Software maintenance project delays prediction using Bayesian Networks

Managing software maintenance is rarely a precise task due to uncertainties concerned with resources and services descriptions. Even when a well-established maintenance process is followed, the risk of delaying tasks remains if the new services are not precisely described or when resources change during process execution. Also, the delay of a task at an early process stage may represent a different delay at the end of the process, depending on complexity or services reliability requirements. This paper presents a knowledge-based representation (Bayesian Networks) for maintenance project delays based on specialists experience and a corresponding tool to help in managing software maintenance projects. (c) 2006 Elsevier Ltd. All rights reserved.

New Improvements in Automatic Structure Elucidation Using the LSD (Logic for Structure Determination) and the SISTEMAT Expert Systems

This article describes the integration of the LSD (Logic for Structure Determination) and SISTEMAT expert systems that were both designed for the computer-assisted structure elucidation of small organic molecules. A first step has been achieved towards the linking of the SISTEMAT database with the LSD structure generator. The skeletal descriptions found by the SISTEMAT programs are now easily transferred to LSD as substructural constraints. Examples of the synergy between these expert systems are given for recently reported natural products.

Dynamic whole system testing of combined renewable heating systems : the current state of the art

Objective: For the evaluation of the energetic performance of combined renewable heating systems that supply space heat and domestic hot water for single family houses, dynamic behaviour, component interactions, and control of the system play a crucial role and should be included in test methods. Methods: New dynamic whole system test methods were developed based on “hardware in the loop” concepts. Three similar approaches are described and their differences are discussed. The methods were applied for testing solar thermal systems in combination with fossil fuel boilers (heating oil and natural gas), biomass boilers, and/or heat pumps. Results: All three methods were able to show the performance of combined heating systems under transient operating conditions. The methods often detected unexpected behaviour of the tested system that cannot be detected based on steady state performance tests that are usually applied to single components. Conclusion: Further work will be needed to harmonize the different test methods in order to reach comparable results between the different laboratories. Practice implications: A harmonized approach for whole system tests may lead to new test standards and improve the accuracy of performance prediction as well as reduce the need for field tests.

Speed prediction for triggering vehicle activated signs

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.

Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for large-scale systems. Nonetheless, a critical obstacle, which needs to be overcome in MPC, is the large computational burden when a large-scale system is considered or a long prediction horizon is involved. In order to solve this problem, we use an adaptive prediction accuracy (APA) approach that can reduce the computational burden almost by half. The proposed MPC scheme with this scheme is tested on the northern Dutch water system, which comprises Lake IJssel, Lake Marker, the River IJssel and the North Sea Canal. The simulation results show that by using the MPC-APA scheme, the computational time can be reduced to a large extent and a flood protection problem over longer prediction horizons can be well solved.

A Modular and digitally programmable interface based on band-pass sigma-delta modulator for mixed-signal systems-on-chip

The focus of this thesis is to discuss the development and modeling of an interface architecture to be employed for interfacing analog signals in mixed-signal SOC. We claim that the approach that is going to be presented is able to achieve wide frequency range, and covers a large range of applications with constant performance, allied to digital configuration compatibility. Our primary assumptions are to use a fixed analog block and to promote application configurability in the digital domain, which leads to a mixed-signal interface. The use of a fixed analog block avoids the performance loss common to configurable analog blocks. The usage of configurability on the digital domain makes possible the use of all existing tools for high level design, simulation and synthesis to implement the target application, with very good performance prediction. The proposed approach utilizes the concept of frequency translation (mixing) of the input signal followed by its conversion to the ΣΔ domain, which makes possible the use of a fairly constant analog block, and also, a uniform treatment of input signal from DC to high frequencies. The programmability is performed in the ΣΔ digital domain where performance can be closely achieved according to application specification. The interface performance theoretical and simulation model are developed for design space exploration and for physical design support. Two prototypes are built and characterized to validate the proposed model and to implement some application examples. The usage of this interface as a multi-band parametric ADC and as a two channels analog multiplier and adder are shown. The multi-channel analog interface architecture is also presented. The characterization measurements support the main advantages of the approach proposed.

