Search results for: cluster model approach

Authors: Ana María Rojas Chaparro, Cristian Camilo Sarmiento Chaves

Abstract:

Given the growing importance of logistics as a discipline for efficient management of materials flow and information, the adoption of tools that permit to create facilities in making decisions based on a global perspective of the system studied has been essential. The article shows how from a concepts-based model is possible to organize and represent in appropriate way the reality, showing accurate and timely information, features that make this kind of models an ideal component to support an information system, recognizing that information as relevant to establish particularities that allow get a better performance about the evaluated sector.

Keywords: system, information, conceptual model, logistics

Procedia PDF Downloads 496

Authors: B. Sir, M. Podhoranyi, S. Kuchar, T. Kocyan

Abstract:

Rainfall-runoff models play important role in hydrological predictions. However, the model is only one part of the process for creation of flood prediction. The aim of this paper is to show the process of successful prediction for flood event (May 15–May 18 2014). The prediction was performed by rainfall runoff model HEC–HMS, one of the models computed within Floreon+ system. The paper briefly evaluates the results of automatic hydrologic prediction on the river Olše catchment and its gages Český Těšín and Věřňovice.

Keywords: flood, HEC-HMS, prediction, rainfall, runoff

Procedia PDF Downloads 395

Authors: Fezzani Boubaker

Abstract:

The modified activated sludge model (ASM1 or Mantis) is a generic structured model and a common platform for dynamic simulation of varieties of aerobic processes for optimization and upgrading of existing plants and for new facilities design. In this study, the modified ASM1 included in the GPS-X software was used to simulate the wastewater treatment plant (WWTP) of Beja treating domestic sewage mixed with baker‘s yeast factory effluent. The results of daily measurements and operating records were used to calibrate the model. A sensitivity and an automatic optimization analysis were conducted to determine the most sensitive and optimal parameters. The results indicated that the ASM1 model could simulate with good accuracy: the COD concentration of effluents from the WWTP of Beja for all months of the year 2012. In addition, it prevents the disruption observed at the output of the plant by injecting the baker‘s yeast factory effluent at high concentrations varied between 20 and 80 g/l.

Keywords: ASM1, activated sludge, baker’s yeast effluent, modelling, simulation, GPS-X 5.1 software

Procedia PDF Downloads 343

Authors: A. Clementking, C. Jothi Venkateswaran

Abstract:

Mathematical and computational functionalities such as descriptive mining, optimization, and predictions are espoused to resolve natural resource planning. The water quality prediction and its attributes influence determinations are adopted optimization techniques. The water properties are tainted while merging water resource one with another. This work aimed to predict influencing water resource distribution connectivity in accordance to water quality and sediment using an innovative proposed master-slave neural network back-propagation model. The experiment results are arrived through collecting water quality attributes, computation of water quality index, design and development of neural network model to determine water quality and sediment, master–slave back propagation neural network back-propagation model to determine variations on water quality and sediment attributes between the water resources and the recommendation for connectivity. The homogeneous and parallel biochemical reactions are influences water quality and sediment while distributing water from one location to another. Therefore, an innovative master-slave neural network model [M (9:9:2)::S(9:9:2)] designed and developed to predict the attribute variations. The result of training dataset given as an input to master model and its maximum weights are assigned as an input to the slave model to predict the water quality. The developed master-slave model is predicted physicochemical attributes weight variations for 85 % to 90% of water quality as a target values.The sediment level variations also predicated from 0.01 to 0.05% of each water quality percentage. The model produced the significant variations on physiochemical attribute weights. According to the predicated experimental weight variation on training data set, effective recommendations are made to connect different resources.

Keywords: master-slave back propagation neural network model(MSBPNNM), water quality analysis, multivariate analysis, environmental mining

Procedia PDF Downloads 477

Authors: Tiwi Kamidin

Abstract:

After years of effort trying to integrate environmental education, studies keep revealing that Malaysian still not reached the certain level of desired commitment toward the environment. Some researchers mentioned that our planet healthy is depending on our mentally health especially our psychological and spiritual is split from the natural. Therefore, this study discussed on ecopcyhological approach in order to enhance space consciousness toward the environment. Space consciousness represents not only freedom from ego but also from dependency on the things of this world, from materialism and materiality. It is the spiritual dimension which alone can give transcendent and true meaning to this world. If pupils can balance this internal awareness will put an individual to respect the environment as part of yourself and your family against only as contributors to the continuance of human’s life. Qualitative findings showed that the informants considered their consciousness toward environment has been changed.

