ProbD: Faulty Path Detection Based on Probability in Software-Defined Networking

With the increasing number of switches in Software-Defined Network-ing(SDN),there are more and more faults rising in the data plane.However,due to the existence of link redundancy and multi-path forwarding mechanisms,these problems cannot be detected in time.The current faulty path detection mechan-isms have problems such as the large scale of detection and low efficiency,which is difficult to meet the requirements of efficient faulty path detection in large-scale SDN.Concerning this issue,we propose an efficient network path fault testing model ProbD based on probability detection.This model achieves a high prob-ability of detecting arbitrary path fault in the form of small-scale random sam-pling.Under a certain path fault rate,ProbD obtains the curve of sample size and probability of detecting arbitrary path fault by randomly sampling network paths several times.After a small number of experiments,the ProbD model can cor-rectly estimate the path fault rate of the network and calculate the total number of paths that need to be detected according to the different probability of detecting arbitrary path fault and the path fault rate of the network.Thefinal experimental results show that,compared with the full path coverage test,the ProbD model based on probability detection can achieve efficient network testing with less overhead.Besides,the larger the network scale is,the more overhead will be saved.

Robust adaptive synchronization of uncertain and delayed dynamical complex networks with faulty network

This paper presents a new robust adaptive synchronization method for a class of uncertain dynamical complex networks with network failures and coupling time-varying delays. Adaptive schemes are proposed to adjust controller parameters for the faulty network compensations, as well as to estimate the upper and lower bounds of delayed state errors and perturbations to compensate the effects of delay and perturbation on-line without assuming symmetry or irreducibility of networks. It is shown that, through Lyapunov stability theory, distributed adaptive controllers con- structed by the adaptive schemes are successful in ensuring the achievement of asymptotic synchronization of networks in the present of faulty and delayed networks, and perturbation inputs. A Chua＇s circuit network example is finally given to show the effectiveness of the proposed synchronization criteria.

Effect of Wind Farm and Thyristor Switched Series Capacitors on a Faulty Network

Controlling voltage and active or reactive losses are one of the most important issues in each power grid.In this paper,the influence of wind farm and thyristors switched capacitors on the network are considered.TSSC and Wind turbines are one of the significant components of each network.These instruments are also one of the resources of producing active and reactive power.In this study,wind farm and TSSC are already located optimally by Genetic algorithm.This network studied when a fault considered in one of buses.So that,in first step none of wind farm and TSSC are in the power grid.In the second step,both wind farm and TSSC are connected while a short circuit accrues in one of the busses or lines of the network.At the end,it will be observed that using thyristor switched capacitors and wind farm influence the network.So that,the capacity of producing the reactive and active power will be increased and totally the loss of the system will be decreased.Furthermore,the voltage profile will be in a suitable range.

Faulty Feeder Identification in Resonant Grounding Distribution Networks Based on Deep Learning and Transfer Learning

Identification of faulty feeders in resonant grounding distribution networks remains a significant challenge dueto the weak fault current and complicated working conditions.In this paper, we present a deep learning-based multi-labelclassification framework to reliably distinguish the faulty feeder.Three different neural networks (NNs) including the multilayerperceptron, one-dimensional convolutional neural network (1DCNN), and 2D CNN are built. However, the labeled data maybe difficult to obtain in the actual environment. We use thesimplified simulation model based on a full-scale test field (FSTF)to obtain sufficient labeled source data. Being different frommost learning-based methods, assuming that the distribution ofsource domain and target domain is identical, we propose asamples-based transfer learning method to improve the domainadaptation by using samples in the source domain with properweights. The TrAdaBoost algorithm is adopted to update theweights of each sample. The recorded data obtained in the FSTFare utilized to test the domain adaptability. According to ourvalidation and testing, the validation accuracies are high whenthere is sufficient labeled data for training the proposed NNs.The proposed 2D CNN has the best domain adaptability. TheTrAdaBoost algorithm can help the NNs to train an efficientclassifier that has better domain adaptation. It has been thereforeconcluded that the proposed method, especially the 2D CNN, issuitable for actual distribution networks.

