Research on static characteristic of double redundance double nozzle flapper valve based on AMESim

The feedback spring rod of the armature assembly is cancelled in the double redundance double nozzle flapper valve(DRDNFV),and the difficulty of valve core displacement control is increased.Therefore,this paper intends to study the static characteristic of DRDNFV through the AMESet and AMESim simulation.It is explored under the circumstance of the fixed orifices being clogged and experimentally verified on the test bench.The results show that the pressure gain increases and the flow gain decreases with the increasing clogged degree of the fixed orifices on both sides.The zero bias increases synchronously with the increasing clogged degree of the unilateral fixed orifice.The experimental results are basically consistent with the theoretical curves and the theoretical correctness of the simulation model is effectively verified.The results can provide the theoretical reference for design,debugging,maintenance and fault diagnosis of DRDNFV.

Fault-tolerant FADS system development for a hypersonic vehicle via neural network algorithms

Hypersonic vehicles suffer from extreme aerodynamic heating during flights, especially around the area of leading edge due to its small curvature. Therefore, flush air data sensing(FADS) system has been developed to perform accurate measurement of the air data parameters. In the present study, the method to develop the FADS algorithms with fail-operational capability for a sharp-nosed hypersonic vehicle is provided. To be specific, the FADS system implemented with 16 airframe-integrated pressure ports is used as a case study. Numerical simulations of different freestream conditions have been conducted to generate the database for the FADS targeting in 2 ≤ Ma ≤ 5 and 0 km ≤ H ≤ 30 km. Four groups of neural network algorithms have been developed based on four different pressure port configurations, and the accuracy has been validated by 280 groups of simulations. Particularly, the algorithms based on the 16-port configuration show an excellent ability to serve as the main solver of the FADS, where 99. 5% of the angle-of-attack estimations are within the error band ±0. 2°. The accuracy of the algorithms is discussed in terms of port configuration. Furthermore, diagnosis of the system health is present in the paper. A fault-tolerant FADS system architecture has been designed, which is capable of continuously sensing the air data in the case that multi-port failure occurs, with a reduction in the system accuracy.

Redundant Transmission Control Algorithm for Information-Centric Vehicular IoT Networks

Vehicular Adhoc Networks(VANETs)enable vehicles to act as mobile nodes that can fetch,share,and disseminate information about vehicle safety,emergency events,warning messages,and passenger infotainment.However,the continuous dissemination of information fromvehicles and their one-hop neighbor nodes,Road Side Units(RSUs),and VANET infrastructures can lead to performance degradation of VANETs in the existing hostcentric IP-based network.Therefore,Information Centric Networks(ICN)are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile,dynamic,and errorprone domains.In ICN-based Vehicular-IoT networks,consumer mobility is implicitly supported,but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular nodes.This paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information dissemination.The proposed protocol,called Network Cording Multiple Solutions Scheduling(NCMSS),is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information sources.Experimental results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching,data retrieval delay,and end-to-end application throughput.The end-to-end throughput in proposed NCMSS is 22%higher(for 1024 byte data)than existing solutions whereas delay in NCMSS is reduced by 5%in comparison with existing solutions.

Performance analysis of the tight combination of GPS, BDS, GALILEO, and QzsS mixed frequencies signals

With the gradual development and modernization of satellite navigation systems,using observation information from multi-GNss has become one of the hot-spot issues in recent years.Multi-system loose combinations form double-difference observation equations within their respective systems,and the positioning effect is improved.However,the interchangeability and compatible interoperability between global navigation satellite systems(GNSS)cannot be truly realized.At the same time,when the number of visible satellites decreases abruptly,the positioning performance deteriorates sharply.This paper focuses on the GNsS multi-system tight combination relative positioning technique,gives a mathe-matical model of multi-system tight combination relative positioning considering differential inter-system bias(DISB),and analyzes the time-varying characteristics of DISB at overlapping and non-overlapping frequencies among GPS/Galileo,GPS/BDS,and GPS/QZSS in terms of receiver brand,tem-perature,and receiver restart.The GNsS tight combination relative positioning performance is verified by static data from Curtin University and dynamic data measured at Taiyuan University of Technology.The results show that compared with loose combination,the ambiguity-fixed rate increases from 62.18%to 97.60%for static data and from 74.97%to 99.53%for dynamic data when the elevation mask angle is 50°,resulting in a significant improvement in positioning performance.

