Backup Strategy Research Articles

Circuit Breaker Failure Protection Strategy for HVDC Grids

HVDC grids demand the fast and reliable operation of the protection system. The failure of any protection element should initialize a backup protection almost immediately in order to assure the system’s stability. This paper proposes a novel backup strategy that covers the failure of the primary protection including the malfunctioning of the HVDC circuit breaker. Only local voltage measurements are employed in the proposed backup protection and the voltage derivative is calculated at both sides of the limiting inductor. Consequently, the speed and reliability of the protection system are enhanced, since no communication channel is needed. This paper contains a thorough specification of the proposed protection strategy. This strategy is validated in a four-terminal HVDC grid with various fault case scenarios, including high-resistance fault cases. The operation of the backup protection is reliable and remarkably fast.

Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets

The rapid growth in biomedical datasets has generated high dimensionality features that negatively impact machine learning classifiers. In machine learning, feature selection (FS) is an essential process for selecting the most significant features and reducing redundant and irrelevant features. In this study, an equilibrium optimization algorithm (EOA) is used to minimize the selected features from high-dimensional medical datasets. EOA is a novel metaheuristic physics-based algorithm and newly proposed to deal with unimodal, multi-modal, and engineering problems. EOA is considered as one of the most powerful, fast, and best performing population-based optimization algorithms. However, EOA suffers from local optima and population diversity when dealing with high dimensionality features, such as in biomedical datasets. In order to overcome these limitations and adapt EOA to solve feature selection problems, a novel metaheuristic optimizer, the so-called improved equilibrium optimization algorithm (IEOA), is proposed. Two main improvements are included in the IEOA: The first improvement is applying elite opposite-based learning (EOBL) to improve population diversity. The second improvement is integrating three novel local search strategies to prevent it from becoming stuck in local optima. The local search strategies applied to enhance local search capabilities depend on three approaches: mutation search, mutation–neighborhood search, and a backup strategy. The IEOA has enhanced the population diversity, classification accuracy, and selected features, and increased the convergence speed rate. To evaluate the performance of IEOA, we conducted experiments on 21 biomedical benchmark datasets gathered from the UCI repository. Four standard metrics were used to test and evaluate IEOA’s performance: the number of selected features, classification accuracy, fitness value, and p-value statistical test. Moreover, the proposed IEOA was compared with the original EOA and other well-known optimization algorithms. Based on the experimental results, IEOA confirmed its better performance in comparison to the original EOA and the other optimization algorithms, for the majority of the used datasets.

Quench detection method for superconducting magnets with a phase difference measurement system based on multiple-correlation

Quench detection for superconducting magnets is an ongoing and key issue in magnet design. Quench detection systems must be able to detect quenching rapidly in any area of a certain magnet so that the stored energy of the magnet can be safely discharged. Resistance voltage detection signals are easily submerged in high induced voltages. This paper presents a quench detection method for superconducting magnets with a phase difference measurement system based on multiple correlations to monitor impedance changes from the superconducting state to the normal state during the quench propagation process. The principle behind the proposed method is analysed and a quench detection scheme for superconducting testing coil is designed on the basis of this principle. The multiple-correlation method can suppress all types of coupled voltages to improve the sensitivity and interference resistance of the quench detection system. Experimental results indicate that the proposed method is capable of identifying any type of quenching in a timely and accurate manner. Thus, this method can be used as a backup scheme for quench detection in the Experimental Advanced Superconducting Tokamak.

Sustainable and resilience planning for the supply chain of online hyperlocal grocery services

In an uncertain Supply Chain (SC) environment dealing with suppliers and transport-level disruptions in addition to fulfilling customers’ requirements is a complex task. Such challenges are more taxing in case of perishable products SCs. The concurrent effect of all such events affects the economic stability of SC and impedes sustainability on a longer planning horizon. The proposed study presents a case example of e-commerce based SC addressing an operational and distribution planning problem. For this purpose, the study simulates online grocery business operations by proposing a mixed-integer linear programming problem to minimize SC's expected cost. It includes the costs of purchase, transportation, supply-demand mismatch, and cost of economic loss due to the perishable nature of the products. Computational analysis is performed to manifest the performance of the proposed model considering the dataset of a hyperlocal distribution service. The study evaluates the utility of resilience and sustainable strategies to minimize expected economic costs and reduce environmental impacts by effectively managing waste. A backup strategy is devised to promote a resilient SC planning to fight the risk of disruptions and operational failures. Additionally, economic sustainability is achieved by implementing various markdown policies and waste minimization strategies. A sensitivity analysis using Monte-Carlo simulation is presented to recognize the impact of operational risk, demand uncertainty, and perishability on the expected cost. Majorly, it contributes to the scant literature of quantitative modeling for the production and distribution of food SC to promote resiliently sustainable planning. Further, the resilient and sustainable strategies, procurement policies, penalty assessment, and markdown due to perishability are some of the key contributions of the study. The discussion provides plenty of opportunities to pursue future studies in operational and distribution management of e-commerce SCs.

