Unnecessary Distance Research Articles

Multi-Regional Delta-Tracking Method for Neutron Transport Tracking in Monte Carlo Criticality Calculation

The Monte Carlo method has been widely used as a standard method to perform neutron transport simulations in reactor physics. In conventional Monte Carlo codes corresponding to the neutron transport tracking with ray-tracing method, the distances to material boundaries must be computed frequently when the neutron changes its kinetic energy or moving into new material regions to determine the neutron transport length. However, if the neutron’s mean free path length, to some extent, is greater than the macro size of the model, a huge amount of distances need to be computed. As a result, the computational efficiency of the neutron transport tracking will be degraded. An improved multi-regional delta-tracking method based on domain decomposition was introduced to solve this problem, in which the original heterogeneous model would be decomposed into many sub-regions and each sub-region was tracked using a local delta-tracking method. Consequently, the computational efficiency of the neutron transport tracking can be improved theoretically without the unnecessary distance calculations. The improved multi-regional delta-tracking method was incorporated into the MOSRT system, which is a multi-objective modeling and simulation platform for radiation transport system. Finally, the method was validated using the criticality benchmarks and its accuracy and efficiency were demonstrated in Monte Carlo criticality calculation. The results indicated that the new method was consistent with the conventional methods, but with a more competitive run-time performance.

A fast clustering algorithm based on pruning unnecessary distance computations in DBSCAN for high-dimensional data

Clustering is an important technique to deal with large scale data which are explosively created in internet. Most data are high-dimensional with a lot of noise, which brings great challenges to retrieval, classification and understanding. No current existing approach is “optimal” for large scale data. For example, DBSCAN requires O(n2) time, Fast-DBSCAN only works well in 2 dimensions, and ρ-Approximate DBSCAN runs in O(n) expected time which needs dimension D to be a relative small constant for the linear running time to hold. However, we prove theoretically and experimentally that ρ-Approximate DBSCAN degenerates to an O(n2) algorithm in very high dimension such that 2D > > n. In this paper, we propose a novel local neighborhood searching technique, and apply it to improve DBSCAN, named as NQ-DBSCAN, such that a large number of unnecessary distance computations can be effectively reduced. Theoretical analysis and experimental results show that NQ-DBSCAN averagely runs in O(n*log(n)) with the help of indexing technique, and the best case is O(n) if proper parameters are used, which makes it suitable for many realtime data.

Doubly Approximate Nearest Neighbor Classification

Nonparametric classification models, such as K-Nearest Neighbor (KNN), have become particularly powerful tools in machine learning and data mining, due to their simplicity and flexibility. However, the testing time of the KNN classifier becomes unacceptable and the KNN's performance deteriorates significantly when applied to data sets with millions of dimensions. We observe that state-of-the-art approximate nearest neighbor (ANN) methods aim to either reduce the number of distance comparisons based on tree structure or decrease the cost of distance computation by dimension reduction methods. In this paper, we propose a doubly approximate nearest neighbor classification strategy, which marries the two branches which compress the dimensions for decreasing distance computation cost as well as reduce the number of distance comparison instead of full scan. Under this strategy, we build a compressed dimensional tree (CD-Tree) to avoid unnecessary distance calculations. In each decision node, we propose a novel feature selection paradigm by optimizing the feature selection vector as well as the separator (indicator variables for splitting instances) with the maximum margin. An efficient algorithm is then developed to find the globally optimal solution with convergence guarantee. Furthermore, we also provide a data-dependent generalization error bound for our model, which reveals a new insight for the design of ANN classification algorithms. Our empirical studies show that our algorithm consistently obtains competitive or better classification results on all data sets, yet we can also achieve three orders of magnitude faster than state-of-the-art libraries on very high dimensions.

Efficient search for top-k discords in streaming time series

The problem of anomaly detection in streaming time series has received much attention recently. The problem addresses finding the most anomalous subsequence (discord) over a time-series stream, which might arrive at high speed. The fact that finding top-k discords is more useful than finding the most unusual subsequence since users might make a choice among the top-k discords instead of choosing only one. Hence, an efficient method of search for top-k discords in streaming time series is proposed in the paper. The method uses a lower bound threshold, a lower bounding technique on a common dimensionality reduction transform, and a state-of-the-art technique of the distance computation between two time-series subsequences to prune off unnecessary distance calculations. The three techniques are arranged in a cascading fashion to speed up the performance of the method. Furthermore, the proposed method can return a set of top-k discords on the fly. The experimental results show that the proposed method can acquire quality discords nearly identical to those obtained by HOT SAX, a well-known method of anomaly detection. Remarkably, our proposed method demonstrates a fast response in handling time-series streams at high speed.

