Adaptive Distance Research Articles

Approximating adaptive distance measures using scalable feature signatures

The feature signatures in connection with the adaptive distance measures have become a respected similarity model for effective multimedia retrieval. However, the efficiency of the model is still a challenging task because the adaptive distance measures have at least quadratic time complexity according to the number of tuples in feature signatures. In order to reduce the number of tuples in feature signatures, we introduce the scalable feature signatures, a new formal framework enabling definition of new methods based on agglomerative hierarchical clustering. We show the framework can be used to express nontrivial feature signature reduction techniques including also popular agglomerative hierarchical clustering techniques. We experimentally demonstrate our new feature signature reduction techniques can be used to implement order of magnitude faster yet effective filter distances approximating the original adaptive distance measures. We also show the filter distances using our new feature signature reduction techniques can compete or even outperform the filter distances based on the related feature signature reduction techniques.

Development of adaptive distance relay for statcom connected 220 kv transmission line with wavelet transform and ANN

A new scheme to enhance the solution of the problems associated with Transmission line protection with Statcom connected is presented in this paper. Static Synchronous Compensator (STATCOM) is a shunt type FACTS device connected at the midpoint of the transmission line to maintain the voltage at desired level by injecting/absorbing the reactive power. This connection affects the performance of distance protection relay during line faults. The fault detection is carried out by using energy of the detail coefficients of the phase signals and artificial neutral network algorithm used for fault distance location for all the types of faults for 220 kv transmission line. For each type of fault separate neural network is prepared for finding out the fault location. An improved performance is obtained once the neutral network is trained suitably, thus performance correctly when faced with different system parameters and conditions.

Adaptive Energy-Efficient On-Demand Distance Vector Routing Protocol for MANET

A Mobile Ad-Hoc Network (MANET) is a collection of wireless mobile nodes forming a temporary network without using any centralized access point, infrastructure, or centralized administration. The proposed Adaptive Energy-efficient On-demand Distance Vector routing (AEODV) Protocol for MANETs that is based on the Ad-hoc On-demand Distance Vector (AODV). The AEODV makes significant reduction in the energy consumption of the mobile nodes batteries for large no of connections. The balanced energy consumption is done by SARSA Reinforcement learning. At route discovery due to link breakage route is maintained using available backup routes instead of flooding control packets to the whole network area. It saves significant amount of energy which is consumed by rebroadcasting. To show the efficiency of the proposed protocol, it simulated using QualNet. Simulation results show that the AEODV protocol makes an improvement in delay, throughput, total packets received and residual energy compared to AODV and SARSA. General Terms Wireless Mobile Ad-hoc Network, Computational Intelligence, Energy-efficient.

Adaptive Distance Relaying Scheme for Transmission Network Connecting Wind Farms

This article presents an adaptive distance relay setting for a power transmission network connecting more than one wind farm. The ideal trip characteristics of the distance relay is greatly affected in the presence of complex mutual coupling of transmission lines, as the apparent impedance is significantly affected. Similarly, the reach setting of the relay for the lines connecting wind farms is significantly affected, as the relaying voltage fluctuates continuously. Thus, the proposed study focuses on developing an adaptive relay setting for a transmission network including more than one wind farm and considering variations in operating conditions of wind farms and mutual coupling of transmission lines together. The proposed relay algorithm is extensively tested on two-terminal as well as multi-terminal power networks with wide variations in operating parameters. The performance testing of the proposed adaptive relay characteristics for faults and faults during power swing indicates the potential ability of the approach in handling distance relaying in a transmission system.

Exact and Adaptive Signed Distance Fields Computation for Rigid and Deformable Models on GPUs.

Most techniques for real-time construction of a signed distance field, whether on a CPU or GPU, involve approximate distances. We use a GPU to build an exact adaptive distance field, constructed from an octree by using the Morton code. We use rectangle-swept spheres to construct a bounding volume hierarchy (BVH) around a triangulated model. To speed up BVH construction, we can use a multi-BVH structure to improve the workload balance between GPU processors. An upper bound on distance to the model provided by the octree itself allows us to reduce the number of BVHs involved in determining the distances from the centers of octree nodes at successively lower levels, prior to an exact distance query involving the remaining BVHs. Distance fields can be constructed 35-64 times as fast as a serial CPU implementation of a similar algorithm, allowing us to simulate a piece of fabric interacting with the Stanford Bunny at 20 frames per second.

