The generalization of a class of impulse stochastic control models of a geometric Brownian motion

Recently, international academic circles advanced a class of new stochastic control models of a geometric Brownian motion which is an important kind of impulse control models whose cost structure is different from the others before, and it has a broad applying background and important theoretical significance in financial control and management of investment. This paper generalizes substantially the above stochastic control models under quite extensive conditions and describes the models more exactly under more normal theoretical system of stochastic process. By establishing a set of proper variational equations and proving the existence of its solution, and applying the means of stochastic analysis, this paper proves that the generalized stochastic control models have optimal controls. Meanwhile, we also analyze the structure of optimal controls carefully. Besides, we study the solution function of variational equations in a relatively deep-going way, which constitutes the value function of control models to some extent. Because the analysis methods of this paper are greatly different from those of original reference, this paper possesses considerable originality to some extent. In addition, this paper gives the strict proof to the part of original reference which is not fairly well-knit in analyses, and makes analyses and discussions of the model have the exactitude of mathematical sense. Supported by the National Natural Science Foundation of China (Grant No. 19671004)     

A systematic approach to data security

The problem of selecting internal controls, or that subset of those controls we call security measures, in a data processing environment yields rather readily to an orderly, systematic approach. Such an approach requires recognition that a control should not be implemented if it costs more than tolerating the problem. Further, no control should be implemented which is more costly or less effective or displaces less potential loss than does some other control. The basic concept and outline of the systematic approach is described and references to supplemental papers for guidance in specific areas is provided through the bibliography.     

Model reduction in model predictive control of combined water quantity and quality in open channels

Model predictive control (MPC) is an advanced real-time control technique that uses an internal model to predict the future system behavior and generates optimal control actions by solving an optimization problem. MPC has been more and more applied for controlling open water systems, especially open water channels. Most of the research however focuses on water quantity (water level) control. Since water quality management is recently attracting more attention, extending MPC on combined water quantity and quality management is a logical next step.In this paper, we study the application of complex models in MPC to control both water quantity and quality. However, because of the online optimization of MPC, the computational time becomes an issue. In order to reduce the computational time, a model reduction technique, Proper Orthogonal Decomposition (POD), is applied to reduce the model order. The method is tested on a Polder flushing case. The results show that POD can significantly reduce the model order for both water quantity and quality with high accuracy. The MPC using the reduced model performs well in controlling combined water quantity and quality in open water channels.     

Transferring auditors’ internal control evaluation knowledge to management

Due to both regulatory and competitive forces, attention to an organization's internal controls has increased significantly in the 1990s. Although management is ultimately responsible for ensuring internal controls are adequate, managers often lack knowledge of internal control concepts. This study reports on an experiment testing an expert system developed to facilitate the transfer of internal control knowledge to management. Experimental results indicate that expert systems are viable aids for transferring internal control knowledge to managers whose work experience is outside of accounting and control systems.     

A relational framework for assertion management

An important purpose of research on decision support systems is to develop a theory of information management that is as independent as possible of the way in which the information is stored and processed. On framework that has proven useful in this regard is a relational framework, which has been successfully applied to the organization and processing of data files and decision models. In this paper we examine the application of relational theory to the management of assertions — that is, to the organization and processing of statements that may be true or false.     

工业控制系统的信息物理因果流模型

工业控制系统向智能控制的发展随着人工智能的因果革命也应该进行因果建模的思考。通过提出工业过程控制系统的单、多层信息物理结构,引入信息、物理因果流,建立过程控制系统的信息物理因果流模型,为依据因果关系设计工业控制系统、分析控制运行机制、实施系统故障预测和监控等提供理论基础和描述框架。     

Formal verification of conflict detection algorithms

Safety assessment of new air traffic management systems is a main issue for civil aviation authorities. Standard techniques such as testing and simulation have serious limitations in new systems that are significantly more autonomous than the older ones. In this paper, we present an innovative approach for establishing the correctness of conflict detection systems. Fundamental to our approach is the concept of trajectory, and how we represent a continuous physical trajectory by a continuous path in the x-y plane constrained by physical laws and operational requirements. From the model of trajectories, we extract, and formally prove, high-level properties that can serve as a framework to analyze conflict scenarios. We use the AILS (Airborne Information for Lateral Spacing) alerting algorithm as a case study of our approach. Published online: 19 November 2002     

一种基于Hadoop的云运维监控模型设计与实现

随着企业信息化在生产实时监测、海量存储和科学分析决策等方面的需求不断提升,运维监控系统已逐渐成为主要的管理手段。采用最新的云计算技术,设计及搭建一个数据规模易扩展、处理速度快、安全性高、成本低的云运维监控系统;针对运维控制系统中海量监控历史数据实时提取响应速度慢的缺点,设计并实现一种基于Hadoop的分布式海量数据处理模型。仿真实验证明,Hadoop在对云监控系统中的海量数据提取效率优于传统方法,随着数据量的快速增长,优势越明显。     

