IECE - Institute of Emerging and Computer Engineers

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    Academic Editor
    Author
    Contributions by role
    Author 4
    Reviewer 2
    Editor 2
    ORCID
    0009-0000-1079-2564
    Shenglun Yi
    Department of Information Engineering, University of Padua, Italy
    Summary
    Edited Journals
    IECE Transactions on Intelligent Systematics
    IECE Contributions
    Articles
    4
    Edited
    2
    Free Access | Research Article | 18 December 2024
    Adaptive Tunable Predefined-Time Backstepping Control for Uncertain Robotic Manipulators
    by
    Huihui Shi
    ,
    Shuzong Xie
    ,
    Qiang Chen
    ,
    Shuangyi Hu
    ,
    Shenglun Yi
    IECE Transactions on Sensing, Communication, and Control | Volume 1, Issue 2: 126-135, 2024 | DOI:10.62762/TSCC.2024.672831
    Abstract
    In engineering applications, high-precision tracking control is crucial for robotic manipulators to successfully complete complex operational tasks. To achieve this goal, this study proposes an adaptive tunable predefined-time backstepping control strategy for uncertain robotic manipulators with external disturbances and model uncertainties. By establishing a novel practical predefined-time stability criterion, a tunable predefined-time backstepping controller is systematically presented, allowing the upper bound of tracking error settling time to be precisely determined by adjusting only one control parameter. To accurately address lumped uncertainty, two updating laws are designed: a fuzz... More >
    Graphical Abstract
    Adaptive Tunable Predefined-Time Backstepping Control for Uncertain Robotic Manipulators
    Free Access | Research Article | 30 October 2024
    Enhanced Recognition for Finger Gesture-Based Control in Humanoid Robots Using Inertial Sensors
    by
    Jingyi Xie
    ,
    Na Xiang
    ,
    Shenglun Yi
    IECE Transactions on Sensing, Communication, and Control | Volume 1, Issue 2: 89-100, 2024 | DOI:10.62762/TSCC.2024.805710
    Abstract
    Humanoid robots have much weight in many fields. Their efficient and intuitive control input is critically important and, in many cases, requires remote operation. In this paper, we investigate the potential advantages of inertial sensors as a key element of command signal generation for humanoid robot control systems. The goal is to use inertial sensors to detect precisely when the user is moving which enables precise control commands. The finger gestures are initially captured as signals coming from the inertial sensor. Movement commands are extracted from these signals using filtering and recognition. These commands are subsequently translated into robot movements according to the attitud... More >
    Graphical Abstract
    Enhanced Recognition for Finger Gesture-Based Control in Humanoid Robots Using Inertial Sensors
    Free Access | Research Article | 25 October 2024
    Spatio-temporal Feature Soft Correlation Concatenation Aggregation Structure for Video Action Recognition Networks
    by
    Fafa Wang
    ,
    Shenglun Yi
    IECE Transactions on Sensing, Communication, and Control | Volume 1, Issue 1: 60-71, 2024 | DOI:10.62762/TSCC.2024.212751
    Abstract
    The efficient extraction and fusion of video features to accurately identify complex and similar actions has consistently remained a significant research endeavor in the field of video action recognition. While adept at feature extraction, prevailing methodologies for video action recognition frequently exhibit suboptimal performance in the context of complex scenes and similar actions. This shortcoming arises primarily from their reliance on uni-dimensional feature extraction, thereby overlooking the interrelations among features and the significance of multi-dimensional fusion. To address this issue, this paper introduces an innovative framework predicated upon a soft correlation strategy... More >
    Graphical Abstract
    Spatio-temporal Feature Soft Correlation Concatenation Aggregation Structure for Video Action Recognition Networks
    Code (Data) Available | Free Access | Research Article | Feature Paper | 09 August 2024
    LI3D-BiLSTM: A Lightweight Inception-3D Networks with BiLSTM for Video Action Recognition
    by
    Fafa Wang
    ,
    Xuebo Jin
    ,
    Shenglun Yi
    IECE Transactions on Emerging Topics in Artificial Intelligence | Volume 1, Issue 1: 58-70, 2024 | DOI:10.62762/TETAI.2024.628205
    Abstract
    This paper proposes an improved video action recognition method, primarily consisting of three key components. Firstly, in the data preprocessing stage, we developed multi-temporal scale video frame extraction and multi-spatial scale video cropping techniques to enhance content information and standardize input formats. Secondly, we propose a lightweight Inception-3D networks (LI3D) network structure for spatio-temporal feature extraction and design a soft-association feature aggregation module to improve the recognition accuracy of key actions in videos. Lastly, we employ a bidirectional LSTM network to contextualize the feature sequences extracted by LI3D, enhancing the representation capa... More >
    Graphical Abstract
    LI3D-BiLSTM: A Lightweight Inception-3D Networks with BiLSTM for Video Action Recognition
    Free Access | Research Article | 29 May 2024 | Cited: 6
    Parameter Adaptive Non-Model-Based State Estimation Combining Attention Mechanism and LSTM
    by
    Xuebo Jin
    ,
    Tianxiao Sun
    ,
    Wei Chen
    ,
    Huijun Ma
    ,
    Yeqing Wang
    ,
    Yusen Zheng
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 40-48, 2024 | DOI:10.62762/TIS.2024.137329
    Abstract
    Nowadays, state estimation is widely used in fields such as autonomous driving and drone navigation. However, in practical applications, it is difficult to obtain accurate target motion models and noise covariance.This leads to a decrease in the estimation accuracy of traditional Kalman filters. To address this issue, this paper proposes an adaptive model free state estimation method based on attention parameter learning module. This method combines Transformer's encoder with Long Short Term Memory Network (LSTM), and obtains the system's operational characteristics through offline learning of measurement data without modeling the system dynamics and measurement characteristics. In addition,... More >
    Graphical Abstract
    Parameter Adaptive Non-Model-Based State Estimation Combining Attention Mechanism and LSTM
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