Now showing items 221-240 of 1402

    • A Comparison of Graph-Theoretic Approaches for Resilient System of Systems Design 

      Chatterjee, Abheek; Helbig, Cade; Malak, Richard; Layton, Astrid (The American Society of Mechanical Engineers (ASME), 4/19/2023 )
      System of systems (SoS) are networked integration of constituent systems that together achieve new capabilities not possible through the operation of any single system. SoS can be found across all aspects of modern life ...
    • Reflect–Express–Transform: Investigating Speech-Based Iterative Digital Design for Young Designers 

      Vyas, Shantanu; Chen, Ting-Ju; Woodward, Jay; Krishnamurthy, Vinayak R. (The American Society of Mechanical Engineers (ASME), 4/19/2023 )
      We investigate speech-based input as a means to enable reflective thinking for younger individuals (middle- and high-school students) during design iterations. Verbalization offers a unique way to externalize ideas in early ...
    • Effects of Elastoplasticity, Damage, and Environmental Exposure on the Behavior of Adhesive Step-Lap Joints 

      Michopoulos, John G.; Apetre, Nicole A.; Iliopoulos, Athanasios P.; Steuben, John C. (The American Society of Mechanical Engineers (ASME), 12/21/2022)
      The presence of damage in the adhesive material as well as combined environmental excitation in multi-material adhesive step-lap joints (ASLJs) often encountered in aircraft industries are frequently neglected. Historically, ...
    • Physics Informed Synthetic Image Generation for Deep Learning-Based Detection of Wrinkles and Folds 

      Manyar, Omey M.; Cheng, Junyan; Levine, Reuben; Krishnan, Vihan; Barbič, Jernej; Gupta, Satyandra K. (The American Society of Mechanical Engineers (ASME), 12/9/2022 )
      Deep learning-based image segmentation methods have showcased tremendous potential in defect detection applications for several manufacturing processes. Currently, majority of deep learning research for defect detection ...
    • Metric-Based Meta-Learning for Cross-Domain Few-Shot Identification of Welding Defect 

      Xie, Tingli; Huang, Xufeng; Choi, Seung-Kyum (The American Society of Mechanical Engineers (ASME), 12/9/2022 )
      With the development of deep learning and information technologies, intelligent welding systems have been further developed, which achieve satisfactory identification of defective welds. However, the lack of labeled samples ...
    • Upper Extremity Joint Torque Estimation Through an Electromyography-Driven Model 

      Tahmid, Shadman; Font-Llagunes, Josep M.; Yang, James (The American Society of Mechanical Engineers (ASME), 12/9/2022 )
      Cerebrovascular accidents like a stroke can affect the lower limb as well as upper extremity joints (i.e., shoulder, elbow, or wrist) and hinder the ability to produce necessary torque for activities of daily living. In ...
    • Special Section: Highlights of CIE 2022 

      Unknown author (The American Society of Mechanical Engineers (ASME), 4/12/2023 )
      This Special Section contains a selection of six papers from the 42nd American Society of Mechanical Engineers (ASME) Computers and Information in Engineering (CIE) Conference that was held in St. Louis, MO, Aug. 14–17, ...
    • Reviewer's Recognition 

      Unknown author (The American Society of Mechanical Engineers (ASME), 2/23/2023 )
      The Editor and Editorial Board of the Journal of Computing and Information Science in Engineering would like to thank all of the reviewers for volunteering their expertise and time reviewing manuscripts in 2022. Serving ...
    • Physics-Constrained Bayesian Neural Network for Bias and Variance Reduction 

      Malashkhia, Luka; Liu, Dehao; Lu, Yanglong; Wang, Yan (The American Society of Mechanical Engineers (ASME), 11/8/2022 )
      When neural networks are applied to solve complex engineering problems, the lack of training data can make the predictions of the surrogate inaccurate. Recently, physics-constrained neural networks were introduced to ...
    • Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications 

      Tran, Anh; Maupin, Kathryn; Rodgers, Theron (The American Society of Mechanical Engineers (ASME), 10/20/2022)
      Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods ...
    • Spatial Transform Depthwise Over-Parameterized Convolution Recurrent Neural Network for License Plate Recognition in Complex Environment 

