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Prof. Farong Gao
Artificial Intelligence Institute, School of Automation, Hangzhou Dianzi University

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0 Artificial Intelligence
0 Gait Analysis
0 Information Processing
0 Machine Learning
0 Pattern Recognition

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Journal article
Published: 13 August 2021 in Brain Sciences
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Epilepsy is a chronic neurological disorder which can affect 65 million patients worldwide. Recently, network based analyses have been of great help in the investigation of seizures. Now graph theory is commonly applied to analyze functional brain networks, but functional brain networks are dynamic. Methods based on graph theory find it difficult to reflect the dynamic changes of functional brain network. In this paper, an approach to extracting features from brain functional networks is presented. Dynamic functional brain networks can be obtained by stacking multiple functional brain networks on the time axis. Then, a tensor decomposition method is used to extract features, and an ELM classifier is introduced to complete epilepsy prediction. In the prediction of epilepsy, the accuracy and F1 score of the feature extracted by tensor decomposition are higher than the degree and clustering coefficient. The features extracted from the dynamic functional brain network by tensor decomposition show better and more comprehensive performance than degree and clustering coefficient in epilepsy prediction.

ACS Style

Han Li; Qizhong Zhang; Ziying Lin; Farong Gao. Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network. Brain Sciences 2021, 11, 1066 .

AMA Style

Han Li, Qizhong Zhang, Ziying Lin, Farong Gao. Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network. Brain Sciences. 2021; 11 (8):1066.

Chicago/Turabian Style

Han Li; Qizhong Zhang; Ziying Lin; Farong Gao. 2021. "Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network." Brain Sciences 11, no. 8: 1066.

Journal article
Published: 07 July 2021 in Biomedical Signal Processing and Control
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The classification of motor imagery (MI) task based on Electroencephalography (EEG) is an important problem in brain-computer interface (BCI) system. The high-precision classification of MI is a challenging task in which the process of feature extraction is crucial step. In this work, we propose a tensor model of a dynamic brain functional network (DBFN) to decode motion intentions. First, we construct the brain functional network in each small window. Then, the BFN of each time window is superimposed into a DBFN tensor with time as the axis. A tensor decomposition method with orthogonal and partial symmetric constraints is used to analyze the DBFN. Finally, the core tensor features are used as an input of the extreme learning machine (ELM) for classification. The results show that the proposed method is better than the degree, clustering coefficient of network, and principal component analysis of DBFN matrix model and the average accuracies are improved by 17.33%, 12.91%, and 17.5% under ELM, respectively. Moreover, the classification accuracy of the proposed method has the lowest variance, i.e., 5.96, indicating that the core tensor features are more adaptable to the subjects. The proposed method has the highest accuracy of 95% under both ELM and support vector machine (SVM). The average accuracy rates of ELM and SVM are 87.08% and 85.83%, respectively. The proposed method effectively extracts the EEG signal characteristics of MI and has strong robustness. This provides a reference for further research on the feature extraction algorithm of BCI.

ACS Style

Qizhong Zhang; Bin Guo; Wanzeng Kong; Xugang Xi; Yizhi Zhou; Farong Gao. Tensor-based dynamic brain functional network for motor imagery classification. Biomedical Signal Processing and Control 2021, 69, 102940 .

AMA Style

Qizhong Zhang, Bin Guo, Wanzeng Kong, Xugang Xi, Yizhi Zhou, Farong Gao. Tensor-based dynamic brain functional network for motor imagery classification. Biomedical Signal Processing and Control. 2021; 69 ():102940.

Chicago/Turabian Style

Qizhong Zhang; Bin Guo; Wanzeng Kong; Xugang Xi; Yizhi Zhou; Farong Gao. 2021. "Tensor-based dynamic brain functional network for motor imagery classification." Biomedical Signal Processing and Control 69, no. : 102940.

Journal article
Published: 27 June 2021 in Applied Sciences
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In order to obtain panoramic images in a low contrast underwater environment, an underwater panoramic image mosaic algorithm based on image enhancement and improved image registration (IIR) was proposed. Firstly, mixed filtering and sigma filtering are used to enhance the contrast of the original image and de-noise the image. Secondly, scale-invariant feature transform (SIFT) is used to detect image feature points. Then, the proposed IIR algorithm is applied to image registration to improve the matching accuracy and reduce the matching time. Finally, the weighted smoothing method is used for image fusion to avoid image seams. The results show that IIR algorithm can effectively improve the registration accuracy, shorten the registration time, and improve the image fusion effect. In the field of cruise research, instruments equipped with imaging systems, such as television capture and deep-drag camera systems, can produce a large number of image or video recordings. This algorithm provides support for fast and accurate underwater image mosaic and has important practical significance.