A fast DBSCAN clustering algorithm by accelerating neighbor searching using Groups method

Density based clustering methods are proposed for clustering spatial databases with noise. Density Based Spatial Clustering of Applications with Noise (DBSCAN) can discover clusters of arbitrary shape and also handles outliers effectively. DBSCAN obtains clusters by finding the number of points within the specified distance from a given point. It involves computing distances from given point to all other points in the dataset. The conventional index based methods construct a hierarchical structure over the dataset to speed-up the neighbor search operations. The hierarchical index-structures fail to scale for datasets of dimensionality above 20. In this paper, we propose a novel graph-based index structure method Groups that accelerates the neighbor search operations and also scalable for high dimensional datasets. Experimental results show that the proposed method improves the speed of DBSCAN by a factor of about 1.5–2.2 on benchmark datasets. The performance of DBSCAN degrades considerably with noise due to unnecessary distance computations introduced by noise points while the proposed method is robust to noise by pruning out noise points early and eliminating the unnecessary distance computations. The cluster results produced by our method are exactly similar to that of DBSCAN but executed at a much faster pace.

Triangular inequality-based rotation-invariant boundary image matching for smart devices

Nowadays there are many efforts to develop image matching applications exploiting a large number of images stored in smart devices such as smartphones, smart pads, and smart cameras. Boundary image matching converts boundary images to time-series and identifies similar boundary images using time-series matching on those time-series. In boundary image matching, computing the rotation-invariant distance between image time-series is a very time-consuming process since it requires a lot of Euclidean distance computations for all possible rotations. To support the boundary image matching in smart devices, we need to devise a simple but fast computation mechanism for rotation-invariant distances. For this purpose, in this paper we propose a novel rotation-invariant matching solution that significantly reduces the number of distance computations using the triangular inequality. To this end, we first present the notion of self-rotation distance and formally show that the self-rotation distance with the triangular inequality produces a tight lower bound and prunes many unnecessary distance computations. Using the self-rotation distance, we then propose a triangular inequality-based solution to rotation-invariant image matching. We next present the concept of k-self rotation distance as a generalized version of the self-rotation distance and formally show that this $$k$$ k -self rotation distance produces a tighter lower bound and prunes more unnecessary distance computations. Using the $$k$$ k -self rotation distance we also propose an advanced triangular inequality-based solution to rotation-invariant image matching. Experimental results show that our self-rotation distance-based algorithms significantly outperform the existing algorithms by up to one or two orders of magnitude, and we believe that this performance improvement makes our algorithms very suitable for smart devices.

Where do I go? A trauma victim′s plea in an informal trauma system

Background:The three pillars of a good trauma system are the prehospital care, definitive care, and rehabilitative services. The prehospital care is a critical component of the efforts to lower trauma mortality.Objective:To study the prehospital profile of patients who died due to trauma, compute the time taken to reach our facility, find the cause of delay, and make feasible recommendations.Materials and Methods:A hospital-based study was performed at a trauma center in Puducherry from June 2009 to August 2010. Puducherry is a union territory of India in the geographical terrain of the state of Tamil Nadu. A total of 241deaths due to trauma were included. Apart from the demographic and injury characteristics, a detailed prehospital log was constructed regarding the time of incident, the referral patterns, care given in the prehospital phase, the distance travelled, and the total time taken to reach our center.Results:The majority (59%) of patients were referred, with stopovers at two consecutive referral centers (30%), needing at least two vehicles to transport to definitive care (70%), clocking unnecessary distances (67%), and delayed due to non therapeutic intervention (87%). The majority of deaths (66%) were due to head injury. Only 2.96% of referred cases reached us within the first hour. Few of the patients coming directly to us had vehicle change due to local availability and lack of knowledge of predestined definitive care facility. Overall, 94.6% of direct cases arrived within 4 h whereas 93.3% of referred cases required up to 7 h to arrive at definitive care.Conclusions:Seriously injured patients lose valuable prehospital time because there is no direction regarding destination and interfacility transfer, a lack of seamless transport, and no concept of initial trauma care. The lack of direction is compounded in geographical areas that are situated at the border of political jurisdictions.

