高速铁路信号云联锁系统数据容错技术研究

为适应铁路信号系统的数字化发展需求,将云计算技术引入铁路信号联锁系统(云联锁),有助于为联锁系统提供更高的灵活性、可扩展性和计算性能。目前,研究如何确保云联锁系统的安全性问题极具挑战性。为了解决目前商用云计算平台尚不能提供满足铁路信号系统安全苛求等方面的技术保障措施,针对云联锁系统的安全性,提出一种用于云联锁系统的数据容错恢复方案,确保云平台可以运行联锁应用,并保障云联锁功能的安全性和正确性。首先,对既有计算机联锁系统的安全措施和目前针对云平台的安全技术手段进行分析,找出云联锁系统的安全保障措施的难点,提出一种云联锁系统结构,分析进路控制过程,探索适合于针对云联锁的监控方法。然后,根据进路控制过程,使用特征编码方法对联锁进路和输入信息进行编码。运用基于矩阵算法的容错技术,检测联锁运算数据错误,通过监控软件的运行行为来检测云联锁系统潜在的设计缺陷和计算错误等。对多节点的联锁运算故障进行分析,提出针对云联锁可能发生的多重故障的故障恢复方案,并对采取的措施进行详细分析。最后,通过平均危险失效概率定量计算分析云平台的安全性。研究结果表明:与传统技术相比,提出的监控方案具备更高的安全性能。研究结果可为进一步研究类似的高安全铁路信号系统、铁路信号系统数字化和信息化研究提供参考。

2017年中国新闻传播学研究的十个新鲜话题

本文概述了2017年中国新闻传播学界研究的十个新鲜话题。这一年,习近平一系列新理念贯穿中国新闻舆论工作的全过程。技术上,互联网进入"下半场",个性化新闻推送改变着新闻产业的市场格局,人工智能的进展促使新闻生产发生变革,"后真相"冲击着新闻真实的专业要求。笔者认为,目前新闻传播学界对此做出反应较快,但思考的深度不够,用旧思维考虑新问题的惯性较为明显。新闻传播界学人应培养在有限的时间内迅速对问题作出准确判断、加以恰当解释的能力。新闻传播学是应用学科,这种本学科特有的研究能力是必备的素质。

软件开发项目工作量估算技术的比较研究

软件开发项目的工作量估算技术一般分为三类:基于专家判断的技术、基于算法模型的技术和面向学习的技术。不同的估算技术各有自己的优点和局限性,没有一种估算技术能适用于所有开发环境,并且软件开发方法和技术的更新速度也对所有这些估算技术提出了挑战。软件组织应根据具体的项目特征和可获得的信息来选择合适的估算技术,并针对当前项目情况对使用的估算模型加以调整,依据不同技术的特点组合不同的估算技术进行估算,以提高估算准确性。在估算项目工作量时要充分考虑到项目前期阶段的工作量,并建立本组织的软件项目库。

面向GPU的通用矩阵乘法计算的容错研究

矩阵计算是GPU最擅长的工作之一,NVIDIA公司在CUDA中提供了线性代数库cuBLAS,用于矩阵和向量相关的计算.但是GPU容易受到电磁或者宇宙射线影响,而发生"位"反转问题,从而发生静默数据损坏错误.针对这个问题,利用基于算法的容错方法,提出了带容错的,用于通用矩阵乘法计算的方法,并以CUDA库函数的方式实现.论文讨论了算法的原理,用一种高效的方法实现了容错计算,并提出了一个低开销、高准确率的阈值计算方法用于在线的快速纠错和检错.在两款嵌入式GPU平台上对带容错功能的GEMM库函数进行了评估,其纠错和检错能力与预期一致,并且在大部分情况下,额外性能开销能够控制在50%以内,证明了该GEMM函数可以在较低的性能开销情况下,能够很好的实现GEMM计算的检错和纠错,在某些结果-关键的高性能计算中,具有一定的实用价值.