ACS Style

Yinsen Zhao; Farong Gao; Jun Yu; Xing Yu; Zhangyi Yang. Underwater Image Mosaic Algorithm Based on Improved Image Registration. Applied Sciences 2021, 11, 5986 .

AMA Style

Yinsen Zhao, Farong Gao, Jun Yu, Xing Yu, Zhangyi Yang. Underwater Image Mosaic Algorithm Based on Improved Image Registration. Applied Sciences. 2021; 11 (13):5986.

Chicago/Turabian Style

Yinsen Zhao; Farong Gao; Jun Yu; Xing Yu; Zhangyi Yang. 2021. "Underwater Image Mosaic Algorithm Based on Improved Image Registration." Applied Sciences 11, no. 13: 5986.

Journal article
Published: 30 April 2021 in Processes
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This work focuses on the study of robust no-wait flow shop scheduling problem (R-NWFSP) under the interval-valued fuzzy processing time, which aims to minimize the makespan within an upper bound on total completion time. As the uncertainty of actual job processing times may cause significant differences in processing costs, a R-NWFSP model whose objective function involves interval-valued fuzzy sets (IVFSs) is proposed, and an improved SAA is designed for its efficient solution. Firstly, based on the credibility measure, chance constrained programming (CCP) is utilized to make the deterministic transformation of constraints. The uncertain NWFSP is transformed into an equivalent deterministic linear programming model. Then, in order to tackle the deterministic model efficiently, a simulated annealing algorithm (SAA) is specially designed. A powerful neighborhood search method and new acceptance criterion are applied to find better solutions. Numerical computations demonstrate the high efficiency of the SAA. In addition, a sensitivity analysis convincingly shows that the applicability of the proposed model and its solution strategy under interval-valued fuzzy sets.

ACS Style

Hao Sun; Aipeng Jiang; Dongming Ge; Xiaoqing Zheng; Farong Gao. A Chance Constrained Programming Approach for No-Wait Flow Shop Scheduling Problem under the Interval-Valued Fuzzy Processing Time. Processes 2021, 9, 789 .

AMA Style

Hao Sun, Aipeng Jiang, Dongming Ge, Xiaoqing Zheng, Farong Gao. A Chance Constrained Programming Approach for No-Wait Flow Shop Scheduling Problem under the Interval-Valued Fuzzy Processing Time. Processes. 2021; 9 (5):789.

Chicago/Turabian Style

Hao Sun; Aipeng Jiang; Dongming Ge; Xiaoqing Zheng; Farong Gao. 2021. "A Chance Constrained Programming Approach for No-Wait Flow Shop Scheduling Problem under the Interval-Valued Fuzzy Processing Time." Processes 9, no. 5: 789.

Research article
Published: 27 February 2021 in Computational Intelligence and Neuroscience
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Accuracy is a key index of human gait recognition. In this paper, we propose an improved gait recognition algorithm, which combines multiple feature combination and artificial bee colony for optimizing the support vector machine (ABC-SVM). Firstly, considering the complexity characteristics of surface electromyography (sEMG) signals, four types of features are extracted from the denoised sEMG signals, including the time-domain features of integral of absolute value (IAV), variance (VAR), and number of zero-crossing (ZC) points, frequency-domain features of mean power frequency (MPF) and median frequency (MF), and wavelet features and fuzzy entropy features. Secondly, the classifiers of SVM, linear discriminant analysis (LDA), and extreme learning machine (ELM) are employed to recognize the gait with obtained features, including singe-class features, multiple combination features, and optimized features of dimension reduction by principal component analysis (PCA). Thirdly, the penalty coefficient and kernel function parameter of the SVM classifier are optimized by the ABC algorithm, and the influence of different features and classifiers on the recognition results is studied. Finally, the feature samples selected to construct the SVM classifier are trained and recognized. Results show that the classification performance of the ABC-SVM classifier is significantly better than that of the nonoptimized SVM classifier, and the average recognition rate is increased by 3.18%. In addition, the combined feature samples (time-domain, frequency-domain, wavelet, and fuzzy entropy features) not only improve the gait classification accuracy but also enhance the recognition stability.