The metacognitive loop I: Enhancing reinforcement learning with metacognitive monitoring and control for improved perturbation tolerance

Maintaining adequate performance in dynamic and uncertain settings has been a perennial stumbling block for intelligent systems. Nevertheless, any system intended for real-world deployment must be able to accommodate unexpected change—that is, it must be perturbation tolerant. We have found that metacognitive monitoring and control—the ability of a system to self-monitor its own decision-making processes and ongoing performance, and to make targeted changes to its beliefs and action-determining components—can play an important role in helping intelligent systems cope with the perturbations that are the inevitable result of real-world deployment. In this article we present the results of several experiments demonstrating the efficacy of metacognition in improving the perturbation tolerance of reinforcement learners, and discuss a general theory of metacognitive monitoring and control, in a form we call the metacognitive loop.     

集中式集群资源调度框架的可扩展性优化

集中式集群资源管理系统既能够确保全局资源状态的一致性亦拥有多种调度模型,因此被广泛应用于实际系统中.但是,当集中式资源管理器在接收并处理大规模的周期性心跳信息时,由于其采用单一节点来维护全局资源状态,所以资源管理器的负载压力急剧增加,导致调度能力降低,影响了集群系统的可扩展性.针对上述问题,提出一种"没有变化就不更新"...     

Authentication and access control in RFID based logistics-customs clearance service platform

The content security requirements of a radio frequency identification (RFID) based logistics-customs clearance service platform (LCCSP) are analysed in this paper. Then, both the unified identity authentication and the access control modules are designed according to those analyses. Finally, the unified identity authentication and the access control on the business level are implemented separately. In the unified identity authentication module, based on an improved Kerberos-based authentication approach, a new control transfer method is proposed to solve the sharing problem of tickets among different servers of different departments. In the access control module, the functions of access controls are divided into different granularities to make the access control management more flexible. Moreover, the access control module has significant reference value for user management in similar systems.     

浅析计算机信息处理环境下的内部会计控制

计算机及其相关技术已经我国的生活、生产领域中获得了非常广泛地应用。在此大背景之下，会计电算化的发展速度非常快，但是在电算系统的广泛应中用也产生了一些问题。这些问题的解决有赖于健全、高效的内部会计控制制度。在本文中，笔者结合工作经验，对算机信息处理环境下的内部会计控制制度进行了比较深入的分析和探讨。     

Dynamic QoS Adaptation for Mobile Middleware

Computation and networking resources in mobile operating environments are much scarcer and more dynamic than in desktop operating environments. Mobile applications can leverage on the benefits of adaptive computing to optimize the QoS delivery based on contextual situations. Fuzzy control models have been successfully applied to various distributed network QoS management systems. However, existing models are either application-specific or limited to abstract modeling and simple conceptual scenarios which do not take into account overall model scalability. Specifically, the large number of QoS parameters in a mobile operating environment causes an exponential increase in the number of rules correspondingly increases the demand for processing power to infer the rules. Hierarchical fuzzy systems were introduced to reduce the number of rules using hierarchical fuzzy control, in which correlated linguistic variables are hierarchically inferred and grouped into abstract linguistic variables. In this paper, we propose a mobile QoS management framework that uses a hierarchical fuzzy control model to support a highly extensible and structured adaptation paradigm. The proposed framework integrates several levels of QoS abstractions derived from user-perceived requirements.     

Preface

Networked sensing and control has attracted significant interest in recent years due to its wide applications. For example, sensor networks, especially wireless sensor networks, have found important applications in environmental monitoring, agriculture, building and industrial automation, machine condition monitoring, intelligent transportation systems, health care, surveillance, and defense. On the other hand, due to the flexibility and significant cost-saving, there has been an increasing trend for control systems to utilize digital communication networks for exchanging information between sensor and controller and/or controller and actuator, as well as between subsystems or systems sharing the same communication networks. Furthermore, networked multiagent systems, where the collective behavior of a system is realized through interactions among dynamically decoupled systems, have also been the focus of many recent research efforts. In networked systems, the network is an important component with constraints and uncertainties, such as the limited bandwidth, random transmission-delays, possible packet-losses, out-of-sequence measurements, etc. The uncertainties and constraints become more significant in wireless networks because of its limited resources of communication and computation, and the fading that changes the throughput of communication channels. As a result, there is an imperative need of new tools for analysis and synthesis, and new algorithms for control, estimation and decision-making, to take these uncertainties and constraints into consideration and handle the interplay among communication, computation and control. In the past few years, a lot of research efforts have been devoted to this field and significant advancement has been made. This special issue intends to reflect part of those achievements. Indeed, the special issue consists of 17 papers among which 8 discuss the control over communication networks, 1 investigates the traffic control in communication networks, 3 are concerned with multiagent systems, and 5 deal with wireless sensor networks. All papers were selected from numerous submissions and carefully reviewed.We hope that the special issue will promote the research interest in networked sensing, actuation and control systems, and foster new applications of networked systems. Special issue editors: Lihua Xie, Nanyang Technological University, Singapore. Frank L. Lewis, The University of Texas at Arlington, USA.     

