Search results for: prediction method

Authors: Chang Su Woo, Hyun Sung Park

Abstract:

Useful lifetime evaluation of chevron rubber spring was very important in design procedure to assure the safety and reliability. It is, therefore, necessary to establish a suitable criterion for the replacement period of chevron rubber spring. In this study, we performed characteristic analysis and useful lifetime prediction of chevron rubber spring. Rubber material coefficient was obtained by curve fittings of uni-axial tension, equi bi-axial tension and pure shear test. Computer simulation was executed to predict and evaluate the load capacity and stiffness for chevron rubber spring. In order to useful lifetime prediction of rubber material, we carried out the compression set with heat aging test in an oven at the temperature ranging from 50°C to 100°C during a period 180 days. By using the Arrhenius plot, several useful lifetime prediction equations for rubber material was proposed.

Keywords: chevron rubber spring, material coefficient, finite element analysis, useful lifetime prediction

Procedia PDF Downloads 534

Authors: Subhamita Chakraborty, Shubhabrata Datta, Sujay Kumar Mukherjea, Partha Protim Chattopadhyay

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Manufacturing of multiphase high strength steel in hot strip mill have drawn significant attention due to the possibility of forming low temperature transformation product of austenite under continuous cooling condition. In such endeavor, reliable prediction of temperature profile of hot strip coil is essential in order to accesses the evolution of microstructure at different location of hot strip coil, on the basis of corresponding Continuous Cooling Transformation (CCT) diagram. Temperature distribution profile of the hot strip coil has been determined by using finite volume method (FVM) vis-à-vis finite difference method (FDM). It has been demonstrated that FVM offer greater computational reliability in estimation of contact pressure distribution and hence the temperature distribution for curved and irregular profiles, owing to the flexibility in selection of grid geometry and discrete point position, Moreover, use of finite volume concept allows enforcing the conservation of mass, momentum and energy, leading to enhanced accuracy of prediction.

Keywords: simulation, modeling, thermal analysis, coil cooling, contact pressure, finite volume method

Procedia PDF Downloads 438

Authors: Qiang Zhang, Chun Yuan

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Since the PU (Prediction Unit) decision process is the most time consuming part of the emerging HEVC (High Efficient Video Coding) standardin intra and inter frame coding, this paper proposes the fast PU decision algorithm and speed up the algorithm using CUDA (Compute Unified Device Architecture). In intra frame coding, the fast PU decision algorithm uses the texture features to skip intra-frame prediction or terminal the intra-frame prediction for smaller PU size. In inter frame coding of HEVC, the fast PU decision algorithm takes use of the similarity of its own two Nx2N size PU's motion vectors and the hierarchical structure of CU (Coding Unit) partition to skip some modes of PU partition, so as to reduce the motion estimation times. The accelerate algorithm using CUDA is based on the fast PU decision algorithm which uses the GPU to make the motion search and the gradient computation could be parallel computed. The proposed algorithm achieves up to 57% time saving compared to the HM 10.0 with little rate-distortion losses (0.043dB drop and 1.82% bitrate increase on average).

Keywords: HEVC, PU decision, inter prediction, intra prediction, CUDA, parallel

Procedia PDF Downloads 370

Authors: J. K. Alhassan, C. S. Actsu

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This study is on the prediction of feature performance of undergraduate students with Artificial Neural Networks (ANN). With the growing decline in the quality academic performance of undergraduate students, it has become essential to predict the students’ feature academic performance early in their courses of first and second years and to take the necessary precautions using such prediction-based information. The feed forward multilayer neural network model was used to train and develop a network and the test carried out with some of the input variables. A result of 80% accuracy was obtained from the test which was carried out, with an average error of 0.009781.

Keywords: academic performance, artificial neural network, prediction, students

Procedia PDF Downloads 428

Authors: Lee P. Leon, Raymond Charles

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This paper presents a method of evaluating the effect of aggregate angularity on hot mix asphalt (HMA) properties and its relationship to the Permanent Deformation resistance. The research concluded that aggregate particle angularity had a significant effect on the Permanent Deformation performance, and also that with an increase in coarse aggregate angularity there was an increase in the resistance of mixes to Permanent Deformation. A comparison between the measured data and predictive data of permanent deformation predictive models showed the limits of existing prediction models. The numerical analysis described the permanent deformation zones and concluded that angularity has an effect of the onset of these zones. Prediction of permanent deformation help road agencies and by extension economists and engineers determine the best approach for maintenance, rehabilitation, and new construction works of the road infrastructure.