      Deng, Jiehang; Wei, Haomin; Lai, Zhenxiang; Gu, Guosheng; Chen, Zhiqiang; Chen, Leo; Ding, Lei (The American Society of Mechanical Engineers (ASME), 10/10/2022)
      Automatic license plate recognition (ALPR) system has been widely used in intelligent transportation and other fields. However, in complex environments such as vehicle sound source localization, poor illumination, or bad ...
    • Special Issue: Machine Intelligence for Engineering Under Uncertainties 

      Unknown author (The American Society of Mechanical Engineers (ASME), 12/19/2022)
      Machine intelligence integrates computation, data, models, and algorithms to solve problems that are too complex for humans. During the last three decades, machine intelligence has been a highly researched topic and widely ...
    • Untitled 

      Liu, Dehao; Pusarla, Pranav; Wang, Yan (The American Society of Mechanical Engineers (ASME), 2022)
      Data sparsity is still the main challenge to apply machine learning models to solve complex scientific and engineering problems. The root cause is the “curse of dimensionality” in training these models. Training algorithms ...
    • Dynamic Characteristics of Electromechanical Coupling and Fuzzy Control of Intelligent Joints for Robot Drive and Control 

      Mo, Shuai;Zhou, Changpeng;Li, Xu;Yang, Zhenning;Cen, Guojian;Huang, Yunsheng (The American Society of Mechanical Engineers (ASME), 2023)
      In this technical brief, the resonance problem of a robot joint is analyzed. By establishing the electromechanical coupling dynamic equation of the robot joint, the natural vibration characteristics of the electromechanical ...
    • Cervical Spine Finite Element Models for Healthy Subjects: Development and Validation 

      Tahmid, Shadman;Love, Brittany M.;Liang, Ziyang;Yang, James (The American Society of Mechanical Engineers (ASME), 2023)
      Finite element modeling is a popular method for predicting kinematics and kinetics in spine biomechanics. With the advancement of powerful computational equipment, more detailed finite element models have been developed ...
    • ClusteringBased Detection of Debye–Scherrer Rings 

      Sirhindi, Rabia;Khan, Nazar (The American Society of Mechanical Engineers (ASME), 2023)
      Calibration of the Xray powder diffraction (XRPD) experimental setup is a crucial step before data reduction and analysis, and requires correctly extracting individual Debye–Scherrer rings from the 2D XRPD image. This ...
    • A MinimumControlTrajectoryDeviation Time Grid Reconstruction Strategy for CoDesign Approach 

      Zhang, Jinwen;Li, Congbo;Li, Yongsheng;Wang, Ningbo;Li, Wei (The American Society of Mechanical Engineers (ASME), 2023)
      Optimizing dynamic engineering systems (DESs) is quite challenging due to the increasing pursuit of automation and intelligence in modern industry. However, most of the existing studies generally only focus on plant variables ...
    • BiLSTMBased Dynamic Prediction Model for Pulling Speed of Czochralski SingleCrystal Furnace 

      Feng, Zhengyuan;Hu, Xiaoliang;Tian, Zengguo;Jiang, Baozhu;Zhang, Hongshuai;Zhang, Wanli (The American Society of Mechanical Engineers (ASME), 2023)
      With the rapid development of microelectronics science and technology, the quality of ICgrade silicon single crystal directly affects the yield and stability of the performance of semiconductor device production. As the ...
    • Partitioned Active Learning for Heterogeneous Systems 

      Lee, Cheolhei;Wang, Kaiwen;Wu, Jianguo;Cai, Wenjun;Yue, Xiaowei (The American Society of Mechanical Engineers (ASME), 2023)
      Active learning is a subfield of machine learning that focuses on improving the data collection efficiency in expensivetoevaluate systems. Active learningapplied surrogate modeling facilitates costefficient analysis of ...
    • Teeth Mold Point Cloud Completion Via Data Augmentation and Hybrid RLGAN 

      Toscano, Juan Diego;ZunigaNavarrete, Christian;Siu, Wilson David Jo;Segura, Luis Javier;Sun, Hongyue (The American Society of Mechanical Engineers (ASME), 2023)
      Teeth scans are essential for many applications in orthodontics, where the teeth structures are virtualized to facilitate the design and fabrication of the prosthetic piece. Nevertheless, due to the limitations caused by ...