New Approach for K-mean and K-medoids Algorithm

K-means and K-medoids clustering algorithms are widely used for many practical applications. Original k-mean and kmedoids algorithms select initial centroids and medoids randomly that affect the quality of the resulting clusters and sometimes it generates unstable and empty clusters which are meaningless. The original k-means and k-mediods algorithm is computationally expensive and requires time proportional to the product of the number of data items, number of clusters and the number of iterations. The new approach for the k mean algorithm eliminates the deficiency of exiting k mean. It first calculates the initial centroids k as per requirements of users and then gives better, effective and stable cluster. It also takes less execution time because it eliminates unnecessary distance computation by using previous iteration. The new approach for kmedoids selects initial k medoids systematically based on initial centroids. It generates stable clusters to improve accuracy.

Improving DBSCAN’s execution time by using a pruning technique on bit vectors

Clustering is the process of assigning a set of physical or abstract objects into previously unknown groups. The goal of clustering is to group similar objects into the same clusters and dissimilar objects into different clusters. Similarities between objects are evaluated by using the attribute values of objects. There are many clustering algorithms in the literature; among them, DBSCAN is a well known density-based clustering algorithm. We improve DBSCAN’s execution time performance for binary data sets and Hamming distances. We achieve considerable speed gains by using a novel pruning technique, as well as bit vectors, and binary operations. Our novel method effectively discards distant neighbors of an object and computes only the distances between an object and its possible neighbors. By discarding distant neighbors, we avoid unnecessary distance computations and use less CPU time when compared with the conventional DBSCAN algorithm. However, the accuracy of our method is identical to that of the original DBSCAN. Experimental test results on real and synthetic data sets demonstrate that, by using our pruning technique, we obtain considerably faster execution time results compared to DBSCAN.

All-nearest-neighbors finding based on the Hilbert curve

An all-nearest-neighbors (ANN) query retrieves all nearest neighbors to all query objects. We may perform large number of one-nearest-neighbor queries to answer such an ANN query. Due to no total ordering of spatial proximity among spatial objects, the Hilbert curve approach has proposed to preserve the spatial locality. Chen and Chang have proposed a neighbor finding strategy (denoted as the CCSF strategy) based on the Hilbert curve to compute the absolute location of the neighboring blocks. However, it costs much time during the transformation between the Hilbert curve and the Peano curve. On the other hand, in the strategy based on R or R∗-trees for an ANN query, large number of unnecessary distance comparisons have to be done due to the problem of overlaps within the R-tree, resulting in many redundant disk accesses. Therefore, in this paper, we first propose the one-nearest-neighbor finding strategy directly based on the Hilbert curve (denoted as the ONHC strategy) for a one-nearest-neighbor query. By relations among orientations, orders, and quaternary numbers, we compute the relative locations of the query block and the neighboring block in the Hilbert curve. Then, the nearest neighbor of one query point can be found directly from these neighboring blocks. Next, by using our ONHC strategy, we propose the all-nearest-neighbors finding strategy based on the Hilbert curve (denoted as the ANHC strategy) for an ANN query. Finally, from the simulation result, we show that our ONHC strategy needs less response time (the CPU-time and the I/O time) than the CCSF strategy for the one-nearest-neighbor query. We also show that our ANHC strategy needs less response time than the strategy based on R∗-trees for an ANN query.

CliqueStream: Creating an efficient and resilient transport overlay for peer-to-peer live streaming using a clustered DHT

Several overlay-based live multimedia streaming platforms have been proposed in the recent peer-to-peer streaming literature. In most of the cases, the overlay neighbors are chosen randomly for robustness of the overlay. However, this causes nodes that are distant in terms of proximity in the underlying physical network to become neighbors, and thus data travels unnecessary distances before reaching the destination. For efficiency of bulk data transmission like multimedia streaming, the overlay neighborhood should resemble the proximity in the underlying network. In this paper, we exploit the proximity and redundancy properties of a recently proposed clique-based clustered overlay network, named eQuus, to build efficient as well as robust transport overlays for multimedia streaming. To combine the efficiency of content pushing over tree structured overlays and the robustness of data-driven mesh overlays, higher capacity stable nodes are organized in tree structure to carry the long haul traffic and less stable nodes with intermittent presence are organized in localized meshes. The overlay construction and fault-recovery procedures are explained in details. Simulation study demonstrates the good locality properties of the platform. The outage time and control overhead induced by the failure recovery mechanism are minimal as demonstrated by the analysis.