A redundant subspace weighting procedure for clock ensemble

A redundant-subspace-weighting(RSW)-based approach is proposed to enhance the frequency stability on a time scale of a clock ensemble.In this method,multiple overlapping subspaces are constructed in the clock ensemble,and the weight of each clock in this ensemble is defined by using the spatial covariance matrix.The superimposition average of covariances in different subspaces reduces the correlations between clocks in the same laboratory to some extent.After optimizing the parameters of this weighting procedure,the frequency stabilities of virtual clock ensembles are significantly improved in most cases.

Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

With the rapid development of information technology,IoT devices play a huge role in physiological health data detection.The exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge nodes.The storage capacity of edge nodes close to users is limited.We should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging problem.Our paper proposes a redundant data detection method that meets the privacy protection requirements.By scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot data.It has the same effect as zero-knowledge proof,and it will not reveal the privacy of users.In addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the data.We use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is deleted.The feasibility of the scheme is proved through safety analysis and efficiency analysis.

Intelligent Dynamic Heterogeneous Redundancy Architecture for IoT Systems

The conventional dynamic heterogeneous redundancy(DHR)architecture suffers from the security threats caused by the stability differences and similar vulnerabilities among the executors.To overcome these challenges,we propose an intelligent DHR architecture,which is more feasible by intelligently combining the random distribution based dynamic scheduling algorithm(RD-DS)and information weight and heterogeneity based arbitrament(IWHA)algorithm.In the proposed architecture,the random distribution function and information weight are employed to achieve the optimal selection of executors in the process of RD-DS,which avoids the case that some executors fail to be selected due to their stability difference in the conventional DHR architecture.Then,through introducing the heterogeneity to restrict the information weights in the procedure of the IWHA,the proposed architecture solves the common mode escape issue caused by the existence of multiple identical error output results of similar vulnerabilities.The experimental results characterize that the proposed architecture outperforms in heterogeneity,scheduling times,security,and stability over the conventional DHR architecture under the same conditions.

A framework for dynamic modelling of railway track switches considering the switch blades,actuators and control systems

The main contribution of this paper is the development and demonstration of a novel methodology that can be followed to develop a simulation twin of a railway track switch system to test the functionality in a digital environment.This is important because,globally,railway track switches are used to allow trains to change routes;they are a key part of all railway networks.However,because track switches are single points of failure and safety-critical,their inability to operate correctly can cause significant delays and concomitant costs.In order to better understand the dynamic behaviour of switches during operation,this paper has developed a full simulation twin of a complete track switch system.The approach fuses finite element for the rail bending and motion,with physics-based models of the electromechanical actuator system and the control system.Hence,it provides researchers and engineers the opportunity to explore and understand the design space around the dynamic operation of new switches and switch machines before they are built.This is useful for looking at the modification or monitoring of existing switches,and it becomes even more important when new switch concepts are being considered and evaluated.The simulation is capable of running in real time or faster meaning designs can be iterated and checked interactively.The paper describes the modelling approach,demonstrates the methodology by developing the system model for a novel“REPOINT”switch system,and evaluates the system level performance against the dynamic performance requirements for the switch.In the context of that case study,it is found that the proposed new actuation system as designed can meet(and exceed)the system performance requirements,and that the fault tolerance built into the actuation ensures continued operation after a single actuator failure.

