Article
Engineering, Chemical
Chia-Wen Chang, Chin-Wang Tao
Summary: This paper presents a fuzzy motion control algorithm for a monocular vision system in a cooperative transportation system of two humanoid robots. The algorithm includes three stages: object searching, walking towards the transported object, and cooperating to move the object. The algorithm utilizes fuzzy techniques for synchronous movement control.
Article
Engineering, Mechanical
Zebang Pan, Shan Yin, Guilin Wen, Zhao Tan
Summary: Designing a high-performance controller for biped robots' walking gaits is a research area that is still open due to their nonlinearity and non-smooth responses. To overcome these challenges, a humanoid robot with a torso is developed first, followed by the adoption of the twin delayed deep deterministic policy gradient algorithm to design the reinforcement learning controller. A reward function utilizing the Poincare map and the power function is constructed for the specified control targets, providing guidelines for the controller. The proposed controller can adaptively output accurate cosine torques and achieve the goal without relying on pre-designed reference trajectories or unstable periodic gaits.
ACTA MECHANICA SINICA
(2023)
Article
Robotics
Mathieu Hobon, Victor De-Leon-Gomez, Gabriel Abba, Yannick Aoustin, Christine Chevallereau
Summary: The study aims to define the feasible speed range for two walking motions of a particular planar biped robot, revealing that the first gait is more energy-efficient at moderate to fast velocities, while the second gait is more efficient at low walking speeds. The results were obtained through numerical calculations and a parametric optimization algorithm.
Article
Computer Science, Information Systems
Kunming Zheng, Qiuju Zhang, Youmin Hu, Bo Wu
Summary: This study addresses the control problem of complex robot systems with uncertainties and disturbances using the FS-FNN-BSC system, which guarantees accurate, stable and efficient control. By utilizing fuzzy system and fuzzy neural network technologies, modeling and non-modeling information is approximated and predicted, respectively. The stability of the FS-FNN-BSC system is proved based on the Lyapunov stability theorem, and its superiority is demonstrated through quantitative comparison with existing intelligent control methods on the series and parallel robots.
INFORMATION SCIENCES
(2021)
Article
Chemistry, Multidisciplinary
Liang Yang, Guanyu Lai, Yong Chen, Zhihui Guo
Summary: This paper proposes a new online walking controller for biped robots, which integrates a neural-network estimator and an incremental learning mechanism to improve control performance in dynamic environment. An interval type-2 fuzzy weight identifier is developed to address the imbalanced distribution problem of training data. The effectiveness of the control scheme is verified through full-dynamics simulation and practical robot experiment.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Abhishek Jhawar, Chee Kau Lim, Chee Seng Chan
Summary: This paper aims to study fall prevention and develop a ranking system to aid in reducing fall incidents. The proposed approach is validated by experimental data and is proven to be effective and reliable.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Multidisciplinary
Zihan Xu, Qin Fang, Yong Ren, Chengju Liu
Summary: In this paper, an active balance control framework is proposed for position-controlled robots, which includes a compliant controller and a fuzzy footstep planner. The framework addresses the challenges of force estimation and footstep generation. Experimental results validate the effectiveness and robustness of the proposed framework.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Multidisciplinary Sciences
Guanfeng Zhou, Bo Jiang, Tengfei Long, Guirong Jiang
Summary: This article presents a biped robot walking on horizontal ground with two feasible switching patterns of motion (two-phase gait and three-phase gait). By using the first-order Taylor approximate at the equilibrium point, a simplified linear continuous dynamic equation is obtained to discuss the walking dynamics of the biped robot. Conditions for the existence and stability of period-1 gaits (P(1,2),P(1,3)) and period-2 gaits (P(2,2,2),P(2,2,3),P(2,3,3)) are obtained by using a discrete map. Among the periodic gaits, the P(2,2,3) type gait has never been reported in previous studies. Flip bifurcation of periodic gait is investigated. Numerical results for periodic gaits and bifurcation diagram are in good agreement with the theoretical analysis.
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
(2023)
Article
Multidisciplinary Sciences
Pengyu Zhao, Anhuan Xie, Shiqiang Zhu, Lingyu Kong
Summary: This paper proposes an electric-hydraulic hybrid drive system for biped robots to address the issues of low output torque, weak impact resistance, and high energy consumption in traditional drive systems. The robot platform is designed based on the Zhejiang Lab biped robot prototype, and models of the hydraulic drive system and mechanical structure are established to analyze dynamic characteristics and load forces during walking. A value function reflecting the energy consumption of the hydraulic drive system is proposed, with the pressure of the accumulator in the hydraulic power unit selected as the control parameter. The control parameters are optimized using a genetic algorithm to minimize the value function, thereby reducing energy consumption of the hydraulic drive system. Simulation results show that the proposed optimization algorithm improves efficiency by 3.49%.
