Search results for: train drivers
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 1176

Search results for: train drivers

576 Co-Existence of Thai Muslim People and Other in an Ancient Community Located in the Heart of Bangkok: The Case Study of Petchaburi 7 Community

Authors: Saowapa Phaithayawat

Abstract:

The objectives of the study are the following: 1) To study the way of life in terms of one hundred years co-existence of the Muslim and local community in this area 2) To analyze factors affect to this community with happy co-existence. The study requires quantitative research to study a history together with the study of humanity. The result of this study showed that the area of Petchburi 7 community is an ancient area which has owned by the Muslim for almost 100 years. There is a sanctuary as the center of unity. Later Bangkok becomes more developed and provides more infrastructures like the motorway and other transportation: however, the owners of lands in this community still keep their lands and build many buildings to run the business. With this purpose, there are many non-Muslim people come to live here with co-existence. Not only do they convenient to work but also easy to transport by sky train. There are factors that make them live harmonious as following: 1) All Muslims in this area are strict to follow their rules and allocate their community for business. 2) All people, who come and live here, are middle-aged and working men and women. They rent rooms closed to their work. 3) There are Muslim food and desserts, especially Roti, the popular fried flour, and local Chachak, tea originated from the south of Thailand. All these food and deserts are famous for working men and women to home and join after work 4) All Muslim in this area are independent to lead their own lives although a society changes rapidly.

Keywords: co-existence, Muslims, other group of people, the ancient community, social sciences

Procedia PDF Downloads 339
575 Transportation Mode Classification Using GPS Coordinates and Recurrent Neural Networks

Authors: Taylor Kolody, Farkhund Iqbal, Rabia Batool, Benjamin Fung, Mohammed Hussaeni, Saiqa Aleem

Abstract:

The rising threat of climate change has led to an increase in public awareness and care about our collective and individual environmental impact. A key component of this impact is our use of cars and other polluting forms of transportation, but it is often difficult for an individual to know how severe this impact is. While there are applications that offer this feedback, they require manual entry of what transportation mode was used for a given trip, which can be burdensome. In order to alleviate this shortcoming, a data from the 2016 TRIPlab datasets has been used to train a variety of machine learning models to automatically recognize the mode of transportation. The accuracy of 89.6% is achieved using single deep neural network model with Gated Recurrent Unit (GRU) architecture applied directly to trip data points over 4 primary classes, namely walking, public transit, car, and bike. These results are comparable in accuracy to results achieved by others using ensemble methods and require far less computation when classifying new trips. The lack of trip context data, e.g., bus routes, bike paths, etc., and the need for only a single set of weights make this an appropriate methodology for applications hoping to reach a broad demographic and have responsive feedback.

Keywords: classification, gated recurrent unit, recurrent neural network, transportation

Procedia PDF Downloads 137
574 Single Pole-To-Earth Fault Detection and Location on the Tehran Railway System Using ICA and PSO Trained Neural Network

Authors: Masoud Safarishaal

Abstract:

Detecting the location of pole-to-earth faults is essential for the safe operation of the electrical system of the railroad. This paper aims to use a combination of evolutionary algorithms and neural networks to increase the accuracy of single pole-to-earth fault detection and location on the Tehran railroad power supply system. As a result, the Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) are used to train the neural network to improve the accuracy and convergence of the learning process. Due to the system's nonlinearity, fault detection is an ideal application for the proposed method, where the 600 Hz harmonic ripple method is used in this paper for fault detection. The substations were simulated by considering various situations in feeding the circuit, the transformer, and typical Tehran metro parameters that have developed the silicon rectifier. Required data for the network learning process has been gathered from simulation results. The 600Hz component value will change with the change of the location of a single pole to the earth's fault. Therefore, 600Hz components are used as inputs of the neural network when fault location is the output of the network system. The simulation results show that the proposed methods can accurately predict the fault location.

Keywords: single pole-to-pole fault, Tehran railway, ICA, PSO, artificial neural network

Procedia PDF Downloads 123
573 Dynamic Interaction between Two Neighboring Tunnels in a Layered Half-Space

Authors: Chao He, Shunhua Zhou, Peijun Guo

Abstract:

The vast majority of existing underground railway lines consist of twin tunnels. In this paper, the dynamic interaction between two neighboring tunnels in a layered half-space is investigated by an analytical model. The two tunnels are modelled as cylindrical thin shells, while the soil in the form of a layered half-space with two cylindrical cavities is simulated by the elastic continuum theory. The transfer matrix method is first used to derive the relationship between the plane wave vectors in arbitrary layers and the source layer. Thereafter, the wave translation and transformation are introduced to determine the plane and cylindrical wave vectors in the source layer. The solution for the dynamic interaction between twin tunnels in a layered half-space is obtained by means of the compatibility of displacements and equilibrium of stresses on the two tunnel–soil interfaces. By coupling the proposed model with a fully track model, the train-induced vibrations from twin tunnels in a multi-layered half-space are investigated. The numerical results demonstrate that the existence of a neighboring tunnel has a significant effect on ground vibrations.

