Redundancy Ratio Research Articles

Signal Processing Application Based on a Hybrid Wavelet Transform to Fault Detection and Identification in Power System

The power system is one of the most susceptible systems to failures, which are most frequently caused by transmission line faults. Transmission line failures account for 85% of all power system malfunctions. However, over the last decade, numerous fault detection methods have been developed to ensure the reliability and stability of power systems. A hybrid detection method based on the idea of redundancy property is presented in this paper. Because the continuous wavelet transform itself does not extract fault features for small defects effectively, the stationary wavelet transform approach is employed to assist in their detection. As a result of its ability to decompose the signal into high- and low-frequency components, undecimated reconstruction by using the algebraic summation operation (ASO) is used. This approach creates redundancy, which is useful for the feature extraction of small defects and makes faulty parts more evident. The numerical value of the redundancy ratio’s contribution to the original signal is approximately equal to 36%. Following this method for redundant signal reconstruction, a continuous wavelet transform is used to extract the fault characteristic significantly easier in the time-scale (frequency) domain. Finally, the suggested technique has been demonstrated to be an efficient fault detection and identification tool for use in power systems. In fact, using this advanced signal processing technique will help with early fault detection, which is mainly about predictive maintenance. This application provides more reliable operation conditions.

Classifier-Based Nonuniform Time Slicing Method for Local Community Evolution Analysis

With the rapid expansion of the scale of a dynamic network, local community evolution analysis attracts much attention because of its efficiency and accuracy. It concentrates on a particularly interested community rather than considering all communities together. A fundamental problem is how to divide time into slices so that a dynamic network is represented as a sequence of snapshots which accurately capture the evolutionary events of the interested community. Existing time slicing methods lead to inaccurate evolution analysis results. The reason is that they usually rely on a linear strategy while the community evolution is a nonlinear process. This paper investigates the problem and proposes a classifier-based time slicing method for local community evolution analysis. First, a classifier is trained for judging whether there is a community in the given network snapshot is identified as the continuing of the community defined by the given node subset. The features for classification include internal cohesion degree and external coupling degree. Second, a time slicing method is proposed based on the trained classifier. As the network evolves, the method continuously uses the classifier to predict whether there is a community in the newest network identified as the continuing of the interested community. Whenever the answer is negative, an evolutionary event is presumed to have occurred and a new time slice is generated. Experimental results show that compared with existing time slicing methods, our proposed method achieves higher recognition rate for given redundancy ratio.

Hierarchical Q-learning-enabled neutrosophic AHP scheme in candidate relay set size adaption in vehicular networks

Cooperative candidate relay sets (CRS) in opportunistic broadcast paradigms can significantly improve the performance efficiency of traditional broadcast schemes by simultaneously scheduling multiple vehicles in the data packet transmission process. Nonetheless, multiple relay synchronization may cause enormous data redundancy and high delays in data delivery, resulting in a high cooperative transmission cost. High vehicle velocities and vehicular topology restrictions, including intersections, exacerbate the cooperative transmission cost. Motivated by a trade-off between delay and delivery ratio via reducing duplicate data packets and redundant relays, we address this gap in this paper. To this end, we propose an adaptive candidate relay set (ACRS) size scheme based on the Neutrosophic Set Analytic Hierarchy Process (NS-AHP) technique assisted by a Q-learning algorithm. The NS-AHP pair-wise comparison matrix includes; redundancy, cooperative-based efficiency, and relay set stability as the decision-making attributes. Meanwhile, we use a hierarchical Q-learning approach based on the time-varying vehicular environment to adjust the relative preference of the attributes and alternatives (i.e., various CRS sizes) over each other. To optimally learn the relative preference values of the attributes and alternatives over each other, reward functions are set dynamically at both levels. Comprehensive simulations via NS-2 and SUMO are carried out to assess the efficacy of our ACRS scheme compared to three commonly used baseline techniques of fixed size, threshold-based, and probability-based techniques. Overall, the average yields obtained from the experimental outcomes in the urban and highway scenarios show that the ACRS scheme, on average, improves the packet delivery ratio by 3.77%, 5.36%, and 1.35%; reduces the single-hop delay by 34.78%, 42.085%, and 19.39%; reduces the end-to-end delay by 34.76%, 42.72%, and 21.35%; improves the transmission throughput by 34.24%, 41.54%, and 19.305%; and reduces the redundancy ratio by 43.69%, 50.66%, and 24.32% compared to the fixed size, threshold-based, and probability-based techniques, respectively.

