Search results for: whale optimization algorithm

Authors: V. S. S. Kumar, B. Vikram, G. C. S. Reddy

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Time and cost are the two critical objectives of construction project management and are not independent but intricately related. Trade-off between project duration and cost are extensively discussed during project scheduling because of practical relevance. Generally when the project duration is compressed, the project calls for an increase in labor and more productive equipments, which increases the cost. Thus, the construction time-cost optimization is defined as a process to identify suitable construction activities for speeding up to attain the best possible savings in both time and cost. As there is hidden tradeoff relationship between project time and cost, it might be difficult to predict whether the total cost would increase or decrease as a result of compressing the schedule. Different combinations of duration and cost for the activities associated with the project determine the best set in the time-cost optimization. Therefore, the contractors need to select the best combination of time and cost to perform each activity, all of which will ultimately determine the project duration and cost. In this paper, the fuzzy set theory is used to model the uncertainties in the project environment for time-cost trade off analysis.

Keywords: fuzzy sets, uncertainty, qualitative factors, decision making

Procedia PDF Downloads 652

Authors: Senol Dogan, Gunay Karli

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Identification of relevant publications based on a Medline query is time-consuming and error-prone. An all based process has the potential to solve this problem without any manual work. To the best of our knowledge, our study is the first to investigate the ability of machine learning to identify relevant articles accurately. 5 different machine learning algorithms were tested using 23 predictors based on several metadata fields attached to publications. We find that the Boosted model is the best-performing algorithm and its overall accuracy is 96%. In addition, specificity and sensitivity of the algorithm is 97 and 93%, respectively. As a result of the work, we understood that we can apply the same procedure to understand cancer gene expression big data.

Keywords: prediction of publications, machine learning, algorithms, bariatric surgery, comparison of algorithms, boosted, tree, logistic regression, ANN model

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Authors: Usman Jilani, Ibad Khurram, Irshad Hussain

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Environmentally sustainable waste management is a challenging task as it involves multiple and diverse economic, environmental, technical and regulatory issues. Municipal Solid Waste Management (MSWM) is more challenging in developing countries like Pakistan due to lack of awareness, technology and human resources, insufficient funding, inefficient collection and transport mechanism resulting in the lack of a comprehensive waste management system. This work presents an overview of current MSWM practices in Peshawar, the provincial capital of Khyber Pakhtunkhwa, Pakistan and proposes a better and sustainable integrated solid waste management system with incineration (Waste to Energy) option. The diverted waste would otherwise generate revenue; minimize land fill requirement and negative impact on the environment. The proposed optimized solution utilizing scientific techniques (like mathematical modeling, optimization algorithms and GIS) as decision support tools enhances the technical & institutional efficiency leading towards a more sustainable waste management system through incorporating: - Improved collection mechanisms through optimized transportation / routing and, - Resource recovery through incineration and selection of most feasible sites for transfer stations, landfills and incineration plant. These proposed methods shift the linear waste management system towards a cyclic system and can also be used as a decision support tool by the WSSP (Water and Sanitation Services Peshawar), agency responsible for the MSWM in Peshawar.

Keywords: municipal solid waste management, incineration, mathematical modeling, optimization, GIS, Peshawar

Procedia PDF Downloads 376

Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

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Authors: Sweeti, Anoop Kant Godiyal, Neha Singh, Sneh Anand, B. K. Panigrahi, Jayasree Santhosh

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This work is about developing a tool for visualization and segmentation of Electroencephalograph (EEG) signals based on frequency domain features. Change in the frequency domain characteristics are correlated with change in mental state of the subject under study. Proposed algorithm provides a way to represent the change in the mental states using the different frequency band powers in form of segmented EEG signal. Many segmentation algorithms have been suggested in literature having application in brain computer interface, epilepsy and cognition studies that have been used for data classification. But the proposed method focusses mainly on the better presentation of signal and that’s why it could be a good utilization tool for clinician. Algorithm performs the basic filtering using band pass and notch filters in the range of 0.1-45 Hz. Advanced filtering is then performed by principal component analysis and wavelet transform based de-noising method. Frequency domain features are used for segmentation; considering the fact that the spectrum power of different frequency bands describes the mental state of the subject. Two sliding windows are further used for segmentation; one provides the time scale and other assigns the segmentation rule. The segmented data is displayed second by second successively with different color codes. Segment’s length can be selected as per need of the objective. Proposed algorithm has been tested on the EEG data set obtained from University of California in San Diego’s online data repository. Proposed tool gives a better visualization of the signal in form of segmented epochs of desired length representing the power spectrum variation in data. The algorithm is designed in such a way that it takes the data points with respect to the sampling frequency for each time frame and so it can be improved to use in real time visualization with desired epoch length.