ACS Style

Farong Gao; Taixing Tian; Ting Yao; Qizhong Zhang. Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms. Computational Intelligence and Neuroscience 2021, 2021, 1 -14.

AMA Style

Farong Gao, Taixing Tian, Ting Yao, Qizhong Zhang. Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms. Computational Intelligence and Neuroscience. 2021; 2021 ():1-14.

Chicago/Turabian Style

Farong Gao; Taixing Tian; Ting Yao; Qizhong Zhang. 2021. "Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms." Computational Intelligence and Neuroscience 2021, no. : 1-14.

Journal article
Published: 19 February 2021 in Journal of Marine Science and Engineering
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In this study, an underwater image enhancement method based on local contrast correction (LCC) and multi-scale fusion is proposed to resolve low contrast and color distortion of underwater images. First, the original image is compensated using the red channel, and the compensated image is processed with a white balance. Second, LCC and image sharpening are carried out to generate two different image versions. Finally, the local contrast corrected images are fused with sharpened images by the multi-scale fusion method. The results show that the proposed method can be applied to water degradation images in different environments without resorting to an image formation model. It can effectively solve color distortion, low contrast, and unobvious details of underwater images.

ACS Style

Farong Gao; Kai Wang; Zhangyi Yang; Yejian Wang; Qizhong Zhang. Underwater Image Enhancement Based on Local Contrast Correction and Multi-Scale Fusion. Journal of Marine Science and Engineering 2021, 9, 225 .

AMA Style

Farong Gao, Kai Wang, Zhangyi Yang, Yejian Wang, Qizhong Zhang. Underwater Image Enhancement Based on Local Contrast Correction and Multi-Scale Fusion. Journal of Marine Science and Engineering. 2021; 9 (2):225.

Chicago/Turabian Style

Farong Gao; Kai Wang; Zhangyi Yang; Yejian Wang; Qizhong Zhang. 2021. "Underwater Image Enhancement Based on Local Contrast Correction and Multi-Scale Fusion." Journal of Marine Science and Engineering 9, no. 2: 225.

Journal article
Published: 07 July 2020 in Processes
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Multi-stage flash (MSF) desalination plays an important role in achieving large-scale fresh water driven by thermal energy. In this paper, based on first-principle modeling of a typical multi-stage flash desalination system, the effects of different operational parameters on system performance and operational optimization for cost saving were extensively studied. Firstly, the modelled desalination system was divided into flash chamber modules, brine heater modules, mixed modules and split modules, and based on energy and mass conservation laws the equations were formulated and put together to describe the whole process model. Then, with physical parameter calculation the whole process was simulated and analyzed on the platform of MATLAB, and the water production performance effected by operational parameters such as the feed temperature of seawater, the recycle brine from the discharge section, steam temperature and flowrate of recycled brine were discussed and analyzed. Then, the optimal operation to achieve maximize GOR (gained output ratio) with fixed freshwater demand was considered and performed, and thus the optimal flowrate of recycled brine, steam temperature, and seawater output flowrate from rejection section were obtained based on the established model. Finally, considering that minimizing the daily operational cost is a more rational objective, the operational cost equations were formulated and the optimal problem to minimize the daily operational cost was solved and the optimal manipulated variables at different hours were obtained. The study results can be used for guideline of real time optimization of the MSF system.

ACS Style

Hanhan Gao; Aipeng Jiang; Qiuyun Huang; Yudong Xia; Farong Gao; Jian Wang. Mode-Based Analysis and Optimal Operation of MSF Desalination System. Processes 2020, 8, 794 .

AMA Style

Hanhan Gao, Aipeng Jiang, Qiuyun Huang, Yudong Xia, Farong Gao, Jian Wang. Mode-Based Analysis and Optimal Operation of MSF Desalination System. Processes. 2020; 8 (7):794.

Chicago/Turabian Style

Hanhan Gao; Aipeng Jiang; Qiuyun Huang; Yudong Xia; Farong Gao; Jian Wang. 2020. "Mode-Based Analysis and Optimal Operation of MSF Desalination System." Processes 8, no. 7: 794.

