This paper presents a novel tip-following approach for real-time position tracking and avoiding obstacle of continuum robots with joint limit constraints. This type of hyper-redundant robot, which is more flexible and...
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Aiming at the current lack of certification capability in domestic industrial controlsystems, an industrial control system authentication scheme based on identity cryptography algorithm is proposed. When authenticati...
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Aiming at the current lack of certification capability in domestic industrial controlsystems, an industrial control system authentication scheme based on identity cryptography algorithm is proposed. When authenticating between control system devices, the scheme integrates the identity-based digital signature algorithm into the SSL/TLS handshake protocol to complete identity authentication between the engineering station and the trusted PLC. At the same time, when the connection is established for the first time between devices, the connection authentication is selected by the challenge/response based method. The authentication technology based on the identity cryptographic algorithm no longer requires the existence of a public key certificate. This eliminates the need to revoke, store, and issue certificates, simplifies the certification process and reduces the amount of computation required by the authentication process. At the same time, the improved handshake protocol is universal in industrial controlsystems. The authentication technology based on the identity and password algorithm no longer requires the existence of a public key certificate, eliminating the need for certificate revocation, storage, and issuance, simplifying authentication steps, and reducing system maintenance. Finally, by establishing a test environment, the improved handshake protocol is implemented, and the feasibility of the authentication scheme is verified and analyzed.
In this paper, a time series prediction model that merges eXtreme Gradient Boosting (XGBoost) and Gate Recurrent Unit (GRU), XGB-GRU model, is proposed for multivariate time series prediction in industry. The XGB-GRU ...
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ISBN:
(数字)9781728152448
ISBN:
(纸本)9781728152455
In this paper, a time series prediction model that merges eXtreme Gradient Boosting (XGBoost) and Gate Recurrent Unit (GRU), XGB-GRU model, is proposed for multivariate time series prediction in industry. The XGB-GRU model uses XGBoost's strong feature extraction capabilities to extract the hidden information of multiple control variables in industrial data. Next, the model uses GRU's unique gating unit to extract the timing information in the industrial data. Finally, the importance of XGBoost output variables to guide actual production and solve the problem of inexplicability of neural networks. Predicting the temperature of the heating furnace verifies that the proposed XGB-GRU is better than a single XGBoost and GRU model. And the model has a good fit to the predicted value.
Medical diagnostic robot systems have been paid more and more attention due to its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI) is considered an effective means to prevent Alzheimer's...
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Medical diagnostic robot systems have been paid more and more attention due to its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI) is considered an effective means to prevent Alzheimer's disease (AD). Doctors diagnose MCI based on various clinical examinations, which are expensive and the diagnosis results rely on the knowledge of doctors. Therefore, it is necessary to develop a robot diagnostic system to eliminate the influence of human factors and obtain a higher accuracy rate. In this paper, we propose a novel Group Feature Domain Adversarial Neural Network (GF- DANN) for amnestic MCI (aMCI) diagnosis, which involves two important modules. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group- level features through adversarial learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed to reduce the distribution difference between the source and target domain in a domain adaption way. On three types of data set, GF-DANN achieves the best accuracy compared with classic machine learning and deep learning methods. On the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the sensitivity and specificity are 90% and 89%. In addition, by comparing three EEG data collection paradigms, our results demonstrate that the DMS paradigm has the potential to build an aMCI diagnose robot system.
Many previous works have facilitated muscle cell (C2C12) alignment to form fiber-like cell structures. However, there still remains a challenge how to induce C2C12 myoblasts in the cell structures to differentiate int...
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Many previous works have facilitated muscle cell (C2C12) alignment to form fiber-like cell structures. However, there still remains a challenge how to induce C2C12 myoblasts in the cell structures to differentiate into matured myocytes to form a functional muscle tissue, while external mechanical stimulation has been proved to have good effects on proliferation and differentiation of myoblasts. In this paper, we proposed a vision-based micro robotic manipulation system to achieve automatic mechanical stimulation for one single muscle fiber-like cell structures (MFCS). A tube, which is attached to a three degree-of-freedom (DOF) manipulator, and a probe are employed to apply the uniaxial mechanical stimulation to train the MFCS. To measure the force applied on MFCS, a vision-based measuring and correction method is utilized, which decrease the error by 74%. Moreover, based on the viscoelastic property of the MFCS, a feedback control algorithm has been applied to compensate for the force loss to realize the force stimulation. And the final value of force remains 699 ± 1μN after 110s experiment.
This paper studies the velocity and altitude consensus problems of multi-hypersonic vehicle systems with disturbances. By the combination of integral sliding-mode control method and high-order disturbance observer tec...
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ISBN:
(数字)9784888983006
ISBN:
(纸本)9781728102634
This paper studies the velocity and altitude consensus problems of multi-hypersonic vehicle systems with disturbances. By the combination of integral sliding-mode control method and high-order disturbance observer technique, consensus protocols for a class of leaderless multi-hypersonic vehicle systems are proposed to achieve the velocity and altitude consensus. Firstly, high-order disturbance observers are designed to estimate the disturbances affecting each hypersonic vehicle. Then, based on integral sliding-mode control and disturbance estimates, a consensus scheme is presented to achieve the velocity and altitude asymptotic consensus of the multi-hypersonic vehicle systems. Moreover, numerical simulations are given to validate the efficiency of the proposed consensus algorithms.
This paper presents a performance degradation analysis for rotate vector (RV) reducer using acoustic emission (AE) techniques. Unlike the AE signals from fixed gear reducer that are steady and have unchanged route, th...
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Human-machine interface with muscle signals serves as an important role in the field of wearable robotics. To compensate for the limitations of the existing surface Electromyography (sEMG) based technologies, we previ...
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ISBN:
(数字)9781728119908
ISBN:
(纸本)9781728119915
Human-machine interface with muscle signals serves as an important role in the field of wearable robotics. To compensate for the limitations of the existing surface Electromyography (sEMG) based technologies, we previously proposed a noncontact capacitive sensing approach that could record the limb shape changes. The sensing approach frees the human skin from contacting to the metal electrodes, thus enabling the measurement of muscle signals by dressing the sensing front-ends outside of the clothes. We validated the capacitive sensing in human motion intent recognition tasks with the wearable robots and produced comparable results to existing studies. However, the biological significance of the capacitance signals is still unrevealed, which is an indispensable issue for robot intuitive control. In this study, we address the problems of identifying the relationships between the muscle morphological parameters and the capacitance signals. We constructed a measurement system that recorded the noncontact capacitive sensing signals and the muscle ultrasound (US) images simultaneously. With the designed device, five subjects were employed and the US images from the gastrocnemius muscle (GM) and the tibialis anterior (TA) muscle during level walking were sampled. We fitted the calculated muscle morphological parameters (the pinnation angles and the muscle fascicle length) and the capacitance signals of the same gait phases. The results demonstrated that at least one-channel capacitance signal strongly correlated to the muscle morphological parameters (R 2 > 0.5, quadratic regression). The average R 2 s of the most correlated channels were up to 0.86 for pinnation angles and 0.83 for the muscle fascicle length changes. The interesting findings in this preliminary study suggest the biological physical significance of the capacitance signals during human locomotion. Future efforts are worth being paid in this new research direction for more promising results.
Abnormal state accumulation over a long period will cause an electrical equipment fault. Therefore, Substation equipment state forecasting plays a vital role in smart grids. Fault forecasting method based on deep lear...
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This paper presents a grasping convolutional neural network with image segmentation for mobile manipulating robot. The proposed method is cascaded by a feature pyramid network FPN and a grasping network DrGNet. The FP...
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