Keywords: ecopsychological approach, space consciousness, environmental education, environment

Procedia PDF Downloads 308

Authors: Rohan Bhasin

Abstract:

Agrammatism in non-fluent Aphasia Cases can be defined as a language disorder wherein a patient can only use content words ( nouns, verbs and adjectives ) for communication and their speech is devoid of functional word types like conjunctions and articles, generating speech of with extremely rudimentary grammar . Past approaches involve Speech Therapy of some order with conversation analysis used to analyse pre-therapy speech patterns and qualitative changes in conversational behaviour after therapy. We describe this approach as a novel method to generate functional words (prepositions, articles, ) around content words ( nouns, verbs and adjectives ) using a combination of Natural Language Processing and Deep Learning algorithms. The applications of this approach can be used to assist communication. The approach the paper investigates is : LSTMs or Seq2Seq: A sequence2sequence approach (seq2seq) or LSTM would take in a sequence of inputs and output sequence. This approach needs a significant amount of training data, with each training data containing pairs such as (content words, complete sentence). We generate such data by starting with complete sentences from a text source, removing functional words to get just the content words. However, this approach would require a lot of training data to get a coherent input. The assumptions of this approach is that the content words received in the inputs of both text models are to be preserved, i.e, won't alter after the functional grammar is slotted in. This is a potential limit to cases of severe Agrammatism where such order might not be inherently correct. The applications of this approach can be used to assist communication mild Agrammatism in non-fluent Aphasia Cases. Thus by generating these function words around the content words, we can provide meaningful sentence options to the patient for articulate conversations. Thus our project translates the use case of generating sentences from content-specific words into an assistive technology for non-Fluent Aphasia Patients.

Keywords: aphasia, expressive aphasia, assistive algorithms, neurology, machine learning, natural language processing, language disorder, behaviour disorder, sequence to sequence, LSTM

Procedia PDF Downloads 164

Authors: Minami Ito, Akihiro Iijima

Abstract:

On March 11, 2011, the Great East Japan Earthquake occurred off the coast of Sanriku, Japan. It is important to build a sustainable society through the reconstruction process rather than simply restoring the infrastructure. To compare the goals of reconstruction plans of quake-stricken municipalities, Japanese language morphological analysis was performed by using text mining techniques. Frequently-used nouns were sorted into four main categories of “life”, “disaster prevention”, “economy”, and “harmony with environment”. Because Soma City is affected by nuclear accident, sentences tagged to “harmony with environment” tended to be frequent compared to the other municipalities. Results from cluster analysis and principle component analysis clearly indicated that the local government reinforces the efforts to reduce risks from radiation exposure as a top priority.

Keywords: eco-friendly reconstruction, harmony with environment, decontamination, nuclear disaster

Procedia PDF Downloads 220

Authors: Arzu K. Kamberli, Tolga Ulusoy

Abstract:

Technological developments and the resulting global communication have made the 21st century when large capitals are moved from one end to the other via a button. As a result, the flow of capital inflows has accelerated, and capital inflow has brought with it crisis-related infectiousness. Considering the irrational human behavior, the financial crisis in the world under the influence of the whole world has turned into the basic problem of the countries and increased the interest of the researchers in the reasons of the crisis and the period in which they lived. Therefore, the complex nature of the financial crises and its linearly unexplained structure have also been included in the new discipline, econophysics. As it is known, although financial crises have prediction mechanisms, there is no definite information. In this context, in this study, using the concept of electric field from the electrostatic part of physics, an early econophysical approach for global financial crises was studied. The aim is to define a model that can take place before the financial crises, identify financial fragility at an earlier stage and help public and private sector members, policy makers and economists with an econophysical approach. 2001 Turkey crisis has been assessed with data from Turkish Central Bank which is covered between 1992 to 2007, and for 2001 Argentina crisis, data was taken from IMF and the Central Bank of Argentina from 1997 to 2007. As an econophysical method, an analogy is used between the Gauss's law used in the calculation of the electric field and the forecasting of the financial crisis. The concept of Φ (Financial Flux) has been adopted for the pre-warning of the crisis by taking advantage of this analogy, which is based on currency movements and money mobility. For the first time used in this study Φ (Financial Flux) calculations obtained by the formula were analyzed by Matlab software, and in this context, in 2001 Turkey and Argentina Crisis for Φ (Financial Flux) crisis of values has been confirmed to give pre-warning.