Keywords: de-noising, multi-channel data, PCA, power spectra, segmentation

Procedia PDF Downloads 397

Authors: A. Cherifi, B. Bouazza, A. O. Dahmane, B. Yagoubi

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Transmission over Optical channels will introduce inter-symbol interference (ISI) as well as inter-channel (or inter-carrier) interference (ICI). To decrease the effects of ICI, this paper proposes equalizer for the Optical OFDM system based on the fractional Fourier transform (FrFFT). In this FrFT-OFDM system, traditional Fourier transform is replaced by fractional Fourier transform to modulate and demodulate the data symbols. The equalizer proposed consists of sampling the received signal in the different time per time symbol. Theoretical analysis and numerical simulation are discussed.

Keywords: OFDM, (FrFT) fractional fourier transform, optical OFDM, equalization algorithm

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Authors: Basil Manos, Parthena Chatzinikolaou, Fedra Kiomourtzi

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This paper presents a Multicriteria Mathematical Programming model for farm planning and sustainable optimization of agricultural production. The model can be used as a tool for the analysis and simulation of agricultural production plans, as well as for the study of impacts of various measures of Common Agriculture Policy in the member states of European Union. The model can achieve the optimum production plan of a farm or an agricultural region combining in one utility function different conflicting criteria as the maximization of gross margin and the minimization of fertilizers used, under a set of constraints for land, labor, available capital, Common Agricultural Policy etc. The proposed model was applied to the region of Larisa in central Greece. The optimum production plan achieves a greater gross return, a less fertilizers use, and a less irrigated water use than the existent production plan.

Keywords: sustainable optimization, multicriteria analysis, agricultural production, farm planning

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Authors: Taha Anjamrooz, Sareh Rajabi, Salwa Bheiry

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Selection between the available projects in bidding processes for the contractor is one of the essential areas to concentrate on. It is important for the contractor to choose the right projects within its portfolio during the tendering stage based on certain criteria. It should align the bidding process with its origination strategies and goals as a screening process to have the right portfolio pool to start with. Secondly, it should set the proper framework and use a suitable technique in order to optimize its selection process for concertation purpose and higher efforts during the tender stage with goals of success and winning. In this research paper, a two steps framework proposed to increase the efficiency of the contractor’s bidding process and the winning chance of getting the new projects awarded. In this framework, initially, all the projects pass through the first stage screening process, in which the portfolio basket will be evaluated and adjusted in accordance with the organization strategies to the reduced version of the portfolio pool, which is in line with organization activities. In the second stage, the contractor uses linear programming to optimize the portfolio pool based on available resources such as manpower, light equipment, heavy equipment, financial capability, return on investment, and success rate of winning the bid. Therefore, this optimization model will assist the contractor in utilizing its internal resource to its maximum and increase its winning chance for the new project considering past experience with clients, built-relation between two parties, and complexity in the exertion of the projects. The objective of this research will be to increase the contractor's winning chance in the bidding process based on the success rate and expected return on investment.

Keywords: bidding process, internal resources, optimization, contracting portfolio management

Procedia PDF Downloads 142

Authors: Manpreet Kaur

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When legitimate credit card users are mistakenly labelled as fraudulent in numerous financial delated applications, there are numerous ethical problems. The innovative machine learning approach we have suggested in this research outperforms the current models and shows how to model a data set for credit card fraud detection while minimizing false positives. As a result, we advise using random forests as the best machine learning method for predicting and identifying credit card transaction fraud. The majority of victims of these fraudulent transactions were discovered to be credit card users over the age of 60, with a higher percentage of fraudulent transactions taking place between the specific hours.

