Search results for: prediction interval

Authors: Mohammad Hossein Ahmadi, Vahid Haghighatdoost

Abstract:

Spatial databases have been an active area of research over years. In this paper, we study how to answer the General Time-Dependent Sequenced Route queries. Given the origin and destination of a user over a time-dependent road network graph, an ordered list of categories of interests and a departure time interval, our goal is to find the minimum travel time path along with the best departure time that minimizes the total travel time from the source location to the given destination passing through a sequence of points of interests belonging to each of the specified categories of interest. The challenge of this problem is the added complexity to the optimal sequenced route queries, where we assume that first the road network is time dependent, and secondly the user defines a departure time interval instead of one single departure time instance. For processing general time-dependent sequenced route queries, we propose two solutions as Discrete-Time and Continuous-Time Sequenced Route approaches, finding approximate and exact solutions, respectively. Our proposed approaches traverse the road network based on A*-search paradigm equipped with an efficient heuristic function, for shrinking the search space. Extensive experiments are conducted to verify the efficiency of our proposed approaches.

Keywords: trip planning, time dependent, sequenced route query, road networks

Procedia PDF Downloads 322

Authors: Dariush Jafari, S. Mostafa Nowee

Abstract:

In this study a ternary system containing sodium chloride as solute, water as primary solvent and ethanol as the antisolvent was considered to investigate the application of artificial neural network (ANN) in prediction of sodium solubility in the mixture of water as the solvent and ethanol as the antisolvent. The system was previously studied using by Extended UNIQUAC model by the authors of this study. The comparison between the results of the two models shows an excellent agreement between them (R2=0.99), and also approves the capability of ANN to predict the thermodynamic behavior of ternary electrolyte systems which are difficult to model.

Keywords: thermodynamic modeling, ANN, solubility, ternary electrolyte system

Procedia PDF Downloads 385

Authors: Yujiang Wu

Abstract:

As electrical machine (e-machine) power density re-querulents become more stringent in vehicle electrification, mounting a temperature sensor for e-machine stator windings becomes increasingly difficult. This can lead to higher manufacturing costs, complicated harnesses, and reduced reliability. In this paper, we propose a deep-learning method for predicting electric machine winding temperature, which can either replace the sensor entirely or serve as a backup to the existing sensor. We compare the performance of our method, the stateful long short-term memory networks (LSTM) with truncated backpropagation through time (TBTT), with that of linear regression, as well as stateless LSTM with/without residual connection. Our results demonstrate the strength of combining stateful LSTM and TBTT in tackling nonlinear time series prediction problems with long sequence lengths. Additionally, in industrial applications, high-temperature region prediction accuracy is more important because winding temperature sensing is typically used for derating machine power when the temperature is high. To evaluate the performance of our algorithm, we developed a temperature-stratified MSE. We propose a simple but effective data preprocessing trick to improve the high-temperature region prediction accuracy. Our experimental results demonstrate the effectiveness of our proposed method in accurately predicting winding temperature, particularly in high-temperature regions, while also reducing manufacturing costs and improving reliability.

Keywords: deep learning, electrical machine, functional safety, long short-term memory networks (LSTM), thermal management, time series prediction

Procedia PDF Downloads 103

Authors: A. J. Regan, F. J. Lidgey, M. Betteridge, P. Georgiou, C. Toumazou, K. Hayatleh, J. R. Dibble

Abstract:

Our goal is development of an algorithm capable of predicting the directional trend of the Standard and Poor’s 500 index (S&P 500). Extensive research has been published attempting to predict different financial markets using historical data testing on an in-sample and trend basis, with many authors employing excessively complex mathematical techniques. In reviewing and evaluating these in-sample methodologies, it became evident that this approach was unable to achieve sufficiently reliable prediction performance for commercial exploitation. For these reasons, we moved to an out-of-sample strategy based on linear regression analysis of an extensive set of financial data correlated with historical closing prices of the S&P 500. We are pleased to report a directional trend accuracy of greater than 55% for tomorrow (t+1) in predicting the S&P 500.

Keywords: financial algorithm, GPU, S&P 500, stock market prediction

Procedia PDF Downloads 350

Authors: Jong Won, Park, Sung Hyun, Kim

Abstract:

The kinetic energy to pass subjected to shock and chisel reciprocating piston hydraulic power supplied by the excavator using for the purpose of crushing the rock, and roads, buildings, etc., hydraulic breakers blow. Impact frequency, efficiency measurement of the impact energy, hydraulic breakers, to demonstrate the ability of hydraulic breaker manufacturers and users to a very important item. And difficult in order to confirm the initial performance degradation in the life of the hydraulic breaker has been thought to be a problem.In this study, we measure the efficiency of hydraulic breaker, Impact energy and Impact frequency, the degradation analysis of research to predict the life.

