THE LOGIC OF AUTOMATA

Automata are the prime example of general systems over discrete spaces, and yet the theory of automata is fragmentary and it is not clear what makes a general structure an automaton. This paper investigates the logical foundations of automata relating it to the semantics of our notions of uncertainty, state and state-determined. A single framework is established for the conventional spectrum of automata: deterministic, probabilistic, fuzzy, and non-deterministic, which shows this set to be, in some sense, complete. Counter-examples are then developed to show that this spectrum alone is inadequate to describe the behaviour of certain forms of uncertain system. Finally a general formulation is developed based on the fundamental semantics of our notion of a state that shows that the logical structure of an automaton must be at least a positive ordered semiring. The role of probability logic, its relationship to fuzzy logic, the roles of topological models of automata, and the symmetry between inputs and outputs in hyperstate/hyperinput-determined systems are also discussed.     

基于量子逻辑的确定型正则文法

基于量子逻辑的文法理论是量子计算模型的一个重要研究方向.给出了基于量子逻辑的确定型正则文法的概念,证明了基于量子逻辑的确定型正则文法与基于量子逻辑的确定型自动机的等价性.利用此等价性,给出了量子确定正则语言的代数刻画和层次刻画,并得到量子确定正则语言关于正则运算的封闭性.     

多核技术在动态模式识别算法中的应用

利用LabVIEW多核编程环境,采用曙光16核服务器作为硬件平台,实现了动态模式识别下的多模式快速识别。同时,以压气机Mansoux模型为应用背景,设计压气机旋转失速识别快速检测系统,该系统解决了基于确定学习理论的多模式识别算法中涉及的大量计算问题,实验结果和性能分析进一步验证了该检测系统的快速性和可行性。     

中心引力算法收敛分析及在神经网络中的应用

中心引力优化算法(central force optimization,简称CFO)是一种新型的基于天体动力学的多维搜索优化算法.该算法是一种确定性的优化算法,利用一组质子在万有引力作用下的运动,搜索目标函数在决策空间上的最优值.利用天体力学理论对该算法中质子运动方程进行了深入的研究,并利用天体力学中万有引力定理对质子运动方程进行了推导,建立起天体力学与CFO 算法之间的联系,通过天体力学中数学分析的方法对该算法中质子收敛性能进行了分析,最后,通过严格的数学推导证明出:无论初始时质子是何种分布,CFO 算法中所有的质子始终都会收敛于CFO 空间的确定最优解.作为测试效果,将CFO 算法与常见的BP 训练算法相结合,提出了CFO-BP 训练算法,优化前馈型人工神经网络的权值和结构.实验结果表明,采用CFO-BP 算法优化神经网络比其他常见优化算法有更好的收敛精度和收敛速度.     

A stochastic control model for optimal timing of climate policies

A stochastic control model is proposed as a paradigm for the design of optimal timing of greenhouse gas (GHG) emission abatement. The resolution of uncertainty concerning climate sensitivity and the technological breakthrough providing access to a carbon-free production economy are modeled as controlled stochastic jump processes. The optimal policy is characterized using the dynamic programming solution to a piecewise deterministic optimal control problem. A numerical illustration is developed with a set of parameters calibrated on recently proposed models for integrated assessment of climate policies. The results are interpreted and the insights they provide on the timing issue of climate policy are discussed.     

Optimizing a data acquisition system for the nonstationary information energy state by deterministic and fuzzy methods

Two problems are formulated and resolved in optimizing nonstationary fuzzy and deterministic systems. The identity between the optimal relationships enables one to assign these system types to a single group. __________ Translated from Izmeritel’naya Tekhnika, No. 7, pp. 8–10, July, 2008.     

Choice coordination with limited failure

The Choice Coordination Problem requiresn asynchronous processes to reach a common choice of one out ofk possible alternatives. Processes communicate viak shared variables. Up tot, t, of the processes may fail to operate by suddenly quitting the protocol. Rabin (1982) presented lower and upper bounds for the extreme caset=n–1. We present deterministic and randomized algorithms for arbitraryt using an alphabet of sizeO(t ²). A semi-synchronous model is also studied. A reduction to a consensus problem proves the necessity to assume some powerful atomic shared-memory operations.     

Topology-assisted deterministic packet marking for IP traceback

A novel deterministic packet marking (DPM) for IP traceback against denial of service (DoS) and distributed denial of service (DDoS) attacks is presented, which features good scalability and high accuracy. In this scheme, an ingress router pre-calculates a Hash of its IP address and splits the Hash into several fragments. When marking a packet, the router randomly selects a fragment to mark into the packet. In the traceback stage the victim identifies the marked router with the help of the map of its upstream routers. Based on the map, the victim can identify a candidate ingress router after receiving only several marked packets. The scheme overcomes defects in previous deterministic packet marking schemes, where too much packets are required to recover a router and high false positive rate occurs in case of large-scale DDoS. Theoretical analysis, the pseudo code and experimental results are provided. The scheme is proved to be accurate and efficient and can handle large-scale DDoS attacks.     

Optimal dual-model controller of solid oxide fuel cell output voltage using imitation distributed deep reinforcement learning

Solid oxide fuel cell (SOFC) is disadvantaged by significant nonlinearity, which makes it difficult to control output voltage of SOFC and satisfy the constraints of fuel utilization simultaneously. In order to solve this problem, a dual-model control framework (DMCF) is proposed. In particular, there are two controllers deployed under this framework, with an PID controller and a supplementary dynamic controller to track the SOFC output voltage. The supplementary dynamic controller is conducive to the stabilization of tracking by adapting to the uncertainties, considering the constraint on fuel utilization. In addition, an imitation distributed deep deterministic policy gradient (ID3PG) algorithm, which integrates imitation learning and distributed deep reinforcement learning to enhance the robustness and adaptive capacity of this framework, is proposed for the supplementary dynamic controller. The simulation results obtained in this work have demonstrated that the proposed framework is effective in imposing control on SOFC output voltage and preventing constraint violations of fuel utilization.     

Novel hybrid Monte Carlo/deterministic technique for shutdown dose rate analyses of fusion energy systems

The rigorous 2-step (R2S) computational system uses three-dimensional Monte Carlo transport simulations to calculate the shutdown dose rate (SDDR) in fusion reactors. Accurate full-scale R2S calculations are impractical in fusion reactors because they require calculating space- and energy-dependent neutron fluxes everywhere inside the reactor. The use of global Monte Carlo variance reduction techniques was suggested for accelerating the R2S neutron transport calculation. However, the prohibitive computational costs of these approaches, which increase with the problem size and amount of shielding materials, inhibit their ability to accurately predict the SDDR in fusion energy systems using full-scale modeling of an entire fusion plant. This paper describes a novel hybrid Monte Carlo/deterministic methodology that uses the Consistent Adjoint Driven Importance Sampling (CADIS) method but focuses on multi-step shielding calculations. The Multi-Step CADIS (MS-CADIS) methodology speeds up the R2S neutron Monte Carlo calculation using an importance function that represents the neutron importance to the final SDDR. Using a simplified example, preliminary results showed that the use of MS-CADIS enhanced the efficiency of the neutron Monte Carlo simulation of an SDDR calculation by a factor of 550 compared to standard global variance reduction techniques, and that the efficiency enhancement compared to analog Monte Carlo is higher than a factor of 10,000.