Keywords: automated fraud detection, isolation forest method, local outlier factor, ML algorithm, credit card

Procedia PDF Downloads 114

Authors: N. D'Amico, E. Grossi, B. Colombo, F. Rigiroli, M. Buscema, D. Fazzini, G. Cornalba, S. Papa

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Purpose: To characterize, through a radiomic approach, the nature of nodules considered non-specific by expert radiologists, recognized in magnetic resonance mammography (MRm) with T1-weighted (T1w) sequences with paramagnetic contrast. Material and Methods: 47 cases out of 1200 undergoing MRm, in which the MRm assessment gave uncertain classification (non-specific nodules), were admitted to the study. The clinical outcome of the non-specific nodules was later found through follow-up or further exams (biopsy), finding 35 benign and 12 malignant. All MR Images were acquired at 1.5T, a first basal T1w sequence and then four T1w acquisitions after the paramagnetic contrast injection. After a manual segmentation of the lesions, done by a radiologist, and the extraction of 150 radiomic features (30 features per 5 subsequent times) a machine learning (ML) approach was used. An evolutionary algorithm (TWIST system based on KNN algorithm) was used to subdivide the dataset into training and validation test and to select features yielding the maximal amount of information. After this pre-processing, different machine learning systems were applied to develop a predictive model based on a training-testing crossover procedure. 10 cases with a benign nodule (follow-up older than 5 years) and 18 with an evident malignant tumor (clear malignant histological exam) were added to the dataset in order to allow the ML system to better learn from data. Results: NaiveBayes algorithm working on 79 features selected by a TWIST system, resulted to be the best performing ML system with a sensitivity of 96% and a specificity of 78% and a global accuracy of 87% (average values of two training-testing procedures ab-ba). The results showed that in the subset of 47 non-specific nodules, the algorithm predicted the outcome of 45 nodules which an expert radiologist could not identify. Conclusion: In this pilot study we identified a radiomic approach allowing ML systems to perform well in the diagnosis of a non-specific nodule at MR mammography. This algorithm could be a great support for the early diagnosis of malignant breast tumor, in the event the radiologist is not able to identify the kind of lesion and reduces the necessity for long follow-up. Clinical Relevance: This machine learning algorithm could be essential to support the radiologist in early diagnosis of non-specific nodules, in order to avoid strenuous follow-up and painful biopsy for the patient.

Keywords: breast, machine learning, MRI, radiomics

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Authors: Hassan S. Hussein, Rania A. Abul Seoud, Amr M. Refaat

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Scientific researchers face in the analysis of very large data sets that is increasing noticeable rate in today’s and tomorrow’s technologies. Hadoop and Spark are types of software that developed frameworks. Hadoop framework is suitable for many Different hardware platforms. In this research, a scientific Linux cluster for Big Data analysis (SLBD) is presented. SLBD runs open source software with large computational capacity and high performance cluster infrastructure. SLBD composed of one cluster contains identical, commodity-grade computers interconnected via a small LAN. SLBD consists of a fast switch and Gigabit-Ethernet card which connect four (nodes). Cloudera Manager is used to configure and manage an Apache Hadoop stack. Hadoop is a framework allows storing and processing big data across the cluster by using MapReduce algorithm. MapReduce algorithm divides the task into smaller tasks which to be assigned to the network nodes. Algorithm then collects the results and form the final result dataset. SLBD clustering system allows fast and efficient processing of large amount of data resulting from different applications. SLBD also provides high performance, high throughput, high availability, expandability and cluster scalability.

Keywords: big data platforms, cloudera manager, Hadoop, MapReduce

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Authors: Sukhveer Singh, Sandeep Singh

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A bi-objective fuzzy transportation problem with the objectives to minimize the total fuzzy cost and fuzzy time of transportation without according priorities to them is considered. To the best of our knowledge, there is no method in the literature to find efficient solutions of the bi-objective transportation problem under uncertainty. In this paper, a bi-objective transportation problem in an uncertain environment has been formulated. An algorithm has been proposed to find efficient solutions of the bi-objective transportation problem under uncertainty. The proposed algorithm avoids the degeneracy and gives the optimal solution faster than other existing algorithms for the given uncertain transportation problem.