Keywords: econophysics, financial crisis, Gauss's Law, physics

Procedia PDF Downloads 153

Authors: Flevy Lasrado

Abstract:

Purpose: The purpose of this paper is to show the relationship between the service quality dimensions and model them to propose quality improvements using interpretive structural modelling (ISM). Methodology: This paper used an interpretive structural modelling (ISM). The data was collected from the expert opinions that included a questionnaire. The detailed method of using ISM is discussed in the paper. Findings: The present research work provides an ISM based model to understand the relationships among the service quality dimensions. Practical implications or Original Value: An ISM based model has been developed for healthcare facility for improving customer satisfaction and increasing market share. Although there is lot of research on SERVQUAL model adapted to healthcare sector, no study has been done to understand the interactions among these dimensions. So the major contribution of this research work is the development of contextual relationships among identified variables through a systematic framework. The present research work provides an ISM based model to understand the relationships among the service quality dimensions.

Keywords: SERQUAL, healthcare, quality, service quality

Procedia PDF Downloads 405

Authors: Nikki Berrange, Gizelle Willows

Abstract:

Business rescue has become increasingly popular since its inclusion in the Companies Act of South Africa in May 2011. The Alternate Exchange (AltX) of the Johannesburg Stock Exchange has experienced a marked increase in the number of companies entering business rescue. This study sampled twenty companies listed on the AltX to determine whether Altman’s Z-score model for emerging markets (ZEM) or Taffler’s Z-score model is a more accurate model in predicting financial distress for small to medium size companies in South Africa. The study was performed over three different time horizons; one, two and three years prior to the event of financial distress, in order to determine how many companies each model predicted would be unlikely to succeed as well as the predictive ability and accuracy of the respective models. The study found that Taffler’s Z-score model had a greater ability at predicting financial distress from all three-time horizons.

Keywords: Altman’s ZEM-score, Altman’s Z-score, AltX, business rescue, Taffler’s Z-score

Procedia PDF Downloads 372

Authors: G. L. C. Yap

Abstract:

Modelling realized volatility with high-frequency returns is popular as it is an unbiased and efficient estimator of return volatility. A computationally simple model is fitting the logarithms of the realized volatilities with a fractionally integrated long-memory Gaussian process. The Gaussianity assumption simplifies the parameter estimation using the Whittle approximation. Nonetheless, this assumption may not be met in the finite samples and there may be a need to normalize the financial series. Based on the empirical indices S&P500 and DAX, this paper examines the performance of the linear volatility model pre-treated with normalization compared to its existing counterpart. The empirical results show that by including normalization as a pre-treatment procedure, the forecast performance outperforms the existing model in terms of statistical and economic evaluations.

Keywords: Gaussian process, long-memory, normalization, value-at-risk, volatility, Whittle estimator

Procedia PDF Downloads 354

Authors: Felix Schumann, Giorgia Carratta, Tobias Dauth, Liv Jaeckel

Abstract:

In the last few decades, the rapid growth of the use and disposal of plastic items has led to their overaccumulation in the environment. As a result, plastic pollution has become a subject of global concern. Today plastics are used as raw materials in almost every industry. While the recognition of the ecological, social, and economic impact of plastics in academic research is on the rise, the potential role of the ‘plastic industry’ in dealing with such issues is still largely underestimated. Therefore, the literature on sustainable plastic management is still nascent and fragmented. Working towards sustainability requires a fundamental shift in the way companies employ plastics in their day-to-day business. For that reason, the applicability of the business model concept has recently gained momentum in environmental research. Business model innovation is increasingly recognized as an important driver to re-conceptualize the purpose of the firm and to readily integrate sustainability in their business. It can serve as a starting point to investigate whether and how sustainability can be realized under industry- and firm-specific circumstances. Yet, there is no comprehensive view in the plastic industry on how firms start refining their business models to embed sustainability in their operations. Our study addresses this gap, looking primarily at the industrial sectors responsible for the production of the largest amount of plastic waste today: plastic packaging, consumer goods, construction, textile, and transport. Relying on the archetypes of sustainable business models and applying them to the aforementioned sectors, we try to identify companies’ current strategies to make their business models more sustainable. Based on the thematic clustering, we can develop an integrative framework for the plastic industry. The findings are underpinned and illustrated by a variety of relevant plastic management solutions that the authors have identified through a systematic literature review and analysis of existing, empirically grounded research in this field. Using the archetypes, we can promote options for business model innovations for the most important sectors in which plastics are used. Moreover, by linking the proposed business model archetypes to the plastic industry, our research approach guides firms in exploring sustainable business opportunities. Likewise, researchers and policymakers can utilize our classification to identify best practices. The authors believe that the study advances the current knowledge on sustainable plastic management through its broad empirical industry analyses. Hence, the application of business model archetypes in the plastic industry will be useful for shaping companies’ transformation to create and deliver more sustainability and provides avenues for future research endeavors.

