Parameter value selection strategy for complete coverage path planning based on the Lüsystem to perform specific types of missions

We propose a novel parameter value selection strategy for the Lüsystem to construct a chaotic robot to accomplish the complete coverage path planning(CCPP)task.The algorithm can meet the requirements of high randomness and coverage rate to perform specific types of missions.First,we roughly determine the value range of the parameter of the Lüsystem to meet the requirement of being a dissipative system.Second,we calculate the Lyapunov exponents to narrow the value range further.Next,we draw the phase planes of the system to approximately judge the topological distribution characteristics of its trajectories.Furthermore,we calculate the Pearson correlation coefficient of the variable for those good ones to judge its random characteristics.Finally,we construct a chaotic robot using variables with the determined parameter values and simulate and test the coverage rate to study the relationship between the coverage rate and the random characteristics of the variables.The above selection strategy gradually narrows the value range of the system parameter according to the randomness requirement of the coverage trajectory.Using the proposed strategy,proper variables can be chosen with a larger Lyapunov exponent to construct a chaotic robot with a higher coverage rate.Another chaotic system,the Lorenz system,is used to verify the feasibility and effectiveness of the designed strategy.The proposed strategy for enhancing the coverage rate of the mobile robot can improve the efficiency of accomplishing CCPP tasks under specific types of missions.

Solving quantified constraint satisfaction problems with value selection rules

Solving a quantified constraint satisfaction problem(QCSP)is usually a hard task due to its computational complexity.Exact algorithms play an important role in solving this problem,among which backtrack algorithms are effective.In a backtrack algorithm,an important step is assigning a variable by a chosen value when exploiting a branch,and thus a good value selection rule may speed up greatly.In this paper,we propose two value selection rules for existentially and universally quantified variables,respectively,to avoid unnecessary searching.The rule for universally quantified variables is prior to trying failure values in previous branches,and the rule for existentially quantified variables selects the promising values first.Two rules are integrated into the state-of-the-art QCSP solver,i.e.,QCSP-Solve,which is an exact solver based on backtracking.We perform a number of experiments to evaluate improvements brought by our rules.From computational results,we can conclude that the new value selection rules speed up the solver by 5 times on average and 30 times at most.We also show both rules perform well particularly on instances with existentially and universally quantified variables occurring alternatively.

Measured Impedance and Threshold Value of Electrode Line Impedance Supervision Based Protection in HVDC Transmission System

At present,electrode line impedance supervision(ELIS)based protection is widely used to detect faults on grounding electrode lines,which are indispensable elements of high-voltage direct current(HVDC)systems.The existing theoretical analysis of measured impedance is based on lumped line model and the threshold value is generally set according to engineering experience,which have caused the dead zone problem and even accidents.Therefore,a study on measured impedance of ELIS-based protection and its threshold value selection method is carried out to solve this problem.In this study,the expressions of measured impedance under normal operation and fault conditions are deduced based on rigorous and accurate line model.Based on the expressions,the characteristics of the measured impedance are calculated and analyzed.With the characteristics of the measured impedance,the applicability of the protection with the traditional threshold value is further discussed and the distribution of the dead zone can be located.Then,the method to calculate the threshold value of ELIS-based protection is proposed.With a proper threshold value selected by the proposed method,the dead zone of ELIS-based protection is effectively eliminated,and the protection can identify all types of faults even with large transition resistances.Case studies on PSCAD/EMTDC have been conducted to verify the conclusion.

Issues in the Mining of Heart Failure Datasets

This paper investigates the characteristics of a clinical dataset using a combination of feature selection and classification methods to handle missing values and understand the underlying statistical characteristics of a typical clinical dataset. Typically, when a large clinical dataset is presented, it consists of challenges such as missing values, high dimensionality, and unbalanced classes. These pose an inherent problem when implementing feature selection and classification algorithms. With most clinical datasets, an initial exploration of the dataset is carried out, and those attributes with more than a certain percentage of missing values are eliminated from the dataset. Later, with the help of missing value imputation, feature selection and classification algorithms, prognostic and diagnostic models are developed. This paper has two main conclusions: 1) Despite the nature of clinical datasets, and their large size, methods for missing value imputation do not affect the final performance. What is crucial is that the dataset is an accurate representation of the clinical problem and those methods of imputing missing values are not critical for developing classifiers and prognostic/diagnostic models. 2) Supervised learning has proven to be more suitable for mining clinical data than unsupervised methods. It is also shown that non-parametric classifiers such as decision trees give better results when compared to parametric classifiers such as radial basis function networks(RBFNs).