Keywords: aggregate angularity, asphalt concrete, permanent deformation, rutting prediction

Procedia PDF Downloads 369

Authors: Zhaopeng Zhu, Xianzhi Song, Gensheng Li, Shuo Zhu, Shiming Duan, Xuezhe Yao

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Accurate calculation of wellbore pressure is of great significance to prevent wellbore risk during drilling. The traditional mechanism model needs a lot of iterative solving procedures in the calculation process, which reduces the calculation efficiency and is difficult to meet the demand of dynamic control of wellbore pressure. In recent years, many scholars have introduced artificial intelligence algorithms into wellbore pressure calculation, which significantly improves the calculation efficiency and accuracy of wellbore pressure. However, due to the ‘black box’ property of intelligent algorithm, the existing intelligent calculation model of wellbore pressure is difficult to play a role outside the scope of training data and overreacts to data noise, often resulting in abnormal calculation results. In this study, the multi-phase flow mechanism is embedded into the objective function of the neural network model as a constraint condition, and an intelligent prediction model of wellbore pressure under the constraint condition is established based on more than 400,000 sets of pressure measurement while drilling (MPD) data. The constraint of the multi-phase flow mechanism makes the prediction results of the neural network model more consistent with the distribution law of wellbore pressure, which overcomes the black-box attribute of the neural network model to some extent. The main performance is that the accuracy of the independent test data set is further improved, and the abnormal calculation values basically disappear. This method is a prediction method driven by MPD data and multi-phase flow mechanism, and it is the main way to predict wellbore pressure accurately and efficiently in the future.

Keywords: multiphase flow mechanism, pressure while drilling data, wellbore pressure, mechanism constraints, combined drive

Procedia PDF Downloads 147

Authors: Mina Jafari, Kobra Hamraee, Saied Hossein Hosseini

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The prediction of breast cancer disease is one of the challenges in medicine. In this paper we collected 528 records of women’s information who live in Kabul including demographic, life style, diet and pregnancy data. There are many classification algorithm in breast cancer prediction and tried to find the best model with most accurate result and lowest error rate. We evaluated some other common supervised algorithms in data mining to find the best model in prediction of breast cancer disease among afghan women living in Kabul regarding to momography result as target variable. For evaluating these algorithms we used Cross Validation which is an assured method for measuring the performance of models. After comparing error rate and accuracy of three models: Decision Tree, Naive Bays and Rule Induction, Decision Tree with accuracy of 94.06% and error rate of %15 is found the best model to predicting breast cancer disease based on the health care records.

Keywords: decision tree, breast cancer, probability, data mining

Procedia PDF Downloads 108

Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: big data, k-NN, machine learning, traffic speed prediction

Procedia PDF Downloads 329

Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

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One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

Procedia PDF Downloads 67

Authors: Jiun Jeon, Byung-Ju Yi

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This paper investigates modeling and shape prediction of elastic kinematic chains such as colonoscopy. 2D and 3D models of elastic kinematic chains are suggested and their behaviors are demonstrated through simulation. To corroborate the effectiveness of those models, experimental work is performed using a magnetic sensor system.

Keywords: elastic kinematic chain, shape prediction, colonoscopy, modeling

Procedia PDF Downloads 565

Authors: Danilo López, Edwin Rivas, Fernando Pedraza

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Currently, one of the major challenges in wireless networks is the optimal use of radio spectrum, which is managed inefficiently. One of the solutions to existing problem converges in the use of Cognitive Radio (CR), as an essential parameter so that the use of the available licensed spectrum is possible (by secondary users), well above the usage values that are currently detected; thus allowing the opportunistic use of the channel in the absence of primary users (PU). This article presents the results found when estimating or predicting the future use of a spectral transmission band (from the perspective of the PU) for a chaotic type channel arrival behavior. The time series prediction method (which the PU represents) used is ANFIS (Adaptive Neuro Fuzzy Inference System). The results obtained were compared to those delivered by the RNA (Artificial Neural Network) algorithm. The results show better performance in the characterization (modeling and prediction) with the ANFIS methodology.

