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.

A Method for Surveying Control Network Optimization Based on Reliability Properties

Surveying control network optimization design is related to standards, such as precision, reliability, sensitivity and the cost, and these standards are related closely to each other. A new method for surveying control network simulation optimization design is proposed. This method is based on the inner reliability index of the observation values.

Genetic Algorithm-Based Redundancy Optimization Method for Smart Grid Communication Network

This paper proposes a redundancy optimization method for smart grid Advanced Metering Infrastructure(AMI) to realize economy and reliability targets.AMI is a crucial part of the smart grid to measure,collect,and analyze data about energy usage and power quality from customer premises.From the communication perspective,the AMI consists of smart meters,Home Area Network(HAN) gateways and data concentrators;in particular,the redundancy optimization problem focus on deciding which data concentrator needs redundancy.In order to solve the problem,we first develop a quantitative analysis model for the network economic loss caused by the data concentrator failures.Then,we establish a complete redundancy optimization model,which comprehensively consider the factors of reliability and economy.Finally,an advanced redundancy deployment method based on genetic algorithm(GA) is developed to solve the proposed problem.The simulation results testify that the proposed redundancy optimization method is capable to build a reliable and economic smart grid communication network.

Redundancy Elimination in GPRS network

The mechanisms of TCP’s retransmission and reset will result in redundant packets. These redundant packets are often sent unnecessarily to the user over a slow last-hop link delaying useful traffic. This is a problem for wide-area wireless links, such as General Packet Radio Service (GPRS), because unnecessary transmissions waste already limited radio bandwidth, battery power at the mobile terminal and incurs monetary cost due to charging by data volume. The paper first describes a GPRS model, then discusses how to eliminate the redundant packets in GPRS network and presents the simulation results in Network Simulation 2 (NS 2). The more traffic is, the more the network can benefit. In heavy traffic, it can even get more than 30% improvement in throughput. Average delay and loss percent are also lowered.

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.

Adaptive Control of Flexible Redundant Manipulators Using Neural Networks

An investigation on the neural networks based active vibration control of flexible redundant manipulators was conducted. The smart links of the manipulator were synthesized with the flexible links to which were attached piezoceramic actuators and strain gauge sensors. A nonlinear adaptive control strategy named neural networks based indirect adaptive control (NNIAC) was employed to improve the dynamic performance of the manipulator. The mathematical model of the 4-layered dynamic recurrent neural networks (DRNN) was introduced. The neuro-identifier and the neuro-controller featuring the DRNN topology were designed off line so as to enhance the initial robustness of the NNIAC. By adjusting the neuro-identifier and the neuro-controller alternatively, the manipulator was controlled on line for achieving the desired dynamic performance. Finally, a planar 3R redundant manipulator with one smart link was utilized as an illustrative example. The simulation results proved the validity of the control strategy.

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.

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.

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

Losses Calculation of an Aerospace Retraction Wheel Motor with Regarding to Electromagnetic-Field Analysis Investigation

A 3-D FEA （finite element analysis） transient and steady-state design proposal for high-speed with Nd-Fe-Br （reversible） magnets in aerospace application will be examined under design considerations of n = 12,000 rpm, short-duty, sinusoidal drive, low cogging, high efficiency at peak torque, and etc. for an ARWM （aerospace retraction wheel motor）. In construction, the PMs （permanent magnets） fixed on the rotor core which is surface-mOunted magnets retained by a carbon-fiber bandage. Redundant windings, resistant to fault propagation have accounted. Besides, an axial water-jacket housing without end-cap cooling has involved. All performed characteristic performances of the correlated ARWM will verify by comparison through 2-D and 3-D FEA results. In this paper, design process has dealing with determination of various kinds of losses such as electromagnetic and mechanical losses. In terms of both classified losses, copper, stator back iron, stator tooth, PM, rotor back iron, air-friction and sleeve losses were calculated. The 3-D end-winding effects were included in the modeled ARWM by the authors.

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.

