Search results for: back propagation algorithm

Authors: Kaoutar Abbahaddou, Mohammed Salah Chiadmi

Abstract:

The need to find a relevant way to predict the next-day price of a stock index is a real concern for many financial stakeholders and researchers. We have known across years the proliferation of several methods. Nevertheless, among all these methods, the most controversial one is a machine learning algorithm that claims to be reliable, namely neural networks. Thus, the purpose of this article is to study the prediction power of neural networks in the particular case of Islamic finance as it is an under-looked area. In this article, we will first briefly present a review of the literature regarding neural networks and Islamic finance. Next, we present the architecture and principles of artificial neural networks most commonly used in finance. Then, we will show its empirical application on two Islamic stock indexes. The accuracy rate would be used to measure the performance of the algorithm in predicting the right price the next day. As a result, we can conclude that artificial neural networks are a reliable method to predict the next-day price for Islamic indices as it is claimed for conventional ones.

Keywords: Islamic finance, stock price prediction, artificial neural networks, machine learning

Procedia PDF Downloads 237

Authors: Marcelino Caetano Noronha, Vosco Pereira, Jose Soares Pinto, Ferdinando Da C. Saores

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One particularly popular social media platform is Youtube. It’s a video-sharing platform where users can submit videos, and other users can like, dislike or comment on the videos. In this study, we conduct a binary classification task on YouTube’s video comments and review from the users regarding the accession process of Timor Leste to become the eleventh member of the Association of South East Asian Nations (ASEAN). We scrape the data directly from the public YouTube video and apply several pre-processing and weighting techniques. Before conducting the classification, we categorized the data into two classes, namely positive and negative. In the classification part, we apply Support Vector Machine (SVM) algorithm. By comparing with Naïve Bayes Algorithm, the experiment showed SVM achieved 84.1% of Accuracy, 94.5% of Precision, and Recall 73.8% simultaneously.

Keywords: classification, YouTube, sentiment analysis, support sector machine

Procedia PDF Downloads 108

Authors: Rafid A. Al-Khannak, Fawzi M. Al-Naima

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Power load is one of the most important controlling keys which decide power demands and illustrate power usage to shape power market. Hence, power load forecasting is the parameter which facilitates understanding and analyzing all these aspects. In this paper, power load forecasting is solved under MATLAB environment by constructing a neural network for the power load to find an accurate simulated solution with the minimum error. A developed algorithm to achieve load forecasting application with faster technique is the aim for this paper. The algorithm is used to enable MATLAB power application to be implemented by multi machines in the Grid computing system, and to accomplish it within much less time, cost and with high accuracy and quality. Grid Computing, the modern computational distributing technology, has been used to enhance the performance of power applications by utilizing idle and desired Grid contributor(s) by sharing computational power resources.

Keywords: DeskGrid, Grid Server, idle contributor(s), grid computing, load forecasting

Procedia PDF Downloads 475

Authors: Nomfundo Ngombane, Topside E. Mathonsi

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Remote sensing and satellite imaging have been widely used to detect wildfires; nevertheless, the technologies present some limitations in terms of early wildfire detection as the technologies are greatly influenced by weather conditions and can miss small fires. The fires need to have spread a few kilometers for the technologies to provide accurate detection. The South African Advanced Fire Information System uses MODIS (Moderate Resolution Imaging Spectroradiometer) as satellite imaging. MODIS has limitations as it can exclude small fires and can fall short in validating fire vulnerability. Thus in the future, a Machine Learning algorithm will be designed and implemented for the early detection of wildfires. A simulator will be used to evaluate the effectiveness of the proposed solution, and the results of the simulation will be presented.

