Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Target Tracking by Probability Hypothesis Density Based on Dirichlet Distribution

A multiple target state extraction algorithm of probability hypothesis density filter based on Dirichlet distribution is proposed.The instability of Dirichlet distribution with negative exponent parameters is applied in estimating multiple target state by maximum likelihood criterion.Dirichlet distribution drives irrelevant components to extinction while the maximum likelihood solution is being searched by expectation maximum algorithm.In order to balance the successful initialization and the reduction of time cost of algorithm,a k-dimensional tree is applied to initialize Dirichlet distribution.Simulation results show that the multiple target state extraction algorithm based on Dirichlet distribution is superior to the existing algorithms for the probability hypothesis density filter in multiple target tracking.
Application of Rough Set Theory in Multi-index Comprehensive Evaluation

The presence of more information doesn't usually generate a corresponding increase.The rough set theory was introduced into evaluating the significance of the system parameters.The presence of irrelevant information will decrease the effectiveness of the algorithm.The authors proposed a solution to improve the quality,that is an attribute-weighted algorithm based on rough set theory.Firstly,The authors computed values for the weight of every attribute.Then combination of rough set and TOPSIS for comprehensive evaluation is described.The proposed method was applied in example and the result showed that it is effective.
International Journal of Engineering

Targets and objects registration and tracking in a sequence of images play an important role in various areas. One of the methods in image registration is feature-based algorithm which is accomplished in two steps. The ﬁrst step includes ﬁnding features of sensed and reference images. In this step, a scale space is used to reduce the sensitivity of detected features to the scale changes. Afterward, we attribute feature points that obtained in the ﬁrst step, descriptions using brightness value around the feature points. In this paper, a new algorithm is proposed based on Binary Robust Invariant Scalable Keypoints (BRISK) and Scale Invariant Feature Transform (SIFT) algorithms. The proposed algorithm uses the directional pattern to describe the edges which are around the keypoints. This pattern is perpendicular to the direction of keypoints which shows the direction of the edge and provides more useful information regarding brightness around the feature point to make descriptor vector. Furthermore, in the proposed algorithm, the output vector consists of multilevel values instead of binary values which means further useful information is involved in the descriptor vector. Also, levels of output vectors can be adjusted using a single parameter so that the processor with low computing ability can tune the output to a binary vector. Experimental results show that the proposed algorithm is more robust than the BRISK algorithm and the e ﬃ ciency of the algorithm is about the same as BRISK algorithm.
Support Vector Machine for Scene Matching Area Selection

In view of interrelations and constraints of the result of selecting matching area and the information of reference image,the paper presents a method for selecting suitable scene matching area by using support vector machine.First,intensity-based and feature-based measure parameters of reference image were selected to represent the reference image's information accurately and effectively.Then the measured parameters were coded as input feature vectors of training set and matching results were gained as output of training set by normalized cross-correlation algorithm.Finally,after the training with appropriate radial basis kernel function and corresponding optimal parameters,decision function which could separate suitable and unsuitable matching area class was obtained.Thus,matching area can be extracted from random reference image by decision function.The experimental results show that this method holds the capability of flexibility and jamming resistance as well as proper guide to selection of suitable matching area from complex reference image.
A distribution short-term load forecasting based on human comfort index

A short-term load forecasting method is made based on the load curve shape similarity and human comfort index for distribution load characteristics. The characteristics of the load curve shape similarity are researched. Based on the expectation of each moment load difference, a load curve shape similarity calculation method is given. Based on the impact of meteorological factors on the load, a human comfort index is introduced, and according to the daily maximum temperature and minimum temperature, the similarity calculation method of a day human comfort index is given. According to the distribution load type characteristics, the load change characteristics of various types are researched on holidays and normal working days. Combining these features, this paper gives a distribution network short term load forecasting method, and discusses the method and its steps in detail. Nine days of historical data and weather data of a distribution network power point are taken as examples to verify the feasibility of the proposed method.
Particle-swarm optimization algorithm for model predictive control with constraints

We investigate the optimization algorithms for solving the constrained optimization problems in model predictive control(MPC). To deal with the disadvantage of the quadratic programming(QP) algorithm, we introduce and apply the chaotic particle-swarm optimization(CPSO) algorithm to solve the control problem with simultaneous constraints on inputs and states. A practical constrained optimization problem of the discrete-time linear system is solved by QP and PSO, respectively. By comparing the simulation results, we show the advantages of the PSO-based MPC algorithm.
Design of Humanoid Complicated Dynamic Motion with Similarity Considered