Keywords: impact energy, impact frequency, hydraulic breaker, life prediction

Procedia PDF Downloads 441

Authors: Deepak Raj Bhat, Ryuichi Yatabe

Abstract:

In this study, a residual-state creep failure model was developed based on the residual-state creep test results of clayey soils. To develop the proposed model, the regression analyses were done by using the R. The model results of the failure time (tf) and critical displacement (δc) were compared with experimental results and found in close agreements to each others. It is expected that the proposed regression model for residual-state creep failure will be more useful for the prediction of displacement of different clayey soils in the future.

Keywords: regression model, residual-state creep failure, displacement prediction, clayey soils

Procedia PDF Downloads 410

Authors: Nuwan Kuruwitaarachchi, M. K. M. Peiris, C. N. Madawala, K. M. A. R. Perera, V. U. N Perera

Abstract:

Dealing with businesses with the foreign market always took a special place in a country’s economy. Political and social factors came into play making currency rate changes fluctuate rapidly. Currency rate prediction has become an important factor for larger international businesses since large amounts of money exchanged between countries. This research focuses on comparing the accuracy of mainly three models; Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Networks(ANN) and Support Vector Machines(SVM). series of data import, export, USD currency exchange rate respect to LKR has been selected for training using above mentioned algorithms. After training the data set and comparing each algorithm, it was able to see that prediction in SVM performed better than other models. It was improved more by combining SVM and SVR models together.

Keywords: ARIMA, ANN, FFNN, RMSE, SVM, SVR

Procedia PDF Downloads 213

Authors: Hassan Baji, Chun-Qing Li, Wei Yang

Abstract:

Water seepage is one of the most common causes of damage in tunnel structures, which can cause direct and indirect e.g. reinforcement corrosion and calcium leaching damages. Estimation of water seepage or inflow is one of the main challenges in probabilistic assessment of tunnels. The methodology proposed in this study is an attempt for mathematically modeling the water seepage in tunnel structures and further predicting its service life. Using the time-dependent reliability, water seepage is formulated as a failure mode, which can be used for prediction of service life. Application of the formulated seepage failure mode to a case study tunnel is presented.

Keywords: water seepage, tunnels, time-dependent reliability, service life

Procedia PDF Downloads 484

Authors: Fitri CaturLestari

Abstract:

According to National Population and Family Planning Board (BKKBN), demographic bonus will occur in 2025-2035, when the number of people within the productive age bracket is higher than the number of elderly people and children. This time will be a gold moment for Indonesia to achieve maximum productivity and prosperity. But it will be a demographic bomb if it isn’t balanced by economic and social aspect considerations. Therefore it is important to make a prediction mapping of all provinces in Indonesia whether in demographic bomb or bonus condition after 100 years Indonesian independence. The purpose of this research were to make the demographic mapping based on the economic and social aspects of the provinces in Indonesia and categorizing them into demographic bomb and bonus condition. The research data are gained from Statistics Indonesia (BPS) as the secondary data. The multiregional component method, regression and quadrant analysis were used to predict the number of people, economic growth, Human Development Index (HDI), and gender equality in education and employment. There were different characteristic of provinces in Indonesia from economic aspect and social aspect. The west Indonesia was already better developed than the east one. The prediction result, many provinces in Indonesia will get demographic bonus but the others will get demographic bomb. It is important to prepare particular strategy to particular provinces with all of their characteristic based on the prediction result so the demographic bomb can be minimalized.

Keywords: demography, economic growth, gender, HDI

Procedia PDF Downloads 336

Authors: Senol Dogan, Gunay Karli

Abstract:

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

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

Procedia PDF Downloads 210

Authors: Shauma L. Tamba

Abstract:

This study examined whether morality is the most important aspect in actual behavior. The prediction was that people tend to behave in line with moral (as compared to competence) norms, especially when such norms are presented by their ingroup. The actual behavior that was tested was support for a military intervention without a mandate from the UN. In addition, this study also examined whether identification with the ingroup and religion moderated the effect of group and norm on support for the norm that was prescribed by their ingroup. The prediction was that those who identified themselves higher with the ingroup moral would show a higher support for the norm. Furthermore, the prediction was also that those who have religion would show a higher support for the norm in the ingroup moral rather than competence. In an online survey, participants were asked to read a scenario in which a military intervention without a mandate was framed as either the moral (but stupid) or smart (but immoral) thing to do by members of their own (ingroup) or another (outgroup) society. This study found that when people identified themselves with the smart (but immoral) norm, they showed a higher support for the norm. However, when people identified themselves with the moral (but stupid) norm, they tend to show a lesser support towards the norm. Most of the results in the study did not support the predictions. Possible explanations and implications are discussed.