Keywords: underground railway, twin tunnels, wave translation and transformation, transfer matrix method

Procedia PDF Downloads 119
572 Operational Software Maturity: An Aerospace Industry Analysis

Authors: Raúl González Muñoz, Essam Shehab, Martin Weinitzke, Chris Fowler, Paul Baguley

Abstract:

Software applications have become crucial to the aerospace industry, providing a wide range of functionalities and capabilities used during the design, manufacturing and support of aircraft. However, as this criticality increases, so too does the risk for business operations when facing a software failure. Hence, there is a need for new methodologies to be developed to support aerospace companies in effectively managing their software portfolios, avoiding the hazards of business disruption and additional costs. This paper aims to provide a definition of operational software maturity, and how this can be used to assess software operational behaviour, as well as a view on the different aspects that drive software maturity within the aerospace industry. The key research question addressed is, how can operational software maturity monitoring assist the aerospace industry in effectively managing large software portfolios? This question has been addressed by conducting an in depth review of current literature, by working closely with aerospace professionals and by running an industry case study within a major aircraft manufacturer. The results are a software maturity model composed of a set of drivers and a prototype tool used for the testing and validation of the research findings. By utilising these methodologies to assess the operational maturity of software applications in aerospace, benefits in maintenance activities and operations disruption avoidance have been observed, supporting business cases for system improvement.

Keywords: aerospace, software lifecycle, software maintenance, software maturity

Procedia PDF Downloads 326
571 Applying of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Estimation of Flood Hydrographs

Authors: Amir Ahmad Dehghani, Morteza Nabizadeh

Abstract:

This paper presents the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to flood hydrograph modeling of Shahid Rajaee reservoir dam located in Iran. This was carried out using 11 flood hydrographs recorded in Tajan river gauging station. From this dataset, 9 flood hydrographs were chosen to train the model and 2 flood hydrographs to test the model. The different architectures of neuro-fuzzy model according to the membership function and learning algorithm were designed and trained with different epochs. The results were evaluated in comparison with the observed hydrographs and the best structure of model was chosen according the least RMSE in each performance. To evaluate the efficiency of neuro-fuzzy model, various statistical indices such as Nash-Sutcliff and flood peak discharge error criteria were calculated. In this simulation, the coordinates of a flood hydrograph including peak discharge were estimated using the discharge values occurred in the earlier time steps as input values to the neuro-fuzzy model. These results indicate the satisfactory efficiency of neuro-fuzzy model for flood simulating. This performance of the model demonstrates the suitability of the implemented approach to flood management projects.

Keywords: adaptive neuro-fuzzy inference system, flood hydrograph, hybrid learning algorithm, Shahid Rajaee reservoir dam

Procedia PDF Downloads 478
570 A Fuzzy Inference System for Predicting Air Traffic Demand Based on Socioeconomic Drivers

Authors: Nur Mohammad Ali, Md. Shafiqul Alam, Jayanta Bhusan Deb, Nowrin Sharmin

Abstract:

The past ten years have seen significant expansion in the aviation sector, which during the previous five years has steadily pushed emerging countries closer to economic independence. It is crucial to accurately forecast the potential demand for air travel to make long-term financial plans. To forecast market demand for low-cost passenger carriers, this study suggests working with low-cost airlines, airports, consultancies, and governmental institutions' strategic planning divisions. The study aims to develop an artificial intelligence-based methods, notably fuzzy inference systems (FIS), to determine the most accurate forecasting technique for domestic low-cost carrier demand in Bangladesh. To give end users real-world applications, the study includes nine variables, two sub-FIS, and one final Mamdani Fuzzy Inference System utilizing a graphical user interface (GUI) made with the app designer tool. The evaluation criteria used in this inquiry included mean square error (MSE), accuracy, precision, sensitivity, and specificity. The effectiveness of the developed air passenger demand prediction FIS is assessed using 240 data sets, and the accuracy, precision, sensitivity, specificity, and MSE values are 90.83%, 91.09%, 90.77%, and 2.09%, respectively.

