Search results for: linear congruential algorithm

Authors: Osman Turkoglu, Alvin Estilo, Ritu Gupta, Liliam Pineda-Salgado, Rajesh Pandey

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Background: Hepatotoxicity can be linked to a variety of clinical symptoms and histopathological signs, posing a great challenge in the surveillance of suspected drug-induced liver injury (DILI) cases in the safety database. Additionally, the majority of such cases are rare, idiosyncratic, highly unpredictable, and tend to demonstrate unique individual susceptibility; these qualities, in turn, lend to a pharmacovigilance monitoring process that is often tedious and time-consuming. Objective: Develop a multifactorial algorithm to assist pharmacovigilance physicians in identifying high-risk hepatotoxicity cases associated with DILI from the sponsor’s safety database (Argus). Methods: Multifactorial selection criteria were established using Structured Query Language (SQL) and the TIBCO Spotfire® visualization tool, via a combination of word fragments, wildcard strings, and mathematical constructs, based on Hy’s law criteria and pattern of injury (R-value). These criteria excluded non-eligible cases from monthly line listings mined from the Argus safety database. The capabilities and limitations of these criteria were verified by comparing a manual review of all monthly cases with system-generated monthly listings over six months. Results: On an average, over a period of six months, the algorithm accurately identified 92% of DILI cases meeting established criteria. The automated process easily compared liver enzyme elevations with baseline values, reducing the screening time to under 15 minutes as opposed to multiple hours exhausted using a cognitively laborious, manual process. Limitations of the algorithm include its inability to identify cases associated with non-standard laboratory tests, naming conventions, and/or incomplete/incorrectly entered laboratory values. Conclusions: The newly developed multifactorial algorithm proved to be extremely useful in detecting potential DILI cases, while heightening the vigilance of the drug safety department. Additionally, the application of this algorithm may be useful in identifying a potential signal for DILI in drugs not yet known to cause liver injury (e.g., drugs in the initial phases of development). This algorithm also carries the potential for universal application, due to its product-agnostic data and keyword mining features. Plans for the tool include improving it into a fully automated application, thereby completely eliminating a manual screening process.

Keywords: automation, drug-induced liver injury, pharmacovigilance, post-marketing

Procedia PDF Downloads 143

Authors: Xiang Jianhong, Wang Cong, Wang Linyu

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With the rise of medical IoT technologies, Wireless body area networks (WBANs) can collect fetal electrocardiogram (FECG) signals to support telemedicine analysis. The compressed sensing (CS)-based WBANs system can avoid the sampling of a large amount of redundant information and reduce the complexity and computing time of data processing, but the existing algorithms have poor signal compression and reconstruction performance. In this paper, a Joint block multi-orthogonal least squares (JBMOLS) algorithm is proposed. We apply the FECG signal to the Joint block sparse model (JBSM), and a comparative study of sparse transformation and measurement matrices is carried out. A FECG signal compression transmission mode based on Rbio5.5 wavelet, Bernoulli measurement matrix, and JBMOLS algorithm is proposed to improve the compression and reconstruction performance of FECG signal by CS-based WBANs. Experimental results show that the compression ratio (CR) required for accurate reconstruction of this transmission mode is increased by nearly 10%, and the runtime is saved by about 30%.

Keywords: telemedicine, fetal ECG, compressed sensing, joint sparse reconstruction, block sparse signal

Procedia PDF Downloads 120

Authors: Mustafa Engin Başoğlu, Bekir Çakır

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The most important component affecting the efficiency of photovoltaic power systems are solar panels. Efficiency of these systems are significantly affected because of being low efficiency of solar panel. Therefore, solar panels should be operated under maximum power point conditions through a power converter. In this study, design boost converter with maximum power point tracking (MPPT) operation has been designed and performed with Incremental Conductance (Inc-Cond) algorithm by using direct duty control. Furthermore, it is shown that performance of boost converter with MPPT operation fails under low load resistance connection.