Keywords: moderate resolution imaging spectroradiometer, advanced fire information system, machine learning algorithm, detection of wildfires

Procedia PDF Downloads 78

Authors: Abdulrahman S. Alturki, John S. Loomis

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This paper discusses a corner detection algorithm for camera calibration. Calibration is a necessary step in many computer vision and image processing applications. Robust corner detection for an image of a checkerboard is required to determine intrinsic and extrinsic parameters. In this paper, an algorithm for fully automatic and robust X-corner detection is presented. Checkerboard corner points are automatically found in each image without user interaction or any prior information regarding the number of rows or columns. The approach represents each X-corner with a quadratic fitting function. Using the fact that the X-corners are saddle points, the coefficients in the fitting function are used to identify each corner location. The automation of this process greatly simplifies calibration. Our method is robust against noise and different camera orientations. Experimental analysis shows the accuracy of our method using actual images acquired at different camera locations and orientations.

Keywords: camera calibration, corner detector, edge detector, saddle points

Procedia PDF Downloads 406

Authors: Amani A. Saad, Ahmed A. El-Farag, El-Sayed A. Helali

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Cloud computing is an important and promising field in the recent decade. Cloud computing allows sharing resources, services and information among the people of the whole world. Although the advantages of using clouds are great, but there are many risks in a cloud. The data security is the most important and critical problem of cloud computing. In this research a new security model for cloud computing is proposed for ensuring secure communication system, hiding information from other users and saving the user's times. In this proposed model Blowfish encryption algorithm is used for exchanging information or data, and SHA-2 cryptographic hash algorithm is used for data integrity. For user authentication process a simple user-name and password is used, the password uses SHA-2 for one way encryption. The proposed system shows an improvement of the processing time of uploading and downloading files on the cloud in secure form.

Keywords: cloud computing, data security, SAAS, PAAS, IAAS, Blowfish

Procedia PDF Downloads 359

Authors: Wanru Huang, Fujun Wang, Chaoyue Wang, Yuan Tang, Zhifeng Yao, Ruofu Xiao, Xin Chen

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Rotating stall in centrifugal pump is an unsteady flow phenomenon that causes instabilities and high hydraulic losses. It typically occurs at low flow rates due to large flow separation in impeller blade passage. In order to reveal the influence of incoming flow conditions on rotating stall in centrifugal pump, a numerical method for investigating rotating stall was established. This method is based on a modified SST k-ω turbulence model and a fine mesh model was adopted. The calculated flow velocity in impeller by this method was in good agreement with PIV results. The effects of flow rate and sealing-ring leakage on stall characteristics of centrifugal pump were studied by using the proposed numerical approach. The flow structures in impeller under typical flow rates and typical sealing-ring leakages were analyzed. It is found that the stall vortex frequency and circumferential propagation velocity increase as flow rate decreases. With the flow rate decreases from 0.40Qd to 0.30Qd, the stall vortex frequency increases from 1.50Hz to 2.34Hz, the circumferential propagation velocity of the stall vortex increases from 3.14rad/s to 4.90rad/s. Under almost all flow rate conditions where rotating stall is present, there is low frequency of pressure pulsation between 0Hz-5Hz. The corresponding pressure pulsation amplitude increases with flow rate decreases. Taking the measuring point at the leading edge of the blade pressure surface as an example, the flow rate decreases from 0.40Qd to 0.30Qd, the pressure fluctuation amplitude increases by 86.9%. With the increase of leakage, the flow structure in the impeller becomes more complex, and the 8-shaped stall vortex is no longer stable. On the basis of the 8-shaped stall vortex, new vortex nuclei are constantly generated and fused with the original vortex nuclei under large leakage. The upstream and downstream vortex structures of the 8-shaped stall vortex have different degrees of swimming in the flow passage, and the downstream vortex swimming is more obvious. The results show that the proposed numerical approach could capture the detail vortex characteristics, and the incoming flow conditions have significant effects on the stall vortex in centrifugal pumps.