New Efficient Strategy to Accelerate k-Means Clustering Algorithm

One of the most popular clustering techniques is the k-means clustering algorithm. However, the utilization of the k-means is severely limited by its high computational complexity. In this study, we propose a new strategy to accelerate the k-means clustering algorithm through the Partial Distance (PD) logic. The proposed strategy avoids many unnecessary distance calculations by applying efficient PD strategy. Experiments show the efficiency of the proposed strategy when applied to different data sets.

On efficiently searching trajectories and archival data for historical similarities

We study the problem of efficiently evaluating similarity queries on histories, where a history is a d -dimensional time series for d ≥ 1. While there are some solutions for time-series and spatio-temporal trajectories where typically d ≤ 3, we are not aware of any work that examines the problem for larger values of d. In this paper, we address the problem in its general case and propose a class of summaries for histories with a few interesting properties. First, for commonly used distance functions such as the L p -norm, LCSS, and DTW, the summaries can be used to efficiently prune some of the histories that cannot be in the answer set of the queries. Second, histories can be indexed based on their summaries, hence the qualifying candidates can be efficiently retrieved. To further reduce the number of unnecessary distance computations for false positives, we propose a finer level approximation of histories, and an algorithm to find an approximation with the least maximum distance estimation error. Experimental results confirm that the combination of our feature extraction approaches and the indexability of our summaries can improve upon existing methods and scales up for larger values of d and database sizes, based on our experiments on real and synthetic datasets of 17-dimensional histories.

Clustering Objects in a Road Network

Most spatial clustering algorithms deal with the objects in Euclidean space. In many real applications, however, the accessibility of spatial objects is constrained by spatial networks (e.g. road network). It is therefore more realistic to work on clustering objects in a road network. The distance metric in such setting is redefined by the network distance, which has to be computed by the expensive shortest path distance over the network. The existing methods are not applicable to such cases. Therefore, by exploiting unique features of road networks, two new clustering algorithms are presented, which use the information of nodes and edges in the network to prune the search space and avoid some unnecessary distance computations. The experimental results indicate that the algorithms achieve high efficiency for clustering objects in real road network.

A Fast Encoding Method for Vector Quantization by using Multi Euclidean Distance Estimations

Abstract Vector quantization (VQ) is a well-known method for image compression but its encoding process is very heavy computationally. In order to speed up VQ encoding, it is most important to avoid unnecessary Euclidean distance computations (k-D) as much as possible by a lighter (no multiplication operation) difference check fast that uses simpler features (low dimensional) while the searching is going on. Sum (1-D) and partial sums (2-D) are proposed as the appropriate features in this paper because they are the first two simplest features of a vector. Then, Manhattan distance (no multiplication operation but k dimensional computation) is used as a better difference check that basically benefits from no extra memory requirement for codewords at all. Sum difference, partial sum difference and Manhattan distance are computed as the multi estimations of Euclidean distance and they are connected to each other by the Cauchy-Schwarz inequality so as to reject a lot of unlikely codewords. For typical standard...

Adaptive space decomposition for fast visualization of soft objects

AbstractWe present a fast visualization scheme of soft objects by introducing an efficient evaluation method for a field function. The evaluation of a field function includes distance computation between a point in space and the defining primitives of the soft object. If the unnecessary distance computations between a point in space and the components that do not influence the point can be avoided, the evaluation can be accelerated. For this purpose, we decompose the space into adaptive‐sized cells according to the bounding volume of the components and build a data structure called an interval tree through which the influencing components for a point are sought. The bounding volume of a component is generated by considering the radius of a component and k‐DOPs. The proposed scheme can be used in many applications for soft objects such as modeling and rendering, especially in the interactive modeling process. Copyright © 2003 John Wiley & Sons, Ltd.

Mobile robot routing with dynamic programming

Abstract The rise of automated manufacturing systems, and autonomous systems in general, is creating a need for increased intelligence in mobile robotic devices, also known as autonomous guided vehicles (AGVs). One of the most important tasks these vehicles must perform is routing their own path in a cluttered environment. The path must ensure that obstacles are avoided without any unnecessary distance being covered. This paper discusses an approach to solving this routing problem. The method employed digitizes the workspace into a grid of nodes and treats the grid as a transition network. The principles of dynamic programming, with slight modifications, can be used to generate paths through this network that exhibit the desired degree of collision avoidance and economy. The algorithm is quick and simple enough to be executed by an on-board microprocessor, and it can be updated whenever the environment changes. The general principles are discussed, both of dynamic programming in general and of its modifications to fit the AGV routing problem. Two examples are then presented. The first example is generated with the emphasis on creating as short a path as possible, and the second includes a trade-off between path shortness and safety.