Analysis of wheel-rail adhesion redundancy considering the thirdbody medium on the rail surface

Purpose–In response to the problem of insufficient traction/braking adhesion force caused by the existence of the third-body medium on the rail surface,this study aims to analyze the utilization of wheel-rail adhesion coefficient under different medium conditions and propose relevant measures for reasonable and optimized utilization of adhesion to ensure the traction/braking performance and operation safety of trains.Design/methodology/approach–Based on the PLS-160 wheel-rail adhesion simulation test rig,the study investigates the variation patterns of maximum utilized adhesion characteristics on the rail surface under different conditions of small creepage and large slip.Through statistical analysis of multiple sets of experimental data,the statistical distribution patterns of maximum utilized adhesion on the rail surface are obtained,and a method for analyzing wheel-rail adhesion redundancy based on normal distribution is proposed.The study analyzes the utilization of traction/braking adhesion,as well as adhesion redundancy,for different medium under small creepage and large slip conditions.Based on these findings,relevant measures for the reasonable and optimized utilization of adhesion are derived.Findings–When the third-body medium exists on the rail surface,the train should adopt the low-level service braking to avoid the braking skidding by extending the braking distance.Compared with the current adhesion control strategy of small creepage,adopting appropriate strategies to control the train’s adhesion coefficient near the second peak point of the adhesion coefficient-slip ratio curve in large slip can effectively improve the traction/braking adhesion redundancy and the upper limit of adhesion utilization,thereby ensuring the traction/braking performance and operation safety of the train.Originality/value–Most existing studies focus on the wheel-rail adhesion coefficient values and variation patterns under different medium conditions,without considering whether the rail surface with different medium can provide sufficient traction/braking utilized adhesion coefficient for the train.Therefore,there is a risk of traction overspeeding/braking skidding.This study analyzes whether the rail surface with different medium can provide sufficient traction/braking utilized adhesion coefficient for the train and whether there is redundancy.Based on these findings,relevant measures for the reasonable and optimized utilization of adhesion are derived to further ensure operation safety of the train.

Quantitative Comparative Study of the Performance of Lossless Compression Methods Based on a Text Data Model

Data compression plays a key role in optimizing the use of memory storage space and also reducing latency in data transmission. In this paper, we are interested in lossless compression techniques because their performance is exploited with lossy compression techniques for images and videos generally using a mixed approach. To achieve our intended objective, which is to study the performance of lossless compression methods, we first carried out a literature review, a summary of which enabled us to select the most relevant, namely the following: arithmetic coding, LZW, Tunstall’s algorithm, RLE, BWT, Huffman coding and Shannon-Fano. Secondly, we designed a purposive text dataset with a repeating pattern in order to test the behavior and effectiveness of the selected compression techniques. Thirdly, we designed the compression algorithms and developed the programs (scripts) in Matlab in order to test their performance. Finally, following the tests conducted on relevant data that we constructed according to a deliberate model, the results show that these methods presented in order of performance are very satisfactory:- LZW- Arithmetic coding- Tunstall algorithm- BWT + RLELikewise, it appears that on the one hand, the performance of certain techniques relative to others is strongly linked to the sequencing and/or recurrence of symbols that make up the message, and on the other hand, to the cumulative time of encoding and decoding.

Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises

As cyber threats keep changing and business environments adapt, a comprehensive approach to disaster recovery involves more than just defensive measures. This research delves deep into the strategies required to respond to threats and anticipate and mitigate them proactively. Beginning with understanding the critical need for a layered defense and the intricacies of the attacker’s journey, the research offers insights into specialized defense techniques, emphasizing the importance of timely and strategic responses during incidents. Risk management is brought to the forefront, underscoring businesses’ need to adopt mature risk assessment practices and understand the potential risk impact areas. Additionally, the value of threat intelligence is explored, shedding light on the importance of active engagement within sharing communities and the vigilant observation of adversary motivations. “Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises” is a comprehensive guide for organizations aiming to fortify their cybersecurity posture, marrying best practices in proactive and reactive measures in the ever-challenging digital realm.