Keywords: boost converter, incremental conductance (Inc-Cond), MPPT, solar panel

Procedia PDF Downloads 1037

Authors: Arindam Chaudhuri

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Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

Procedia PDF Downloads 483

Authors: Rajib Mia, Badam Singh Kushvah

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In this present paper, we use the photogravitational version of the restricted three body problem (RTBP) in binary systems. In the photogravitational RTBP, an infinitesimal particle (dust grain) is moving under the gravitational attraction and radiation pressure from the two bigger primaries. The third particle does not affect the motion of two bigger primaries. The zero-velocity curves, zero-velocity surfaces and their projections on the plane are studied. We have used existing analytical method to solve the equations of motion. We have obtained the Lagrangian points in some binary stellar systems. It is found that mass reduction factor affects the Lagrangian points. The linear stability of Lagrangian points is studied and found that these points are unstable. Moreover, trajectories of the infinitesimal particle at the triangular points are studied.

Keywords: binary systems, Lagrangian points, linear stability, photogravitational RTBP, trajectories

Procedia PDF Downloads 245

Authors: Lucian Capitanu, Virgil Florescu

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Often for the laboratory studies, modeling of specific tribological processes raises special problems. One such problem is the modeling of some temperatures and extremely high contact pressures, allowing modeling of temperatures and pressures at which the injection or extrusion processing of thermoplastic materials takes place. Tribological problems occur mainly in thermoplastics materials reinforced with glass fibers. They produce an advanced wear to the barrels and screws of processing machines, in short time. Obtaining temperatures around 210 °C and higher, as well as pressures around 100 MPa is very difficult in the laboratory. This paper reports a simple and convenient solution to get these conditions, using friction sliding couples with linear contact, cylindrical liner plastic filled with glass fibers on plate steel samples, polished and super-finished. C120 steel, which is a steel for moulds and Rp3 steel, high speed steel for tools, were used. Obtaining the pressure was achieved by continuous request of the liner in rotational movement up to its elasticity limits, when the dry friction coefficient reaches or exceeds the hardness value of 0.5 HB. By dissipation of the power lost by friction on flat steel sample, are reached contact temperatures at the metal surface that reach and exceed 230 °C, being placed in the range temperature values of the injection. Contact pressures (in load and materials conditions used) ranging from 16.3-36.4 MPa were obtained depending on the plastic material used and the glass fibers content.

Keywords: plastics with glass fibers, dry friction, linear contact, contact temperature, contact pressure, experimental simulation

Procedia PDF Downloads 297

Authors: Tomasz Grzes, Maciej Kopczynski, Jaroslaw Stepaniuk

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The rough sets theory developed by Prof. Z. Pawlak is one of the tools that can be used in the intelligent systems for data analysis and processing. Banking, medicine, image recognition and security are among the possible fields of utilization. In all these fields, the amount of the collected data is increasing quickly, but with the increase of the data, the computation speed becomes the critical factor. Data reduction is one of the solutions to this problem. Removing the redundancy in the rough sets can be achieved with the reduct. A lot of algorithms of generating the reduct were developed, but most of them are only software implementations, therefore have many limitations. Microprocessor uses the fixed word length, consumes a lot of time for either fetching as well as processing of the instruction and data; consequently, the software based implementations are relatively slow. Hardware systems don’t have these limitations and can process the data faster than a software. Reduct is the subset of the decision attributes that provides the discernibility of the objects. For the given decision table there can be more than one reduct. Core is the set of all indispensable condition attributes. None of its elements can be removed without affecting the classification power of all condition attributes. Moreover, every reduct consists of all the attributes from the core. In this paper, the hardware implementation of the two-stage greedy algorithm to find the one reduct is presented. The decision table is used as an input. Output of the algorithm is the superreduct which is the reduct with some additional removable attributes. First stage of the algorithm is calculating the core using the discernibility matrix. Second stage is generating the superreduct by enriching the core with the most common attributes, i.e., attributes that are more frequent in the decision table. Described above algorithm has two disadvantages: i) generating the superreduct instead of reduct, ii) additional first stage may be unnecessary if the core is empty. But for the systems focused on the fast computation of the reduct the first disadvantage is not the key problem. The core calculation can be achieved with a combinational logic block, and thus add respectively little time to the whole process. Algorithm presented in this paper was implemented in Field Programmable Gate Array (FPGA) as a digital device consisting of blocks that process the data in a single step. Calculating the core is done by the comparators connected to the block called 'singleton detector', which detects if the input word contains only single 'one'. Calculating the number of occurrences of the attribute is performed in the combinational block made up of the cascade of the adders. The superreduct generation process is iterative and thus needs the sequential circuit for controlling the calculations. For the research purpose, the algorithm was also implemented in C language and run on a PC. The times of execution of the reduct calculation in a hardware and software were considered. Results show increase in the speed of data processing.