A multi-stage graph based algorithm for survivable Service Function Chain orchestration with backup resource sharing

Network softwarisation introduces flexibility in network management by enabling the deployment of network functions as software modules running on virtual machines. However, this creates new concerns for service availability and reliability due to multiple sources of failures at both software and hardware levels, potentially resulting in service degradations and penalties due to Service Level Agreement (SLA) violations. The survivability of critical services can best be guaranteed by pro-actively provisioning dedicated backup resources for these services, but at the cost of a high resource consumption. Aware of the divergent requirements of future services, a promising alternative is envisaged, allowing non-critical users to use the unused backup resources of high priority users. However, this approach poses a stringent challenge if a critical service disruption occurs, requiring the computation of a traffic rerouting solution for the non-critical requests when preempted from their borrowed resources. In this paper we first propose a generic multi-stage graph based algorithm as an alternative algorithm for Service Function Chain (SFC) deployments. Simulation results demonstrate that the proposed algorithm is optimized in terms of resource utilization, resulting in a 10% improvement in terms of acceptance ratio compared to a given state of the art algorithm, and within a 4% margin of the optimal solution. Based on the mentioned algorithm, we propose a new migration-aware algorithm for the mapping of non-critical services, enabling the non-critical services to borrow the unused backup resources from the critical services while minimizing the probability of preemption they could experience. The migration-aware algorithm results in more than an 8% resource saving, in most scenarios, compared to a dedicated backup strategy, and more than a 70% performance improvement in terms of the number of service preemptions, compared to a cost based algorithm. Additionally, whenever low priority users are preempted from their borrowed resources, we propose a new QoS-aware global-rerouting algorithm for remapping those users, reducing the impact of the service interruption thanks to avoiding the migration of surviving VNFs and virtual links when feasible. The proposed algorithm is shown to outperform a service restoration strategy based on local rerouting, in terms of successful service restoration and resource consumption.

Reliable Service Function Chain Deployment Method Based on Deep Reinforcement Learning.

Network function virtualization (NFV) is a key technology to decouple hardware device and software function. Several virtual network functions (VNFs) combine into a function sequence in a certain order, that is defined as service function chain (SFC). A significant challenge is guaranteeing reliability. First, deployment server is selected to place VNF, then, backup server is determined to place the VNF as a backup which is running when deployment server is failed. Moreover, how to determine the accurate locations dynamically with machine learning is challenging. This paper focuses on resource requirements of SFC to measure its priority meanwhile calculates node priority by current resource capacity and node degree, then, a novel priority-awareness deep reinforcement learning (PA-DRL) algorithm is proposed to implement reliable SFC dynamically. PA-DRL determines the backup scheme of each VNF, then, the model jointly utilizes delay, load balancing of network as feedback factors to optimize the quality of service. In the experimental results, resource efficient utilization, survival rate, and load balancing of PA-DRL were improved by 36.7%, 35.1%, and 78.9% on average compared with benchmark algorithm respectively, average delay was reduced by 14.9%. Therefore, PA-DRL can effectively improve reliability and optimization targets compared with other benchmark methods.

Bi-objective sequence optimization in reliability problems with a matrix-analytic approach

The redundancy allocation problem (RAP) is an intriguing area in the field of reliability optimization to which a lot of research has been devoted in recent years. In this paper, a bi-objective model is developed for RAP with a heterogeneous backup scheme and a mixed redundancy strategy. Elimination of the lower bound estimation and the exact calculation of the reliability of the mixed strategy forms one of the most striking features of the proposed model. Investigating the optimal sequence of components in each subsystem, the study modifies a non-dominated sorting genetic algorithm (NSGA-II), as a powerful multi-objective evolutionary one, to solve the proposed bi-objective model. Two numerical examples will then be used to verify the efficiency of the model in achieving enhanced system reliability. Finally, the results of Pareto optimal set are used to demonstrate that the assumptions made enable the proposed model to improve system reliability by offering various structures while simultaneously considering system limitations.