Broadcasting with Controlled Redundancy and Improved Localization in Wireless Sensor Networks

Wireless sensor networks （WSNs） consist of sensor nodes that broadcast a message within a network. Efficient broadcasting is a key requirement in sensor networks and has been a focal point of research over the last few years. There are many challenging tasks in the network, including redundancy control and sensor node localization that mainly depend on broadcasting. In this paper, we propose a broadcasting algorithm to control redundancy and improve localization （BACRIL） in WSNs. The proposed algorithm incorporates the benefits of the gossip protocol for optimizing message broadcasting within the network. Simulation results show a controlled level of redundancy, which is up to 57.6% if the number of sensor nodes deployed in a 500 m×500 m area are increased from 50 to 500.

Magnetic resonance imaging findings of redundant nerve roots of the cauda equina

BACKGROUND Redundant nerve roots(RNRs)of the cauda equina are often a natural evolutionary part of lumbar spinal canal stenosis secondary to degenerative processes characterized by elongated,enlarged,and tortuous nerve roots in the superior and/or inferior of the stenotic segment.Although magnetic resonance imaging(MRI)findings have been defined more frequently in recent years,this condition has been relatively under-recognized in radiological practice.In this study,lumbar MRI findings of RNRs of the cauda equina were evaluated in spinal stenosis patients.AIM To evaluate RNRs of the cauda equina in spinal stenosis patients.METHODS One-hundred and thirty-one patients who underwent lumbar MRI and were found to have spinal stenosis between March 2010 and February 2019 were included in the study.On axial T2-weighted images(T2WI),the cross-sectional area(CSA)of the dural sac was measured at L2-3,L3-4,L4-5,and L5-S1 levels in the axial plane.CSA levels below 100 mm^2 were considered stenosis.Elongation,expansion,and tortuosity in cauda equina fibers in the superior and/or inferior of the stenotic segment were evaluated as RNRs.The patients were divided into two groups:Those with RNRs and those without RNRs.The CSA cut-off value resulting in RNRs of cauda equina was calculated.Relative length(RL)of RNRs was calculated by dividing the length of RNRs at mid-sagittal T2WI by the height of the vertebral body superior to the stenosis level.The associations of CSA leading to RNRs with RL,disc herniation type,and spondylolisthesis were evaluated.RESULTS Fifty-five patients(42%)with spinal stenosis had RNRs of the cauda equina.The average CSA was 40.99±12.76 mm^2 in patients with RNRs of the cauda equina and 66.83±19.32 mm^2 in patients without RNRs.A significant difference was found between the two groups for CSA values(P<0.001).Using a cut-off value of 55.22 mm^2 for RNRs of the cauda equina,sensitivity,specificity,positive predictive value(PPV),and negative predictive value(NPV)values of 96.4%,96.1%,89.4%,and 98.7%were obtained,respectively.RL was 3.39±1.31(range:0.93-6.01).When the extension of RNRs into the superior and/or inferior of the spinal canal stenosis level was evaluated,it was superior in 54.5%,both superior and inferior in 32.8%,and inferior in 12.7%.At stenosis levels leading to RNRs of the cauda equina,29 disc herniations with soft margins and 26 with sharp margins were detected.Disc herniation type and spondylolisthesis had no significant relationship with RL or CSA of the dural sac with stenotic levels(P>0.05).As the CSA of the dural sac decreased,the incidence of RNRs observed at the superior of the stenosis level increased(P<0.001).CONCLUSION RNRs of the cauda equina are frequently observed in patients with spinal stenosis.When the CSA of the dural sac is<55 mm^2,lumbar MRIs should be carefully examined for this condition.

ENGINE SENSOR FAULT DIAGNOSIS USING MAIN AND DECENTRALIZED NEURAL NETWORKS

This Paper presents a methodology for solving the sensor failure detection, isolation and accommodation of aeroengine control systems using on line learning neural networks(NN), which has one main NN and a set of decentralized NNs. Changes in the system dynamics are monitored by the on line learning NN. When a failure occurs in some sensor, the sensor failure detection can be accomplished with high precision, and the sensor failure accommodation can be achieved by replacing the value from the failed sensor with its estimate from the decentralized NN. By integrating the optimal estimation and failure logic, this method can detect soft failures. Simulation of one kind of turboshaft engine control system with this multiple neural network architecture shows that the ANN developed can detect and isolate hard and soft sensor failures timely and provide accurate accommodation.