Coverage Control for Underwater Sensor Networks Based on Residual Energy Probability

Underwater sensor networks have important application value in the fields of water environment data collection,marine environment monitoring and so on.It has some characteristics such as low available bandwidth,large propagation delays and limited energy,which bring new challenges to the current researches.The research on coverage control of underwater sensor networks is the basis of other related researches.A good sensor node coverage control method can effectively improve the quality of water environment monitoring.Aiming at the problem of high dynamics and uncertainty of monitoring targets,the random events level are divided into serious events and general events.The sensors are set to sense different levels of events and make different responses.Then,an event-driven optimization algorithm for determining sensor target location based on self-organization map is proposed.Aiming at the problem of limited energy of underwater sensor nodes,considering the moving distance,coverage redundancy and residual energy of sensor nodes,an underwater sensor movement control algorithm based on residual energy probability is proposed.The simulation results show that compared with the simple movement algorithm,the proposed algorithm can effectively improve the coverage and life cycle of the sensor networks,and realize real-time monitoring of the water environment.

Reaction Rate Weighted Multilayer Nuclear Reaction Network

Nuclear reaction rate A is a significant factor in processes of nucleosyntheses.A multi-layer directed-weighted nuclear reaction network,in which the reaction rate is taken as the weight,and neutron,proton,4 He and the remainder nuclei as the criteria for different reaction layers,is for the first time built based on all thermonuclear reactions in the JINA REACLIB database.Our results show that with the increase in the stellar temperature T9,the distribution of nuclear reaction rates on the R-layer network demonstrates a transition from unimodal to bimodal distributions.Nuclei on the R-layer in the region of A=[1,2.5×101]have a more complicated out-going degree distribution than that in the region of A=[1011,1013],and the number of involved nuclei at T9=1 is very different from the one at T9=3.The redundant nuclei in the region of A=[1,2.5×101]at T9=3 prefer(γ,p)and(γ,α)reactions to the ones at T9=1,which produce nuclei around theβstable line.This work offers a novel way to the big-data analysis on the nuclear reaction network at stellar temperatures.

A novel sensitivity model for short nets

For modern processes at deep sub-micron technology nodes, yield design, especially the design at the layout stage is an important way to deal with the problem of manufacturability and yield. In order to reduce the yield loss caused by redundancy material defects, the choice of nets to be optimized at first is an important step in the process of layout optimization. This paper provides a new sensitivity model for a short net, which is net-based and reflects the size of the critical area between a single net and the nets around it. Since this model is based on a single net and includes the information of the surrounding nets, the critical area between the single net and surrounding nets can be reduced at the same time. In this way, the efficiency of layout optimization becomes higher. According to experimental observations~ this sensitivity model can be used to choose the position for optimization. Compared with the chip-area-based and basic- layout-based sensitivity models, our sensitivity model not only has higher efficiency, but also confirms that choosing the net to be optimized at first improves the design.

COCP:Coupling Parameters Content Placement Strategy for In-Network Caching-Based Content-Centric Networking

On-path caching is the prominent module in Content-Centric Networking(CCN),equipped with the capability to handle the demands of future networks such as the Internet of Things(IoT)and vehicular networks.The main focus of the CCN caching module is data dissemination within the network.Most of the existing strategies of in-network caching in CCN store the content at the maximumnumber of routers along the downloading path.Consequently,content redundancy in the network increases significantly,whereas the cache hit ratio and network performance decrease due to the unnecessary utilization of limited cache storage.Moreover,content redundancy adversely affects the cache resources,hit ratio,latency,bandwidth utilization,and server load.We proposed an in-network caching placement strategy named Coupling Parameters to Optimize Content Placement(COCP)to address the content redundancy and associated problems.The novelty of the technique lies in its capability tominimize content redundancy by creating a balanced cache space along the routing path by considering request rate,distance,and available cache space.The proposed approach minimizes the content redundancy and content dissemination within the network by using appropriate locations while increasing the cache hit ratio and network performance.The COCP is implemented in the simulator(Icarus)to evaluate its performance in terms of the cache hit ratio,path stretch,latency,and link load.Extensive experiments have been conducted to evaluate the proposed COCP strategy.The proposed COCP technique has been compared with the existing state-of-theart techniques,namely,Leave Copy Everywhere(LCE),Leave Copy Down(LCD),ProbCache,Cache Less forMore(CL4M),and opt-Cache.The results obtained with different cache sizes and popularities show that our proposed caching strategy can achieve up to 91.46%more cache hits,19.71%reduced latency,35.43%improved path stretch and 38.14%decreased link load.These results confirm that the proposed COCP strategy has the potential capability to handle the demands of future networks such as the Internet of Things(IoT)and vehicular networks.

Design of Distributed Fault-Tolerant Industrial Network

A token-bus-based design method of the distributedfault-tolerant industrial network is presented in this pa-per.The dual-link network is of hot-redundancy.The performance of the network is also discussed.