SCIENTIFIC REPORTS
(2023)
Article
Engineering, Biomedical
Yaser Fathi, Abbas Erfanian
Summary: The study aimed to decode hindlimb kinematics during walking by recording sensory and motor information from the spinal cord. Two experimental paradigms were used, with results showing no significant difference in information content between DC and LC signals during walking, but DC signals had higher content during passive movement. Decoding performance using DC signals was comparable to LC during locomotion, with significantly better performance from DC channels. Long-term analysis showed robust decoding performance over 2-3 months.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Alejandro Pena, Juan C. Tejada, Juan David Gonzalez-Ruiz, Lina Maria Sepulveda-Cano, Francisco Chiclana, Fabio Caraffini, Mario Gongora
Summary: This paper presents a model for a serial robotic system with flexible joints (RFJ) using Euler-Lagrange equations. It also proposes a Stochastic Flexible-Adaptive Neural Integrated System (SF-ANFIS) for identifying and controlling the RFJ. The SF-ANFIS model shows better performance in both identification and control stages compared to the MADALINE model, with improved statistical indices and the ability to cancel oscillations.
APPLIED SOFT COMPUTING
(2023)
Article
Chemistry, Analytical
Jiwoo Choi, Sangil Choi, Taewon Kang
Summary: In this paper, a smartphone authentication system based on human gait is proposed. By learning human gait features and implementing filtering techniques, the system can accurately identify legitimate users. The study demonstrates the possibility of using human gait as a new user authentication method with high reliability.
Article
Mathematics
Jiarui Chen, Aimin Tang, Guanfeng Zhou, Ling Lin, Guirong Jiang
Summary: This study presents an ascending stair biped robot model with impulse thrust and obtains the conditions for the existence and stability of period-1 gait through linearization and discrete mapping. Numerical simulations validate the feasibility of theoretical analysis.
ELECTRONIC RESEARCH ARCHIVE
(2022)
Article
Computer Science, Artificial Intelligence
Aiqin Liu, Yuezhong Zhang, Honghua Zhao, Shi Wang, Dianmin Sun
Summary: Attitude detection is crucial for cooperative robots to improve safety, accuracy, and efficiency in their work. It can estimate configuration for robots with unknown parameters, facilitate further kinematics and dynamics analysis. With attitude detection, kinematic parameters of an economical cooperative robot can be corrected.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Hardware & Architecture
Divyendu Kumar Mishra, Aby Thomas, Jinsa Kuruvilla, P. Kalyanasundaram, K. Ramalingeswara Prasad, Anandakumar Haldorai
Summary: To achieve successful autonomous navigation, challenges related to perception, localization, planning, and control functions must be addressed. The introduction of a neuro-fuzzy system explores the benefits of both deliberate and reactive navigation control.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Saikat Sahoo, Madhav Maheshwari, Dilip Kumar Pratihar, Sudipta Mukhopadhyay
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2020)
Article
Engineering, Electrical & Electronic
Saikat Sahoo, Mahesh Saboo, Dilip Kumar Pratihar, Sudipta Mukhopadhyay
IEEE SENSORS JOURNAL
(2020)
Article
Engineering, Electrical & Electronic
Saikat Sahoo, Shivam Kumar Panda, Dilip Kumar Pratihar, Sudipta Mukhopadhyay
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2020)
Article
Engineering, Mechanical
Debasish Das, Amit Kr Das, D. K. Pratihar, G. G. Roy
Summary: Machine learning algorithms were used to predict residual stress during electron beam welding of stainless steel, with support vector regression and locally weighted learning showing consistent good and bad performance respectively. Experimental validation through X-ray diffraction showed good agreements, while statistical tests and Monte-Carlo simulations were used to analyze the reliability of the employed models.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
(2021)
Review
Materials Science, Multidisciplinary
Debasish Das, Sanjib Jaypuria, Dilip Kumar Pratihar, Gour Gopal Roy
Summary: Optimisation has been extensively used in welding to determine optimal input parameters for desired weld attributes and accurately establish an input-output relationship of the welding process. Both traditional and nature-inspired optimisation tools have been utilized for these purposes. Future research areas in the role of optimisation in welding have also been identified.
