Search results for: real estate valuation model

Authors: Shady Adib, Vladimir Vinogradov, Peter Gosling

Abstract:

Digital Twin (DT) technology is a new technology that appeared in the early 21st century. The DT is defined as the digital representation of living and non-living physical assets. By connecting the physical and virtual assets, data are transmitted smoothly, allowing the virtual asset to fully represent the physical asset. Although there are lots of studies conducted on the DT concept, there is still limited information about the ability of the DT models for monitoring and detecting unexpected changes in structural behaviour in real time. This is due to the large computational efforts required for the analysis and an excessively large amount of data transferred from sensors. This paper aims to develop the DT concept to be able to detect the abnormal changes in structural behaviour in real time using advanced modelling techniques, deep learning algorithms, and data acquisition systems, taking into consideration model uncertainties. finite element (FE) models were first developed offline to be used with a reduced basis (RB) model order reduction technique for the construction of low-dimensional space to speed the analysis during the online stage. The RB model was validated against experimental test results for the establishment of a DT model of a two-dimensional truss. The established DT model and deep learning algorithms were used to identify the location of damage once it has appeared during the online stage. Finally, the RB model was used again to identify the damage severity. It was found that using the RB model, constructed offline, speeds the FE analysis during the online stage. The constructed RB model showed higher accuracy for predicting the damage severity, while deep learning algorithms were found to be useful for estimating the location of damage with small severity.

Keywords: data acquisition system, deep learning, digital twin, model uncertainties, reduced basis, reduced order model

Procedia PDF Downloads 67

Authors: Sunday B. Alabi, Edikan P. Felix, Aniediong M. Umo

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Crystal size distribution is of great importance in the sugar factories. It determines the market value of granulated sugar and also influences the cost of production of sugar crystals. Typically, sugar is produced using fed-batch vacuum evaporative crystallizer. The crystallization quality is examined by crystal size distribution at the end of the process which is quantified by two parameters: the average crystal size of the distribution in the mean aperture (MA) and the width of the distribution of the coefficient of variation (CV). Lack of real-time measurement of the sugar crystal size hinders its feedback control and eventual optimisation of the crystallization process. An attractive alternative is to use a soft sensor (model-based method) for online estimation of the sugar crystal size. Unfortunately, the available models for sugar crystallization process are not suitable as they do not contain variables that can be measured easily online. The main contribution of this paper is the development of a regression model for estimating the sugar crystal size as a function of input variables which are easy to measure online. This has the potential to provide real-time estimates of crystal size for its effective feedback control. Using 7 input variables namely: initial crystal size (Lo), temperature (T), vacuum pressure (P), feed flowrate (Ff), steam flowrate (Fs), initial super-saturation (S0) and crystallization time (t), preliminary studies were carried out using Minitab 14 statistical software. Based on the existing sugar crystallizer models, and the typical ranges of these 7 input variables, 128 datasets were obtained from a 2-level factorial experimental design. These datasets were used to obtain a simple but online-implementable 6-input crystal size model. It seems the initial crystal size (Lₒ) does not play a significant role. The goodness of the resulting regression model was evaluated. The coefficient of determination, R² was obtained as 0.994, and the maximum absolute relative error (MARE) was obtained as 4.6%. The high R² (~1.0) and the reasonably low MARE values are an indication that the model is able to predict sugar crystal size accurately as a function of the 6 easy-to-measure online variables. Thus, the model can be used as a soft sensor to provide real-time estimates of sugar crystal size during sugar crystallization process in a fed-batch vacuum evaporative crystallizer.

Keywords: crystal size, regression model, soft sensor, sugar, vacuum evaporative crystallizer

Procedia PDF Downloads 176

Authors: A. Amar

Abstract:

The angular distributions of the nuclear systems under consideration have been analyzed in the framework of the optical model (OM), where the real part was taken in the crystal model form. A crystal model (CM) has been applied to deuteron elastically scattered by ⁶,⁷Li and ⁹Be. A crystal model (CM) + distorted-wave Born approximation (DWBA) + dynamic polarization potential (DPP) potential has been applied to deuteron elastically scattered by ⁶,⁷Li and 9Be. Also, a crystal model has been applied to ⁶Li elastically scattered by ¹⁶O and ²⁸Sn in addition to the ⁷Li+⁷Li system and the ¹²C(alpha,⁸Be) ⁸Be reaction. The continuum-discretized coupled-channels (CDCC) method has been applied to the ⁷Li+⁷Li system and agreement between the crystal model and the continuum-discretized coupled-channels (CDCC) method has been observed. In general, the models succeeded in reproducing the differential cross sections at the full angular range and for all the energies under consideration.