Keywords: data reduction, digital systems design, field programmable gate array (FPGA), reduct, rough set

Procedia PDF Downloads 212

Authors: Vahid Bayrami Rad

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In this research, common methods and techniques have been investigated with a focus on intelligent fault finding and monitoring systems in the oil industry. In fact, remote and intelligent control methods are considered a necessity for implementing various operations in the oil industry, but benefiting from the knowledge extracted from countless data generated with the help of data mining algorithms. It is a avoid way to speed up the operational process for monitoring and troubleshooting in today's big oil companies. Therefore, by comparing data mining algorithms and checking the efficiency and structure and how these algorithms respond in different conditions, The proposed (Bayesian) algorithm using data clustering and their analysis and data evaluation using a colored Petri net has provided an applicable and dynamic model from the point of view of reliability and response time. Therefore, by using this method, it is possible to achieve a dynamic and consistent model of the remote control system and prevent the occurrence of leakage in oil pipelines and refineries and reduce costs and human and financial errors. Statistical data The data obtained from the evaluation process shows an increase in reliability, availability and high speed compared to other previous methods in this proposed method.

Keywords: wireless sensors, petroleum equipment troubleshooting, Bayesian algorithm, colored Petri net, rapid miner, data mining-reliability

Procedia PDF Downloads 57

Authors: Amara Prakasa Rao, N. V. S. N. Sarma

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This paper describes the synthesis of linear array geometry with minimum sidelobe level and null control using the Taguchi method. Based on the concept of the orthogonal array, Taguchi method effectively reduces the number of tests required in an optimization process. Taguchi method has been successfully applied in many fields such as mechanical, chemical engineering, power electronics, etc. Compared to other evolutionary methods such as genetic algorithms, simulated annealing and particle swarm optimization, the Taguchi method is much easier to understand and implement. It requires less computational/iteration processing to optimize the problem. Different cases are considered to illustrate the performance of this technique. Simulation results show that this method outperforms the other evolution algorithms (like GA, PSO) for smart antenna systems design.

Keywords: array factor, beamforming, null placement, optimization method, orthogonal array, Taguchi method, smart antenna system

Procedia PDF Downloads 381

Authors: S. Valarmathi, P. B. Harathi, R. Sridhar, S. Balasubramanian

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Machine learning tools in medical diagnosis is increasing due to the improved effectiveness of classification and recognition systems to help medical experts in diagnosing breast cancer. In this study, ID3 chooses the splitting attribute with the highest gain in information, where gain is defined as the difference between before the split versus after the split. It is applied for age, location, taluk, stage, year, period, martial status, treatment, heredity, sex, and habitat against Very Serious (VS), Very Serious Moderate (VSM), Serious (S) and Not Serious (NS) to calculate the gain of information. The ranked histogram gives the gain of each field for the breast cancer data. The doctors use TNM staging which will decide the risk level of the breast cancer and play an important decision making field in fuzzy logic for perception based measurement. Spatial risk area (taluk) of the breast cancer is calculated. Result clearly states that Coimbatore (North and South) was found to be risk region to the breast cancer than other areas at 20% criteria. Weighted value of taluk was compared with criterion value and integrated with Map Object to visualize the results. ID3 algorithm shows the high breast cancer risk regions in the study area. The study has outlined, discussed and resolved the algorithms, techniques / methods adopted through soft computing methodology like ID3 algorithm for prognostic decision making in the seriousness of the breast cancer.

Keywords: ID3 algorithm, breast cancer, fuzzy logic, MATLAB

Procedia PDF Downloads 509

Authors: Angelina Anani, Ignacio Ortiz Flores, Haitao Li

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The use of underground mining methods have increased significantly over the past decades. This increase has also been spared on by several mines transitioning from surface to underground mining. However, determining the transition depth can be a challenging task, especially when coupled with production schedule optimization. Several researchers have simplified the problem by excluding operational features relevant to production schedule optimization. Our research objective is to investigate the extent to which operational features of transition mines accounted for affect the optimal production schedule. We also provide a framework for factors to consider in production schedule optimization for transition mines. An integrated mixed-integer linear programming (MILP) model is developed that maximizes the NPV as a function of production schedule and transition depth. A case study is performed to validate the model, with a comparative sensitivity analysis to obtain operational insights.

