Search results for: time series generation
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 21838

Search results for: time series generation

21118 Increasing System Adequacy Using Integration of Pumped Storage: Renewable Energy to Reduce Thermal Power Generations Towards RE100 Target, Thailand

Authors: Mathuravech Thanaphon, Thephasit Nat

Abstract:

The Electricity Generating Authority of Thailand (EGAT) is focusing on expanding its pumped storage hydropower (PSH) capacity to increase the reliability of the system during peak demand and allow for greater integration of renewables. To achieve this requirement, Thailand will have to double its current renewable electricity production. To address the challenges of balancing supply and demand in the grid with increasing levels of RE penetration, as well as rising peak demand, EGAT has already been studying the potential for additional PSH capacity for several years to enable an increased share of RE and replace existing fossil fuel-fired generation. In addition, the role that pumped-storage hydropower would play in fulfilling multiple grid functions and renewable integration. The proposed sites for new PSH would help increase the reliability of power generation in Thailand. However, most of the electricity generation will come from RE, chiefly wind and photovoltaic, and significant additional Energy Storage capacity will be needed. In this paper, the impact of integrating the PSH system on the adequacy of renewable rich power generating systems to reduce the thermal power generating units is investigated. The variations of system adequacy indices are analyzed for different PSH-renewables capacities and storage levels. Power Development Plan 2018 rev.1 (PDP2018 rev.1), which is modified by integrating a six-new PSH system and RE planning and development aftermath in 2030, is the very challenge. The system adequacy indices through power generation are obtained using Multi-Objective Genetic Algorithm (MOGA) Optimization. MOGA is a probabilistic heuristic and stochastic algorithm that is able to find the global minima, which have the advantage that the fitness function does not necessarily require the gradient. In this sense, the method is more flexible in solving reliability optimization problems for a composite power system. The optimization with hourly time step takes years of planning horizon much larger than the weekly horizon that usually sets the scheduling studies. The objective function is to be optimized to maximize RE energy generation, minimize energy imbalances, and minimize thermal power generation using MATLAB. The PDP2018 rev.1 was set to be simulated based on its planned capacity stepping into 2030 and 2050. Therefore, the four main scenario analyses are conducted as the target of renewables share: 1) Business-As-Usual (BAU), 2) National Targets (30% RE in 2030), 3) Carbon Neutrality Targets (50% RE in 2050), and 5) 100% RE or full-decarbonization. According to the results, the generating system adequacy is significantly affected by both PSH-RE and Thermal units. When a PSH is integrated, it can provide hourly capacity to the power system as well as better allocate renewable energy generation to reduce thermal generations and improve system reliability. These results show that a significant level of reliability improvement can be obtained by PSH, especially in renewable-rich power systems.

Keywords: pumped storage hydropower, renewable energy integration, system adequacy, power development planning, RE100, multi-objective genetic algorithm

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21117 Strabismus Management in Retinoblastoma Survivors

Authors: Babak Masoomian, Masoud Khorrami Nejad, Hamid Riazi Esfahani

Abstract:

Purpose: To report the result of strabismus surgery in eye-salvaged retinoblastoma (Rb) patients. Methods: A retrospective case series including 18 patients with Rb and strabismus who underwent strabismus surgery after completing tumor treatment by a single pediatric ophthalmologist. Results: A total of 18 patients (10 females and 8 males) were included with a mean age of 13.3 ± 3.0 (range, 2-39) months at the time tumor presentation and 6.0 ± 1.5 (range, 4-9) years at the time of strabismus surgery. Ten (56%) patients had unilateral, and 8(44%) had bilateral involvement, and the most common worse eye tumor’s group was D (n=11), C (n=4), B (n=2) and E (n=1). Macula was involved by the tumors in 12 (67%) patients. The tumors were managed by intravenous chemotherapy (n=8, 47%), intra-arterial chemotherapy (n=7, 41%) and both (n=3, 17%). After complete treatment, the average time to strabismus surgery was 29.9 ± 20.5 (range, 12-84) months. Except for one, visual acuity was equal or less than 1.0 logMAR (≤ 20/200) in the affected eye. Seven (39%) patients had exotropia, 11(61%) had esotropia (P=0.346) and vertical deviation was found in 8 (48%) cases. The angle of deviation was 42.0 ± 10.4 (range, 30-60) prism diopter (PD) for esotropic and 35.7± 7.9 (range, 25-50) PD for exotropic patients (P=0.32) that after surgery significantly decreased to 8.5 ± 5.3 PD in esotropic cases and 5.9±6.7 PD in exotropic cases (P<0.001). The mean follow-up after surgery was 15.2 ± 2.0 (range, 10-24) months, in which 3 (17%) patients needed a second surgery. Conclusion: Strabismus surgery in treated Rb is safe, and results of the surgeries are acceptable and close to the general population. There was not associated with tumor recurrence or metastasis.

