Search Speed Research Articles

Fast artificial bee colony algorithm with complex network and naive bayes classifier for supply chain network management

In supply chain network (SCN) management, multi-objective Pareto optimization means the network can meet the demand for both minimal cost and minimal lead-time in SCN. Due to the compromise between cost and lead-time, it is a non-trivial issue to search for multi-objective Pareto optimal solutions (POS) in SCN. Furthermore, with the wide application of the internet, an increasing number of SCN applications have been based on the internet. As a result, the complexity of SCN increases exponentially with the number of suppliers increasing. It is really a big challenge to find the global multi-objective POS within a limited time in SCN management. In order to solve this problem, first, this paper proposes an artificial bee colony (ABC) optimization algorithm with two improvements: (1) a novel solution framework designed to extend the application field of the SCN based on complex network; (2) the acceleration of search speed by adopting naive Bayes classifier. Second, the paper provides a case example of optimizing a three-echelon SCN with the objective of minimizing both cost and lead-time. After the simulation with this example, it turns out that the enhanced ABC algorithm can satisfy the requirements of: (1) finding the global multi-objective POS; (2) improving the speed of finding optimal solutions in SCN management.

REGISTOR

This article presents REGISTOR, a platform for r egular e xpression g rabbing i nside stor age. The main idea of Registor is accelerating regular expression (regex) search inside storage where large data set is stored, eliminating the I/O bottleneck problem. A special hardware engine for regex search is designed and augmented inside a flash SSD that processes data on-the-fly during data transmission from NAND flash to host. To make the speed of regex search match the internal bus speed of a modern SSD, a deep pipeline structure is designed in Registor hardware consisting of a file semantics extractor, matching candidates finder, regex matching units (REMUs), and results organizer. Furthermore, each stage of the pipeline makes the use of maximal parallelism possible. To make Registor readily usable by high-level applications, we have developed a set of APIs and libraries in Linux allowing Registor to process files in the SSD by recombining separate data blocks into files efficiently. A working prototype of Registor has been built in our newly designed NVMe-SSD. Extensive experiments and analyses have been carried out to show that Registor achieves high throughput, reduces the I/O bandwidth requirement by up to 97%, and reduces CPU utilization by as much as 82% for regex search in large datasets.

Dynamical Model Identification for a Small-Scale Unmanned Helicopter Using an Integrated Approach

This article presents an integrated approach for the parameter identification of a small-scale unmanned helicopter. With the flight experiment data collection and preprocessing, a hybrid identified algorithm combining the improved artificial bee colony algorithm and prediction error method is proposed to obtain the unknown dynamical parameters of the linear model. The proposed algorithm is valid to use thanks to an adaptive search equation, a novel probability-scaling method, and a chaotic operator and has a good performance in search speed and quality. Afterwards, we design a wind tunnel test to modify the main rotor time constant of the identified model. The identified accuracy and feasibility of the proposed approach are verified by making a time-domain comparison with three other algorithms. Results show that the dynamical characteristics of the helicopter can be determined accurately by the identified model. And the proposed approach is propitious to enhance the reliability and availability of the identified dynamical model.

Improved dynamic match phone lattice search for Persian spoken term detection system in online and offline applications

Spoken term detection (STD) refers to discovering all occurrences of a given term in a set of speech utterances. One of the well-known approaches for the STD system is the phone lattice search (PLS) that produces a phone-based lattice of speech utterances. Since the accuracy of a phone recognizer affects the accuracy of the STD system, the PLS approach utilizes the minimum edit distance (MED) measure to compensate the phone recognizer errors. While this measure increases the detection rate, it also raises the false alarm rate. In this paper, we consider the PLS approach as the baseline. Then, we use Viterbi scoring and Jaro-Winkler similarity measure in order to decrease the false alarm rate. Since the proposed approach uses more techniques than the baseline approach, the search speed may decrease. To overcome this problem, we use lattice pruning and indexing techniques such as depth first search algorithm to increase the search speed in online and offline applications, respectively. We report the experimental results for monophone-based and triphone-based STD system. The results indicate that using triphone-based STD system improved the performance about 2% in comparison with monophone-based STD system. Moreover, when we used triphone-based models, the proposed approach including MED measure, Viterbi scores and Jaro-Winkler similarity measure improved the accuracy of the method with only MED measure, about 17%.