Keywords: underground mining, transition mines, mixed-integer linear programming, production schedule

Procedia PDF Downloads 158

Authors: Mohammad Izadkhah, Mojtaba Hoseini, Alireza Khalili Tehrani

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In this paper we presented a new method for tracking flying targets in color video sequences based on contour and kernel. The aim of this work is to overcome the problem of losing target in changing light, large displacement, changing speed, and occlusion. The proposed method is made in three steps, estimate the target location by particle filter, segmentation target region using neural network and find the exact contours by greedy snake algorithm. In the proposed method we have used both region and contour information to create target candidate model and this model is dynamically updated during tracking. To avoid the accumulation of errors when updating, target region given to a perceptron neural network to separate the target from background. Then its output used for exact calculation of size and center of the target. Also it is used as the initial contour for the greedy snake algorithm to find the exact target's edge. The proposed algorithm has been tested on a database which contains a lot of challenges such as high speed and agility of aircrafts, background clutter, occlusions, camera movement, and so on. The experimental results show that the use of neural network increases the accuracy of tracking and segmentation.

Keywords: video tracking, particle filter, greedy snake, neural network

Procedia PDF Downloads 331

Authors: Heri Suryoatmojo, Adi Kurniawan, Feby A. Pamuji, Nursalim, Syaffaruddin, Herbert Innah

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Photovoltaic (PV) system hybrid with diesel system is utilized widely for electrification in remote area. PV output power fluctuates due to uncertainty condition of temperature and sun irradiance. When the penetration of PV power is large, the reliability of the power utility will be disturbed and seriously impact the unstable frequency of system. Therefore, designing a robust frequency controller in PV-diesel hybrid power system is very important. This paper proposes new method of frequency control application in hybrid PV-diesel system based on artificial neural network (ANN). This method can minimize the frequency deviation without smoothing PV output power that controlled by maximum power point tracking (MPPT) method. The neural network algorithm controller considers average irradiance, change of irradiance and frequency deviation. In order the show the effectiveness of proposed algorithm, the addition of battery as energy storage system is also presented. To validate the proposed method, the results of proposed system are compared with the results of similar system using MPPT only. The simulation results show that the proposed method able to suppress frequency deviation smaller compared to the results of system using MPPT only.

Keywords: energy storage system, frequency deviation, hybrid power generation, neural network algorithm

Procedia PDF Downloads 490

Authors: B. X. Tchomeni, A. A. Alugongo, T. B. Tengen

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In this paper, de Laval rotor system has been characterized by a hinge model and its transient response numerically treated for a dynamic solution. The effect of the ensuing non-linear disturbances namely rub and breathing crack is numerically simulated. Subsequently, three analysis methods: Orbit Analysis, Fast Fourier Transform (FFT) and Wavelet Transform (WT) are employed to extract features of the vibration signal of the faulty system. An analysis of the system response orbits clearly indicates the perturbations due to the rotor-to-stator contact. The sensitivities of WT to the variation in system speed have been investigated by Continuous Wavelet Transform (CWT). The analysis reveals that features of crack, rubs and unbalance in vibration response can be useful for condition monitoring. WT reveals its ability to detect non-linear signal, and obtained results provide a useful tool method for detecting machinery faults.

Keywords: Continuous wavelet, crack, discrete wavelet, high acceleration, low acceleration, nonlinear, rotor-stator, rub

Procedia PDF Downloads 339

Authors: Amir Hossein Hejazi, Nima Amjady

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In recent years, due to environmental issues traditional energy sources had been replaced by renewable ones. Wind energy as the fastest growing renewable energy shares a considerable percent of energy in power electricity markets. With this fast growth of wind energy worldwide, owners and operators of wind farms, transmission system operators, and energy traders need reliable and secure forecasts of wind energy production. In this paper, a new forecasting strategy is proposed for short-term wind power prediction based on Echo State Networks (ESN). The forecast engine utilizes state-of-the-art training process including dynamical reservoir with high capability to learn complex dynamics of wind power or wind vector signals. The study becomes more interesting by incorporating prediction of wind direction into forecast strategy. The Big Bang-Big Crunch (BB-BC) evolutionary optimization algorithm is adopted for adjusting free parameters of ESN-based forecaster. The proposed method is tested by real-world hourly data to show the efficiency of the forecasting engine for prediction of both wind vector and wind power output of aggregated wind power production.