Keywords: centrifugal pump, rotating stall, numerical simulation, flow condition, vortex frequency

Procedia PDF Downloads 137

Authors: Amr Mohamadien, Ali Imanpour, Sylvester Agbo, Nader Yoosef-Ghodsi, Samer Adeeb

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The fracture toughness resistance curve (e.g., J-R curve and crack tip opening displacement (CTOD) or δ-R curve) is important in facilitating strain-based design and integrity assessment of oil and gas pipelines. This paper aims to present laboratory experimental data to characterize the fracture behavior of pipeline steel. The influential parameters associated with the fracture of API 5L X52 pipeline steel, including different initial crack sizes, were experimentally investigated for a single notch edge bend (SENB). A total of 9 small-scale specimens with different crack length to specimen depth ratios were conducted and tested using single edge notch bending (SENB). ASTM E1820 and BS7448 provide testing procedures to construct the fracture resistance curve (Load-CTOD, CTOD-R, or J-R) from test results. However, these procedures are limited by standard specimens’ dimensions, displacement gauges, and calibration curves. To overcome these limitations, this paper presents the use of small-scale specimens and a 3D-digital image correlation (DIC) system to extract the parameters required for fracture toughness estimation. Fracture resistance curve parameters in terms of crack mouth open displacement (CMOD), crack tip opening displacement (CTOD), and crack growth length (∆a) were carried out from test results by utilizing the DIC system, and an improved regression fitting resistance function (CTOD Vs. crack growth), or (J-integral Vs. crack growth) that is dependent on a variety of initial crack sizes was constructed and presented. The obtained results were compared to the available results of the classical physical measurement techniques, and acceptable matchings were observed. Moreover, a case study was implemented to estimate the maximum strain value that initiates the stable crack growth. This might be of interest to developing more accurate strain-based damage models. The results of laboratory testing in this study offer a valuable database to develop and validate damage models that are able to predict crack propagation of pipeline steel, accounting for the influential parameters associated with fracture toughness.

Keywords: fracture toughness, crack propagation in pipeline steels, CTOD-R, strain-based damage model

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Authors: Lina Wu, Ye Li, Jia Liu

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We are interested in solving Liouville-type problems to explore constancy properties for maps or differential forms on Riemannian manifolds. Geometric structures on manifolds, the existence of constancy properties for maps or differential forms, and energy growth for maps or differential forms are intertwined. In this article, we concentrate on discovery of solutions to Liouville-type problems where manifolds are Euclidean spaces (i.e. flat Riemannian manifolds) and maps become real-valued functions. Liouville-type results of vanishing properties for functions are obtained. The original work in our research findings is to extend the q-energy for a function from finite in L^q space to infinite in non-L^q space by applying p-balanced technique where q = p = 2. Calculation skills such as Hölder's Inequality and Tests for Series have been used to evaluate limits and integrations for function energy. Calculation ideas and computational techniques for solving Liouville-type problems shown in this article, which are utilized in Euclidean spaces, can be universalized as a successful algorithm, which works for both maps and differential forms on Riemannian manifolds. This innovative algorithm has a far-reaching impact on research work of solving Liouville-type problems in the general settings involved with infinite energy. The p-balanced technique in this algorithm provides a clue to success on the road of q-energy extension from finite to infinite.

Keywords: differential forms, holder inequality, Liouville-type problems, p-balanced growth, p-harmonic maps, q-energy growth, tests for series

Procedia PDF Downloads 235

Authors: Vijaya K. Srivastava, Davide Spinello

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This paper presents performance of two robust gradient-based heuristic optimization procedures based on 3ⁿ enumeration and tunneling approach to seek global optimum of constrained integer problems. Both these procedures consist of two distinct phases for locating the global optimum of integer problems with a linear or non-linear objective function subject to linear or non-linear constraints. In both procedures, in the first phase, a local minimum of the function is found using the gradient approach coupled with hemstitching moves when a constraint is violated in order to return the search to the feasible region. In the second phase, in one optimization procedure, the second sub-procedure examines 3ⁿ integer combinations on the boundary and within hypercube volume encompassing the result neighboring the result from the first phase and in the second optimization procedure a tunneling function is constructed at the local minimum of the first phase so as to find another point on the other side of the barrier where the function value is approximately the same. In the next cycle, the search for the global optimum commences in both optimization procedures again using this new-found point as the starting vector. The search continues and repeated for various step sizes along the function gradient as well as that along the vector normal to the violated constraints until no improvement in optimum value is found. The results from both these proposed optimization methods are presented and compared with one provided by popular MS Excel solver that is provided within MS Office suite and other published results.