Keywords: morality, competence, ingroup identification, religion, group norm

Procedia PDF Downloads 408

Authors: Nima Dastanboo, Xiao-Qing Li, Hamed Gharibdoost

Abstract:

The purpose of this study is to investigate about the geological properties ahead of a tunnel face with using Electrical Resistivity Tomography ERT and Tunnel Seismic Prediction TSP303 methods. In deep tunnels with hydro-geological conditions, it is important to study the geological structures of the region before excavating tunnels. Otherwise, it would lead to unexpected accidents that impose serious damage to the project. For constructing Nosoud tunnel in west of Iran, the ERT and TSP303 methods are employed to predict the geological conditions dynamically during the excavation. In this paper, based on the engineering background of Nosoud tunnel, the important results of applying these methods are discussed. This work demonstrates seismic method and electrical tomography as two geophysical techniques that are able to detect a tunnel. The results of these two methods were being in agreement with each other but the results of TSP303 are more accurate and quality. In this case, the TSP 303 method was a useful tool for predicting unstable geological structures ahead of the tunnel face during excavation. Thus, using another geophysical method together with TSP303 could be helpful as a decision support in excavating, especially in complicated geological conditions.

Keywords: tunnel seismic prediction (TSP303), electrical resistivity tomography (ERT), seismic wave, velocity analysis, low-velocity zones

Procedia PDF Downloads 150

Authors: Behzad Behnia, Noah LaRussa-Trott

Abstract:

In recent years, fibers have been successfully used as an additive to reinforce asphalt concrete materials and to enhance the sustainability and resiliency of transportation infrastructure. Roads covered with fiber-reinforced asphalt concrete (FRAC) require less frequent maintenance and tend to have a longer lifespan. The present work investigates the application of sasobit-coated aramid fibers in asphalt pavements and employs machine learning to develop prediction models to evaluate the cracking performance of FRAC materials. For the experimental part of the study, the effects of several important parameters such as fiber content, fiber length, and testing temperature on fracture characteristics of FRAC mixtures were thoroughly investigated. Two mechanical performance tests, i.e., the disk-shaped compact tension [DC(T)] and indirect tensile [ID(T)] strength tests, as well as the non-destructive acoustic emission test, were utilized to experimentally measure the cracking behavior of the FRAC material in both macro and micro level, respectively. The experimental results were used to train the supervised machine learning approach in order to establish prediction models for fracture performance of the FRAC mixtures in the field. Experimental results demonstrated that adding fibers improved the overall fracture performance of asphalt concrete materials by increasing their fracture energy, tensile strength and lowering their 'embrittlement temperature'. FRAC mixtures containing long-size fibers exhibited better cracking performance than regular-size fiber mixtures. The developed prediction models of this study could be easily employed by pavement engineers in the assessment of the FRAC pavements.

Keywords: fiber reinforced asphalt concrete, machine learning, cracking performance tests, prediction model

Procedia PDF Downloads 142

Authors: Yusuf S. Dambatta, Ahmed A. D. Sarhan

Abstract:

Fused deposition modelling (FDM) is one of the most prominent rapid prototyping (RP) technologies which is being used to efficiently fabricate CAD 3D geometric models. However, the process is coupled with many drawbacks, of which the surface quality of the manufactured RP parts is among. Hence, studies relating to improving the surface roughness have been a key issue in the field of RP research. In this work, a technique of modelling the surface roughness in FDM is presented. Using experimentally measured surface roughness response of the FDM parts, an ANFIS prediction model was developed to obtain the surface roughness in the FDM parts using the main critical process parameters that affects the surface quality. The ANFIS model was validated and compared with experimental test results.

Keywords: surface roughness, fused deposition modelling (FDM), adaptive neuro fuzzy inference system (ANFIS), orientation

Procedia PDF Downloads 462

Authors: Niranjana Devi N.