Keywords: optical model (OM), crystal model (CM), distorted-wave born approximation (DWBA), dynamic polarization potential (DPP), the continuum-discretized coupled-channels (CDCC) method, and deuteron elastically scattered by ⁶, ⁷Li and ⁹Be

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Authors: Kristina Ancutiene

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The goal of this research is to perform comparative analysis between the virtual fit of the woman's dress and the fit on a real person. The dress fitting was done using mechanical and structural parameters of the 100 % linen fabric and using Modaris_3D_Fit software (CAD Lectra). The dress was also sawn after which garment fit differences of real and virtual dress was researched. Four respondents whose figures were similar were used to evaluate the ease and strain deformations of the real and virtual dress. The scores that were given by the respondents wearing the real dress were compared to the ease and strain results that were given by the software. The main result was that respondents feel similar to the virtual stretch deformations but their ease feeling is not always matching the virtual ones. The results may be influenced by psychological factors and different understanding about purpose of garment.

Keywords: virtual garment, 3D CAD, garment fit, mechanical properties

Procedia PDF Downloads 290

Authors: Sahar Navabakhsh, Rokia Raslan, Yair Schwartz

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Decision-making on regeneration of housing estates, whether to refurbish or re-build, has been mostly triggered by economic factors. To enable sustainable growth, it is vital that environmental and social impacts of different scenarios are also taken into account. The methodology used to include all the three sustainable development pillars is called Life Cycle Sustainability Assessment (LCSA), which comprises of Life Cycle Assessment (LCA) for the assessment of environmental impacts of buildings. Current practice of LCA is regularly conducted post design stage and by sustainability experts. Not only is undertaking an LCA at this stage less effective, but issues such as the limited scope for the definition and assessment of environmental impacts, the implication of changes in the system boundary and the alteration of each of the variable metrics, employment of different Life Cycle Impact Assessment Methods and use of various inventory data for Life Cycle Inventory Analysis can result in considerably contrasting results. Given the niche nature and scarce specialist domain of LCA of buildings, the majority of the stakeholders do not contribute to the generation or interpretation of the impact assessment, and the results can be generated and interpreted subjectively due to the mentioned uncertainties. For an effective and democratic assessment of environmental impacts, different stakeholders, and in particular the community and design team should collaborate in the process of data collection, assessment and analysis. This paper examines and evaluates a participatory approach to LCSA through the analysis of a case study of a housing estate in South West London. The study has been conducted throughout tier-based collaborative methods to collect and share data through surveys and co-design workshops with the community members and the design team as the main stakeholders. The assessment of lifecycle impacts is conducted throughout the process and has influenced the decision-making on the design of the Community Plan. The evaluation concludes better assessment transparency and outcome, alongside other socio-economic benefits of identifying and engaging the most contributive stakeholders in the process of conducting LCSA.

Keywords: life cycle assessment, participatory LCA, life cycle sustainability assessment, participatory processes, decision-making, housing estate regeneration

Procedia PDF Downloads 116

Authors: T. Hadj Sadok, R. Hattab Bey, K. Rebiha

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The valuation of juice from prickly pear of Opuntia ficus indica inermis as cocktails appears an attractive alternative because of their nutritional intake and functional compound has anti-radical activity (polyphenols, vitamin C, carotenoids, Betalaines, fiber and minerals). The juice from the fruit pulp is characterized by a high pH 5.85 which makes it difficult for its conservation and preservation requires a thermal treatment at high temperatures (over 100 °C) harmful for bioactive constituents compared to juice orange more acidic and processed at temperatures < 100 °C. The valuation as fig cocktails-orange is particularly interesting thanks to the contribution of polyph2nols, fiber, vitamin C, reducing sugar (sweetener) and betalaine, minerals while allowing lower temperature processing to decrease pH. The heat treatment of these juices: orange alone or in cocktails showed that the antioxidant power decreases by 12% in presence of 30% of juice treated by the heat and of 28 and 32% in the presence of 10 and 20% juice which shows the effect prickly pear juice of Opuntia. During storage for 4 weeks the loss of vitamin C is 40 and 38% in the presence of 10 and 20% juice and 33% in the presence of 30% pear juice parallel, a treatment of stabilization by heat affects relatively the polyphenols rate which decreases from 10.5% to 30% in the cocktail, and 6.11-6.71pour cocktails at 10% and 20%. Vitamin C decreases to 12 to 24 % after a heat treatment at 85°C for 30 minutes respectively for the orange juice and pear juice; this reduction is higher when the juice is in the form of cocktails composed of 10 to 30 % pear juice.