基于AIOps的VoLTE质量管理解决方案

针对VoLTE(Voice over LTE)网络复杂性导致的VoLTE业务分析难点和痛点,提出基于AIOps(基于算法的IT运维)的VoLTE质量管理解决方案。将VoLTE业务流程分解为四大应用场景,然后针对每个场景提出相应的质量监控规则和问题定界算法,建立和实现VoLTE业务质量自动化监控和分析体系,极大地提升运维效率和降低VoLTE质量分析对分析人员的技术能力要求。

Retrieving reuse component based on semantic

According to the current research status of component retrieval, the component description model based on facet classification is improved by adding semantic features. Furthermore, the component retrieval process model is put forward by combining the domain ontology with the relative concept match algorithm. A detailed illustration of a component reasoning engine and a component classification engine is given and the component classification algorithm is provided by using the Naive Bayes algorithm based on domain ontology. The experimental results show that the recall ratio and the precision ratio are obviously improved by using the method based on semantics, and demonstrate the feasibility and effectiveness of the proposed method.

Efficient Virtual Network Embedding Algorithm Based on Restrictive Selection and Optimization Theory Approach

Network virtualization(NV) is widely considered as a key component of the future network and promises to allow multiple virtual networks(VNs) with different protocols to coexist on a shared substrate network(SN). One main challenge in NV is virtual network embedding(VNE). VNE is a NPhard problem. Previous VNE algorithms in the literature are mostly heuristic, while the remaining algorithms are exact. Heuristic algorithms aim to find a feasible embedding of each VN, not optimal or sub-optimal, in polynomial time. Though presenting the optimal or sub-optimal embedding per VN, exact algorithms are too time-consuming in smallscaled networks, not to mention moderately sized networks. To make a trade-off between the heuristic and the exact, this paper presents an effective algorithm, labeled as VNE-RSOT(Restrictive Selection and Optimization Theory), to solve the VNE problem. The VNERSOT can embed virtual nodes and links per VN simultaneously. The restrictive selection contributes to selecting candidate substrate nodes and paths and largely cuts down on the number of integer variables, used in the following optimization theory approach. The VNE-RSOT fights to minimize substrate resource consumption and accommodates more VNs. To highlight the efficiency of VNERSOT, a simulation against typical and stateof-art heuristic algorithms and a pure exact algorithm is made. Numerical results reveal that virtual network request(VNR) acceptance ratio of VNE-RSOT is, at least, 10% higher than the best-behaved heuristic. Other metrics, such as the execution time, are also plotted to emphasize and highlight the efficiency of VNE-RSOT.

Decentralized Multiagent Task Planning for Heterogeneous UAV Swarm

A decentralized task planning algorithm is proposed for heterogeneous unmanned aerial vehicle(UAV)swarm with different capabilities.The algorithm extends the consensus-based bundle algorithm(CBBA)to account for a more realistic and complex environment.The extension of the algorithm includes handling multi-agent task that requires multiple UAVs collaboratively completed in coordination,and consideration of avoiding obstacles in task scenarios.We propose a new consensus algorithm to solve the multi-agent task allocation problem and use the Dubins algorithm to design feasible paths for UAVs to avoid obstacles and consider motion constraints.Experimental results show that the CBBA extension algorithm can converge to a conflict-free and feasible solution for multi-agent task planning problems.

Fuzzy-second order sliding mode control optimized by genetic algorithm applied in direct torque control of dual star induction motor

The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parametric variations. Among the most evoked control strategies adopted in this field to overcome these drawbacks presented in classical drive, it is worth mentioning the use of the second order sliding mode control(SOSMC) based on the super twisting algorithm(STA) combined with the fuzzy logic control(FSOSMC). In order to realize the optimal control performance, the FSOSMC parameters are adjusted using an optimization algorithm based on the genetic algorithm(GA). The performances of the envisaged control scheme, called G-FSOSMC, are investigated against G-SOSMC, G-PI and BBO-FSOSMC algorithms. The proposed controller scheme is efficient in reducing the torque and flux ripples, and successfully suppresses chattering. The effects of parametric uncertainties do not affect system performance.

Turnout fault diagnosis based on DBSCAN/PSO-SOM

In order to diagnose the common faults of railway switch control circuit,a fault diagnosis method based on density-based spatial clustering of applications with noise(DBSCAN)and self-organizing feature map(SOM)is proposed.Firstly,the three-phase current curve of the switch machine recorded by the micro-computer monitoring system is dealt with segmentally and then the feature parameters of the three-phase current are calculated according to the action principle of the switch machine.Due to the high dimension of initial features,the DBSCAN algorithm is used to separate the sensitive features of fault diagnosis and construct the diagnostic sensitive feature set.Then,the particle swarm optimization(PSO)algorithm is used to adjust the weight of SOM network to modify the rules to avoid“dead neurons”.Finally,the PSO-SOM network fault classifier is designed to complete the classification and diagnosis of the samples to be tested.The experimental results show that this method can judge the fault mode of switch control circuit with less training samples,and the accuracy of fault diagnosis is higher than that of traditional SOM network.

