Curing behavior and mechanical behavior of fully and semi-interpenetrating polymer networks based on polyurethane and acrylics

Polyurethane (PU) was made by reacting stoichiometric equivalent of trimethylol propane (TMP) and desmodur L. Fully interpenetrating polymer networks (fully IPN's) of various compositions based on PU and poly(ethylene glycol) diacrylate (PEGDA) were prepared by blending various ratios of PU/PEGDA, and cured by benzoyl peroxide (BPO). Semi-interpenetrating polymer networks based on PU and poly(ethylene glycol) monomethyl ether of acrylate (PEGMEA) were prepared in a similar way. Shift of exothermic peaks during IPN formation were examined with dynamic DSC. Viscosity increases were investigated with a Brookfield RVT type viscometer. Dynamic mechanical properties were probed via a rheometric dynamic spectroscopy (RDS).Expermintal results revealed a good compatibility of both IPN systems, as evidenced from the single damping peak of the RDS curves for each composition. Shifts of exothermic peaks to higher temperatures during the formation of fully IPN were observed, especially for the composition of PU/PEGDA = 50/50, which showed an exothermic peak at the highest temperature. Experimental results also revealed delayed viscosity increases and decreased gel fractions for all fully IPN's. On the contrary, the semi-IPN did not exhibibt similar phenomena. All these findings supported an effect of network interlock during fully IPN formation. The existence of a network not only provided a sterically hindered environment, but also restrained the chain mobility of the growing network, and vice versa, thus retarding the curing rates of both networks. Network interlock also broadened the width of the half damping peak, T_1/2, and subsequently led to improved mechanical properties such as the impact resistance and Young's modulus of fully IPN material.     

Exploiting temporal information in Web search

Time plays important roles in Web search, because most Web pages contain temporal information and a lot of Web queries are time-related. How to integrate temporal information in Web search engines has been a research focus in recent years. However, traditional search engines have little support in processing temporal-textual Web queries. Aiming at solving this problem, in this paper, we concentrate on the extraction of the focused time for Web pages, which refers to the most appropriate time associated with Web pages, and then we used focused time to improve the search efficiency for time-sensitive queries. In particular, three critical issues are deeply studied in this paper. The first issue is to extract implicit temporal expressions from Web pages. The second one is to determine the focused time among all the extracted temporal information, and the last issue is to integrate focused time into a search engine. For the first issue, we propose a new dynamic approach to resolve the implicit temporal expressions in Web pages. For the second issue, we present a score model to determine the focused time for Web pages. Our score model takes into account both the frequency of temporal information in Web pages and the containment relationship among temporal information. For the third issue, we combine the textual similarity and the temporal similarity between queries and documents in the ranking process. To evaluate the effectiveness and efficiency of the proposed approaches, we build a prototype system called Time-Aware Search Engine (TASE). TASE is able to extract both the explicit and implicit temporal expressions for Web pages, and calculate the relevant score between Web pages and each temporal expression, and re-rank search results based on the temporal-textual relevance between Web pages and queries. Finally, we conduct experiments on real data sets. The results show that our approach has high accuracy in resolving implicit temporal expressions and extracting focused time, and has better ranking effectiveness for time-sensitive Web queries than its competitor algorithms.     

An architectural design for effective information retrieval in semantic web

The current web IR system retrieves relevant information only based on the keywords which is inadequate for that vast amount of data. It provides limited capabilities to capture the concepts of the user needs and the relation between the keywords. These limitations lead to the idea of the user conceptual search which includes concepts and meanings. This study deals with the Semantic Based Information Retrieval System for a semantic web search and presented with an improved algorithm to retrieve the information in a more efficient way.This architecture takes as input a list of plain keywords provided by the user and the query is converted into semantic query. This conversion is carried out with the help of the domain concepts of the pre-existing domain ontologies and a third party thesaurus and discover semantic relationship between them in runtime. The relevant information for the semantic query is retrieved and ranked according to the relevancy with the help of an improved algorithm. The performance analysis shows that the proposed system can improve the accuracy and effectiveness for retrieving relevant web documents compared to the existing systems.     

Reactive Search strategies using Reinforcement Learning,local search algorithms and Variable Neighborhood Search

Optimization techniques known as metaheuristics have been applied successfully to solve different problems, in which their development is characterized by the appropriate selection of parameters (values) for its execution. Where the adjustment of a parameter is required, this parameter will be tested until viable results are obtained. Normally, such adjustments are made by the developer deploying the metaheuristic. The quality of the results of a test instance [The term instance is used to refer to the assignment of values to the input variables of a problem.] will not be transferred to the instances that were not tested yet and its feedback may require a slow process of “trial and error” where the algorithm has to be adjusted for a specific application. Within this context of metaheuristics the Reactive Search emerged defending the integration of machine learning within heuristic searches for solving complex optimization problems. Based in the integration that the Reactive Search proposes between machine learning and metaheuristics, emerged the idea of putting Reinforcement Learning, more specifically the Q-learning algorithm with a reactive behavior, to select which local search is the most appropriate in a given time of a search, to succeed another local search that can not improve the current solution in the VNS metaheuristic. In this work we propose a reactive implementation using Reinforcement Learning for the self-tuning of the implemented algorithm, applied to the Symmetric Travelling Salesman Problem.     