Faulty Feeder Identification and Fault Area Localization in Resonant Grounding System Based on Wavelet Packet and Bayesian Classifier

Accurate fault area localization is a challenging problem in resonant grounding systems(RGSs).Accordingly,this paper proposes a novel two-stage localization method for single-phase earth faults in RGSs.Firstly,a faulty feeder identification algorithm based on a Bayesian classifier is proposed.Three characteristic parameters of the RGS(the energy ratio,impedance factor,and energy spectrum entropy)are calculated based on the zero-sequence current(ZSC)of each feeder using wavelet packet transformations.Then,the values of three parameters are sent to a pre-trained Bayesian classifier to recognize the exact fault mode.With this result,the faulty feeder can be finally identified.To find the exact fault area on the faulty feeder,a localization method based on the similarity comparison of dominant frequency-band waveforms is proposed in an RGS equipped with feeder terminal units(FTUs).The FTUs can provide the information on the ZSC at their locations.Through wavelet-packet transformation,ZSC dominant frequency-band waveforms can be obtained at all FTU points.Similarities of the waveforms of characteristics at all FTU points are calculated and compared.The neighboring FTU points with the maximum diversity are the faulty sections finally determined.The proposed method exhibits higher accuracy in both faulty feeder identification and fault area localization compared to the previous methods.Finally,the effectiveness of the proposed method is validated by comparing simulation and experimental results.

Legal-Ease FAULTY OR INEFFICIENT BUSINESS APPLICATIONS

WHOLLY FOREIGN-OWNED ENTERPRISES, FOREIGN-INVESTED COMMERCIAL ENTERPRISES AND JOINT VENTURES Faulty license applications & tax structuring problems The scenarios and problems below apply to all the above categories of foreign investment but for sake of convenience we have used the "Foreign-invested Enterprise" (FIE) term to cater for all. Sending start-up funds to somebody else before FIE is

Legal-Ease Faulty or Inefficient Business Applications

In China, some business applications can be complicated. Then of course there is the language-and then again the nuances of law that determine the huge difference in operational quality between the so-called "cookie-cutter" applications and those in which time and attention have been spent. Here we identify some of the common mistakes we are asked regularly to fix when clients have either been badly advised, or simply were not aware of the full application and structural procedures when making an investm...

BOARD-LEVEL BUILT-IN SELF-REPAIR METHOD OF RAM

This paper describes the method of built-in self-repairing of RAM on board, designs hardware circuit, and logic for the RAM's faults self-repairing system based on FPGA. The key technology is that it utilizes FPGA to test RAM according to some algorithm to find out failure memory units and replace the faulty units with FPGA. Then it can build a memory that has no fault concern to external controller, and realizes the logic binding between external controller and RAM. Micro Controller Unit (MCU) can operate external RAM correctly even if RAM has some fault address units. Conventional MCS-51 is used to simulate the operation of MCU operating external memory. Simulation shows FPGA can complete the faulty address units' mapping and MCU can normally read and write external RAM. This design realizes the RAM's built-in self-repairing on board.

New Classes of Interconnection Topology Structures and Their Properties

In the first part of this- paper, three generalizations of arrangement graph A.,k of [1], namely Bn,k, Cn,k and Dn,k , are introduced. We prove that all the three classes of graphs are vertex symmetric, two of them are edge symmetric. They have great faulty tolerance and high connectivity. We give the diameters of B..k and Cn,k, the Hamiltonian cycle of Cn,k and Hamiltonian path of B.,k. We list several open problems, one of them related to the complexity of sorting algorithm on the arrangement graphs. All these graphs can be thought as generalizations of star graph but are more flexible so that they can be considered as new interconnection network topologies. In the second part of this paper, we provide other four classes of combinatorial graphes, Chn , Cyn, Zhn and Zyn. Many good properties of them, such as high node--connectivity, node symmetry, edge symmetry, diameter, ets., are shown in this paper.