Original research article
Published: 17 March 2020 in Frontiers in Neurology
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Motor Unit Number Index (MUNIX) is a technique that provides a susceptive biomarker for monitoring innervation conditions in patients with neurodegenerative diseases. A satisfactory repeatability is essential for the interpretation of MUNIX results. This study aims to examine the effect of channel number and location on the repeatability of MUNIX. In this study, 128 channels of high-density surface electromyography (EMG) signals were recorded from the biceps brachii muscles of eight healthy participants, at 10, 20, 30, 40, 50, 60, 70, 80, and 100% of maximal voluntary contraction. The repeatability was defined by the coefficient of variation (CV) of MUNIX estimated from three experiment trials. Single-channel MUNIX (sMUNIX) was calculated on a channel-specific basis and a multi-channel MUNIX (mMUNIX) approach as the weighted average of multiple sMUNIX results. Results have shown (1) significantly improved repeatability with the proposed mMUNIX approach; (2) a higher variability of sMUNIX when the recording channel is positioned away from the innervation zone. Our results have demonstrated that (1) increasing the number of EMG channels and (2) placing recording channels close to the innervation zone (IZ) are effective methods to improve the repeatability of MUNIX. This study investigated two potential causes of MUNIX variations and provided novel perspectives to improve the repeatability, using high-density surface EMG. The mMUNIX technique proposed can serve as a promising tool for reliable neurodegeneration evaluation.

ACS Style

Farong Gao; Yueying Cao; Chuan Zhang; Yingchun Zhang. A Preliminary Study of Effects of Channel Number and Location on the Repeatability of Motor Unit Number Index (MUNIX). Frontiers in Neurology 2020, 11, 1 .

AMA Style

Farong Gao, Yueying Cao, Chuan Zhang, Yingchun Zhang. A Preliminary Study of Effects of Channel Number and Location on the Repeatability of Motor Unit Number Index (MUNIX). Frontiers in Neurology. 2020; 11 ():1.

Chicago/Turabian Style

Farong Gao; Yueying Cao; Chuan Zhang; Yingchun Zhang. 2020. "A Preliminary Study of Effects of Channel Number and Location on the Repeatability of Motor Unit Number Index (MUNIX)." Frontiers in Neurology 11, no. : 1.

Journal article
Published: 08 January 2020 in Symmetry
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Surface electromyogram (sEMG) signals are easy to record and offer valuable motion information, such as symmetric and periodic motion in human gait. Due to these characteristics, sEMG is widely used in human-computer interaction, clinical diagnosis and rehabilitation medicine, sports medicine and other fields. This paper aims to improve the estimation accuracy and real-time performance, in the case of the knee joint angle in the lower limb, using a sEMG signal, in a proposed estimation algorithm of the continuous motion, based on the principal component analysis (PCA) and the regularized extreme learning machine (RELM). First, the sEMG signals, collected during the lower limb motion, are preprocessed, while feature samples are extracted from the acquired and preconditioned sEMG signals. Next, the feature samples dimensions are reduced by the PCA, as well as the knee joint angle system is measured by the three-dimensional motion capture system, are followed by the normalization of the feature variable value. The normalized sEMG feature is used as the input layer, in the RELM model, while the joint angle is used as the output layer. After training, the RELM model estimates the knee joint angle of the lower limbs, while it uses the root mean square error (RMSE), Pearson correlation coefficient and model training time as key performance indicators (KPIs), to be further discussed. The RELM, the traditional BP neural network and the support vector machine (SVM) estimation results are compared. The conclusions prove that the RELM method, not only has ensured the validity of results, but also has greatly reduced the learning train time. The presented work is a valuable point of reference for further study of the motion estimation in lower limb.

ACS Style

Yanxia Deng; Farong Gao; Huihui Chen. Angle Estimation for Knee Joint Movement Based on PCA-RELM Algorithm. Symmetry 2020, 12, 130 .

AMA Style

Yanxia Deng, Farong Gao, Huihui Chen. Angle Estimation for Knee Joint Movement Based on PCA-RELM Algorithm. Symmetry. 2020; 12 (1):130.

Chicago/Turabian Style

Yanxia Deng; Farong Gao; Huihui Chen. 2020. "Angle Estimation for Knee Joint Movement Based on PCA-RELM Algorithm." Symmetry 12, no. 1: 130.