Keywords: aviation industry, fuzzy inference system, membership function, graphical user interference

Procedia PDF Downloads 72
569 Examining Ethiopian Banking Industry in Relation to Factors Affecting Profitability: From 2008 to 2012

Authors: Zelalem Zerihun

Abstract:

In this study, attempts were made to assess the bank-specific, industry-specific, and macro-economic factors affecting bank profitability. Data were collected from ten commercial banks in Ethiopia, covering the period of 2008-2012. A mixed method research approach was adopted for this research. Documentary analysis and in-depth interview were also used to substantiate the data. The study found out that capital strength, income diversification, bank size and gross domestic product are statistically significant and they have a positive relationship with banks’ profitability. However, operational efficiency and asset quality have a negative relationship with banks’ profitability. The relationship for liquidity risk, concentration and inflation were found to be statistically insignificant. The study revealed that focusing and reengineering the banks in light of the key internal drivers could enhance the profitability as well as the performance of the commercial banks in Ethiopia. In addition to this, the study suggests that banks in Ethiopia should not only be concerned about internal structures but also they must consider both the internal environment and the macro-economic environment in designing strategies to improve their profit or their performance.

Keywords: Ethiopian banking industry, macro-economic factors, documentary analysis, capital strength, income diversification

Procedia PDF Downloads 341
568 Performance Evaluation of Distributed Deep Learning Frameworks in Cloud Environment

Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang

Abstract:

2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.

Keywords: artificial intelligence, machine learning, deep learning, convolutional neural networks

Procedia PDF Downloads 211
567 Changes in When and Where People Are Spending Time in Response to COVID-19

Authors: Nicholas Reinicke, Brennan Borlaug, Matthew Moniot

Abstract:

The COVID-19 pandemic has resulted in a significant change in driving behavior as people respond to the new environment. However, existing methods for analyzing driver behavior, such as travel surveys and travel demand models, are not suited for incorporating abrupt environmental disruptions. To address this, we analyze a set of high-resolution trip data and introduce two new metrics for quantifying driving behavioral shifts as a function of time, allowing us to compare the time periods before and after the pandemic began. We apply these metrics to the Denver, Colorado metropolitan statistical area (MSA) to demonstrate the utility of the metrics. Then, we present a case study for comparing two distinct MSAs, Louisville, Kentucky, and Des Moines, Iowa, which exhibit significant differences in the makeup of their labor markets. The results indicate that although the regions of study exhibit certain unique driving behavioral shifts, emerging trends can be seen when comparing between seemingly distinct regions. For instance, drivers in all three MSAs are generally shown to have spent more time at residential locations and less time in workplaces in the time period after the pandemic started. In addition, workplaces that may be incompatible with remote working, such as hospitals and certain retail locations, generally retained much of their pre-pandemic travel activity.

Keywords: COVID-19, driver behavior, GPS data, signal analysis, telework

Procedia PDF Downloads 111
566 The Design and Modeling of Intelligent Learners Assistance System (ILASS)

Authors: Jelili Kunle Adedeji, Toeb Akorede Akinbola

Abstract:

The problem of vehicle mishap as a result of miscalculation, recklessness, or malfunction of some part in a vehicle is acknowledged to be a global issue. In most of the cases, it results into death or life injuries, all over the world; the issue becomes a nightmare to the stakeholders on how to curb mishaps on our roads due to these endemic factors. Hence this research typically examined the design of a device, specifically for learners that can lead to a society of intelligent vehicles (traffic) without withdrawing the driving authority from them, unlike pre-existing systems. Though ILASS shears a lot of principle with existing advance drivers assistance systems, yet there are two fundamental differences between ILASS system and existing systems. Firstly ILASS is meant to accept continuous input from the throttle at all time such that the devices will not constraint the driving process unnecessarily and ensure a change of speed at any point in time. Secondly, it made use of a variable threshold distance between the host vehicle and front vehicle which can be set by the host driver under the constraint of road maintenance agency, who communicates the minimum possible threshold for a different lane to the host vehicle. The results obtained from the simulation of the ILASS system concluded that ILASS is a good solution to road accidents, particularly road accident which occurs as a result of driving at high speed.