Keywords: retinoblastoma, strabismus, chemotherapy, surgery

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21116 Starting Order Eight Method Accurately for the Solution of First Order Initial Value Problems of Ordinary Differential Equations

Authors: James Adewale, Joshua Sunday

Abstract:

In this paper, we developed a linear multistep method, which is implemented in predictor corrector-method. The corrector is developed by method of collocation and interpretation of power series approximate solutions at some selected grid points, to give a continuous linear multistep method, which is evaluated at some selected grid points to give a discrete linear multistep method. The predictors were also developed by method of collocation and interpolation of power series approximate solution, to give a continuous linear multistep method. The continuous linear multistep method is then solved for the independent solution to give a continuous block formula, which is evaluated at some selected grid point to give discrete block method. Basic properties of the corrector were investigated and found to be zero stable, consistent and convergent. The efficiency of the method was tested on some linear, non-learn, oscillatory and stiff problems of first order, initial value problems of ordinary differential equations. The results were found to be better in terms of computer time and error bound when compared with the existing methods.

Keywords: predictor, corrector, collocation, interpolation, approximate solution, independent solution, zero stable, consistent, convergent

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21115 Nonstationary Modeling of Extreme Precipitation in the Wei River Basin, China

Authors: Yiyuan Tao

Abstract:

Under the impact of global warming together with the intensification of human activities, the hydrological regimes may be altered, and the traditional stationary assumption was no longer satisfied. However, most of the current design standards of water infrastructures were still based on the hypothesis of stationarity, which may inevitably result in severe biases. Many critical impacts of climate on ecosystems, society, and the economy are controlled by extreme events rather than mean values. Therefore, it is of great significance to identify the non-stationarity of precipitation extremes and model the precipitation extremes in a nonstationary framework. The Wei River Basin (WRB), located in a continental monsoon climate zone in China, is selected as a case study in this study. Six extreme precipitation indices were employed to investigate the changing patterns and stationarity of precipitation extremes in the WRB. To identify if precipitation extremes are stationary, the Mann-Kendall trend test and the Pettitt test, which is used to examine the occurrence of abrupt changes are adopted in this study. Extreme precipitation indices series are fitted with non-stationary distributions that selected from six widely used distribution functions: Gumbel, lognormal, Weibull, gamma, generalized gamma and exponential distributions by means of the time-varying moments model generalized additive models for location, scale and shape (GAMLSS), where the distribution parameters are defined as a function of time. The results indicate that: (1) the trends were not significant for the whole WRB, but significant positive/negative trends were still observed in some stations, abrupt changes for consecutive wet days (CWD) mainly occurred in 1985, and the assumption of stationarity is invalid for some stations; (2) for these nonstationary extreme precipitation indices series with significant positive/negative trends, the GAMLSS models are able to capture well the temporal variations of the indices, and perform better than the stationary model. Finally, the differences between the quantiles of nonstationary and stationary models are analyzed, which highlight the importance of nonstationary modeling of precipitation extremes in the WRB.

Keywords: extreme precipitation, GAMLSSS, non-stationary, Wei River Basin

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21114 Random Access in IoT Using Naïve Bayes Classification

Authors: Alhusein Almahjoub, Dongyu Qiu

Abstract:

This paper deals with the random access procedure in next-generation networks and presents the solution to reduce total service time (TST) which is one of the most important performance metrics in current and future internet of things (IoT) based networks. The proposed solution focuses on the calculation of optimal transmission probability which maximizes the success probability and reduces TST. It uses the information of several idle preambles in every time slot, and based on it, it estimates the number of backlogged IoT devices using Naïve Bayes estimation which is a type of supervised learning in the machine learning domain. The estimation of backlogged devices is necessary since optimal transmission probability depends on it and the eNodeB does not have information about it. The simulations are carried out in MATLAB which verify that the proposed solution gives excellent performance.

Keywords: random access, LTE/LTE-A, 5G, machine learning, Naïve Bayes estimation

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21113 A Conceptual Study for Investigating the Preliminary State of Energy at the Birth of Universe and Understanding Its Emergence From the State of Nothing

Authors: Mahmoud Reza Hosseini

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The universe is in a continuous expansion process, resulting in the reduction of its density and temperature. Also, by extrapolating back from its current state, the universe at its early times is studied known as the big bang theory. According to this theory, moments after creation, the universe was an extremely hot and dense environment. However, its rapid expansion due to nuclear fusion led to a reduction in its temperature and density. This is evidenced through the cosmic microwave background and the universe structure at a large scale. However, extrapolating back further from this early state reaches singularity which cannot be explained by modern physics and the big bang theory is no longer valid. In addition, one can expect a nonuniform energy distribution across the universe from a sudden expansion. However, highly accurate measurements reveal an equal temperature mapping across the universe which is contradictory to the big bang principles. To resolve this issue, it is believed that cosmic inflation occurred at the very early stages of the birth of the universe. According to the cosmic inflation theory, the elements which formed the universe underwent a phase of exponential growth due to the existence of a large cosmological constant. The inflation phase allows the uniform distribution of energy so that an equal maximum temperature could be achieved across the early universe. Also, the evidence of quantum fluctuations of this stage provides a means for studying the types of imperfections the universe would begin with. Although well-established theories such as cosmic inflation and the big bang together provide a comprehensive picture of the early universe and how it evolved into its current state, they are unable to address the singularity paradox at the time of universe creation. Therefore, a practical model capable of describing how the universe was initiated is needed. This research series aims at addressing the singularity issue by introducing a state of energy called a “neutral state” possessing an energy level which is referred to as the “base energy”. The governing principles of base energy are discussed in detail in our second paper in the series “A Conceptual Study for Addressing the Singularity of the Emerging Universe” which is discussed in detail. To establish a complete picture, the origin of the base energy should be identified and studied. In this research paper, the mechanism which led to the emergence of this natural state and its corresponding base energy is proposed. In addition, the effect of the base energy in the space-time fabric is discussed. Finally, the possible role of the base energy in quantization and energy exchange is investigated. Therefore, the proposed concept in this research series provides a road map for enhancing our understating of the universe's creation from nothing and its evolution and discusses the possibility of base energy as one of the main building blocks of this universe.

