Different from existing protocols for achieving fixed-time convergence, a novel fixed-time protocol with an adaptive gain is proposed for solving the distributed optimization problem over networks. Based on the zero-g...
Different from existing protocols for achieving fixed-time convergence, a novel fixed-time protocol with an adaptive gain is proposed for solving the distributed optimization problem over networks. Based on the zero-gradient-sum straetgy, the problem will reduced to the fixed-time consensus problem and the convergence time is determined by the frequency of a sine function. Besides, some comments on the practical implementation are also given and it is found that the proposed protocol maintains strong robustness to input saturations. Two illustrative examples are finally provided to validate all the mentioned results.
This paper investigates two distributed optimization schemes with or without feasible set constraints over unbalanced graphs,*** overcome the inaccuracy convergence problem of unbalanced graphs,we propose two continuo...
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This paper investigates two distributed optimization schemes with or without feasible set constraints over unbalanced graphs,*** overcome the inaccuracy convergence problem of unbalanced graphs,we propose two continuous time algorithms with an inexact gradient tracking *** first one is a distributed optimization algorithm without feasible set constraints and the second one is improved from the first one with feasible set ***,we provide the convergence analyses for the two optimization algorithms.
This paper presents a low delay architecture design method for the hardware implementation of the Advanced Encryption Standard (AES) algorithm. The proposed architecture is designed by using constant matrix multiplica...
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Sparse reconstruction is widely popular in many *** Bayesian learning(SBL) approach has been applied to capture and recover the spectra variables via a small number of ***,the computational cost of the reconstruction ...
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Sparse reconstruction is widely popular in many *** Bayesian learning(SBL) approach has been applied to capture and recover the spectra variables via a small number of ***,the computational cost of the reconstruction is high because an inverse of large matrix needs to be calculated in each *** this paper,a fast method to sparse reconstruction for near-infrared(NIR) spectra is proposed based on block sparse Bayesian learning ***,the minimum cost function obtained the optimal hyper-parameters is taken by using the fast marginalized likelihood maximization method,which is greatly helpful to reduce the computational ***,a real NIR spectra is used to verified the proposed method by comparing with full-spectral based PLS and SBL+PLS *** experimental results explained that the proposed method has better performance than other two methods.
Visual SLAM based on ORB features will increase the computational pressure of SLAM system due to the large amount of feature extraction and matching computation and the need to screen a large number of mismatched poin...
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Visual SLAM based on ORB features will increase the computational pressure of SLAM system due to the large amount of feature extraction and matching computation and the need to screen a large number of mismatched point pairs. It cannot completely eliminate the mismatched point pairs, which will affect the camera positioning accuracy of visual SLAM system to a certain extent. To solve the two questions, the PROSAC algorithm is used, screening point of mismatch on, all matching points by calculation of evaluation function, selection to match point to build the model with the highest quality, through continuous to join interior point, build the final model, screening point pairs of mismatch. Provide high quality data for camera pose estimation and back end optimization of SLAM system. Through the comparison of RANSAC algorithm and PROSAC algorithm false match screening time, as well as tracking error. PROSAC algorithm effectively reduced the time of mismatching screening, with a maximum improvement of 100 times. The tracking error has also improved significantly.
In this paper, an event-triggered optimal adaptive control is developed for robot trajectory tracking system. Due to the nonlinearity of Hamilton function, we apply the actor-critic neural network structure to solve i...
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ISBN:
(数字)9781728177090
ISBN:
(纸本)9781728177106
In this paper, an event-triggered optimal adaptive control is developed for robot trajectory tracking system. Due to the nonlinearity of Hamilton function, we apply the actor-critic neural network structure to solve it. Firstly, the critic network is used to estimate the cost function and the actor network is used to estimate the optimal event-triggered control law. Due to the advantage of event-triggered method, the weight update rate of actor-critic neural network only occurs when the triggering condition is violated, which save a lot of communication resources. Then, the event-triggered robot trajectory tracking system is ultimately bounded by Lyapunov stability analysis. Finally, the simulation show that the proposed method is effective.
This paper presents a low delay architecture design method for the hardware implementation of the Advanced Encryption Standard (AES) algorithm. The proposed architecture is designed by using constant matrix multiplica...
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ISBN:
(纸本)9781665409858
This paper presents a low delay architecture design method for the hardware implementation of the Advanced Encryption Standard (AES) algorithm. The proposed architecture is designed by using constant matrix multiplications (CMM) merging technologies. To reduce the area cost in hardware implementations, S-box/InvS-box is usually implemented with composite field arithmetic (CFA) technologies. In this paper, CMM in CFA-based S-box/InvS-box are further merged with constant coefficient multiplications in MixColumns/ InvMixColumns, which can also be expressed as CMM forms. By the merging, the delay of the hardware implementation of encryption round transform is reduced at the cost of slight area cost increasing, and both delay and area cost are reduced in hardware implementations of decryption round transform. Hardware complexities analysis indicates that our designs have less delay compared with previous works.
This work studies the event-triggered control problem for networked control systems. The plant is controlled directly by a dynamic local controller, which receives the reference control signal from the remote controll...
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ISBN:
(数字)9781728177090
ISBN:
(纸本)9781728177106
This work studies the event-triggered control problem for networked control systems. The plant is controlled directly by a dynamic local controller, which receives the reference control signal from the remote controller. The measurement signal of the plant and the reference control signal of the remote controller has their separate event trigger, and thus the remote controller and the local controller can decide when to transmit signals on their own. It is proved that with the proposed control approach and the dual event trigger, the closed loop system is globally asymptotically stable, which has been illustrated by simulation results.
High resolution and large range force/torque (F/T) measurements are usually required in many engineering tasks. However, most existing F/T sensors only have a fixed resolution over their whole ranges. The key lies in ...
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High resolution and large range force/torque (F/T) measurements are usually required in many engineering tasks. However, most existing F/T sensors only have a fixed resolution over their whole ranges. The key lies in that it is difficult to well balance high resolution and large range in the sensor design. Taking the torque sensor for example, this paper presents a better compromise for this problem i.e., a novel variable resolution torque sensor based on variable stiffness principle. From the structural points of view, the sensor is constructed with multiple radial flexures to achieve a pure rotational motion with negligible parasitic center motions. Two resistive strain gauges (RSGs) are selected as the measuring units of the sensor to detect the applied external torque and meanwhile provide variable resolutions in the two different measuring ranges (each RSG for one range). Static and dynamic models of the sensor are established in details and validated through finite element analysis (FEA) to evaluate its characteristics. A principle prototype is finally fabricated and tested to verify the effectiveness of the presented design. RSGs are calibrated through a commercial six-axis F/T sensor from ATI Industrial Automation, Inc. Experimental results show that the torque sensor can provide high and low resolutions in the small and large ranges respectively and possesses the first natural frequency of 67.3 Hz. In addition, the proposed variable resolution method can also be applied to the development of multi-axis F/T sensors.
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