ABM-V: An Adaptive Backoff Mechanism for Mitigating Broadcast Storm in VANETs

In vehicular ad hoc networks (VANETs), the broadcast storm problem may disable road safety applications and cause congestion or traffic accidents. Existing schemes propose broadcast storm mitigation by reducing the number of relay vehicles. Concurrently, in improving reachability, these schemes stipulate that vehicles expected to transmit packets to more next-hop neighbors have higher relay priority. However, the potential redundancy caused by duplicate reception by neighbors is ignored, which exacerbates the contention and collisions in transmission. This paper proposes an adaptive backoff mechanism for mitigating broadcast storm in VANETs (ABM-V). Specifically, the receiver estimates the expected benefit and redundancy by combining the distribution of neighbors. Then, the receiver adaptively adjusts the backoff time by utilizing Dempster-Shafer evidence theory. Finally, the receiver with the shortest backoff time becomes the relay and rebroadcasts the packet to its neighbors. Simulation results illustrate that ABM-V significantly reduces the rebroadcast ratio and redundancy ratio compared with the existing typical schemes while maintaining stable reachability.

K-Level Extended Sparse Array Design for Direction-of-Arrival Estimation

Sparse arrays based on the concept of a sum-difference coarray (SDCA) have increased degrees of freedom and enlarged effective array aperture compared to those only considering a difference coarray. Nevertheless, there still exist a number of overlapping virtual sensors between the difference coarray and the sum coarray, yielding high coarray redundancy. In this paper, we propose a k-level extended sparse array configuration consisting of one sparse subarray with k-level expansion and one uniform linear subarray. By systematically analyzing the inherent structure of the k-level extended sparse array, the closed-form expressions for sensor locations, uniform DOF and coarray redundancy ratio (CARR) are derived. Moreover, with the utilization of a k-level extended strategy, the proposed array remains a hole-free property and achieves low coarray redundancy. According to the proposed sparse array, the spatial and temporal information of the incident sources are jointly exploited for underdetermined direction-of-arrival estimation. The theoretical propositions are proven and numerical simulations are performed to demonstrate the superior performance of the proposed array.

DEM studies on the effect of particle breakage on the critical state behaviours of granular soils under undrained shear conditions

A number of discrete element analyses of undrained triaxial shear tests on crushable assemblies are performed using 3-dimensional particle flow code (PFC3D). The undrained shear of the samples is simulated by assigning different velocities at the boundaries to control the constant volume of the samples during the shear. Particle breakage is studied using the octahedral shear stress breakage criterion and the fragment spawning mode satisfying an Apollonian sphere packing. The microparameters of the soils are obtained by simulating the test results reported on a Nansha calcareous sand. The effects of particle breakage and drainage condition on the critical state behaviours of the sand are intensively examined. It is found that particle breakage and drainage condition do not affect the slope of the critical state p′−q line. In the ecs−(p′cs/pa)0.7 space, the critical state line translates downward and rotates clockwise when particle breakage is considered, whilst drainage condition does not affect the critical state line. The intrinsic macro–micro correlations underlying the critical state behaviours are explored by analysing the evolution of representative micromechanical indicators such as coordination number, redundancy ratio, and fabric and force anisotropies.

A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things.

Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using k-mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only; that is, data of nonneighbor is not compared at all. These algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches.