Interactive fuzzy stochastic multi-level 0–1 programming using tabu search and probability maximization

In this paper, we consider interactive fuzzy programming for multi-level 0–1 programming problems involving random variable coefficients both in objective functions and constraints. Following the probability maximization model together with the concept of chance constraints, the formulated stochastic multi-level 0–1 programming problems are transformed into deterministic ones. Taking into account vagueness of judgments of the decision makers, we present interactive fuzzy programming. In the proposed interactive method, after determining the fuzzy goals of the decision makers at all levels, a satisfactory solution is derived efficiently by updating satisfactory levels of the decision makers with considerations of overall satisfactory balance among all levels. For solving the transformed deterministic problems efficiently, we also introduce novel tabu search for general 0–1 programming problems. A numerical example for a three-level 0–1 programming problem is provided to illustrate the proposed method.     

Context matters: The structure of task goals affects accuracy in multiple-target visual search

Career visual searchers such as radiologists and airport security screeners strive to conduct accurate visual searches, but despite extensive training, errors still occur. A key difference between searches in radiology and airport security is the structure of the search task: Radiologists typically scan a certain number of medical images (fixed objective), and airport security screeners typically search X-rays for a specified time period (fixed duration). Might these structural differences affect accuracy? We compared performance on a search task administered either under constraints that approximated radiology or airport security. Some displays contained more than one target because the presence of multiple targets is an established source of errors for career searchers, and accuracy for additional targets tends to be especially sensitive to contextual conditions. Results indicate that participants searching within the fixed objective framework produced more multiple-target search errors; thus, adopting a fixed duration framework could improve accuracy for career searchers.     

Tabu-based GIS for solving the vehicle routing problem

Besides being a hard combinatorial problem, the VRP is also a spatial problem. Hence, effective decision making in this field strongly requires the integration of GIS and optimization systems (GIS-O). This article integrates GIS and optimization tools for solving the vehicle routing problem with loading and distance requirements (DCVRP). A general outline of the multi-step integration is pointed out showing the interaction of the GIS and the spatial optimization according to the loose coupling strategy. The computational performance of the TS-VRP algorithm for the DCVRP turned out to be quite efficient on both computation time and solution quality. The Tunisian case study well illustrates the incentive behind using such a spatial decision support system that allows the management of the problem from the data acquisition to the visualization of possible simulation scenarios in a more realistic way.     

An improved global-best harmony search algorithm for faster optimization

In this paper, an improved global-best harmony search algorithm, named IGHS, is proposed. In the IGHS algorithm, initialization based on opposition-based learning for improving the solution quality of the initial harmony memory, a new improvisation scheme based on differential evolution for enhancing the local search ability, a modified random consideration based on artificial bee colony algorithm for reducing randomness of the global-best harmony search (GHS) algorithm, as well as two perturbation schemes for avoiding premature convergence, are integrated. In addition, two parameters of IGHS, harmony memory consideration rate and pitch adjusting rate, are dynamically updated based on a composite function composed of a linear time-varying function, a periodic function and a sign function in view of approximate periodicity of evolution in nature. Experimental results tested on twenty-eight benchmark functions indicate that IGHS is far better than basic harmony search (HS) algorithm and GHS. In further study, IGHS has also been compared with other eight well known metaheuristics. The results show that IGHS is better than or at least similar to those approaches on most of test functions.     

求解工程结构优化问题的改进布谷鸟搜索算法

针对布谷鸟搜索算法局部搜索能力不强的缺点, 提出一种基于随机局部搜索的改进布谷鸟搜索算法用于求解工程结构优化问题。引入惯性权重以平衡算法的勘探和开采能力; 利用随机局部搜索方法对当前最优解进行局部搜索, 以加快算法的收敛速度。两个工程结构优化问题的实验结果表明了该算法的可行性和有效性。     

面向旋翼无人机的高压输电线在线检测方法

面向旋翼无人机电力巡线的高压输电线在线检测对算法实时性、鲁棒性要求高的问题,提出了一种基于边界搜索和Radon变换(BSRT)的高压输电线识别算法.分析了高压输电线在图像中的边界贯穿特征,提出了以图像四条边界作为起始点的搜索策略,给出了边界约束下的直线特征Radon变换能量函数和求解方法.复杂度分析结果表明本算法与经典的Radon算法相比,复杂度降低了一个数量级.以人工合成图像和无人机实际航拍图像,对本算法、Radon算法和LSD算法在实时性和有效性方面进行了实验对比分析,实验结果表明,本算法的处理速度较Radon算法有很大的提高,与LSD算法的处理速度基本处于同一量级,但本算法的高压输电线检测精度大幅优于传统的Radon和LSD算法.理论分析及实验结果证明,提出的BSRT方法有效地解决了经典Radon算法的高复杂度和LSD算法的复杂背景高敏感性的问题,具有较好的应用价值.