Keywords: front-vehicle, host-speed, threshold-distance, ILASS

Procedia PDF Downloads 181
565 Key Drivers Motivating Prospective International Students to Study Abroad and Their Attitude to University Rankings

Authors: Dasha Karzunina, Laura Bridgestock

Abstract:

Our oral presentation will be based on our qualitative and quantitative findings into motivations and challenges faced by international and UK students when choosing a university abroad. The insight was gathered through a series of over 60 focus groups held with prospective university students all over the world, including masters and PhD applicants. We spoke to students face-to-face in Latin America, North American, India, China, South East Asia and the major European cities. A survey was carried out alongside, to gather additional insight on their priorities and attitudes to universities’ reputation, collecting over 1,800 responses. The session’s aims are to break down some of the myths about the perspectives of international students and inform university leaders interested in recruiting more highly talented students from abroad and those currently working with international students. As a provider of one of the most demanded resources in higher education, QS World University Rankings, we specialize in understanding universities’ performance, their institutional brand and the impact of rankings on student recruitment. We therefore feel we are well placed to carry out and present this research. We hope for our findings to act as a bridge between bright students and their future universities abroad. We intend for our session to be interactive and so are happy to go into more depth on any of the destinations we visited, depending on what the audience is most interested in and which questions we receive.

Keywords: international student recruitment, market research, rankings, study abroad

Procedia PDF Downloads 236
564 Public Bus Transport Passenger Safety Evaluations in Ghana: A Phenomenological Constructivist Exploration

Authors: Enoch F. Sam, Kris Brijs, Stijn Daniels, Tom Brijs, Geert Wets

Abstract:

Notwithstanding the growing body of literature that recognises the importance of personal safety to public transport (PT) users, it remains unclear what PT users consider regarding their safety. In this study, we explore the criteria PT users in Ghana use to assess bus safety. This knowledge will afford a better understanding of PT users’ risk perceptions and assessments which may contribute to theoretical models of PT risk perceptions. We utilised phenomenological research methodology, with data drawn from 61 purposively sampled participants. Data collection (through focus group discussions and in-depth interviews) and analyses were done concurrently to the point of saturation. Our inductive data coding and analyses through the constant comparison and content analytic techniques resulted in 4 code categories (conceptual dimensions), 27 codes (safety items/criteria), and 100 quotations (data segments). Of the number of safety criteria participants use to assess bus safety, vehicle condition, driver’s marital status, and transport operator’s safety records were the most considered. With each criterion, participants rightly demonstrated its respective relevance to bus safety. These findings imply that investment in and maintenance of safer vehicles, and responsible and safety-conscious drivers, and prioritization of passengers’ safety are key-targets for public bus/minibus operators in Ghana.

Keywords: safety evaluations, public bus/minibus, passengers, phenomenology, Ghana

Procedia PDF Downloads 338
563 Evaluation and Compression of Different Language Transformer Models for Semantic Textual Similarity Binary Task Using Minority Language Resources

Authors: Ma. Gracia Corazon Cayanan, Kai Yuen Cheong, Li Sha

Abstract:

Training a language model for a minority language has been a challenging task. The lack of available corpora to train and fine-tune state-of-the-art language models is still a challenge in the area of Natural Language Processing (NLP). Moreover, the need for high computational resources and bulk data limit the attainment of this task. In this paper, we presented the following contributions: (1) we introduce and used a translation pair set of Tagalog and English (TL-EN) in pre-training a language model to a minority language resource; (2) we fine-tuned and evaluated top-ranking and pre-trained semantic textual similarity binary task (STSB) models, to both TL-EN and STS dataset pairs. (3) then, we reduced the size of the model to offset the need for high computational resources. Based on our results, the models that were pre-trained to translation pairs and STS pairs can perform well for STSB task. Also, having it reduced to a smaller dimension has no negative effect on the performance but rather has a notable increase on the similarity scores. Moreover, models that were pre-trained to a similar dataset have a tremendous effect on the model’s performance scores.

Keywords: semantic matching, semantic textual similarity binary task, low resource minority language, fine-tuning, dimension reduction, transformer models

Procedia PDF Downloads 211
562 Use of Gaussian-Euclidean Hybrid Function Based Artificial Immune System for Breast Cancer Diagnosis

Authors: Cuneyt Yucelbas, Seral Ozsen, Sule Yucelbas, Gulay Tezel

Abstract:

Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems.