Keywords: prickly pear juice, orange cocktail, polyphenol, Opuntia ficus indica, vitamin

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Authors: Afef Dridi, Salah Mezlini

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Since experimental devices cannot calculate stress and deformation of complex structures. The Finite Element Method FEM has been widely used in several fields of research. One of these fields is orthodontics. The advantage of using such a method is the use of an accurate and non invasive method that allows us to have a sufficient data about the physiological reactions can happening in soft tissues. Most of researches done in this field were interested in the study of stresses and deformations induced by orthodontic apparatus in soft tissues (alveolar tissues). Only few studies were interested in the distribution of stress and strain in the orthodontic brackets. These studies, although they tried to be as close as possible to real conditions, their models did not reproduce the clinical cases. For this reason, the model generated by our research is the closest one to reality. In this study, a numerical model was developed to explore the stress and strain distribution under the application of real conditions. A comparison between different material properties was also done.

Keywords: visco-hyperelasticity, FEM, orthodontic treatment, inverse method

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Authors: Yifu Ding, Vijay Avinash, Malcolm McCulloch

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As the proliferation of decentralized energy systems, the UK power system allows small-scale entities such as microgrids (MGs) to tender multiple energy services including energy arbitrage and frequency responses (FRs). However, its operation requires the balance between the uncertain renewable generations and loads in real-time and has to fulfill their provision requirements of contract services continuously during the time window agreed, otherwise it will be penalized for the under-delivered provision. To hedge against risks due to uncertainties and maximize the economic benefits, we propose a stochastic model predictive control (SMPC) framework to optimize its operation for the multi-service provision. Distinguished from previous works, we include a detailed economic-degradation model of the lithium-ion battery to quantify the costs of different service provisions, as well as accurately describe the changing dynamics of the battery. Considering a branch of load and generation scenarios and the battery aging, we formulate a risk-averse cost function using conditional value at risk (CVaR). It aims to achieve the maximum expected net revenue and avoids severe losses. The framework will be performed on a case study of a PV-battery grid-tied microgrid in the UK with real-life data. To highlight its performance, the framework will be compared with the case without the degradation model and the deterministic formulation.

Keywords: model predictive control (MPC), battery degradation, frequency response, microgrids

Procedia PDF Downloads 94

Authors: Junfeng Yan, Wenkui Hou

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Prognostics and Health Management (PHM) is a technology to monitor the equipment status and predict impending faults. It is used to predict the potential fault and provide fault information and track trends of system degradation by capturing characteristics signals. So how to detect characteristics signals is very important. The select of test point plays a very important role in detecting characteristics signal. Traditionally, we use dependency model to select the test point containing the most detecting information. But, facing the large complicated system, the dependency model is not built so easily sometimes and the greater trouble is how to calculate the matrix. Rely on this premise, the paper provide a highly effective method to select test point without dependency model. Because signal flow model is a diagnosis model based on failure mode, which focuses on system’s failure mode and the dependency relationship between the test points and faults. In the signal flow model, a fault information can flow from the beginning to the end. According to the signal flow model, we can find out location and structure information of every test point and module. We break the signal flow model up into serial and parallel parts to obtain the final relationship function between the system’s testability or prediction metrics and test points. Further, through the partial derivatives operation, we can obtain every test point’s importance degree in determining the testability metrics, such as undetected rate, false alarm rate, untrusted rate. This contributes to installing the test point according to the real requirement and also provides a solid foundation for the Prognostics and Health Management. According to the real effect of the practical engineering application, the method is very efficient.

Keywords: false alarm rate, importance degree, signal flow model, undetected rate, untrusted rate

Procedia PDF Downloads 349

Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

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Authors: Meriem Bahij, Ahmed Nafidi, Boujemâa Achchab, Sílvio M. A. Gama, José A. O. Matos