SCIENCE AND TECHNOLOGY OF WELDING AND JOINING
(2021)
Article
Computer Science, Artificial Intelligence
Amit Kumar Das, Dilip Kumar Pratihar
Summary: A novel mutation scheme and crossover operator are proposed for improving the performance of genetic algorithm in solving real-coded problems, and experiments show that the proposed algorithm outperforms others in terms of accuracy, convergence rate, and computational time.
Article
Computer Science, Artificial Intelligence
Debasish Das, Amit Kumar Das, Abhishek Rudra Pal, Sanjib Jaypuria, Dilip Kumar Pratihar, Gour Gopal Roy
Summary: The study models electron beam welding through Elman and Jordan recurrent neural networks, trained using nature-inspired optimization tools. Experimental results show that the flower pollination-tuned Jordan RNN yields the best prediction results.
NEURAL PROCESSING LETTERS
(2021)
Article
Engineering, Mechanical
Kondalarao Bhavanibhatla, Sulthan Suresh-Fazeela, Dilip Kumar Pratihar
Summary: This study conducted numerical analysis to determine the optimal mounting location for the base of a serial manipulator on a six-legged mobile platform, taking into account factors such as workspace, manipulability, and foot forces' distribution.
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Amit Kumar Das, Dilip Kumar Pratihar
Summary: This paper introduces an intelligent optimization technique called Bonobo Optimizer (BO), which mimics the reproductive strategies and social behavior of Bonobos. The BO is designed to efficiently solve optimization problems by incorporating natural strategies and a unique searching mechanism. Results from testing BO on various functions and real-life optimization problems show its applicability and superior performance compared to other algorithms.
APPLIED INTELLIGENCE
(2022)
Article
Engineering, Electrical & Electronic
Saikat Sahoo, Shivam Kumar Panda, Dilip Kumar Pratihar, Sudipta Mukhopadhyay
Summary: This article proposes a novel method for recognizing locomotion mode and estimating environmental features using laser distance sensors and inertial measurement units. The efficiency of this method was tested with healthy subjects and proved to be accurate and effective for controlling lower limb prosthetic devices.
IEEE SENSORS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Amit Kumar Das, Saikat Sahoo, Dilip Kumar Pratihar
Summary: The necessity of developing orthotic devices for stroke or spinal injury patients has led to the need for more compact and energy efficient designs. In this study, a novel optimal design of an energy-economic knee orthosis has been obtained using the Self-adaptive Bonobo Optimizer (SaBO) algorithm. SaBO has been proven to outperform other optimization techniques in solving difficult benchmark functions and has successfully yielded the most energy-efficient design for knee orthosis, reducing the required maximum motor torque by up to 22%.
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
(2023)
Article
Robotics
Pushpendra Gupta, Dilip Kumar Pratihar, Kalyanmoy Deb
Summary: The gait cycle of the 25-degree of freedom humanoid robot NAO consists of single support phase and double support phase. Both dynamic and stability analyses are conducted to determine its power consumption and dynamic stability margin. Particle swarm optimization and genetic algorithms are used to optimize the single support phase and double support phase separately, and the results show that particle swarm optimization performs better. The study also reveals that a humanoid robot with higher hip height, lower swing height, and slower pace consumes less power during the gait cycle.
INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS
(2023)
Article
Multidisciplinary Sciences
Sanjib Jaypuria, Amit Kumar Das, P. K. C. Kanigalpula, Debasish Das, Dilip Kumar Pratihar, Debalay Chakrabarti, M. N. Jha
Summary: The input-output relationships of electron beam welding are nonlinear and complex. ANFIS-based input-output modeling is used to predict the severity of spiking. Optimization algorithms like grey wolf optimizer, particle swarm optimization, and bonobo optimizer are used to optimize the ANFIS architecture and achieve precise predictions. Multi-objective optimization algorithms are used to solve the conflicting multi-objective criteria problems.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Saikat Sahoo, Dilip Kumar Pratihar, Sudipta Mukhopadhyay
Summary: A novel gait event detection strategy was proposed in this study, which demonstrated improved overall performance by adapting to different locomotion modes through LM classification and rule-base selection.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Multidisciplinary Sciences
Amit Kumar Das, Debasish Das, Sanjib Jaypuria, Dilip Kumar Pratihar, Gour Gopal Roy
Summary: This study utilized ANFIS models to predict weld attributes during EBW of SS201 and found the best prediction accuracy in multi-objective optimization. Interesting observations were made during the experiment, such as the fixed input power and squeezed experimental range for welding speed.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)