Keywords: business models, environmental economics, plastic management, plastic pollution, sustainability

Procedia PDF Downloads 99

Authors: James Kinsella

Abstract:

There is currently very little understanding of how the interaction between in- vestors, consumers, the firms (agents) affect a) the transmission of information, and b) the creation and transfer of value and wealth between these two groups. Employing scholarly ideas from multiple research areas (behavioural finance, emotional finance, econo-biology, and game theory) we develop a conceptual the- oretic model (the ‘bow-tie’ model) as a framework for considering this interaction. Our bow-tie model views information transfer, value and wealth creation, and transfer through the lens of “investor-consumer connection facilitated through the communicative medium of the ‘firm’ (agents)”. We confront our bow-tie model with theoretical and practical examples. Next, we utilise consumer and business confidence data alongside index data, to conduct quantitative analy- sis, to support our bow-tie concept, and to introduce the concept of “investor- consumer connection”. We highlight the importance of information persuasiveness, knowledge, and emotional categorization of characteristics in facilitating a communicative relationship between investors, consumers, and the firm (agents), forming academic and practical applications of the conceptual bow-tie model, alongside applications to wider instances, such as those seen within the Covid-19 pandemic.

Keywords: behavioral finance, emotional finance, economy-biology, social mood

Procedia PDF Downloads 127

Authors: Rawaa El Ayoubi

Abstract:

Many researchers, academics and practitioners have developed leadership theories during the 20th century. This substantial effort has built more leadership theories, generating considerable organisational research on leadership models in contemporary literature. This paper explores the stages and drivers of leadership theory evolution based on the researcher’s personal conclusions and review of leadership theories. The purpose of this paper is to create a Leadership Process Model (LPM) that can provide guidance in dealing with the key challenges within the organisation. This integrative model of organisational leadership is based on inner meaning, leader values and vision. It further addresses the relationships between leadership theory, practice and development, exploring why challenges exist within the field of leadership theory and how these challenges can be mitigated.

Keywords: leadership challenges, leadership process model, leadership |theories, organisational leadership, paradigm development

Procedia PDF Downloads 78

Authors: A. Shebani, C. Pislaru

Abstract:

Wear of materials is an everyday experience and has been observed and studied for long time. The prediction of wear is a fundamental problem in the industrial field, mainly correlated to the planning of maintenance interventions and economy. Pin-on-disc test is the most common test which is used to study the wear behaviour. In this paper, the pin-on-disc (AEROTECH UNIDEX 11) is used for the investigation of the effects of normal load and hardness of material on the wear under dry and sliding conditions. In the pin-on-disc rig, two specimens were used; one, a pin which is made of steel with a tip, is positioned perpendicular to the disc, where the disc is made of aluminium. The pin wear and disc wear were measured by using the following instruments: The Talysurf instrument, a digital microscope, and the alicona instrument; where the Talysurf profilometer was used to measure the pin/disc wear scar depth, and the alicona was used to measure the volume loss for pin and disc. After that, the Archard model, American Society for Testing and Materials model (ASTM), and neural network model were used for pin/disc wear modelling and the simulation results are implemented by using the Matlab program. This paper focuses on how the alicona can be considered as a powerful tool for wear measurements and how the neural network is an effective algorithm for wear estimation.