Keywords: ANFIS, cognitive radio, prediction primary user, RNA

Procedia PDF Downloads 392

Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: deep learning, convolutional neural network, LSTM, housing prediction

Procedia PDF Downloads 275

Authors: O. P. Oladipo, O. A. Falode

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The determination of Recovery Factor is of great importance to the reservoir engineer since it relates reserves to the initial oil in place. Reserves are the producible portion of reservoirs and give an indication of the profitability of a field Development. The core objective of this project is to develop an artificial neural network model using selected reservoir data to predict Recovery Factors (RF) of hydrocarbon reservoirs and compare the model with a couple of the existing correlations. The type of Artificial Neural Network model developed was the Single Layer Feed Forward Network. MATLAB was used as the network simulator and the network was trained using the supervised learning method, Afterwards, the network was tested with input data never seen by the network. The results of the predicted values of the recovery factors of the Artificial Neural Network Model, API Correlation for water drive reservoirs (Sands and Sandstones) and Guthrie and Greenberger Correlation Equation were obtained and compared. It was noted that the coefficient of correlation of the Artificial Neural Network Model was higher than the coefficient of correlations of the other two correlation equations, thus making it a more accurate prediction tool. The Artificial Neural Network, because of its accurate prediction ability is helpful in the correct prediction of hydrocarbon reservoir factors. Artificial Neural Network could be applied in the prediction of other Petroleum Engineering parameters because it is able to recognise complex patterns of data set and establish a relationship between them.

Keywords: recovery factor, reservoir, reserves, artificial neural network, hydrocarbon, MATLAB, API, Guthrie, Greenberger

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Authors: Wei Tian, Jie Liang, Hammad Naveed

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Beta-barrel membrane proteins are found in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. They carry out diverse biological functions, including pore formation, membrane anchoring, enzyme activity, and bacterial virulence. In addition, beta-barrel membrane proteins increasingly serve as scaffolds for bacterial surface display and nanopore-based DNA sequencing. Due to difficulties in experimental structure determination, they are sparsely represented in the protein structure databank and computational methods can help to understand their biophysical principles. We have developed a novel computational method to predict the 3D structure of beta-barrel membrane proteins using evolutionary coupling (EC) constraints and a reduced state space. Combined with an empirical potential function, we can successfully predict strand register at > 80% accuracy for a set of 49 non-homologous proteins with known structures. This is a significant improvement from previous results using EC alone (44%) and using empirical potential function alone (73%). Our method is general and can be applied to genome-wide structural prediction.

Keywords: beta-barrel membrane proteins, structure prediction, evolutionary constraints, reduced state space

Procedia PDF Downloads 579

Authors: S. Adjiri-Bailiche, S. M. Boudia, H. Daaou, S. Hadouche, A. Benzaoui

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In this work the study of changes in the wind speed depending on the altitude is calculated and described by the model of the neural networks, the use of measured data, the speed and direction of wind, temperature and the humidity at 10 m are used as input data and as data targets at 50m above sea level. Comparing predict wind speeds and extrapolated at 50 m above sea level is performed. The results show that the prediction by the method of artificial neural networks is very accurate.

Keywords: MATLAB, neural network, power low, vertical extrapolation, wind energy, wind speed

Procedia PDF Downloads 651

Authors: Marjan Golmaryami, Marzieh Behzadi

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

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

Procedia PDF Downloads 523

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 59

Authors: Xiaogang Li, Jieqiong Miao

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As for the problem of the grey forecasting model prediction accuracy is low, an improved grey prediction model is put forward. Firstly, use trigonometric function transform the original data sequence in order to improve the smoothness of data , this model called SGM( smoothness of grey prediction model), then combine the improved grey model with support vector machine , and put forward the grey support vector machine model (SGM - SVM).Before the establishment of the model, we use trigonometric functions and accumulation generation operation preprocessing data in order to enhance the smoothness of the data and weaken the randomness of the data, then use support vector machine (SVM) to establish a prediction model for pre-processed data and select model parameters using genetic algorithms to obtain the optimum value of the global search. Finally, restore data through the "regressive generate" operation to get forecasting data. In order to prove that the SGM-SVM model is superior to other models, we select the battery life data from calce. The presented model is used to predict life of battery and the predicted result was compared with that of grey model and support vector machines．For a more intuitive comparison of the three models, this paper presents root mean square error of this three different models .The results show that the effect of grey support vector machine (SGM-SVM) to predict life is optimal, and the root mean square error is only 3.18%. Keywords: grey forecasting model, trigonometric function, support vector machine, genetic algorithms, root mean square error