Developmental trajectories of strategy use in children with mathematical anxiety

The main goal of the present study was to explore strategy selection in high mathematical anxiety (MA) individuals, and to test the role of development in the selection of strategy. We tested 2nd, 3rd and 5th graders with high or low MA in simple and complex addition problems. Participants first solved the problems and were then asked to report the strategy that they used. During elementary school, typically developing children change strategy use. In the first years backup strategies of counting are very frequent, but with maturation and schooling, they can shift to memory- based strategies. Hence, we tested finger counting and advanced memory based strategies in high MA children. In finger counting, high MA children showed developmental delay. For example, in the third grade, low MA children stopped using finger counting, while high MA participants continued to use it. However, in the case of advanced strategy use, we found a different pattern: regardless of age, high MA children used less advanced strategies than low MA participants. Moreover, usage of advanced memory based strategies was modulated by visuospatial working memory abilities in the two groups. The present results suggest that the MA participant has atypical developmental trajectories in strategy use.

Deep Reinforcement-Learning-Guided Backup for Energy Harvesting Powered Systems

Energy harvesting technology has been widely developed as a promising alternative of battery to power embedded systems. However, energy harvesting powered embedded systems may have potential frequent power interruptions due to unstable energy supply. Nonvolatile processors (NVPs) are proposed to survive power failures by saving volatile data to nonvolatile memory (NVM) upon power failures and resuming them after power comes back. Traditionally, backup is triggered immediately when an energy warning occurs. However, it is also possible to more aggressively utilize the residual energy for program execution to improve forward progress. In this work, we propose a deep reinforcement-learning-guided backup strategy to improve forward progress in energy harvesting powered intermittent embedded systems. The experimental results show an average of 8.3&#x0025;, 51.6&#x0025;, and 325.3&#x0025; improved forward progress compared with <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-learning, the related work ALD, and traditional instant backup, respectively.

Data-Driven Safety-Critical Control: Synthesizing Control Barrier Functions With Koopman Operators

Control barrier functions (CBFs) are a powerful tool to guarantee safety of autonomous systems, yet they rely on the computation of control invariant sets, which is notoriously difficult. A backup strategy employs an implicit control invariant set computed by forward integrating the system dynamics. However, this integration is prohibitively expensive for high dimensional systems, and inaccurate in the presence of unmodelled dynamics. We propose to learn discrete-time Koopman operators of the closed-loop dynamics under a backup strategy. This approach replaces forward integration by a simple matrix multiplication, which can mostly be computed offline. We also derive an error bound on the unmodeled dynamics in order to robustify the CBF controller. Our approach extends to multi-agent systems, and we demonstrate the method on collision avoidance for wheeled robots and quadrotors.

Now is the time to move past traditional 3-2-1 back-ups

Strangely enough, we have a professional photographer to thank for the 3-2-1 back-up strategy that many organisations around the world employ today. Peter Krogh originally shared the concept in his 2009 book, The DAM Book: Digital Asset Management for Photographers . 1 This back-up strategy has helped to prevent data losses for an untold number of organisations. However, in the world of digital, 11 years is a very long time and, arguably, the strategy is now ripe for an update, particularly in the face of the ongoing and growing scourge of ransomware attacks.

Research on Backup Strategy of BeiDou Global Navigation System Constellation

BeiDou Global Navigation System (BDS-3) will be fully completed by 2020, and provide global users with high-quality services including positioning, navigation and timing, global short messages communication (GSMC), international search and rescue(SAR) etc. Compared with the regional navigation system, the global navigation system serves a wider area and more users, and the consequences are more serious in the event of a service outage. It is necessary to study the backup strategy of the space segment as early as possible in the early stage of system operation. Firstly, the paper analyses the effects of three kinds of satellite in different positions on the system performance, then analyses the probability distribution of multiple satellites failure, and then puts forward the satellite backup strategy. From the perspectives of improving GSMC coverage and increasing the available time of SAR, combined with the remaining life and configuration of the satellites in orbit, the orbit positions of redundancy satellites are suggested. The analysis results show that if one or two satellites are backed up in three MEO orbital planes, the robustness of the system will be improved effectively, and the availability of the system will be guaranteed even if any satellite fails. Furthermore, if four satellites equipped with SAR and GSMC load are backed up in proper phase on the first and third orbit planes, the availability of GSMC service will reach 100% in the global scope, and the availability of SAR service will be significantly improved.

