Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural Network

Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions(EOC),which are inevitable in the normal inspection of civil and mechanical structures.This paper thus presents a robust guided wave-based method for damage detection and localization under complex environmental conditions by singular value decomposition-based feature extraction and one-dimensional convolutional neural network(1D-CNN).After singular value decomposition-based feature extraction processing,a temporal robust damage index(TRDI)is extracted,and the effect of EOCs is well removed.Hence,even for the signals with a very large temperature-varying range and low signal-to-noise ratios(SNRs),the final damage detection and localization accuracy retain perfect 100%.Verifications are conducted on two different experimental datasets.The first dataset consists of guided wave signals collected from a thin aluminum plate with artificial noises,and the second is a publicly available experimental dataset of guided wave signals acquired on a composite plate with a temperature ranging from 20℃to 60℃.It is demonstrated that the proposed method can detect and localize the damage accurately and rapidly,showing great potential for application in complex and unknown EOC.

Remaining Useful Life Prediction of Aeroengine Based on Principal Component Analysis and One-Dimensional Convolutional Neural Network

In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.

Research on the Application of the Radiative Transfer Model Based on Deep Neural Network in One-dimensional Variational Algorithm

As a typical physical retrieval algorithm for retrieving atmospheric parameters,one-dimensional variational(1 DVAR)algorithm is widely used in various climate and meteorological communities and enjoys an important position in the field of microwave remote sensing.Among algorithm parameters affecting the performance of the 1 DVAR algorithm,the accuracy of the microwave radiative transfer model for calculating the simulated brightness temperature is the fundamental constraint on the retrieval accuracies of the 1 DVAR algorithm for retrieving atmospheric parameters.In this study,a deep neural network(DNN)is used to describe the nonlinear relationship between atmospheric parameters and satellite-based microwave radiometer observations,and a DNN-based radiative transfer model is developed and applied to the 1 DVAR algorithm to carry out retrieval experiments of the atmospheric temperature and humidity profiles.The retrieval results of the temperature and humidity profiles from the Microwave Humidity and Temperature Sounder(MWHTS)onboard the Feng-Yun-3(FY-3)satellite show that the DNN-based radiative transfer model can obtain higher accuracy for simulating MWHTS observations than that of the operational radiative transfer model RTTOV,and also enables the 1 DVAR algorithm to obtain higher retrieval accuracies of the temperature and humidity profiles.In this study,the DNN-based radiative transfer model applied to the 1 DVAR algorithm can fundamentally improve the retrieval accuracies of atmospheric parameters,which may provide important reference for various applied studies in atmospheric sciences.

Tele-Network Design Based on Queue Competition Algorithm

In this paper, we report research on how to design the tele-network. First of all, we defined the reliability of tele-network. According to the definition, we divide the whole reliability into two parts:the reliability of the mini-way and that of the whole system. Then we do algebra unintersection of the mini-way, deriving a function of reliability of tele-network. Also, we got a function of the cost of tele-network after analyzing the cost of arcs and points. Finally, we give a mathematical model to design a tele-network. For the algorithm, we define the distance of a network and adjacent area within certain boundaries . We present a new algorithm--Queue Competition Algorithm (QCA) based on the adjacent area . The QCA correlates sequence of fitnesses in their father-generations with hunting zone of mutation and the number of individuals generated by mutation, making the stronger fitness in a small zone converge at a local extreme value, but the weaker one takes the advantage of lots of individuals and a big zone to hunt a new local extreme value. In this way, we get the overall extreme value. Numerical simulation shows that we can get the efficient hunting and exact solution by using QCA. The QCA efficient hunting and exact solution.

The queueing model for quantum key distribution network

This paper develops a QKD （quantum key distribution）-based queueing model to investigate the data delay on QKD link and network, especially that based on trusted relays. It shows the mean packet delay performance of the QKD system. Furthermore, it proposes a key buffering policy which could effectively improve the delay performance in practice. The results will be helpful for quality of service in practical QKD systems.

