Knowledge-based control and case-based diagnosis based upon empirical knowledge and fuzzy logic for the SBR plant.

Because biological wastewater treatment plants (WWTPs) involve a long time-delay and various disturbances, in general, skilled operators manually control the plant based on empirical knowledge. And operators usually diagnose the plant using similar cases experienced in the past. For the effective management of the plant, system automation has to be accomplished based upon operating recipes. This paper introduces automatic control and diagnosis based upon the operator's knowledge. Fuzzy logic was employed to design this knowledge-based controller because fuzzy logic can convert the linguistic information to rules. The controller can manage the influent and external carbon in considering the loading rate. The input of the controller is not the loading rate but the dissolved oxygen (DO) lag-time, which has a strong relation to the loading rate. This approach can replace an expensive sensor, which measures the loading rate and ammonia concentration in the reactor, with a cheaper DO sensor. The proposed controller can assure optimal operation and prevent the over-feeding problem. Case-based diagnosis was achieved by the analysis of profile patterns collected from the past. A new test profile was diagnosed by comparing it with template patterns containing normal and abnormal cases. The proposed control and diagnostic system will guarantee the effective and stable operation of WWTPs.     

A study on flowshop scheduling problem combining Taguchi experimental design and genetic algorithm

As genetic algorithm parameters vary depending on different problem types when applying genetic algorithm to reach global optimum, appropriate design value selection has significant impact on the efficiency of genetic algorithm. However, most users adjust parameters manually based on the reference values of previous literature. Such trial-and-error method is time-consuming, ineffective, and often it could not locate the optimal combination. Therefore, in flowshop scheduling problems, this research anticipates to complete optimal parameter combination design in genetic algorithm using Taguchi experimental design. According to the research results, different ways of producing initial solution have significant influence on this research topic. Consequently, confirmation experiment is conducted using the optimal parameter combination obtained from the research results. It is found that the predicted value of signal-to-noise ratio (S/N ratio) and its actual value exists deviation of 0.238%, indicating repetitiveness and robustness of the obtained parameter combination. Hence, this research method can effectively reduce time spent on parameter design using genetic algorithm and increase efficiency of algorithm.     

Design space exploration of reliable networked embedded systems

In this paper, a new methodology is presented for topology optimization of networked embedded systems as they occur in automotive and avionic systems as well as wireless sensor networks. By introducing a model which is (1) suitable for heterogeneous networks with different communication bandwidths, (2) modeling of routing restrictions, and (3) flexible binding of tasks onto processors, current design issues of networked embedded systems can be investigated. On the basis of this model, the presented methodology firstly allocates the required resources which can be communication links as well as computational nodes and secondly binds the functionality onto the nodes and the data dependencies onto the links such that no routing restrictions will be violated or capacities on communication links will be exceeded. Due to the often error-prone communication in networks, we allow for routing each data dependency over multiple routes in the networks. With this strategy, our methodology is able to increase the reliability of the entire system. This reliability analysis is based on Binary Decision Diagrams (BDDs) and is integrated in our multi-objective design space exploration. By applying Evolutionary Algorithms, we are able to consider multiple objectives simultaneously during the optimization process and allow for a subsequent unbiased decision making. An experimental evaluation as well as a demonstration of a case study from the field of automotive electronics will show the applicability of the presented approach.     

Tracking-error model-based predictive control for mobile robots in real time

In this paper, a model-predictive trajectory-tracking control applied to a mobile robot is presented. Linearized tracking-error dynamics is used to predict future system behavior and a control law is derived from a quadratic cost function penalizing the system tracking error and the control effort. Experimental results on a real mobile robot are presented and a comparison of the control obtained with that of a time-varying state-feedback controller is given. The proposed controller includes velocity and acceleration constraints to prevent the mobile robot from slipping and a Smith predictor is used to compensate for the vision-system dead-time. Some ideas for future work are also discussed.     