Abstract:

Test case selection is to select the subset of only the fit test cases and remove the unfit, ambiguous, redundant, unnecessary test cases which in turn improve the quality and reduce the cost of software testing. Test cases optimization is the problem of finding the best subset of test cases from a pool of the test cases to be audited. It will meet all the objectives of testing concurrently. But most of the research have evaluated the fitness of test cases only on single parameter fault detecting capability and optimize the test cases using a single objective. In the proposed approach, nine parameters are considered for test case selection and the best subset of parameters for test case selection is obtained using Interval Type-2 Fuzzy Rough Set. Test case selection is done in two stages. The first stage is the fuzzy entropy-based filtration technique, used for estimating and reducing the ambiguity in test case fitness evaluation and selection. The second stage is the ant colony optimization-based wrapper technique with a forward search strategy, employed to select test cases from the reduced test suite of the first stage. The results are evaluated using the Coverage parameters, Precision, Recall, F-Measure, APSC, APDC, and SSR. The experimental evaluation demonstrates that by this approach considerable computational effort can be avoided.

Keywords: ant colony optimization, fuzzy entropy, interval type-2 fuzzy rough set, test case selection

Procedia PDF Downloads 670

Authors: Aneta Oblouková, Eva Vítková

Abstract:

The article deals with the issue of water and sewerage charge rate prices in the Czech Republic. The research is specifically focused on the analysis of the development of the average prices of water and sewerage charge rate in the Czech Republic in the years 1994-2021 and on the validation of the chosen methodology relevant for the prediction of the development of the average prices of water and sewerage charge rate in the Czech Republic. The research is based on data collection. The data for this research was obtained from the Czech Statistical Office. The aim of the paper is to validate the relevance of the mathematical linear trend estimate technique for the calculation of the predicted average prices of water and sewerage charge rates. The real values of the average prices of water and sewerage charge rates in the Czech Republic in the years 1994-2018 were obtained from the Czech Statistical Office and were converted into a mathematical equation. The same type of real data was obtained from the Czech Statistical Office for the years 2019-2021. Prediction of the average prices of water and sewerage charge rates in the Czech Republic in the years 2019-2021 were also calculated using a chosen method -a linear trend estimation technique. The values obtained from the Czech Statistical Office and the values calculated using the chosen methodology were subsequently compared. The research result is a validation of the chosen mathematical technique to be a suitable technique for this research.

Keywords: Czech Republic, linear trend estimation, price prediction, water and sewerage charge rate

Procedia PDF Downloads 120

Authors: Malo Pocheau-Lesteven, Olivier Le Maître

Abstract:

Engineering and optimisation of complex systems is often achieved through multi-disciplinary analysis of the system, where each subsystem is modeled and interacts with other subsystems to model the complete system. The coherence of the output of the different sub-systems is achieved through the use of compatibility constraints, which enforce the coupling between the different subsystems. Due to the complexity of some sub-systems and the computational cost of evaluating their respective models, it is often necessary to build surrogate models of these subsystems to allow repeated evaluation these subsystems at a relatively low computational cost. In this paper, gaussian processes are used, as their probabilistic nature is leveraged to evaluate the likelihood of satisfying the compatibility constraints. This paper presents infilling strategies to build accurate surrogate models of the subsystems in areas where they are likely to meet the compatibility constraint. It is shown that these infilling strategies can reduce the computational cost of building surrogate models for a given level of accuracy. An application of these methods to velocity prediction programs used in offshore racing naval architecture further demonstrates these method's applicability in a real engineering context. Also, some examples of the application of uncertainty quantification to field of naval architecture are presented.

Keywords: infilling strategy, gaussian process, multi disciplinary analysis, velocity prediction program

Procedia PDF Downloads 158

Authors: Aragorn Joaquin Pineda Dela Cruz

Abstract:

Road traffic congestion is continually deteriorating in Hong Kong. The largest contributing factor is the increase in vehicle fleet size, resulting in higher competition over the utilisation of road space. This study proposes a project that can process closed-circuit television images and videos to provide real-time traffic detection and prediction capabilities. Specifically, a deep-learning model involving computer vision techniques for video and image-based vehicle counting, then a separate model to detect and predict traffic congestion levels based on said data. State-of-the-art object detection models such as You Only Look Once and Faster Region-based Convolutional Neural Networks are tested and compared on closed-circuit television data from various major roads in Hong Kong. It is then used for training in long short-term memory networks to be able to predict traffic conditions in the near future, in an effort to provide more precise and quicker overviews of current and future traffic conditions relative to current solutions such as navigation apps.