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Stochastic modeling concerns the use of probability to model real-world situations in which uncertainty is present. Therefore, the purpose of stochastic modeling is to estimate the probability of outcomes within a forecast, i.e. to be able to predict what conditions or decisions might happen under different situations. In the present study, we present a model of a stochastic diffusion process based on the bi-Weibull distribution function (its trend is proportional to the bi-Weibull probability density function). In general, the Weibull distribution has the ability to assume the characteristics of many different types of distributions. This has made it very popular among engineers and quality practitioners, who have considered it the most commonly used distribution for studying problems such as modeling reliability data, accelerated life testing, and maintainability modeling and analysis. In this work, we start by obtaining the probabilistic characteristics of this model, as the explicit expression of the process, its trends, and its distribution by transforming the diffusion process in a Wiener process as shown in the Ricciaardi theorem. Then, we develop the statistical inference of this model using the maximum likelihood methodology. Finally, we analyse with simulated data the computational problems associated with the parameters, an issue of great importance in its application to real data with the use of the convergence analysis methods. Overall, the use of a stochastic model reflects only a pragmatic decision on the part of the modeler. According to the data that is available and the universe of models known to the modeler, this model represents the best currently available description of the phenomenon under consideration.

Keywords: diffusion process, discrete sampling, likelihood estimation method, simulation, stochastic diffusion process, trends functions, bi-parameters weibull density function

Procedia PDF Downloads 263

Authors: Elham Taghizadeh, Mostafa Abedzadeh, Mostafa Setak

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Logistics network is expected that opened facilities work continuously for a long time horizon without any failure; but in real world problems, facilities may face disruptions. This paper studies a reliable joint inventory location problem to optimize cost of facility locations, customers’ assignment, and inventory management decisions when facilities face failure risks and doesn’t work. In our model we assume when a facility is out of work, its customers may be reassigned to other operational facilities otherwise they must endure high penalty costs associated with losing service. For defining the model closer to real world problems, the model is proposed based on p-median problem and the facilities are considered to have limited capacities. We define a new binary variable (Z_is) for showing that customers are not assigned to any facilities. Our problem involve a bi-objective model; the first one minimizes the sum of facility construction costs and expected inventory holding costs, the second one function that mention for the first one is minimizes maximum expected customer costs under normal and failure scenarios. For solving this model we use NSGAII and MOSS algorithms have been applied to find the pareto- archive solution. Also Response Surface Methodology (RSM) is applied for optimizing the NSGAII Algorithm Parameters. We compare performance of two algorithms with three metrics and the results show NSGAII is more suitable for our model.

Keywords: joint inventory-location problem, facility location, NSGAII, MOSS

Procedia PDF Downloads 489

Authors: Ali Heidari

Abstract:

This paper presents the principles for improving flood mitigation operation in multipurpose dams and maximizing reservoir performance during flood occurrence with a focus on the real-time operation of gated spillways. The criteria of operation include the safety of dams during flood management, minimizing the downstream flood risk by decreasing the flood hazard and fulfilling water supply and other purposes of the dam operation in mid and long terms horizons. The parameters deemed to be important include flood inflow, outlet capacity restrictions, downstream flood inundation damages, economic revenue of dam operation, and environmental and sedimentation restrictions. A simulation model was used to determine the real-time release of the Dez dam located in the Dez rivers in southwest Iran, considering the gate regulation curves for the gated spillway. The results of the simulation model show that there is a possibility to improve the current procedures used in the real-time operation of the dams, particularly using gate regulation curves and early flood forecasting system results. The Dez dam operation data shows that in one of the best flood control records, % 17 of the total active volume and flood control pool of the reservoir have not been used in decreasing the downstream flood hazard despite the availability of a flood forecasting system.

Keywords: dam operation, flood control criteria, Dez dam, Iran

Procedia PDF Downloads 189

Authors: Wong Hock Tsen

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This study aims to investigate the impact of exchange rate volatility on real export in Malaysia. The moving standard deviation with order three (MSD(3)) is used for the measurement of exchange rate volatility. The conventional and partially asymmetric autoregressive distributed lag (ARDL) models are used in the estimations. This study finds exchange rate volatility to have significant impact on real total export and some sub-categories of real total export. Moreover, this study finds that the positive or negative exchange rate volatility tends to have positive or negative impact on real export. Exchange rate volatility can be harmful to export of Malaysia.

Keywords: exchange rate volatility, autoregressive distributed lag, export, Malaysia