Keywords: uncertain transportation problem, efficient solution, ranking function, fuzzy transportation problem

Procedia PDF Downloads 525

Authors: Wenjiang Deng, Tian Mou, Yudi Pawitan, Trung Nghia Vu

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Estimation of isoform-level gene expression from RNA-seq data depends on simplifying assumptions, such as uniform reads distribution, that are easily violated in real data. Such violations typically lead to biased estimates. Most existing methods provide a bias correction step(s), which is based on biological considerations, such as GC content–and applied in single samples separately. The main problem is that not all biases are known. For example, new technologies such as single-cell RNA-seq (scRNA-seq) may introduce new sources of bias not seen in bulk-cell data. This study introduces a method called XAEM based on a more flexible and robust statistical model. Existing methods are essentially based on a linear model Xβ, where the design matrix X is known and derived based on the simplifying assumptions. In contrast, XAEM considers Xβ as a bilinear model with both X and β unknown. Joint estimation of X and β is made possible by simultaneous analysis of multi-sample RNA-seq data. Compared to existing methods, XAEM automatically performs empirical correction of potentially unknown biases. XAEM implements an alternating expectation-maximization (AEM) algorithm, alternating between estimation of X and β. For speed XAEM utilizes quasi-mapping for read alignment, thus leading to a fast algorithm. Overall XAEM performs favorably compared to other recent advanced methods. For simulated datasets, XAEM obtains higher accuracy for multiple-isoform genes, particularly for paralogs. In a differential-expression analysis of a real scRNA-seq dataset, XAEM achieves substantially greater rediscovery rates in an independent validation set.

Keywords: alternating EM algorithm, bias correction, bilinear model, gene expression, RNA-seq

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Authors: Mohammad Besharatloo

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Intrusion detection is an important research topic in network security because of increasing growth in the use of computer network services. Intrusion detection is done with the aim of detecting the unauthorized use or abuse in the networks and systems by the intruders. Therefore, the intrusion detection system is an efficient tool to control the user's access through some predefined regulations. Since, the data used in intrusion detection system has high dimension, a proper representation is required to show the basis structure of this data. Therefore, it is necessary to eliminate the redundant features to create the best representation subset. In the proposed method, a hybrid model of differential evolution and firefly algorithms was employed to choose the best subset of properties. In addition, decision tree and support vector machine (SVM) are adopted to determine the quality of the selected properties. In the first, the sorted population is divided into two sub-populations. These optimization algorithms were implemented on these sub-populations, respectively. Then, these sub-populations are merged to create next repetition population. The performance evaluation of the proposed method is done based on KDD Cup99. The simulation results show that the proposed method has better performance than the other methods in this context.

Keywords: intrusion detection system, differential evolution, firefly algorithm, support vector machine, decision tree

Procedia PDF Downloads 91

Authors: Ahmed Fradi

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In a digital world in perpetual evolution and acceleration, data more and more voluminous, rich and varied, the new software solutions emerged with the Big Data phenomenon offer new opportunities to the company enabling it not only to optimize its business and to evolve its production model, but also to reorganize itself to increase competitiveness and to identify new strategic axes. Design and manufacturing industrial companies, like the others, face these challenges, data represent a major asset, provided that they know how to capture, refine, combine and analyze them. The objective of our paper is to propose a solution allowing geometric and topological information extraction from 3D CAD model (precisely STEP files) databases, with specific algorithm based on the programming paradigm MapReduce. Our proposal is the first step of our future approach to 3D CAD object retrieval.

Keywords: Big Data, MapReduce, 3D object retrieval, CAD, STEP format

Procedia PDF Downloads 541

Authors: H. E. Budak, B. Arslan-Ilkiz, N. Cakmakci, I. Gocek, U. K. Sahin, H. Acikgoz-Tufan, M. H. Arslan

