Article
Construction & Building Technology
Xinwu Zhou, Sho Takeda, Tetsuya Uchimoto, Mitsuo Hashimoto, Toshiyuki Takagi
Summary: An EPAT method was investigated for evaluating debonding between rebar and concrete. Debonding specimens were prepared by wrapping polystyrene foam around a rebar. Elastic wave signals were collected using an acoustic emission sensor. EPAT was demonstrated to be useful for non-destructive evaluation of debonding by comparing the differences in signal reach time between specimens with and without debonding. Finite element simulations also validated the reliability of EPAT for examining debonding in reinforced concrete.
CEMENT & CONCRETE COMPOSITES
(2023)
Article
Computer Science, Information Systems
Zhen Zheng, Yingjian Yan, Yanjiang Liu, Linyuan Li, Yajing Chang
Summary: The proposed framework of PCA-TVLA-based leakage detection achieves a balance between accuracy and efficiency by reducing the number of power traces while ensuring accuracy.
Article
Computer Science, Artificial Intelligence
Pei Li, Wenlin Zhang, Chengjun Lu, Rui Zhang, Xuelong Li
Summary: A novel robust kernel principal component analysis method with optimal mean (RKPCA-OM) is proposed to enhance the robustness of KPCA by automatically eliminating the optimal mean. The theoretical proof guarantees the convergence of the algorithm and the obtained optimal subspaces and means. Exhaustive experimental results validate the superiority of the proposed method.
Article
Computer Science, Artificial Intelligence
James A. Mckelvy, Irina Novikova, Eugeniy E. Mikhailov, Mario A. Maldonado, Isaac Fan, Yang Li, Ying-Ju Wang, John Kitching, Andrey B. Matsko
Summary: In this study, an unsupervised machine learning algorithm and nonlinear dimensionality reduction technique were used to accurately determine the longitudinal angle of the local magnetic field through spectroscopic observations of EIT spectra. The algorithm represented each EIT spectrum measurement as a coordinate in a new reduced dimensional feature space, and a supervised support vector regression machine modeled the relationship between the KPCA projections and field direction. The results showed that the proposed method could predict the longitudinal angle of the local magnetic field with high accuracy and resolution.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2023)
Article
Chemistry, Physical
Petr Louda, Aleksandr Sharko, Dmitry Stepanchikov, Artem Sharko
Summary: This study explores the propagation of acoustic emission signals in continuous conjugated media under real-time loading. The results demonstrate the plastic deformation properties of polymer coatings on a metal base using the acoustic emission method. The principal component method is used to analyze the acoustic emission spectra and trace the evolution of deformation transformation processes. The study also investigates the propagation of acoustic emission vibrations in different combinations of materials.
Article
Materials Science, Characterization & Testing
Nitin Nagesh Kulkarni, Shweta Dabetwar, Jason Benoit, Tzuyang Yu, Alessandro Sabato
Summary: This study improves the accuracy of infrared thermography (IRT) in detecting voids underneath roadways by comparing and validating three advanced image-processing techniques. Among them, sparse principal component thermography (S-PCT) allows determining the physical size of voids with an accuracy above 95%. This research provides a foundation for advancing the use of IRT as a more accurate and cost-effective method for road condition monitoring.
NDT & E INTERNATIONAL
(2022)
Article
Computer Science, Artificial Intelligence
Zhaozhao Chi, Juncheng Jiang, Xu Diao, Qiang Chen, Lei Ni, Zhirong Wang, Guodong Shen
Summary: This study proposed a novel leakage detection method based on an improved adaptive filter and particle swarm optimization, proving its high efficiency and reliability in processing pipeline leak signals. Through signal denoising and feature dimension reduction, it is possible to accurately identify leakage states and sizes.
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
(2022)
Article
Environmental Sciences
Erfan Ghasemi Tousi, Jennifer G. Duan, Patricia M. Gundy, Kelly R. Bright, Charles P. Gerba
Summary: This study assessed the impact of incorporating sediment information on improving machine learning models to quantify E. coli levels in irrigation water. The support vector machine model performed the best and including sediment features improved the performance of all models.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Engineering, Multidisciplinary
Shen-Bin Zhu, Zhen-Lin Li, Xiang Li, Hao-hao Xu, Xi-ming Wang
Summary: The traditional methods for diagnosing valve internal leakage have limitations, leading to the proposal of a new method using convolutional neural networks to recognize valve internal leakage. Experimental results show that this method can effectively identify internal leakage signals, with a maximum prediction error of less than 3%, serving as a new approach for valve leakage diagnosis.