Keywords: intelligent transportation system, vehicle detection, traffic analysis, deep learning, machine learning, computer vision, traffic prediction

Procedia PDF Downloads 103

Authors: Atchara Sankom, Warapa Mahakarnchanakul, Ronnarit Rittiron, Tanaboon Sajjaanantakul, Thammasak Thongket

Abstract:

Vegetables are frequently contaminated with pesticides residues resulting in the most food safety concern among agricultural products. The objective of this work was to develop a method to detect the organophosphate (OP) pesticides residues in vegetables using Near Infrared (NIR) spectroscopy technique. Low concentration (ppm) of OP pesticides in vegetables were investigated. The experiment was divided into 2 sections. In the first section, Chinese kale spiked with different concentrations of chlorpyrifos pesticide residues (0.5-100 ppm) was chosen as the sample model to demonstrate the appropriate conditions of sample preparation, both for a solution or solid sample. The spiked samples were extracted with acetone. The sample extracts were applied as solution samples, while the solid samples were prepared by the dry-extract system for infrared (DESIR) technique. The DESIR technique was performed by embedding the solution sample on filter paper (GF/A) and then drying. The NIR spectra were measured with the transflectance mode over wavenumber regions of 12,500-4000 cm⁻¹. The QuEChERS method followed by gas chromatography-mass spectrometry (GC-MS) was performed as the standard method. The results from the first section showed that the DESIR technique with NIR spectroscopy demonstrated good accurate calibration result with R² of 0.93 and RMSEP of 8.23 ppm. However, in the case of solution samples, the prediction regarding the NIR-PLSR (partial least squares regression) equation showed poor performance (R² = 0.16 and RMSEP = 23.70 ppm). In the second section, the DESIR technique coupled with NIR spectroscopy was applied to the detection of OP pesticides in vegetables. Vegetables (Chinese kale, cabbage and hot chili) were spiked with OP pesticides (chlorpyrifos ethion and profenofos) at different concentrations ranging from 0.5 to 100 ppm. Solid samples were prepared (based on the DESIR technique), then samples were scanned by NIR spectrophotometer at ambient temperature (25+2°C). The NIR spectra were measured as in the first section. The NIR- PLSR showed the best calibration equation for detecting low concentrations of chlorpyrifos residues in vegetables (Chinese kale, cabbage and hot chili) according to the prediction set of R2 and RMSEP of 0.85-0.93 and 8.23-11.20 ppm, respectively. For ethion residues, the best calibration equation of NIR-PLSR showed good indexes of R² and RMSEP of 0.88-0.94 and 7.68-11.20 ppm, respectively. As well as the results for profenofos pesticide, the NIR-PLSR also showed the best calibration equation for detecting the profenofos residues in vegetables according to the good index of R² and RMSEP of 0.88-0.97 and 5.25-11.00 ppm, respectively. Moreover, the calibration equation developed in this work could rapidly predict the concentrations of OP pesticides residues (0.5-100 ppm) in vegetables, and there was no significant difference between NIR-predicted values and actual values (data from GC-MS) at a confidence interval of 95%. In this work, the proposed method using NIR spectroscopy involving the DESIR technique has proved to be an efficient method for the screening detection of OP pesticides residues at low concentrations, and thus increases the food safety potential of vegetables for domestic and export markets.

Keywords: NIR spectroscopy, organophosphate pesticide, vegetable, food safety

Procedia PDF Downloads 151

Authors: Dieudonne Uwizeye

Abstract:

Introduction: Diarrhoea is one of the major causes of morbidity and mortality among communities living in urban informal settlements of developing countries. It is assumed that mountainous environment introduces variations of the burden among residents of the same settlements. Design and Objective: A cross-sectional study was done in Kigali to explore the effect of mountainous informal settlements on diarrhoea risk variations. Data were collected among 1,152 households through household survey and transect walk to observe the status of sanitation. The outcome variable was the incidence of diarrhoea among household members of any age. The study used the most knowledgeable person in the household as the main respondent. Mostly this was the woman of the house as she was more likely to know the health status of every household member as she plays various roles: mother, wife, and head of the household among others. The analysis used cross tabulation and logistic regression analysis. Results: Results suggest that risks for diarrhoea vary depending on home location in the settlements. Diarrhoea risk increased as the distance from the road increased. The results of the logistic regression analysis indicate the adjusted odds ratio of 2.97 with 95% confidence interval being 1.35-6.55 and 3.50 adjusted odds ratio with 95% confidence interval being 1.61-7.60 in level two and three respectively compared with level one. The status of sanitation within and around homes was also significantly associated with the increase of diarrhoea. Equally, it is indicated that stable households were less likely to have diarrhoea. The logistic regression analysis indicated the adjusted odds ratio of 0.45 with 95% confidence interval being 0.25-0.81. However, the study did not find evidence for a significant association between diarrhoea risks and household socioeconomic status in the multivariable model. It is assumed that environmental factors in mountainous settings prevailed. Households using the available public water sources were more likely to have diarrhoea in their households. Recommendation: The study recommends the provision and extension of infrastructure for improved water, drainage, sanitation and wastes management facilities. Equally, studies should be done to identify the level of contamination and potential origin of contaminants for water sources in the valleys to adequately control the risks for diarrhoea in mountainous urban settings.