Kernel-based hard clustering methods in the feature space with automatic variable weighting

This paper presents variable-wise kernel hard clustering algorithms in the feature space in which dissimilarity measures are obtained as sums of squared distances between patterns and centroids computed individually for each variable by means of kernels. The methods proposed in this paper are supported by the fact that a kernel function can be written as a sum of kernel functions evaluated on each variable separately. The main advantage of this approach is that it allows the use of adaptive distances, which are suitable to learn the weights of the variables on each cluster, providing a better performance. Moreover, various partition and cluster interpretation tools are introduced. Experiments with synthetic and benchmark datasets show the usefulness of the proposed algorithms and the merit of the partition and cluster interpretation tools.

Adaptive distance relay algorithm for double circuit line with series compensation

With the onset of fault, nonlinear operation of Metal Oxide Varistor (MOV) in series compensated line introduces error in distance estimation. Distance relay reach accuracy depends on several factors. Mutual coupling effect in double circuit line is highly nonlinear and depends on circuit configuration, fault resistance and remote source infeed. This may lead to inadvertent operation of distance relay for faults outside the operating zone. Variations in system condition, effect of shunt capacitance effect and fault resistance influence distance relay healthy operation. Adaptive setting in digital platform is a greater advantage to estimate the change-in system condition and modify the relay characteristics using the recent information. This paper presents an adaptive algorithm for protection of double circuit series compensated line using local end information. The adaptive algorithm is derived for single-line-to-ground fault condition. A 400kV two source system is considered and simulated using EMTDC/PSCAD.

Adaptive Distance Metric Learning for Diffusion Tensor Image Segmentation

High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

Intelligent coordinated adaptive distance relaying

Malfunctioning of distance relay third zone has been identified as one of the leading factors of power system blackouts in several nations. This inconsistency in operation happens when heavy loads start encroaching the zone 3 settings. In this paper, a novel multi-agent system based methodology for power system protection coordination is proposed. Various system agents are designed to collaborate and adaptively modify the setting of distance relay zone 3 via communication with neighboring substations. The proposed coordination scheme aids in preventing fault misjudgment and possible network breakdown because of load encroachment. Furthermore, it enhances the relay selectivity and loadability. It has been tested using MATLAB®/Simulink™ with the agent programming in Java Agent DEvelopment Framework (JADE) platform. Analysis and test results demonstrate that the proposed method effectively mitigates distance relay malfunction under load encroachment.

Study on Adaptive Distance Relay to Prevent Cascading Trips in Complex Power Grid

Cascading trips caused by flow transferring are the crucial reason for a lot of well-known blackouts. Aiming at the distance relay which is most applied in power grid, this paper puts forward an adaptive setting scheme to prevent cascading trips in complex power grid. In this paper, using two-port network model to fast calculating apparent impedance, and than calculating the apparent impedance change rate which is caused by flow transferring. According to the apparent impedance change rate modify the setting value which would contract the range of the impedance circle. After accomplishing the contraction, the relay action check will start. Through the process of modification and checking, the adaptive setting scheme which is not affected by the flow transferring will be formed. Taking the New England 10-unit 39-bus system which also used this method as the example, in this case, this method is confirmed by the BPA simulation.

Wykorzystanie niekonwencjonalnych technik decyzyjnych do poprawy działania zabezpieczenia odległościowego linii napowietrznej WN o zmiennych w szerokich granicach zdolnościach przesyłowych, część II

Problems with zones forming and parameterization of distance protection for HV overhead line with widely varying capacity limit are discussed in the paper. The developed solutions Adaptive Line Distance Protection AKOL, using unconventional decision taking methods and adaptive adjustment of the parameters for the actual operating conditions of the line are also presented. (Using unconventional decision taking methods in distance protection of HV overhead line with widely varying capacity limit, part II).

A novel adaptive level set segmentation method.

The adaptive distance preserving level set (ADPLS) method is fast and not dependent on the initial contour for the segmentation of images with intensity inhomogeneity, but it often leads to segmentation with compromised accuracy. And the local binary fitting model (LBF) method can achieve segmentation with higher accuracy but with low speed and sensitivity to initial contour placements. In this paper, a novel and adaptive fusing level set method has been presented to combine the desirable properties of these two methods, respectively. In the proposed method, the weights of the ADPLS and LBF are automatically adjusted according to the spatial information of the image. Experimental results show that the comprehensive performance indicators, such as accuracy, speed, and stability, can be significantly improved by using this improved method.