Keywords: artificial immune system, breast cancer diagnosis, Euclidean function, Gaussian function

Procedia PDF Downloads 435
561 Modeling the Philippine Stock Exchange Index Closing Value Using Artificial Neural Network

Authors: Frankie Burgos, Emely Munar, Conrado Basa

Abstract:

This paper aimed at developing an artificial neural network (ANN) model specifically for the Philippine Stock Exchange index closing value. The inputs to the ANN are US Dollar and Philippine Peso(USD-PHP) exchange rate, GDP growth of the country, quarterly inflation rate, 10-year bond yield, credit rating of the country, previous open, high, low, close values and volume of trade of the Philippine Stock Exchange Index (PSEi), gold price of the previous day, National Association of Securities Dealers Automated Quotations (NASDAQ), Standard and Poor’s 500 (S & P 500) and the iShares MSCI Philippines ETF (EPHE) previous closing value. The target is composed of the closing value of the PSEi during the 627 trading days from November 3, 2011, to May 30, 2014. MATLAB’s Neural Network toolbox was employed to create, train and simulate the network using multi-layer feed forward neural network with back-propagation algorithm. The results satisfactorily show that the neural network developed has the ability to model the PSEi, which is affected by both internal and external economic factors. It was found out that the inputs used are the main factors that influence the movement of the PSEi closing value.

Keywords: artificial neural networks, artificial intelligence, philippine stocks exchange index, stocks trading

Procedia PDF Downloads 297
560 An Evaluation of the Efficacy of School-Based Suicide Prevention Programs

Authors: S. Wietrzychowski

Abstract:

The following review has identified specific programs, as well as the elements of these programs, that have been shown to be most effective in preventing suicide in schools. Suicide is an issue that affects many students each year. Although this is a prominent issue, there are few prevention programs used within schools. The primary objective of most prevention programs is to reduce risk factors such as depression and hopelessness, and increase protective factors like support systems and help-seeking behaviors. Most programs include a gatekeeper training model, education component, peer support group, and/or counseling/treatment. Research shows that some of these programs, like the Signs of Suicide and Youth Aware of Mental Health Programme, are effective in reducing suicide behaviors and increasing protective factors. These programs have been implemented in many countries across the world and have shown promising results. Since schools can provide easy access to adolescents, implement education programs, and train staff members and students how to identify and to report suicide behaviors, school-based programs seem to be the best way to prevent suicide among adolescents. Early intervention may be an effective way to prevent suicide. Although, since early intervention is not always an option, school-based programs in high schools have also been shown to decrease suicide attempts by up to 50%. As a result of this presentation, participants will be able to 1.) list at least 2 evidence-based suicide prevention programs, 2.) identify at least 3 factors which protect against suicide, and 3.) describe at least 3 risk factors for suicide.

Keywords: school, suicide, prevention, programs

Procedia PDF Downloads 346
559 How Teachers Comprehend and Support Children's Needs to Be Scientists

Authors: Anita Yus

Abstract:

Several Elementary Schools (SD) ‘favored’ by parents, especially those live in big cities in Indonesia, implicitly demand each child enrolled in the first grade of SD to be able to read, write and calculate. This condition urges the parents to push the teachers in PAUD (Kindergarten) to train their children to read, write, and calculate so they have a set of knowledge. According to Piaget, each child is capable of acquiring knowledge when he is given the opportunity to interact with his environment (things, people, and atmosphere). Teachers can make the interaction occur. There are several learning approaches suitable for the characteristics and needs of child’s growth. This paper talks about a research result conducted to investigate how twelve teachers of early childhood program comprehend the constructivist theory of Piaget, and how they inquire, how the children acquire and construct a number of knowledge through occurred interactions. This is a qualitative research with an observation method followed up by a focus group discussion (FGD). The research result shows that there is a reciprocal interaction between the behaviors of teachers and children affected by the size of the classroom and learning source, teaching experiences, education background, teachers’ attitude and motivation, as well as the way the teachers interpret and support the children’s needs. The teachers involved in this research came up with varied perspective on how knowledge acquired by children at first and how they construct it. This research brings a new perspective in understanding children as scientists.

Keywords: constructivist approach, young children as a scientist, teacher practice, teacher education

Procedia PDF Downloads 249
558 Approach for Demonstrating Reliability Targets for Rail Transport during Low Mileage Accumulation in the Field: Methodology and Case Study

Authors: Nipun Manirajan, Heeralal Gargama, Sushil Guhe, Manoj Prabhakaran

Abstract:

In railway industry, train sets are designed based on contractual requirements (mission profile), where reliability targets are measured in terms of mean distance between failures (MDBF). However, during the beginning of revenue services, trains do not achieve the designed mission profile distance (mileage) within the timeframe due to infrastructure constraints, scarcity of commuters or other operational challenges thereby not respecting the original design inputs. Since trains do not run sufficiently and do not achieve the designed mileage within the specified time, car builder has a risk of not achieving the contractual MDBF target. This paper proposes a constant failure rate based model to deal with the situations where mileage accumulation is not a part of the design mission profile. The model provides appropriate MDBF target to be demonstrated based on actual accumulated mileage. A case study of rolling stock running in the field is undertaken to analyze the failure data and MDBF target demonstration during low mileage accumulation. The results of case study prove that with the proposed method, reliability targets are achieved under low mileage accumulation.

