平盖开孔的有限元分析及其补强方法的探讨

为了使平盖开孔补强结构更为合理、可靠,使用圆形平板的理论解和有限元分析方法,对不同开孔直径的27个平盖模型进行了应力计算和分析比较,进一步探讨了不同的开孔补强方法。结果表明,对于开孔率小于0.5的平盖开孔补强采用均匀增加平盖厚度的方法,不能解决孔边产生的应力集中问题,难以达到补强的效果;而采用外加补强元件的方法,则应明确其补强范围应在2d范围内。     

Modeling of time to corrosion-induced cover cracking in reinforced concrete structures

Service life of the concrete structures depends on the protective action provided by the cover concrete against the susceptibility of the reinforcement to the corrosive environment. Depending on the level of the oxidation of metallic iron, corrosion products may have much greater volume than the original iron that gets consumed by the process of corrosion. This volume expansion is mainly responsible for exerting the expansive radial pressure at the steel-concrete interface and development of hoop tensile stresses in the surrounding concrete resulting ultimately in the through cracking of the cover concrete. This cover cracking would indicate the loss of the service life for the corrosion-affected structures. In the present paper, an attempt has been made to develop analytical models for predicting the time to cover cracking by considering the residual strength of the cracked concrete and the stiffness provided by the combination of the reinforcement and expansive corrosion products. The problem is modeled as a boundary value problem wherein the governing equations are expressed in terms of the radial displacement and the analytical solutions are presented considering a simple 2-zone model for the cover concrete viz. cracked or uncracked. The analytical models are then evaluated through their ability to reproduce available experimental trends and subsequently a sensitivity analysis has also been carried out to show the influence of the various variable parameters of the proposed models with reference to the experimental trends.     

聚丙烯工程化研究

采用纳米填充、交联和转晶的复合技术对聚丙烯进行了改性，优选了纳米碳酸钙为填料。当β成核剂用量为0．5％、助交联剂为3％时，复合材料的综合性能达到最好，接近普通工程塑料的各项力学性能。红外光谱分析和熔体质量流动速率的测定表明，复合材料体系产生了微交联；DSC分析发现，PP材料的聚集态结构中含有β球晶。     

电脑安全加固技术研究

从防止黑客攻击角度对电脑安全加固技术进行探讨。     

Rollout sampling approximate policy iteration

Several researchers have recently investigated the connection between reinforcement learning and classification. We are motivated by proposals of approximate policy iteration schemes without value functions, which focus on policy representation using classifiers and address policy learning as a supervised learning problem. This paper proposes variants of an improved policy iteration scheme which addresses the core sampling problem in evaluating a policy through simulation as a multi-armed bandit machine. The resulting algorithm offers comparable performance to the previous algorithm achieved, however, with significantly less computational effort. An order of magnitude improvement is demonstrated experimentally in two standard reinforcement learning domains: inverted pendulum and mountain-car.     

Supervised and Reinforcement Evolutionary Learning for Wavelet-based Neuro-fuzzy Networks

This study presents a wavelet-based neuro-fuzzy network (WNFN). The proposed WNFN model combines the traditional Takagi–Sugeno–Kang (TSK) fuzzy model and the wavelet neural networks (WNN). This study adopts the non-orthogonal and compactly supported functions as wavelet neural network bases. A novel supervised evolutionary learning, called WNFN-S, is proposed to tune the adjustable parameters of the WNFN model. The proposed WNFN-S learning scheme is based on dynamic symbiotic evolution (DSE). The proposed DSE uses the sequential-search-based dynamic evolutionary (SSDE) method. In some real-world applications, exact training data may be expensive or even impossible to obtain. To solve this problem, the reinforcement evolutionary learning, called WNFN-R, is proposed. Computer simulations have been conducted to illustrate the performance and applicability of the proposed WNFN-S and WNFN-R learning algorithms.     