Sparse compression algorithm for MBOK signals

To cope with the limitations of traditional direct-sequence spread spectrum system application, MBOK signals have been considered owing to their high gain, fast rate, and high frequency band transmission capability. However, to address the problems of large search range, slow speed, and large complexity when receiving and searching for MBOK signals, the sparse compression search (SCS) algorithm is proposed. The idea of the SCS algorithm is to compress the frequency dimension in order to improve the frequency compensation efficiency, and to then establish the sparse processing from the code dimension and the frequency dimension, thus improving the search speed. Next, the code and frequency values are calculated by channel mapping matching. The test and analysis results show that the SCS algorithm can quickly estimate the code and frequency values, and the detection probability and the computation speed are much better than other algorithms.

A Novel Simplification Method for 3D Geometric Point Cloud Based on the Importance of Point

3D point cloud simplification is an important pretreatment in surface reconstruction for sparing computer resources and improving reconstruction speed. However, existing methods often sacrifice the simplification precision to improve the simplification speed, or sacrifice the speed to improve precision. A proper balance between the simplification speed and the simplification accuracy is still a challenge. In this paper, we propose a new simplification method based on the importance of point. Named as detail feature points simplified algorithm (DFPSA), this algorithm has distinct processes to achieve improvements in three aspects. First, a rule of k neighborhood search is set to ensure the points found are the closest to the sample point. In this way, the accuracy of calculated normal vector of the point cloud is significantly improved, and the search speed is largely increased. Second, a formula that considers multiple characteristics for measuring the importance of point is proposed. Thereupon, the main detail features of the point cloud are preserved. Finally, an octree structure is employed to simplify the remaining points, through which holes in reconstructing point cloud are obviously reduced. The DFPSA is applied to four different data sets, and the corresponding results are compared with those of other five algorithms. The experimental results demonstrate that the DFPSA brings better simplification effects than existing counterparts, and the DFPSA not only can simplify point cloud but also has good effect in simplifying subject's narrow contours.

УСОВЕРШЕНСТВОВАНИЕ ОЦЕНКИ ДВИЖЕНИЯ В ВЫСОКОЭФФЕКТИВНОМ ВИДЕОКОДИРОВАНИИ

The paper deals with video coding based on the use of dependent sources, which is caused by the need to accelerate the coding process. The coding time is directly dependent on the computing power, which with the advent of new standards is not enough. Therefore, the article suggests the results of the study, which are designed to improve existing methods, as well as to continue the progress of scientific research in this area. The results consist of using an approach to compression based on coding methods of dependent sources, which can significantly reduce the complexity of processing on the side of the encoder. The results of the study were obtained using two coding algorithms for fixed and dynamic plots. For comparison, JPEG and, respectively, MPEG encoding standards were defined, as well as an improved algorithm that was considered by the authors in previous papers. It solves the problem of efficiently recovering intermediate frames on the decoder side. When evaluating the movement, search styles have a significant impact on the performance of the algorithm, both the search speed and the quality of the search. The search style should be designed considering the characteristics of the distribution of the motion vector probability (MVP) of the sequences of the real plot. The purpose of this study is to improve the coding efficiency of video sequences using the Diamond Search (DS) algorithm. The results show that DS is efficient and reliable, it can always give a higher search speed in different sequences than other algorithms.

UISFuzz: An Efficient Fuzzing Method for CPU Undocumented Instruction Searching

With the rapid development of network security and the frequent appearance of CPU vulnerabilities, CPU security have gradually raised great attention and become a crucial issue in the computer field. Undocumented instructions, as one of the important threats to system security, is an important entry for CPU security research. Using fuzzing technology can automatically test the CPU instruction set and discover potential undocumented instructions, but the existing methods are of slow search speed and low accuracy. Therefore, this paper designs an efficient fuzzing method (UISFuzz) for undocumented instruction searching. This method has the following merits: (1) the instruction search speed is greatly improved by an automatic instruction format recognition, as the low efficient part of the known instruction format is skipped and therefore the instruction search space is much narrowed; (2) the false positive rate is reduced by a recheck mechanism based on the expert knowledge database to filter the wrongly found instructions; (3) the overhead of the method is decreased by optimizing the result analysis program, and the scope of the system is expanded, where more processors with lower performance are compatible. Typical CPU experimental results show that, the UISFuzz can successfully find undocumented instructions in the CPUs and simultaneously improve the time efficiency by 5 times compared with existing tools.

