混沌序列变换成均匀伪随机序列的普适算法的FPGA实现

针对现有的PRNG的均匀性差的特性,文献[1]提出了一种将混沌序列变换成均匀伪随机序列的普适算法。我们首次提出该算法的FPGA实现方案,方案由上位机软件、UART控制器、初值缓存器、均匀化算法实现单元、尾数序列缓存转换器组成。采用VHDL完成各模块设计,芯片选用逻辑资源为100万门的CycloneIIEP2C35F672C6,硬件电路共占6721逻辑单元,资源率20%,工作频率为50MHz。     

Robust suboptimal feedback control for a fed-batch nonlinear time-delayed switched system

The optimal control strategy constructed in the form of a state feedback is effective for small state perturbations caused by changes in modeling uncertainty. In this paper, we investigate a robust suboptimal feedback control (RSPFC) problem governed by a nonlinear time-delayed switched system with uncertain time delay arising in a 1,3-propanediol (1,3-PD) microbial fed-batch process. The feedback control strategy is designed based on the radial basis function to balance the two (possibly competing) objectives: (i) the system performance (concentration of 1,3-PD at the terminal time of the fermentation) is to be optimal; and (ii) the system sensitivity (the system performance with respect to the uncertainty of the time-delay) is to be minimized. The RSPFC problem is subject to the continuous state inequality constraints. An exact penalty method and a novel time scaling transformation approach are used to transform the RSPFC problem into the one subject only to box constraints. The resulting problem is solved by a hybrid optimization algorithm based on a filled function method and a gradient-based algorithm. Numerical results are given to verify the effectiveness of the developed hybrid optimization algorithm.     

Parameter estimation for an input nonlinear state space system with time delay

This paper researches parameter estimation problems for an input nonlinear system with state time-delay. Combining the linear transformation and the property of the shift operator, the system is transformed into a bilinear parameter identification model. A gradient based and a least squares based iterative parameter estimation algorithms are presented for identifying the state time-delay system. The simulation results confirm that the proposed two algorithms are effective and the least squares based iterative algorithm has faster convergence rates than the gradient based iterative algorithm.     

Design and performance analysis of PID controller for an automatic voltage regulator system using simplified particle swarm optimization

This paper presents the design and performance analysis of Proportional Integral Derivate (PID) controller for an Automatic Voltage Regulator (AVR) system using recently proposed simplified Particle Swarm Optimization (PSO) also called Many Optimizing Liaisons (MOL) algorithm. MOL simplifies the original PSO by randomly choosing the particle to update, instead of iterating over the entire swarm thus eliminating the particles best known position and making it easier to tune the behavioral parameters. The design problem of the proposed PID controller is formulated as an optimization problem and MOL algorithm is employed to search for the optimal controller parameters. For the performance analysis, different analysis methods such as transient response analysis, root locus analysis and bode analysis are performed. The superiority of the proposed approach is shown by comparing the results with some recently published modern heuristic optimization algorithms such as Artificial Bee Colony (ABC) algorithm, Particle Swarm Optimization (PSO) algorithm and Differential Evolution (DE) algorithm. Further, robustness analysis of the AVR system tuned by MOL algorithm is performed by varying the time constants of amplifier, exciter, generator and sensor in the range of ?50% to +50% in steps of 25%. The analysis results reveal that the proposed MOL based PID controller for the AVR system performs better than the other similar recently reported population based optimization algorithms.     

网络安全数据可视化与自动告警系统研究

为了从海量数据中识别网络安全状况,及时发现网络安全问题,文章提出了一种适用于大规模网络安全数据的实时可视化与自动告警系统。该系统通过实时监控网络安全设备,评估安全状况;通过计算关联权值,评估告警等级,实现自动告警;通过融合网络安全数据,实现网络安全可视化。通过部署验证,该系统能够有效识别分布式拒绝服务等网络攻击,并进行可视化展现与告警。     

Reliable automatic word spacing using a space insertion and correction model based on neural networks in Korean