Keywords: big bang, cosmic inflation, birth of universe, energy creation, universe evolution

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21112 A Comparative Study of the Techno-Economic Performance of the Linear Fresnel Reflector Using Direct and Indirect Steam Generation: A Case Study under High Direct Normal Irradiance

Authors: Ahmed Aljudaya, Derek Ingham, Lin Ma, Kevin Hughes, Mohammed Pourkashanian

Abstract:

Researchers, power companies, and state politicians have given concentrated solar power (CSP) much attention due to its capacity to generate large amounts of electricity whereas overcoming the intermittent nature of solar resources. The Linear Fresnel Reflector (LFR) is a well-known CSP technology type for being inexpensive, having a low land use factor, and suffering from low optical efficiency. The LFR was considered a cost-effective alternative option to the Parabolic Trough Collector (PTC) because of its simplistic design, and this often outweighs its lower efficiency. The LFR has been found to be a promising option for directly producing steam to a thermal cycle in order to generate low-cost electricity, but also it has been shown to be promising for indirect steam generation. The purpose of this important analysis is to compare the annual performance of the Direct Steam Generation (DSG) and Indirect Steam Generation (ISG) of LFR power plants using molten salt and other different Heat Transfer Fluids (HTF) to investigate their technical and economic effects. A 50 MWe solar-only system is examined as a case study for both steam production methods in extreme weather conditions. In addition, a parametric analysis is carried out to determine the optimal solar field size that provides the lowest Levelized Cost of Electricity (LCOE) while achieving the highest technical performance. As a result of optimizing the optimum solar field size, the solar multiple (SM) is found to be between 1.2 – 1.5 in order to achieve as low as 9 Cent/KWh for the direct steam generation of the linear Fresnel reflector. In addition, the power plant is capable of producing around 141 GWh annually and up to 36% of the capacity factor, whereas the ISG produces less energy at a higher cost. The optimization results show that the DSG’s performance overcomes the ISG in producing around 3% more annual energy, 2% lower LCOE, and 28% less capital cost.

Keywords: concentrated solar power, levelized cost of electricity, linear Fresnel reflectors, steam generation

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21111 Software Quality Assurance in 5G Technology-Redefining Wireless Communication: A Comprehensive Survey

Authors: Sumbal Riaz, Sardar-un-Nisa, Mehreen Sirshar

Abstract:

5G - The 5th generation of mobile phone and data communication standards is the next edge of innovation for whole mobile industry. 5G is Real Wireless World System and it will provide a totally wireless communication system all over the world without limitations. 5G uses many 4g technologies and it will hit the market in 2020. This research is the comprehensive survey on the quality parameters of 5G technology.5G provide High performance, Interoperability, easy roaming, fully converged services, friendly interface and scalability at low cost. To meet the traffic demands in future fifth generation wireless communications systems will include i) higher densification of heterogeneous networks with massive deployment of small base stations supporting various Radio Access Technologies (RATs), ii) use of massive Multiple Input Multiple Output (MIMO) arrays, iii) use of millimetre Wave spectrum where larger wider frequency bands are available, iv) direct device to device (D2D) communication, v) simultaneous transmission and reception, vi) cognitive radio technology.

Keywords: 5G, 5th generation, innovation, standard, wireless communication

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21110 Methaheuristic Bat Algorithm in Training of Feed-Forward Neural Network for Stock Price Prediction

Authors: Marjan Golmaryami, Marzieh Behzadi

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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

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21109 Changes in Foreign Direct Investment Policy of India and Its Impact on Economic Development

Authors: Kishor P. Kadam

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Foreign direct investment policy (FDI) is defined as an investment involving a long term relationship and reflecting a long duration interest and control of a resident entity in the home country (foreign direct investor or parent firm) in the host country. India has been one of the most translucent and open-minded FDI regimes among the emerging and developing economies. There is clear cut mentioned about the sectoral caps for foreign investment. The policy problems that have been identified by time to time surveys as acting as additional hurdles for FDI are laws, regulatory systems and government monopolies that do not have contemporary relevance. Foreign investment policies in the post-reforms period have emphasized greater encouragement and mobilization of non-debt creating private inflows for plunging reliance on debt flows. This paper will focus on how foreign direct investment policy changed from 1990-91 up to now. A time series data of 25 years is used for analysing the policy changes. It is observed that India has more liberal policy. The growth in number of Greenfield investments in India has been more impressive than the number of M&A deals whereas equity capital for incorporated bodies FDI inflows has been increased continuously 2014-15. India has made major changes in FDI Policy, and it has positive impact on economic development.

