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558 results on '"Hao, Lina"'

52. Design of Modular Humanoid Robotic Hand Driven by SMA Actuator

Author

Chen, Yang, Guo, Shaofei, Yang, Hui, Hao, Lina, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Sun, Yu, editor, and Falco, Joe, editor

Published
2018
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57. Extreme Learning Machine Based Modified Deep Auto-Encoder Network Classifier Algorithm

Author

Cao, Ruimin, Wang, Fengli, Hao, Lina, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Sun, Fuchun, editor, Liu, Huaping, editor, and Hu, Dewen, editor

Published
2017
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77. A Method for Precise Tracking Control of Pneumatic Artificial-Muscle-Driven Exoskeletal Robot.

Author

Ma, Gaoke, Jia, Hongyun, Xiao, Jichun, and Hao, Lina

Subjects
PARTICLE swarm optimization, PNEUMATIC control, ARTIFICIAL muscles, ELBOW, ROBOT dynamics, ROBOT design & construction, TORQUE control
Abstract

Exoskeletal robots are of critical importance in the domain of mechanical boosting. The pneumatic artificial muscle (PAM) is commonly used as a flexible actuator in exoskeletal robots designed for upper limbs due to its high power-to-weight ratio, conformability, and safety. This study establishes a new model based on the existing model to improve its control precision by implementing elastic and frictional forces and empirical coefficients, battling against the time-variant hysteresis that PAM's output force exhibits. In the meantime, a BP neural network is employed in reverse modeling, followed by the adoption of the least-square-based particle swarm optimization algorithm in order to determine the optimized parameter values. PAM provides the Upper Limb Exoskeletal Robot with appropriate auxiliary power, which can be adjusted to accommodate variations in posture change during the lifting process. PAM is also capable of handling variable loads based on the principle of torque balance, constructing a control system according to the inverse dynamics of exoskeletal robots accompanied by an inverse model of PAM's output force, and finally, rendering tracking control of the elbow angle during the auxiliary process possible. Finally, the tracking error results are calculated and shown; the maximum angular error in the tracking process is 0.0175 rad, MAE value is 0.0038 rad, RMSE value is 0.0048 rad, and IEAT value is 4.6426 rad. This control method is able to improve the precision of tracking control of the elbow angle of the upper limb–exoskeleton coupled system during the process of lifting goods. [ABSTRACT FROM AUTHOR]

Published
2023
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89. Changes in the Toolik Lake Ecosystem of Alaska in the Past Decades

Author

HAO, LINA and Hao, Xianjun

Abstract

Arctic regions are experiencing faster climate change than any other region in the world. Since the Arctic plays a critical role in the carbon cycle and strongly affects the thermal state of the surrounding permafrost, it is important to understand the impact of Arctic climate changes. This study investigates the climate and ecosystem changes of the Toolik Lake area in Alaska. Satellite remote sensing measurements and long-term baseline environmental and biological data from the Environmental Data Center (EDC) of the Toolik Field Station (TFS) were used to analyze this climate change and its impact on ecosystems. Land surface temperature (LST) data from the Terra/MODIS satellite remote sensing measurements show significant warming of the Toolik lake area in the past two decades, with an increasing trend of 1.049K/decade and 1.186K/decade for daytime and nighttime LST respectively. MODIS NDVI data during the summer season also shows a significant greening trend. EDC field observations show that air temperature above the Toolik lake has an increasing trend of 0.6762K/Decade from 1988 to the present, and 0.8598K/decade from 2000 to present, indicating that there has been faster warming in the past two decades. The thawing times of the Toolik Lake and the inlet river are becoming earlier, and the freezing times of the Toolik Lake and the inlet river are becoming later, causing the ice-free period of the lake to increase significantly. These changes have considerable impacts on local and regional ecosystems; for example, the first seen times of the Arctic ground squirrel around the study area are becoming earlier, and the hibernation periods are becoming shorter, as shown from the EDC field survey data. This study confirmed the rapid warming of Arctic regions and showed changes in the Toolik Lake ecosystem caused by climate change. In future research, these methods can be applied to other regions near the Arctic to gain further insight into changes in the Arctic regions caused by climate change., Journal of Student-Scientists' Research, Vol. 2 (2020)

Published
2022
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90. Structure and Electromagnetic Actuation Systems of Microrobot

Author

Zhu, Shuqiang, Hao, Lina, Wang, Bo, Li, Zhong, Dong, Jie, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Su, Chun-Yi, editor, Rakheja, Subhash, editor, and Liu, Honghai, editor

Published
2012
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91. Acceptance and needs of medication literacy education among children by their caregivers: A multicenter study in mainland China

