Search results for: Accidents Prediction Models (APMs)

Authors: V. Bezlepkin, A. Frolov, L. Lebedev, E. Kharchenko

Abstract:

Iodine radionuclides in accident releases under severe accident conditions at NPP with VVER are the most radiationimportant with a view to population dose generation at the beginning of the accident. To decrease radiation consequences of severe accidents the technical solutions for severe accidents management have been proposed in MIR.1200 project, with consideration of the measures for suppression of volatile iodine forms generation in the containment. Behavior dynamics of different iodine forms in the containment under severe accident conditions has been analyzed for the purpose of these technical solutions justification.

Keywords: Iodine radionuclides, VVER, severe accident.

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Authors: E. Movahednejad, F. Ommi

Abstract:

Droplet size distributions in the cold spray of a fuel are important in observed combustion behavior. Specification of droplet size and velocity distributions in the immediate downstream of injectors is also essential as boundary conditions for advanced computational fluid dynamics (CFD) and two-phase spray transport calculations. This paper describes the development of a new model to be incorporated into maximum entropy principle (MEP) formalism for prediction of droplet size distribution in droplet formation region. The MEP approach can predict the most likely droplet size and velocity distributions under a set of constraints expressing the available information related to the distribution. In this article, by considering the mechanisms of turbulence generation inside the nozzle and wave growth on jet surface, it is attempted to provide a logical framework coupling the flow inside the nozzle to the resulting atomization process. The purpose of this paper is to describe the formulation of this new model and to incorporate it into the maximum entropy principle (MEP) by coupling sub-models together using source terms of momentum and energy. Comparison between the model prediction and experimental data for a gas turbine swirling nozzle and an annular spray indicate good agreement between model and experiment.

Keywords: Droplet, instability, Size Distribution, Turbulence, Maximum Entropy

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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 a 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.

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Authors: Siti Nor Hafizah bt Mohd Zaid, Mohamed Abdel Maguid, Abdel Hamid Soliman

Abstract:

Road traffic accidents are a major cause of death worldwide. In an attempt to reduce accidents, some research efforts have focused on creating Advanced Driver Assistance Systems (ADAS) able to detect vehicle, driver and environmental conditions and to use this information to identify cues for potential accidents. This paper presents continued work on a novel Non-intrusive Intelligent Driver Assistance and Safety System (Ni-DASS) for assessing driver point of regard within vehicles. It uses an on-board CCD camera to observe the driver-s face. A template matching approach is used to compare the driver-s eye-gaze pattern with a set of eye-gesture templates of the driver looking at different focal points within the vehicle. The windscreen is divided into cells and comparison of the driver-s eye-gaze pattern with templates of a driver-s eyes looking at each cell is used to determine the driver-s point of regard on the windscreen. Results indicate that the proposed technique could be useful in situations where low resolution estimates of driver point of regard are adequate. For instance, To allow ADAS systems to alert the driver if he/she has positively failed to observe a hazard.

Keywords: Head rotation, Eye-gestures, Windscreen, Template matching.

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Authors: Ticijana Prijon, Branko Ermenc

Abstract:

Background: Blunt aortic trauma (BAT) includes various morphological changes that occur during deceleration, acceleration and/or body compression in traffic accidents. The various forms of BAT, from limited laceration of the intima to complete transection of the aorta, depends on the force acting on the vessel wall and the tolerance of the aorta to injury. The force depends on the change in velocity, the dynamics of the accident and of the seating position in the car. Tolerance to aortic injury depends on the anatomy, histological structure and pathomorphological alterations due to aging or disease of the aortic wall. An overview of the literature and medical documentation reveals that different terms are used to describe certain forms of BAT, which can lead to misinterpretation of findings or diagnoses. We therefore, propose a classification that would enable uniform systematic screening of all forms of BAT. We have classified BAT into three morphologycal types: TYPE I (intramural), TYPE II (transmural) and TYPE III (multiple) aortic ruptures with appropriate subtypes. Methods: All car accident casualties examined at the Institute of Forensic Medicine from 2001 to 2009 were included in this retrospective study. Autopsy reports were used to determine the occurrence of each morphological type of BAT in deceased drivers, front seat passengers and other passengers in cars and to define the morphology of BAT in relation to the accident dynamics and the age of the fatalities. Results: A total of 391 fatalities in car accidents were included in the study. TYPE I, TYPE II and TYPE III BAT were observed in 10,9%, 55,6% and 33,5%, respectively. The incidence of BAT in drivers, front seat and other passengers was 36,7%, 43,1% and 28,6%, respectively. In frontal collisions, the incidence of BAT was 32,7%, in lateral collisions 54,2%, and in other traffic accidents 29,3%. The average age of fatalities with BAT was 42,8 years and of those without BAT 39,1 years. Conclusion: Identification and early recognition of the risk factors of BAT following a traffic accident is crucial for successful treatment of patients with BAT. Front seat passengers over 50 years of age who have been injured in a lateral collision are the most at risk of BAT.