Journal article
Published: 01 January 2020 in IFAC-PapersOnLine
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A regionalized environmental knowledge model (REKModel) is presented to describe the environment in the paper. The REKModel is a hierarchical structure in which small regions are grouped together to form superordinate regions. The REKModel is intrinsically hierarchical iterative and nested. Thus an extended nested-graph(ENG) is proposed to construct REKModel. An biomimetic navigation system for mobile robots is presented that is inspired by the fine-to-coarse planning heuristic, a human wayfinding strategy. A online fine-to-coarse pathfinding algorithm designed here allows robots to derives the route with decreasing the level of detail along the route. By using spatial information at different levels of detail for close and coarseness for distance, the memory load and plan complexity are all reduced. The simulation on MobileSim platform verifies the effectiveness and feasibility of the navigation system.

ACS Style

Chaoliang Zhong; Shirong Liu; Qiang Lu; Botao Zhang; Jian Wang; Qiuxuan Wu; Farong Gao. Mobile Robot Navigation Based on Regionalized Spatial Knowledge Representation and Reasoning. IFAC-PapersOnLine 2020, 53, 9728 -9733.

AMA Style

Chaoliang Zhong, Shirong Liu, Qiang Lu, Botao Zhang, Jian Wang, Qiuxuan Wu, Farong Gao. Mobile Robot Navigation Based on Regionalized Spatial Knowledge Representation and Reasoning. IFAC-PapersOnLine. 2020; 53 (2):9728-9733.

Chicago/Turabian Style

Chaoliang Zhong; Shirong Liu; Qiang Lu; Botao Zhang; Jian Wang; Qiuxuan Wu; Farong Gao. 2020. "Mobile Robot Navigation Based on Regionalized Spatial Knowledge Representation and Reasoning." IFAC-PapersOnLine 53, no. 2: 9728-9733.

Journal article
Published: 12 December 2019 in Sensors
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A gait event is a crucial step towards the effective assessment and rehabilitation of motor dysfunctions. However, for the data acquisition of a three-dimensional motion capture (3D Mo-Cap) system, the high cost of setups, such as the high standard laboratory environment, limits widespread clinical application. Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. Inertial sensors are now sufficiently small in size and light in weight to be part of a body sensor network for the collection of human gait data. The acceleration signal has found important applications in human gait recognition. In this paper, using the experimental data from the heel and toe, first the wavelet method was used to remove noise from the acceleration signal, then, based on the threshold of comprehensive change rate of the acceleration signal, the signal was primarily segmented. Subsequently, the vertical acceleration signals, from heel and toe, were integrated twice, to compute their respective vertical displacement. Four gait events were determined in the segmented signal, based on the characteristics of the vertical displacement of heel and toe. The results indicated that the gait events were consistent with the synchronous record of the motion capture system. The method has achieved gait event subdivision, while it has also ensured the accuracy of the defined gait events. The work acts as a valuable reference, to further study gait recognition.

ACS Style

Chang Mei; Farong Gao; Ying Li. A Determination Method for Gait Event Based on Acceleration Sensors. Sensors 2019, 19, 5499 .

AMA Style

Chang Mei, Farong Gao, Ying Li. A Determination Method for Gait Event Based on Acceleration Sensors. Sensors. 2019; 19 (24):5499.

Chicago/Turabian Style

Chang Mei; Farong Gao; Ying Li. 2019. "A Determination Method for Gait Event Based on Acceleration Sensors." Sensors 19, no. 24: 5499.

Conference paper
Published: 01 December 2019 in 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO)
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Considering the complexity and functionality of multi-muscle synergies, involved in the human lower limb gait movement, a gait function evaluation method, based on synergy structure, is proposed. Firstly, the surface electromyography (sEMG) from selected muscles are collected and pretreated, to extract its envelope. Next, it is decoupled by the algorithm of non-negative matrix decomposition, so that the synergy elements of the gait action can be extracted and the corresponding activation coefficients calculated, while subsequently these data are converted into normalized form. Then, the energy distribution and complexity of the synergy motion are analyzed. Finally, the function of gait motion is mapped, according to synergy structure, to determine whether the gait phases are normal or not. This study is helpful for quantitative analysis of lower limb gait and evaluation of motor function, in the framework of rehabilitation therapy.

