider an LLM as a potential BIM control method using natural language, a specific method of utilizing LLMs for automated BIM model detailing has not yet been proposed. This paper proposes an LLM-BIM
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Development of a spatial dimension-based taxonomy for classifying the defect patterns in a wafer bin map Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-12 Seung-Hyun Choi, Dong-Hee Lee, Eun-Su Kim, Young-Mok Bae, Young-Chan Oh, Kwang-Jae Kim
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Object-based terminal positioning solution within task-boosted global constraint for improving mobile robotic stacking accuracy Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-12 Zhiyuan Chen, Yixiao Feng, Tiemin Li, Yao Jiang
Wall building is labor-intensive and time-consuming as a typical construction task. It is expected to perform this heavy task using robots. However, laborers are still largely employed, mainly because high continuous stacking precision for mobile robots has always been a bottleneck problem. This paper examines the characteristics of continuous robotic stacking in wall-building tasks and finds the key
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SR-M−GAN: A generative model for high-fidelity stress fields prediction of the composite bolted joints Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-11 Yuming Liu, Qingyuan Lin, Wei Pan, Wencai Yu, Yu Ren, Yong Zhao
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Pre-training enhanced unsupervised contrastive domain adaptation for industrial equipment remaining useful life prediction Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-11 Haodong Li, Peng Cao, Xingwei Wang, Ying Li, Bo Yi, Min Huang
An essential task in industrial intelligence is to accurately predict the remaining useful life(RUL) of industrial equipment, and there has been tremendous progress in RUL prediction based on data-driven methods. However, these methods rely heavily on the data representation ability of the model and the assumption of consistency in data distribution. In practical industrial environments, due to different
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ACEPSO: A multiple adaptive co-evolved particle swarm optimization for solving engineering problems Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-10 Gang Hu, Mao Cheng, Guanglei Sheng, Guo Wei
Particle swarm optimization (PSO) is one of the most classical metaheuristic algorithms that has gained significant attention since its inception. It has some inherent advantages, such as easy implementation, rapid convergence, low computational complexity and so on. However, the drawbacks of being prone to local optimization and insufficient diversity cannot be ignored. Therefore, a new multiple adaptive
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An information freshness-based digital twin model to support multi-level complementary dynamic scheduling in Shared Manufacturing Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-09 Huagang Tong, Jianjun Zhu, Bin Wu, Zhenzhen Ma
Shared Manufacturing (SM) seeks to efficiently utilize idle manufacturing capacity by matching it with demand. However, disruptions in the production process are common due to the open nature of the social environment. Therefore, rescheduling becomes crucial. First, previous rescheduling efforts have primarily focused on individual aspects, overlooking integrated multilevel rescheduling. To address
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A hierarchical feature-logit-based knowledge distillation scheme for internal defect detection of magnetic tiles Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-08 Luofeng Xie, Xuexiang Cen, Houhong Lu, Guofu Yin, Ming Yin
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Advancing RUL prediction in mechanical systems: A hybrid deep learning approach utilizing non-full lifecycle data Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-08 Tianjiao Lin, Liuyang Song, Lingli Cui, Huaqing Wang
This paper addresses the significant challenge of predicting the Remaining Useful Life (RUL) of mechanical equipment, a critical aspect of predictive maintenance and reliability engineering. Traditional deep learning methods in RUL prediction have been hindered by key challenges, including the scarcity of comprehensive lifecycle data, the prevalence of high-frequency noise in sensor readings, and a
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Light-weight residual convolution-based capsule network for EEG emotion recognition Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-08 Cunhang Fan, Jinqin Wang, Wei Huang, Xiaoke Yang, Guangxiong Pei, Taihao Li, Zhao Lv
In recent years, electroencephalography (EEG) emotion recognition has achieved excellent progress. However, the applied shallow convolutional neural networks (CNNs) cannot characterize the spatial relations between different features well, which affects the performance of these models. In addition, because the amount of EEG sample data is small, it is challenging to collect and annotate enough EEG
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Unknown working condition fault diagnosis of rotate machine without training sample based on local fault semantic attribute Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-08 Xuejun Liu, Wei Sun, Hongkun Li, Qiang Li, Zhenhui Ma, Chen Yang
Data-driven fault diagnosis techniques for rotating machinery have exhibited highly promising results. However, these methods heavily rely on sufficient faulty data and presuppose that the source (model training) and target domains (model diagnosis) share a matching data distribution. In practical industrial settings, acquiring target domain data can be quite challenging, and the distribution between
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Logic-Informed Graph Neural Networks for Structural Form-Finding Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-05 Lazlo Bleker, Kam-Ming Mark Tam, Pierluigi D’Acunto
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Cloud-Edge Test-Time Adaptation for Cross-Domain Online Machinery Fault Diagnosis via Customized Contrastive Learning Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-04 Mengliang Zhu, Jie Liu, Zhongxu Hu, Jiawei Liu, Xingxing Jiang, Tielin Shi