BM3D Image Denoising Algorithm with Adaptive Distance Hard-threshold

Block-matching and 3D filtering (BM3D) denoising algorithm [1] proposed recently has a problem of computational burden especially for low noise level and a sharp performance drop for high noise level. To solve it, an improved version of BM3D is proposed. The solution combines the digital image characteristic with added noise pollution levels, and adaptively selects block-matching threshold in grouping stage. Experimental results demonstrate it outperforms not only in terms of objective criteria of PSNR and running time, but also in visual quality.

Profile of Paul Schulze-Lefert

Growing up on his family’s farm in northwestern Germany, Paul Schulze-Lefert received an early introduction to plant biology that blossomed into a lifelong interest. In a fruitful career as a plant biologist, Schulze-Lefert has played a crucial role in elucidating mechanisms by which the plant immune system detects and fights harmful microbes, and his findings could lead to the development of pathogen-resistant crops. More recently, he has focused on fundamental questions about the complex interactions between plants and their microbial communities, called the plant microbiota. In his Inaugural Article (1), Schulze-Lefert describes the diversification of the bacterial microbiota in the roots of four plant species representing one phylogenetic lineage and shows that microbiota diversification is driven by host species–specific ecological adaptation and phylogenetic distance. In addition, he identified a few core bacterial families that were conserved over at least a 30-million-year evolutionary range and across biogeographical host specialization. For his many discoveries in the field of plant innate immunity and plant–microbe interactions, Schulze-Lefert was elected as a Foreign Associate to the National Academy of Sciences in 2010. Paul Schulze-Lefert. Image courtesy of Max Planck Institute for Plant Breeding Research. Schulze-Lefert, now the director of the Department of Plant–Microbe Interactions at the Max Planck Institute for Plant Breeding Research [Max-Planck-Institut fur Zuchtungsforschung (MPIPZ)] in Cologne, Germany, says he gained an early appreciation of the importance of plant domestication and breeding in human history. “Food production is of utmost importance in the face of a growing world population, and will be one of the grand challenges over the next decades,” he says. “There is tremendous scope to improve not only yield, but also how we produce food.” To that end, Schulze-Lefert is focused on understanding how plants protect themselves from microbial pathogens. Current methods of crop protection use mainly chemicals to control …

Dynamic clustering of histogram data based on adaptive squared Wasserstein distances

This paper presents a Dynamic Clustering Algorithm for histogram data with an automatic weighting step of the variables by using adaptive distances. The Dynamic Clustering Algorithm is a k-means-like algorithm for clustering a set of objects into a predefined number of classes. Histogram data are realizations of particular set-valued descriptors defined in the context of Symbolic Data Analysis. We propose to use the ℓ2 Wasserstein distance for clustering histogram data and two novel adaptive distance based clustering schemes. The ℓ2 Wasserstein distance allows to express the variability of a set of histograms in two components: the first related to the variability of their averages and the second to the variability of the histograms related to different size and shape. The weighting step aims to take into account global and local adaptive distances as well as two components of the variability of a set of histograms. To evaluate the clustering results, we extend some classic partition quality indexes when the proposed adaptive distances are used in the clustering criterion function. Examples on synthetic and real-world datasets corroborate the proposed clustering procedure.

Image Set Based Face Recognition Using Self-Regularized Non-Negative Coding and Adaptive Distance Metric Learning

Simple nearest neighbor classification fails to exploit the additional information in image sets. We propose self-regularized nonnegative coding to define between set distance for robust face recognition. Set distance is measured between the nearest set points (samples) that can be approximated from their orthogonal basis vectors as well as from the set samples under the respective constraints of self-regularization and nonnegativity. Self-regularization constrains the orthogonal basis vectors to be similar to the approximated nearest point. The nonnegativity constraint ensures that each nearest point is approximated from a positive linear combination of the set samples. Both constraints are formulated as a single convex optimization problem and the accelerated proximal gradient method with linear-time Euclidean projection is adapted to efficiently find the optimal nearest points between two image sets. Using the nearest points between a query set and all the gallery sets as well as the active samples used to approximate them, we learn a more discriminative Mahalanobis distance for robust face recognition. The proposed algorithm works independently of the chosen features and has been tested on gray pixel values and local binary patterns. Experiments on three standard data sets show that the proposed method consistently outperforms existing state-of-the-art methods.