Keywords: Grey prediction model, trigonometric functions, support vector machines, genetic algorithms, root mean square error

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Authors: Hyon I. Paek, Sang Rim Kim, Hyon A. Ryu

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In functional linear regression, one typical problem is to reduce dimension. Compared with multivariate linear regression, functional linear regression is regarded as an infinite-dimensional case, and the main task is to reduce dimensions of functional response and functional predictors. One common approach is to adapt functional principal component analysis (FPCA) on functional predictors and then use a few leading functional principal components (FPC) to predict the functional model. The leading FPCs estimated by the typical FPCA explain a major variation of the functional predictor, but these leading FPCs may not be mostly correlated with the functional response, so they may not be significant in the prediction for response. In this paper, we propose a supervised functional principal component analysis method for a functional response model with FPCs obtained by considering the correlation of the functional response. Our method would have a better prediction accuracy than the typical FPCA method.

Keywords: supervised, functional principal component analysis, functional response, functional linear regression

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Authors: Dimitrios Triantakonstantis, Demetris Stathakis

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Urban areas have been expanded throughout the globe. Monitoring and modeling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modeling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.

Keywords: artificial neural networks, CORINE, urban atlas, urban growth prediction

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Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

Procedia PDF Downloads 309

Authors: Dong Wu, Michael Grenn

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Managing software projects effectively is crucial for meeting deadlines, ensuring quality, and managing resources well. Traditional methods often struggle with predicting project timelines accurately due to uncertain schedules and complex data. This study addresses these challenges by combining time prediction algorithms with Large Language Models (LLMs). It makes use of real-world software project data to construct and validate a model. The model takes detailed project progress data such as task completion dynamic, team Interaction and development metrics as its input and outputs predictions of project timelines. To evaluate the effectiveness of this model, a comprehensive methodology is employed, involving simulations and practical applications in a variety of real-world software project scenarios. This multifaceted evaluation strategy is designed to validate the model's significant role in enhancing forecast accuracy and elevating overall management efficiency, particularly in complex software project environments. The results indicate that the integration of time prediction algorithms with LLMs has the potential to optimize software project progress management. These quantitative results suggest the effectiveness of the method in practical applications. In conclusion, this study demonstrates that integrating time prediction algorithms with LLMs can significantly improve the predictive accuracy and efficiency of software project management. This offers an advanced project management tool for the industry, with the potential to improve operational efficiency, optimize resource allocation, and ensure timely project completion.

Keywords: software project management, time prediction algorithms, large language models (LLMS), forecast accuracy, project progress prediction

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Authors: Jon Cobb, Nasir

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Due to higher switching frequencies, the conducted Electromagnetic interference (EMI) noise is generated in a converter. It degrades the performance of a switching converter. Therefore, it is an essential requirement to mitigate EMI noise of high performance converter. Moreover, it includes two types of emission such as common mode (CM) and differential mode (DM) noise. CM noise is due to parasitic capacitance present in a converter and DM noise is caused by switching current. However, there is dire need to understand the main cause of EMI noise. Hence, we propose a novel method to predict conducted EMI noise of different converter topologies during early stage. This paper also presents the comparison of conducted electromagnetic interference (EMI) noise due to different SMPS topologies. We also make an attempt to develop an EMI noise model for a converter which allows detailed performance analysis. The proposed method is applied to different converter, as an example, and experimental results are verified the novel prediction technique.

Keywords: EMI, electromagnetic interference, SMPS, switch-mode power supply, common mode, CM, differential mode, DM, noise

Procedia PDF Downloads 1173

Authors: S. H. Arnaud Kanga, O. Hili, S. Dabo-Niang

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Spatial prediction is an issue appealing and attracting several fields such as agriculture, environmental sciences, ecology, econometrics, and many others. Although multiple non-parametric prediction methods exist for spatial data, those are based on the conditional expectation. This paper took a different approach by examining a non-parametric spatial predictor of the conditional quantile. The study especially observes the stationary multidimensional spatial process over a rectangular domain. Indeed, the proposed quantile is obtained by inverting the conditional distribution function. Furthermore, the proposed estimator of the conditional distribution function depends on three kernels, where one of them controls the distance between spatial locations, while the other two control the distance between observations. In addition, the almost complete convergence and the convergence in mean order q of the kernel predictor are obtained when the sample considered is alpha-mixing. Such approach of the prediction method gives the advantage of accuracy as it overcomes sensitivity to extreme and outliers values.

