Abstract: The purpose of the study is to analyze the load rejection transient of ABWR by using TRACE, PARCS, and SNAP codes. This study has some steps. First, using TRACE, PARCS, and SNAP codes establish the model of ABWR. Second, the key parameters are identified to refine the TRACE/PARCS/SNAP model further in the frame of a steady state analysis. Third, the TRACE/PARCS/SNAP model is used to perform the load rejection transient analysis. Finally, the FSAR data are used to compare with the analysis results. The results of TRACE/PARCS are consistent with the FSAR data for the important parameters. It indicates that the TRACE/PARCS/SNAP model of ABWR has a good accuracy in the load rejection transient.
Abstract: The using of finite element programs in analyzing and designing buildings are becoming very popular, but there are many engineers still using the tributary area method (TAM) in designing the structural members such as columns. This study is an attempt to investigate the accuracy of the TAM results with different load condition (gravity and lateral load), different floors numbers, and different columns stiffness's. To conduct this study, linear elastic analysis in ETABS program is used. The results from finite element method are compared to those obtained from TAM. According to the analysis of the data obtained, it can be seen that there is significance difference between the real load carried by columns and the load which is calculated by using the TAM. Thus, using 3-D models are the best choice to calculate the real load effected on columns and design these columns according to this load.
Abstract: This research presents the three-dimensional mechanical characteristics of a commercial gas diffusion layer by experiment and simulation results. Although the mechanical performance of gas diffusion layers has attracted much attention, its reliability and accuracy are still a major challenge. With the help of simulation analysis methods, it is beneficial to the gas diffusion layer’s extensive commercial development and the overall stress analysis of proton electrolyte membrane fuel cells during its pre-production design period. Therefore, in this paper, a three-dimensional constitutive model of a commercial gas diffusion layer, including its material stiffness matrix parameters, is developed and coded, in the user-defined material model of a commercial finite element method software for simulation. Then, the model is validated by comparing experimental results as well as simulation outcomes. As a result, both the experimental data and simulation results show a good agreement with each other, with high accuracy.
Abstract: Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.
Abstract: The quality of press-fit assembly is closely related to
reliability and safety of product. The paper proposed a keypoint
detection method based on convolutional neural network to improve
the accuracy of keypoint detection in press-fit curve. It would
provide an auxiliary basis for judging quality of press-fit assembly.
The press-fit curve is a curve of press-fit force and displacement.
Both force data and distance data are time-series data. Therefore,
one-dimensional convolutional neural network is used to process
the press-fit curve. After the obtained press-fit data is filtered, the
multi-layer one-dimensional convolutional neural network is used to
perform the automatic learning of press-fit curve features, and then
sent to the multi-layer perceptron to finally output keypoint of the
curve. We used the data of press-fit assembly equipment in the actual
production process to train CNN model, and we used different data
from the same equipment to evaluate the performance of detection.
Compared with the existing research result, the performance of
detection was significantly improved. This method can provide a
reliable basis for the judgment of press-fit quality.
Abstract: Eyes are considered to be the most sensitive and
important organ for human being. Thus, any eye disorder will affect
the patient in all aspects of life. Cataract is one of those eye disorders
that lead to blindness if not treated correctly and quickly. This paper
demonstrates a model for automatic detection, classification, and
grading of cataracts based on image processing techniques and
artificial intelligence. The proposed system is developed to ease the
cataract diagnosis process for both ophthalmologists and patients.
The wavelet transform combined with 2D Log Gabor Wavelet
transform was used as feature extraction techniques for a dataset of
120 eye images followed by a classification process that classified the
image set into three classes; normal, early, and advanced stage. A
comparison between the two used classifiers, the support vector
machine SVM and the artificial neural network ANN were done for
the same dataset of 120 eye images. It was concluded that SVM gave
better results than ANN. SVM success rate result was 96.8%
accuracy where ANN success rate result was 92.3% accuracy.