An edge-adaptive demosaicking method based on image correlation

To reduce the cost, size and complexity, a consumer digital camera usually uses a single sensor overlaid with a color filter array(CFA) to sample one of the red-green-blue primary color values, and uses demosaicking algorithm to estimate the missing color values at each pixel. A novel image correlation and support vector machine(SVM) based edge-adaptive algorithm was proposed, which can reduce edge artifacts and false color artifacts, effectively. Firstly, image pixels were separated into edge region and smooth region with an edge detection algorithm. Then, a hybrid approach switching between a simple demosaicking algorithm on the smooth region and SVM based demosaicking algorithm on the edge region was performed. Image spatial and spectral correlations were employed to create middle planes for the interpolation. Experimental result shows that the proposed approach produced visually pleasing full-color result images and obtained higher CPSNR and smaller S-CIELAB*ab?E than other conventional demosaicking algorithms.

QIM digital watermarkingbased on LDPC code and messagepassingunder scalingattacks

Watermarking system based on quantization index modulation （QIM） is increasingly popular in high payload applications,but it is inherently fragile against amplitude scaling attacks.In order to resist desynchronization attacks of QIM digital watermarking,a low density parity check （LDPC） code-aided QIM watermarking algorithm is proposed,and the performance of QIM watermarking system can be improved by incorporating LDPC code with message passing estimation/detection framework.Using the theory of iterative estimation and decoding,the watermark signal is decoded by the proposed algorithm through iterative estimation of amplitude scaling parameters and decoding of watermark.The performance of the proposed algorithm is closer to the dirty paper Shannon limit than that of repetition code aided algorithm when the algorithm is attacked by the additive white Gaussian noise.For constant amplitude scaling attacks,the proposed algorithm can obtain the accurate estimation of amplitude scaling parameters.The simulation result shows that the algorithm can obtain similar performance compared to the algorithm without desynchronization.

A fast MPC algorithm for reducing computation burden of MIMO

The computation burden in the model-based predictive control algorithm is heavy when solving QR optimization with a limited sampling step, especially for a complicated system with large dimension. A fast algorithm is proposed in this paper to solve this problem, in which real-time values are modulated to bit streams to simplify the multiplication. In addition, manipulated variables in the prediction horizon are deduced to the current control horizon approximately by a recursive relation to decrease the dimension of QR optimization. The simulation results demonstrate the feasibility of this fast algorithm for MIMO systems.

Intrusion Detection Algorithm Based on Density,Cluster Centers,and Nearest Neighbors

Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls.It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic firewalls.Many intrusion detection methods are processed through machine learning.Previous literature has shown that the performance of an intrusion detection method based on hybrid learning or integration approach is superior to that of single learning technology.However,almost no studies focus on how additional representative and concise features can be extracted to process effective intrusion detection among massive and complicated data.In this paper,a new hybrid learning method is proposed on the basis of features such as density,cluster centers,and nearest neighbors(DCNN).In this algorithm,data is represented by the local density of each sample point and the sum of distances from each sample point to cluster centers and to its nearest neighbor.k-NN classifier is adopted to classify the new feature vectors.Our experiment shows that DCNN,which combines K-means,clustering-based density,and k-NN classifier,is effective in intrusion detection.

A weighted selection combining scheme for cooperative spectrum prediction in cognitive radio networks

A weighted selection combining （WSC） scheme is proposed to improve prediction accuracy for cooperative spectrum prediction in cognitive radio networks by exploiting spatial diversity. First, a genetic algorithm-based neural network （GANN） is designed to perform spectrum prediction in consideration of both the characteristics of the primary users （PU） and the effect of fading. Then, a fusion selection method based on the iterative self-organizing data analysis （ISODATA） algorithm is designed to select the best local predictors for combination. Additionally, a reliability-based weighted combination rule is proposed to make an accurate decision based on local prediction results considering the diversity of the predictors. Finally, a Gaussian approximation approach is employed to study the performance of the proposed WSC scheme, and the expressions of the global prediction precision and throughput enhancement are derived. Simulation results reveal that the proposed WSC scheme outperforms the other cooperative spectrum prediction schemes in terms of prediction accuracy, and can achieve significant throughput gain for cognitive radio networks.