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The aim of this study is to design a novel laundry algorithm for colored textiles which have significant decoloring problem. During the experimental work, bleached knitted single jersey fabric made of 100% cotton and dyed with reactive dyestuff was utilized, since according to a conducted survey textiles made of cotton are the most demanded textile products in the textile market by the textile consumers and for coloration of textiles reactive dyestuffs are the ones that are the most commonly used in the textile industry for dyeing cotton-made products. Therefore, the fabric used in this study was selected and purchased in accordance with the survey results. The fabric samples cut out of this fabric were dyed with different dyeing parameters by using Remazol Brilliant Red 3BS dyestuff in Gyrowash machine at laboratory conditions. From the alternative reactive-dyed cotton fabric samples, the ones that have high tendency to color loss were determined and examined. Accordingly, the parameters of the dyeing process used for these fabric samples were evaluated and the dyeing process which was chosen to be used for causing high tendency to color loss for the cotton fabrics was determined in order to reveal the level of improvement in color loss during this study clearly. Afterwards, all of the untreated fabric samples cut out of the fabric purchased were dyed with the dyeing process selected. When dyeing process was completed, an experimental design was created for the laundering process by using Minitab® program considering temperature, time and mechanical action as parameters. All of the washing experiments were performed in domestic washing machine. 16 washing experiments were performed with 8 different experimental conditions and 2 repeats for each condition. After each of the washing experiments, water samples of the main wash of the laundering process were measured with UV spectrophotometer. The values obtained were compared with the calibration curve of the materials used for the dyeing process. The results of the washing experiments were statistically analyzed with Minitab® program. According to the results, the most suitable washing algorithm to be used in terms of the parameters temperature, time and mechanical action for domestic washing machines for minimizing fabric color loss was chosen. The laundry algorithm proposed in this study have the ability of minimalizing the problem of color loss of colored textiles in washing machines by eliminating the negative effects of the parameters of laundering process on color of textiles without compromising the fundamental effects of basic cleaning action being performed properly. Therefore, since fabric color loss is minimized with this washing algorithm, dyestuff residuals will definitely be lower in the grey water released from the laundering process. In addition to this, with this laundry algorithm it is possible to wash and clean other types of textile products with proper cleaning effect and minimized color loss.

Keywords: color loss, laundry algorithm, textiles, domestic washing process

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Authors: Heru Syah Putra, Aji Tri Pamungkas Nurcahyo, Chuang-Jan Chang

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MOIL-SDK method refers to the spatial angle that forms a view with a different perspective from the Fisheye image. Visual Odometry forms a trusted application for extending projects by tracking using image sequences. A real-time, precise, and persistent approach that is able to contribute to the work when taking datasets and generate ground truth as a reference for the estimates of each image using the FAST Algorithm method in finding Keypoints that are evaluated during the tracking process with the 5-point Algorithm with RANSAC, as well as produce accurate estimates the camera trajectory for each rotational, translational movement on the X, Y, and Z axes.

Keywords: MOIL-SDK, intel realsense T265, Fisheye image, monocular visual odometry

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Authors: Mihai Rebenciuc, Simona Mihaela Bibic

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Branch of modern mathematics, graphs represent instruments for optimization and solving practical applications in various fields such as economic networks, engineering, network optimization, the geometry of social action, generally, complex systems including contemporary urban problems (path or transport efficiencies, biourbanism, & c.). In this paper is studied the interconnection of some urban network, which can lead to a simulation problem of a digraph through another digraph. The simulation is made univoc or more general multivoc. The concepts of fragment and atom are very useful in the study of connectivity in the digraph that is simulation - including an alternative evaluation of k- connectivity. Rough set approach in (bi)digraph which is proposed in premier in this paper contribute to improved significantly the evaluation of k-connectivity. This rough set approach is based on generalized rough sets - basic facts are presented in this paper.

Keywords: (bi)digraphs, rough set theory, systems of interacting agents, complex systems

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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

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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

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Authors: Han Ik Park, Jeong Hyeon Seong, Dong Seop Han, Su-Chul Shin, Young Chul Park

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A Spudcan is mounted on the lower leg of the jack-up rig, a device for preventing a rollover of a structure and to support the structure in a stable sea floor. At the time of inserting the surface of the spud can to penetrate when the sand layer is stable and smoothly pulled to the clay layer, and at that time of recovery when uploading the spud can is equipped with a water injection device. In this study, it is significant to optimize the shape of pipelines holes for water injection device and it was set in two kinds of shape, the oval and round. Interpretation of the subject into the site of Gulf of Mexico offshore Wind Turbine Installation Vessels (WTIV)was chosen as a target platform. Using the ANSYS Workbench commercial programs, optimal design was conducted. The results of this study can be applied to the hole-shaped design of various marine structures.