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Prior-knowledge-guided mode filtering network for interpretable equipment intelligent diagnosis under varying speed conditions Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-04 Rui Liu, Xiaoxi Ding, Yimin Shao
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Digital Twin for wear degradation of sliding bearing based on PFENN Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-04 Jingzhou Dai, Ling Tian, Tianlin Han, Haotian Chang
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A transportation Revitalization index prediction model based on Spatial-Temporal attention mechanism Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-03 Zhiqiang Lv, Zhaobin Ma, Fengqian Xia, Jianbo Li
The global outbreak of COVID-19 has had a substantial impact on various sectors worldwide, including the economy, healthcare, entertainment, policy formulation, and international relations, with the transportation industry being particularly hard-hit. To curb the widespread transmission of the virus, many regions globally have implemented policies and measures to restrict transportation. These actions
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Faulty rolling bearing digital twin model and its application in fault diagnosis with imbalanced samples Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-03 Yi Qin, Hongyu Liu, Yongfang Mao
The simulation signals generated by the bearing dynamics model have a big gap with the actual signals, which limits their efficacy in bearing fault diagnosis. Therefore, it is valuable to build an accurate digital twin model of faulty rolling bearing. Firstly, a multi-degree-of-freedom bearing fault dynamics model is constructed in the virtual space for generating the vibration responses of bearing
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A hybrid deep semantic mining method considering fuzzy expressions for the automatic recognition of construction safety hazard information Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-03 Xiaojian Zhang, Dan Tian, Qiubing Ren, Mingchao Li, Yang Shen, Shuai Han
Safety hazards are a key consideration in construction management. The efficient recognition of safety hazard information can help managers formulate safety hazard management measures and improve the efficiency of construction safety management. However, construction site safety hazard data are stored in semistructured and unstructured text formats, which cannot be directly converted into understandable
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A federated cross-machine diagnostic framework for machine-level motors with extreme label shortage Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-01 Yiming He, Weiming Shen
A start-up company is usually only able to collect normal samples, resulting in extreme label shortage and inability to establish effective intelligent diagnostic models. Especially for machine-level motors with more expensive experimental labeling, cooperation from multiple partners is often required. The phenomenon of signal global domain shift caused by inherent assembly differences and dynamic
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Predictive maintenance system for high-end equipment in nuclear power plant under limited degradation knowledge Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-04-01 Xue Liu, Wei Cheng, Ji Xing, Xuefeng Chen, Linying Li, Yuxin Guan, Baoqing Ding, Zelin Nie, Rongyong Zhang, Yifan Zhi
To ensure the safe operation of high-end equipment, the three-stage maintenance strategy comprising unplanned shutdown, temporary shutdown, and scheduled shutdown is currently employed in nuclear power plants. However, this strategy hinders the acquisition of degradation knowledge (run-to-failure data or degradation mechanism), thereby impeding the application of traditional predictive maintenance
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Decision-guidance method for knowledge discovery and reuse in multi-goal engineering design problems Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-30 Ru Wang, Lin Guo, Yu Huang, Yan Yan
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Situation modeling and evaluation for complex systems: A case study of satellite attitude control system Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-29 Jintao Wang, Yulong Yin, Jiayi Qu, Huaiqi Chen, Xiaohui Lian
Because the dependence, competition, correlation, and other complex interactions among the components and between system and environment, the complex system is difficult to be modeled directly. As an important spacecraft system, satellite needs to meet the high reliability demand. However, the increasingly complex satellite system structure and complex space environment interference make the on-orbit
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Cascade operation-enhanced high-resolution representation learning for meticulous segmentation of bridge cracks Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-29 Honghu Chu, Weiwei Chen, Lu Deng
High-resolution (HR) crack images have proven valuable for bridge inspection using unmanned aerial vehicles (UAVs), offering fine details crucial for accurate segmentation. Traditional deep learning (DL) struggles with HR images due to downsampling issues and limited computational resources. To address this, we propose Cascade-FcaHRNet, a HR representation learning-based multiscale architecture. It
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NAS-ASDet: An adaptive design method for surface defect detection network using neural architecture search Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-27 Zhenrong Wang, Bin Li, Weifeng Li, Shuanlong Niu, Miao Wang, Tongzhi Niu
Deep convolutional neural networks (CNNs) have been widely used in surface defect detection. However, no CNN architecture is suitable for all detection tasks and designing effective task-specific architectures requires considerable effort. The neural architecture search (NAS) technology makes it possible to automatically generate adaptive data-driven networks. Here, we propose a new method called NAS-ASDet