Keywords: constrained integer problems, enumerative search algorithm, Heuristic algorithm, Tunneling algorithm

Procedia PDF Downloads 325

Authors: Salah Al-Din I. Badran, Samad Ahmadi, Dylan Menzies, Ismail Shahin

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This paper describes a new efficient blind source separation method; in this method we use a non-uniform filter bank and a new structure with different sub-bands. This method provides a reduced permutation and increased convergence speed comparing to the full-band algorithm. Recently, some structures have been suggested to deal with two problems: reducing permutation and increasing the speed of convergence of the adaptive algorithm for correlated input signals. The permutation problem is avoided with the use of adaptive filters of orders less than the full-band adaptive filter, which operate at a sampling rate lower than the sampling rate of the input signal. The decomposed signals by analysis bank filter are less correlated in each sub-band than the input signal at full-band, and can promote better rates of convergence.

Keywords: Blind source separation, estimates, full-band, mixtures, sub-band

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Authors: Medani Khaled Ben Oualid

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Most of researchers solved and analyzed the ORPD problem in the normal conditions. However, network collapses appear in contingency conditions. In this paper, ORPD under several contingencies is presented using the proposed method WOA. To ensure viability of the power system in contingency conditions, several critical cases are simulated in order to prevent and prepare the power system to face such situations. The results obtained are carried out in IEEE 30 bus test system for the solution of ORPD problem in which control of bus voltages, tap position of transformers and reactive power sources are involved. Moreover, another method, namely, Particle Swarm Optimization with Time Varying Acceleration Coefficient (PSO-TVAC) has been compared with the proposed technique. Simulation results indicate that the proposed WOA gives remarkable solution in terms of effectiveness in case of outages.

Keywords: optimal reactive power dispatch, metaheuristic techniques, whale optimization algorithm, real power loss minimization, contingency conditions

Procedia PDF Downloads 90

Authors: Ezio Bassi, Francesco Vercesi, Francesco Benzi

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The aim of this study is to evaluate the potentiality of synchronous reluctance motors for lift systems by also evaluating the vibroacoustic behaviour of the motor. Two types of synchronous machines are designed, analysed, and compared with an equivalent induction motor, which is the more common solution in such gearbox applications. The machines' performance are further improved with optimization procedures based on multiobjective optimization genetic algorithm (MOGA). The difference between the two synchronous motors consists in the rotor geometry; a symmetric and an asymmetric rotor design were investigated. The evaluation of the vibroacoustic performance has been conducted with a multi-variable model and finite element software taking into account electromagnetic, mechanical, and thermal features of the motor, therefore carrying out a multi-physics analysis of the electrical machine.

Keywords: synchronous reluctance motor, vibro-acoustic, lift systems, genetic algorithm

Procedia PDF Downloads 178

Authors: H. Bakhshi, E. Khayyamian

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Cognitive radios have been recognized as one of the most promising technologies dealing with the scarcity of the radio spectrum. In cognitive radio systems, secondary users are allowed to utilize the frequency bands of primary users when the bands are idle. Hence, how to accurately detect the idle frequency bands has attracted many researchers’ interest. Detection performance is sensitive toward noise power and gain fluctuation. Since signal to noise ratio (SNR) between primary user and secondary users are not the same and change over the time, SNR and noise power estimation is essential. In this paper, we present a cooperative spectrum sensing algorithm using SNR estimation to improve detection performance in the real situation.