ACS Style

Yanxia Deng; Farong Gao; Chao Chen; Ying Cao. Gait Motor Function Evaluation Based on Muscle Synergy Method*. 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO) 2019, 2072 -2078.

AMA Style

Yanxia Deng, Farong Gao, Chao Chen, Ying Cao. Gait Motor Function Evaluation Based on Muscle Synergy Method*. 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). 2019; ():2072-2078.

Chicago/Turabian Style

Yanxia Deng; Farong Gao; Chao Chen; Ying Cao. 2019. "Gait Motor Function Evaluation Based on Muscle Synergy Method*." 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO) , no. : 2072-2078.

Conference paper
Published: 02 August 2019 in Algorithms and Data Structures
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The shape changes of soft organisms demonstrate the survival rules in the evolution of self life. It is important for the transform control of soft robots about how to envolve suitable for the shape demands. In this paper, the compositional pattern producing networks (CPPN) algorithm was used to evolve soft robot. By taking simple random functions as genotype inputs, the functions can be weighted combinations to generate the desired phenotype, which mapping relationship between genotypes and phenotypes can be achieved. The VoxCAD simulation software was used to build the three-dimensional topological structure of soft robot and the evolution process of the virtual life was realized by using the specific rule shape to simulate the real environment. The evolutionary analysis of the four-legged walking soft robot was carried out in the simulation experiment, which the effectiveness of the method was verified.

ACS Style

Yueqin Gu; Xuecheng Zhang; Qiuxuan Wu; Yancheng Li; Botao Zhang; Farong Gao; Yanbin Luo. Research on Motion Evolution of Soft Robot Based on VoxCAD. Algorithms and Data Structures 2019, 26 -37.

AMA Style

Yueqin Gu, Xuecheng Zhang, Qiuxuan Wu, Yancheng Li, Botao Zhang, Farong Gao, Yanbin Luo. Research on Motion Evolution of Soft Robot Based on VoxCAD. Algorithms and Data Structures. 2019; ():26-37.

Chicago/Turabian Style

Yueqin Gu; Xuecheng Zhang; Qiuxuan Wu; Yancheng Li; Botao Zhang; Farong Gao; Yanbin Luo. 2019. "Research on Motion Evolution of Soft Robot Based on VoxCAD." Algorithms and Data Structures , no. : 26-37.

Conference paper
Published: 01 August 2019 in 2019 IEEE International Conference on Real-time Computing and Robotics (RCAR)
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For soft robots, establishing accurate models is the key to research. The traditional modeling methods are based on the ideal conditions, and the physical properties of the materials in the actual modeling are not considered, and the accuracy and complexity of the model are unsatisfactory. Therefore, it is necessary to further analyze and study the kinematics model of the soft robot. So this paper is inspired by the flexible wrist arm of the octopus in the ocean. It proposes a modular-based modeling idea, establishes the physical model of a single module in SolidWorks software, and the model is transformed into MATLAB to build a soft robot model, and then transforms the model into Matlab to build a soft robot model. And give a certain force and control signal. Through the simulation analysis of the four-arm soft robot, it is found that the flexible arm model can follow the given control signal, achieve stable output, and walk a certain distance, thus verifying the accuracy of the modular modeling idea.

ACS Style

Yueqin Gu; Xuecheng Zhang; Qiuxuan Wu; Botao Zhang; Farong Gao; Jian Wang. Modeling and Motion Analysis of Flexible Arm Inspired by Octopus. 2019 IEEE International Conference on Real-time Computing and Robotics (RCAR) 2019, 557 -561.

AMA Style

Yueqin Gu, Xuecheng Zhang, Qiuxuan Wu, Botao Zhang, Farong Gao, Jian Wang. Modeling and Motion Analysis of Flexible Arm Inspired by Octopus. 2019 IEEE International Conference on Real-time Computing and Robotics (RCAR). 2019; ():557-561.

Chicago/Turabian Style

Yueqin Gu; Xuecheng Zhang; Qiuxuan Wu; Botao Zhang; Farong Gao; Jian Wang. 2019. "Modeling and Motion Analysis of Flexible Arm Inspired by Octopus." 2019 IEEE International Conference on Real-time Computing and Robotics (RCAR) , no. : 557-561.