Digital Computer Modulation Signal Classification Based on Neural Network

Computer is one of the indispensable tools in the human world, and human needs for it are increasing, so the emergence and application of more advanced computers are needed. The current computers do not respond intelligently, and it is difficult to meet people’s needs for information processing in the era of big data. In order to solve these problems, this paper proposes the application of a neural network-based data classification algorithm in computers, aiming to study the practical application of the algorithm in computers. The research method of this paper is to introduce the BP neural network, select the appropriate method of classification features, and then study the data classification algorithm. The function of the research method is to compare the classification error and convergence speed of the BP network composed of different hidden layer nodes, to study whether a certain feature item of the data exists and the difference in the amount of information classification of the entire document, and to select high efficiency, accuracy, and scalability algorithm. This paper compares the forward reasoning time of the model before and after cutting through experiments based on neural network model design, algorithm design, and man-machine dialogue model design. The results show that, in terms of computing speed, the adaptive model compression method based on the accuracy and redundancy ratio compresses the model after the forward reasoning time is greatly reduced, and the reasoning time becomes 35% of the original, and in terms of calculation accuracy, the absolute error after using the SOM method in this article has not reached 0.5.

Efficiency Evaluation of Major Coastal Ports in China: Application of the Three-stage DEA Model

With the increasing competition between ports, how to improve port efficiency and promote the sustainable development of ports becomes the focus of attention. The paper applies the three-stage data envelopment analysis (T-S DEA) model to measure the efficiency of top 10 Chinese coastal ports, including the scale efficiency (SE) and technical efficiency (TE). Besides, the principle component analysis approach is adopted to reduce the influence of redundant information of evaluation indicators on results. The stochastic frontier analysis regression model is applied to eliminate the effects of environmental factors and stochastic errors under similar exterior conditions. The results show: (1) The ports efficiency (SE and TE) in different regions is quite different; the efficiency of Chinese coastal ports is generally low, which is mainly caused by scale inefficiency; (2) The SE and TE are improved and the input and output redundancy ratio are decreased significantly by comparing the results of traditional DEA model. (3) Shanghai, Ningbo-Zhoushan, Shenzhen, Hong Kong and Kaohsiung have relatively high comprehensive efficiency. This study provides valuable information for estimating port efficiency and establishing competitive strategies in the future.

On the Utility of Power Spectral Techniques With Feature Selection Techniques for Effective Mental Task Classification in Noninvasive BCI

In this paper classification of mental task-root Brain-Computer Interfaces (BCI) is being investigated, as those are a dominant area of investigations in BCI and are of utmost interest as these systems can be augmented life of people having severe disabilities. The BCI model's performance is primarily dependent on the size of the feature vector, which is obtained through multiple channels. In the case of mental task classification, the availability of training samples to features are minimal. Very often, feature selection is used to increase the ratio for the mental task classification by getting rid of irrelevant and superfluous features. This paper proposes an approach to select relevant and non-redundant spectral features for the mental task classification. This can be done by using four very known multivariate feature selection methods viz, Bhattacharya's Distance, Ratio of Scatter Matrices, Linear Regression and Minimum Redundancy & Maximum Relevance. This work also deals with a comparative analysis of multivariate and univariate feature selection for mental task classification. After applying the above-stated method, the findings demonstrate substantial improvements in the performance of the learning model for mental task classification. Moreover, the efficacy of the proposed approach is endorsed by carrying out a robust ranking algorithm and Friedman's statistical test for finding the best combinations and comparing different combinations of power spectral density and feature selection methods.