Article
Energy & Fuels
Mengchao Yi, Fachao Jiang, Languang Lu, Jianqiao Ren, Mingxin Jin, Yuebo Yuan, Yong Xiang, Xiaofeng Geng, Xingong Zhang, Xuebing Han, Minggao Ouyang
Summary: This study proposed a method of in situ testing of batteries using ultrasound, and analyzed the acoustic energy to evaluate the state of the active material. The results showed a high correlation between acoustic energy and the calculated acoustic impedance of the active material, indicating that ultrasound is an effective method for studying the status of Li-ion batteries.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Automation & Control Systems
Guohao Song, Jianhua Zhang, Yingshang Ge, Kangyi Zhu, Zhensheng Fu, Luchuan Yu
Summary: A novel tool wear predicting method is proposed in this paper, utilizing a weighted multi-kernel relevance vector machine and integrated radial basis function-based probabilistic kernel principal component analysis for modeling and feature extraction, which significantly improves the accuracy and robustness of the model. Experimental results demonstrate the effectiveness of the method in accurately monitoring tool wear in industrial applications, with strong practical value.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Engineering, Multidisciplinary
Shenglan Ma, Shurong Ren, Zhining Chen, Chen Wu, Shaofei Jiang
Summary: This paper proposes a comprehensive evaluation index based on acoustic emission (AE) technology and principal component analysis (PCA) to address the issue of low accuracy of any single AE parameter in identifying damage to wooden beams caused by accumulative damage. The proposed index can divide the damage evolution process of wooden beams into four stages and determine the time of crack initiation. The proposed wooden beam damage detection method based on AE technique provides a reference for further research on the damage evolution mechanism of wooden structures and in situ monitoring methods.
Article
Thermodynamics
Kevin M. Gitushi, Rishikesh Ranade, Tarek Echekki
Summary: This study investigates the trade-off between flamelet-like and PDF-like models in turbulent combustion modeling, aiming to accelerate PDF-based methods through key ingredients like dimensional reduction and deep learning frameworks. By using PCA for dimensional reduction and DeepONet for constructing joint PCs' PDFs, the models can simplify and enhance statistical independence.
COMBUSTION AND FLAME
(2022)
Article
Engineering, Mechanical
Mingjiang Shi, Yanbing Liang, Liansheng Qin, Zhen Zheng, Zhiqiang Huang
Summary: This article introduces a method for detecting internal leakage of ball valves based on acoustic emission technology, which predicts the leakage rate by optimizing a neural network mathematical model. Experimental results validate the accuracy of the proposed method.
FLOW MEASUREMENT AND INSTRUMENTATION
(2021)
Article
Engineering, Electrical & Electronic
Spoorthy Venkatesh, Manjunath Mulimani, Shashidhar G. Koolagudi
Summary: Acoustic Scene Classification (ASC) is achieved by introducing a Fisher network that mimics the working mechanism of a feed-forward Convolutional Neural Network (CNN). The proposed model consists of a feature extraction step and a Fisher layer, which includes sub-layers for Fisher Vector encoding, temporal pyramid, normalization, and PCA. The model achieved high classification accuracy on multiple datasets.
