Article
Construction & Building Technology
Songyuan Geng, Qiling Luo, Kun Liu, Yunchao Li, Yuchen Hou, Wujian Long
Summary: 3D printing brings new opportunities for civil engineering development, and machine learning is widely used in various fields of 3D printing. This paper reviews and analyzes the main research issues and potential applications of machine learning in construction 3D printing, provides an overview of current research progress, and discusses current challenges and future research scope. It introduces the classification and working principle of 3D printing and machine learning technology, discusses the application status of machine learning in construction 3D printing in terms of material design, printing process control, and component quality inspection. It also summarizes the future potential and challenges of machine learning in construction 3D printing, aiming to promote high-efficiency, intelligence, and sustainability in civil engineering.
CASE STUDIES IN CONSTRUCTION MATERIALS
(2023)
Review
Computer Science, Interdisciplinary Applications
Chen Wang, Ling-han Song, Zhou Yuan, Jian-sheng Fan
Summary: With the advancement of the building and infrastructure industry's informatization, traditional analysis methods are inadequate for the demands of the new era. Artificial intelligence (AI) technology has emerged as a promising alternative due to its efficiency and scalability. This study provides a comprehensive review of AI-based computation in material and structural analyses in civil engineering, covering the methodology, AI models, applications, strengths, and weaknesses. It also highlights the accuracy and efficiency of an end-to-end deep learning framework for structural analysis as compared to conventional numerical methods, and suggests future research directions.
JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
(2023)
Article
Engineering, Multidisciplinary
Maarten R. Dobbelaere, Pieter P. Plehiers, Ruben van de Vijver, Christian V. Stevens, Kevin M. Van Geem
Summary: Machine learning has significant advantages in the field of chemical engineering, including flexibility, accuracy, and execution speed, but also comes with weaknesses such as lack of interpretability. The biggest opportunities lie in using machine learning for real-time optimization and planning, while the greatest threat is inappropriate use.
Review
Geosciences, Multidisciplinary
Abolfazl Baghbani, Tanveer Choudhury, Susanga Costa, Johannes Reiner
Summary: This study reviewed the application of artificial intelligence methods in geotechnical engineering and identified nine prominent areas. Artificial Neural Network (ANN) emerged as the most widely used AI method. The analysis shows that the success and accuracy of AI applications depends on the number and type of datasets and selection of input parameters.
EARTH-SCIENCE REVIEWS
(2022)
Article
Computer Science, Interdisciplinary Applications
Leonardo Rossi, Mark H. M. Winands, Christoph Butenweg
Summary: Monte Carlo Tree Search (MCTS) has emerged as a significant breakthrough in artificial intelligence applications, particularly in board and video games. The success of MCTS in the game of Go has opened up new perspectives for its application in various scientific and technical problems. The adaptation of MCTS in civil engineering design problems showcases its potential as a valuable tool for civil engineers.
ENGINEERING WITH COMPUTERS
(2022)
Review
Biochemical Research Methods
Woo Dae Jang, Gi Bae Kim, Yeji Kim, Sang Yup Lee
Summary: This paper reviews recent studies on AI-aided protein engineering and design, focusing on directed evolution that uses AI approaches to efficiently construct mutant libraries. Additionally, recent works on AI-aided pathway design strategies, including template-based and template-free approaches, are discussed.
CURRENT OPINION IN BIOTECHNOLOGY
(2022)
Article
Green & Sustainable Science & Technology
Adriano Bressane, Marianne Spalding, Daniel Zwirn, Anna Isabel Silva Loureiro, Abayomi Oluwatobiloba Bankole, Rogerio Galante Negri, Irineu de Brito Junior, Jorge Kennety Silva Formiga, Liliam Cesar de Castro Medeiros, Luana Albertani Pampuch Bortolozo, Rodrigo Moruzzi
Summary: Understanding key factors in students' performance can improve teaching and learning. Artificial intelligence methods have the potential to revolutionize education. This study compares different AI models to predict student performance and finds that a fuzzy AI-based model shows the highest accuracy. The model can assist in identifying at-risk students and implementing preventive interventions.
Article
Computer Science, Artificial Intelligence
Kerstin N. Vokinger, Urs Gasser
Summary: Regulatory frameworks for artificial intelligence are being developed on both sides of the Atlantic, eagerly anticipated by the scientific and industrial community. Commonalities and differences in approaches to AI in medicine are beginning to emerge.
NATURE MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Miao Su, Hui Peng, Shaofan Li
Summary: This study conducted a visualized bibliometric analysis of machine learning in engineering (MLE) research trends based on articles indexed in the Web of Science Core Collection from 2000 to 2019. The USA was found to be the most productive country, and the research hotspots and knowledge bases from 2000 to 2019 were identified.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Review
Construction & Building Technology
Yue Pan, Limao Zhang
Summary: The adoption of artificial intelligence in construction engineering and management has become a major focus of research, with an increasing number of papers focusing on topics like building information modeling and digital twins. Future research is expected to further explore areas such as intelligent optimization and computer vision to enhance automation and efficiency in CEM.