From Apoptosis to Necroptosis: The Death Wishes to Cancer.

DNA mutation is a common event in the human body, but in most situations, it is fixed right away by the DNA damage response program. In case the damage is too severe to repair, the programmed cell death system will be activated to get rid of the cell. However, if the damage affects some critical components of this system, the genetic scars are kept and multiply through mitosis, possibly leading to cancer someday. There are many forms of programmed cell death, but apoptosis and necroptosis represent the default and backup strategy, respectively, in the maintenance of optimal cell population as well as in cancer prevention. For the same reason, the ideal approach for cancer treatment is to induce apoptosis in the cancer cells because it proceeds 20 times faster than tumor cell proliferation and leaves no mess behind. Induction of necroptosis can be the second choice in case apoptosis becomes hard to achieve, however, necroptosis finishes the job at a cost—inflammation.

Ballast Water Exchange Plus Treatment Lowers Species Invasion Rate in Freshwater Ecosystems.

The movement of ballast water by commercial shipping is a prominent pathway for aquatic invasions. Ships' ballast water management is now transitioning from open ocean exchange to a ballast water performance standard that will effectively require use of onboard treatment systems. Neither strategy is perfect, therefore, combined use of ballast water exchange plus treatment has been suggested to provide greatest protection of aquatic ecosystems. This study compared the performance of exchange plus treatment against treatment alone by modeling establishment rates of nonindigenous zooplankton introduced by ballast water across different habitat types (fresh, brackish, and marine) in Canada. Treatment was modeled under two efficacy scenarios (100% and 50% of ship trips) to consider the possibility that treatment may not always be successful. The model results indicate that exchange plus treatment will be more effective than treatment alone at reducing establishments when recipient ports are freshwater (58 140 vs 11 338 trips until ≥1 establishment occurs, respectively). Exchange plus treatment also serves as an important backup strategy if treatment systems are partially effective (50% of trips), primarily for freshwater recipient ecosystems (1442 versus 585 trips until ≥1 establishment occurs, respectively).

A lightweight online backup manager for energy harvesting powered nonvolatile processor systems

Abstract With the explosive growth of battery-free and energy-harvesting devices, the energy harvesting powered system has gained more attentions and been widely used in different fields. However, unstable harvested energy is a challenge of energy-harvesting devices since the program execution would be interrupted frequently. Non-volatile processor (NVP) is proposed to back up volatile logics before energy depletion and recover the system status after energy resumption. This paper takes the challenge of forward progress improvement issue in NVP system and proposes a lightweight online backup strategy which tries to aggressively use energy in capacitor after receiving energy warnings. We also build a flexible and accurate simulation tool for NVP system evaluation. The experimental results show an average of 24.2% and 13.7% improved forward progress compared with instant backup method and the most related work, respectively.

A methodology for the decryption of encrypted smartphone backup data on android platform: A case study on the latest samsung smartphone backup system

Backups on smartphones protect user data from the risk of data corruption and loss by storing personal information, media data, application data, and other settings. Although backups were originally designed to maintain and protect user data, these data can be important in criminal investigations requiring the verification of suspect behavior-related information at the time of an incident. However, backup data are often encrypted by each manufacturer using different scheme to protect user privacy. Since the encryption acts as a disturbance to the use of backup data in investigations, it is necessary to decrypt backup data by analyzing the encryption schemes of each manufacturer.In this paper, we propose a widely applicable methodology that efficiently analyzes various encryption backup schemes. Our methodology checks the backup features, identifies the backup data, and their encrypting locations reverses encryption schemes used in the backup and finally decrypts encrypted backup data. As a case study, we apply our methodology to the latest Samsung smartphone backup system consisting of a Samsung SmartSwitch Mobile and a Samsung SmartSwitch PC. We acquired the backup data including the encrypted data generated by the Samsung smartphone backup in plain form, and revealed a technique to recover the Personal Identification Number (PIN) used for encryption through the authenticator included in the backup data. We also identified, through reverse engineering, a hidden feature that could be used to extract more data than was possible using the normal backup. Finally, we developed a decryption tool to verify that the encrypted backup data were correctly decrypted. Although, in this paper, we focused on the Samsung smartphone backup, our methodology could be applied to any smartphone backup system on Android platform. We believe that our work will be very helpful to mobile investigators.

