Machine learning-based multi-modal information perception for soft robotic hands

Huang, Haiming; Lin, Junhao; Wu, Linyuan; Fang, Bin; Wen, Zhenkun; Sun, Fei

doi:10.26599/tst.2019.9010009

Cited by 42 publications

(26 citation statements)

References 22 publications

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“…In future work, we will further refine our algorithm by introducing more optimization goals and context factors, such as those in Refs. [26][27][28][29][30][31][32][33]. In addition, how to improve the recommendation performances by optimizing the network load balance [34][35][36] is another research topic that requires intensive study.…”

Section: Discussionmentioning

confidence: 99%

A truncated SVD-based ARIMA model for multiple QoS prediction in mobile edge computing

Yan

Zhang

Zhong

et al. 2022

Tsinghua Sci. Technol.

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In the mobile edge computing environments, Quality of Service (QoS) prediction plays a crucial role in web service recommendation. Because of distinct features of mobile edge computing, i.e., the mobility of users and incomplete historical QoS data, traditional QoS prediction approaches may obtain less accurate results in the mobile edge computing environments. In this paper, we treat the historical QoS values at different time slots as a temporal sequence of QoS matrices. By incorporating the compressed matrices extracted from QoS matrices through truncated Singular Value Decomposition (SVD) with the classical ARIMA model, we extend the ARIMA model to predict multiple QoS values simultaneously and efficiently. Experimental results show that our proposed approach outperforms the other state-of-the-art approaches in accuracy and efficiency.

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Section: Discussionmentioning

confidence: 99%

A truncated SVD-based ARIMA model for multiple QoS prediction in mobile edge computing

Yan

Zhang

Zhong

et al. 2022

Tsinghua Sci. Technol.

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show abstract

“…Therefore, the curvature of the finger is obtained by detecting the light energy loss. A detailed description can be found in [26], written by the authors of this paper.…”

Section: Curvature Detection Sensorsmentioning

confidence: 99%

“…Therefore, the curvature of the finger is obtained by detecting the light energy loss. A detailed description can be found in [26], written by the authors of this paper. The GPS is embedded in a proportional valve, which is used to measure the absolute pressure of gas and connected in the gas channel of each finger in series mode.…”

Section: Curvature Detection Sensorsmentioning

confidence: 99%

Trigger-Based Dexterous Operation with Multimodal Sensors for Soft Robotic Hand

Huang

Lin

et al. 2021

Applied Sciences

Self Cite

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This paper focuses on how to improve the operation ability of a soft robotic hand (SRH). A trigger-based dexterous operation (TDO) strategy with multimodal sensors is proposed to perform autonomous choice operations. The multimodal sensors include optical-based fiber curvature sensor (OFCS), gas pressure sensor (GPS), capacitive pressure contact sensor (CPCS), and resistance pressure contact sensor (RPCS). The OFCS embedded in the soft finger and the GPS series connected in the gas channel are used to detect the curvature of the finger. The CPCS attached on the fingertip and the RPCS attached on the palm are employed to detect the touch force. The framework of TDO is divided into sensor detection and action operation. Hardware layer, information acquisition layer, and decision layer form the sensor detection module; action selection layer, actuator drive layer, and hardware layer constitute the action operation module. An autonomous choice decision unit is used to connect the sensor detecting module and action operation module. The experiment results reveal that the TDO algorithm is effective and feasible, and the actions of grasping plastic framework, pinching roller ball pen and screwdriver, and handshake are executed exactly.

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“…SVM demonstrates its powerful ability in learning from data and shows a strong generalization ability. 65 Denoting a data set of HP-PPIs by the form of…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…Support vector machine (SVM) is widely applied in different areas, including the classification and regression tasks. SVM demonstrates its powerful ability in learning from data and shows a strong generalization ability 65 . Denoting a data set of HP‐PPIs by the form of { x i , y i }, i =1,2,…, N , SVM model generates the prediction with following Equation ().…”

Section: A Two‐layer Model For Prediction Of Hp‐ppismentioning

confidence: 99%

APEX2S: A two‐layer machine learning model for discovery of host‐pathogen protein‐protein interactions on cloud‐based multiomics data

Chen

Shen

Wang

et al. 2020

Concurrency and Computation

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Presented by the avalanche of biological interactions data, computational biology is now facing greater challenges on big data analysis and solicits more studies to mine and integrate cloud-based multiomics data, especially when the data are related to infectious diseases. Meanwhile, machine learning techniques have recently succeeded in different computational biology tasks. In this article, we have calibrated the focus for host-pathogen protein-protein interactions study, aiming to apply the machine learning techniques for learning the interactions data and making predictions. A comprehensive and practical workflow to harness different cloud-based multiomics data is discussed. In particular, a novel two-layer machine learning model, namely APEX2S, is proposed for discovery of the protein-protein interactions data. The results show that our model can better learn and predict from the accumulated host-pathogen protein-protein interactions.

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Machine learning-based multi-modal information perception for soft robotic hands

Cited by 42 publications

References 22 publications

A truncated SVD-based ARIMA model for multiple QoS prediction in mobile edge computing

A truncated SVD-based ARIMA model for multiple QoS prediction in mobile edge computing

Trigger-Based Dexterous Operation with Multimodal Sensors for Soft Robotic Hand

APEX2S: A two‐layer machine learning model for discovery of host‐pathogen protein‐protein interactions on cloud‐based multiomics data

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