Keywords: FDI, India, economic development, government

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21108 Application of Monitoring of Power Generation through GPRS Network in Rural Residênias Cabo Frio/Rj

Authors: Robson C. Santos, David D. Oliveira, Matheus M. Reis, Gerson G. Cunha, Marcos A. C. Moreira

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The project demonstrates the construction of a solar power generation, integrated inverter equipment to a "Grid-Tie" by converting direct current generated by solar panels, into alternating current, the same parameters of frequency and voltage concessionaire distribution network. The energy generated is quantified by smart metering module that transmits the information in specified periods of time to a microcontroller via GSM modem. The modem provides the measured data on the internet, using networks and cellular antennas. The monitoring, fault detection and maintenance are performed by a supervisory station. Employed board types, best inverter selection and studies about control equipment and devices have been described. The article covers and explores the global trend of implementing smart distribution electrical energy networks and the incentive to use solar renewable energy. There is the possibility of the excess energy produced by the system be purchased by the local power utility. This project was implemented in residences in the rural community of the municipality of Cabo Frio/RJ. Data could be seen through daily measurements during the month of November 2013.

Keywords: rural residence, supervisory, smart grid, solar energy

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21107 Review on Future Economic Potential Stems from Global Electronic Waste Generation and Sustainable Recycling Practices.

Authors: Shamim Ahsan

Abstract:

Abstract Global digital advances associated with consumer’s strong inclination for the state of art digital technologies is causing overwhelming social and environmental challenges for global community. During recent years not only economic advances of electronic industries has taken place at steadfast rate, also the generation of e-waste outshined the growth of any other types of wastes. The estimated global e-waste volume is expected to reach 65.4 million tons annually by 2017. Formal recycling practices in developed countries are stemming economic liability, opening paths for illegal trafficking to developing countries. Informal crude management of large volume of e-waste is transforming into an emergent environmental and health challenge in. Contrariwise, in several studies formal and informal recycling of e-waste has also exhibited potentials for economic returns both in developed and developing countries. Some research on China illustrated that from large volume of e-wastes generation there are recycling potential in evolving from ∼16 (10−22) billion US$ in 2010, to an anticipated ∼73.4 (44.5−103.4) billion US$ by 2030. While in another study, researcher found from an economic analysis of 14 common categories of waste electric and electronic equipment (WEEE) the overall worth is calculated as €2.15 billion to European markets, with a potential rise to €3.67 billion as volumes increase. These economic returns and environmental protection approaches are feasible only when sustainable policy options are embraced with stricter regulatory mechanism. This study will critically review current researches to stipulate how global e-waste generation and sustainable e-waste recycling practices demonstrate future economic development potential in terms of both quantity and processing capacity, also triggering complex some environmental challenges.

Keywords: E-Waste, , Generation, , Economic Potential, Recycling

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21106 Multi-Label Approach to Facilitate Test Automation Based on Historical Data

Authors: Warda Khan, Remo Lachmann, Adarsh S. Garakahally

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The increasing complexity of software and its applicability in a wide range of industries, e.g., automotive, call for enhanced quality assurance techniques. Test automation is one option to tackle the prevailing challenges by supporting test engineers with fast, parallel, and repetitive test executions. A high degree of test automation allows for a shift from mundane (manual) testing tasks to a more analytical assessment of the software under test. However, a high initial investment of test resources is required to establish test automation, which is, in most cases, a limitation to the time constraints provided for quality assurance of complex software systems. Hence, a computer-aided creation of automated test cases is crucial to increase the benefit of test automation. This paper proposes the application of machine learning for the generation of automated test cases. It is based on supervised learning to analyze test specifications and existing test implementations. The analysis facilitates the identification of patterns between test steps and their implementation with test automation components. For the test case generation, this approach exploits historical data of test automation projects. The identified patterns are the foundation to predict the implementation of unknown test case specifications. Based on this support, a test engineer solely has to review and parameterize the test automation components instead of writing them manually, resulting in a significant time reduction for establishing test automation. Compared to other generation approaches, this ML-based solution can handle different writing styles, authors, application domains, and even languages. Furthermore, test automation tools require expert knowledge by means of programming skills, whereas this approach only requires historical data to generate test cases. The proposed solution is evaluated using various multi-label evaluation criteria (EC) and two small-sized real-world systems. The most prominent EC is ‘Subset Accuracy’. The promising results show an accuracy of at least 86% for test cases, where a 1:1 relationship (Multi-Class) between test step specification and test automation component exists. For complex multi-label problems, i.e., one test step can be implemented by several components, the prediction accuracy is still at 60%. It is better than the current state-of-the-art results. It is expected the prediction quality to increase for larger systems with respective historical data. Consequently, this technique facilitates the time reduction for establishing test automation and is thereby independent of the application domain and project. As a work in progress, the next steps are to investigate incremental and active learning as additions to increase the usability of this approach, e.g., in case labelled historical data is scarce.