Text File Compression Using Hybrid Run Length Encoding (Rle) Algorithm With Even Rodeh Code (Erc) And Variable Length Binary Encoding (Vlbe) To Save Storage Space

The increase of data usage causes problems in data storage, indirectly making the need for data storage also to increase. One alternative solution that can be done is to compress the file so that the file becomes smaller in size so it saves storage space. The algorithm used in this research is the Run Length Encoding algorithm, the Even Rodeh Code algorithm, and the Variable Length Binary Encoding algorithm which are the types of lossless compression. The algorithm will calculate its performance based on Compression Ratio, Ratio of Compression, Redundancy, Compression Time, and Decompression Time. The file that will be used in the data compression process is the file extension *.txt. This study used homogeneous strings (strings that have the same character) and heterogeneous strings (strings that have different characters) in testing the algorithm. In the compression process with a homogeneous string, the combination of the Run Length Encoding algorithm with the Variable Length Binary Encoding algorithm is better than the combination of the Run Length Encoding algorithm and the Even Rodeh Code algorithm with a Compression Ratio of 18.84% and a decompression time of 0.01295 ms. While the compression process on heterogeneous strings from the combination of the Run Length Encoding algorithm with the Even Rodeh Code algorithm is better than the combination of the Run Length Encoding algorithm with Variable Length Binary Encoding algorithms with Compression Ratio of an average of 52.45% and fewer decompression times of 4.93002 ms

ИНФОРМАЦИОННАЯ ХАРАКТЕРИСТИКА ПЕРВИЧНОЙ ПОЧКИ НА ЭТАПАХ ЭМБРИОГЕНЕЗА У СИРИЙСКОГО ХОМЯКА

The state of the system «primary kidney of a Syrian hamster» at the stages of embryonic development was studied. The informational characteristic of the complexity and organization of the morphological system «primary kidney» was carried out by calculating the indicators: informational maximum entropy (Нmax): (Нmax) = Iog2n, where n is the number of classes, informational entropy (Н), as a measure of information uncertainty according to Shannon’s formula Н = -∑Рi Log2Pi, where Р – volume fraction of tissue components of the primary kidney; information organization of the system (S): relative entropy (h), coeffi cient of relative organization of the system (or coeffi cient of redundancy) (R): R = (Hmax-H) / Hmax 100%. Based on the studies carried out, it was found that the entropy of the «primary kidney» system in embryos increases by the end of the 1st stage of the organ’s life cycle to 1.07 bit, at the 2nd stage it decreases to 0.80 bit, which is associated with an increase in the proportion of mesenchyme and its predominance. over other structural components. The redundancy ratio increases in the middle of stage 1, then decreases towards the beginning of stage 2, then from the middle of stage 2, the redundancy ratio increases until the end of stage 3, which is associated with an increase in the stability of the system. The relative entropy during the observation period decreases from 52.54% at the beginning of stage 1 of the organ’s life cycle to 39.93% at stage 3.

Dynamic feature selection method with minimum redundancy information for linear data

Feature selection plays a fundamental role in many data mining and machine learning tasks. In this paper, we proposed a novel feature selection method, namely, Dynamic Feature Selection Method with Minimum Redundancy Information (MRIDFS). In MRIDFS, the conditional mutual information is used to calculate the relevance and the redundancy among multiple features, and a new concept, the feature-dependent redundancy ratio, was introduced. Such ratio can represent redundancy more accurately. To evaluate our method, MRIDFS is tested and compared with seven popular methods on 16 benchmark data sets. Experimental results show that MRIDFS outperforms in terms of average classification accuracy.

Исследование способов повышения безотказности пассивно резервированных подсистем управления летательными аппаратами с учетом допусков

The paper studies the reliability of passively redundant subsystems of aircraft, taking into account tolerances for a decrease in their output parameters in case of sudden component failures. The influence of the reliability values of elements, tolerances of two levels and the redundancy ratio 
 on the reliability of passively redundant subsystems as a whole have been investigated; the examples of such subsystems have been given. There have been presented the results of analysis 
 of the aircraft subsystems features with allowance for tolerances. First of all, these include the availability of technical specifications and realized tolerances determined by the reservation structure; implementing each tolerance with different values of the multiplicities of redundancy; using the main tolerance grid by multiple backup methods; presence of critical probabilities narrowing the reliability range of the elements, where this type of reservation is beneficial from the range 
 0–1 to the range of the supercritical area (pkr–1). The influence of the element reliability values, tolerances of two levels and the redundancy ratio on the failure-free operation of passively redundant subsystems in general and in supercritical areas has been investigated. It is shown that the minimum redundancy multiplicities in the interests of increasing the reliability of the considered subsystems of aircraft are advantageous to use only with large tolerances and low probabilities of failure-free operation of their elements. Under close tolerances and any reliability values of elements, as well as with large tolerances (more than 25%), high reliability values of this class of aircraft subsystems can be achieved only with large redundancy multiplicities. It has been inferred that there are extreme reliability differences of a passively redundant subsystem and its element, which 
 allows to set the task of developing highly reliable passively redundant subsystems of aircraft taking into account tolerances from relatively unreliable elements.