Keywords: urbanisation, diarrhoea risk, mountainous environment, urban informal settlements in Rwanda

Procedia PDF Downloads 173

Authors: Amir Hossein Hejazi, Nima Amjady

Abstract:

In recent years, due to environmental issues traditional energy sources had been replaced by renewable ones. Wind energy as the fastest growing renewable energy shares a considerable percent of energy in power electricity markets. With this fast growth of wind energy worldwide, owners and operators of wind farms, transmission system operators, and energy traders need reliable and secure forecasts of wind energy production. In this paper, a new forecasting strategy is proposed for short-term wind power prediction based on Echo State Networks (ESN). The forecast engine utilizes state-of-the-art training process including dynamical reservoir with high capability to learn complex dynamics of wind power or wind vector signals. The study becomes more interesting by incorporating prediction of wind direction into forecast strategy. The Big Bang-Big Crunch (BB-BC) evolutionary optimization algorithm is adopted for adjusting free parameters of ESN-based forecaster. The proposed method is tested by real-world hourly data to show the efficiency of the forecasting engine for prediction of both wind vector and wind power output of aggregated wind power production.

Keywords: wind power forecasting, echo state network, big bang-big crunch, evolutionary optimization algorithm

Procedia PDF Downloads 573

Authors: Amiel Satvedi, Richard Pyne

Abstract:

Injuries in professional sports occur on a regular basis. Some may be minor, while others can cause huge impact on a player's career and earning potential. In soccer, there is a high risk of players picking up injuries during game time. This research work seeks to help soccer players reduce the risk of getting injured by predicting the likelihood of injury while playing in the near future and then providing recommendations for intervention. The injury prediction tool will use a soccer player's number of minutes played on the field, number of appearances, distance covered and performance data for the current and previous seasons as variables to conduct statistical analysis and provide injury predictive results using a machine learning linear regression model.

Keywords: injury predictor, soccer injury prevention, machine learning in soccer, big data in soccer

Procedia PDF Downloads 183

Authors: Selin Guney, Andres Riquelme

Abstract:

Among the main purposes of optimal and efficient forecasts of agricultural commodity prices is to guide the firms to advance the economic decision making process such as planning business operations and marketing decisions. Governments are also the beneficiaries and suppliers of agricultural price forecasts. They use this information to establish a proper agricultural policy, and hence, the forecasts affect social welfare and systematic errors in forecasts could lead to a misallocation of scarce resources. Various empirical approaches have been applied to forecast commodity prices that have used different methodologies. Most commonly-used approaches to forecast commodity sectors depend on classical time series models that assume values of the response variables are precise which is quite often not true in reality. Recently, this literature has mostly evolved to a consideration of fuzzy time series models that provide more flexibility in terms of the classical time series models assumptions such as stationarity, and large sample size requirement. Besides, fuzzy modeling approach allows decision making with estimated values under incomplete information or uncertainty. A number of fuzzy time series models have been developed and implemented over the last decades; however, most of them are not appropriate for forecasting repeated and nonconsecutive transitions in the data. The modeling scheme used in this paper eliminates this problem by introducing Markov modeling approach that takes into account both the repeated and nonconsecutive transitions. Also, the determination of length of interval is crucial in terms of the accuracy of forecasts. The problem of determining the length of interval arbitrarily is overcome and a methodology to determine the proper length of interval based on the distribution or mean of the first differences of series to improve forecast accuracy is proposed. The specific purpose of this paper is to propose and investigate the potential of a new forecasting model that integrates methodologies for determining the proper length of interval based on the distribution or mean of the first differences of series and Fuzzy Time Series- Markov Chain model. Moreover, the accuracy of the forecasting performance of proposed integrated model is compared to different univariate time series models and the superiority of proposed method over competing methods in respect of modelling and forecasting on the basis of forecast evaluation criteria is demonstrated. The application is to daily corn and soybean prices observed at three commercially important North Carolina markets; Candor, Cofield and Roaring River for corn and Fayetteville, Cofield and Greenville City for soybeans respectively. One main conclusion from this paper is that using fuzzy logic improves the forecast performance and accuracy; the effectiveness and potential benefits of the proposed model is confirmed with small selection criteria value such MAPE. The paper concludes with a discussion of the implications of integrating fuzzy logic and nonarbitrary determination of length of interval for the reliability and accuracy of price forecasts. The empirical results represent a significant contribution to our understanding of the applicability of fuzzy modeling in commodity price forecasts.