Keywords: wear modelling, Archard Model, ASTM Model, Neural Networks Model, Pin-on-disc Test, Talysurf, digital microscope, Alicona

Procedia PDF Downloads 456

Authors: R. Ramesh, K. K. Shivaraman

Abstract:

The main aim of this paper is to handle the energy requirement in an efficient manner by merging the advanced digital communication and control technologies for smart grid applications. In order to reduce user home load during peak load hours, utility applies several incentives such as real-time pricing, time of use, demand response for residential customer through smart meter. However, this method provides inconvenience in the sense that user needs to respond manually to prices that vary in real time. To overcome these inconvenience, this paper proposes a convolutional neural network (CNN) with k-means clustering machine learning model which have ability to forecast energy requirement in short term, i.e., hour of the day or day of the week. By integrating our proposed technique with home energy management based on Bluetooth low energy provides predicted value to user for scheduling appliance in advanced. This paper describes detail about CNN configuration and k-means clustering algorithm for short-term energy forecasting.

Keywords: convolutional neural network, fuzzy logic, k-means clustering approach, smart home energy management

Procedia PDF Downloads 305

Authors: Rodrigo Arnaldo Scarpel

Abstract:

Trend following is a widely accepted investment strategy that adopts a rule-based trading mechanism that rather than striving to predict market direction or on information gathering to decide when to buy and when to sell a stock. Thus, in trend following one must respond to market’s movements that has recently happen and what is currently happening, rather than on what will happen. Optimally, in trend following strategy, is to catch a bull market at its early stage, ride the trend, and liquidate the position at the first evidence of the subsequent bear market. For applying the trend following strategy one needs to find the trend and identify trade signals. In order to avoid false signals, i.e., identify fluctuations of short, mid and long terms and to separate noise from real changes in the trend, most academic works rely on moving averages and other technical analysis indicators, such as the moving average convergence divergence (MACD) and the relative strength index (RSI) to uncover intelligible stock trading rules following trend following strategy philosophy. Recently, some works has applied machine learning techniques for trade rules discovery. In those works, the process of rule construction is based on evolutionary learning which aims to adapt the rules to the current environment and searches for the global optimum rules in the search space. In this work, instead of focusing on the usage of machine learning techniques for creating trading rules, a time series trend classification employing a semi-supervised approach was used to early identify both the beginning and the end of upward and downward trends. Such classification model can be employed to identify trade signals and the decision-making procedure is that if an up-trend (down-trend) is identified, a buy (sell) signal is generated. Semi-supervised learning is used for model training when only part of the data is labeled and Semi-supervised classification aims to train a classifier from both the labeled and unlabeled data, such that it is better than the supervised classifier trained only on the labeled data. For illustrating the proposed approach, it was employed daily trade information, including the open, high, low and closing values and volume from January 1, 2000 to December 31, 2022, of the São Paulo Exchange Composite index (IBOVESPA). Through this time period it was visually identified consistent changes in price, upwards or downwards, for assigning labels and leaving the rest of the days (when there is not a consistent change in price) unlabeled. For training the classification model, a pseudo-label semi-supervised learning strategy was used employing different technical analysis indicators. In this learning strategy, the core is to use unlabeled data to generate a pseudo-label for supervised training. For evaluating the achieved results, it was considered the annualized return and excess return, the Sortino and the Sharpe indicators. Through the evaluated time period, the obtained results were very consistent and can be considered promising for generating the intended trading signals.

Keywords: evolutionary learning, semi-supervised classification, time series data, trading signals generation

Procedia PDF Downloads 89

Authors: H. D. Ibrahim, H. C. Chinwenyi, T. Danjuma

Abstract:

An option is defined as a financial contract that provides the holder the right but not the obligation to buy or sell a specified quantity of an underlying asset in the future at a fixed price (called a strike price) on or before the expiration date of the option. This paper examined two approaches for derivation of Partial Differential Equation (PDE) options price valuation formula for the Heston stochastic volatility model. We obtained various PDE option price valuation formulas using the riskless portfolio method and the application of Feynman-Kac theorem respectively. From the results obtained, we see that the two derived PDEs for Heston model are distinct and non-unique. This establishes the fact of incompleteness in the model for option price valuation.

Keywords: Black-Scholes partial differential equations, Ito process, option price valuation, partial differential equations

Procedia PDF Downloads 145

Authors: B. I. Zhumagaliyev, L. Sh. Balgabayeva, G. S. Nabiyeva, B. A. Tulegenova, P. Oralkhan, B. S. Kalenova, S. S. Akhmetov

Abstract:

The article describes an approach to build competence in research of Bachelor and Master, which is now an important feature of modern specialist in the field of engineering. Provides an example of methodical teaching methods with the research aspect, is including the formulation of the problem, the method of conducting experiments, analysis of the results. Implementation of methods allows the student to better consolidate their knowledge and skills at the same time to get research. Knowledge on the part of the media requires some training in the subject area and teaching methods.