Keywords: cognitive radio, cooperative spectrum sensing, energy detection, SNR estimation, spectrum sensing, rayleigh fading channel

Procedia PDF Downloads 451

Authors: I Made Ginarsa, Agung Budi Muljono, I Made Ari Nrartha

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Transient voltage response appears in power system operation when an additional loading is forced to load bus of power systems. In this research, improvement of transient voltage response is done by using power system stabilizer-static var compensator (PSS-SVC) based on adaptive neuro-fuzzy inference system (ANFIS)-algorithm. The main function of the PSS is to add damping component to damp rotor oscillation through automatic voltage regulator (AVR) and excitation system. Learning process of the ANFIS is done by using off-line method where data learning that is used to train the ANFIS model are obtained by simulating the PSS-SVC conventional. The ANFIS model uses 7 Gaussian membership functions at two inputs and 49 rules at an output. Then, the ANFIS-PSS and ANFIS-SVC models are applied to power systems. Simulation result shows that the response of transient voltage is improved with settling time at the time of 4.25 s.

Keywords: improvement, transient voltage, PSS-SVC, ANFIS, settling time

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Authors: Matthew Ferguson, Tatyana Konkova, Ioannis Violatos

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Microstructure analysis of additively manufactured (AM) materials is an important step in understanding the interrelationship between mechanical properties and materials performance. Literature on the effect of laser-based AM process parameters on the microstructure in the substrate-deposit interface is limited. The interface region, the adjoining area of substrate and deposit, is characterized by the presence of the fusion zone (FZ) and heat-affected zone (HAZ), experiencing rapid thermal gyrations resulting in thermal-induced transformations. Inconel 718 was utilized as work material for both the substrate and deposit. Three blocks of Inconel 718 material were deposited by Direct Energy Deposition (DED) using three different laser powers, 550W, 750W and 950W, respectively. A coupled thermo-mechanical transient approach was utilized to correlate temperature history to the evolution of microstructure. The thermal history of the deposition process was monitored with the thermocouples installed inside the substrate material. The interface region of the blocks was analyzed with Optical Microscopy (OM) and Scanning Electron Microscopy (SEM), including the electron back-scattered diffraction (EBSD) technique. Laser power was found to influence the dissolution of intermetallic precipitated phases in the substrate and grain growth in the interface region. Microstructure and thermal history data were utilized to draw conclusive comparisons between the investigated process parameters.

Keywords: additive manufacturing, direct energy deposition, electron back-scattered diffraction, finite element analysis, inconel 718, microstructure, optical microscopy, scanning electron microscopy, substrate-deposit interface region

Procedia PDF Downloads 203

Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the model-agnostic meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to an exponential growth of computation, this paper also proposes a key data extraction method that only extract part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: mutex task generation, data augmentation, meta-learning, text classification.

Procedia PDF Downloads 143

Authors: K. A. Chinmaya, M. Udaya Bhaskar

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Multiphase motor drives are now a day considered for numerous applications due to the advantages that they offer when compared to their three-phase counterparts. Proper modeling of inverters and motors are important in devising an appropriate control algorithm. This paper develops a complete modeling of a five-phase inverter and five-phase space vector modulation schemes which can be used for five-phase motor drives. A novel modified algorithm is introduced which enables the sinusoidal output voltages up to certain voltage value. The waveforms of phase to neutral voltage are compared with the different modulation techniques and also different modulation indexes in terms of Low-order Harmonic (LH) voltage of 3rd and 7th present. A detailed performance evolution of existing and newly modified schemes is done in terms of Total Harmonic Distortion (THD).

Keywords: multi-phase drives, space vector modulation, voltage source inverter, low order harmonic voltages, total harmonic distortion

Procedia PDF Downloads 404

Authors: Mojtaba Valinataj

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Nowadays reversible logic has received many attentions as one of the new fields for reducing the power consumption. On the other hand, the processing systems have weaknesses against different external effects. In this paper, some error detecting reversible logic serial multipliers are proposed by incorporating the parity-preserving gates. This way, the new designs are presented for signed parity-preserving serial multipliers based on the Booth's algorithm by exploiting the new arrangements of existing gates. The experimental results show that the proposed 4×4 multipliers in this paper reach up to 20%, 35%, and 41% enhancements in the number of constant inputs, quantum cost, and gate count, respectively, as the reversible logic criteria, compared to previous designs. Furthermore, all the proposed designs have been generalized for n×n multipliers with general formulations to estimate the main reversible logic criteria as the functions of the multiplier size.