Keywords: machine learning, multi-class, multi-label, supervised learning, test automation

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21105 On the End-of-Life Inventory Problem

Authors: Hans Frenk, Sonya Javadi, Semih Onur Sezer

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We consider the so-called end of life inventory problem for the supplier of a product in its final phase of the service life cycle. This phase starts when the production of the items stops and continues until the warranty of the last sold item expires. At the beginning of this phase, the supplier places a final order for spare parts to serve customers coming with defective items. At any time during the final phase, the supplier may also decide to switch to an alternative and more cost-effective policy. This alternative policy may be in the form of replacing a defective item with a substitutable product or offering discounts / rebates on new generation products. In this setup, the objective is to find a final order quantity and also a switching time which will minimize the total expected discounted cost. We study this problem under a general cost structure in a continuous-time framework where arrivals of defective items are given by a non-homogeneous Poisson process. We consider four formulations which differ by the nature of the switching time. These formulations are studied in detail and properties of the objective function are derived in each case. Using these properties, we provide exact algorithms for efficient numerical implementations. Numerical examples are provided illustrating the application of these algorithms. In these examples, we also compare the costs associated with these different formulations.

Keywords: End-of-life inventory control, martingales, optimization, service parts

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21104 Solution of Some Boundary Value Problems of the Generalized Theory of Thermo-Piezoelectricity

Authors: Manana Chumburidze

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We have considered a non-classical model of dynamical problems for a conjugated system of differential equations arising in thermo-piezoelectricity, which was formulated by Toupin – Mindlin. The basic concepts and the general theory of solvability for isotropic homogeneous elastic media is considered. They are worked by using the methods the Laplace integral transform, potential method and singular integral equations. Approximate solutions of mixed boundary value problems for finite domain, bounded by the some closed surface are constructed. They are solved in explicitly by using the generalized Fourier's series method.

Keywords: thermo-piezoelectricity, boundary value problems, Fourier's series, isotropic homogeneous elastic media

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21103 Voltage Stability Margin-Based Approach for Placement of Distributed Generators in Power Systems

Authors: Oludamilare Bode Adewuyi, Yanxia Sun, Isaiah Gbadegesin Adebayo

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Voltage stability analysis is crucial to the reliable and economic operation of power systems. The power system of developing nations is more susceptible to failures due to the continuously increasing load demand, which is not matched with generation increase and efficient transmission infrastructures. Thus, most power systems are heavily stressed, and the planning of extra generation from distributed generation sources needs to be efficiently done so as to ensure the security of the power system. Some voltage stability index-based approach for DG siting has been reported in the literature. However, most of the existing voltage stability indices, though sufficient, are found to be inaccurate, especially for overloaded power systems. In this paper, the performance of a relatively different approach using a line voltage stability margin indicator, which has proven to have better accuracy, has been presented and compared with a conventional line voltage stability index for DG siting using the Nigerian 28 bus system. Critical boundary index (CBI) for voltage stability margin estimation was deployed to identify suitable locations for DG placement, and the performance was compared with DG placement using the Novel Line Stability Index (NLSI) approach. From the simulation results, both CBI and NLSI agreed greatly on suitable locations for DG on the test system; while CBI identified bus 18 as the most suitable at system overload, NLSI identified bus 8 to be the most suitable. Considering the effect of the DG placement at the selected buses on the voltage magnitude profile, the result shows that the DG placed on bus 18 identified by CBI improved the performance of the power system better.

Keywords: voltage stability analysis, voltage collapse, voltage stability index, distributed generation

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21102 Load Forecasting in Microgrid Systems with R and Cortana Intelligence Suite

Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

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21101 Investigation of Ground Disturbance Caused by Pile Driving: Case Study

Authors: Thayalan Nall, Harry Poulos

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Piling is the most widely used foundation method for heavy structures in poor soil conditions. The geotechnical engineer can choose among a variety of piling methods, but in most cases, driving piles by impact hammer is the most cost-effective alternative. Under unfavourable conditions, driving piles can cause environmental problems, such as noise, ground movements and vibrations, with the risk of ground disturbance leading to potential damage to proposed structures. In one of the project sites in which the authors were involved, three offshore container terminals, namely CT1, CT2 and CT3, were constructed over thick compressible marine mud. The seabed was around 6m deep and the soft clay thickness within the project site varied between 9m and 20m. CT2 and CT3 were connected together and rectangular in shape and were 2600mx800m in size. CT1 was 400m x 800m in size and was located on south opposite of CT2 towards its eastern end. CT1 was constructed first and due to time and environmental limitations, it was supported on a “forest” of large diameter driven piles. CT2 and CT3 are now under construction and are being carried out using a traditional dredging and reclamation approach with ground improvement by surcharging with vertical drains. A few months after the installation of the CT1 piles, a 2600m long sand bund to 2m above mean sea level was constructed along the southern perimeter of CT2 and CT3 to contain the dredged mud that was expected to be pumped. The sand bund was constructed by sand spraying and pumping using a dredging vessel. About 2000m length of the sand bund in the west section was constructed without any major stability issues or any noticeable distress. However, as the sand bund approached the section parallel to CT1, it underwent a series of deep seated failures leading the displaced soft clay materials to heave above the standing water level. The crest of the sand bund was about 100m away from the last row of piles. There were no plausible geological reasons to conclude that the marine mud only across the CT1 region was weaker than over the rest of the site. Hence it was suspected that the pile driving by impact hammer may have caused ground movements and vibrations, leading to generation of excess pore pressures and cyclic softening of the marine mud. This paper investigates the probable cause of failure by reviewing: (1) All ground investigation data within the region; (2) Soil displacement caused by pile driving, using theories similar to spherical cavity expansion; (3) Transfer of stresses and vibrations through the entire system, including vibrations transmitted from the hammer to the pile, and the dynamic properties of the soil; and (4) Generation of excess pore pressure due to ground vibration and resulting cyclic softening. The evidence suggests that the problems encountered at the site were primarily caused by the “side effects” of the pile driving operations.

