A web-based software tool for participatory optimization of conservation practices in watersheds

WRESTORE (Watershed Restoration Using Spatio-Temporal Optimization of Resources) is a web-based, participatory planning tool that can be used to engage with watershed stakeholder communities, and involve them in using science-based, human-guided, interactive simulation–optimization methods for designing potential conservation practices on their landscape. The underlying optimization algorithms, process simulation models, and interfaces allow users to not only spatially optimize the locations and types of new conservation practices based on quantifiable goals estimated by the dynamic simulation models, but also to include their personal subjective and/or unquantifiable criteria in the location and design of these practices. In this paper, we describe the software, interfaces, and architecture of WRESTORE, provide scenarios for implementing the WRESTORE tool in a watershed community's planning process, and discuss considerations for future developments.     

Superior foetal electrocardiogram signal elicitation using a novel artificial intelligent Bayesian methodology

Innumerable casualties due to intrauterine hypoxia are a major worry during prenatal phase besides advanced patient monitoring with latest science and technology. Hence, the analysis of foetal electrocardiogram (fECG) signals is very vital in order to evaluate the foetal heart status for timely recognition of cardiac abnormalities. Regrettably, the latest technology in the cutting edge field of biomedical signal processing does not seem to yield the desired quality of fECG signals required by physicians, which is the major cause for the pathetic condition. The focus of this work is to extort non-invasive fECG signal with highest possible quality with a motive to support physicians in utilizing the methodology for the latest intrapartum monitoring technique called STAN (ST analysis) for forecasting intrapartum foetal hypoxia. However, the critical quandary is that the non-invasive fECG signals recorded from the maternal abdomen are affected by several interferences like power line interference, baseline drift interference, electrode motion interference, muscle movement interference and the maternal electrocardiogram (mECG) being the dominant interference. A novel hybrid methodology called BANFIS (Bayesian adaptive neuro fuzzy inference system) is proposed. The BANFIS includes a Bayesian filter and an adaptive neuro fuzzy filter for mECG elimination and non-linear artefacts removal to yield high quality fECG signal. Kalman filtering frame work has been utilized to estimate the nonlinear transformed mECG component in the abdominal electrocardiogram (aECG). The adaptive neuro fuzzy filter is employed to discover the nonlinearity of the nonlinear transformed version of mECG and to align the estimated mECG signal with the maternal component in the aECG signal for annulment. The outcomes of the investigation by the proposed BANFIS system proved valuable for STAN system for efficient prediction of foetal hypoxia.     

Protection of power transformer using multi criteria decision-making

Power transformers are protected by different relays that operate independently. Malfunction of each relay has a major role in reducing the reliability of the protection system. In order to mitigate the main drawbacks of the power transformer relays, an overall protection scheme is presented in this paper. This scheme proposes a novel multi criterion algorithm using decision-making based on fuzzy logic. In this paper the outputs of restricted earth fault relay and a directional check unit, are combined with the output of the differential protection relay. Therefore, problems that are pertaining to independent operation of each relay have been mitigated and the relays cover protection blind spots of each other. The improved power transformer protection (IPTP) scheme enhances the sensitivity and reliability of the power transformer protection. Extensive simulations are used to measure the effectiveness and merit of the proposed IPTP relay. The above efforts result in a multi criteria approach for protection of power transformers.     

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.     

An effective coding approach for multiobjective integrated resource selection and operation sequences problem

In this paper, we consider an integrated Resource Selection and Operation Sequences (iRS/OS) problem in Intelligent Manufacturing System (IMS). Several kinds of objectives are taken into account, in which the makespan for orders should be minimized; workloads among machine tools should be balanced; the total transition times between machines in a local plant should also be minimized. To solve this multiobjective iRS/OS model, a new two vectors-based coding approach has been proposed to improve the efficiency by designing a chromosome containing two kinds of information, i.e., operation sequences and machine selection. Using such kind of chromosome, we adapt multistage operation-based Genetic Algorithm (moGA) to find the Pareto optimal solutions. Moreover a special technique called left-shift hillclimber has been used as one kind of local search to improve the efficiency of our algorithm. Finally, the experimental results of several iRS/OS problems indicate that our proposed approach can obtain best solutions. Further more comparing with previous approaches, moGA performs better for finding Pareto solutions. Received: May 2005/Accepted: December 2005     

硫酸厂主鼓风机的主要设计准则

论述现代硫酸厂主鼓风机设计时必须考虑的关键因素，通过实例讨论操作费用与投资之间的平衡关系以及对装置竞争力的影响。建议在硫酸厂严酷的操作条件下改进风机特性，提高耐磨性并采用维修友好的设计。     

管道风险管理方法研究

按照管道风险管理的流程分别对管道风险评价、风险控制和决策支持、效能测试和响应进行了论述。针对目前国内管道行业的情况，提出了进行管道风险评价的有效方法及维护措施。着重介绍了国外管道风险可接受标准的情况，作为国内制定管道风险评价标准的参考。     

A New Criteria Decision Applied on the Primary Synchronization in the DS-WCDMA Interface of the UMTS System

In DS-WCDMA mobile systems such the UMTS, asynchronous cell site operation,assigning different long spreading code to each cell, yields the advantageof flexible system deployment. We can design an indoor system basedon an outdoor one. However, in general, much longer search time isrequired in asynchronous operation than in synchronous. This paperproposes three techniques to take decisions about synchronizationbased on observation of correlated signals. Classical decision criterialike maximum and threshold criterion are presented. A new decisioncriteria that we call Threshold&Max combined decision criteriais analyzed. The results of this new introduced technique is comparedwith the classical ones.     

Model Selection in Applied Science and Engineering: A Decision-Theoretic Approach

Mathematical models are developed and used to study the properties of complex systems in just about every area of applied science and engineering. Information on the system being modeled is, in general, incomplete, so that there may be two or more models consistent with the available information. The collection of these models is called the class of candidate models. A decision-theoretic method is developed for selecting the optimal member from the collection. The optimal model depends on the available information, the class of candidate models, and the model use. The candidate models may be deterministic or random. Classical methods for model selection, including the method of maximum likelihood and Bayesian methods, are briefly reviewed. These methods ignore model use and require data to be available. In addition, examples are used to show that classical methods for model selection can be unreliable in the sense that they can deliver unsatisfactory models when data is limited. The proposed decision-theoretic method for model selection does not have these limitations. The method accounts for model use via a utility function. This feature is especially important when modeling high-risk systems where the consequences of using an inappropriate model for the system can be disastrous.