Keywords: conditional quantile, kernel, nonparametric, stationary

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Authors: Haozhe Xiang

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With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

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Authors: Emad A. Mohammed

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The concept of hydraulic flow units (HFU) has been used for decades in the petroleum industry to improve the prediction of permeability. This concept is strongly related to the flow zone indicator (FZI) which is a function of the reservoir rock quality index (RQI). Both indices are based on reservoir porosity and permeability of core samples. It is assumed that core samples with similar FZI values belong to the same HFU. Thus, after dividing the porosity-permeability data based on the HFU, transformations can be done in order to estimate the permeability from the porosity. The conventional practice is to use the power law transformation using conventional HFU where percentage of error is considerably high. In this paper, neural network technique is employed as a soft computing transformation method to predict permeability instead of power law method to avoid higher percentage of error. This technique is based on HFU identification where Amaefule et al. (1993) method is utilized. In this regard, Kozeny and Carman (K–C) model, and modified K–C model by Hasan and Hossain (2011) are employed. A comparison is made between the two transformation techniques for the two porosity-permeability models. Results show that the modified K-C model helps in getting better results with lower percentage of error in predicting permeability. The results also show that the use of artificial intelligence techniques give more accurate prediction than power law method. This study was conducted on a heterogeneous complex carbonate reservoir in Oman. Data were collected from seven wells to obtain the permeability correlations for the whole field. The findings of this study will help in getting better estimation of permeability of a complex reservoir.

Keywords: permeability, hydraulic flow units, artificial intelligence, correlation

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Authors: Gonzalez Carlos, Martinez Fransisco

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The creation of a seismic prediction model that considers all the regional variations and perfectly adjusts its results to the response spectra is very complicated. To achieve statistically acceptable results, it is necessary to process a sufficiently robust data set, and even if high efficiencies are achieved, this model will only work properly in this region. However, when using it in other regions, differences are found due to different parameters that have not been calibrated to other regions, such as the site effect. The fact that impedance contrasts, as well as other factors belonging to the site, have a great influence on the local response is well known, which is why this work, using the residual method, is intended to establish a regional calibration of the corresponding parameters site effect for the Spain region in the global GMPM BSSA 14.

Keywords: GMPM, seismic prediction equations, residual method, response spectra, impedance contrast

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Authors: Yong-Il Lee, Do-Yeon Hwang, Won-Seog Jeong, Duckshin Park

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Recently, because of the high-speed trains forced ventilation, it is important to control the ventilation. The ventilation is for controlling various contaminants, temperature, and humidity. The high-speed train route is straight to a destination having a high speed. And there are many mountainous areas in Korea. So, tunnel rate is higher then other country. KTX HVAC block off the outdoor air, when entering tunnel. So the high tunnel rate is an effect of ventilation in the KTX cabin. It is important to reduction rate in CO2 concentration prediction. To meet the air quality of the public transport vehicles recommend standards, the KTX cabin of CO2 concentration should be managed. In this study, the concentration change was predicted by CO2 prediction simulation in route to be opened.

Keywords: CO2 prediction, KTX, ventilation, infrastructure and transportation engineering

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Authors: Soha A. Bahanshal, Byung G. Kim

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Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor.

Keywords: machine learning, prediction, classification, hybrid fuzzy weighted k-nearest neighbor, diabetic hospital readmission

Procedia PDF Downloads 156

Authors: Stepan Kuchar, Martin Golasowski, Radim Vavrik, Michal Podhoranyi, Boris Sir, Jan Martinovic

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The main goal of this article is to describe the online flood monitoring and prediction system Floreon+ primarily developed for the Moravian-Silesian region in the Czech Republic and the basic process it uses for running automatic rainfall-runoff and hydrodynamic simulations along with their calibration and uncertainty modeling. It takes a long time to execute such process sequentially, which is not acceptable in the online scenario, so the use of high-performance computing environment is proposed for all parts of the process to shorten their duration. Finally, a case study on the Ostravice river catchment is presented that shows actual durations and their gain from the parallel implementation.

Keywords: flood prediction process, high performance computing, online flood prediction system, parallelization

Procedia PDF Downloads 461