Keywords: pile driving, ground vibration, excess pore pressure, cyclic softening

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21100 Assessment Power and Oscillation Damping Using the POD Controller and Proposed FOD Controller

Authors: Tohid Rahimi, Yahya Naderi, Babak Yousefi, Seyed Hossein Hoseini

Abstract:

Today’s modern interconnected power system is highly complex in nature. In this, one of the most important requirements during the operation of the electric power system is the reliability and security. Power and frequency oscillation damping mechanism improve the reliability. Because of power system stabilizer (PSS) low speed response against of major fault such as three phase short circuit, FACTs devise that can control the network condition in very fast time, are becoming popular. However, FACTs capability can be seen in a major fault present when nonlinear models of FACTs devise and power system equipment are applied. To realize this aim, the model of multi-machine power system with FACTs controller is developed in MATLAB/SIMULINK using Sim Power System (SPS) blockiest. Among the FACTs device, Static synchronous series compensator (SSSC) due to high speed changes its reactance characteristic inductive to capacitive, is effective power flow controller. Tuning process of controller parameter can be performed using different method. However, Genetic Algorithm (GA) ability tends to use it in controller parameter tuning process. In this paper, firstly POD controller is used to power oscillation damping. But in this station, frequency oscillation dos not has proper damping situation. Therefore, FOD controller that is tuned using GA is using that cause to damp out frequency oscillation properly and power oscillation damping has suitable situation.

Keywords: power oscillation damping (POD), frequency oscillation damping (FOD), Static synchronous series compensator (SSSC), Genetic Algorithm (GA)

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21099 Temperature Fields in a Channel Partially-Filled by Porous Material with Internal Heat Generations: On Exact Solution

Authors: Yasser Mahmoudi, Nader Karimi

Abstract:

The present work examines analytically the effect internal heat generation on temperature fields in a channel partially-filled with a porous under local thermal non-equilibrium condition. The Darcy-Brinkman model is used to represent the fluid transport through the porous material. Two fundamental models (models A and B) represent the thermal boundary conditions at the interface between the porous medium and the clear region. The governing equations of the problem are manipulated, and for each interface model, exact solutions for the solid and fluid temperature fields are developed. These solutions incorporate the porous material thickness, Biot number, fluid to solid thermal conductivity ratio Darcy number, as the non-dimensional energy terms in fluid and solid as parameters. Results show that considering any of the two models and under zero or negative heat generation (heat sink) and for any Darcy number, an increase in the porous thickness increases the amount of heat flux transferred to the porous region. The obtained results are applicable to the analysis of complex porous media incorporating internal heat generation, such as heat transfer enhancement (THE), tumor ablation in biological tissues and porous radiant burners (PRBs).

Keywords: porous media, local thermal non-equilibrium, forced convection, heat transfer, exact solution, internal heat generation

Procedia PDF Downloads 441
21098 Leadership and Whether It Stems from Innate Abilities or from Situation

Authors: Salwa Abdelbaki

Abstract:

This research investigated how leaders develop, asking whether they have been leaders due to their innate abilities or they gain leadership characteristics through interactions based on requirements of a situation. If the first is true, then a leader should be successful in any situation. Otherwise, a leader may succeed only in a specific situation. A series of experiments were carried out on three groups including of males and females. First; a group of 148 students with different specializations had to select a leader. Another group of 51 students had to recall their previous experiences and their knowledge of each other to identify who were leaders in different situations. Then a series of analytic tools were applied to the identified leaders and to the whole groups to find out how leaders were developed. A group of 40 young children was also experimented with to find young leaders among them and to analyze their characteristics.

Keywords: leadership, innate characteristics, situation, leadership theories

Procedia PDF Downloads 269
21097 A High Compression Ratio for a Losseless Image Compression Based on the Arithmetic Coding with the Sorted Run Length Coding: Meteosat Second Generation Image Compression

Authors: Cherifi Mehdi, Lahdir Mourad, Ameur Soltane

Abstract:

Image compression is the heart of several multimedia techniques. It is used to reduce the number of bits required to represent an image. Meteosat Second Generation (MSG) satellite allows the acquisition of 12 image files every 15 minutes and that results in a large databases sizes. In this paper, a novel image compression method based on the arithmetic coding with the sorted Run Length Coding (SRLC) for MSG images is proposed. The SRLC allows us to find the occurrence of the consecutive pixels of the original image to create a sorted run. The arithmetic coding allows the encoding of the sorted data of the previous stage to retrieve a unique code word that represents a binary code stream in the sorted order to boost the compression ratio. Through this article, we show that our method can perform the best results concerning compression ratio and bit rate unlike the method based on the Run Length Coding (RLC) and the arithmetic coding. Evaluation criteria like the compression ratio and the bit rate allow the confirmation of the efficiency of our method of image compression.