Keywords: Booth’s algorithm, error detection, multiplication, parity-preserving gates, quantum computers, reversible logic

Procedia PDF Downloads 228

Authors: Arash Rahmani, Ahmad Ghanbari, Abbas Baghernezhad, Babak Safaei

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In the design of parallel manipulators, usually mean value of a dexterity measure over the workspace volume is considered as the objective function to be used in optimization algorithms. The mentioned indexes in a hybrid parallel manipulator (HPM) are quite complicated to solve thanks to infinite solutions for every point within the workspace of the redundant manipulators. In this paper, spatial isotropic design axioms are extended as a well-known method for optimum design of manipulators. An upper limit for the isotropy measure of HPM is calculated and instead of computing and minimizing isotropy measure, minimizing the obtained limit is considered. To this end, two different objective functions are suggested which are obtained from objective functions of comprising modules. Finally, by using genetic algorithm (GA), the best geometric parameters for a specific hybrid parallel robot which is composed of two modified Gough-Stewart platforms (MGSP) are achieved.

Keywords: hybrid manipulator, spatial isotropy, genetic algorithm, optimum design

Procedia PDF Downloads 336

Authors: Fatima Khurshid, Noor Ul Ain, Syed Abdul Rehman Kashif, Zainab Riaz, Abdullah Usman Khan, Muhammad Imran

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As the world is moving towards extra-high voltage (EHV) and ultra-high voltage (UHV) power systems, the performance requirements of power transformers are becoming crucial to the system reliability and security. With the transformers being an essential component of a power system, low health index of transformers poses greater risks for safe and reliable operation. Therefore, to meet the rising demands of the power system and transformer performance, researchers are being prompted to provide solutions for enhanced thermal and electrical properties of transformers. This paper proposes an approach to improve the health index of a transformer by using nano-technology in conjunction with bio-degradable oils. Vegetable oils can serve as potential dielectric fluid alternatives to the conventional mineral oils, owing to their numerous inherent benefits; namely, higher fire and flashpoints, and being environment-friendly in nature. Moreover, the addition of nanoparticles in the dielectric fluid further serves to improve the dielectric strength of the insulation medium. In this research, using the finite element method (FEM) in COMSOL Multiphysics environment, and a 2D space dimension, three different oil samples have been modelled, and the electric field distribution is computed for each sample at various electric potentials, i.e., 90 kV, 100 kV, 150 kV, and 200 kV. Furthermore, each sample has been modified with the addition of nanoparticles of different radii (50 nm and 100 nm) and at different interparticle distance (5 mm and 10 mm), considering an instant of time. The nanoparticles used are non-conductive and have been modelled as alumina (Al₂O₃). The geometry has been modelled according to IEC standard 60897, with a standard electrode gap distance of 25 mm. For an input supply voltage of 100 kV, the maximum electric field stresses obtained for the samples of synthetic vegetable oil, olive oil, and mineral oil are 5.08 ×10⁶ V/m, 5.11×10⁶ V/m and 5.62×10⁶ V/m, respectively. It is observed that for the unmodified samples, vegetable oils have a greater dielectric strength as compared to the conventionally used mineral oils because of their higher flash points and higher values of relative permittivity. Also, for the modified samples, the addition of nanoparticles inhibits the streamer propagation inside the dielectric medium and hence, serves to improve the dielectric properties of the medium.

Keywords: dielectric strength, finite element method, health index, nanotechnology, streamer propagation

Procedia PDF Downloads 141

Authors: Chaouch Souad, Abdou Latifa, Larbi Chrifi Alaoui

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This paper deals with sensorless backstepping control of induction motor using MRAS technique associated to sliding mode approach. A high order genetic algorithm structure is used to approximate a control law designed by the Backstepping technique, and to find the best parameters globally optimized. However, the Backstepping control approach is unsuitable for high performance applications because the need of a speed sensor for increased accuracy and the absence of any error decay mechanism. In this paper a nonlinear observer, obtained by combining sliding mode structure and model reference adaptive system (MRAS), is designed for the rotor flux and rotor speed estimations. To validate the proposed method, the results are presented for showing the improved drive characteristics and performances.