Entropy-Based Approach to Remove Redundant Monitoring Wells from Regional-Scale Groundwater Network

An entropy-based approach is applied to identify redundant wells in thenetwork. In the process of this research, groundwater-monitoring network is considered as acommunication system with a capability to transfer information, and monitoring wells are taken asinformation receivers. The concepts of entropy and mutual information are then applied to measurethe information content of individual monitoring well and information relationship betweenmonitoring well pairs. The efficiency of information transfer among monitoring wells is the basis tojudge the redundancy in the network. And the capacity of the monitoring wells to provideinformation on groundwater is the point of evaluation to identify redundant monitoring wells. Thisapproach is demonstrated using the data from a regional-scale ground-water network in Hebei plain,China The result shows that the entropy-based method is recommendable in optimizing groundwaternetworks, especially for those within media of higher heterogeneities and ani-sotropies.

SOLVING INVERSE KINEMATICS OF REDUNDANT MANIPULATOR BASED ON NEURAL NETWORK

For the redundant manipulators, neural network is used to tackle the velocityinverse kinematics of robot manipulators. The neural networks utilized are multi-layeredperceptions with a back-propagation training algorithm. The weight table is used to save the weightssolving the inverse kinematics based on the different optimization performance criteria.Simulations verity the effectiveness of using neural network.

Characterization of genetically engineered mouse models carrying Col2a1-cre-induced deletions of Lrp5 and/or Lrp6

Mice carrying Collagen2a1-cre-mediated deletions of Lrp5 and/or Lrp6 were created and characterized.Mice lacking either gene alone were viable and fertile with normal knee morphology.Mice in which both Lrp5 and Lrp6 were conditionally ablated via Collagen2al-cre-mediated deletion displayed severe defects in skeletal development during embryogenesis.In addition,adult mice carrying Collagen2al-cre-mediated deletions of Lrp5 and/or Lrp6 displayed low bone mass suggesting that the Collagen2a1-cre transgene was active in cells that subsequently differentiated into osteoblasts.In both embryonic skeletal development and establishment of adult bone mass,Lrp5 and Lrp6 carry out redundant functions.

An Improved Method for Predicting Linear B-cell Epitope Using Deep Maxout Networks

To establish a relation between an protein amino acid sequence and its tendencies to generate antibody response,and to investigate an improved in silico method for linear B-cell epitope（LBE）prediction.We present a sequence-based LBE predictor developed using deep maxout network（DMN）with dropout training techniques.A graphics processing unit（GPU）was used to reduce the training time of the model.A 10-fold cross-validation test on a large,non-redundant and

READA: Redundancy Elimination for Accurate Data Aggregation in Wireless Sensor Networks

In monitoring systems, multiple sensor nodes can detect a single target of interest simultaneously and the data collected are usually highly correlated and redundant. If each node sends data to the base station, energy will be wasted and thus the network energy will be depleted quickly. Data aggregation is an important paradigm for compressing data so that the energy of the network is spent efficiently. In this paper, a novel data aggregation algorithm called Redundancy Elimination for Accurate Data Aggregation (READA) has been proposed. By exploiting the range of spatial correlations of data in the network, READA applies a grouping and compression mechanism to remove duplicate data in the aggregated set of data to be sent to the base station without largely losing the accuracy of the final aggregated data. One peculiarity of READA is that it uses a prediction model derived from cached values to confirm whether any outlier is actually an event which has occurred. From the various simulations conducted, it was observed that in READA the accuracy of data has been highly preserved taking into consideration the energy dissipated for aggregating the

Pattern Analysis and Regressive Linear Measure for Botnet Detection

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various researchers.However,certain limitations need to be addressed efficiently.The provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective constraints.The bots’patterns or features over the network have to be analyzed in both linear and non-linear manner.The linear and non-linear features are composed of high-level and low-level features.The collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier model.Here,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor model.Finally,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets detection.The simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so on.Here,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's reliability.The F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.