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In this paper, the relevance of deep neural network (DNN) is studied in big data scenarios, specifically for prognostics applications. It is observed that fault predictions can be performed more efficiently when DNN is used with a pre-processing step. A novel hierarchical dimension reduction (HDR) approach is therefore proposed as a pre-processing step to DNN. This two-step approach is shown to be...
Accurately and fast detection of weight of objects has an important place for lots of academic and industrial application at the present time. In this work, it was aimed to estimate weight of eggs in a distance independent manner using image processing and artificial neural networks (ANN). The constituted system consists of a camera, artificial lighting system, reflector and reference image. Object...
Low sucrose concentrations in solutions is estimated by means of localized surface plasmon resonance of immobilized gold nanoparticles. The ultraviolet-visible spectra (UV-Vis) of samples with different sucrose concentrations were prepared and used to train artificial neural networks. In our study, MATLAB Neural Networks Toolbox was used and effect of different input sizes and network structures on...
Power load estimation, especially short-term power load estimation, plays an important role in the management of a power system in terms of system security and electricity costs. Therefore, estimation of short-term power load accurately is a popular research issue. In this paper, the generalized behavioral learning method (GBLM), a method developed based on human's behavioral learning theories, was...
A sufficient data length can play an important role in a proper estimation drought index, leading to a better appraisal for drought risk reduction. The South Central Region of Vietnam is one of drought prone areas but it has poor data conditions. A collection of meteorological data in the study area during a period of 38 years 1977–2014 found out a fact that there existed missing values in 10 out...
This paper addresses the multi-armed bandit problem in a multi-player framework. Players explore a finite set of arms with stochastic rewards, and the reward distribution of each arm is player-dependent. The goal is to find the best global arm, i.e., the one with the largest expected reward when averaged out among players. To achieve this goal, we develop a distributed variant of the well-known UCB1...
The effort required for the development of a software system is predicted through the cost of software estimation. Completion of project within time and budget limits is required for accurate cost estimation. Effort and cost estimation can be done through various modes. A new hybrid algorithm which is a combination of concepts of Artificial Bee Colony (ABC) and Local search procedures is used here...
Aiming at maintaining the accuracy of grasping pattern recognition meanwhile evaluating the required force, this paper uses Linear discriminant analysis (LDA) to realize pattern recognition and artificial neural networks to establish the relationship between surface EMG signals and fingertip force in each grasping mode. Once the grasping pattern identified, the program calls the corresponding force...
In industrial environments, it is often difficult and expensive to collect a good amount of data to adequately train expert systems for regression purposes. Therefore the usage of already available data, related to environments showing similar characteristics, could represent an effective approach to find a good balance between regression performance and the amount of data to gather for training....
In software project management, software development effort estimation (SDEE) is one of the critical activities. Analogy-Based Estimation (ABE) is most popular estimation technique suggested in SDEE literature [1, 7, 22]. Researchers have proposed various methods to improve the accuracy of ABE by adjusting the retrieved solution. The research suggests all published calibration methods depend on linear...
In this paper we assess the performance of Support Vector Machine Regression (SVR) based on Radial Basis Function (RBF) and Artificial Neural Network (ANN) based on Scaled Conjugate Gradient Backpropagation (SCG) algorithms to estimate the channel variations using the reference signal structure standardized for LTE Downlink system. Complex SVR and ANN where applied to estimate real channel environment...
Inference systems basically aim to provide and present the knowledge (outputs) that decision-makers can take advantage of in their decision-making process. Nowadays one of the most commonly used inference systems for time series prediction is the computational inference system based on artificial neural networks. Although they have the ability of handling uncertainties and are capable of solving real...
We propose a training method for multiple sound source localization (SSL) based on deep neural networks (DNNs). Such networks function as posterior probability estimator of sound location in terms of position labels and achieve high localization correctness. Since the previous DNNs' configuration for SSL handles one-sound-source cases, it should be extended to multiple-sound-source cases to apply...
The following paper discusses the development of a risk-based cost estimation model for completing non-standard manufacturing orders. The model in question is a hybrid of Monte Carlo Simulation (MCS), which constitutes the main module of the applied model. Vector of order risk probability, which is the input data for the MCS module, is highly difficult to assess and is burdened to a considerable degree...
The paper proposes a new method based on artificial neural network (ANN) for estimation of pressure loss coefficients in Tee Junction for dividing flows. The selected features are given as input to the ANN including flow and geometry parameters of Tee Junction for training and pressure loss coefficients are estimated in an efficient manner. The paper also gives comparison results of ANN based approach...
In the last decade, many advances have been made in the field of automatic temperature estimation, including wearable sensor technologies (WST), infrared thermography (IRT), and non-contact infrared thermometer (NCIT). In contrast with the WST and IRT, NCIT is inexpensive without the risk of potential skin irritation. Nevertheless, NCIT is limited in short valid estimation distance (<12 cm), resulting...
This paper introduces Aicyber's system for IALP 2016 shared task, the Dimensional Sentiment Analysis of Chinese Words. The system is an ensemble of several boosted one layer neural networks, each one is trained on a different type of Chinese word vector. Our best system mainly use position-based character-enhanced word embedding and FastText as word vectors and achieve Mean Absolute Error 0.577(1st)...
Electricity is one of the most important needs of human life. In order to provide this need sufficiently, demand for the electricity needs to be predicted in advance. Conducting production oriented studies based on the estimation results is a must. In this study, electricity consumption data of Turkey between the years 1970 and 2014 were collected from Turkish Statistical Institute. Using these data,...
This paper proposes a hybrid control scheme based on Non-singular Terminal Sliding Mode Control (NTSMC), Higher Order Sliding Mode (HOMS) and Neural Network (NN) structure for n-DOF robot manipulator. The NTSMC is used with time delay estimation method (TDE). Through TDE the equivalent control is designed without needs of system model. The HOMS consists of the Super Twisting algorithm which is estimated...
The time-dependent origin-destination (TDOD) demand estimation problem aims at estimating dynamic demand that represents the observed traffic flow patterns in a transportation network. Errors in TDOD demand are often propagated into the network outputs causing unreliable planning and operational policies. In this study, a bi-level optimization problem is proposed where the upper level is an Ordinary...
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