Keywords: mean distance between failures, mileage-based reliability, reliability target appropriations, rolling stock reliability

Procedia PDF Downloads 267
557 Novel Adaptive Radial Basis Function Neural Networks Based Approach for Short-Term Load Forecasting of Jordanian Power Grid

Authors: Eyad Almaita

Abstract:

In this paper, a novel adaptive Radial Basis Function Neural Networks (RBFNN) algorithm is used to forecast the hour by hour electrical load demand in Jordan. A small and effective RBFNN model is used to forecast the hourly total load demand based on a small number of features. These features are; the load in the previous day, the load in the same day in the previous week, the temperature in the same hour, the hour number, the day number, and the day type. The proposed adaptive RBFNN model can enhance the reliability of the conventional RBFNN after embedding the network in the system. This is achieved by introducing an adaptive algorithm that allows the change of the weights of the RBFNN after the training process is completed, which will eliminates the need to retrain the RBFNN model again. The data used in this paper is real data measured by National Electrical Power co. (Jordan). The data for the period Jan./2012-April/2013 is used train the RBFNN models and the data for the period May/2013- Sep. /2013 is used to validate the models effectiveness.

Keywords: load forecasting, adaptive neural network, radial basis function, short-term, electricity consumption

Procedia PDF Downloads 345
556 Fused Structure and Texture (FST) Features for Improved Pedestrian Detection

Authors: Hussin K. Ragb, Vijayan K. Asari

Abstract:

In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.

Keywords: pedestrian detection, phase congruency, local phase, LBP features, CSLBP features, FST descriptor

Procedia PDF Downloads 488
555 Analyzing Energy Consumption Behavior of Migrated Population in Turkey Using Bayesian Belief Approach

Authors: Ebru Acuner, Gulgun Kayakutlu, M. Ozgur Kayalica, Sermin Onaygil

Abstract:

In Turkey, emigration, especially from Syria, has been continuously increasing together with rapid urbanization. In parallel to this, total energy consumption has been growing, rapidly. Unfortunately, domestic energy sources could not meet this energy demand. Hence, there is a need for reliable predictions. For this reason, before making a survey study for the migrated people, an informative questionnaire was prepared to take the opinions of the experts on the main drivers that shape the energy consumption behavior of the migrated people. Totally, 17 experts were answered, and they were analyzed by means of Netica program considering Bayesian belief analysis method. In the analysis, factors affecting energy consumption behaviors as well as strategies, institutions, tools and financing methods to change these behaviors towards efficient consumption were investigated. On the basis of the results, it can be concluded that changing the energy consumption behavior of the migrated people is crucial. In order to be successful, electricity and natural gas prices and tariffs in the market should be arranged considering energy efficiency. In addition, support mechanisms by not only the government but also municipalities should be taken into account while preparing related policies. Also, electric appliance producers should develop and implement strategies and action in favor of the usage of more efficient appliances. Last but not least, non-governmental organizations should support the migrated people to improve their awareness on the efficient consumption for the sustainable future.

Keywords: Bayesian belief, behavior, energy consumption, energy efficiency, migrated people

Procedia PDF Downloads 111
554 Assessment of the Economic Factors and Motivations towards De-Dollarization since the Early 2000s and Their Implications

Authors: Laila Algalal, Chen Xi

Abstract:

The US dollar has long served as the world's primary reserve currency. However, this dominance faces growing challenges from internal US economic pressures and the rise of alternative currencies. Internally, issues like high debt, inflation, reduced competitiveness, and economic instability due to inequality in economic policies threaten the dollar's position. Externally, more countries are establishing alternative currencies, payment systems, and regional financial institutions to reduce dollar dependence. These drivers have contributed to a decline in the dollar's share of global foreign exchange reserves from 71% in 2001 to an estimated 58% in 2022. While this 13-percentage point drop took two decades, recent initiatives suggest de-dollarization could accelerate in the coming few decades. Efforts to establish non-dollar trade deals and alternative financial systems show more substantial progress compared to initiatives in the early 2000s. As the nature of the world system is anarchic, states make either individual or group efforts to guarantee their economic security and achieve their interests. Based on neoclassical realism, this paper analyzes both internal and external US economic factors driving current and future de-dollarization and the implications on the international monetary system, in addition to examining the motivation for such moves.