This paper explores a method of designing humanoid motion with rhythm considered based on human motion capture.Firstly,human motion is segmented into primitive motions.Secondly,the kinematics constraints and stability adjustment method are formulated.Thirdly,the similarity evaluation with rhythm considered is made,and a method to derive humanoid motion with high similarity is presented.Finally,an experiment of Chinese swordsmanship was performed using our developed humanoid robot BHR-2 to illustrate the effectiveness of this method.
A Fuzzy Partition Entropy Approach for Multi-Thresholding Segmentation Based on the Recursive Artificial Bee Colony Algorithm

A new recursive artificial bee colony fuzzy partition entropy algorithm(RAFPEA) for multi-thresholding image segmentation is proposed to solve the inefficiency and repeated computation in fuzzy partition entropy approach for selecting the thresholds in the process of image segmentation.The membership functions with attached boundary conditions and gray weights are selected to build the image fuzzy entropy model.The combined computation of different variables in this model is converted to the recursive process and the no-repetitive results of the processing moments are stored.Then the artificial bee colony algorithm(ABCA) uses the stored results to calculate the fitness value of individual species in the ABCA so that the repeated calculations can be reduced and the optimal thresholds can be searched effectively.Experimental results and comparisons with common algorithms indicate that the run time accounts for 5% of ones of the fuzzy partition entropy approaches based on exhaustive algorithm and genetic algorithm.And the uniformity obtained by the proposed scheme is equivalent to the one via exhaustive search.Moreover,as the number of required thresholds increases,the run time keeps stable.The RAFPEA can effectively segment images by multiple thresholds with ensured precision.
Shared steering control combined with driving intention for vehicle obstacle avoidance

This paper presents a framework of shared steering control for vehicle obstacle avoidance, which is combined with the driver’s driving intention and risk assessment. The driving intention is recognized by a support vector machine model through the steering wheel angle, the relative lateral offset, and lateral velocity to the road center line. The elastic band theory is adopted to plan the obstacle avoidance path, which takes the driving intention and environment information into consideration. A linear model predictive control algorithm based on a bicycle vehicle model is used to realize path following. The risk assessment of current traffic situation is calculated by time to collision and the time required from the starting position of lane changing to the obstacle to collide. The shared steering control strategy is designed by the risk assessment and driving intention, simultaneously, so that the cooperative coefficient and the driving mode would be determined. In order to validate the framework proposed in this paper, the shared steering control system is tested on four obstacle avoidance scenarios, including the static and moving obstacles on a straight road and a curvy road. The results show that the shared steering control system could help the human driver avoid obstacles safely. Besides, both the path and steering wheel angle curve of the shared steering control system are smooth, and the vehicle stability performance also maintains well through a suitable shared strategy.
Study on Probability Distribution Model Of Designed Ice Load For Transmission Lines

Based on the ice load data of Studnice Meteorological Station from 1940 to 1999, the design ice load of the area is calculated and analyzed by normal distribution, Gumbel extreme vaule type I distribution, Frechet extreme vaule type II distribution and Weibull extreme vaule type III distribution respectively. The test result of The Kolmogorov goodness fit λ shows that the Frechet extreme value type II distribution is the optimum curve to fit ice distribution of the district. Because of the effect of topography, the probability distribution model differs from one place to another. The ice load is a key factor of the design of transmission lines to obtain economy and safety, therefore it is suggested that probability distribution model of ice load be chosen based on the records of the sites.
A Robust Interference Alignment Scheme with Imperfect CSI Used in Multi-Cell System

In practical communication systems,the prefect channel state information(CSI) is hardly obtained by transmitters owing to imperfect estimate or feedback of the channel coefficients.In order to mitigate the adverse impact of imperfect CSI on bit error rate,based on the constraint of zero-forcing channel error,a robust minimum interference leakage algorithm is proposed for multi-cell multi-user multi-input multi-output system.The formulation of system sum rate is derived under new objective constraint with channel error.Simulations show that if the imperfect CSI is known,the performance on bit error rate and average sum rate of the user in cell edge can be improved by the proposed robust algorithm,especially at the high signal noise rate values.
Multi-view modeling of 3-D target based on Zernike moments

Modeling is an important step in 3-D target recognition. Avoiding the recovery of 3-D obje- ct, multi-view modeling is used widely in 3-D object modeling. Multiple 2-D views are obtained by proje- cting a tessellated regular polyhedron onto the viewing sphere.In order to describe all shape and pose info- rmation, a fine partition of the viewing sphere is necessary,which leads to computation complexity. Zernike moments have a rotation invariant property. A method is used to decrease the number of 2-D projections view by extracting the Zernike moments of each 2-D view, whose max order is decided by the difference between the original image and the reconstruction one.Those partitions with Zernike moments of correspon- ding views are similar are merged and a characteristic view is obtained for a new partition. Experimental results show the validity of the method.
A Simplifed Calculating Method on Reliability