双马尔可夫决策过程联合模型

人类在处理问题中往往分为两个层次,首先在整体上把握问题,即提出大体方案,然后再具体实施.也就是说人类就是具有多分辨率智能系统的极好例子,他能够在多个层次上从底向上泛化(即看问题角度粒度变"粗",它类似于抽象),并且又能从顶向下进行实例化(即看问题角度变"细",它类似于具体化).由此构造了由在双层(理想空间即泛化和实际空间即实例化)上各自运行的马尔可夫决策过程组成的半马尔可夫决策过程,称之为双马尔可夫决策过程联合模型.然后讨论该联合模型的最优策略算法,最后给出一个实例说明双马尔可夫决策联合模型能够经济地节约"思想",是运算有效性和可行性的一个很好的折中.     

Stochastic learning and optimization—A sensitivity-based approach

We introduce a sensitivity-based view to the area of learning and optimization of stochastic dynamic systems. We show that this sensitivity-based view provides a unified framework for many different disciplines in this area, including perturbation analysis, Markov decision processes, reinforcement learning, identification and adaptive control, and singular stochastic control; and that this unified framework applies to both the discrete event dynamic systems and continuous-time continuous-state systems. Many results in these disciplines can be simply derived and intuitively explained by using two performance sensitivity formulas. In addition, we show that this sensitivity-based view leads to new results and opens up new directions for future research. For example, the n th bias optimality of Markov processes has been established and the event-based optimization may be developed; this approach has computational and other advantages over the state-based approaches.     

Parallel hyper heuristic algorithm based on reinforcement learning for the corridor allocation problem and parallel row ordering problem

Hyper heuristics is a relatively new optimisation algorithm. Numerous studies have reported that hyper heuristics are well applied in combinatorial optimisation problems. As a classic combinatorial optimisation problem, the row layout problem has not been publicly reported on applying hyper heuristics to its various sub-problems. To fill this gap, this study proposes a parallel hyper-heuristic approach based on reinforcement learning for corridor allocation problems and parallel row ordering problems. For the proposed algorithm, an outer layer parallel computing framework was constructed based on the encoding of the problem. The simulated annealing, tabu search, and variable neighbourhood algorithms were used in the algorithm as low-level heuristic operations, and Q-learning in reinforcement learning was used as a high-level strategy. A state space containing sequences and fitness values was designed. The algorithm performance was then evaluated for benchmark instances of the corridor allocation problem (37 groups) and parallel row ordering problem (80 groups). The results showed that, in most cases, the proposed algorithm provided a better solution than the best-known solutions in the literature. Finally, the meta-heuristic algorithm applied to three low-level heuristic operations is taken as three independent algorithms and compared with the proposed hyper-heuristic algorithm on four groups of parallel row ordering problem instances. The effectiveness of Q-learning in selection is illustrated by analysing the comparison results of the four algorithms and the number of calls of the three low-level heuristic operations in the proposed method.     

Linear Least-Squares Algorithms for Temporal Difference Learning

We introduce two new temporal difference (TD) algorithms based on the theory of linear least-squares function approximation. We define an algorithm we call Least-Squares TD (LS TD) for which we prove probability-one convergence when it is used with a function approximator linear in the adjustable parameters. We then define a recursive version of this algorithm, Recursive Least-Square TD (RLS TD). Although these new TD algorithms require more computation per time-step than do Suttons TD() algorithms, they are more efficient in a statistical sense because they extract more information from training experiences. We describe a simulation experiment showing the substantial improvement in learning rate achieved by RLS TD in an example Markov prediction problem. To quantify this improvement, we introduce the TD error variance of a Markov chain, TD, and experimentally conclude that the convergence rate of a TD algorithm depends linearly on TD. In addition to converging more rapidly, LS TD and RLS TD do not have control parameters, such as a learning rate parameter, thus eliminating the possibility of achieving poor performance by an unlucky choice of parameters.