Keywords: image compression, arithmetic coding, Run Length Coding, RLC, Sorted Run Length Coding, SRLC, Meteosat Second Generation, MSG

Procedia PDF Downloads 335
21096 Attention-based Adaptive Convolution with Progressive Learning in Speech Enhancement

Authors: Tian Lan, Yixiang Wang, Wenxin Tai, Yilan Lyu, Zufeng Wu

Abstract:

The monaural speech enhancement task in the time-frequencydomain has a myriad of approaches, with the stacked con-volutional neural network (CNN) demonstrating superiorability in feature extraction and selection. However, usingstacked single convolutions method limits feature represen-tation capability and generalization ability. In order to solvethe aforementioned problem, we propose an attention-basedadaptive convolutional network that integrates the multi-scale convolutional operations into a operation-specific blockvia input dependent attention to adapt to complex auditoryscenes. In addition, we introduce a two-stage progressivelearning method to enlarge the receptive field without a dra-matic increase in computation burden. We conduct a series ofexperiments based on the TIMIT corpus, and the experimen-tal results prove that our proposed model is better than thestate-of-art models on all metrics.

Keywords: speech enhancement, adaptive convolu-tion, progressive learning, time-frequency domain

Procedia PDF Downloads 100
21095 Using Hemicellulosic Liquor from Sugarcane Bagasse to Produce Second Generation Lactic Acid

Authors: Regiane A. Oliveira, Carlos E. Vaz Rossell, Rubens Maciel Filho

Abstract:

Lactic acid, besides a valuable chemical may be considered a platform for other chemicals. In fact, the feasibility of hemicellulosic sugars as feedstock for lactic acid production process, may represent the drop of some of the barriers for the second generation bioproducts, especially bearing in mind the 5-carbon sugars from the pre-treatment of sugarcane bagasse. Bearing this in mind, the purpose of this study was to use the hemicellulosic liquor from sugarcane bagasse as a substrate to produce lactic acid by fermentation. To release of sugars from hemicellulose it was made a pre-treatment with a diluted sulfuric acid in order to obtain a xylose's rich liquor with low concentration of inhibiting compounds for fermentation (≈ 67% of xylose, ≈ 21% of glucose, ≈ 10% of cellobiose and arabinose, and around 1% of inhibiting compounds as furfural, hydroxymethilfurfural and acetic acid). The hemicellulosic sugars associated with 20 g/L of yeast extract were used in a fermentation process with Lactobacillus plantarum to produce lactic acid. The fermentation process pH was controlled with automatic injection of Ca(OH)2 to keep pH at 6.00. The lactic acid concentration remained stable from the time when the glucose was depleted (48 hours of fermentation), with no further production. While lactic acid is produced occurs the concomitant consumption of xylose and glucose. The yield of fermentation was 0.933 g lactic acid /g sugars. Besides, it was not detected the presence of by-products, what allows considering that the microorganism uses a homolactic fermentation to produce its own energy using pentose-phosphate pathway. Through facultative heterofermentative metabolism the bacteria consume pentose, as is the case of L. plantarum, but the energy efficiency for the cell is lower than during the hexose consumption. This implies both in a slower cell growth, as in a reduction in lactic acid productivity compared with the use of hexose. Also, L. plantarum had shown to have a capacity for lactic acid production from hemicellulosic hydrolysate without detoxification, which is very attractive in terms of robustness for an industrial process. Xylose from hydrolyzed bagasse and without detoxification is consumed, although the hydrolyzed bagasse inhibitors (especially aromatic inhibitors) affect productivity and yield of lactic acid. The use of sugars and the lack of need for detoxification of the C5 liquor from sugarcane bagasse hydrolyzed is a crucial factor for the economic viability of second generation processes. Taking this information into account, the production of second generation lactic acid using sugars from hemicellulose appears to be a good alternative to the complete utilization of sugarcane plant, directing molasses and cellulosic carbohydrates to produce 2G-ethanol, and hemicellulosic carbohydrates to produce 2G-lactic acid.

Keywords: fermentation, lactic acid, hemicellulosic sugars, sugarcane

Procedia PDF Downloads 353
21094 Epistemic Uncertainty Analysis of Queue with Vacations

Authors: Baya Takhedmit, Karim Abbas, Sofiane Ouazine

Abstract:

The vacations queues are often employed to model many real situations such as computer systems, communication networks, manufacturing and production systems, transportation systems and so forth. These queueing models are solved at fixed parameters values. However, the parameter values themselves are determined from a finite number of observations and hence have uncertainty associated with them (epistemic uncertainty). In this paper, we consider the M/G/1/N queue with server vacation and exhaustive discipline where we assume that the vacation parameter values have uncertainty. We use the Taylor series expansions approach to estimate the expectation and variance of model output, due to epistemic uncertainties in the model input parameters.

Keywords: epistemic uncertainty, M/G/1/N queue with vacations, non-parametric sensitivity analysis, Taylor series expansion

Procedia PDF Downloads 415
21093 Automating 2D CAD to 3D Model Generation Process: Wall pop-ups

Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski

Abstract:

In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.

Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection

Procedia PDF Downloads 130
21092 Generation of Ultra-Broadband Supercontinuum Ultrashort Laser Pulses with High Energy

Authors: Walid Tawfik

Abstract:

The interaction of intense short nano- and picosecond laser pulses with plasma leads to reach variety of important applications, including time-resolved laser induced breakdown spectroscopy (LIBS), soft x-ray lasers, and laser-driven accelerators. The progress in generating of femtosecond down to sub-10 fs optical pulses has opened a door for scientists with an essential tool in many ultrafast phenomena, such as femto-chemistry, high field physics, and high harmonic generation (HHG). The advent of high-energy laser pulses with durations of few optical cycles provided scientists with very high electric fields, and produce coherent intense UV to NIR radiation with high energy which allows for the investigation of ultrafast molecular dynamics with femtosecond resolution. In this work, we could experimentally achieve the generation of a two-octave-wide supercontinuum ultrafast pulses extending from ultraviolet at 3.5 eV to the near-infrared at 1.3 eV in neon-filled capillary fiber. These pulses are created due to nonlinear self-phase modulation (SPM) in neon as a nonlinear medium. The measurements of the generated pulses were performed using spectral phase interferometry for direct electric-field reconstruction. A full characterization of the output pulses was studied. The output pulse characterization includes the pulse width, the beam profile, and the spectral bandwidth. Under optimization conditions, the reconstructed pulse intensity autocorrelation function was exposed for the shorts possible pulse duration to achieve transform-limited pulses with energies up to 600µJ. Furthermore, the effect of variation of neon pressure on the pulse-width was studied. The nonlinear SPM found to be increased with the neon pressure. The obtained results may give an opportunity to monitor and control ultrafast transit interaction in femtosecond chemistry.

Keywords: femtosecond laser, ultrafast, supercontinuum, ultra-broadband

Procedia PDF Downloads 192
21091 MHD Non-Newtonian Nanofluid Flow over a Permeable Stretching Sheet with Heat Generation and Velocity Slip

Authors: Rama Bhargava, Mania Goyal

Abstract:

The problem of magnetohydrodynamics boundary layer flow and heat transfer on a permeable stretching surface in a second grade nanofluid under the effect of heat generation and partial slip is studied theoretically. The Brownian motion and thermophoresis effects are also considered. The boundary layer equations governed by the PDE’s are transformed into a set of ODE’s with the help of local similarity transformations. The differential equations are solved by variational finite element method. The effects of different controlling parameters on the flow field and heat transfer characteristics are examined. The numerical results for the dimensionless velocity, temperature and nanoparticle volume fraction as well as the reduced Nusselt and Sherwood number have been presented graphically. The comparison confirmed excellent agreement. The present study is of great interest in coating and suspensions, cooling of metallic plate, oils and grease, paper production, coal water or coal-oil slurries, heat exchangers technology, materials processing exploiting.

Keywords: viscoelastic nanofluid, partial slip, stretching sheet, heat generation/absorption, MHD flow, FEM

Procedia PDF Downloads 294
21090 The Impact of CO2 on Learning and Memory Duration of Bombus terrestris

Authors: Gholizadeh F. F., Goldansaz S. H., Bandani A. R., A. Ashouri

Abstract:

This study aimed to investigate the direct effects of increasing carbon dioxide (CO₂) concentration on the behavior of Bombus terrestris bumblebees in laboratory conditions to understand the outcomes of the augmentation of this gas in the Earth's atmosphere on the decline of populations of these pollinators. Learning and memory duration of bumblebees were evaluated as two main behavioral factors in social insects at different concentrations of CO₂. In both series of experiments, the behavior of bees under the influence of CO₂ changes compared to the control. Insects kept at high CO₂ concentrations learn less than control bees and spend more time identifying and navigating to discover their food source and access time (nectar consumption). These results showed that bees maybe lose some of their food resources due to poorer identification and act weaker on searching due to less memory and avoiding the enemy in higher CO₂ concentration. Therefore, CO₂ increasing concentration can be one of the reasons for the decline of these pollinating insects' populations by negatively affecting their fitness.

Keywords: Bombus terrestris, CO₂, learning, memory duration

Procedia PDF Downloads 160
21089 Rainfall Analysis in the Contest of Climate Change for Jeddah Area, Western Saudi Arabia

Authors: Ali M. Subyani

Abstract:

The increase in the greenhouse gas emission has had a severe impact on global climate change and is bound to affect the weather patterns worldwide. This climate change impacts are among the future significant effects on any society. Rainfall levels are drastically increasing with flash floods in some places and long periods of droughts in others, especially in arid regions. These extreme events are causes of interactions concerning environmental, socio-economic and cultural life and their implementation. This paper presents the detailed features of dry and wet spell durations and rainfall intensity series available (1971-2012) on daily basis for the Jeddah area, Western, Saudi Arabia. It also presents significant articles for combating the climate change impacts on this area. Results show trend changes in dry and wet spell durations and rainfall amount on daily, monthly and annual time series. Three rain seasons were proposed in this investigation: high rain, low rain, and dry seasons. It shows that the overall average dry spell durations is about 80 continuous days while the average wet spell durations is 1.39 days with an average rainfall intensity of 8.2 mm/day. Annual and seasonal autorun analyses confirm that the rainy seasons are tending to have more intense rainfall while the seasons are becoming drier. This study would help decision makers in future for water resources management and flood risk analysis.

Keywords: climate change, daily rainfall, dry and wet spill, Jeddah, Saudi Arabia

Procedia PDF Downloads 324