Keywords: professional competence, model of it-specialties, teaching methods, educational technology, decision making

Procedia PDF Downloads 437

Authors: Isik Cetintav, Cenk Misirli, Yilmaz Can

Abstract:

This work investigates upsetting of the tri-metallic cylindrical billets both experimentally and analytically with a reduction ratio 30%. Steel, brass, and copper are used for the outer and outmost rings and aluminum for the inner core. Two different models have been designed to show material flow and the cavity took place over the two interfaces during forming after this reduction ratio. Each model has an outmost ring material as steel. Model 1 has an outer ring between the outmost ring and the solid core material as copper and Model 2 has a material as brass. Solid core is aluminum for each model. Billets were upset in press machine by using parallel flat dies. Upsetting load was recorded and compared for models and single billets. To extend the tests and compare with experimental procedure to a wider range of inner core and outer ring geometries, finite element model was performed. ABAQUS software was used for the simulations. The aim is to show how contact between outmost ring, outer ring and the inner core are carried on throughout the upsetting process. Results have shown that, with changing in height, between outmost ring, outer ring and inner core, the Model 1 and Model 2 had very good interaction, and the contact surfaces of models had various interface behaviour. It is also observed that tri-metallic materials have lower weight but better mechanical properties than single materials. This can give an idea for using and producing these new materials for different purposes.

Keywords: tri-metallic, upsetting, copper, brass, steel, aluminum

Procedia PDF Downloads 342

Authors: T. Nakpun, T. Kanjanarach, T. Kittisopee

Abstract:

Turnover of community pharmacist can affect continuity of patient care and most importantly the quality of care and also the costs of a pharmacy. It was hypothesized that organizational resources, job characteristics, and social supports had direct effect on pharmacist turnover intention, and indirect effect on pharmacist turnover intention via pharmacist engagement. This research aimed to study influencing factors on pharmacist engagement and pharmacist turnover intention by testing the proposed structural hypothesized model to explain the relationship among organizational resources, job characteristics, and social supports that effect on pharmacist turnover intention and pharmacist engagement in Thai community pharmacists. A cross sectional study design with self-administered questionnaire was conducted in 209 Thai community pharmacists. Data were analyzed using Structural Equation Modeling technique with analysis of a moment structures AMOS program. The final model showed that only organizational resources had significant negative direct effect on pharmacist turnover intention (β =-0.45). Job characteristics and social supports had significant positive relationship with pharmacist engagement (β = 0.44, and 0.55 respectively). Pharmacist engagement had significant negative relationship with pharmacist turnover intention (β = - 0.24). Thus, job characteristics and social supports had significant negative indirect effect on turnover intention via pharmacist engagement (β =-0.11 and -0.13, respectively). The model fit the data well (χ2/ degree of freedom (DF) = 2.12, the goodness of fit index (GFI)=0.89, comparative fit index (CFI) = 0.94 and root mean square error of approximation (RMSEA) = 0.07). This study can be concluded that organizational resources were the most important factor because it had direct effect on pharmacist turnover intention. Job characteristics and social supports were also help decrease pharmacist turnover intention via pharmacist engagement.

Keywords: community pharmacist, influencing factor, turnover intention, work engagement

Procedia PDF Downloads 204

Authors: Hilya Mudrika Arini, Nur Aini Masruroh, Budi Hartono

Abstract:

There are more than 20 terrorist threats from 2002 to 2012 in Indonesia. Despite of this fact, preventive solution through studies in the field of national security in Indonesia has not been conducted comprehensively. This study aims to provide a preventive solution by developing prediction model of the terrorist threat in Indonesia by using Bayesian network. There are eight stages to build the model, started from literature review, build and verify Bayesian belief network to what-if scenario. In order to build the model, four experts from different perspectives are utilized. This study finds several significant findings. First, news and the readiness of terrorist group are the most influent factor. Second, according to several scenarios of the news portion, it can be concluded that the higher positive news proportion, the higher probability of terrorist threat will occur. Therefore, the preventive solution to reduce the terrorist threat in Indonesia based on the model is by keeping the positive news portion to a maximum of 38%.