Keywords: de-dollarization, US dollar, monetary system, economic security, economic policies.

Procedia PDF Downloads 92
553 Transferring World Athletic Championship-Winning Principles to Entrepreneurship: The Case of Abdelkader El Mouaziz

Authors: Abderrahman Hassi, Omar Bacadi, Khaoula Zitouni

Abstract:

Abdelkader El Mouaziz is a Moroccan long-distance runner with a career-best time of 2:06:46 in the Chicago Marathon. El Mouaziz is a winner of the Madrid Marathon in 1994, the London Marathon in 1999 and 2001, as well as the New York Marathon in 2001. While he was playing for the Moroccan national team, he used to train in the Ifrane-Azrou region owing to its altitude, fresh forests, non-polluted air and quietness. After winning so many international competitions and retiring, he left his native Casablanca and went back to the Ifrane-Azrou region and started a business that employs ten people. In March 2010, El Mouaziz opened a bed and breakfast called Tourtite with a nice view on the mountain near the city of Ifrane in the way to Azrou. He wanted to give back to the region that helped him become a sport legend. His management style is not different than his sport style: performance and competitiveness combined with fair play. The objective of the present case study is to further enhance the understanding of the dynamics of transferring athletic championship-winning principles to entrepreneurial activities. The case study is a real-life situation and experience designed to provoke and stimulate reflections about a particular approach of management, especially for start-up businesses.

Keywords: sport, winning principles, entrepreneurship, Abdelkader El Mouaziz

Procedia PDF Downloads 277
552 Regenerative City Regions: Exploring the Connections between Regenerative Development, Collaborative Governance and Progressive Regionalism

Authors: Lorena F. Axinte

Abstract:

Territorial rescaling is a universal practice in the UK, following a logic of agglomeration and competition as the only chance for cities to thrive. Cardiff Capital Region is one of the latest examples, and its governance structures and developmental narratives are currently being shaped. Its evolution must be compatible with the Wellbeing of Future Generations Act, a Welsh legislation that requires public bodies to put sustainability at the core of all actions. Departing from this case study, the project follows the evolution of Cardiff Capital Region and assesses it based on a new a conceptual framework that connects the notions of regenerative development, collaborative governance, and progressive regionalism. The hypothetical synergies between these different theoretical perspectives are demonstrated, inferring that if regenerative development is aimed at, it must necessarily start with collaborative modes of governance. The objective is to explore (a) whether expanding the network of active stakeholders who get to intervene in the governance structure can contribute to a more progressive definition and development of the city region and (b) whether this can be considered a pathway towards regenerative development. The exploratory fieldwork conducted during the initial phase of the project used qualitative methods, which will be complemented next by different participatory research approaches, as well as a quantitative analysis. Despite being in its early days, the study is showing that a wider range of voices can indeed change priorities, reconcile and balance between the economic drivers and the wider social, economic, cultural and environmental aspects.

Keywords: Cardiff Capital Region, collaborative governance, progressive regionalism, regenerative development

Procedia PDF Downloads 310
551 Numerical Modelling and Soil-structure Interaction Analysis of Rigid Ballast-less and Flexible Ballast-based High-speed Rail Track-embankments Using Software

Authors: Tokirhusen Iqbalbhai Shaikh, M. V. Shah

Abstract:

With an increase in travel demand and a reduction in travel time, high-speed rail (HSR) has been introduced in India. Simplified 3-D finite element modelling is necessary to predict the stability and deformation characteristics of railway embankments and soil structure interaction behaviour under high-speed design requirements for Indian soil conditions. The objective of this study is to analyse the rigid ballast-less and flexible ballast-based high speed rail track embankments for various critical conditions subjected to them, viz. static condition, moving train condition, sudden brake application, and derailment case, using software. The input parameters for the analysis are soil type, thickness of the relevant strata, unit weight, Young’s modulus, Poisson’s ratio, undrained cohesion, friction angle, dilatancy angle, modulus of subgrade reaction, design speed, and other anticipated, relevant data. Eurocode 1, IRS-004(D), IS 1343, IRS specifications, California high-speed rail technical specifications, and the NHSRCL feasibility report will be followed in this study.