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A hybrid method combining Lévy process and neural network for predicting remaining useful life of rotating machinery Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-27 Shuai Lv, Shujie Liu, Hongkun Li, Yu Wang, Gengshuo Liu, Wei Dai
The accurate prediction of remaining useful life (RUL) for rotating machinery with gears and bearings at its core plays a crucial role in ensuring equipment's safe operation and preventing catastrophic accidents. Therefore, this paper focuses on the RUL issue of rotating machinery, proposing a novel RUL prediction framework. Initially, leveraging multi-domain feature extraction and self-organizing
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Integrating object detection and natural language processing models to build a personalized attraction recommendation agent in a smart product service system Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-27 Ming-Chuan Chiu, Cheng-Zhou Tsai, Yu-Chen Huang
Product Service System (PSS) is a new business model that integrates tangible products and intangible services to better meet customer needs and expectations. In recent years, scholars had some efforts to enhance the capability of PSS with the support of artificial intelligence (AI) which is known as Smart PSS(SPSS). So far, most previous studies adopted a single model which cannot handle multiple
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Global principal planes aided LiDAR-based mobile mapping method in artificial environments Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-27 Bao Sheng, Shi Wenzhong, Yang Daping, Xiang Haodong, Yu Yue
3-D mapping of buildings is crucial for urban renewal, but traditional LiDAR-based mapping methods are often less effective for buildings with narrow spaces and limited geometric features. Current methods attempt to overcome this by integrating additional sensors, such as cameras, which increases cost and complexity. This paper proposes a novel LiDAR-based mobile mapping framework using global principal
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Efficacy assessment for multi-vehicle formations based on data augmentation considering reliability Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-26 Haoran Zhang, Ruohan Yang, Wei He
Nowadays, aerial vehicle swarm (AVS) formations have been widely applied to military actions. Meanwhile, assessing their efficacy has also received increasing attention due to the significance for offline tactic planning and online formation switching, which places extra emphasis on the combination of empirical knowledge and experimental data, the transparency of assessment process and the explainability
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Classification of architectural and MEP BIM objects for building performance evaluation Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-26 Duygu Utkucu, Huaquan Ying, Zijian Wang, Rafael Sacks
Numerous factors in current workflows reduce the completeness, accuracy, and reliability of the information exchanged between Building Information Modelling (BIM) authoring tools and building performance simulation and analysis software. Automated classification of BIM objects could ameliorate most of the interoperability problems. However, architectural, structural, mechanical, electrical, and plumbing
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Application of artificial intelligence in digital twin models for stormwater infrastructure systems in smart cities Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-26 Abbas Sharifi, Ali Tarlani Beris, Amir Sharifzadeh Javidi, Mohammadsadegh Nouri, Ahmad Gholizadeh Lonbar, Mohsen Ahmadi
Digital twins provide insights into physical objects by serving as advanced virtual representations. Their sensors capture detailed information about an object’s functionality through their use of various sensors. It is possible to gain a deep understanding of the object’s performance and potential areas for improvement by collecting data, which includes metrics such as energy output, temperature,
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NC process information mining based optimization method of roughing tool sequence selection for pocket features Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-25 Changhong Xu, Shusheng Zhang, Jiachen Liang, Bian Rong, Junming Hou
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Local maximum instantaneous extraction transform based on extended autocorrelation function for bearing fault diagnosis Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-25 Tao Liu, Laixing Li, Khandaker Noman, Yongbo Li
Extracting weak impulses from vibration signals is a tricky technique in fault diagnosis and condition monitoring of the rotating machinery. The autocorrelation function is an effective method for enhancing repetitive signals, however, the effect deteriorates as the point number of autocorrelation calculation decreases. To resolve this problem, a novel algorithm entitled extended autocorrelation function
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STFE-Net: A multi-stage approach to enhance statistical texture feature for defect detection on metal surfaces Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-25 Hao Zhong, Daxing Fu, Ling Xiao, Fang Zhao, Jie Liu, Youmin Hu, Bo Wu
Statistical texture features are essential for metal surface defect detection. However, low contrast and cluttered backgrounds exacerbate the intrinsic blurriness and variability of defect textures, hampering accurate identification and localization. To tackle these challenges, we propose a Statistical Texture Feature Enhancement Network (STFE-Net) based on YOLO model, a multi-stage approach based
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FTSDC: A novel federated transfer learning strategy for bearing cross-machine fault diagnosis based on dual-correction training Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-22 Zhenhao Yan, Zifeng Xu, Yixiang Zhang, Jiachen Sun, Lilan Liu, Yanning Sun
In recent years, although traditional intelligent fault diagnosis methods have achieved satisfactory development in transfer learning tasks, the sample information that the single client can generally provide is extremely limited in real industrial scenarios. And the private data needs to be guaranteed not to leave the local storage during the application process, which leads to obstacles for fault