Abstract: Driven by the demand of intelligent monitoring in
rehabilitation centers or hospitals, a high accuracy real-time location
system based on UWB (ultra-wideband) technology was proposed.
The system measures precise location of a specific person, traces his
movement and visualizes his trajectory on the screen for doctors or
administrators. Therefore, doctors could view the position of the
patient at any time and find them immediately and exactly when
something emergent happens. In our design process, different
algorithms were discussed, and their errors were analyzed. In addition,
we discussed about a , simple but effective way of correcting the
antenna delay error, which turned out to be effective. By choosing the
best algorithm and correcting errors with corresponding methods, the
system attained a good accuracy. Experiments indicated that the
ranging error of the system is lower than 7 cm, the locating error is
lower than 20 cm, and the refresh rate exceeds 5 times per second. In
future works, by embedding the system in wearable IoT (Internet of
Things) devices, it could provide not only physical parameters, but
also the activity status of the patient, which would help doctors a lot in
performing healthcare.
Abstract: Ultrasonic infrared nondestructive testing is a kind of testing method with high speed, accuracy and localization. However, there are still some problems, such as the detection requires manual real-time field judgment, the methods of result storage and viewing are still primitive. An intelligent non-destructive detection system based on embedded linux is put forward in this paper. The hardware part of the detection system is based on the ARM (Advanced Reduced Instruction Set Computer Machine) core and an embedded linux system is built to realize image processing and defect detection of thermal images. The CLAHE algorithm and the Butterworth filter are used to process the thermal image, and then the boa server and CGI (Common Gateway Interface) technology are used to transmit the test results to the display terminal through the network for real-time monitoring and remote monitoring. The system also liberates labor and eliminates the obstacle of manual judgment. According to the experiment result, the system provides a convenient and quick solution for industrial non-destructive testing.
Abstract: In this work, we present an efficient approach for
solving variable-order time-fractional partial differential equations,
which are based on Legendre and Laguerre polynomials. First, we
introduced the pseudo-operational matrices of integer and variable
fractional order of integration by use of some properties of
Riemann-Liouville fractional integral. Then, applied together with
collocation method and Legendre-Laguerre functions for solving
variable-order time-fractional partial differential equations. Also, an
estimation of the error is presented. At last, we investigate numerical
examples which arise in physics to demonstrate the accuracy of the
present method. In comparison results obtained by the present method
with the exact solution and the other methods reveals that the method
is very effective.
Abstract: Autofluorescence Imaging (AFI) is a technology for detecting early carcinogenesis of the gastrointestinal tract in recent years. Compared with traditional white light endoscopy (WLE), this technology greatly improves the detection accuracy of early carcinogenesis, because the colors of normal tissues are different from cancerous tissues. Thus, edge detection can distinguish them in grayscale images. In this paper, based on the traditional Sobel edge detection method, optimization has been performed on this method which considers the environment of the gastrointestinal, including adaptive threshold and morphological processing. All of the processes are implemented on our self-designed system based on the image sensor OV6930 and Field Programmable Gate Array (FPGA), The system can capture the gastrointestinal image taken by the lens in real time and detect edges. The final experiments verified the feasibility of our system and the effectiveness and accuracy of the edge detection algorithm.
Abstract: Electronic apex locators (EAL) has been widely used
clinically for measuring root canal working length with high accuracy,
which is crucial for successful endodontic treatment. In order to
maintain high accuracy in different measurement environments,
this study presented a system for root canal length measurement
based on multifrequency impedance method. This measuring system
can generate a sweep current with frequencies from 100 Hz to
1 MHz through a direct digital synthesizer. Multiple impedance
ratios with different combinations of frequencies were obtained
and transmitted by an analog-to-digital converter and several of
them with representatives will be selected after data process. The
system analyzed the functional relationship between these impedance
ratios and the distance between the file and the apex with statistics
by measuring plenty of teeth. The position of the apical foramen
can be determined by the statistical model using these impedance
ratios. The experimental results revealed that the accuracy of
the system based on multifrequency impedance ratios method to
determine the position of the apical foramen was higher than the
dual-frequency impedance ratio method. Besides that, for more
complex measurement environments, the performance of the system
was more stable.