Clustering Accuracies on Concepts of Nursing

The purpose of this study is to integrate fuzzy clustering algorithm based on common Mahalanobis distance. Fuzzy clustering could distinguish characteristics of concept structures on nursing. Finally, some limitations and suggestions of this study are discussed. It shows that knowledge structures will be feasible for remedial instruction and help students get more chance to get professional certification on nurses. Based on the findings and results, combined with fuzzy clustering algorithm based on normalized Mahalanobis distance could be very feasible for cognition diagnosis in the future. The algorithm provided by Bezdek (1) is used in this study. Fuzzy c-mean algorithm based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. Gustafson-Kessel clustering algorithm needs added constraint of fuzzy covariance matrix, whereas Gath-Geva clustering algorithm can only be used for the data with multivariate Gaussian distribution. In GK-algorithm, modified Mahalanobis distance with preserved volume was used. However, the added fuzzy covariance matrices in their distance measure were not directly derived from the objective function. The fuzzy covariance matrices in the Mahalanobis distance can be directly derived by minimizing the objective function. II. LITERATURE REVIEW FCM can only work well for spherical shaped clusters. In the objective function the distances between data points to the centers of the clusters are calculated by Euclidian distances. To overcome the above drawback, we could try to extend the distance measure to Mahalanobis distance (MD). However, Krishnapuram and Kim (1999) pointed out that the Mahalanobis distance can not be used directly in clustering algorithm. Gustafson and Kessel (1979) extended the Euclidian distances of the standard FCM by employing an adaptive norm, in order to detect clusters of different geometrical shape without changing the clusters' sizes in one data set. Gath-Geva (GG) fuzzy clustering algorithm is an extension of Gustafson-Kessel (GK) fuzzy clustering algorithm, and also takes the size and density of clusters for classification. Hence, it has better behaviors for irregular features. For improving the limitation of GK algorithm and GG algorithm, we added a regulating factor of covariance matrix to each class in the objective function, and deleted the constraint of the determinant of covariance matrices. We can obtain the Fuzzy C-Means based on adaptive Mahalanobis distance (FCM-M) as following (2, 3). For improving the stability of the clustering results, we replace all of the covariance matrices with the same common covariance matrix in the objective function in the FCM-M algorithm, and then, an improved fuzzy clustering method, called the Fuzzy C-Means algorithm based on common Mahalanobis distance (FCM-CM) is proposed.

Fast local binary fitting optimization approach for image segmentation

It is difficult to get the correct segmentation results for the intensity inhomogeneity images,and the segmentation results are very sensitive to the initial contours.Thus,a fast and stable approach was proposed to overcome these disadvantages.First,an Adaptive Distance Preserving Level Set(ADPLS) method was utilized to get a better initial contour.Second,the Local Binary Fitting(LBF) model was used for a further segmentation.The experimental results show that the improved model can achieve good performance and is better to solve the contradiction among the segmentation speed,accuracy and stability.

SCALABLE AND EFFICIENT CLUSTER-BASED FRAMEWORK FOR MULTIDIMENSIONAL INDEXING

Indexing high dimensional data has its utility in m any real world applications. Especially the informa tion retrieval process is dramatically improved. The existing techniques coul d overcome the problem of “Curse of Dimensionality” of high dimensional data sets by using a technique known as Vector Approxima tion-File which resulted in sub-optimal performance . When compared with VAFile clustering results in more compact data set as it uses inter-dimensional correlations. However, p runing of unwanted clusters is important. The existing pruning techniques are base d on bounding rectangles, bounding hyper spheres ha ve problems in NN search. To overcome this problem Ramaswamy and Rose propose d an approach known as adaptive cluster distance bo unding for high dimensional indexing which also includes an efficie nt spatial filtering. In this paper we implement th is high-dimensional indexing approach. We built a prototype application to for p roof of concept. Experimental results are encouragi ng and the prototype can be used in real time applications.

Based on LBF Model of Adaptive Distance Keeping Level Set Evolution on Medical Image Segmentation

Because LBF model is sensitive to the initial position and regional model may causes excessive segmentation or inner cavity. The paper adds adaptive distance keeping level set method on LBF model. Evolution curves reduce limitation to the initial position. Meanwhile, an improved area weight coefficient makes evolution curve move inward or outward adaptively according to image information and it can detect object boundaries when it is in a region with intensity homogeneity. The method enhances capability of capturing boundary concavities. In addition, xconv2 instead of conv2 function during Matlab programming for improving the evolution speed in some sense. The experimental results show that the proposed method can get local characteristics of the brain.