A FUZZY-LOGIC CONTROL ALGORITHM FOR ACTIVE QUEUE MANAGEMENT IN IP NETWORKS

Active Queue Management (AQM) is an active research area in the Internet community. Random Early Detection (RED) is a typical AQM algorithm, but it is known that it is difficult to configure its parameters and its average queue length is closely related to the load level. This paper proposes an effective fuzzy congestion control algorithm based on fuzzy logic which uses the pre- dominance of fuzzy logic to deal with uncertain events. The main advantage of this new congestion control algorithm is that it discards the packet dropping mechanism of RED, and calculates packet loss according to a preconfigured fuzzy logic by using the queue length and the buffer usage ratio. Theo- retical analysis and Network Simulator (NS) simulation results show that the proposed algorithm achieves more throughput and more stable queue length than traditional schemes. It really improves a router's ability in network congestion control in IP network.

Design and Implementation of an Adaptive Feedback Queue Algorithm over Open Flow Networks

The concurrent presence of different types of traffic in multimedia applications might aggravate a burden on the underlying data network, which is bound to affect the transmission quality of the specified traffic. Recently, several proposals for fulfilling the quality of service(QoS) guarantees have been presented. However, they can only support coarse-grained QoS with no guarantee of throughput, jitter, delay or loss rate for different applications. To address these more challenging problems, an adaptive scheduling algorithm for Parallel data Processing with Multiple Feedback(PPMF) queues based on software defined networks(SDN) is proposed in this paper, which can guarantee the quality of service of high priority traffic in multimedia applications. PPMF combines the queue bandwidth feedback mechanism to realise the automatic adjustment of the queue bandwidth according to the priority of the packet and network conditions, which can effectively solve the problem of network congestion that has been experienced by some queues for a long time. Experimental results show PPMF significantly outperforms other existing scheduling approaches in achieving 35--80% improvement on average time delay by adjusting the bandwidth adaptively, thus ensuring the transmission quality of the specified traffic and avoiding effectively network congestion.

Congestion control scheme in ATM networks based on fast tracing-queue

One of the more challenging and unresolved issues in ATM networks is the congestion control of available bit rate （ABR）. The dynamic controller is designed based on the control theory and the feedback mechanism of explicit rates With the given method of a chosen parameter, it can guarantee the stability of the controller and closed loop system with propagation delay and bandwidth oscillation. It needs less parameters（only one） to be designed. The queue length can converge to the given value in the least steps. The fairness of different connections is considered further. The simulations show better performance and good quality of service（QoS） is achieved.

Analytical Modeling of a Multi-queue Nodes Network Router

This paper presents the derivation of an analytical model for a multi-queue nodes network router, which is referred to as the multi-queue nodes （mQN） model. In this model, expressions are derived to calculate two performance metrics, namely, the queue node and system utilization factors. In order to demonstrate the flexibility and effectiveness of the mQN model in analyzing the performance of an mQN network router, two scenarios are performed. These scenarios investigated the variation of queue nodes and system utilization factors against queue nodes dropping probability for various system sizes and packets arrival routing probabilities. The performed scenarios demonstrated that the mQN analytical model is more flexible and effective when compared with experimental tests and computer simulations in assessing the performance of an mQN network router.

Major impact of queue-rule choice on the performance of dynamic networks with limited buffer size

We investigate the similarities and differences among three queue rules,the first-in-first-out(FIFO)rule,last-in-firstout(LIFO)rule and random-in-random-out(RIRO)rule,on dynamical networks with limited buffer size.In our network model,nodes move at each time step.Packets are transmitted by an adaptive routing strategy,combining Euclidean distance and node load by a tunable parameter.Because of this routing strategy,at the initial stage of increasing buffer size,the network density will increase,and the packet loss rate will decrease.Packet loss and traffic congestion occur by these three rules,but nodes keep unblocked and lose no packet in a larger buffer size range on the RIRO rule networks.If packets are lost and traffic congestion occurs,different dynamic characteristics are shown by these three queue rules.Moreover,a phenomenon similar to Braess’paradox is also found by the LIFO rule and the RIRO rule.