Procedia PDF Downloads 277

Authors: O. Benabdeljelil, A. Karioun, S. Amami, R. Rouger, M. Hamidine

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A model for preventing the risks related to climate conditions in the agricultural sector is presented. It will determine the yearly optimum premium to be paid by a producer in order to reach his required turnover. The model is based on both climatic stability and 'soft' responses of usually grown species to average climate variations at the same place and inside a safety ball which can be determined from past meteorological data. This allows the use of linear regression expression for dependence of production result in terms of driving meteorological parameters, the main ones of which are daily average sunlight, rainfall and temperature. By simple best parameter fit from the expert table drawn with professionals, optimal representation of yearly production is determined from records of previous years, and yearly payback is evaluated from minimum yearly produced turnover. The model also requires accurate pricing of commodity at N+1. Therefore, a pricing model is developed using 3 state variables, namely the spot price, the difference between the mean-term and the long-term forward price, and the long-term structure of the model. The use of historical data enables to calibrate the parameters of state variables, and allows the pricing of commodity. Application to beet sugar underlines pricer precision. Indeed, the percentage of accuracy between computed result and real world is 99,5%. Optimal premium is then deduced and gives the producer a useful bound for negotiating an offer by insurance companies to effectively protect its harvest. The application to beet production in French Oise department illustrates the reliability of present model with as low as 6% difference between predicted and real data. The model can be adapted to almost any agricultural field by changing state parameters and calibrating their associated coefficients.

Keywords: agriculture, production model, optimal price, meteorological factors, 3-factor model, parameter calibration, forward price

Procedia PDF Downloads 340

Authors: Shadab Shishegar, Sophie Duchesne, Genevieve Pelletier

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Sustainability as one of the key elements of Smart cities, can be realized by employing Real Time Control Strategies for city’s infrastructures. Nowadays Stormwater management systems play an important role in mitigating the impacts of urbanization on natural hydrological cycle. These systems can be managed in such a way that they meet the smart cities standards. In fact, there is a huge potential for sustainable management of urban stormwater and also its adaptability to global challenges like climate change. Hence, a dynamically managed system that can adapt itself to instability of the environmental conditions is desirable. A Global Predictive Real Time Control approach is proposed in this paper to optimize the performance of stormwater management basins in terms of flooding prevention. To do so, a mathematical optimization model is developed then solved using Genetic Algorithm (GA). Results show an improved performance at system-level for the stormwater basins in comparison to static strategy.

Keywords: environmental sustainability, optimization, real time control, storm water management

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Authors: Riko Wahyudi, Rezky Lasekti Wicaksono, Ayu Satya Damayanti, Ridhasepta Multi Kenrosa

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Implementing Forest Management Unit (FMU) is considered as the best solution for forest management in developing countries. However, when FMU has been formed, many parties then blame the FMU and assume it is not working on. Currently, there are two main issues that make FMU not be functional i.e. institutional and financial issues. This paper is addressing financial issues to make FMUs in Aceh Province can be functional. A mixed financing scheme is proposed here, both direct and indirect financing. The direct financing scheme derived from two components i.e. public funds and businesses. Non-tax instruments of intergovernmental fiscal transfer (IFT) system and FMU’s businesses are assessed. Meanwhile, indirect financing scheme is conducted by assessing public funds within villages around forest estate as about 50% of total villages in Aceh Province are located surrounding forest estate. Potential instruments under IFT system are forest and mining utilization royalties. In order to make these instruments become direct financing for FMU, interventions on allocation and distribution aspects of them are conducted. In the allocation aspect, alteration in proportion of allocation is required as the authority to manage forest has shifted from district to province. In the distribution aspect, Government of Aceh can earmark usage of the funds for FMUs. International funds for climate change also encouraged to be domesticated and then channeled through these instruments or new instrument under public finance system in Indonesia. Based on FMU’s businesses both from forest products and forest services, FMU can impose non-tax fees for each forest product and service utilization. However, for doing business, the FMU need to be a Public Service Agency (PSA). With this status, FMU can directly utilize the non-tax fees without transferring them to the state treasury. FMU only need to report the fees to Ministry of Finance. Meanwhile, indirect financing scheme is conducted by empowering villages around forest estate as villages in Aceh Province is receiving average village fund of IDR 800 million per village in 2017 and the funds will continue to increase in subsequent years. These schemes should be encouraged in parallel to establish a mixed financing scheme in order to ensure sustainable financing for FMU in Aceh Province, Indonesia.

Keywords: forest management, public funds, mixed financing, village

Procedia PDF Downloads 159

Authors: Saad M. Darwish

Abstract:

Recently, software quality issues have come to be seen as important subject as we see an enormous growth of agencies involved in software industries. However,these agencies cannot guarantee the quality of their products, thus leaving users in uncertainties. Software certification is the extension of quality by means that quality needs to be measured prior to certification granting process. This research participates in solving the problem of software assessment by proposing a model for assessment and certification of software product that uses a fuzzy inference engine to integrate both of process–driven and application-driven quality assurance strategies. The key idea of the on hand model is to improve the compactness and the interpretability of the model’s fuzzy rules via employing an ant colony optimization algorithm (ACO), which tries to find good rules description by dint of compound rules initially expressed with traditional single rules. The model has been tested by case study and the results have demonstrated feasibility and practicability of the model in a real environment.

