Accurate and robust power system state estimation (PSSE) is an essential prerequisite for reliable operation of smart power grids. In contrast to the commonly employed weighted least squares (WLS) one, the least-absol...
Accurate and robust power system state estimation (PSSE) is an essential prerequisite for reliable operation of smart power grids. In contrast to the commonly employed weighted least squares (WLS) one, the least-absolute-value (LAV) estimator is well documented for its robustness. Due to the non-convexity and non-smoothness however, existing LAV implementations are typically slow, thus inadequate for real-time system monitoring. In this context, this paper puts forward a novel LAV estimator leveraging recent algorithmic advances in composite optimization. Concretely, the estimator is based on a proximal linear procedure that deals with a sequence of convex quadratic problems, each efficiently solvable by means of either standard convex optimization methods, or the alternating direction method of multipliers. Simulated tests using two IEEE benchmark networks showcase its improved robustness and computational efficiency relative to several competing alternatives.
Traffic congestion is a serious problem around the world and to a great extent influences urban communities in various manners including increased stress levels, delayed deliveries, fuel wastage, and monetary losses. ...
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Traffic congestion is a serious problem around the world and to a great extent influences urban communities in various manners including increased stress levels, delayed deliveries, fuel wastage, and monetary losses. Therefore, an accurate congestion prediction algorithm to limit these misfortunes is fundamental. This paper presents a comparative study of traffic congestion prediction systems including decision tree, logistic regression, and neural networks. Five days of traffic information (1,231,200 samples) are utilized to drive the prediction model. The TensorFlow and the Clementine machine learning platforms are used for data preprocessing, training, and testing of the model. The confusion matrix clears that decision tree has better prediction performance and leads the other two methods with accuracy (97%), macro-average precision (95%), macro-average recall (96%), and macro-average F1_score (96%) in the python programming environment. Moreover, performance of the three prediction models is verified in Clementine environment and decision tree outperforms all other models with an accuracy of 97.65%.
In an electro-hydraulic servo control system,the force servo system is an important ***,due to the nonlinear characteristic of hydraulic systems,traditional control methods cannot achieve satisfactory control *** deal...
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In an electro-hydraulic servo control system,the force servo system is an important ***,due to the nonlinear characteristic of hydraulic systems,traditional control methods cannot achieve satisfactory control *** deal with this issue,a load velocity compensation algorithm based on the structural invariant principle is proposed in this ***,the theoretical analysis of the hydraulic and cylindrical force control system is presented,and the mathematical model of the force control system is *** the open-loop frequency response characteristics of the system are analyzed,in which the Bode diagram shows that the bandwidth of the system is obviously expanded after adopting the load velocity compensation ***,apractical hydraulic and cylindrical force servo system is introduced to validate the feasibility of the proposed controller,the experimental results demonstrate that the proposed method can improve the performance of force control and eliminate the influence of load stiffness on the dynamic characteristics of the system through a set of comparative experiments with different elastic loads.
To address the problem of data fusion between monocular camera image with 3 D data from laser detection and ranging(LADAR)sensor,this paper proposes a novel simplified scheme based on the planar feature method,which c...
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To address the problem of data fusion between monocular camera image with 3 D data from laser detection and ranging(LADAR)sensor,this paper proposes a novel simplified scheme based on the planar feature method,which can meet the accuracy requirements of the joint calibration with fewer checkerboard calibration plate(CP)positions than traditional ***,a mathematical model of the joint calibration is established to obtain the calibration ***,the selection of positions and orientations of the CP are introduced and the corresponding influence to the calibration is ***,the calibration result is optimized by using a nonlinear Levenberg-Marquardt(LM)optimization approach,and the distance residual method is utilized to estimate the ***,experimental results conclude that the minimum number of positions required to meet the joint calibration accuracy in the proposed method is 5,which is less than 12 in traditional methods.
In this paper, a new concept, the fuzzy rate of an operator in linear spaces is proposed for the very first time. Some properties and basic principles of it are studied. Fuzzy rate of an operator B which is specific i...
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Sensor network deployment is the key for sensors to play an important performance. Based on game theory, first, the authors propose a multi-type sensor target allocation method for the autonomous deployment of sensors...
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Sensor network deployment is the key for sensors to play an important performance. Based on game theory, first, the authors propose a multi-type sensor target allocation method for the autonomous deployment of sensors, considering exploration cost, target detection value, exploration ability and other factors. Then, aiming at the unfavorable environment, e.g., obstacles and enemy interference, the authors design a method to maintain the connectivity of sensor network, under the conditions of effective detection of the targets. Simulation result shows that the proposed deployment strategy can achieve the dynamic optimization deployment under complex conditions.
A novel adaptive robust control (ARC) is presented for the four-motor driving servo systems with the uncertain nonlinearities and actuation failures, such that the load tracking control is achieved with the proximat...
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A novel adaptive robust control (ARC) is presented for the four-motor driving servo systems with the uncertain nonlinearities and actuation failures, such that the load tracking control is achieved with the proximate optimal-time. By applying the proposed scheme, several control objectives are achieved. First, the nonlinear synchronization algorithm is presented to maintain the velocity synchronization of each motor, which provides fast convergence without chatting. Moreover, the time-varying bias torque is applied to eliminate the effect of backlash and reduce the waste of energy. Then, the ARC is designed to achieve the proximate optimal-time output tracking with the transient performance in L2 norm, where the friction and actuation failures are addressed by the adaptive scheme based on the norm estimation of unknown parameter vector. Finally, the extensive simulated and experimental results validate the effectiveness of the proposed method.
Multi-agent task assignment problem exists in collaborative target tracking, collaborative rescue, regional search, etc. Most researchers regarding multi-agent task assignment only consider static tasks. However, in c...
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Cooperative monitoring targets of mobile robots is of great importance in military, civil, and medical applications. In order to achieve multi-robot coordinated monitoring, this paper proposes a new distributed path p...
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In this paper, we first consider a pinning node selection and control gain co-design problem for complex networks. A necessary and sufficient condition for the synchronization of the pinning controlled networks at a h...
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