Privacy-Preserving and Trustworthy Device-to-Device (D2D) Offloading Scheme

In device-to-device (D2D) offloading, frequent offloading to a specific mobile device (i.e., an offloadee) can violate the privacy of a task owner and proper offloading results cannot always be guaranteed due to untruthful mobile devices. To address these problems, we propose a privacy-preserving and trustworthy D2D offloading scheme (PPTS) with two steps: 1) a privacy-preserving offloading step and 2) a blockchain-based verification step. In the first step, a task owner selects several offloadees and offloads the tasks redundantly to them to obtain reliable offloading results with a minimum task completion delay while preserving its own privacy at a sufficient level. In addition, the task owner chooses another mobile device as a verifier. In the second step, the task owner and verifier exploit blockchain networks to check whether the offloading results are appropriately processed. We formulate a static game to decide an appropriate redundancy ratio of offloading tasks and incentives for the offloadees.The evaluation results demonstrate that PPTS can provide reliable offloading results with a reduced task completion delay and guarantee a sufficient level of privacy of the task owner.

Enhanced Event Coverage and Redundant Data Minimization Employing Pentagonal Scalar Premier Selection in Wireless Multimedia Sensor Networks (WMSNs)

In the modern era, data redundancy has become one among the predominant ultimatums encountered in Wireless Multimedia Sensor Networks (WMSNs), which occurs because of event information reporting through the scalars residing at the superimposing zones of field of views (FoVs) of multiple camera sensors. As a result, same data is transferred many times, thus leading to redundancy in data transfer. Therefore, the aim is to select the representatives of scalar sensors called scalar premiers (SPs) that can report the event information in lieu of all the scalars while diminishing the redundant data transfer and improving the event coverage. We have proffered a pentagonal scheme of SP selection that chooses five SPs in each of the virtual compartments of the monitored zone efficiently. The chosen SPs operate as nominee of scalars for event information transmittal. Extensive experiments have been accomplished to affirm the efficiency of our proffered method. We changed the number of cameras deployed (noc) and the number of scalars deployed (nos). The results attained from the experimental studies in terms of number of camera sensors activated (nca), coverage ratio (cr), redundancy ratio (rr), event loss ratio (elr) and energy expenditure for camera actuation (eeca) assert the superiority of our profferred approach over existing approaches.

Adaptive path splitting based survivable virtual network embedding in elastic optical networks

Abstract Virtualization facilitates heterogeneous cloud applications to share the same physical infrastructure with admirable flexibility, while resource efficiency and survivability are critical concerns for virtual network embedding (VNE). In this paper, we study two-stage coordinated survivable VNE (SVNE) problem and propose an adaptive path splitting based SVNE (APSS) scheme for elastic optical networks (EONs). For APSS, we first develop a concise anchor node strategy to balance the node resource utilization and restrict the solution space of the candidate substrate nodes, which coordinates node mapping with link mapping to limit the distance spans of the virtual links. Then, we employ an adaptive path splitting policy to provide full protection against single-link failures with partial backup resource, and design an agile frequency slot windows choosing mechanism to mitigate the spectrum fragmentation for link resource efficiency. We evaluate the proposed APSS scheme and compare its performances with some counterpart schemes. Simulation results demonstrate that the proposed APSS scheme can achieve satisfactory performance in terms of spectrum utilization and blocking ratio, even if with higher backup redundancy ratio.