Keywords: optimization technique, quality assurance, software certification model, software assessment

Procedia PDF Downloads 456

Authors: Yosra Ben Mhamed

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Most previous research that investigates the importance of earnings volatility for a firm’s value has focused on the effects of earnings volatility on the cost of capital. Many study illustrate that earnings volatility can reduce the firm’s value by enhancing the cost of capital. However, a few recent studies directly examine the relation between earnings volatility and subsequent earnings levels. In our study, we further explore the role of volatility in forecasting. Our study makes two primary contributions to the literature. First, taking into account the level of current firm’s performance, we provide causal theory to the link between volatility and earnings predictability. Nevertheless, previous studies testing the linearity of this relationship have not mentioned any underlying theory. Secondly, our study contributes to the vast body of fundamental analysis research that identifies a set of variables that improve valuation, by showing that earnings volatility affects the estimation of future earnings. Projections of earnings are used by valuation research and practice to derive estimates of firm value. Since we want to examine the impact of volatility on earnings predictability, we sort the sample into three portfolios according to the level of their earnings volatility in ascending order. For each quintile, we present the predictability coefficient. In a second test, each of these portfolios is, then, sorted into three further quintiles based on their level of current earnings. These yield nine quintiles. So we can observe whether volatility strongly predicts decreases on earnings predictability only for highest quintile of earnings. In general, we find that earnings volatility has an inverse relationship with earnings predictability. Our results also show that the sensibility of earnings predictability to ex-ante volatility is more pronounced among profitability firms. The findings are most consistent with overinvestment and persistence explanations.

Keywords: earnings volatility, earnings predictability, earnings persistence, current profitability

Procedia PDF Downloads 399

Authors: Budi Nurani Ruchjana, Atje Setiawan Abdullah, I. Gede Nyoman Mindra Jaya, Eddy Hermawan

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In this paper, we propose the application package to estimate parameters of spatiotemporal model based on the multivariate time series analysis using the R open-source software. We build packages mainly to estimate the parameters of the Generalized Space Time Autoregressive (GSTAR) model. GSTAR is a combination of time series and spatial models that have parameters vary per location. We use the method of Ordinary Least Squares (OLS) and use the Mean Average Percentage Error (MAPE) to fit the model to spatiotemporal real phenomenon. For case study, we use oil production data from volcanic layer at Jatibarang Indonesia or climate data such as rainfall in Indonesia. Software R is very user-friendly and it is making calculation easier, processing the data is accurate and faster. Limitations R script for the estimation of model parameters spatiotemporal GSTAR built is still limited to a stationary time series model. Therefore, the R program under windows can be developed either for theoretical studies and application.

Keywords: GSTAR Model, MAPE, OLS method, oil production, R software

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Authors: Sunghae Jun

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Technology management (TM) is important issue in a company improving the competitiveness. Among many activities of TM, technology analysis (TA) is important factor, because most decisions for management of technology are decided by the results of TA. TA is to analyze the developed results of target technology using statistics or Delphi. TA based on Delphi is depended on the experts’ domain knowledge, in comparison, TA by statistics and machine learning algorithms use objective data such as patent or paper instead of the experts’ knowledge. Many quantitative TA methods based on statistics and machine learning have been studied, and these have been used for technology forecasting, technological innovation, and management of technology. They applied diverse computing tools and many analytical methods case by case. It is not easy to select the suitable software and statistical method for given TA work. So, in this paper, we propose a methodology for quantitative TA using statistical computing software called R and data science to construct a general framework of TA. From the result of case study, we also show how our methodology is applied to real field. This research contributes to R&D planning and technology valuation in TM areas.

Keywords: technology management, R system, R data science, statistics, machine learning

Procedia PDF Downloads 424

Authors: Atinkut Atinafu Yilma, Eyob Messele Sefene

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Anomaly detection is one of the essential mechanisms to control and reduce production loss, especially in today's smart manufacturing. Quick anomaly detection aids in reducing the cost of production by minimizing the possibility of producing defective products. However, developing an anomaly detection model that can rapidly detect a production change is challenging. This paper proposes Gated Recurrent Unit (GRU) combined with Random Forest (RF) to detect anomalies in the production process in real-time quickly. The GRU is used as a feature detector, and RF as a classifier using the input features from GRU. The model was tested using various synthesis and real-world datasets against benchmark methods. The results show that the proposed GRU-RF outperforms the benchmark methods with the shortest time taken to detect anomalies in the production process. Based on the investigation from the study, this proposed model can eliminate or reduce unnecessary production costs and bring a competitive advantage to manufacturing industries.