Abstract: This paper presents state estimation with Phasor Measurement Unit (PMU) allocation to obtain complete observability of network. A matrix is designed with modeling of zero injection constraints to minimize PMU allocations. State estimation algorithm is developed with optimal allocation of PMUs to find accurate states of network. The incorporation of PMU into traditional state estimation process improves accuracy and computational performance for large power systems. The nonlinearity integrated with zero injection (ZI) constraints is remodeled to linear frame to optimize number of PMUs. The problem of optimal PMU allocation is regarded with modeling of ZI constraints, PMU loss or line outage, cost factor and redundant measurements. The proposed state estimation with optimal PMU allocation has been compared with traditional state estimation process to show its importance. MATLAB programming on IEEE 14, 30, 57, and 118 bus networks is implemented out by Binary Integer Programming (BIP) method and compared with other methods to show its effectiveness.
Abstract: Studies estimate that there will be 266,120 new cases
of invasive breast cancer and 40,920 breast cancer induced deaths
in the year of 2018 alone. Despite the pervasiveness of this
affliction, the current process to obtain an accurate breast cancer
prognosis is tedious and time consuming. It usually requires a
trained pathologist to manually examine histopathological images and
identify the features that characterize various cancer severity levels.
We propose MITOS-RCNN: a region based convolutional neural
network (RCNN) geared for small object detection to accurately
grade one of the three factors that characterize tumor belligerence
described by the Nottingham Grading System: mitotic count. Other
computational approaches to mitotic figure counting and detection
do not demonstrate ample recall or precision to be clinically viable.
Our models outperformed all previous participants in the ICPR 2012
challenge, the AMIDA 2013 challenge and the MITOS-ATYPIA-14
challenge along with recently published works. Our model achieved
an F- measure score of 0.955, a 6.11% improvement in accuracy from
the most accurate of the previously proposed models.
Abstract: The aim of this paper is to show that the observation
of the external effort and the sensor-less control of a system is
limited by the mechanical system. First, the model of a one-joint
robot with a prismatic joint is presented. Based on this model,
two different procedures were performed in order to identify the
mechanical parameters of the system and observe the external effort
applied on it. Experiments have proven that the accuracy of the force
observer, based on the DC motor current, is limited by the mechanics
of the robot. The sensor-less control will be limited by the accuracy in
estimation of the mechanical parameters and by the maximum static
friction force, that is the minimum force which can be observed in
this case. The consequence of this limitation is that industrial robots
without specific design are not well adapted to perform sensor-less
precision tasks. Finally, an efficient control law is presented for high
effort applications.
Abstract: Image registration is an important topic for many imaging systems and computer vision applications. The standard image registration techniques such as Mutual information/ Normalized mutual information -based methods have a limited performance because they do not consider the spatial information or the relationships between the neighbouring pixels or voxels. In addition, the amount of image noise may significantly affect the registration accuracy. Therefore, this paper proposes an efficient method that explicitly considers the relationships between the adjacent pixels, where the gradient information of the reference and scene images is extracted first, and then the cosine similarity of the extracted gradient information is computed and used to improve the accuracy of the standard normalized mutual information measure. Our experimental results on different data types (i.e. CT, MRI and thermal images) show that the proposed method outperforms a number of image registration techniques in terms of the accuracy.
Abstract: The reduction in dapped end beams depth nearby the supports tends to produce stress concentration and hence results in shear cracks, if it does not have an adequate reinforcement detailing. This study investigates numerically the efficiency of applying different external strengthening techniques to the dapped end of such beams. A two-dimensional finite element model was built to predict the structural behavior of dapped ends strengthened with different techniques. The techniques included external bonding of the steel angle at the re-entrant corner, un-bounded bolt anchoring, external steel plate jacketing, exterior carbon fiber wrapping and/or stripping and external inclined steel plates. The FE analysis results are then presented in terms of the ultimate load capacities, load-deflection and crack pattern at failure. The results showed that the FE model, at various stages, was found to be comparable to the available test data. Moreover, it enabled the capture of the failure progress, with acceptable accuracy, which is very difficult in a laboratory test.