Geographical information systems applied in the field of renewable energy sources

This article presents a synthetic vision of geographical information systems (GIS) applications that are state of the art in the renewable energy field. The objective is to analyze the main qualities and problems of these applications, focusing on specific samples, and to carry out a methodological proposal in this genre.From this point of view, the study synthesizes the analyzed applications in three big groups: Decisions Support Systems (DSS) based on GIS; renewable energy and distributed generation of electricity; and decentralized generation for the rural electrification. In addition, a synthetic table and bibliographical references is provided for each group.Finally, several conclusions and a methodological outline are contributed for GIS application in the rural electrification with renewable energy.     

An objective comparison of the cost effectiveness of three testing methods

Branch testing is a well established method for exercising software. JJ-path testing, whilst employed by some practitioners, is less popular, and the testing of JJ-pairs finds few adherents. In this paper an objective, practical study of the cost-effectiveness of these three testing methods is reported. The effectiveness of each method is assessed, in the presence of infeasible paths, not only on its ability to cover the specific structural element of code that it targets, but also on its ability to cover the structural elements targeted by the other two methods – the collateral coverage it achieves. The assessment is based on the results derived from experiments in which each of the three methods is applied to 35 units of program code.     

Forming consensus in the networks of knowledge

Information granules, such as e.g., fuzzy sets, capture essential knowledge about data and the key dependencies between them. Quite commonly, we may envision that information granules (fuzzy sets) have become a result of fuzzy clustering and therefore could be succinctly represented in the form of some fuzzy partition matrices. Interestingly, the same data set could be represented from various standpoints and this multifaceted view yields a collection of different partition matrices being reflective of the higher-order granular knowledge about the data. The levels of specificity of the clusters the data are organized into could be quite different—the larger the number of clusters, the more detailed insight into the structure of data becomes available. Given the granularity of the resulting constructs (rather than plain data themselves), one could view a collection of partition matrices as a certain type of a network of knowledge. Considering a variety of sources of knowledge encountered across the network, we are interested in forming consensus between them. In a nutshell, this leads to the construction of certain fuzzy partition matrices which “reconcile” the knowledge captured by the individual partition matrices. Given that the granularity of the sources of knowledge under consideration could vary quite substantially, we develop a unified optimization perspective by introducing fuzzy proximity matrices that are induced by the corresponding partition matrices. In the sequel, the optimization is realized on a basis of these proximity matrices. We offer a detailed algorithm and illustrate its performance using a series of numeric experiments.     

Introducing task-based general computer self-efficacy: An empirical comparison of three general self-efficacy instruments

Computer self-efficacy (CSE) operates at multiple levels, including general and specific, although many studies operationalize it at the general level. GCSE, or a judgment of ability across all computing domains, is particularly useful because it can be generalized to all computing environments and it matches the level of specificity for general constructs. However, there have been problems with current instruments, among them the inability to capture the entire computing domain and using levels of assistance instead of task-based items. This study introduces and tests a new method of measuring general CSE, one that is task-based and calculated from specific CSEs, called summated GCSE (SGCSE). This instrument is empirically compared to two other GCSE instruments in their relationship with three common self-efficacy outcomes. Results demonstrate that SGCSE significantly related to all outcomes and that task-based GCSE is the preferred instrument to use for some, but not all outcomes. Implications are discussed.     

System gains configuration and coordination of redundant degrees of freedom by genetic algorithms for multi-axis machine system in manufacturing

In multi-axis machine tool systems, the configuration of system gains and the coordination of redundant degrees of freedom are often a problem of insurmountable difficulty. This study explores the use of a nontraditional scheme, the genetic algorithm, in the configuration of system gains and exploitation of redundant degrees of freedom. The off-line gains configuration functioned as a kind of system design which may serve as a starting point for on-line adaptation. The allocation of redundant DOF was done on-line. The success in this work inspired the idea of future on-line GA application and the possibility of integration of GA with other non-traditional algorithms for manufacturing.