A Robot Odor Source Localization Strategy Based on Bionic Behavior

In this paper, a robot bionic odor source localization algorithm based on kinesis response and tropotaxis behavior of flatworm is proposed, the algorithm does not need to detect wind direction information and odor absolute concentration information. Experiments show that the proposed algorithm is not affected by both wind direction and wind speed, only by judging the rate of change of odor concentration, which improves the searching efficiency, has good stability and environmental adaptability. it is a reliable initiative olfactory strategy with simple, search speed, high efficiency advantages.

Suffix Arrays with a Twist

The suffix array is a classic full-text index, combining effectiveness with simplicity. We discuss three approaches aiming to improve its efficiency even more: changes to the navigation, data layout and adding extra data. In short, we show that i) the way how we search for the right interval boundary impacts significantly the overall search speed, ii) a B-tree data layout easily wins over the standard one, iii) the well-known idea of a lookup table for the prefixes of the suffixes can be refined with using compression, iv) caching prefixes of the suffixes in a helper array can pose another practical space-time tradeoff.

A Novel Heuristic Artificial Neural Network Model for Urban Computing

Urban computing brings powerful computational techniques to bear on such urban challenges as pollution, energy consumption, and traffic congestion. After decades of rapid development, artificial neural networks (ANN) have been successfully applied in many disciplines and have enabled many remarkable research achievements. However, no quantitative method has yet been found that can identify every parameter to optimize a neural network. The BP neural network is most frequently used but suffers from the following defects with respect to complex and multidimensional training data or setting of different parameters, i.e., overfitting, slow convergence speed, trapping in local optima and poor prediction effect, and these obstacles have greatly restricted its practical applications. Therefore, this paper proposes a method that uses ant colony optimization (ACO) to train the parameters and structure of the neural network, optimizes its weight and threshold to solve its defects, and applies the model in the optimization scheme of urban operation and management to verify its effect. The experimental simulation proves that the method in this paper is effective and that it makes certain improvements in the local and global search ability, speed, and accuracy of the neural network.

A BP Neural Network Prediction Model Based on Dynamic Cuckoo Search Optimization Algorithm for Industrial Equipment Fault Prediction

The fault prediction problem for modern industrial equipment is a hot topic in current research. So, this paper first proposes a dynamic cuckoo search algorithm. The algorithm improves the step size and discovery probability. Then, it introduces the change trend of fitness function value into the step size update formula to balance the search speed and accuracy. At the same time, the algorithm initial global search step is larger, while the step size of the local search is smaller in the latter part of the algorithm. In the process of discovering the global optimal solution, the probability of preserving the offspring with good fitness is increased, and the uncertainty of preference random walk is improved. As the search progresses, the probability of discovery is reduced, which makes it easy to produce new individuals in the later stage of evolution. Based on this, a BP neural network prediction model based on dynamic cuckoo search algorithm optimization is established. And the experimental results show that the proposed prediction model has faster convergence and higher accuracy.

Distributed data-dependent locality sensitive hashing

Locality sensitive hashing (LSH) is a popular algorithm for approximate nearest neighbour (ANN) search. As LSH partitions vector space uniformly and the distribution of vectors is usually non-uniform, it poorly fits real datasets and has limited efficiency. In this paper, we propose a novel data-dependent locality sensitive hashing (DP-LSH) algorithm, which has a two-level structure. In the first level, we train a number of cluster centres, and use the centres to divide the dataset. So the vectors in each cluster have near uniform distribution. In the second level, we construct LSH tables for each cluster. Given a query, we first determine a few clusters that it belongs to, and perform ANN search in the corresponding LSH tables. Furthermore, we present an optimised distributed scheme and a distributed DP-LSH algorithm. Experimental results on the reference datasets show that the search speed of DP-LSH can be increased by 48 times compared to E2LSH, while keeping high search precision; and the distributed DP-LSH can further improve search efficiency.

