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.     

《新编鲁般营造正式》中的门尺法研究

《新编鲁般营造正式》中详细记载了门尺法的原理和应用,分析其应用规则,以此为线索解读民间传统匠人的营造技术和营造思想。     

WF油田强底水断块油藏开发规律研究及认识

为改善底水油藏开发效果,结合WF油田强底水断块油藏历年开发实践的基础上,分别对不同开发阶段的影响因素进行了详细分析,总结了强底水断块油藏的开发规律。通过计算临界产量和统计生产初期打开程度与含水的关系,早期应充分利用天然隔板,合理打开程度在30%左右;中期含水20%~80%时,合理控制生产压差;后期提掖稳产,晚期上返挖潜,确保了底水油藏的有效开发。该方法从不同生产阶段来分别制定对策,能充分挖掘油藏的生产潜力,来提高开发效果,其做法及经验可供同类型油藏开发参考借鉴。     

Intelligent building energy management system using rule sets

The current study presents an intelligent decision support model using rule sets based on a typical building energy management system. In addition, the model's impact on the energy consumption and indoor quality of a typical office building in Greece is presented. The model can control how the building operational data deviates from the settings as well as carry out diagnosis of internal conditions and optimize building's energy operation. In this context, the integrated “decision support model” can contribute to the management of the daily energy operations of a typical building, related to the energy consumption, by incorporating the following requirements in the best possible way: (a) the guarantee of the desirable levels of living quality in all building's rooms and (b) the necessity for energy savings.     

An interval-valued exceedance method in MCDM with uncertain satisfactions

Multicriteria decision-making problems have been applied to many applications for its practicality. Nevertheless, when the evaluated satisfactions are more complex, such as interval-valued distributions, how to reasonably obtain the aggregation results of alternatives is still an open issue. In this paper, an interval-valued exceedance method is proposed to solve such a question based on the Golden Rule representative value and probabilistic exceedance method. Due to good performance of expressing uncertain information, the Golden Rule representative value method is used to order interval-valued satisfactions after an effective normalization process. In addition, a quantifier-based ordered weighted averaging operator is also introduced to consider the preferences of decision makers. A realistic application of supplier selection is shown to illustrate the practicality of the proposed method.     

An effective hybrid learning system for telecommunication churn prediction

Customer churn has emerged as a critical issue for Customer Relationship Management and customer retention in the telecommunications industry, thus churn prediction is necessary and valuable to retain the customers and reduce the losses. Moreover, high predictive accuracy and good interpretability of the results are two key measures of a classification model. More studies have shown that single model-based classification methods may not be good enough to achieve a satisfactory result. To obtain more accurate predictive results, we present a novel hybrid model-based learning system, which integrates the supervised and unsupervised techniques for predicting customer behaviour. The system combines a modified k-means clustering algorithm and a classic rule inductive technique (FOIL).Three sets of experiments were carried out on telecom datasets. One set of the experiments is for verifying that the weighted k-means clustering can lead to a better data partitioning results; the second set of experiments is for evaluating the classification results, and comparing it to other well-known modelling techniques; the last set of experiment compares the proposed hybrid-model system with several other recently proposed hybrid classification approaches. We also performed a comparative study on a set of benchmarks obtained from the UCI repository. All the results show that the hybrid model-based learning system is very promising and outperform the existing models.     

Experimental and numerical assessment of mechanical properties of welded tubes for hydroforming

The identification of welded tubes properties considering the weld bead and Heat Affected Zone (HAZ) is important for reliable and accurate finite element simulation of tubular plastic forming processes such as tube hydroforming and rotary draw bending processes. Therefore, a simplified method is proposed to extract the weld bead and HAZ properties. Full size standard tensile specimens cut from the welded tube and comprising the weld parallel to the load direction are extended to failure. Mechanical properties obtained from uniaxial tensile test are correlated with the microhardness data measured across the welded specimen and by using the rule of mixtures; the constitutive model parameters of weld bead and HAZ regions are identified. Accuracy of the proposed method is assessed by comparing finite element simulation predictions to experimental measurements obtained from two mechanical tests: the first one is the uniaxial tensile test performed on specimens comprising the weld line perpendicular to the loading direction and the second test is the free bulge hydroforming test achieved on seamed tubular samples. This investigation has shown that the presented method is practical in use and sufficiently accurate to extract the weld metal properties of seamed tubes.     

Churn prediction using comprehensible support vector machine: An analytical CRM application

Support vector machine (SVM) is currently state-of-the-art for classification tasks due to its ability to model nonlinearities. However, the main drawback of SVM is that it generates “black box” model, i.e. it does not reveal the knowledge learnt during training in human comprehensible form. The process of converting such opaque models into a transparent model is often regarded as rule extraction. In this paper we proposed a hybrid approach for extracting rules from SVM for customer relationship management (CRM) purposes. The proposed hybrid approach consists of three phases. (i) During first phase; SVM-RFE (SVM-recursive feature elimination) is employed to reduce the feature set. (ii) Dataset with reduced features is then used in the second phase to obtain SVM model and support vectors are extracted. (iii) Rules are then generated using Naive Bayes Tree (NBTree) in the final phase. The dataset analyzed in this research study is about Churn prediction in bank credit card customer (Business Intelligence Cup 2004) and it is highly unbalanced with 93.24% loyal and 6.76% churned customers. Further we employed various standard balancing approaches to balance the data and extracted rules. It is observed from the empirical results that the proposed hybrid outperformed all other techniques tested. As the reduced feature dataset is used, it is also observed that the proposed approach extracts smaller length rules, thereby improving the comprehensibility of the system. The generated rules act as an early warning expert system to the bank management.     

Development of a maintenance effectiveness monitoring program for CANDU reactors

A procedural program to monitor the effectiveness of maintenance activities was developed for CANDU reactors and, to confirm its applicability, was tested on a CANDU power plant being operated by the Korea Hydro & Nuclear Power Co. The monitoring program is based on a methodology utilizing probabilistic risk information to meet US regulation 10CFR50.65, which is known as the Maintenance Rule. There are many cases in which the Maintenance Rule is applied to Light Water Reactor systems, including PWRs and BWRs. However, it has not been applied to a CANDU Reactor System thus far. In this paper, a procedure to set up a maintenance effectiveness monitoring program is presented with an emphasis on its application to the CANDU system. Relevant solutions to problems that were encountered are introduced to make the program more suitable for the characteristics of CANDU systems. In the end, an application of the program to an operating CANDU power plant is discussed to evaluate the performance status of the plant.