A data estimation for failing nodes using fuzzy logic with integrated microcontroller in wireless sensor networks

Continuous data transmission in wireless sensor networks (WSNs) is one of the most important characteristics which makes sensors prone to failure. a backup strategy needs to co-exist with the infrastructure of the network to assure that no data is missing. The proposed system relies on a backup strategy of building a history file that stores all collected data from these nodes. This file is used later on by fuzzy logic to estimate missing data in case of failure. An easily programmable microcontroller unit is equipped with a data storage mechanism used as cost worthy storage media for these data. An error in estimation is calculated constantly and used for updating a reference “optimal table” that is used in the estimation of missing data. The error values also assure that the system doesn’t go into an incremental error state. This paper presents a system integrated of optimal data table, microcontroller, and fuzzy logic to estimate missing data of failing sensors. The adapted approach is guided by the minimum error calculated from previously collected data. Experimental findings show that the system has great potentials of continuing to function with a failing node, with very low processing capabilities and storage requirements.

Climate variability and its effect on normalized difference vegetation index (NDVI) using remote sensing in semi-arid area

In arid and semi areas, understanding the impact of climate variability on vegetation is a key to managing a new adaptation strategy and safeguarding crop production. The aim of this paper is to investigate the relationship between Normalized difference vegetation index (NDVI) and rainfall (R) in the arid area of Gabes. The relationship analysis was carried out in three phases: Firstly, NDVI and LST were derived from Landsat TM (1987,1995), Landsat ETM + (2003) and Landsat 8 Oli/TIRS (2015) using GIS and remote-sensing tools. Then, the trend of LST has been analyzed according to on the obtained map. The rainfall data was homogenized over 10 stations rainfall from 1974 to 2015 using the Regional Vector Method and the variability. It was analyzed using the graphical chronological method in information processing (MGCTI) and tests for rupture detection. Finally, the response of vegetation to climatic parameters was portrayed using zonal statistics. Results show a decrease in rainfall and an increase in LST on 100% of the area. On an annual scale, linear regression reported a positive relation between NDVI and rainfall, while this relation was has been negative between LST and NDVI. Thus, during the study period, the vegetation growth decreased by about 70% in response to the observed climatic variation. The findings show that vegetation growth is affected by rainfall and also by LST. Accordingly, the backup strategy should be based on irrigation management measures monitored by the control of LST.

PFPMine: A parallel approach for discovering interacting data entities in data-intensive cloud workflows

With the evolution of cloud computing, communities and companies deployed their workflows on cloud to support end-to-end business processes that are usually syndicated with other external services. To improve the efficiency of the system as well as reducing energy consumption, data placement and backup strategies should be carefully designed. One of the most challenging problems is the discovery of interacting data entities in date-intensive workflows. To tackle this challenge, this paper presents a frequent pattern-based approach named FPMine for interacting data entity discovery in cloud workflows. A direct discriminative mining algorithm is first proposed to determine the minimum support threshold, based on which FP-tree is constructed to formulate the frequent item pairs. Next, FP-matrix is applied to avoid traversing the FP-trees during data entity discovery, and a pruning approach is introduced to reduce the redundancy of frequent item pairs. Furthermore, we propose a parallel data entity mining algorithm using MapReduce framework, namely PFPMine, and then design a primitive data placement and backup strategy. Finally, we evaluate the efficiency of our approach by experiments using real-life data, based on which we show that our approach can facilitate the discovery of interacting data entities with efficiency for cloud workflows. Comparing with traditional FP-growth approach, we pay only a multiplicative factor for making our approach able to extract fine-grained frequent item pairs rather than frequent patterns, which can bring significant advantages to data placement. After parallelization, the PFPMine algorithm performs better with high efficiency for both sparse datasets and dense datasets than FP-growth. The results show that PFPMine can reduce the running time by at least 25%, and preforms with significantly higher efficiency than FP-growth approach.

Cooperative optimisation strategy of comprehensive vulnerability of activedistribution network considering information interrupted

As the scale of distributed generations connecting to the power system increases continuously, communication system considering the requirements of real-time control is becoming much more important in active distribution system. Therefore, in order to improve the responsiveness of the distribution network during the communication interruption, an offline backup strategy is proposed considering the combined output of distributed generations and energy storage systems involving the characteristics of the active distribution system. Based on a typical set of clusters which describe the features of wind turbines and photovoltaics, the strategy analyses operational vulnerability combining structural characteristics of the active distribution system and solves the output level of each distributed generation according to the improved algorithm. A numerical case study is presented to demonstrate the effectiveness of this model.