Feature Data Selection for Improving the Performance of Entity Similarity Searches in the Internet of Things

Sensors are used to sense the state information of physical entities in the Internet of Things (IoT). Thus, a large amount of dynamic real-time data is generated. The entity similarity search based on the quantitative dynamic sensor data is thus worth studying. To the best of our knowledge, there is no research on the entity similarity search based on feature data selection for the quantitative dynamic sensor data in the IoT. This paper proposes a selection mechanism for the entity main features (SMEF). The SMEF is a feature data selection method based on the quantitative dynamic sensor data. It uses the feature matrix to delete the irrelevant entity features, applies an improved relief algorithm (iRelief) to calculate the feature data relevance and proposes a three-component storage relation table of the entities, models, and features (TEMF) for the dynamic feature weights calculation. The experimental results show that the similarity search algorithm based on feature data selection can improve the average search accuracy by more than 10%, as well as increase the search speed and reduce the data transmission and storage costs.

Crow Search Algorithm Based on Neighborhood Search of Non-Inferior Solution Set

Characterized by less parameter settings, easy implementation, and strong optimization capacity, the crow search algorithm has been successfully applied to solve the optimization problem. As the basic crow search algorithm is a new kind of swarm intelligent algorithm only based on the crow's memory foraging mode, it also contains defects like slow search speed and low optimization precision in later iterations, which are especially obvious for the optimization of high-dimensional functions. In order to overcome these shortcomings, a new crow search algorithm based on neighborhood search of non-inferior solution set (NICSA) is proposed. The proposed algorithm makes the crow individual choose the memory search mode or neighborhood search mode automatically in the course of evolution by the determination factor of non-inferior solution. With this strategy, the local exploitation and the global exploration of the algorithm became more balanced. In the neighborhood search, the selectivity factor is used to guide non-inferior solutions to adaptively execute neighborhood search of Levy flight or Gaussian flight, to enhance the neighborhood searchability of the algorithm and improve the optimization precision. The result of simulation experiments with 23 benchmark test functions verifies that the proposed algorithm has good optimization effect in the aspects of search veracity, convergence rate and robustness.

An improved distributed storage model of remote sensing images based on the HDFS and pyramid structure

With the rapidly growing amount of remote sensing data, data management needs to adopt a new architecture. Some improvements have been made in some areas. For example, the Hadoop distributed file system (HDFS) can be utilised for large files. However, small files will be produced when storing remote sensing images in the pyramid-based structure. In this paper, we propose a method based on the Hadoop system with MapFile. The method is an improved storage model for efficient storage and allows for access of the small files on the HDFS. The proposed method combines small files into the MapFile serialised container, and it reduces the number of small files. The metadata sensor information is stored in the index to improve the search speed. Results demonstrate the efficiency of remote sensing image processing using the pyramid model and the reduced execution time compared with the HDFS, HDWebGIS and Hadoop archives.

Optimal Dispatch of Reactive Power Using Modified Stochastic Fractal Search Algorithm

This paper applies a proposed modified stochastic fractal search algorithm (MSFS) for dealing with all constraints of optimal reactive power dispatch (ORPD) and finding optimal solutions for three different cases including power loss optimization, voltage deviation optimization, and L‐index optimization. The proposed MSFS method is newly constructed in the paper by modifying three new solution update mechanisms on standard stochastic fractal search algorithm (SSFS). The first modification is to keep only one formula and abandon one formula in the diffusion process while the second modification and the third modification are used in the first update and the second update. In two updates of SSFS, solutions with low quality are updated with high probability while other solutions with high quality do not get chances to be updated. This manner results in the fact that some promising solutions around the high quality solutions can be missed. In order to tackle this restriction, the second modification of MSFS is to newly update the worst solutions in the first update and the best solutions in the second update. In the third modification, all existing formulas of SSFS in the two updates are abandoned and the same new proposed technique is used for updating such solutions in two updates. Compared to SSFS, the three modifications can bring advantages to MSFS such as using smaller number of produced solutions per iteration, spending shorter execution time, finding better optimal solutions, and owning more stable search ability. Furthermore, the proposed method also sees its effectiveness and robustness over SSFS by testing on IEEE 30‐bus system and IEEE 118‐bus system with three different single objectives for each system. The proposed method can find less minimum, average, and maximum for all the cases in addition to faster search speed. Besides, the proposed method is also compared to other methods such as PSO‐based method group, GA‐based method group, DE‐based method group, and other recent methods. Result comparisons also indicate that the proposed method can be more efficient than almost all these methods with respect to less minimum and smaller values of control parameters. As a result, evaluation of the performance of the proposed method is that it should be used for seeking solutions of ORPD problem.