Keywords: Aorta, blunt trauma, car accidents, morphology, risk factors.

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Authors: Yang Zhang, Jian He

Abstract:

Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper presents sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for CHD prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network.

Keywords: Bi-LSTM, CHD, coronary heart disease, ECG, electrocardiogram, ResNet, sliding window.

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Authors: Sae-Rom Pak, Seung Hwan Park, Jeong Ho Cho, Daewoong An, Cheong-Sool Park, Jun Seok Kim, Jun-Geol Baek

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It is important to predict yield in semiconductor test process in order to increase yield. In this study, yield prediction means finding out defective die, wafer or lot effectively. Semiconductor test process consists of some test steps and each test includes various test items. In other world, test data has a big and complicated characteristic. It also is disproportionably distributed as the number of data belonging to FAIL class is extremely low. For yield prediction, general data mining techniques have a limitation without any data preprocessing due to eigen properties of test data. Therefore, this study proposes an under-sampling method using support vector machine (SVM) to eliminate an imbalanced characteristic. For evaluating a performance, randomly under-sampling method is compared with the proposed method using actual semiconductor test data. As a result, sampling method using SVM is effective in generating robust model for yield prediction.

Keywords: Yield Prediction, Semiconductor Test Process, Support Vector Machine, Under Sampling

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Authors: C. I. Abiodun, S. O. Azi, J. S. Ojo, P. Akinyemi

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The design of accurate and reliable mobile communication systems depends majorly on the suitability of path loss prediction methods and the adaptability of the methods to various environments of interest. In this research, the results of the adaptability of radio channel behavior are presented based on practical measurements carried out in the 1800 MHz frequency band. The measurements are carried out in typical urban, suburban and rural environments in Ekiti State, Southwestern part of Nigeria. A total number of seven base stations of MTN GSM service located in the studied environments were monitored. Path loss and break point distances were deduced from the measured received signal strength (RSS) and a practical path loss model is proposed based on the deduced break point distances. The proposed two slope model, regression line and four existing path loss models were compared with the measured path loss values. The standard deviations of each model with respect to the measured path loss were estimated for each base station. The proposed model and regression line exhibited lowest standard deviations followed by the Cost231-Hata model when compared with the Erceg Ericsson and SUI models. Generally, the proposed two-slope model shows closest agreement with the measured values with a mean error values of 2 to 6 dB. These results show that, either the proposed two slope model or Cost 231-Hata model may be used to predict path loss values in mobile micro cell coverage in the well-considered environments. Information from this work will be useful for link design of microwave band wireless access systems in the region.

Keywords: Break-point distances, path loss models, path loss exponent, received signal strength.

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Authors: Gregor A. Morrison, Dominic P. Searson, Mark J. Willis

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The purpose of this paper is to demonstrate the ability of a genetic programming (GP) algorithm to evolve a team of data classification models. The GP algorithm used in this work is “multigene" in nature, i.e. there are multiple tree structures (genes) that are used to represent team members. Each team member assigns a data sample to one of a fixed set of output classes. A majority vote, determined using the mode (highest occurrence) of classes predicted by the individual genes, is used to determine the final class prediction. The algorithm is tested on a binary classification problem. For the case study investigated, compact classification models are obtained with comparable accuracy to alternative approaches.

Keywords: classification, genetic programming.

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Authors: J. K. Alhassan, B. Attah, S. Misra

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Human beings have the ability to make logical decisions. Although human decision - making is often optimal, it is insufficient when huge amount of data is to be classified. Medical dataset is a vital ingredient used in predicting patient’s health condition. In other to have the best prediction, there calls for most suitable machine learning algorithms. This work compared the performance of Artificial Neural Network (ANN) and Decision Tree Algorithms (DTA) as regards to some performance metrics using diabetes data. WEKA software was used for the implementation of the algorithms. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were the two algorithms used for ANN, while RegTree and LADTree algorithms were the DTA models used. From the results obtained, DTA performed better than ANN. The Root Mean Squared Error (RMSE) of MLP is 0.3913 that of RBF is 0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206 respectively.

Keywords: Artificial neural network, classification, decision tree, diabetes mellitus.

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Authors: Dawit Mengistu, Lars Lundberg, Paul Davidsson

Abstract:

A major requirement for Grid application developers is ensuring performance and scalability of their applications. Predicting the performance of an application demands understanding its specific features. This paper discusses performance modeling and prediction of multi-agent based simulation (MABS) applications on the Grid. An experiment conducted using a synthetic MABS workload explains the key features to be included in the performance model. The results obtained from the experiment show that the prediction model developed for the synthetic workload can be used as a guideline to understand to estimate the performance characteristics of real world simulation applications.