Keywords: Backstepping Control, Induction Motor, Genetic Algorithm, Sliding Mode observer

Procedia PDF Downloads 731

Authors: Hiesik Kim, Yong-Beom Kim, Jaheon Gu

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Internet of Things (IoT) applications are widely serviced and spread worldwide. Local wireless data transmission technique must be developed to rate up with some technique. Bluetooth wireless data communication is wireless technique is technique made by Special Inter Group (SIG) using the frequency range 2.4 GHz, and it is exploiting Frequency Hopping to avoid collision with a different device. To implement experiment, equipment for experiment transmitting measured data is made by using Arduino as open source hardware, gas sensor, and Bluetooth module and algorithm controlling transmission rate is demonstrated. Experiment controlling transmission rate also is progressed by developing Android application receiving measured data, and controlling this rate is available at the experiment result. It is important that in the future, improvement for communication algorithm be needed because a few error occurs when data is transferred or received.

Keywords: Arduino, Bluetooth, gas sensor, IoT, transmission

Procedia PDF Downloads 278

Authors: Sheldon Liu, Gordon Wang, Lei Liu, Xuefeng Liu

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Anomaly detection, also referred to as one-class classification, plays a crucial role in identifying product images that deviate from the expected distribution. This study introduces Data-centric Anomaly Detection with Diffusion Models (DCADDM), presenting a systematic strategy for data collection and further diversifying the data with image generation via diffusion models. The algorithm addresses data collection challenges in real-world scenarios and points toward data augmentation with the integration of generative AI capabilities. The paper explores the generation of normal images using diffusion models. The experiments demonstrate that with 30% of the original normal image size, modeling in an unsupervised setting with state-of-the-art approaches can achieve equivalent performances. With the addition of generated images via diffusion models (10% equivalence of the original dataset size), the proposed algorithm achieves better or equivalent anomaly localization performance.

Keywords: diffusion models, anomaly detection, data-centric, generative AI

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Authors: Shelby Simpson, William Stanley, Namir Naba, Xiaodi Wang

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Both t-SNE and UMAP are brand new state of art tools to predominantly preserve the local structure that is to group neighboring data points together, which indeed provides a very informative visualization of heterogeneity in our data. In this research, we develop a t-SNE and UMAP base neural network image classification algorithm to embed the original dataset to a corresponding low dimensional dataset as a preprocessing step, then use this embedded database as input to our specially designed neural network classifier for image classification. We use the fashion MNIST data set, which is a labeled data set of images of clothing objects in our experiments. t-SNE and UMAP are used for dimensionality reduction of the data set and thus produce low dimensional embeddings. Furthermore, we use the embeddings from t-SNE and UMAP to feed into two neural networks. The accuracy of the models from the two neural networks is then compared to a dense neural network that does not use embedding as an input to show which model can classify the images of clothing objects more accurately.

Keywords: t-SNE, UMAP, fashion MNIST, neural networks

Procedia PDF Downloads 198

Authors: Saad M. Darwish

Abstract:

Recently, software quality issues have come to be seen as important subject as we see an enormous growth of agencies involved in software industries. However,these agencies cannot guarantee the quality of their products, thus leaving users in uncertainties. Software certification is the extension of quality by means that quality needs to be measured prior to certification granting process. This research participates in solving the problem of software assessment by proposing a model for assessment and certification of software product that uses a fuzzy inference engine to integrate both of process–driven and application-driven quality assurance strategies. The key idea of the on hand model is to improve the compactness and the interpretability of the model’s fuzzy rules via employing an ant colony optimization algorithm (ACO), which tries to find good rules description by dint of compound rules initially expressed with traditional single rules. The model has been tested by case study and the results have demonstrated feasibility and practicability of the model in a real environment.