Keywords: soil structure interaction, high speed rail, numerical modelling, PLAXIS3D

Procedia PDF Downloads 110
550 Enabling Integrated Production of Electric Vehicles in Automotive Final Assembly: Realization of an Expert Study

Authors: Achim Kampker, Heiner Hans Heimes, Mathias Ordung, Jan-Philip Ganser

Abstract:

In the past years, the automotive industry has changed significantly. Innovative mobility concepts have become more important, and electric vehicles see a chance of replacing vehicles with combustion engines in the long term. However, the coming years will be characterized by coexistence. In this context, there are two possible production scenarios: One the one hand, electric vehicles could be manufactured in bespoke assembly lines. Concerning the uncertainty regarding sales figures development, this alternative boasts a high investment risk. Therefore, an integrated assembly building upon existing structures also seems a feasible solution. This empirical study aims at validating hypotheses concerning theoretical and practical challenges of the integrated production in the final assembly. In order to take a test of approaches of the research by analyzing censored feedback of professionals, these hypotheses are validated in the framework of an expert study. For this purpose, hypotheses have been generated on the basis of a requirements analysis and a concept specification. Thereupon, a list of question has been implemented and deduced from the hypotheses to execute an online- and written-survey and interviews with professionals. The interpretation and evaluation of the findings includes an inter-component comparison for the electric drivetrain. Furthermore, key drivers for a sufficient integrated product and process design are presented.

Keywords: automotive industry, final assembly, integrated manufacturing, product and process development

Procedia PDF Downloads 338
549 Partnerships between Public Administration and Private Social Investment for Territorial Development: Lessons after 15 Brazilian Cases

Authors: Graziela D. de Azevedo, Livia M. Pagotto, Mario P. Monzoni, Neto

Abstract:

This article aims to discuss partnerships between public administration and private social investment aimed at territorial development. There has been some approximation in Brazil from private social investors with initiatives aiming at territorial development policies in highly vulnerable territories or in places where the business sector operates. This represents this paper’s major justification: on the advance of academic debate about how businesses, institutes, and foundations have been working alongside local governments, taking the territory as the reference for joint action. The research was based on the literature on governance and territorial development and adopted a mixed iterative approach (inductive and deductive) through an interpretative lens so as to develop an analysis structure that complements and expands knowledge about the contribution of public policies and private social investments for territorial development in Brazil. The analysis of 15 cases based on three distinct blocks (territorial development plans, articulation for education, and thematic approaches) has made it possible to identify common elements regarding the motivations of partnerships, the specific needs of the actors involved, and the priority drivers for stimulating development. Findings include discussion on the leading role of territories in their development paths, on the institutionalization and strengthening of capacities, and on long-term perspectives in development strategies.

Keywords: private social investment, public administration, territorial governance, territorial development

Procedia PDF Downloads 213
548 Anomaly Detection in a Data Center with a Reconstruction Method Using a Multi-Autoencoders Model

Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

Abstract:

Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

Procedia PDF Downloads 194
547 Oil Demand Forecasting in China: A Structural Time Series Analysis

Authors: Tehreem Fatima, Enjun Xia

Abstract:

The research investigates the relationship between total oil consumption and transport oil consumption, GDP, oil price, and oil reserve in order to forecast future oil demand in China. Annual time series data is used over the period of 1980 to 2015, and for this purpose, an oil demand function is estimated by applying structural time series model (STSM). The technique also uncovers the Underline energy demand trend (UEDT) for China oil demand and GDP, oil reserve, oil price and UEDT are considering important drivers of China oil demand. The long-run elasticity of total oil consumption with respect to GDP and price are (0.5, -0.04) respectively while GDP, oil reserve, and price remain (0.17; 0.23; -0.05) respectively. Moreover, the Estimated results of long-run elasticity of transport oil consumption with respect to GDP and price are (0.5, -0.00) respectively long-run estimates remain (0.28; 37.76;-37.8) for GDP, oil reserve, and price respectively. For both model estimated underline energy demand trend (UEDT) remains nonlinear and stochastic and with an increasing trend of (UEDT) and based on estimated equations, it is predicted that China total oil demand somewhere will be 9.9 thousand barrel per day by 2025 as compare to 9.4 thousand barrel per day in 2015, while transport oil demand predicting value is 9.0 thousand barrel per day by 2020 as compare to 8.8 thousand barrel per day in 2015.

Keywords: china, forecasting, oil, structural time series model (STSM), underline energy demand trend (UEDT)

Procedia PDF Downloads 283