Keywords: Grid computing, Performance modeling, Performance prediction, Multi-agent simulation.

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Authors: A. Elyousfi, A. Tamtaoui, E. Bouyakhf

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The H.264/AVC video coding standard contains a number of advanced features. Ones of the new features introduced in this standard is the multiple intramode prediction. Its function exploits directional spatial correlation with adjacent block for intra prediction. With this new features, intra coding of H.264/AVC offers a considerably higher improvement in coding efficiency compared to other compression standard, but computational complexity is increased significantly when brut force rate distortion optimization (RDO) algorithm is used. In this paper, we propose a new fast intra prediction mode decision method for the complexity reduction of H.264 video coding. for luma intra prediction, the proposed method consists of two step: in the first step, we make the RDO for four mode of intra 4x4 block, based the distribution of RDO cost of those modes and the idea that the fort correlation with adjacent mode, we select the best mode of intra 4x4 block. In the second step, we based the fact that the dominating direction of a smaller block is similar to that of bigger block, the candidate modes of 8x8 blocks and 16x16 macroblocks are determined. So, in case of chroma intra prediction, the variance of the chroma pixel values is much smaller than that of luma ones, since our proposed uses only the mode DC. Experimental results show that the new fast intra mode decision algorithm increases the speed of intra coding significantly with negligible loss of PSNR.

Keywords: Intra prediction, H264/AVC, video coding, encodercomplexity.

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Authors: Mladen Dragičević, Mirjana Pejić Bach, Vanja Šimičević

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The purpose of this paper is to develop models that would enable predicting student success. These models could improve allocation of students among colleges and optimize the newly introduced model of government subsidies for higher education. For the purpose of collecting data, an anonymous survey was carried out in the last year of undergraduate degree student population using random sampling method. Decision trees were created of which two have been chosen that were most successful in predicting student success based on two criteria: Grade Point Average (GPA) and time that a student needs to finish the undergraduate program (time-to-degree). Decision trees have been shown as a good method of classification student success and they could be even more improved by increasing survey sample and developing specialized decision trees for each type of college. These types of methods have a big potential for use in decision support systems.

Keywords: Data mining, knowledge discovery in databases, prediction models, student success.

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Authors: Sun-Hee Han, Hun-Jung Lim, Tai-Myoung Chung

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Vehicular Ad-Hoc Networks (VANET) can provide communications between vehicles or infrastructures. It provides the convenience of driving and the secure driving to reduce accidents. In VANET, the security is more important because it is closely related to accidents. Additionally, VANET raises a privacy issue because it can track the location of vehicles and users- identity when a security mechanism is provided. In this paper, we analyze the problem of an existing solution for security requirements required in VANET, and resolve the problem of the existing method when a key management mechanism is provided for the security operation in VANET. Therefore, we show suitability of the Long Term Evolution (LTE) in VANET for the solution of this problem.

Keywords: VANET, Privacy, Security, LTE

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Authors: Monika Chuchro

Abstract:

This paper presents a preliminary attempt to apply classification of time series using meta-clusters in order to improve the quality of regression models. In this case, clustering was performed as a method to obtain subgroups of time series data with normal distribution from the inflow into wastewater treatment plant data, composed of several groups differing by mean value. Two simple algorithms, K-mean and EM, were chosen as a clustering method. The Rand index was used to measure the similarity. After simple meta-clustering, a regression model was performed for each subgroups. The final model was a sum of the subgroups models. The quality of the obtained model was compared with the regression model made using the same explanatory variables, but with no clustering of data. Results were compared using determination coefficient (R2), measure of prediction accuracy- mean absolute percentage error (MAPE) and comparison on a linear chart. Preliminary results allow us to foresee the potential of the presented technique.

Keywords: Clustering, Data analysis, Data mining, Predictive models.

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Authors: Arun Goel

Abstract:

The present paper attempts to investigate the prediction of air entrainment rate and aeration efficiency of a free overfall jets issuing from a triangular sharp crested weir by using regression based modelling. The empirical equations, Support vector machine (polynomial and radial basis function) models and the linear regression techniques were applied on the triangular sharp crested weirs relating the air entrainment rate and the aeration efficiency to the input parameters namely drop height, discharge, and vertex angle. It was observed that there exists a good agreement between the measured values and the values obtained using empirical equations, Support vector machine (Polynomial and rbf) models and the linear regression techniques. The test results demonstrated that the SVM based (Poly & rbf) model also provided acceptable prediction of the measured values with reasonable accuracy along with empirical equations and linear regression techniques in modelling the air entrainment rate and the aeration efficiency of a free overfall jets issuing from triangular sharp crested weir. Further sensitivity analysis has also been performed to study the impact of input parameter on the output in terms of air entrainment rate and aeration efficiency.