Keywords: Bayesian network, decision analysis, national security system, text mining

Procedia PDF Downloads 392

Authors: Hadas Sopher, Davide Schaumann, Yehuda E. Kalay

Abstract:

This paper addresses the shortcomings of architectural computation tools in representing human behavior in built environments, prior to construction and occupancy of those environments. Evaluating whether a design fits the needs of its future users is currently done solely post construction, or is based on the knowledge and intuition of the designer. This issue is of high importance when designing complex buildings such as hospitals, where the quality of treatment as well as patient and staff satisfaction are of major concern. Existing computational pre-occupancy human behavior evaluation methods are geared mainly to test ergonomic issues, such as wheelchair accessibility, emergency egress, etc. As such, they rely on Agent Based Modeling (ABM) techniques, which emphasize the individual user. Yet we know that most human activities are social, and involve a number of actors working together, which ABM methods cannot handle. Therefore, we present an event-based model that manages the interaction between multiple Actors, Spaces, and Activities, to describe dynamically how people use spaces. This approach requires expanding the computational representation of Actors beyond their physical description, to include psychological, social, cultural, and other parameters. The model presented in this paper includes cognitive abilities and rules that describe the response of actors to their physical and social surroundings, based on the actors’ internal status. The model has been applied in a simulation of hospital wards, and showed adaptability to a wide variety of situated behaviors and interactions.

Keywords: agent based modeling, architectural design evaluation, event modeling, human behavior simulation, spatial cognition

Procedia PDF Downloads 264

Authors: Ji-Hyok Kim, Il-Gyong Paek, Yong-Jun Kim

Abstract:

We propose a numerical model based on drift-diffusion theory and lattice Boltzmann method (LBM) to analyze the electro-hydrodynamic behavior in low-pressure direct current (DC) glow discharge plasmas. We apply the drift-diffusion theory for 4-species and employ the standard lattice Boltzmann model (SLBM) for the electron, the finite difference-lattice Boltzmann model (FD-LBM) for heavy particles, and the finite difference model (FDM) for the electric potential, respectively. Our results are compared with those of other methods, and emphasize the necessity of a two-dimensional analysis for glow discharge.

Keywords: glow discharge, lattice Boltzmann method, numerical analysis, plasma simulation, electro-hydrodynamic

Procedia PDF Downloads 120

Authors: Georgia Pozoukidou

Abstract:

TELUM software is a land use model designed specifically to help metropolitan planning organizations (MPOs) prepare their transportation improvement programs and fulfill their numerous planning responsibilities. In this context obtaining, preparing, and validating socioeconomic forecasts are becoming fundamental tasks for an MPO in order to ensure that consistent population and employment data are provided to travel demand models. Chittenden County Metropolitan Planning Organization of Vermont State was used as a case study to test the applicability of TELUM land use model. The technical insights and lessons learned from the land use model application have transferable value for all MPOs faced with land use forecasting development and transportation modelling.

Keywords: calibration data requirements, land use models, land use planning, metropolitan planning organizations

Procedia PDF Downloads 292

Authors: M. Z. Raqab, Debasis Kundu, M. A. Meraou

Abstract:

In this paper, we have analyzed maximum precipitation data during a particular period of time obtained from different stations in the Global Historical Climatological Network of the USA. One important point to mention is that some stations are shut down on certain days for some reason or the other. Hence, the maximum values are recorded by excluding those readings. It is assumed that the number of stations that operate follows zero-truncated Poisson random variables, and the daily precipitation follows a lognormal random variable. We call this model a compound truncated Poisson lognormal model. The proposed model has three unknown parameters, and it can take a variety of shapes. The maximum likelihood estimators can be obtained quite conveniently using Expectation-Maximization (EM) algorithm. Approximate maximum likelihood estimators are also derived. The associated confidence intervals also can be obtained from the observed Fisher information matrix. Simulation results have been performed to check the performance of the EM algorithm, and it is observed that the EM algorithm works quite well in this case. When we analyze the precipitation data set using the proposed model, it is observed that the proposed model provides a better fit than some of the existing models.