DIGITAL SIGNAL PROCESSING
(2023)
Article
Engineering, Environmental
Xingxing Hu, Lingjie Liu, Yanmeng Bi, Lu Li, Chunsheng Qiu, Jingjie Yu, Shaopo Wang
Summary: In this study, the impact of exogenous folate on the start-up process of single-stage partial nitritation-anammox (SPNA) was evaluated using two lab-scale reactors. The results showed that folate addition can enhance nitrogen removal rate, extracellular polymeric substances production, hydrazine oxidase and dehydrogenase activity, as well as the relative abundance of Candidatus Brocadia.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2024)
Article
Engineering, Environmental
Shaocang He, Tingting Shen, Jing Sun, Haoqi Pan, Chenxu Sun, Tianpeng Li, Runyao Li, Enshan Zhang
Summary: A novel process of acid leaching neutralization was developed for the preparation of inorganic polymeric composite ferric aluminum silicate coagulant (CFAS) using solid waste coal gasification coarse slag (CGCS). The optimized preparation process was determined through single-factor experiment and the performance of CFAS was evaluated for domestic sewage treatment. The results showed that CFAS exhibited excellent coagulation ability and achieved significant removal efficiency for turbidity, ammonia nitrogen, and chemical oxygen demand.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2024)
Article
Engineering, Environmental
Kinga Szatmari, Sandor Nemeth, Alex Kummer
Summary: In this article, a resilience-based reinforcement learning approach is proposed to address the potential thermal runaway issue in batch reactors. By calculating the resilience metric for reactors and utilizing Deep Q-learning to decide when to intervene in the system, resilient-based mitigation systems can be effectively developed.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2024)
Article
Engineering, Environmental
Bingyou Jiang, Qi Yao, Mingqing Su, Jingjing Li, Kunlun Lu, Dawei Ding, Han Hong
Summary: This study investigates the inhibitory characteristics and mechanisms of ABC powder on coal powder explosion. The addition of ABC powder significantly decreases the maximum explosion pressure and can completely suppress coal dust explosions. The study also reveals the thermal decomposition characteristics and reaction kinetics of the mixed system.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2024)
Article
Engineering, Environmental
Kubilay Bayramoglu, Mustafa Nuran
Summary: This study examines the feasibility of using pyrolytic oil from waste tires as fuel in diesel engines, and evaluates its energy, exergy, and sustainability. The results indicate that pyrolytic oil has potential as a renewable fuel source with relatively high thermal efficiency.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2024)
Article
Engineering, Environmental
Xinru Duan, Yejia Lv, Jiaxing Hong, Jianzhong Wu, Jia Zhang, Yang Yue, Guangren Qian
Summary: This study successfully prepared a tube reactor with optimized catalyst formula, which showed good performance in removing dioxins and other pollutants in the experiments.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2024)
Article
Engineering, Environmental
Jiaoyang Du, Xueming Dang, Xiaorong Gan, Xin Cui, Huimin Zhao
Summary: In this study, a photocatalyst-enzyme hybrid system was constructed, which solved the issue of enzyme inactivation caused by high concentration of H2O2 through photocatalytic in-situ H2O2 production, and improved the stability and catalytic efficiency of the enzyme. The effectiveness of the system in treating phenolic EDCs was confirmed through experiments.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2024)
Article
Engineering, Environmental
Fu-Rong Xiu, Longsheng Zhan, Yingying Qi, Xinyue Lei, Jiali Wang, Haipeng Zhou, Wenting Shao
Summary: This study developed a synergetic and high-efficiency treatment of waste tantalum capacitors (WTCs) and polyvinyl chloride (PVC) using subcritical water process. The treatment significantly reduced the temperature and reaction time for metal tantalum recovery from WTCs, and improved the dechlorination efficiency of PVC. The optimized conditions resulted in 100% resin conversion efficiency of WTCs and 97.39% dechlorination efficiency of PVC. The interaction between decomposition products of WTCs and PVC produced a high level of benzoic acid.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2024)
Article
Engineering, Environmental
Bingda Li, Jiaming Song, Yuting Li, Chaoying Meng, Shuxian Wang, Linghao Zong, Honggang Ye, Yishuai Jing, Feng Teng, Peng Hu, Haibo Fan, Guangde Chen, Xin Zhao
Summary: CdPS3 nanosheets, especially those exfoliated by sodium cholate, have shown highly efficient photocatalytic degradation performance. The strong dark adsorption and dye-sensitized photocatalytic properties of CdPS3 nanosheets contribute to high degradation efficiencies of various pollutants.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2024)
Article
Engineering, Environmental
Dongxu Ouyang, Yimei Pang, Bo Liu, Zhirong Wang
Summary: This study investigates the thermal runaway features of lithium-ion cells under tunnel conditions, considering different states of charge and tunnel ceilings. The results show that the tunnel visibility is affected by the smoke generated during thermal runaway, and the shape of the tunnel ceiling influences the temperature rise differently.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2024)