RELATIONS BETWEEN PERFORMANCE POTENTIALS AND INFINITESIMAL REALIZATION FACTORS IN CLOSED QUEUEING NETWORKS

In this paper,the concept of the infinitesimal realization factor is extended to the parameter dependent performance functions in closed queueing networks.Then the concepts of realization matrix (its elements are called realization factors) and performance potential are introduced,and the relations between infinitesimal realization factors and these two quantities are discussed.This provides a united framework for both IPA and non IPA approaches.Finally,another physical meaning of the service rate is given.

A neural network algorithm for queue length estimation based on the concept of k-leader connected vehicles

This paper presents a novel method to estimate queue length at signalised intersections using connected vehicle(CV)data.The proposed queue length estimation method does not depend on any conventional information such as arrival flow rate and parameters pertaining to traffic signal controllers.The model is applicable for real-time applications when there are sufficient training data available to train the estimation model.To this end,we propose the idea of “k-leader CVs” to be able to predict the queue which is propagated after the communication range of dedicated short-range communication(the communication platform used in CV system).The idea of k-leader CVs could reduce the risk of communication failure which is a serious concern in CV ecosystems.Furthermore,a linear regression model is applied to weigh the importance of input variables to be used in a neural network model.Vissim traffic simulator is employed to train and evaluate the effectiveness and robustness of the model under different travel demand conditions,a varying number of CVs(i.e.CVs'market penetration rate)as well as various traffic signal control scenarios.As it is expected,when the market penetration rate increases,the accuracy of the model enhances consequently.In a congested traffic condition(saturated flow),the proposed model is more accurate compared to the undersaturated condition with the same market penetration rates.Although the proposed method does not depend on information of the arrival pattern and traffic signal control parameters,the results of the queue length estimation are still comparable with the results of the methods that highly depend on such information.The proposed algorithm is also tested using large size data from a CV test bed(i.e.Australian Integrated Multimodal Ecosystem)currently underway in Melbourne,Australia.The simulation results show that the model can perform well irrespective of the intersection layouts,traffic signal plans and arrival patterns of vehicles.Based on the numerical results,20%penetration rate of CVs is a critical threshold.For penetration rates below 20%,prediction algorithms fail to produce reliable outcomes.

Diffusion approximations for multiclass queueing networks under preemptive priority service discipline

We prove a heavy traffic limit theorem to justify diffusion approximations for multiclass queueing networks under preemptive priority service discipline and provide effective stochastic dynamical models for the systems. Such queueing networks appear typically in high-speed integrated services packet networks about telecommunication system. In the network, there is a number of packet traffic types. Each type needs a number of job classes （stages） of processing and each type of jobs is assigned the same priority rank at every station where it possibly receives service. Moreover, there is no inter-routing among different traffic types throughout the entire network.

Computational analysis of(MAP_1,MAP_2)/(PH_1,PH_2)/N queues with finite buffer in wireless cellular networks

This paper studies a queueing model with the finite buffer of capacity K in wireless cellular networks, which has two types of arriving calls--handoff and originating calls, both of which follow the Markov arriving process with different rates. The channel holding times of the two types of calls follow different phase-type distributions. Firstly, the joint distribution of two queue lengths is derived, and then the dropping and blocking probabilities, the mean queue length and the mean waiting time from the joint distribution are gotten. Finally, numerical examples show the impact of different call arrival rates on the performance measures.

Open Queueing Network Model of Shanghai Public Transportation Problem

In this paper, the open queueing network model is proposed for solving the problem of public transportation in cities. The vertices of the networks(i.e., the bus stops) are determined by means of the fuzzy clustering method. The arcs (i.e., the paths of the public transportation) can be set up by using the shortest path model in the time sense or the 0 1 integer programming method.Applying the statistics method, we can calculate the parameters(such as the passenger flow's distribution, passenger flow's transition probability, mean waiting time for the bus etc. ) of the public transportation network. In this paper, we suggest to divide the network into two or three stages to implement the public transportation system in the form of ``frog jumping' fast transfer and ``permeation' fast dispersion.Combining the computer simulation and the evaluation of the achievement and effect of public transportation system, we modify the model so as to solve the public transportation problem better.