AUTOMATION IN CONSTRUCTION
(2021)
Review
Biochemistry & Molecular Biology
Xinglong Wang, Kangjie Xu, Yameng Tan, Song Liu, Jingwen Zhou
Summary: Food enzymes play a crucial role in improving food characteristics, such as texture, safety, carbohydrates production, and flavor/appearance enhancement. With the development of artificial meats, food enzymes are being utilized for more diverse functions, especially in converting non-edible biomass into edible foods. This article highlights the significance of enzyme engineering through de novo design, which provides potential solutions for screening desired enzymes. It discusses the functions and applications of food enzymes, reviews protein modeling and de novo design methods, and emphasizes future challenges in designing food enzymes.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Engineering, Civil
M. Z. Naser, Brandon Ross
Summary: This article presents a comprehensive view of the dos and don'ts of adopting AI into civil engineering. It addresses the mystique and hesitancy surrounding the application of AI in this field, while highlighting the growing acceptance and adoption of AI technologies.
CIVIL ENGINEERING AND ENVIRONMENTAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Carlo Combi, Beatrice Amico, Riccardo Bellazzi, Andreas Holzinger, Jason H. Moore, Marinka Zitnik, John H. Holmes
Summary: This paper focuses on the importance of explainable artificial intelligence (XAI) in the field of biomedicine. By bringing together researchers with different roles and perspectives, it explores XAI in depth and presents a series of requirements for achieving explainability in AI.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2022)
Review
Agriculture, Multidisciplinary
Feiyin Ge, Gang Chen, Minjing Qian, Cheng Xu, Jiao Liu, Jiaqi Cao, Xinchao Li, Die Hu, Yangsen Xu, Ya Xin, Dianlong Wang, Jia Zhou, Hao Shi, Zhongbiao Tan
Summary: With the development of artificial intelligence, methods for enzyme engineering have been expanded. Various network models and algorithms have been used to optimize lipase production and properties. However, there is still a research gap in utilizing AI to engineer lipase.
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY
(2023)
Article
Environmental Sciences
Francesca Larosa, Sergio Hoyas, Javier Garcia-Martinez, J. Alberto Conejero, Francesco Fuso Nerini, Ricardo Vinuesa
Summary: Large language models provide an opportunity to advance climate and sustainability research. We believe that regulating and validating generative artificial intelligence models would benefit society more than stopping development.
NATURE CLIMATE CHANGE
(2023)
Article
Computer Science, Artificial Intelligence
Haigen Hu, Aizhu Liu, Qiu Guan, Hanwang Qian, Xiaoxin Li, Shengyong Chen, Qianwei Zhou
Summary: This study proposes a novel methodology to adaptively customize activation functions, enhancing the nonlinearity and mapping abilities of neural networks. Experimental results demonstrate its significant performance in convergence speed, precision, and generalization, surpassing other popular methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Mianzhao Wang, Fan Shi, Xu Cheng, Meng Zhao, Yao Zhang, Chen Jia, Weiwei Tian, Shengyong Chen
Summary: This article introduces the importance of visual object tracking in the field of computer vision, as well as one of its main challenges. To overcome this challenge, the article proposes a new representation for multiview images, called macro-epipolar plane image (macro-EPI), which highlights the spatial topological and angular information of the target and distractors. The macro-EPI is obtained by slicing and restacking the original multiview images, and mapped into 2-D space. The article also presents a modified autoencoder network for training a macro-EPI feature extractor, and a composite framework based on discriminative correlation filters for object tracking.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Qin Song, Yu-Jun Zheng, Jun Yang, Yu-Jiao Huang, Wei-Guo Sheng, Sheng-Yong Chen
Summary: The COVID-19 pandemic has increased the demand for medical resources. This study proposes a co-evolutionary transfer learning method to predict the demands of medical materials. The method achieves high prediction accuracy compared to other transfer learning and multitask learning models.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Zan Gao, Xinglei Cui, Tao Zhuo, Zhiyong Cheng, An-An Liu, Meng Wang, Shenyong Chen
Summary: This paper proposes a novel multitemporal-scale spatial-temporal transformer (MSST) network for temporal action localization, which predicts actions on a feature space of multiple temporal scales. The proposed method outperforms state-of-the-art approaches on the THUMOS14 dataset and achieves comparable performance on the ActivityNet1.3 dataset.
IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Chao Zhang, Fan Shi, Xinpeng Zhang, Shengyong Chen
Summary: This article introduces a lightweight network called ICFF-YOLOv5 for detecting birds flying at high altitudes. To address the challenges of inconspicuous bird features and unfriendly deep networks for edge devices, the authors designed a feature fusion module and a double combination convolution technique. Experimental results demonstrate the accurate detection of flying birds and high evaluation metric scores.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Civil
Jianhua Zhang, Rucen Wang, Ruyu Liu, Dongyan Guo, Bo Li, Shengyong Chen
Summary: IoT-based intelligent transportation, specifically traffic video monitoring, requires accurate vehicle and pedestrian detection. Deep learning methods have high accuracy but are computationally expensive for IoT devices. This study proposes optimization tactics for object detection CNN models on digital signal processors and evaluates the performance. Results show that the proposed method achieves faster speed with minimal accuracy loss compared to running the same model on a desktop CPU.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jiajun Meng, Zhenhua Wang, Kaining Ying, Jianhua Zhang, Dongyan Guo, Zhen Zhang, Javen Qinfeng Shi, Shengyong Chen
Summary: Compared to human activity classification, there has been less progress on human interaction understanding (HIU). This is mainly due to the challenge of the task and the limitations of shallow graphical representations used in recent approaches. In this paper, a deep consistency-aware framework is proposed to tackle the grouping and labeling inconsistencies in HIU. The framework consists of three components: a backbone CNN for image feature extraction, a factor graph network for learning higher-order consistencies, and a consistency-aware reasoning module for enforcing consistencies. Experimental results show that the proposed approach achieves leading performance on three HIU benchmarks, demonstrating its effectiveness.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Engineering, Electrical & Electronic
Xinpeng Zhang, Meng Zhao, Yao Zhang, Ji Ao, Hongxia Yang, Congcong Wang, Shengyong Chen
Summary: This article proposes a hierarchical pyramid network with a T structure to detect microaneurysm (MA) in retinal fundus images. The method overcomes the difficulties caused by limited information and different sizes by using adaptive computation in data preparation and generating multisize datasets for training. Experimental results show that the proposed method achieves state-of-the-art performance.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Electrical & Electronic
Jihao Si, Binbin Song, Jixuan Wu, Wei Lin, Wei Huang, Shengyong Chen
Summary: Remote sensing ship recognition technology is an important research area for ocean security monitoring. This paper proposes a new ship detection algorithm YOLO-remote sensing ship detection (YOLO-RSSD) based on YOLOv5. The algorithm improves the accuracy of ship detection while ensuring detection speed through four optimization measures, including improved data preprocessing, feature fusion, loss function, and convolutional unit.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Engineering, Electrical & Electronic
Dongtian Zhang, Weiwei Tian, Xu Cheng, Fan Shi, Hong Qiu, Xiufeng Liu, Shengyong Chen
Summary: Prediction of icing on wind turbine blades is crucial, and traditional approaches have limitations. FedBIP, a novel FL model, addresses these challenges by employing feature selection, oversampling, aggregation, and knowledge distillation.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Artificial Intelligence
Zan Gao, Hongwei Wei, Weili Guan, Jie Nie, Meng Wang, Shengyong Chen
Summary: This study proposes a novel method for cloth-changing person ReID, which shields clues related to the appearance of clothes and focuses on visual semantic information that is not sensitive to view/posture changes, resulting in more robust feature representations.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zhibin Zhang, Wanli Xue, Kaihua Zhang, Bo Liu, Chengwei Zhang, Jingen Liu, Shengyong Chen
Summary: This article introduces a self-corrective network-based long-term tracker (SCLT) with a self-modulated tracking reliability evaluator (STRE) and a self-adjusting proposal postprocessor (SPPP). The STRE improves tracking reliability using an adaptive self-labeling strategy and network modulation mechanism, while the SPPP recaptures the target using a dynamic NMS. Experimental results demonstrate the superior performance of the proposed SCLT on multiple benchmark datasets.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Civil
Xu Cheng, Tian He, Fan Shi, Meng Zhao, Xiufeng Liu, Shengyong Chen
Summary: This paper presents an innovative neural network architecture for automated crack detection on highways. The proposed method effectively models irregular crack objects by selectively integrating features from multiple levels, and it has been demonstrated to outperform baseline methods in extensive evaluations.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Xu Cheng, Kexin Wang, Xiufeng Liu, Qian Yu, Fan Shi, Zhengru Ren, Shengyong Chen
Summary: Sea state estimation is crucial for the development of autonomous ships. Traditional methods have limitations, while deep learning models show superior performance. However, due to imbalanced sea state samples, this research proposes a class-imbalanced cross-scale model to improve sea state estimation by learning coarse and fine-level features from ship motion data.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Automation & Control Systems
Guanhe Huang, Jingyue Shi, Jun Xu, Jing Li, Shengyong Chen, Yingjun Du, Xiantong Zhen, Honghai Liu
Summary: This article proposes a generative approach for gaze estimation, generating multiple candidate gaze maps and fusing predicted gaze directions with an attention mechanism. Experimental results demonstrate the superior performance of this method in gaze estimation.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