Automatic word spacing in Korean remains a significant task in natural language processing owing to the extremely complex word spacing rules involved. Most previous models remove all spaces in input sentences and insert new spaces in the modified input sentences. If input sentences include only a small number of spacing errors, the previous models often return sentences with even more spacing errors than the input sentences because they remove the correct spaces that were typed intentionally by the users. To reduce this problem, we propose an automatic word spacing model based on a neural network that effectively uses word spacing information from input sentences. The proposed model comprises a space insertion layer and a spacing-error correction layer. Using an approach similar to previous models, the space insertion layer inserts word spaces into input sentences from which all spaces have been removed. The spacing error correction layer post-corrects the spacing errors of the space insertion model using word spacing typed by users. Because the two layers are tightly connected in the proposed model, the backpropagation flows are not blocked. As a result, the space insertion and error correction are performed simultaneously. In experiments, the proposed model outperformed all compared models on all measures on the same test data. In addition, it exhibited reliable performance (word-unit F1-measures of 94.17%～97.87%) regardless of how many word spacing errors were present in the input sentences.     

Combined state and parameter estimation for a bilinear state space system with moving average noise

This paper considers the identification problem of bilinear systems with measurement noise in the form of the moving average model. In particular, we present an interactive estimation algorithm for unmeasurable states and parameters based on the hierarchical identification principle. For unknown states, we formulate a novel bilinear state observer from input-output measurements using the Kalman filter. Then a bilinear state observer based multi-innovation extended stochastic gradient (BSO-MI-ESG) algorithm is proposed to estimate the unknown system parameters. A linear filter is utilized to improve the parameter estimation accuracy and a filtering based BSO-MI-ESG algorithm is presented using the data filtering technique. In the numerical example, we illustrate the effectiveness of the proposed identification methods.     

Adaptive fuzzy output feedback controller design for a HAGC system with input saturation and output error constraints

The comprehensive effect of external disturbance, measurement delay, unmeasurable states and input saturation makes the difficulties and challenges for a HAGC system. In this paper, an adaptive fuzzy output feedback control scheme is designed for a HAGC system under the simultaneous consideration of those factors. At the first place, by state transformation technique, the dynamic model of a HAGC system is simply expressed as a strict feedback form, where measurement delay is converted into input delay. Then, an auxiliary system is employed to compensate for the effect of input delay. Furthermore, an asymmetric barrier Lyapunov function (BLF) is constructed to ensure the output error constraint requirement of thickness error and the fuzzy observer is established to solve unmeasurable states, unknown nonlinear functions at the same time. With the aid of backstepping method, adaptive fuzzy controller is developed to assure that the closed-loop system is semi-globally boundedness and the output error of thickness error doesn’t violate its constraint. At the end, compared simulations are carried out to verify the efficiency of the proposed control scheme.     

建立社会稳定预警机制完善政府应急管理体系

中国自从改革开放以后迎来了长达二十多年的高速发展期,经济总量已经达到了一个相当的高度,根据中国国家统计局2005年公布的数据,中国2005年GDP为22400亿美元,超过英国的20300亿美元,成为仅次于美国、日本和德国的世界第四大经济体。与此同时,我国的人均GDP在2005年也已达到了1700美元。根据世界发展进程的规律,在国家和地区的人均GDP处于500～3000美元的发展阶段,往往对应着人口、资源、环境、效率、公平等社会矛盾的瓶颈约束最为严重的时期,也是“经济容易失调、社会容易失序、心理容易失衡、社会伦理需要调整重建”的关键时期,建立社…     

Bifurcation and control of a planktonic ecological system with double delays by delayed feedback control

In this paper, a delayed feedback controller with the delay-dependent coefficient is introduced into a multiple delay phytoplankton-zooplankton system. For uncontrolled system, choosing delays as the bifurcation parameters, we prove that Hopf bifurcation can occur when the delays change and cross some values. Then, the delays are still chosen as the bifurcation parameters to research the dynamic behaviors of the controlled system. Under this control mechanism, the onset of Hopf bifurcation can be delayed by selecting the appropriate control parameters and the stability domain can be extended as feedback gain (the decay rate) decreases (increases), and the influence of the decay rate cannot be ignored. Furthermore, using the crossing curve methods, the stable changes of equilibrium in two delay plane can be obtained. Some numerical simulations are given to verify the correctness and validity of the delayed feedback controller in the bifurcation control.     