Conference paper
Published: 02 October 2018 in Lecture Notes in Control and Information Sciences
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Motor imagery electroencephalography (EEG) has been successfully used in the brain-computer interface (BCI) systems. Broad learning (BL) is an effective and efficient incremental learning algorithm with simple neural network structure. In this work, a novel EEG multi-classification method is proposed by combining with BL and common spatial pattern (CSP). Firstly, the CSP algorithm with the one-versus-the-test scheme is exploited to extract the discriminative multiclass brain patterns from raw EEG data, and then the BL algorithm is applied to the extracted features to discriminate the classes of EEG signals during different motor imagery tasks. Finally, the effectiveness of the proposed method has been verified on four-class motor imagery EEG data from BCI Competition IV Dataset 2a. Compare with other methods including ELM, HELM, DBN and SAE, the proposed method has yielded higher average classification test accuracy with less training time-consuming. The proposed method is meaningful and may have potential to apply into BCI field.

ACS Style

Jie Zou; Qingshan She; Farong Gao; Ming Meng. Multi-task Motor Imagery EEG Classification Using Broad Learning and Common Spatial Pattern. Lecture Notes in Control and Information Sciences 2018, 3 -10.

AMA Style

Jie Zou, Qingshan She, Farong Gao, Ming Meng. Multi-task Motor Imagery EEG Classification Using Broad Learning and Common Spatial Pattern. Lecture Notes in Control and Information Sciences. 2018; ():3-10.

Chicago/Turabian Style

Jie Zou; Qingshan She; Farong Gao; Ming Meng. 2018. "Multi-task Motor Imagery EEG Classification Using Broad Learning and Common Spatial Pattern." Lecture Notes in Control and Information Sciences , no. : 3-10.

Conference paper
Published: 26 September 2018 in Security Education and Critical Infrastructures
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In order to evaluate the effects of time domain (TD) and frequency domain (FD) features as well as muscle number on gait classification recognition, eight channels of electromyography (EMG) signals were collected from four thigh and four lower leg muscles, and two TD features and two FD features were extracted in this study. The method of support vector machine (SVM) was presented to investigate the classification property. For the classification stability and accuracy, 3-fold cross validation was verified and selected to classify the lower limb gait. The results show that the FD features can obtain higher accuracy than TD features. In addition, accuracy of gait recognition increased with the augment of muscle number.

ACS Style

Yueying Cao; Farong Gao; Liling Yu; Qingshan She. Gait Recognition Based on EMG Information with Multiple Features. Security Education and Critical Infrastructures 2018, 402 -411.

AMA Style

Yueying Cao, Farong Gao, Liling Yu, Qingshan She. Gait Recognition Based on EMG Information with Multiple Features. Security Education and Critical Infrastructures. 2018; ():402-411.

Chicago/Turabian Style

Yueying Cao; Farong Gao; Liling Yu; Qingshan She. 2018. "Gait Recognition Based on EMG Information with Multiple Features." Security Education and Critical Infrastructures , no. : 402-411.

Conference paper
Published: 09 August 2018 in Privacy Enhancing Technologies
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Human walking is the composite movement of the musculoskeletal system in lower limbs. The interaction mechanism of the different muscle groups in a combination action is of great importance. To this end, under the stand-squat and squat-stand tasks, the problems of the motion model decomposition and the muscle synergy were studied in this paper. Firstly, the envelopes were extracted from acquired and de-noised surface electromyography (sEMG) signals. Secondly, the non-negative matrix factorization (NMF) algorithm was explored to decompose the four synergistic modules and the corresponding activation coefficients under the two tasks. Finally, the relationship between the muscle synergy and the lower limb movement was discussed in normal and fatigue subjects. The results show that muscle participation of each synergistic module is consistent with the physiological function, and exhibit some differences in muscle synergies between normal and fatigue states. This work can help to understand the control strategies of the nervous system in lower extremity motor and have some significance for the evaluation of limb rehabilitation.

ACS Style

Chao Chen; Farong Gao; Chunling Sun; Qiuxuan Wu. Muscle Synergy Analysis for Stand-Squat and Squat-Stand Tasks with sEMG Signals. Privacy Enhancing Technologies 2018, 545 -552.

AMA Style

Chao Chen, Farong Gao, Chunling Sun, Qiuxuan Wu. Muscle Synergy Analysis for Stand-Squat and Squat-Stand Tasks with sEMG Signals. Privacy Enhancing Technologies. 2018; ():545-552.