Keywords: wind power forecasting, echo state network, big bang-big crunch, evolutionary optimization algorithm

Procedia PDF Downloads 561

Authors: Tai-Ping Chang

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In the present study, the effects on chaotic motion of single-walled carbon nanotube (SWCNT) due to the linear and nonlinear damping are investigated. By using the Hamilton’s principle, the nonlinear governing equation of the single-walled carbon nanotube embedded in a matrix is derived. The Galerkin’s method is adopted to simplify the integro-partial differential equation into a nonlinear dimensionless governing equation for the SWCNT, which turns out to be a forced Duffing equation. The variations of the Lyapunov exponents of the SWCNT with damping and harmonic forcing amplitudes are investigated. Based on the computations of the top Lyapunov exponent, it is concluded that the chaotic motion of the SWCNT occurs when the amplitude of the periodic excitation exceeds certain value, besides, the chaotic motion of the SWCNT occurs with small linear damping and tiny nonlinear damping.

Keywords: chaotic motion, damping, Lyapunov exponents, single-walled carbon nanotube

Procedia PDF Downloads 311

Authors: Ghodhbani Salah, Jemili Farah

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With the growth of cyber attacks, information safety has become an important issue all over the world. Many firms rely on security technologies such as intrusion detection systems (IDSs) to manage information technology security risks. IDSs are considered to be the last line of defense to secure a network and play a very important role in detecting large number of attacks. However the main problem with today’s most popular commercial IDSs is generating high volume of alerts and huge number of false positives. This drawback has become the main motivation for many research papers in IDS area. Hence, in this paper we present a data mining technique to assist network administrators to analyze and reduce false positive alarms that are produced by an IDS and increase detection accuracy. Our data mining technique is unsupervised clustering method based on hybrid ANT algorithm. This algorithm discovers clusters of intruders’ behavior without prior knowledge of a possible number of classes, then we apply K-means algorithm to improve the convergence of the ANT clustering. Experimental results on real dataset show that our proposed approach is efficient with high detection rate and low false alarm rate.

Keywords: intrusion detection system, alarm filtering, ANT class, ant clustering, intruders’ behaviors, false alarms

Procedia PDF Downloads 395

Authors: Hamid Sharif, Nazish Saleem Abbas, Muhammad Haris Jamil

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An adaptive filter is a filter that self-adjusts its transfer function according to an optimization algorithm driven by an error signal. Because of the complexity of the optimization algorithms, most adaptive filters are digital filters. Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications, including adaptive noise cancellation and echo cancellation. Acoustic echo cancellation is a common occurrence in today’s telecommunication systems. The signal interference caused by acoustic echo is distracting to both users and causes a reduction in the quality of the communication. In this paper, we review different techniques of adaptive filtering to reduce this unwanted echo. In this paper, we see the behavior of techniques and algorithms of adaptive filtering like Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Variable Step-Size Least Mean Square (VSLMS), Variable Step-Size Normalized Least Mean Square (VSNLMS), New Varying Step Size LMS Algorithm (NVSSLMS) and Recursive Least Square (RLS) algorithms to reduce this unwanted echo, to increase communication quality.

Keywords: adaptive acoustic, echo cancellation, LMS algorithm, adaptive filter, normalized least mean square (NLMS), variable step-size least mean square (VSLMS)

Procedia PDF Downloads 70

Authors: Toshinori Nawata

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In this paper a nonlinear feedback control called augmented automatic choosing control (AACC) for a class of nonlinear systems with constrained input is presented. When designing the control, a constant term which arises from linearization of a given nonlinear system is treated as a coefficient of a stable zero dynamics. Parameters of the control are suboptimally selected by maximizing the stable region in the sense of Lyapunov with the aid of a genetic algorithm. This approach is applied to a field excitation control problem of power system to demonstrate the splendidness of the AACC. Simulation results show that the new controller can improve performance remarkably well.