Multi-objective optimization based optimal setting control for industrial double-stream alumina digestion process

The operation variables,including feed rate of ore slurry,caustic solution and live steams in the double-stream alumina digestion process,determine the product quality,process costs and the environment pollution.Previously,they were set by the technical workers according to the offline analysis results and an empirical formula,which leads to unstable process indices and high consumption frequently.So,a multi-objective optimization model is built to maintain the balance between resource consumptions and process indices by taking technical indices and energy efficiency as objectives,where the key technical indices are predicted based on the digestion kinetics of diaspore.A multi-objective state transition algorithm(MOSTA)is improved to solve the problem,in which a self-adaptive strategy is applied to dynamically adjust the operator factors of the MOSTA and dynamic infeasible threshold is used to handle constraints to enhance searching efficiency and ability of the algorithm.Then a rule based strategy is designed to make the final decision from the Pareto frontiers.The method is integrated into an optimal control system for the industrial digestion process and tested in the actual production.Results show that the proposed method can achieve the technical target while reducing the energy consumption.

WinoNet:Reconfigurable look-up table-based Winograd accelerator for arbitrary precision convolutional neural network inference

To solve the hardware deployment problem caused by the vast demanding computational complexity of convolutional layers and limited hardware resources for the hardware network inference,a look-up table(LUT)-based convolution architecture built on a field-programmable gate array using integer multipliers and addition trees is used.With the help of the Winograd algorithm,the optimization of convolution and multiplication is realized to reduce the computational complexity.The LUT-based operator is further optimized to construct a processing unit(PE).Simultaneously optimized storage streams improve memory access efficiency and solve bandwidth constraints.The data toggle rate is reduced to optimize power consumption.The experimental results show that the use of the Winograd algorithm to build basic processing units can significantly reduce the number of multipliers and achieve hardware deployment acceleration,while the time-division multiplexing of processing units improves resource utilization.Under this experimental condition,compared with the traditional convolution method,the architecture optimizes computing resources by 2.25 times and improves the peak throughput by 19.3 times.The LUT-based Winograd accelerator can effectively solve the deployment problem caused by limited hardware resources.

A New Feature-Based Image Registration Algorithm

IR （Image Registration） is one of the important operation of image processing system which is the process of aligning two or more images into one coordinate system that are taken at different times, from different sensors, or from different viewpoints. It has a lot of applications especially medical imaging and remote sensing. The main purpose of this paper is to provide a comprehensive review of existing literatures available on image registration system and proposed a new feature-based IR technique using edge of images. We used edges as a feature of images for registration. It will be a useful document for researchers who will work on feature-based image registration regardless for specific applications.

Dynamic reactive power planning method for CSP-PV hybrid power generation system

Aiming at the faults of some weak nodes in the concentrated solar power-photovoltaic(CSP-PV)hybrid power generation system,it is impossible to restore the transient voltage only relying on the reactive power regulation capability of the system itself.We propose a dynamic reactive power planning method suitable for CSP-PV hybrid power generation system.The method determines the installation node of the dynamic reactive power compensation device and its compensation capacity based on the reactive power adjustment capability of the system itself.The critical fault node is determined by the transient voltage stability recovery index,and the weak node of the system is initially determined.Based on this,the sensitivity index is used to determine the installation node of the dynamic reactive power compensation device.Dynamic reactive power planning optimization model is established with the lowest investment cost of dynamic reactive power compensation device and the improvement of system transient voltage stability.Furthermore,the component of the reactive power compensation node is optimized by particle swarm optimization based on differential evolution(DE-PSO).The simulation results of the example system show that compared with the dynamic position compensation device installation location optimization method,the proposed method can improve the transient voltage stability of the system under the same reactive power compensation cost.

Study on Automatic Generation Algorithm of LOD Model based on Complex Features

For certain applications, the paper proposes a LOD generation algorithm based on model feature, to measure the value of certain features as weight, and to just the geometrical characteristics and retains the full feature in the simplified model. Algorithm is simple and intuitive, LOD models can be generated by a continuous smooth transition and meet the requirements of real-time display. Experimental results show the effectiveness of the algorithm. Algorithm is simple and quick, broad applicability, the improve simplified algorithm is very suitable for modulation texture operator, generating algorithm based on a lookup table to meet the model requirements for real-time display.