Abstract: Over the last few years, the amount of data available on the globe has been increased rapidly. This came up with the emergence of recent concepts, such as the big data and the Internet of Things, which have furnished a suitable solution for the availability of data all over the world. However, managing this massive amount of data remains a challenge due to their large verity of types and distribution. Therefore, locating the required file particularly from the first trial turned to be a not easy task, due to the large similarities of names for different files distributed on the web. Consequently, the accuracy and speed of search have been negatively affected. This work presents a method using Electroencephalography signals to locate the files based on their contents. Giving the concept of natural mind waves processing, this work analyses the mind wave signals of different people, analyzing them and extracting their most appropriate features using multi-objective metaheuristic algorithm, and then classifying them using artificial neural network to distinguish among files with similar names. The aim of this work is to provide the ability to find the files based on their contents using human thoughts only. Implementing this approach and testing it on real people proved its ability to find the desired files accurately within noticeably shorter time and retrieve them as a first choice for the user.
Abstract: In this paper, we propose an optimized brain computer
interface (BCI) system for unspoken speech recognition, based on
the fact that the constructions of unspoken words rely strongly on the
Wernicke area, situated in the temporal lobe. Our BCI system has four
modules: (i) the EEG Acquisition module based on a non-invasive
headset with 14 electrodes; (ii) the Preprocessing module to remove
noise and artifacts, using the Common Average Reference method;
(iii) the Features Extraction module, using Wavelet Packet Transform
(WPT); (iv) the Classification module based on a one-hidden layer
artificial neural network. The present study consists of comparing
the recognition accuracy of 5 Arabic words, when using all the
headset electrodes or only the 4 electrodes situated near the Wernicke
area, as well as the selection effect of the subbands produced by
the WPT module. After applying the articial neural network on the
produced database, we obtain, on the test dataset, an accuracy of
83.4% with all the electrodes and all the subbands of 8 levels of the
WPT decomposition. However, by using only the 4 electrodes near
Wernicke Area and the 6 middle subbands of the WPT, we obtain
a high reduction of the dataset size, equal to approximately 19% of
the total dataset, with 67.5% of accuracy rate. This reduction appears
particularly important to improve the design of a low cost and simple
to use BCI, trained for several words.
Abstract: Wind tunnel experiments for aerodynamic profiles display numerous advantages, such as: clean steady laminar flow, controlled environmental conditions, streamlines visualization, and real data acquisition. However, the experiment instrumentation usually is expensive, and hence, each test implies a incremented in design cost. The aim of this work is to select and implement a low-cost static pressure data acquisition system for a NACA 2412 airfoil in an open cycle wind tunnel. This work compares wind tunnel experiment with Computational Fluid Dynamics (CFD) simulation and parametric analysis. The experiment was evaluated at Reynolds of 1.65 e5, with increasing angles from -5° to 15°. The comparison between the approaches show good enough accuracy, between the experiment and CFD, additional parametric analysis results differ widely from the other methods, which complies with the lack of accuracy of the lateral approach due its simplicity.
Abstract: Cartesian Genetic Programming (CGP) is explored to
design an optimal circuit capable of early stage breast cancer
detection. CGP is used to evolve simple multiplexer circuits for
detection of malignancy in the Fine Needle Aspiration (FNA) samples
of breast. The data set used is extracted from Wisconsins Breast
Cancer Database (WBCD). A range of experiments were performed,
each with different set of network parameters. The best evolved
network detected malignancy with an accuracy of 99.14%, which is
higher than that produced with most of the contemporary non-linear
techniques that are computational expensive than the proposed
system. The evolved network comprises of simple multiplexers
and can be implemented easily in hardware without any further
complications or inaccuracy, being the digital circuit.