Keywords: anomaly detection, multivariate time series data, smart manufacturing, gated recurrent unit network, random forest

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Authors: Gamze Ozel, Selen Cakmakyapan

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We introduce a new model called the Marshall-Olkin Rayleigh distribution which extends the Rayleigh distribution using Marshall-Olkin transformation and has increasing and decreasing shapes for the hazard rate function. Various structural properties of the new distribution are derived including explicit expressions for the moments, generating and quantile function, some entropy measures, and order statistics are presented. The model parameters are estimated by the method of maximum likelihood and the observed information matrix is determined. The potentiality of the new model is illustrated by means of real life data set.

Keywords: Marshall-Olkin distribution, Rayleigh distribution, estimation, maximum likelihood

Procedia PDF Downloads 459

Authors: Saad M. Darwish

Abstract:

Recently, software quality issues have come to be seen as important subject as we see an enormous growth of agencies involved in software industries. However, these agencies cannot guarantee the quality of their products, thus leaving users in uncertainties. Software certification is the extension of quality by means that quality needs to be measured prior to certification granting process. This research participates in solving the problem of software assessment by proposing a model for assessment and certification of software product that uses a fuzzy inference engine to integrate both of process–driven and application-driven quality assurance strategies. The key idea of the on hand model is to improve the compactness and the interpretability of the model’s fuzzy rules via employing an ant colony optimization algorithm (ACO), which tries to find good rules description by dint of compound rules initially expressed with traditional single rules. The model has been tested by case study and the results have demonstrated feasibility and practicability of the model in a real environment.

Keywords: software quality, quality assurance, software certification model, software assessment

Procedia PDF Downloads 489

Authors: M. Khairudin

Abstract:

This paper investigates the system identification and design quantitative feedback theory (QFT) for the robust control of a lathe spindle. The dynamic of the lathe spindle is uncertain and time variation due to the deepness variation on cutting process. System identification was used to obtain the dynamics model of the lathe spindle. In this work, real time system identification is used to construct a linear model of the system from the nonlinear system. These linear models and its uncertainty bound can then be used for controller synthesis. The real time nonlinear system identification process to obtain a set of linear models of the lathe spindle that represents the operating ranges of the dynamic system. With a selected input signal, the data of output and response is acquired and nonlinear system identification is performed using Matlab to obtain a linear model of the system. Practical design steps are presented in which the QFT-based conditions are formulated to obtain a compensator and pre-filter to control the lathe spindle. The performances of the proposed controller are evaluated in terms of velocity responses of the the lathe machine spindle in corporating deepness on cutting process.

Keywords: lathe spindle, QFT, robust control, system identification

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Authors: Shreyas Gambhirrao, Aditya Vichare, Aniket Tembhurne, Shahuraj Bhosale

Abstract:

In today's rapidly evolving environment, stress has emerged as a significant health concern across different age groups. Stress that isn't controlled, whether it comes from job responsibilities, health issues, or the never-ending news cycle, can have a negative effect on our well-being. The problem is further aggravated by the ongoing connection to technology. In this high-tech age, identifying and controlling stress is vital. In order to solve this health issue, the study focuses on three key metrics for stress detection: body temperature, heart rate, and galvanic skin response (GSR). These parameters along with the Support Vector Machine classifier assist the system to categorize stress into three groups: 1) Stressed, 2) Not stressed, and 3) Moderate stress. Proposed training model, a NodeMCU combined with particular sensors collects data in real-time and rapidly categorizes individuals based on their stress levels. Real-time stress detection is made possible by this creative combination of hardware and software.

Keywords: real time stress detection, NodeMCU, sensors, heart-rate, body temperature, galvanic skin response (GSR), support vector machine

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Authors: Matt S. Van Deren, Michael Papenfus

Abstract:

Seattle is one of the hundreds of cities in the United States that relies on a combined sewer system to collect and convey municipal wastewater. By design, these systems convey all wastewater, including industrial and commercial wastewater, human sewage, and stormwater runoff, through a single network of pipes. Serious problems arise for combined sewer systems during heavy precipitation events when treatment plants and storage facilities are unable to accommodate the influx of wastewater needing treatment, causing the sewer system to overflow into local waterways through sewer outfalls. CSOs (Combined Sewer Overflows) pose a serious threat to human and environmental health. Principal pollutants found in CSO discharge include microbial pathogens, comprising of bacteria, viruses, parasites, oxygen-depleting substances, suspended solids, chemicals or chemical mixtures, and excess nutrients, primarily nitrogen and phosphorus. While concentrations of these pollutants can vary between overflow events, CSOs have the potential to spread disease and waterborne illnesses, contaminate drinking water supplies, disrupt aquatic life, and effect a waterbody’s designated use. This paper estimates the economic impact of CSOs on residential property values. Using residential property sales data from Seattle, Washington, this paper employs a hedonic valuation model that controls for housing and neighborhood characteristics, as well as spatial and temporal effects, to predict a consumer’s willingness to pay for improved water quality near their homes. Initial results indicate that a 100,000-gallon decrease in the average annual overflow discharged from a sewer outfall within 300 meters of a home is associated with a 0.053% increase in the property’s sale price. For the average home in the sample, the price increase is estimated to be $18,860.23. These findings reveal some of the important economic benefits of improving water quality by reducing the frequency and severity of combined sewer overflows.

Keywords: benefits, hedonic, Seattle, sewer

Procedia PDF Downloads 138

Authors: Pongsiri Kamkankaew

Abstract:

This paper experiments to consider brand equity thought in Thai academic works. This essay employs that the first emerging of brand equity in Thai academic works and the components of brand equity which explore the extent to the convoluted approach with other Thai social condition. In Thailand, brand equity is supposed to provide branding and brand management replacement. However, the commitment of brand equity imposes in its proposal for seemly application in Thai context – to develop the brand equity framework by the Thai social – culture and Thai utilization style which it is questionable whether the brand equity in western conception is useful for characterizing the brand equity in Thailand context. In this position, brand equity also aspects several major questions: How can western conception lead to apply in Thai business? How can diversification be given within Thai SMEs business running? Can corporate brand valuation approach adopt in real business doing? So this paper argues that Thai brand equity notion should reduce disturb over improvement of its self-restraint and business area. Instead, Thai academic who are interested in brand equity can harmonize different mature bodies of discipline and other investigative a frame of references to complete and open the recognizing of brand equity.

Keywords: Thai brand equity, knowledge critical, brand management, branding

Procedia PDF Downloads 291

Authors: Zhang Songning, Guan Zheng, Ci Yan, Ding Gangyi

Abstract:

The application of physical simulation engines in collaborative editing systems has an important background and role. Firstly, physical simulation engines can provide real-world physical simulations, enabling users to interact and collaborate in real time in virtual environments. This provides a more intuitive and immersive experience for collaborative editing systems, allowing users to more accurately perceive and understand various elements and operations in collaborative editing. Secondly, through physical simulation engines, different users can share virtual space and perform real-time collaborative editing within it. This real-time sharing and collaborative editing method helps to synchronize information among team members and improve the efficiency of collaborative work. Through experiments, the average model transmission speed of a single person in the collaborative editing system has increased by 141.91%; the average model processing speed of a single person has increased by 134.2%; the average processing flow rate of a single person has increased by 175.19%; the overall efficiency improvement rate of a single person has increased by 150.43%. With the increase in the number of users, the overall efficiency remains stable, and the physical simulation engine running status collaborative editing system also has horizontal scalability. It is not difficult to see that the design and implementation of a collaborative editing system based on physical simulation engines not only enriches the user experience but also optimizes the effectiveness of team collaboration, providing new possibilities for collaborative work.

Keywords: physics engine, simulation technology, collaborative editing, system design, data transmission

Procedia PDF Downloads 30

Authors: Saif Ul Haq

Abstract:

The current study intends to examine the safety and quality considerations of clients of housing projects and their impact on the adoption of Quality Function Deployment (QFD) by the construction firm. Mixed method research technique has been used to collect and analyze the data wherein a survey was conducted to collect the data from 220 clients of housing projects in Saudi Arabia. Then, the telephonic and Skype interviews were conducted to collect data of 15 professionals working in the top ten real estate companies of Saudi Arabia. Data were analyzed by using partial least square (PLS) and thematic analysis techniques. Findings reveal that today’s customer prioritizes the safety and quality requirements of their houses and as a result, construction firms adopt QFD to address the needs of customers. The findings are of great importance for the clients of housing projects as well as for the construction firms as they could apply QFD in housing projects to address the safety and quality concerns of their clients.

Keywords: construction industry, quality considerations, quality function deployment, safety considerations

Procedia PDF Downloads 88