MODELING CONCEPTS FOR DATA REDUCTION IN CONTROL OF MANUFACTURING SYSTEMS

Manufacturing systems consist of machines and robots together with a cell controller that coordinates and supervises the flow of parts through the cell. To reduce the amount of data exchanged between the controller and the processing components, a concept based on the theory of hierarchical control is developed. It uses on-line simulation to gain the expected behavior of the working components. Therefore for every robot and machine a corresponding model inside the cell controller exists that runs synchronized with the robot or machine. In addition, these internal models supervise their external counterparts to realize the task of monitoring. Because the internal models run synchronized with the external components, their state can be used by the higher levels of the cell controller to guarantee proper flow of parts through the cell.     

PASIM: A spreadsheet-based decision support system for performance analysis of production workers

Electronic spreadsheets offer low cost approach to information management and implementation of decision models for online decision support systems. In this paper, we have combined quantitative models, database management, and simulation to develop a decision support system for real-time performance monitoring of production workers.     

Improving the performance of passive network monitoring applications with memory locality enhancements

Passive network monitoring is the basis for a multitude of systems that support the robust, efficient, and secure operation of modern computer networks. Emerging network monitoring applications are more demanding in terms of memory and CPU resources due to the increasingly complex analysis operations that are performed on the inspected traffic. At the same time, as the traffic throughput in modern network links increases, the CPU time that can be devoted for processing each network packet decreases. This leads to a growing demand for more efficient passive network monitoring systems in which runtime performance becomes a critical issue.In this paper we present locality buffering, a novel approach for improving the runtime performance of a large class of CPU and memory intensive passive monitoring applications, such as intrusion detection systems, traffic characterization applications, and NetFlow export probes. Using locality buffering, captured packets are being reordered by clustering packets with the same port number before they are delivered to the monitoring application. This results in improved code and data locality, and consequently, in an overall increase in the packet processing throughput and decrease in the packet loss rate. We have implemented locality buffering within the widely used libpcap packet capturing library, which allows existing monitoring applications to transparently benefit from the reordered packet stream without modifications. Our experimental evaluation shows that locality buffering improves significantly the performance of popular applications, such as the Snort IDS, which exhibits a 21% increase in the packet processing throughput and is able to handle 67% higher traffic rates without dropping any packets.     

Mining Wi-Fi Channel State Information for breathing and heart rate classification

Current vital signs monitoring systems require that the subject wears sensing devices. An alternative approach is using device-free technologies such as the Channel State Information (CSI) of a Wi-Fi signal. However, recent works using CSI for vital signs monitoring rely on complex signal processing techniques to improve its reliability. Considering that breathing and heart rate provide relevant information about the current health status of a subject, in this paper we develop an experimental system that combines signal processing techniques, such as filters and time and frequency domain analysis, with Data Mining techniques for breathing and heart rate monitoring. We also provide a thorough analysis for understanding CSI data as a technology for vital signs monitoring. Using K-Nearest Neighbors, Support Vector Machines, and Quadratic Discriminant Classifier models, our system achieves an accuracy of 99.18% for breathing rate classification while identifying heart rate monitoring challenges that are also stated in this paper.     

智能化网络考试监控系统开发

近年来随着高等教育事业的快速发展,为了节约教育成本、提高教学质量和教育的信息化程度,网络化、无纸化考试在高校得到了快速推广。但由于计算机网络是一个开放的系统,考试中电子文档资料可以十分隐秘地被复制和在学生间传递,造成很多严重的作弊行为。如何防范当前网络考试中考生的作弊行为及保障考试结果的公平性成为了急需解决的重要问题。本论文所开发的智能化网络考试监控系统克服了现有考试监控系统的不足,将各种网络考试实时监控信息机地结合在一起,实现了作弊行为检测、作弊行为发现及作弊行为报警三位一体的立体化管理。同时,所开发的网络考试监控系统独立于现有各网络考试系统,在不影响现有考试系统正常工作的情况下,实现了考试作弊行为的有效监控,使其具有良好的可推广性及可维护性。