High Power Interleaved Boost Converter for Photovoltaic Applications

Interfacing DC sources to load/power grid requires DC converters that produce minimum level of current ripples. This is to limit the losses and hence increase the life span of these sources. This article proposes a simple inter-leaved boost converter that interfaces PhotoVoltaic (PV) module into a common DC-link. The article also addresses the faulty mode operation of the proposed circuit while advising the appropriate remedy actions. A MATLAB and Simulink dynamic platform are used to simulate the transient performance of the proposed converter. The results revealed the effectiveness and the viability of the proposed converter in reducing the ripples in the PV current without employing bulky input inductors or increasing the switching frequency.

Review of Fault Types, Impacts, and Management Solutions in Smart Grid Systems

Fault management study in smart grid systems (SGSs) is important to ensure the stability of the system. Also, it is important to know the major types of power failures for the effective operation of the SGS. This paper reviews diverse types of faults that might appear in the SGS and gives a survey about the impact of renewable energy resources (RERs) on the behavior of the system. Moreover, this paper offers different fault detection and localization techniques that can be used for SGSs. Furthermore, a potential fault management case study is proposed in this paper. The SGS model in this paper is investigated using both of the Matlab/Simulink and the Real Time Digital Simulation (RTDS) to compute the fault management study. Simulation results show the fast response to a power failure in the system which improves the stability of the SGS.

Fault Detection for PMSM Motor Drive Systems by Monitoring Inverter Input Currents

This paper has proposed a fault detecting method for DC supplied permanent magnet synchronize motor(PMSM)drive systems by monitoring the drive DC input current.This method is based on the fault signal propagation from the torque disturbance on the motor shaft to the inverter input currents.The accuracy of this fault signal propagation is verified by the Matlab simulation and experiment tests with the emulated faulty conditions.The feasible of this approach is shown by the experimental test conducted by the Spectra test rig with the real gearbox fault.This detection scheme is also suitable for monitoring other drive components such as the power converter or the motor itself using only one set of current transducers mounted at the DC input side.

Cooperative Sensor Anomaly Detection Using Global Information

Sensor networks are deployed in many application areas nowadays ranging from environment monitoring, industrial monitoring, and agriculture monitoring to military battlefield sensing. The accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. Therefore, this work is motivated to propose approaches that can detect and repair erroneous （i.e., dirty） data caused by inevitable system problems involving various hardware and software components of sensor networks. As information about a single event of interest in a sensor network is usually reflected in multiple measurement points, the inconsistency among multiple sensor measurements serves as an indicator for data quality problem. The focus of this paper is thus to study methods that can effectively detect and identify erroneous data among inconsistent observations based on the inherent structure of various sensor measurement series from a group of sensors. Particularly, we present three models to characterize the inherent data structures among sensor measurement traces and then apply these models individually to guide the error detection of a sensor network. First, we propose a multivariate Gaussian model which explores the correlated data changes of a group of sensors. Second, we present a Principal Component Analysis （PCA） model which captures the sparse geometric relationship among sensors in a network. The PCA model is motivated by the fact that not all sensor networks have clustered sensor deployment and clear data correlation structure. Further, if the sensor data show non-linear characteristic, a traditional PCA model can not capture the data attributes properly. Therefore, we propose a third model which utilizes kernel functions to map the original data into a high dimensional feature space and then apply PCA model on the mapped linearized data. All these three models serve the purpose of capturing the underlying phenomenon of a sensor network from its global view, and then guide the error detection to discover any anomaly observations. We conducted simulations for each of the proposed models, and evaluated the performance by deriving the Receiver Operating Characteristic （ROC） curves.

Fault-Tolerant Tree-Based Multicasting in Mesh Multicomputers

We propose a fault-tolerant tree-based multicast algorithm for 2-dimensional (2-D) meshes based on the concept of the extended safety level which is a vector associated with each node to capture fault information in the neighborhood. In this approach each destination is reached through a minimum number of hops. In order to minimize the total number of traffic steps, three heuristic strategies are proposed. This approach can be easily implemented by pipelined circuit switching (PCS). A simulation study is conducted to measure the total number of traffic steps under different strategies. Our approach is the first attempt to address the fault- tolerant tree-based multicast problem in 2-D meshes based on limited global information with a simple model and succinct information.