Keywords: augmented automatic choosing control, nonlinear control, genetic algorithm, zero dynamics

Procedia PDF Downloads 471

Authors: Xiaolin Zeng, Lei Cheng, Zhirong Li, Xueping Liu

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Aiming at the problem of low accuracy and susceptibility to cultural relic diseases in the generation of high-resolution archaeology drawings by current image generation algorithms, an archaeology drawings generation algorithm based on a conditional generative adversarial network is proposed. An attention mechanism is added into the high-resolution image generation network as the backbone network, which enhances the line feature extraction capability and improves the accuracy of line drawing generation. A dual-branch parallel architecture consisting of two backbone networks is implemented, where the semantic translation branch extracts semantic features from orthophotographs of cultural relics, and the gradient screening branch extracts effective gradient features. Finally, the fusion fine-tuning module combines these two types of features to achieve the generation of high-quality and high-resolution archaeology drawings. Experimental results on the self-constructed archaeology drawings dataset of grotto temple statues show that the proposed algorithm outperforms current mainstream image generation algorithms in terms of pixel accuracy (PA), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) and can be used to assist in drawing archaeology drawings.

Keywords: archaeology drawings, digital heritage, image generation, deep learning

Procedia PDF Downloads 43

Authors: A. Mansouri, F. Krim

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This paper discusses the implementation and design of both linear PI and fuzzy controllers for DC-DC boost converters. Design of PI controllers is based on temporal response of closed-loop converters, while fuzzy controllers design is based on heuristic knowledge of boost converters. Linear controller implementation is quite straightforward relying on mathematical models, while fuzzy controller implementation employs one or more artificial intelligences techniques. Comparison between these boost controllers is made in design aspect. Experimental results show that the proposed fuzzy controller system is robust against input voltage and load resistance changing and in respect of start-up transient. Results indicate that fuzzy controller can achieve best control performance concerning faster transient response, steady-state response good stability and accuracy under different operating conditions. Fuzzy controller is more suitable to control boost converters.

Keywords: boost DC-DC converter, fuzzy, PI controllers, power electronics and control system

Procedia PDF Downloads 466

Authors: Carlos Huertas, Reyes Juarez-Ramirez

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Public health is one of the most critical issues today; therefore, there is great interest to improve technologies in the area of diseases detection. With machine learning and feature selection, it has been possible to aid the diagnosis of several diseases such as cancer. In this work, we present an extension to the Heat Map Based Feature Selection algorithm, this modification allows automatic threshold parameter selection that helps to improve the generalization performance of high dimensional data such as mass spectrometry. We have performed a comparison analysis using multiple cancer datasets and compare against the well known Recursive Feature Elimination algorithm and our original proposal, the results show improved classification performance that is very competitive against current techniques.

Keywords: biomarker discovery, cancer, feature selection, mass spectrometry

Procedia PDF Downloads 329

Authors: Jasbir Singh Gill, Baljit Singh

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Scheduling and mapping the application task graph on multiprocessor parallel systems is considered as the most crucial and critical NP-complete problem. Many genetic algorithms have been proposed to solve such problems. In this paper, two genetic approach based algorithms have been designed and developed with or without task duplication. The proposed algorithms work on two fitness functions. The first fitness i.e. task fitness is used to minimize the total finish time of the schedule (schedule length) while the second fitness function i.e. process fitness is concerned with allocating the tasks to the available highly efficient processor from the list of available processors (load balance). Proposed genetic-based algorithms have been experimentally implemented and evaluated with other state-of-art popular and widely used algorithms.

Keywords: parallel computing, task scheduling, task duplication, genetic algorithm

Procedia PDF Downloads 337

Authors: Hooman Torabifard

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In the last few years, with the progress of technology and computers and artificial intelligence entry into all kinds of scientific and industrial fields, the lifestyles of human life have changed and in general, the way of humans live on earth has many changes and development. Until now, some of the changes has occurred in the context of digital images and image processing and still continues. However, besides all the benefits, there have been disadvantages. One of these disadvantages is the multiplicity of images with high volume and data; the focus of this paper is on improving and developing a method for summarizing and enhancing the productivity of these images. The general method used for this purpose in this paper consists of a set of methods based on data obtained from image processing and using the PSO (Particle swarm optimization) algorithm. In the remainder of this paper, the method used is elaborated in detail.