水资源资产负债表表式结构初探

探索编制自然资源资产负债表是党的十八届三中全会在生态文明建设领域中的一项重要制度创新。水资源资产负债表是自然资产负债表在水资源领域的重要应用,同时也是自然资源资产负债表的重要组成部分。目前,对于水资源资产负债表的研究还处于理论探索阶段,在相关概念、核算指标、核算框架以及核算表式方面仍存在争议。本文以探索构建水资源资产负债核算框架为目标,在回顾国内外水资源资产负债表和水资源环境经济核算相关研究进展的基础上,从辨析水资源资产、负债概念入手,分析了开展水资源资产、负债核算的相应指标,进而,提出了从存量到流量,从实物到价值,从分类到综合的水资源资产负债核算框架与相应的核算表式,为开展区域水资源资产负债核算提供参考。     

Management system for a scientific research institute based on the assessment of scientific publications

This study depicts ihe management system of a research institute based on input-output principles. The elements of the system are the scientific departments. The main categories of the controlling system arc the planning and supervision or research activities, evaluation of scientific and economic results and rewarding of researchers. The evaluation of scientific results focuses on the analysis of publications. The quality of scientific papers is characterized by a scoring system based on impact factors of periodicals and by citations received. Possible reasons for the lack of citations are discussed. The results of mission-oriented research (MOR) are evaluated on the basis of revenues and earnings. The amount of bonus given to researchers is based on a scoring system taking publication and MOR activity into account. A marked difference in the number of publications and citations among individual departments was observed.     

Decision support system through automatic algorithms and electronic request in diagnosis of anaemia for primary care patients

IntroductionAn appropriate management of anaemia laboratory tests is crucial for a correct diagnosis and treatment. A non-sequential “shotgun” approach (where every anaemia related test is ordered) causes workload and cost increases and could be potentially harmful. We have implemented a Decision Support System through our laboratory information system (LIMS) based on reflexive algorithms and automatic generation of interpretative reports specifically in diagnosis of anaemia for primary care patients.Materials and methodsWhen a request contained an “Anaemia Suspicion Study” profile, more than twenty automatic reflexive rules were activated in our LIMS based upon laboratory results. These rules normally involved the addition of reflexive tests. A final report was automatically generated for each interpretation which was always reviewed for their validity by two staff pathologists. We measured the impact of this system in the ordering of most common anaemia related tests and if a proper treatment was established based on the interpretive report.ResultsFrom all the studies performed, only 12% were positive being “iron deficiency” and “anaemia of chronic disease” the most frequent causes, 62% and 17%, respectively. Proper treatment was established in 88% of these anaemic patients. Total iron, transferrin, ferritin, folate and vitamin B12 demand decreased substantially after implementation representing a cost reduction of 40% only for these five tests.ConclusionsOur system has easily improved patient outcomes, advising on individual clinical cases. We have also noticeably reduced the number of over-requested tests and laboratory costs.     

A hybrid partial feedback linearization and deadbeat control scheme for a nonlinear gantry crane

This paper presents the design of a hybrid partial feedback linearization and deadbeat control scheme for a nonlinear gantry crane with friction to control its position and sway angle. The partial feedback linearization is used to linearize the nonlinear model and to stabilize its internal dynamics. In many crane applications, it's necessary to accelerate the system response. As a result, this will cause oscillation in the position as well as the sway angle. So, the deadbeat controller is added to get the desirable accelerated response without any oscillation or adverse effects on the internal dynamics stability. By using Lyapunov stability method, the proposed scheme is proved to be globally stable, with converging tracking errors to the desired performance. The simulation results are accomplished to evaluate the effectiveness of the proposed scheme and to demonstrate its reliability to control crane systems with comparative results.     