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Autonomous damage segmentation of post-fire reinforced concrete structural components Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-22 Caiwei Liu, Pengfei Wang, Xinyu Wang, Jijun Miao
The existing methods for detecting fire damage in reinforced concrete (RC) structures rely heavily on manual visual inspection, which is time-consuming and challenging due to poor visibility and safety concerns. To address this, an automatic damage segmentation network at the pixel level is proposed. Using a dataset of 403 high-resolution images of fire-damaged RC components, the study employed MobileNetv3
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Spectral boundary detecting model: A promising tool for adaptive mode extraction and machinery fault diagnosis Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-22 Xingxing Jiang, Qiuyu Song, Qian Wang, Wanliang Zhang, Chuancang Ding, Zhongkui Zhu
Extraction of weak transients is vital for realizing the early machinery fault diagnosis. However, a significant challenge lies in an efficient determination of the spectral boundaries of expected modes for excluding interferential information. Through a new perspective exploration on the fundamental principle of variational mode decomposition, we find a relevance between the filtering structure of
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Integrating MBD with BOM for consistent data transformation during lifecycle synergetic decision-making of complex products Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-22 Xin Zhao, Shuangshuang Wei, Shan Ren, Weihua Cai, Yingfeng Zhang
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Digital twin-driven assembly accuracy prediction method for high performance precision assembly of complex products Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-21 Yang Yi, Anqi Zhang, Xiaojun Liu, Di Jiang, Yi Lu, Bin Wu
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A classification and quantitative assessment method for internal and external surface defects in pipelines based on ASTC-Net Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-21 Jie Yuan, Mengtian Qiao, Chun Hu, Yufei Cheng, Zhen Wang, Dezhi Zheng
Classification and size quantification of defects on both the internal and external surfaces of pipelines are critical to pipeline integrity assessment. However, defect classification is challenging because of the similarities of defect signals on the internal and external surfaces. In addition, most existing size quantification methods are not sufficiently accurate. To solve these problems, this paper
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Traffic pattern-aware elevator dispatching via deep reinforcement learning Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-20 Jiansong Wan, Kanghoon Lee, Hayong Shin
This study addresses the elevator dispatching problem using deep reinforcement learning, with a specific emphasis on traffic pattern awareness. Previous studies on reinforcement learning-based elevator dispatching have largely focused on training separate models for single traffic patterns, such as up-peak, down-peak, lunch-peak, and inter-floor. This separate training approach not only introduces
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The flex-route transit service routing plan considering heterogeneous requests and time windows Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-20 Mingyang Li, Jinjun Tang
Flex-route transit (FRT) service has become increasingly popular in low-density suburban areas as a shared transportation mode. Improving the service quality of FRT heavily relies on effective routing plans. To end of this, this paper addresses the FRT routing plan problem considering heterogeneous requests and time windows (FRTRPP-HRTW), which is formulated as a mixed-integer programming (MIP) model
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Enhanced prediction of highway flood inundation through Bayesian generalized linear geostatistical models Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-20 Yitong Li, Chaowei Yang, Wenying Ji
Transportation infrastructure facilitates the mobility of goods and humans. Following flooding, blocked road access would prevent vulnerable communities from accessing essential services and disaster relief resources. To reduce the impact of damaged transportation infrastructure on community lifelines, efficient infrastructure restoration is desired. Conventionally, damage identification is often performed
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A circular intuitionistic fuzzy assignment model with a parameterized scoring rule for multiple criteria assessment methodology Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-19 Ting-Yu Chen
The notion of circular intuitionistic fuzzy (C-IF) sets, which utilizes a malleable circle to depict uncertainties and encompasses membership and non-membership constituents at its core, constitutes a progressive advancement of standard intuitionistic fuzzy sets. This paper concerns the utilization of a C-IF assignment model along with a parameterized scoring rule for a methodology involving multiple
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Design recommendations for voluntary blink interactions based on pressure sensors Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-18 Lin-Han Fan, Wei-Chi Huang, Xue-Qi Shao, Ya-Feng Niu
Voluntary blink control is vital in eye-controlled human–computer interaction (ECHCI). Pressure sensors, known for their low power consumption and strong resistance to interference, are suitable for blink detection. Existing research has predominantly focused on sensor materials and signal processing, and studies on the characteristics and optimized design of voluntary blink actions in ECHCI remain
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A novel SCDM algorithm with offset centroid-driven weight adaptation and its application to appearance design of automotive steering wheels Adv. Eng. Inform. (IF 8.8) Pub Date : 2024-03-18 Lingwan Huang, Aimin Zhou, Ziyi Zhang, Yueyue Shan, Zenghui Wang, Shijian Cang