Author

Xu, Xiaolin, Wang, Zhonglan, Li, Xialei, Li, Ying, Wang, Yu, Wu, Xuexin, Hao, Lina, and Wang, Xiaoling

Subjects
Pharmacology, Pharmacology (medical)
Abstract

Background: This study aims to investigate the needs of child caregivers for popular science about safe medication for children, to deeply explore the characteristics of child caregivers’ demand for safe medication and the shortcomings of current popular science work, and then to seek better coping strategies to ensure children’s safe medication.Methods: A questionnaire was designed based on Lasswell’s “5W” communication model to investigate the needs of child caregivers in terms of content, channels, and forms of popular healthcare science on the safe usage of children’s medication.Results: The primary ways caregivers receive popular healthcare science education concerning safe medication usage knowledge are through medical institutions, notification by medical staff, and personal media. The caregivers of children have a high demand for the presentation of text, pictures, and videos in three forms of popular healthcare science content. Caregivers placed significant importance on the popularization of safe medication usage for children. The survey results showed that the top 3 ways for caregivers to think that the quality of popular healthcare science content was “very good” came from medical institutions, medical staff notifications, and personal media, effectively increasing popular healthcare information accuracy. The intelligibility and pertinence of content expression are urgently needed within the caregiver population.Conclusion: Caregivers are very concerned about the popular science of safe medication for children, and are willing to learn about relevant content. Guided by the demand, we should actively disseminate accurate and easy-to-understand popular science about safe medication for children to caregivers through online or offline channels so as to promote safe medication for children.

Published
2022

97. Distributed Intelligent Traffic Data Processing and Analysis Based on Improved Longhorn Whisker Algorithm

Author

Li, Xing, Hu, Zhenlong, Shen, Yajing, Hao, Lina, and Shang, Wanfeng

Abstract

The purpose is to optimize the Beetle Antenna Search (BAS) algorithm and apply it to the Intelligent Transportation System (ITS) to process traffic data in time and solve traffic congestion. This work studies the development status of ITS and the application status of the BAS algorithm. It optimizes BAS to converge to local optimization prematurely in high-dimensional space, affecting the prediction accuracy. Then, combined with the Least Squares Support Vector Machine Algorithm (LSSVM), the algorithm with quadratic interpolation optimization is proposed. The proposed algorithm is named the Quadratic Interpolation Beetle Antenna Search (QIBAS). On this basis, a traffic flow prediction model based on QIBAS-LSSVM is established. Finally, the improved QIBAS algorithm and Traffic Flow Prediction (TFP) model are verified. The results show that the test Mean Square Error (MSE) of the TFP model based on QIBAS-LSSVM increases by 4.28%, 7.38%, and 18.23%, respectively compared with the other three models. The test Mean Absolute Percentage Error (MAPE) increases by 0.09%, 0.06%, and 0.36% respectively. The proposed QIBAS algorithm has a good effect and high accuracy in short-term TFP. The research has important reference value for the digital transformation of transportation systems in modern smart cities.

Published
2023
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98. Intelligent Traffic Data Transmission and Sharing Based on Optimal Gradient Adaptive Optimization Algorithm

Author

Li, Xing, Zhang, Haotian, Shen, Yajing, Hao, Lina, and Shang, Wanfeng

Abstract

This work aims to improve the transmission and sharing efficiency of intelligent transportation data and promote the further development of intelligent transportation and smart city. In this work, an EEMR (Energy Efficient Multi-hop Routing) is designed for the intelligent transportation wireless sensor network. In the EEMR algorithm, the base station runs the IAP clustering algorithm for network clustering after receiving the information of all surviving nodes. A method called ACRR (Adaptive Cluster-head Round Robin) is proposed for local dynamic election of cluster heads. In addition, the deep learning-based stochastic gradient descent algorithm and its evolution algorithm are sorted out, and its application in practical scenarios is analyzed. Due to the disadvantages of the adaptive algorithm in the current data processing process, the gradient optimization algorithm based on deep learning is adopted and the concept of adaptive friction coefficient is applied to the Adam algorithm to obtain a new adaptive algorithm (TAdam). The simulation experiment reveals that the proportion of network surviving nodes of the EEMR algorithm is still as high as 90% in 1,500 rounds of data collection. This shows that the EEMR algorithm achieves the energy balance of the network nodes as much as possible while minimizing the system energy consumption. In application scenario 1, when the network surviving nodes of the four data collection algorithms compares dropped below 40%, the number of surviving nodes in the EEMR algorithm is still as high as 97.6%. On the PTB (Penn Tree Bank) test data set, the TAdam algorithm shows the fastest convergence speed and the best generalization performance. The TAdam algorithm based on deep learning discussed in this work was of great significance for improving the transmission and sharing efficiency of intelligent transportation data.

Published
2023
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