A novel intelligent system to nitrogen content prediction in plants using indirect chlorophyll measurements

The accurate identification of the nitrogen content in plants is extremely important since it involves economic aspects and environmental impacts, Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification in plants involves a lot of non-linear parameters and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought SPAD index using artificial neural networks (ANN). The network acts as identifier of relationships among, crop varieties, fertilizer treatments, type of leaf and nitrogen content in the plants (target). So, nitrogen content can be generalized and estimated and from an input parameter set. This approach can form the basis for development of an accurate real time system to predict nitrogen content in plants.

Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the Backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.

In silico network topology-based prediction of gene essentiality

The identification of genes essential for survival is important for the understanding of the minimal requirements for cellular life and for drug design. As experimental studies with the purpose of building a catalog of essential genes for a given organism are time-consuming and laborious, a computational approach which could predict gene essentiality with high accuracy would be of great value. We present here a novel computational approach, called NTPGE (Network Topology-based Prediction of Gene Essentiality), that relies on the network topology features of a gene to estimate its essentiality. The first step of NTPGE is to construct the integrated molecular network for a given organism comprising protein physical, metabolic and transcriptional regulation interactions. The second step consists in training a decision-tree-based machine-learning algorithm on known essential and non-essential genes of the organism of interest, considering as learning attributes the network topology information for each of these genes. Finally, the decision-tree classifier generated is applied to the set of genes of this organism to estimate essentiality for each gene. We applied the NTPGE approach for discovering the essential genes in Escherichia coli and then assessed its performance. (C) 2007 Elsevier B.V. All rights reserved.

Prediction of a weakly bound excited state in the He-4(2)-Li-7 molecule

A scale-independent approach, valid for weakly bound three-body systems, is used to analyze the existence of excited Thomas-Efimov states in molecular systems with three atoms: a helium dimer together with isotopes of lithium (Li-6 and Li-7) and sodium (Na-23). With the present study and the available data, we can clearly predict that the He-4(2)-Li-7 system supports an excited state with binding energy close to 2.31 mK. (C) 2000 American Institute of Physics. [S0021-9606(00)30442-1].

Multivariate calibrations in gas chromatographic profiles for prediction of several physicochemical parameters of Brazilian commercial gasoline

Several Brazilian commercial gasoline physicochemical parameters, such as relative density, distillation curve (temperatures related to 10%, 50% and 90% of distilled volume, final boiling point and residue), octane numbers (motor and research octane number and anti-knock index), hydrocarbon compositions (olefins, aromatics and saturates) and anhydrous ethanol and benzene content was predicted from chromatographic profiles obtained by flame ionization detection (GC-FID) and using partial least square regression (PLS). GC-FID is a technique intensively used for fuel quality control due to its convenience, speed, accuracy and simplicity and its profiles are much easier to interpret and understand than results produced by other techniques. Another advantage is that it permits association with multivariate methods of analysis, such as PLS. The chromatogram profiles were recorded and used to deploy PLS models for each property. The standard error of prediction (SEP) has been the main parameter considered to select the "best model". Most of GC-FID-PLS results, when compared to those obtained by the Brazilian Government Petroleum, Natural Gas and Biofuels Agency - ANP Regulation 309 specification methods, were very good. In general, all PLS models developed in these work provide unbiased predictions with lows standard error of prediction and percentage average relative error (below 11.5 and 5.0, respectively). (C) 2007 Elsevier B.V. All rights reserved.

An intelligent system to real time rainfall prediction using radar data

This work presents a new approach for rainfall measurements making use of weather radar data for real time application to the radar systems operated by institute of Meteorological Research (IPMET) - UNESP - Bauru - SP-Brazil. Several real time adjustment techniques has been presented being most of them based on surface rain-gauge network. However, some of these methods do not regard the effect of the integration area, time integration and distance rainfall-radar. In this paper, artificial neural networks have been applied for generate a radar reflectivity-rain relationships which regard all effects described above. To evaluate prediction procedure, cross validation was performed using data from IPMET weather Doppler radar and rain-gauge network under the radar umbrella. The preliminary results were acceptable for rainfalls prediction. The small errors observed result from the spatial density and the time resolution of the rain-gauges networks used to calibrate the radar.