Hop distance fairness for wireless mesh network based on queue management

Wireless mesh network （WMN） is a new multi-hop network for broadband accessing to intemet. However, there exists a server unfairness problem based on different hop distances in WMN. To solve this problem, the unfairness issue was analyzed in test-bed experiment and NS2 simulation. A dynamic queue management scheme E-QMMN was proposed, which allocates the queue buffer according to the hop distance of every flow. The experimental results show that the proposed scheme can not only increase the hop distance fairness of the legacy scheme at most 50%, but also reduce the average round trip time at least 29% in congested WMN environments.

A Service Ratio-Based Dynamic Fair Queueing Algorithm for Packet-Switching Networks

A new weighted fair queueing algorithm is proposed, which uses the novel flow-based service ratio parameters to schedule flows. This solves the main drawback of traditional weighted fair queneing algorithms- the packet-based calculation of the weight parameters. In addition, this paper proposes a novel service ratio calculation method and a queue mangement technology. The former adjusts the service ratio parameters adaptively based on the dynamics of the packet lengths and thee solves the unfairness problem induced by the variable packet length. The latter improves the utilization of the server＇s queue buffer and reduces the delay jitter through restricting the buffer length for each flow.

Steady-State Analysis of the Distributed Queueing Algorithm in a Single-Channel M2M Network

The Distributed Queuing (DQ) algorithm is predicted as one of the solutions to the issues currently found in IoT networks over the use of Aloha based algorithms. Since recently, the algorithm has been of interest to many IoT researchers as a replacement of those Aloha variants for channel access. However, previous works analyzed and evaluated the DQ algorithm without any consideration of the stability of its queues, assuming it is stable for any given number of nodes in the network. In this paper, we define the DQ stability condition in a single-channel M2M environment considering a traffic model of periodic and urgent frames from each node in the network. Besides, a steady-state evaluation of the algorithm’s performance metrics is also presented. In general, the DQ algorithm, when it is stable, was observed not to efficiently use the contention slots for the collision resolution. In a single-channel environment, the DQ algorithm is found to outperform the Aloha based algorithms only in an idle-to-saturation scenario.

On Utilizing Model Transformation for the Performance Analysis of Queueing Networks

In this paper, we present an approach for model transformation from Queueing Network Models (QNMs) into Queueing Petri Nets (QPNs). The performance of QPNs can be analyzed using a powerful simulation engine, SimQPN, designed to exploit the knowledge and behavior of QPNs to improve the efficiency of simulation. When QNMs are transformed into QPNs, their performance can be analyzed efficiently using SimQPN. To validate our approach, we apply it to analyze the performance of several queueing network models including a model of a database system. The evaluation results show that the performance analysis of the transformed QNMs has high accuracy and low overhead. In this context, model transformation enables the performance analysis of queueing networks using different ways that can be more efficient.

Sea surface temperature retrieval based on simulated microwave polarimetric measurements of a one-dimensional synthetic aperture microwave radiometer

Compared with traditional real aperture microwave radiometers,one-dimensional synthetic aperture microwave radiometers have higher spatial resolution.In this paper,we proposed to retrieve sea surface temperature using a one-dimensional synthetic aperture microwave radiometer that operates at frequencies of 6.9 GHz,10.65 GHz,18.7 GHz and 23.8 GHz at multiple incidence angles.We used the ERA5 reanalysis data provided by the European Centre for Medium-Range Weather Forecasts and a radiation transmission forward model to calculate the model brightness temperature.The brightness temperature measured by the spaceborne one-dimensional synthetic aperture microwave radiometer was simulated by adding Gaussian noise to the model brightness temperature.Then,a backpropagation(BP)neural network algorithm,a random forest(RF)algorithm and two multiple linear regression algorithms(RE1 and RE2)were developed to retrieve sea surface temperature from the measured brightness temperature within the incidence angle range of 0°-65°.The results show that the retrieval errors of the four algorithms increase with the increasing Gaussian noise.The BP achieves the lowest retrieval errors at all incidence angles.The retrieval error of the RE1 and RE2 decrease first and then increase with the incidence angle and the retrieval error of the RF is contrary to that of RE1 and RE2.