Keywords: image summarization, particle swarm optimization, image threshold, image processing

Procedia PDF Downloads 124

Authors: A. K. M. Kamrul Islam, Abdelhamid Bouchachia, Suang Cang, Hongnian Yu

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Forecasting model has a great impact in terms of prediction and continues to do so into the future. Although many forecasting models have been studied in recent years, most researchers focus on different forecasting methods based on fuzzy time series to solve forecasting problems. The forecasted models accuracy fully depends on the two terms that are the length of the interval in the universe of discourse and the content of the forecast rules. Moreover, a hybrid forecasting method can be an effective and efficient way to improve forecasts rather than an individual forecasting model. There are different hybrids forecasting models which combined fuzzy time series with evolutionary algorithms, but the performances are not quite satisfactory. In this paper, we proposed a hybrid forecasting model which deals with the first order as well as high order fuzzy time series and particle swarm optimization to improve the forecasted accuracy. The proposed method used the historical enrollments of the University of Alabama as dataset in the forecasting process. Firstly, we considered an automatic clustering algorithm to calculate the appropriate interval for the historical enrollments. Then particle swarm optimization and fuzzy time series are combined that shows better forecasting accuracy than other existing forecasting models.

Keywords: fuzzy time series (fts), particle swarm optimization, clustering algorithm, hybrid forecasting model

Procedia PDF Downloads 241

Authors: Seydou Sinde

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The aims of this paper are to formulate mathematical expressions that can be used to estimate the standpipe pressure (SPP). The developed formulas take into account the main factors that, directly or indirectly, affect the behavior of SPP values. Fluid rheology and well hydraulics are some of these essential factors. Mud Plastic viscosity, yield point, flow power, consistency index, flow rate, drillstring, and annular geometries are represented by the frictional pressure (Pf), which is one of the input independent parameters and is calculated, in this paper, using Herschel-Bulkley rheological model. Other input independent parameters include the rate of penetration (ROP), applied load or weight on the bit (WOB), bit revolutions per minute (RPM), bit torque (TRQ), and hole inclination and direction coupled in the hole curvature or dogleg (DL). The technique of repeating parameters and Buckingham PI theorem are used to reduce the number of the input independent parameters into the dimensionless revolutions per minute (RPMd), the dimensionless torque (TRQd), and the dogleg, which is already in the dimensionless form of radians. Multivariable linear and polynomial regression technique using PTC Mathcad Prime 4.0 is used to analyze and determine the exact relationships between the dependent parameter, which is SPP, and the remaining three dimensionless groups. Three models proved sufficiently satisfactory to estimate the standpipe pressure: multivariable linear regression model 1 containing three regression coefficients for vertical wells; multivariable linear regression model 2 containing four regression coefficients for deviated wells; and multivariable polynomial quadratic regression model containing six regression coefficients for both vertical and deviated wells. Although that the linear regression model 2 (with four coefficients) is relatively more complex and contains an additional term over the linear regression model 1 (with three coefficients), the former did not really add significant improvements to the later except for some minor values. Thus, the effect of the hole curvature or dogleg is insignificant and can be omitted from the input independent parameters without significant losses of accuracy. The polynomial quadratic regression model is considered the most accurate model due to its relatively higher accuracy for most of the cases. Data of nine wells from the Middle East were used to run the developed models with satisfactory results provided by all of them, even if the multivariable polynomial quadratic regression model gave the best and most accurate results. Development of these models is useful not only to monitor and predict, with accuracy, the values of SPP but also to early control and check for the integrity of the well hydraulics as well as to take the corrective actions should any unexpected problems appear, such as pipe washouts, jet plugging, excessive mud losses, fluid gains, kicks, etc.

Keywords: standpipe, pressure, hydraulics, nondimensionalization, parameters, regression

Procedia PDF Downloads 72

Authors: Ajish Sreedharan

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With the fast evolution of digital data exchange, security information becomes much important in data storage and transmission. Due to the increasing use of images in industrial process, it is essential to protect the confidential image data from unauthorized access. As encryption process is applied to the whole image in AES ,it is difficult to improve the efficiency. In this paper, wavelet decomposition is used to concentrate the main information of image to the low frequency part. Then, AES encryption is applied to the low frequency part. The high frequency parts are XORed with the encrypted low frequency part and a wavelet reconstruction is applied. Theoretical analysis and experimental results show that the proposed algorithm has high efficiency, and satisfied security suits for image data transmission.

