Learning fuzzy controllers in mobile robotics with embedded preprocessing

The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance.     

Error in the UNITY substitution rule for subscripted operators

UNITY, introduced by Chandy and Misra [ChM88], is a programming logic intended to reason about temporal properties of distributed programs. Despite the fact that UNITY does not have the full power of, for example, linear temporal logic, it enjoys popularity due to its simplicity.There was however a serious problem with the Substitution Rule. The logic is incomplete without the rule, and with the rule it is inconsistent.     

Pruning Algorithms for Rule Learning

Pre-pruning and Post-pruning are two standard techniques for handling noise in decision tree learning. Pre-pruning deals with noise during learning, while post-pruning addresses this problem after an overfitting theory has been learned. We first review several adaptations of pre- and post-pruning techniques for separate-and-conquer rule learning algorithms and discuss some fundamental problems. The primary goal of this paper is to show how to solve these problems with two new algorithms that combine and integrate pre- and post-pruning.     

Coliphage prevalence in high school septic effluent and associated ground water

At the present time, somatic and male-specific coliphage and human enterovirus groups are being considered as indicators of possible pathogenic human enteric virus contamination from fecal contamination. A primary attribute for any indicator of fecal contamination is its prevalence at the source and in associated ground water. It must be consistently found in the source material at concentrations that are measurable with available techniques. Over a period of ten months, male-specific and somatic coliphage ranged from 7000 to 4,000,000 PFU/L in the effluent from a multi-user septic-tank. Unlike the values determined for septic-tank effluent, coliphage concentrations measured in ground water over this same period only varied by five-fold. Coliphage concentration in ground water under the down-gradient edge of the drainfield contained 1000 PFU/L. This concentration decreased at −1 log₁₀/5 m during 17.4 m of ground-water transport. From these data, coliphage concentrations in septic-tank effluent seem sufficient to allow their use as indicators of fecal contamination in ground water.     

各种状态的液体通过核孔膜的规律

介绍了液体通过核孔膜规律方面取得的新认识，包括：（１）纯净液体通过核孔膜的规律；（２）核孔膜测定液体粘滞系数的各种方法；（３）各种物质溶液浓度的核孔膜测定；（４）液相混合物快速分离和化学分离；（５）流体中固体微粒对核孔膜的堵塞及其公式；（６）用核孔膜滤除液中各各斩规律。     

柳钢质量管理系统的构建及展望

介绍柳钢质量管理系统的结构和功能以及存在问题，并提出改进措施。     

Novel associative classifier based on dynamic adaptive PSO: Application to determining candidates for thoracic surgery

Association rule mining is a data mining technique for discovering useful and novel patterns or relationships from databases. These rules are simple to infer and intuitive and can be easily used for classification in any domain that requires explanation for and investigation into how the classification works. Examples of such areas are medicine, agriculture, education, etc. For such a system to find wide adoptability, it should give output that is correct and comprehensible. The amount of data has been growing very fast and so has the search space of these problems. So we need to change traditional methods. This paper discusses a rule mining classifier called DA-AC (dynamic adaptive-associative classifier) which is based on a Dynamic Particle Swarm Optimizer. Due to its seeding method, exemplar selection, adaptive parameters, dynamic reconstruction of regions and velocity update, it avoids premature convergence and provides a better value in every dimension. Quality evaluation is done both for individual rules as well as entire rulesets. Experiments were conducted over fifteen benchmark datasets to evaluate performance of proposed algorithm in comparison with six other state-of-the-art non associative classifiers and eight associative classifiers. Results demonstrate competitive performance of proposed DA-AC while considering predictive accuracy and number of mined patterns as parameters. The method was then applied to predict life expectancy of post operative thoracic surgery patients.     

Start-Up and Optimization of the Ozone Disinfection Process At the Sebago Lake Water Treatment Facility

The purpose of the ozone process at the Sebago Lake Water Treatment Facility (SLWTF) is to meet the disinfection requirements of the Surface Water Treatment Rule with ozone and without filtration. The start-up experience is presented including initial operating strategies to meet disinfection requirements. An ozone system performance test conducted during the first month of plant operation is described and the results are reviewed. The initial and proposed activities of an ozone optimization project are discussed.     

Correlation of vapor-liquid equilibria for binary mixtures with free energy-based equation of state mixing rules: Carbon dioxide with alcohols, hydrocarbons, and several other compounds

The correlation of vapor-liquid equilibrium data for high-pressure carbon dioxide systems is of interest in a number of industrial applications, including supercritical extraction. Here, we consider the correlation of data for 12 binary systems of carbon dioxide separately with alcohols, with hydrocarbons, and with acetone, benzene, and water. The Wong-Sandler (W-S) and modified Huron — Vidal first order (MHV1) free energy-based equation of state mixing rules (the W-S and MHV1 models) were used in the calculations. Both combined equation of state+free energy models generally resulted in good correlations of the experimental data over wide ranges of temperature and pressure with temperature — independent parameters. However, for the carbon dioxide+water system, the W-S model produced an 11% average absolute deviation in pressure, while no parameter that resulted in an AAD in pressure of less than 20% could be found for the MHV1 model.     

医疗健康数据的模糊粗糙集规则挖掘方法研究

医疗健康数据通常属性较多,且存在连续型、离散型并存的混合数据,这在很大程度上限制了知识发现方法对医疗健康数据的挖掘效率。以模糊粗糙集理论为基础,研究混合数据上的分类规则挖掘方法,通过引入规则获取算法的泛化阈值,来控制获取规则集的大小和复杂程度,提高粗糙集知识发现方法在医疗健康数据上的分类效率。最后通过对比实验验证了该算法在医疗决策表上挖掘规则的有效性。