Keywords: compound Poisson lognormal distribution, EM algorithm, maximum likelihood estimation, approximate maximum likelihood estimation, Fisher information, skew distribution

Procedia PDF Downloads 108

Authors: Faizul Azly Abd Dzubir, Muhammad F. Othman

Abstract:

Conventional structural health monitoring approach for mechanical equipment uses inspection data from Non-Destructive Testing (NDT) during plant shut down window and fitness for service evaluation to estimate the integrity of the equipment that is prone to crack damage. Yet, this forecast is fraught with uncertainty because it is often based on assumptions of future operational parameters, and the prediction is not continuous or online. Advanced Diagnostic and Prognostic Technology (ADaPT) uses Acoustic Emission (AE) technology and a stochastic prognostic model to provide real-time monitoring and prediction of mechanical defects or cracks. The forecast can help the plant authority handle their cracked equipment before it ruptures, causing an unscheduled shutdown of the facility. The ADaPT employs process historical data trending, finite element analysis, fitness for service, and probabilistic statistical analysis to develop a prediction model for crack initiation and growth due to mechanical damage. The prediction model is combined with live equipment operating data for real-time prediction of the remaining life span owing to fracture. ADaPT was devised at a hot combined feed exchanger (HCFE) that had suffered creep crack damage. The ADaPT tool predicts the initiation of a crack at the top weldment area by April 2019. During the shutdown window in April 2019, a crack was discovered and repaired. Furthermore, ADaPT successfully advised the plant owner to run at full capacity and improve output by up to 7% by April 2019. ADaPT was also used on a coke drum that had extensive fatigue cracking. The initial cracks are declared safe with ADaPT, with remaining crack lifetimes extended another five (5) months, just in time for another planned facility downtime to execute repair. The prediction model, when combined with plant information data, allows plant operators to continuously monitor crack propagation caused by mechanical damage for improved maintenance planning and to avoid costly shutdowns to repair immediately.

Keywords: mechanical damage, cracks, continuous monitoring tool, remaining life, acoustic emission, prognostic model

Procedia PDF Downloads 76

Authors: Kirill Filianin, Satu-Pia Reinikainen, Tuomo Sainio

Abstract:

Frequent measurements of product steam quality create a data overload that becomes more and more difficult to handle. In the current study, plant history data with multiple variables was successfully treated by principal component analysis to detect abnormal process behavior, particularly, in copper solvent extraction. The multivariate model is based on the concentration levels of main process metals recorded by the industrial on-stream x-ray fluorescence analyzer. After mean-centering and normalization of concentration data set, two-dimensional multivariate model under principal component analysis algorithm was constructed. Normal operating conditions were defined through control limits that were assigned to squared score values on x-axis and to residual values on y-axis. 80 percent of the data set were taken as the training set and the multivariate model was tested with the remaining 20 percent of data. Model testing showed successful application of control limits to detect abnormal behavior of copper solvent extraction process as early warnings. Compared to the conventional techniques of analyzing one variable at a time, the proposed model allows to detect on-line a process failure using information from all process variables simultaneously. Complex industrial equipment combined with advanced mathematical tools may be used for on-line monitoring both of process streams’ composition and final product quality. Defining normal operating conditions of the process supports reliable decision making in a process control room. Thus, industrial x-ray fluorescence analyzers equipped with integrated data processing toolbox allows more flexibility in copper plant operation. The additional multivariate process control and monitoring procedures are recommended to apply separately for the major components and for the impurities. Principal component analysis may be utilized not only in control of major elements’ content in process streams, but also for continuous monitoring of plant feed. The proposed approach has a potential in on-line instrumentation providing fast, robust and cheap application with automation abilities.

Keywords: abnormal process behavior, failure detection, principal component analysis, solvent extraction

Procedia PDF Downloads 309

Authors: Mais Khader, Xingjie Wei

Abstract:

This research sheds light on female entrepreneurs by providing new insights on the survival predictions of companies led by females in the UK. This study aims to build a predictive machine learning model of the survival of female-led & co-led small & medium enterprises (SMEs) in the UK over the period 2000-2020. The predictive model built utilised a combination of financial and non-financial features related to both companies and their directors to predict SMEs' survival. These features were studied in terms of their contribution to the resultant predictive model. Five machine learning models are used in the modelling: Decision tree, AdaBoost, Naïve Bayes, Logistic regression and SVM. The AdaBoost model had the highest performance of the five models, with an accuracy of 73% and an AUC of 80%. The results show high feature importance in predicting companies' survival for company size, management experience, financial performance, industry, region, and females' percentage in management.

Keywords: company survival, entrepreneurship, females, machine learning, SMEs

Procedia PDF Downloads 101

Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

Abstract:

This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

Procedia PDF Downloads 129