Keywords: commodity, forecast, fuzzy, Markov

Procedia PDF Downloads 218

Authors: Hui Zhang, Sherif Beskhyroun

Abstract:

Structural Health monitoring systems (SHM) are commonly used to identify and assess structural damage. In terms of damage detection, SHM needs to periodically collect data from sensors placed in the structure as damage-sensitive features. This includes abnormal changes caused by the strain field and abnormal symptoms of the structure, such as damage and deterioration. Currently, deploying sensors on a large scale in a building structure is a challenge. In this study, a highly stretchable strain sensors are used in this study to collect data sets of strain generated on the surface of full-size reinforced concrete (RC) frames under extreme cyclic load application. This sensing sheet can be switched freely between the test bending strain and the axial strain to achieve two different configurations. On this basis, the deep neural network prediction model of the frame beam and frame column is established. The training results show that the method can accurately predict the strain value and has good generalization ability. The two deep neural network prediction models will also be deployed in the SHM system in the future as part of the intelligent strain sensor system.

Keywords: strain sensing sheets, deep neural networks, strain measurement, SHM system, RC frames

Procedia PDF Downloads 101

Authors: Ren Moses, Emmanuel Kidando, Eren Ozguven, Yassir Abdelrazig

Abstract:

This study applied traffic speed and occupancy to develop clustering models that identify different traffic conditions. Particularly, these models are based on the Dirichlet Process Mixture of Generalized Linear regression (DML) and change-point regression (CR). The model frameworks were implemented using 2015 historical traffic data aggregated at a 15-minute interval from an Interstate 295 freeway in Jacksonville, Florida. Using the deviance information criterion (DIC) to identify the appropriate number of mixture components, three traffic states were identified as free-flow, transitional, and congested condition. Results of the DML revealed that traffic occupancy is statistically significant in influencing the reduction of traffic speed in each of the identified states. Influence on the free-flow and the congested state was estimated to be higher than the transitional flow condition in both evening and morning peak periods. Estimation of the critical speed threshold using CR revealed that 47 mph and 48 mph are speed thresholds for congested and transitional traffic condition during the morning peak hours and evening peak hours, respectively. Free-flow speed thresholds for morning and evening peak hours were estimated at 64 mph and 66 mph, respectively. The proposed approaches will facilitate accurate detection and prediction of traffic congestion for developing effective countermeasures.

Keywords: traffic congestion, multistate speed distribution, traffic occupancy, Dirichlet process mixtures of generalized linear model, Bayesian change-point detection

Procedia PDF Downloads 294

Authors: Pedro Paulo Galindo Francisco, Eli Dhadad Junior

Abstract:

This study addresses the Meese-Rogoff Puzzle by introducing the latest machine learning techniques as alternatives for predicting the exchange rates. Using RMSE as a comparison metric, Meese and Rogoff discovered that economic models are unable to outperform the random walk model as short-term exchange rate predictors. Decades after this study, no statistical prediction technique has proven effective in overcoming this obstacle; although there were positive results, they did not apply to all currencies and defined periods. Recent advancements in artificial intelligence technologies have paved the way for a new approach to exchange rate prediction. Leveraging this technology, we applied five machine learning techniques to attempt to overcome the Meese-Rogoff puzzle. We considered daily data for the real, yen, British pound, euro, and Chinese yuan against the US dollar over a time horizon from 2010 to 2023. Our results showed that none of the presented techniques were able to produce an RMSE lower than the Random Walk model. However, the performance of some models, particularly LSTM and N-BEATS were able to outperform the ARIMA model. The results also suggest that machine learning models have untapped potential and could represent an effective long-term possibility for overcoming the Meese-Rogoff puzzle.