Distributed cooperative learning for a group of uncertain systems via output feedback and neural networks

This paper studies the problem of adaptive neural network (NN) output-feedback control for a group of uncertain nonlinear multi-agent systems (MASs) from the viewpoint of cooperative learning. It is assumed that all MASs have identical unknown nonlinear dynamic models but carry out different periodic control tasks, i.e., each agent system has its own periodic reference trajectory. By establishing a network topology among systems, we propose a new consensus-based distributed cooperative learning (DCL) law for the unknown weights of radial basis function (RBF) neural networks appearing in output-feedback control laws. The main advantage of such a learning scheme is that all estimated weights converge to a small neighborhood of the optimal value over the union of all system estimated state orbits. Thus, the learned NN weights have better generalization ability than those obtained by traditional NN learning laws. Our control approach also guarantees the convergence of tracking errors and the stability of closed-loop system. Under the assumption that the network topology is undirected and connected, we give a strict proof by verifying the cooperative persisting excitation condition of RBF regression vectors. This condition is defined in our recent work and plays a key role in analyzing the convergence of adaptive parameters. Finally, two simulation examples are provided to verify the effectiveness and advantages of the control scheme proposed in this paper.     

Data-driven fault diagnosis for an automobile suspension system by using a clustering based method

This paper concerns the issues of fault diagnosis and monitoring for an automobile suspension system where only accelerator sensors in the four corners of the car body are available. A clustering based method is proposed to detect the fault happened in the spring, and the Fisher discriminant analysis is applied to isolate the root factor for the fault. Different from most of the existing approaches, the pure data-driven characteristic enables this method to serve as an on-line fault diagnosis and monitoring tool without suspension model or fault features known as a prior. Moreover, this method can classify different reductions in the spring coefficient into one fault rather than different faults. The effectiveness of the proposed method is finally illustrated on an automobile suspension benchmark.     

Output feedback control for a class of high-order nonholonomic systems with complicated nonlinearity and time-varying delay

This paper considers the output feedback control problem for high-order nonholonomic time-delay system. Remarkably, the studied system allows the polynomial time-delay growing conditions. Moreover, the applicable power ranges of nonlinear drift and diffusion terms are further relaxed to be a interval rather than a fix point. By choosing a new Lyapunov–Krasovskii (L–K) functional, and by modifying the adding a power integrator method, a delay-independent output feedback controller is designed such that the system is globally asymptotically stable. A simulation example is given to show the validity of the proposed theory.     

Financial system modeling using deep neural networks (DNNs) for effective risk assessment and prediction

Since the risk of loan defaulting in peer-to-peer (P2P) lending is notoriously difficult to evaluate, a deep neural network-based decision-making approach is proposed in this work for more effective assessment of P2P lending risks. Although normally a dozen features were used for neural network modeling in previous studies carried out by other researchers on similar topics, more comprehensive features including both numeric and categorical ones (e.g. home ownership and purpose of loan), are considered in this work for improved modeling. Since categorical data cannot be used directly as the input of neural networks, they are converted to numerical data using one-hot encoding function. The deep neural network (DNN) used in this work is a multilayer perceptron (MLP) with three hidden layers trained by the back-propagation algorithm. In empirical analysis, the loan data issued by the Lending Club through 2007–2015 are classified into three classes, i.e. safe loan, risky loan and bad loan using TensorFlow. The training and test data sets consist of 221,712 and 55,428 data observations, respectively. Since most of the data belong to the class of safe loan, Synthetic Minority Over-Sampling Technique (SMOTE) is used to improve the DNN prediction accuracy. It is shown that with the proposed approach the test data are classified at an accuracy of 93%, which is much higher than the predication accuracy of 75% obtained using MLP with only one hidden layer.     

Making a space for taste: Context and discourse in the specialty coffee scene

Connoisseur consumption is continuing to grow in popularity, with more niche retailers and specialty firms servicing increasingly discerning consumers. Despite the wealth of consumer data from social media platforms, there has been little empirical focus on how consumers make sense of their experiences after interacting with cultural interlocutors from niche industries with highly specialized knowledge. In order to scrutinize the process of distinction making in practice and reception, this study employs a mixed methods approach to triangulate the production, reception, and practice of taste-making at four coffee fairs held in Toronto, Ontario, and Hamilton, Ontario. Through ethnographic fieldwork, conventional content analysis, and a discourse network analysis of social media usage from attendees, this study finds that there are important contextual differences that affect which discourses are present in-person and appear online.