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The ever increasing need for energy efficient systems has led to various ingenious ideas about energy management. A major offshoot of this search for energy efficient solutions is demand management in power systems. The goal of any demand management program is to control the demand for electric power among customers thereby creating load relief for electric utilities and improving system security...
Price forecasting in competitive electricity markets plays a crucial role for any decision making. This is a difficult task since price time series are non-stationary, and with variable mean and variance, and also have periodic monthly and seasonal behavior. This paper introduces an approach to forecast several-hours-ahead electricity locational marginal price (LMP) using locally linear neuro-fuzzy...
This paper introduces a method for the fault diagnosis of a rotor system. For a vibration signal of a rotor system fault, an AR model is established first, and then the related parameter and amplitude spectrum of this mode can be obtained, etc. The experiments show the above-mentioned method can effectively diagnose the fault of a rotor system.
This paper seeks to implement and test a financial forecasting agent which employs time series, derived time series data, and news that are retrieved and extracted from the Web. This research focuses on the time series data of some individual stocks from the Indonesian Stock Exchange as well as the index data. The financial forecasting agent implemented is based on a Multilayer Neural Network trained...
In the mining industry, knowing the position of miners and/or equipments is an important safety measure that reduces risks and improves the security of that facility. Being an indoor environment, wireless transmitted signals in underground narrow-vein mines suffer multiple kinds of distortions due to extreme multipath and non-line of sight (NLOS) conditions. One of the proposed solutions to accurate...
This paper presents the use of a Wavelet Neural Network (WNN) as an efficient classifier of Electromyographic (EMG) signals. Generally, an EMG signal requires advanced methods for detection, decomposition, processing and classification. In this paper a WNN model will relate the firing frequency of motor unit action potentials (MUAPs) and three different muscle force levels, in order to improve the...
A novel neural network method to predict the spectral signature in the predicted meteorological image is presented here. Back propagation algorithm has been used in this work. Based on computation cost, three different dimensional feature vectors are provided from two consecutive images as input to neural net for training and testing. Various kinds of testing are made depending upon position of predicted...
We present a cognitive modeling framework called Neural Modeling Fields (NMF) and its application to situation learning and categorization. We discuss how this framework is related to the perceptual symbol systems theory of cognition (PSS). Essentially, the mathematical apparatus of NMF is a way to learn the frames and simulators described qualitatively by PSS. For the purposes of this work, a situation...
Depression is a common but ominous psychological disorder that threatens one's quality of life. The screening and grading of depression is still a manual process and grades are often determined in ranges, e.g., "mild to moderate' and "moderate to severe' instead of making them more specific as "mild', "moderate', and "severe'. Such grading is confusing and affects the management...
Information acquired from any species genomic sequence is expected to contribute massively to advances in various fields, such as medicine, forensics and agriculture. This huge impact of DNA sequencing leads to the need for efficient automation of mapping chromatogram traces to their corresponding string of bases through base-calling. This paper attempts to solve the problem of base-calling by modeling...
This paper presents neural networks based approach for estimation of the control and operating parameters of Statcom used for improving voltage profile in a power system, which is emerging as a major problem in the day-to-day operation of stressed power systems. Statcom is an important voltage source converter FACTS device, which can be used in voltage control mode or reactive power injection mode...
The interaction experiment, between a robot and a rat, will benefit significantly when the rat's actions can be recognized automatically in real time. Regarding quantitative behavior analysis, the number and duration of a rat's actions should be measured efficiently and accurately. Therefore, aiming at the above-mentioned objectives, a novel cognition system capable of detecting rats' actions has...
Fingerprint image quality estimation is crucial in eliminating poor fingerprint images, which will affect the performance of the automatic fingerprint identification system. In this paper, we present an image quality estimation method based on neural network. Unlike other methods, which are also based on neural network, we directly take the gray value of fingerprints as inputs into the network. This...
In this paper, a faster supervised algorithm (BPfast) for the neural network training is proposed that maximizes the derivative of sigmoid activation function during back-propagation (BP) training. BP adjusts the weights of neural network with minimizing an error function. Due to the presence of derivative information in the weight update rule, BP goes to `premature saturation' that slows down the...
Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight women. In this paper we develop a hybrid intelligent system for diagnosis, prognosis and prediction for breast cancer using SANE (Symbiotic, Adaptive Neuro-evolution) and compare with ensemble ANN, modular neural network, fixed architecture evolutionary neural network (F-ENN) and Variable...
The traditional prediction models of business failure are usually constructed upon the research sample without missing values, that is, the training and testing procedure of the prediction model are not able to be completed if some observations of the relevant variables are missing. This study solves this problem by applying for the data imputation technique of which the autoassociative neural networks...
This paper forwards a neural network based VLSI power estimation Simulator (NBPE) for VLSI specification design with a graphical user interface developed. The user can enter parameters from VLSI specification such as IO number, frequency, flash depth and parameters on neural network structure such as layer number, learning algorithm etc. This simulator then estimate VLSI's power based on given information...
This paper presents the prediction and analysis of a mobility model of people, in short mobility prediction for the mobile wireless network architecture as the essential part of future wireless market trends. To show a mobile connectivity and wireless connection in wireless mesh-network, we utilized urban mobility simulation time with prediction method, and also investigated a large number of mobility...
The absence of standard in black tea assessment was one main obstacle in its quality assurance. This research were contains process of black tea assessment software development, software problem solving concept, and the software evaluation made. This paper was a proof of simple concept that an expert system should automatically find and chose relevant parameters from relationship between raw image...
We design and implement a system to reduce the risk of heat stress, a recognized occupational health hazard (OHH), in two labor intensive industries using a job-combination approach. A novel feature of the system is employing artificial neural networks (ANNs) as model free estimators to evaluate perceived discomforts (PDs) of workers for different job combinations proposed in the work.
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