Based on the generalized reliability index given by Ditlevsen and the first order second moment method, a simplifed calculating method on structural reliability is presented. An iterative scheme for the analysis of structural reliability and adirect procedure for the design of approximate probabilistic limit state are developed in this paper. The calculated examples show that the method is simpler than the JC method and its precision is high.
Summary of the Time Delay Estimation

The time delay is a very important parameter for charactering the signal.Precise and quick estimation of the time delay is essential to locate the targets.Many researches have been focused on the time delay estimation in recent years.In this paper,the summary of the time delay estimation is given in view of studying amount of literatures about the time delay estimation.The methods of time delay estimation are introduced,including the 2nd order statistic based approaches,higher-order statistics based approaches,the novel signal processing tool—the fractional Fourier transform and its application in the time delay estimation.Analysis and comparison are also made for different methods in terms of the corresponding performance.
SHORT TERM LOAD FORECASTING USING A MULTILAYER NEURAL=NETWORK WITH BP-GA MIXED ALGORITHMS

In this paper, a modified method (BP-GA) for short-term load forecast is presented, which can quicken the learning speed of the network and improve the predicting precision compared with the traditional artificial neural network. We use GAs to train connection weights of multi-layer feed forward neural network (BP) until the learning error has tended to stability, here, the best initial weights have been found. Then we use BP method to finish short-term load forecast process. We also consider the influence of climate for the short-term load and make it as one of the input for the BP. Experimental results show that the short-term load forecast system based on BP-GA has high precision and high learning rate.
The Simulation for Treatment Process of Activated Sludge with Three-Dimensional Cellular Automata

For the treatment process of activated sludge method,a simulation method with three-dimensional cellular automation model is proposed.The method simulates the dynamic evolvement of activated sludge treatment process in the bioreactor,and the treatment process is reflected intuitively.Actual data is used to prove that the method could show the treatment process of activated sludge method effectively.The visualization is realized,and the growth curve of activated sludge is also plotted.
A Compensation Method Based on Dividing Time Intervals for Time Delay of Networked Control System

Time delay of networked control system is a key factor affecting system stability and dynamic perfor-(mance.) On the basis of measurement and analysis on network time delay,this paper modifies the structure of traditional networked control system and presents a time-delay compensation method based on dividing time intervals.The(simulation) results show that the method can remarkably improve the performance of the networked control system.
One-dimensional Image Recognition of Radar Target Based on Particle Swarm Classifier

A novel particle swarm classifier is designed for one-dimensional image recognition of radar target.After pretreating the data,the classification rules are discovered by particle swarm optimization algorithm based on the training samples,and then the one-dimensional image of radar target is recognized by the discovered rules.The convergence of the new algorithm based on Bayes′s theorem and stochastic transform process are analyzed.Experimental results on the data of three airplanes obtained in a microwave anechoic chamber show that the proposed method has a stable performance in recognizing both the one-dimensional images without noise and the one-dimensional images with noise.
A general class of enumerations arising in genetics.

Fisher [1950] gave a general combinatorial method of solution for a class of enumerations in genetics where one is concerned with sets of formulae generated by a given permutation group. As similar problems arise in fields other than genetics, Fisher's enumerations seem to deserve wider attenLtion than they have received. One series of expressions giving the number of partitions of an integer n in k dimensions (or equally the number of sets of isomorphic n-somic genotypes heterogenic at k linked loci) appears to be of very general interest. Foulkes [1965], using group representation theory, has shown that further analysis of Fisher's enumerations is possible in terms of subgroups of the basic permutation group and he has raised the question as to how these subgroups enter into the genetical problems. This article shows how, using an extension of Fisher's method, the subgroups to which Foulkes has drawn attention arise naturally in the genetical context. The enumerations given by Foulkes refer to sets of genetical formulae which are invariant under various subgroups of permutations.
Lifshitz black holes with a time-dependent scalar field in a Horndeski theory

In arbitrary dimensions, we consider a particular Horndeski action given by the Einstein-Hilbert Lagrangian with a cosmological constant term, while the source part is described by a real scalar field with its usual kinetic term together with a nonminimal kinetic coupling. In order to evade the no-hair theorem, we look for solutions where the radial component of the conserved current vanishes identically. Under this hypothesis, we prove that this model can not accommodate Lifshitz solutions with a radial scalar field. This problem is finally circumvented by turning on the time dependence of the scalar field, and we obtain a Lifshitz black hole solution with a fixed value of the dynamical exponent z=1/3. The same metric is also shown to satisfy the field equations arising only from the variation of the matter source.
Land cover classification using LiDAR height texture and ANNs