Keywords: exchage rate, prediction, machine learning, deep learning

Procedia PDF Downloads 33

Authors: Danilo López, Johana Hernández, Edwin Rivas

Abstract:

The use of the Multilayer Perceptron Neural Networks (MLPNN) technique is presented to estimate the future state of use of a licensed channel by primary users (PUs); this will be useful at the spectral decision stage in cognitive radio networks (CRN) to determine approximately in which time instants of future may secondary users (SUs) opportunistically use the spectral bandwidth to send data through the primary wireless network. To validate the results, sequences of occupancy data of channel were generated by simulation. The results show that the prediction percentage is greater than 60% in some of the tests carried out.

Keywords: cognitive radio, neural network, prediction, primary user

Procedia PDF Downloads 372

Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

Abstract:

In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: deep learning, indoor quality, metabolism, predictive model

Procedia PDF Downloads 258

Authors: Fathul Mubin, Budi E. Nurcahya

Abstract:

In 2006, a large earthquake ever occurred in the province of Yogyakarta, which caused considerable damage. This is the basis need to investigate the seismic vulnerability index in around of the earthquake zone. This research is called microzonation of earthquake hazard. This research has been conducted at the site and surrounding of Prambanan Temple, includes homes and civil buildings. The reason this research needs to be done because in the event of an earthquake in 2006, there was damage to the temples at Prambanan temple complex and its surroundings. In this research, data collection carried out for 60 minutes using three component seismograph measurements at 165 points with spacing of 1000 meters. The data recorded in time function were analyzed using the spectral ratio method, known as the Horizontal to Vertical Spectral Ratio (HVSR). Results from this analysis are dominant frequency (Fg) and maximum amplification factor (Ag) are used to obtain seismic vulnerability index. The results of research showed the dominant frequency range from 0.5 to 30 Hz and the amplification is in interval from 0.5 to 9. Interval value for seismic vulnerability index is 0.1 to 50. Based on distribution maps of seismic vulnerability index and impact of buildings damage seemed for suitability. For further research, it needs to survey to the east (klaten) and south (Bantul, DIY) to determine a full distribution maps of seismic vulnerability index.

Keywords: amplification factor, dominant frequency, microzonation analysis, seismic vulnerability index

Procedia PDF Downloads 197

Authors: Onyekachi Ogbonnaya Iroanya, Taiye Abdullahi Gegele, Frank Tochukwu Egwuatu

Abstract:

Introduction: Forensic taphonomy is a multifaceted area that incorporates decomposition, chemical and biological cadaver exposure in post-mortem event chronology and reconstruction to predict the Post Mortem Interval (PMI). The aim of this study was to evaluate the integrity of DNA extracted from the remains of embalmed decomposed Sus domesticus tissues buried in different soil types. Method: A total of 12 limbs of Sus domesticus weighing between 0.7-1.4 kg were used. Each of the samples across the groups was treated with 10% formaldehyde, absolute methanol and 50% Pine oil for 24 hours before burial except the control samples, which were buried immediately. All samples were buried in shallow simulated Clay, Sandy and Loamy soil graves for 12 months. The DNA for each sample was extracted and quantified with Nanodrop Spectrophotometer (6305 JENWAY spectrometers). The rate of decomposition was examined through the modified qualitative decomposition analysis. Extracted DNA was amplified through PCR and bands visualized via gel electrophoresis. A biochemical enzyme assay was done for each burial grave soil. Result: The limbs in all burial groups had lost weight over the burial period. There was a significant increase in the soil urease level in the samples preserved in formaldehyde across the 3 soil type groups (p≤0.01). Also, the control grave soils recorded significantly higher alkaline phosphatase, dehydrogenase and calcium carbonate values compared to experimental grave soils (p≤0.01). The experimental samples showed a significant decrease in DNA concentration and purity when compared to the control groups (p≤0.01). Obtained findings of the soil biochemical analysis showed the embalming treatment altered the relationship between organic matter decomposition and soil biochemical properties as observed in the fluctuations that were recorded in the soil biochemical parameters. The PCR amplified DNA showed no bands on the gel electrophoresis plates. Conclusion: In criminal investigations, factors such as burial grave soil, grave soil biochemical properties, antemortem exposure to embalming chemicals should be considered in post-mortem interval (PMI) determination.

Keywords: forensic taphonomy, post-mortem interval (PMI), embalmment, decomposition, grave soil

Procedia PDF Downloads 166