Keywords: discrete wavelet transforms, AES, dynamic SBox

Procedia PDF Downloads 425

Authors: Archana Gupta, Rajesh Kumar

Abstract:

The chemistry of chalcones has generated intensive scientific studies throughout the world. Especially, interest has been focused on the synthesis and biodynamic activities of chalcones. The blue light transmittance, excellent crystallizability and the two planar rings connected through a conjugated double bond show that chalcone derivatives are superior nonlinear organic compounds. 3-(2-Chloro-6-fluoro¬phen¬yl)-1-(2-thien¬yl) prop-2-en-1-one, 3-(2, 4- Dichlorophenyl) – 1 - (4-methylphenyl) – prop -2-en-1-one, (2E)-3-[4-(methylsulfanyl) phenyl]-1-(4-nitrophenyl) prop-2-en-1-one are some chalcone derivatives exhibiting non linear optical (NLO) properties. NLO materials have been extensively investigated in recent years as they are the key elements for photonic technologies of optical communication, optical interconnect oscillator, amplifier, frequency converter etc. Due to their high molecular hyperpolarizabilities, organic materials display a number of significant NLO properties. Experimental measurements and theoretical calculations on molecular hyperpolarizability β have become one of the key factors in the design of second order NLO materials. Theoretical determination of hyperpolarizability is quite useful both in understanding the relationship between the molecular structure and NLO properties. It also provides a guideline to experimentalists for the design and synthesis of organic NLO materials. Quantum-chemical calculations have made an important contribution to the understanding of the electronic polarization underlying the molecular NLO processes and the establishment of structure–property relationships. In the present investigation, the detailed vibrational analysis of some chalcone derivatives is taken up to understand the correlation of the charge transfer interaction and the NLO activity of the molecules based on density functional theory calculations. The vibrational modes contributing toward the NLO activity have been identified and analyzed. Rather large hyperpolarizability derived by theoretical calculations suggests the possible future use of these compounds for non-linear optical applications. The study suggests the importance of π - conjugated systems for non-linear optical properties and the possibility of charge transfer interactions. We hope that the results of the present study of chalcone derivatives are of assistance in development of new efficient materials for technological applications.

Keywords: hyperpolarizability, molecular structure, NLO material, quantum chemical calculations

Procedia PDF Downloads 226

Authors: Salman Hameed

Abstract:

In this paper, a self-tuning fuzzy PI controller (STFPIC) is proposed for thyristor controlled series capacitor (TCSC) to improve power system dynamic performance. In a STFPIC controller, the output scaling factor is adjusted on-line by an updating factor (α). The value of α is determined from a fuzzy rule-base defined on error (e) and change of error (Δe) of the controlled variable. The proposed self-tuning controller is designed using a very simple control rule-base and the most natural and unbiased membership functions (MFs) (symmetric triangles with equal base and 50% overlap with neighboring MFs). The comparative performances of the proposed STFPIC and the standard fuzzy PI controller (FPIC) have been investigated on a multi-machine power system (namely, 4 machine two area system) through detailed non-linear simulation studies using MATLAB/SIMULINK. From the simulation studies it has been found out that for damping oscillations, the performance of the proposed STFPIC is better than that obtained by the standard FPIC. Moreover, the proposed STFPIC as well as the FPIC have been found to be quite effective in damping oscillations over a wide range of operating conditions and are quite effective in enhancing the power carrying capability of the power system significantly.

Keywords: genetic algorithm, power system stability, self-tuning fuzzy controller, thyristor controlled series capacitor

Procedia PDF Downloads 416

Authors: Ersan Aktas, Eren Turanoğuz

Abstract:

In this study, the quad-electrical rotor driven unmanned aerial vehicle system is designed and modeled using fundamental dynamic equations. After that, mechanical, electronical and control system of the air vehicle are designed and implemented. Brushless motor speeds are altered via electronic speed controllers in order to achieve desired controllability. The vehicle's fundamental Euler angles (i.e., roll angle, pitch angle, and yaw angle) are obtained via AHRS sensor. These angles are provided as an input to the control algorithm that run on soft the processor on the electronic card. The vehicle control algorithm is implemented in the electronic card. Controller is designed and improved for each Euler angles. Finally, flight tests have been performed to observe and improve the flight characteristics.

Keywords: quadrotor, UAS applications, control architectures, PID

Procedia PDF Downloads 352