Layered Lossless Compression Method of Massive Fault Recording Data

In order to overcome the problems of large error and low precision in traditional power fault record data compression, a new layered lossless compression method for massive fault record data is proposed in this paper. The algorithm applies LZW (Lempel Ziv Welch) algorithm, analyzes the LZW algorithm and existing problems, and improves the LZW algorithm. Use the index value of the dictionary to replace the input string sequence, and dynamically add unknown strings to the dictionary. The parallel search method is to divide the dictionary into several small dictionaries with different bit widths to realize the parallel search of the dictionary. According to the compression and decompression of LZW, the optimal compression effect of LZW algorithm hardware is obtained. The multi tree structure of the improved LZW algorithm is used to construct the dictionary, and the multi character parallel search method is used to query the dictionary. The multi character parallel search method is used to query the dictionary globally. At the same time, the dictionary size and update strategy of LZW algorithm are analyzed, and the optimization parameters are designed to construct and update the dictionary. Through the calculation of lossless dictionary compression, the hierarchical lossless compression of large-scale fault record data is completed. Select the optimal parameters, design the dictionary size and update strategy, and complete the lossless compression of recorded data. The experimental results show that compared with the traditional compression method, under this compression method, the mean square error percentage is effectively reduced, and the compression error and compression rate are eliminated, so as to ensure the integrity of fault record data, achieve the compression effect in a short time, and achieve the expected goal.

Parallel implementation of the primer search algorithm for loop-mediated isothermal amplification

In the paper, the implementation of an algorithm of search for primers in a DNA sequence with a size varying between one nucleotide and multiple million nucleotides was discussed. This analysis was done within the objective of finding a set of six specific primers that are used for a conduction of the loop-mediated isothermal amplification (LAMP). For the fastest search result possible, a parallel search for the primer with the use of the Rabin-Karp Algorithm which enables the search for a primer’s entry in DNA sequence in each thread was proposed. A new software for search for primers was developed using Python with BioPython library which implements the algorithm.

Path Planning and Control of a Quadrotor UAV Based on an Improved APF Using Parallel Search

Control and path planning are two essential and challenging issues in quadrotor unmanned aerial vehicle (UAV). In this paper, an approach for moving around the nearest obstacle is integrated into an artificial potential field (APF) to avoid the trap of local minimum of APF. The advantage of this approach is that it can help the UAV successfully escape from the local minimum without collision with any obstacles. Moreover, the UAV may encounter the problem of unreachable target when there are too many obstacles near its target. To address the problem, a parallel search algorithm is proposed, which requires UAV to simultaneously detect obstacles between current point and target point when it moves around the nearest obstacle to approach the target. Then, to achieve tracking of the planned path, the desired attitude states are calculated. Considering the external disturbance acting on the quadrotor, a nonlinear disturbance observer (NDO) is developed to guarantee observation error to exponentially converge to zero. Furthermore, a backstepping controller synthesized with the NDO is designed to eliminate tracking errors of attitude. Finally, comparative simulations are carried out to illustrate the effectiveness of the proposed path planning algorithm and controller.

Query Expansion for Slovak to Bulgarian Language Machine Translation using Parallel Search

The paper presents results of the application of a statistical approach for Slovak to Bulgarian language machine translation. It uses Information Retrieval inspired search techniques and employs sever alalgorithmic steps of parallel statistical search with query expansion in Slovak-Bulgarian EUROPARL 7 Corpus using the Sketch Engine software and its scoring. The search includes the generation of concordances,collocations, word sketch differences, word sketches, and thesauri of the studied keyword (query) by using a statistical scoring, which is regarded as intermediate (inter-lingual) semantic standard presentation by means of which the studied keyword (from the source language) is mapped together with its possible translation equivalents (onto the target language. The results present the study of adjectival collocabillity in both Slovak and Bulgarian language from the corpus of political speech texts outlining the standard semantic relations based on the evaluation of statistical scoring. Finally, the advantages and shortcomings of the approach are discussed.

Multi-UAV Distributed Collaborative Coverage for Target Search Using Heuristic Strategy

Collaborative coverage for target search using a group of unmanned aerial vehicles (UAVs) has received increasing attention in recent years. However, the design of distributed control strategy and coordination mechanisms remains a challenge. This paper presents a distributed and online heuristic strategy to solve the problem of multi-UAV collaborative coverage. As a basis, each UAV maintains a probability grid map in the form of a locally stored matrix, without shared memory. Then we design two evaluation functions and related technical strategies to enable UAVs to make state transfer or area transfer decisions in an online self-organizing way. The simulation results show that the algorithm integrates geometric features such as parallel search and internal spiral search, and is not interfered by factors such as sudden failure of UAVs, changes in detection range, and target movement. Compared with other commonly used methods for target search, our strategy has high search efficiency, good robustness, and fault tolerance.

Search on a Line by Byzantine Robots

We consider the problem of fault-tolerant parallel search on an infinite line by [Formula: see text] robots. Starting from the origin, the robots are required to find a target at an unknown location. The robots can move with maximum speed [Formula: see text] and can communicate wirelessly among themselves. However, among the [Formula: see text] robots, there are [Formula: see text] robots that exhibit byzantine faults. A faulty robot can fail to report the target even after reaching it, or it can make malicious claims about having found the target when in fact it has not. Given the presence of such faulty robots, the search for the target can only be concluded when the non-faulty robots have sufficient evidence that the target has been found. We aim to design algorithms that minimize the value of [Formula: see text], the time to find a target at a (unknown) distance [Formula: see text] from the origin by [Formula: see text] robots among which [Formula: see text] are faulty. We give several different algorithms whose running time depends on the ratio [Formula: see text], the density of faulty robots, and also prove lower bounds. Our algorithms are optimal for some densities of faulty robots.