Keywords: optimization technique, quality assurance, software certification model, software assessment

Procedia PDF Downloads 487

Authors: Gholamreza Ghodrati Amiri, Mojtaba Jafarian Abyaneh, Ali Zare Hosseinzadeh

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This paper presents an effective model updating strategy for damage localization and quantification in frames by defining damage detection problem as an optimization issue. A generalized version of the Modal Residual Force (MRF) is employed for presenting a new damage-sensitive cost function. Then, Grey Wolf Optimization (GWO) algorithm is utilized for solving suggested inverse problem and the global extremums are reported as damage detection results. The applicability of the presented method is investigated by studying different damage patterns on the benchmark problem of the IASC-ASCE, as well as a planar shear frame structure. The obtained results emphasize good performance of the method not only in free-noise cases, but also when the input data are contaminated with different levels of noises.

Keywords: frame, grey wolf optimization algorithm, modal residual force, structural damage detection

Procedia PDF Downloads 389

Authors: Mary Anne Roa

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Congestion in network occurs due to exceed in aggregate demand as compared to the accessible capacity of the resources. Network congestion will increase as network speed increases and new effective congestion control methods are needed, especially for today’s very high speed networks. To address this undeniably global issue, the study focuses on the development of a fuzzy-based congestion control model concerned with allocating the resources of a computer network such that the system can operate at an adequate performance level when the demand exceeds or is near the capacity of the resources. Fuzzy logic based models have proven capable of accurately representing a wide variety of processes. The model built is based on bandwidth, the aggregate incoming traffic and the waiting time. The theoretical analysis and simulation results show that the proposed algorithm provides not only good utilization but also low packet loss.

Keywords: congestion control, queue management, computer networks, fuzzy logic

Procedia PDF Downloads 397

Authors: Du Kim, Nelson Baro

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According to a report of International Atomic Energy Agency (IAEA), there are approximately 437 nuclear power stations are in operation in the present around the world in order to meet increasing energy demands. Indeed, nearly, a third of the world’s energy demands are met through nuclear power because it is one of the most efficient and long-lasting sources of energy. However, there are also consequences when a major event takes place at a nuclear power station. Over the past years, a few major nuclear accidents have occurred around the world. According to a report of International Nuclear and Radiological Event Scale (INES), there are six nuclear accidents that are considered to be high level (risk) of the events: Fukushima Dai-chi (Level 7), Chernobyl (Level 7), Three Mile Island (Level 5), Windscale (Level 5), Kyshtym (Level 6) and Chalk River (Level 5). Today, many people still have doubt about using nuclear power. There is growing number of people who are against nuclear power after the serious accident occurred at the Fukushima Dai-chi nuclear power plant in Japan. In other words, there are public concerns about radiation impacts which emphasize Linear-No-Threshold (LNT) Issues, Radiation Health Effects, Radiation Protection and Social Impacts. This paper will address those keywords by looking back at the history of these major nuclear accidents worldwide, based on INES. This paper concludes that all major mistake from nuclear accidents are preventable due to the fact that most of them are caused by human error. In other words, the human factor has played a huge role in the malfunction and occurrence of most of those events. The correct handle of a crisis is determined, by having a good radiation protection program in place, it’s what has a big impact on society and determines how acceptable people are of nuclear.

Keywords: linear-no-threshold (LNT) issues, radiation health effects, radiation protection, social impacts

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Authors: Vahid Anari, Leila Shahmohammadi

Abstract:

Diagnosing and interpreting manually from a large cohort dataset of immunohistochemically stained tissue of tumors using an optical microscope involves subjectivity and also is tedious for pathologist specialists. Moreover, digital pathology today represents more of an evolution than a revolution in pathology. In this paper, we develop and test an unsupervised algorithm that can automatically enhance the IHC image of a meningioma tumor and classify cells into positive (proliferative) and negative (normal) cells. A dataset including 150 images is used to test the scheme. In addition, a new adaptive color image enhancement method is proposed based on a vector directional filter (VDF) and statistical properties of filtering the window. Since the cells are distinguishable by the human eye, the accuracy and stability of the algorithm are quantitatively compared through application to a wide variety of real images.

Keywords: digital pathology, cell segmentation, immunohistochemically, noise reduction

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