Chicago/Turabian Style

Chao Chen; Farong Gao; Chunling Sun; Qiuxuan Wu. 2018. "Muscle Synergy Analysis for Stand-Squat and Squat-Stand Tasks with sEMG Signals." Privacy Enhancing Technologies , no. : 545-552.

Conference paper
Published: 01 July 2018 in 2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM)
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Unlike traditional rigid robots, soft robots can deform in a wide range to suit environment and contact compliantly with operating objects. Therefore, soft robots have wide potential application prospects in terms of physical rehabilitation, minimally invasive surgery and other fields. In this paper, we present an omni-directional flexural inflatable flexible arm model, which consists of three independent controllable pneumatic components. The components have different elongations under the effect of pressure and flexible arm is bending deformation. Under ideal conditions, it can be achieved 90° bending and 360° twisting. Based on the Yeoh model of hyperelastic rubber material and the geometric analysis method, we present the mathematical model of the flexible arm and FEM capabilities. We use finite element analysis to simulate the actuation characteristics of these modules. We compared the analytical and computational results to experimental results and can be used for the future design and control of soft robotic actuators.

ACS Style

Zhou Xue; Qiuxuan Wu; Farong Gao. Design and Modeling of Omni-directional Bending Pneumatic Flexible Arm. 2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM) 2018, 835 -839.

AMA Style

Zhou Xue, Qiuxuan Wu, Farong Gao. Design and Modeling of Omni-directional Bending Pneumatic Flexible Arm. 2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM). 2018; ():835-839.

Chicago/Turabian Style

Zhou Xue; Qiuxuan Wu; Farong Gao. 2018. "Design and Modeling of Omni-directional Bending Pneumatic Flexible Arm." 2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM) , no. : 835-839.

Conference paper
Published: 06 August 2017 in Transactions on Petri Nets and Other Models of Concurrency XV
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To improve the recognition accuracy of the lower limb gait, a classification method based on genetic algorithm (GA) optimizing the support vector machine (SVM) was proposed. Firstly, electromyography (EMG) signals were collected from four thigh muscles related to lower limb movements. Then the values of variance and integral of absolute were extracted as the useful features from de-noised EMG signals. Finally, the penalty parameter and the kernel parameter were optimized by GA. The results show that the GA-SVM classifier can effectively identify five gait phases of the extremity motion, and the average accuracy is increased by 6.56%, higher than the non-parameter-optimized SVM method.

ACS Style

Ying Li; Farong Gao; Xiao Zheng; Haitao Gan. Gait Recognition Using GA-SVM Method Based on Electromyography Signal. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 10462, 313 -322.

AMA Style

Ying Li, Farong Gao, Xiao Zheng, Haitao Gan. Gait Recognition Using GA-SVM Method Based on Electromyography Signal. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; 10462 ():313-322.

Chicago/Turabian Style

Ying Li; Farong Gao; Xiao Zheng; Haitao Gan. 2017. "Gait Recognition Using GA-SVM Method Based on Electromyography Signal." Transactions on Petri Nets and Other Models of Concurrency XV 10462, no. : 313-322.

Journal article
Published: 01 July 2017 in IFAC-PapersOnLine
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ACS Style

Chaoliang Zhong; Shirong Liu; Botao Zhang; Qiang Lu; Jian Wang; Qiuxuan Wu; Farong Gao. A Fast On-line Global Path Planning Algorithm Based on Regionalized Roadmap for Robot Navigation. IFAC-PapersOnLine 2017, 50, 319 -324.

AMA Style

Chaoliang Zhong, Shirong Liu, Botao Zhang, Qiang Lu, Jian Wang, Qiuxuan Wu, Farong Gao. A Fast On-line Global Path Planning Algorithm Based on Regionalized Roadmap for Robot Navigation. IFAC-PapersOnLine. 2017; 50 (1):319-324.

Chicago/Turabian Style

Chaoliang Zhong; Shirong Liu; Botao Zhang; Qiang Lu; Jian Wang; Qiuxuan Wu; Farong Gao. 2017. "A Fast On-line Global Path Planning Algorithm Based on Regionalized Roadmap for Robot Navigation." IFAC-PapersOnLine 50, no. 1: 319-324.