ANALISIS PERBANDINGAN KINERJA ALGORITMA FIXED LENGTH BINARY ENCODING DAN ALGORITMA ELIAS GAMMA CODE DALAM KOMPRESI FILE TEKS

Now days, there are many algorithms developed for data compression, but there is no one that is good for compressing various types of files because of different characteristics or file structures. This research explained the result of two compression algorithms in order to know the performance comparison between the Fixed Length Binary Encoding (FLBE) algorithm and the elias gamma code algorithm in compressing text files, especially in format rtf. The parameters being compared are the ratio of compression, compression ratio, redundancy and time. Based on the test results show that the fixed length binary encoding algorithm is better than the elias gamma code algorithm where the average ratio of compression results of fixed length binary encoding algorithm is 1.66 bits while the elias gamma code is 1.62 bits. The average compression ratio of fixed length binary encoding algorithm is 60.9% while Elias Gamma Code is 62.20%. The average value of the redundancy of the fixed length binary encoding algorithm is 39.1% while the gamma code elias is 37.79%. The average time compression value of the fixed length binary encoding algorithm is 16 ms while the elias gamma code is 21 ms.Keywords: comparison, compression, FLBE algorithm, Elias Gamma Code Algorithm, text, rtf

Fuzzy logic approach to repair coverage holes in internet of things monitoring applications

Using the internet of things paradigm for monitoring applications requires sufficient coverage of a monitored region to collect accurate data and observe events. The problem of a coverage hole arises when a sensor fails (loses connectivity with its neighbouring sensors) for reasons such as energy exhaustion or physical damage due to deployment in harsh environments. It is vital to detect and repair coverage holes as soon as they appear because they can have debilitating effects on network coverage and connectivity if left unattended. The authors propose a novel approach that employs a fuzzy inference system to select a neighbouring sensor that moves to recover the lost area. All neighbouring sensors communicate with each other to estimate the size and location of the coverage hole. Each sensor considers its own energy level, its distance from the approximated hole, and its redundancy ratio to evaluate its eligibility. The most eligible sensor is then selected to patch the coverage hole. Through extensive simulations, they analysed the performance of the proposed approach and compared it with baseline approaches in terms of coverage recovery effectiveness. The results show that the proposed approach is much more effective in repairing the holes as compared to the baseline approaches.

A Predictive Acknowledgement Model for Enhancing Traffic Redundancy Elimination in Cloud

Due to the huge increase in the utilization of cloud storage in recent days, it leads to a massive growth in data traffic from application based servers to applications like smart phones, which not only influences batteries and computational capacities but as well swamp down multi-hopping strategies during data transmission. To resolve this crisis, traffic redundancy elimination (TRE) is an effectual solution, where the chunks to be transmitted will be directly fetched out from the receivers’ cache. Moreover, prevailing solutions cannot be directly applied or it is not appropriate for smart phones owing to its energy overhead ad high computation that is imposed on the applications. In order to overcome this problem, in this investigation, a novel a Predictive Acknowledgement for Eliminating Traffic (PACKET) is proposed which comprises of three significant elements. Initially, every application possess a clone in cloud that are responsible for calculating intensive tasks like detecting redundancy and parsing traffic. Secondly, consider that every cloud user has some specific applications like Facebook to be used in regular day to day life, every clones of cloud has to selectively determine the applications that are most frequently utilized and also reduce the high redundancy ratio. Thirdly, some cloud users always possess certain common applications; the proposed PACKET clusters those clones to co-operatively perform redundancy detection so as to diminish cache resource consumption in cloud. The simulation is carried out in MATLAB environment; the traces of applications are collected from online available data and are utilized for simulation purpose. Experimental outcomes demonstrates that PACKET can attain much higher hit ratio, reduced E2E delay, increased E2E throughput, energy efficiency and effectual bandwidth utilization in contrast to existing approaches. The proposed PACKET shows better and efficient trade-off than prevailing techniques.