The method of strict slope threshold algorithm is not sufficient to achieve complex object identification or ground features classification from LiDAR data.In this research,artificial intelligence is used to classify the ground features based on the LiDAR height texture.Average elevation image,average intensity image and ground roughness index image are derived from LiDAR points.Then,4 GLCM texture features including entropy,various,second moment and homogeneity texture are measured.Finally,BP-ANNs are used to classify the texture measure into five ground feature types.A coastal area of Zhujiang Delta,South of China,is taken as the study area.The method employed in this research can efficiently work with single LiDAR data source and the accuracy of classification result is 90%,and the classification accuracy of Maximal Likelihood method(ML) is 86.8% for comparison.When the result of ANNs classification is compared with the result of optical image classification,it can be found that 76.5% sample points are in accord.
Two-stage designs for clinical trials.

Several papers recently have discussed the design of clinical trials from a gain function viewpoint (Anscombe [1963], Colton [1963, 1965], and, on a very similar topic, Wetherill and Campling [1966] among others). This paper considers the situation in which two treatments are available for a particular ailment. The response to the treatment is a continuous variable, and the gain in using a treatment is some monotonic function of the value of the response. A given number N of patients will be in need of one or other of the two treatments, and the trial is aimed at allocating the treatments to the patients in such a way as to make the expected gain as large as possible. The trial which achieves the largest expected gain is of considerable complexity, involving a decision with each patient as to which treatment to use, and usually one confines attention to trials with a simpler design. Colton [1963] considered single-stage designs and sequential probability ratio tests, and in a later paper [1965] two types of two-stage designs. In this paper, less restricted and unrestricted two-stage designs are constructed and evaluated. The model used here is the same as used by Colton, and is as follows:
Strukturerhaltende Approximation linearer verteilt-parametrischer PCHD-Systeme

Zusammenfassung In diesem Beitrag wird ein Approximationsverfahren für lineare verteilt-parametrische Port-Hamiltonsche Systeme mit Dämpfung (PCHD-Systeme) vorgestellt, welches die Erhaltung der PCHD-Struktur des Originalsystems ermöglicht. Darüber hinaus kann sichergestellt werden, dass die Approximation eine von der Dissipativität des unendlich-dimensionalen Systemoperators abhängige Mindeststabilitätsreserve besitzt. Das vorgestellte Approximationsverfahren basiert auf einer Verallgemeinerung der Petrov-Galerkin-Approximation und besitzt Freiheitsgrade, die dazu genutzt werden können, Approximationen mit übereinstimmenden Momenten zu erhalten. Anhand der Ordnungsreduktion eines einfachen Wärmeleitungssystems werden die Ergebnisse der Arbeit demonstriert. Abstract In this contribution an approximation approach for linear distributed-parameter port controlled Hamiltonian systems with damping (PCHD systems) is presented that allows for the preservation of the PCHD structure of the original system. Futhermore, it is possible to assure that the stability margin of the approximation satisfies a lower bound that depends on the dissipativity of the infinite-dimensional system operator. The presented approximation method is based on a generalized version of the Petrov-Galerkin approximation and has free parameters that can be used to achieve approximations with matching moments. The results of the paper are demonstrated by approximating a heat conducting system.
Assessing Field Dependence–Independence Cognitive Abilities Through EEG-Based Bistable Perception Processing

Field Dependence–Independence (FDI) is a widely studied dimension of cognitive styles designed to measure an individual’s ability to identify embedded parts of an organized visual field as entities separate from that given field. The research aims to determine whether the brain activity features that are considered to be perceptual switching indicators could serve as robust features, differentiating Field-Dependent (FD) from Field-Independent (FI) participants. Previous research suggests that various features derived from event related potentials (ERP) and frequency features are associated with the perceptual reversal occurring during the observation of a bistable image. In this study, we combined these features in the context of a different experimental scheme using ambiguous and unambiguous stimuli during participants’ perceptual observations. We assessed the participants’ FD-I classification with the use of the Hidden Figures Test (HFT). Results show that the peak amplitude of the frontoparietal positivity, the late positive deflection in frontal and parietal areas, is higher for the FD group at specific locations of the left lobe, whereas it occurs later for the FD group at the central and occipital electrodes. Additionally, the FD group exhibits higher levels of gamma power before stimulus onset at channel TP10 and higher gamma power during reversal at the right centroparietal electrodes (T8, CP6, and TP10). The peak amplitude of the reversal positivity, the positive deflection during the reversal, is higher for the FD group at the rear right lobe (P4).