Keywords: kriging method, jack-up rig, shape optimization, spudcan

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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 1046

Authors: Pitak Keawbunsong, Sathaporn Promwong

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This paper presents an investigation on the efficiency of the optimized Davison path loss model in order to look for a suitable path loss model to design and planning DTTV propagation for small and medium urban areas in southern Thailand. Hadyai City in Songkla Province is chosen as the case study to collect the analytical data on the electric field strength. The optimization is conducted through the least square method while the efficiency index is through the statistical value of relative error (RE). The result of the least square method is the offset and slop of the frequency to be used in the optimized process. The statistical result shows that RE of the old Davidson model is at the least when being compared with the optimized Davison and the Hata models. Thus, the old Davison path loss model is the most accurate that further becomes the most optimized for the plan on the propagation network design.

Keywords: DTTV propagation, path loss model, Davidson model, least square method

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Authors: Satish Babu Rajulapati

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The removal of toxic and heavy metal contaminants from wastewater streams and industrial effluents is one of the most important environmental issues being faced world over. In the present study three bacterial cultures tolerating high concentrations of chromium were isolated from the soil and wastewater sample collected from the tanneries located in Warangal, Telangana state. The bacterial species were identified as Bacillus sp., Staphylococcus sp. and pseudomonas sp. Preliminary studies were carried out with the three bacterial species at various operating parameters such as pH and temperature. The results indicate that pseudomonas sp. is the efficient one in the uptake of Cr(VI). Further, detailed investigation of Pseudomonas sp. have been carried out to determine the efficiency of removal of Cr(VI). The various parameters influencing the biosorption of Cr(VI) such as pH, temperature, initial chromium concentration, innoculum size and incubation time have been studied. Response Surface Methodology (RSM) was applied to optimize the removal of Cr(VI). Maximum Cr(VI) removal was found to be 85.72% Cr(VI) atpH 7, temperature 35 °C, initial concentration 67mg/l, inoculums size 9 %(v/v) and time 60 hrs.

Keywords: Staphylococcus sp, chromium, RSM, optimization, Cr(IV)

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Authors: Melvin Ballera, Aldrich Olivar, Mary Soriano

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Digital computers nowadays must be able to have a utility that is capable of generating random numbers. Usually, computer-generated random numbers are not random given predefined values such as starting point and end points, making the sequence almost predictable. There are many applications of random numbers such business simulation, manufacturing, services domain, entertainment sector and other equally areas making worthwhile to design a unique method and to allow unpredictable random numbers. Applying stochastic simulation using linear congruential algorithm, it shows that as it increases the numbers of the seed and range the number randomly produced or selected by the computer becomes unique. If this implemented in an environment where random numbers are very much needed, the reliability of the random number is guaranteed.

Keywords: stochastic simulation, random numbers, linear congruential algorithm, pseudorandomness

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Authors: Subrato Saha, Yun-Hyun Cho

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In electric power steering (EPS), spoke type brushless ac (BLAC) motors offer distinct advantages over other electric motor types in terms torque smoothness, reliability and efficiency. This paper deals with the shape optimization of spoke type BLAC motor, in order to reduce cogging torque. This paper examines 3 steps skewing rotor angle, optimizing rotor core edge and rotor overlap length for reducing cogging torque in spoke type BLAC motor. The methods were applied to existing machine designs and their performance was calculated using finite- element analysis (FEA). Prototypes of the machine designs were constructed and experimental results obtained. It is shown that the FEA predicted the cogging torque to be nearly reduce using those methods.

Keywords: EPS, 3-Step skewing, spoke type BLAC, cogging torque, FEA, optimization

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Authors: Tomasz Grzes, Maciej Kopczynski, Jaroslaw Stepaniuk

Abstract:

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

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Authors: Vahid Bayrami Rad

Abstract:

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 66

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

Abstract:

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

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Authors: Mohammad Izadkhah, Mojtaba Hoseini, Alireza Khalili Tehrani

Abstract:

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 343