Toward Efficient Encrypted Image Retrieval in Cloud Environment

Outsourcing image search services to public clouds is an ever-increasing trend. However, directly outsourcing image datasets to untrusted clouds introduces privacy concerns. Several secure image retrieval schemes have been proposed recently. However, most of them require participation from image owners when building secure indexes, which wastes many computational resources of the image owners. Several schemes are proposed to solve this problem, but they suffer from low search accuracy on large datasets. In this paper, we propose the first secure image retrieval scheme that simultaneously solves these two problems. To obtain higher search accuracy, we extract image features via fine-tuned convolutional neural networks. Then, the image features are encrypted by using the secure k-Nearest Neighbor algorithm. To improve search speed and reduce the cost of image owners, we let cloud servers locally build a secure hierarchical index graph by using the encrypted image features. Besides, the secure index can be built and updated in parallel. We provide security analysis for the proposed scheme. Performance evaluations on the CIFAR-10 dataset show that the proposed scheme is practical. Moreover, compared with a recent scheme, our scheme can save more index construction time and cost of image owners when building secure indexes.

An Optimization Strategy Based on Dimension Reduction Method in Wireless Charging System Design

This study proposes an optimization strategy, which is based on a dimension reduction method with sensitivity analysis, for fast multi-objective optimization of coil design in the wireless power transmission (WPT) system. The multi-objective optimization process of coil in WPT usually involves general genetic algorithm (GA) and derivatives that suffer from common problems of slow search speed and heavy calculation burden, especially in the presence of multiple optimization variables. Moreover, in a complicated WPT system with ferrite, the optimization objective, such as the coupling factor k, is difficult to calculate analytically; in practice, it is acquired numerically using the finite element method (FEM), which also adds to the optimization complexity and time consumption. As a remedy, we propose the new strategy by simplifying the original complex optimization problem firstly with sensitivity assessment, and then applying NSGA-II convergence algorithm for optimization. Firstly, the optimization strategy is introduced, including the evaluation method, dimension reduction method and entire multi-objective optimization process. Then the optimization strategy is implemented to optimize the design parameters of a coil system. The sensitivity of design parameters is assessed with FEM analysis to realize the dimension reduction, and the optimization framework is established. Afterwards, the high sensitivity parameters will be optimized utilizing the multi-objective algorithm to obtain the optimal parameters. The simulation results demonstrate that, compared to the traditional NSGA-II algorithm, the proposed strategy yields a 44% reduction in the iteration time required for system convergence. Furthermore, the good agreement between the simulation and the measured sensitivity data and optimal parameters presents additional validation for the optimization strategy. The experiment further shows that implementation of the proposed strategy could lead to an increase in the maximum efficiency of the WPT system from 79.85% to 93.2%.

A novel computation method for 2D deformation of fish scale based on SURF and N-R optimisation

Fish scales were unique structural materials that served as a form of natural armour and affected the mechanical properties which had important applications in bionics. Digital image correlation (DIC) method was used to determine the mechanical properties, but it took a long time to calculate the uniaxial tensile deformation. In this investigation a DIC optimisation algorithm method based on speeded-up robust features (SURF) and Newton-Raphson (N-R) was conducted on specimens prepared from the scales. First, the SURF algorithm was used to detect the matched points and their coordinate values in the digital images before and after deformation. Then, the initial displacement of the interest point was estimated from the affine transformation fitted to the matched feature points inside the subset area. Last, the zero-mean normalised sum of squared differences (ZNSSD) metric function was optimised by the N-R iterative method, and the optimised displacement value of the interest points would be gained. The numerical translation experiments and simulation results showed that this method improved the search speed and the measurement accuracy effectively. So the deformation of fish scales for axial tension would be calculated by this method.