Keywords: Air entrainment rate, dissolved oxygen, regression, SVM, weir.

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Authors: M. Ben Slimene, M.-S. Ouali

Abstract:

Asset management of urban infrastructures faces a multitude of challenges that need to be overcome to obtain a reliable measurement of performances. Predicting the performance of slowly aging systems is one of those challenges, which helps the asset manager to investigate specific failure modes and to undertake the appropriate maintenance and rehabilitation interventions to avoid catastrophic failures as well as to optimize the maintenance costs. This article presents a methodology for modeling the deterioration of slowly degrading assets based on an operating history. It consists of extracting degradation profiles by grouping together assets that exhibit similar degradation sequences using an unsupervised classification technique derived from artificial intelligence. The obtained clusters are used to build the performance prediction models. This methodology is applied to a sample of a stormwater drainage culvert dataset.

Keywords: Artificial intelligence, clustering, culvert, regression model, slow degradation.

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: Classification, classifier fusion, CNN, Deep Learning, prediction, SNR.

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Authors: Muhammad Nizam, Azah Mohamed, Majid Al-Dabbagh, Aini Hussain

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This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines. Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN.

Keywords: Dynamic voltage collapse, prediction, artificial neural network, support vector machines

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Authors: Josephine Mutwale-Ziko, Nonde Lushinga, Inonge Akakandelwa

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The study discusses the effectiveness of health and safety induction practices on construction sites against the background of the Zambian construction industry experience. The research design included the literature review of relevant literature. Questionnaires and interviews were administered to regulatory bodies, health, and safety personnel. Observation was also employed on construction sites to assess the health and safety practices being used. Health and safety in the construction industry are not something to be ignored or overlooked. The construction industry needs to take heed of the serious consequences of inadequate health and safety induction practices. The implications of inadequate health and safety induction procedures included among others threats to profitability, corporate social responsibility and increased turnover of the workforce leading to poor productivity. Adequate health and safety practices can improve the health and wellbeing of employees, reduce financial implications on firms and encourage productivity on construction sites. Despite this, accidents are still prevalent on construction sites in Zambia. The overall result of this research denotes that the implementation of health and safety induction practices is inadequate, as indicated by the negligent and non-adherent attitude to health and safety induction aspects on the sites by most stakeholders on construction sites. Therefore, health and safety induction practices are ineffective as preventive measures for reduction of accidents on construction sites in Zambia.

Keywords: Accidents, employees, health and safety, inadequate induction.

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Authors: C. Manjula, Lilly Florence

Abstract:

Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy.

Keywords: Decision tree, genetic algorithm, machine learning, software defect prediction.

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Authors: Zongyou He, Bashu Tsai, Chinhung Ko, Tainchi Lu

Abstract:

A procedural-animation-based approach which rapidly synthesize the adaptive locomotion for quadruped characters that they can walk or run in any directions on an uneven terrain within a dynamic environment was proposed. We devise practical motion models of the quadruped animals for adapting to a varied terrain in a real-time manner. While synthesizing locomotion, we choose the corresponding motion models by means of the footstep prediction of the current state in the dynamic environment, adjust the key-frames of the motion models relying on the terrain-s attributes, calculate the collision-free legs- trajectories, and interpolate the key-frames according to the legs- trajectories. Finally, we apply dynamic time warping to each part of motion for seamlessly concatenating all desired transition motions to complete the whole locomotion. We reduce the time cost of producing the locomotion and takes virtual characters to fit in with dynamic environments no matter when the environments are changed by users.

Keywords: Dynamic environment, motion synthesis, procedural animation, quadruped locomotion

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Authors: Srinath Shetty K., Shivashankar R., Rashmi P. Shetty

Abstract:

Earth reinforcing techniques have become useful and economical to solve problems related to difficult grounds and provide satisfactory foundation performance. In this context, this paper uses radial basis function neural network (RBFNN) for predicting the bearing pressure of strip footing on reinforced granular bed overlying weak soil. The inputs for the neural network models included plate width, thickness of granular bed and number of layers of reinforcements, settlement ratio, water content, dry density, cohesion and angle of friction. The results indicated that RBFNN model exhibited more than 84 % prediction accuracy, thereby demonstrating its application in a geotechnical problem.

Keywords: Bearing pressure, granular bed, radial basis function neural network, strip footing.

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Authors: M. Hassani, Y. Hassani, N. Ajudanioskooei, N. N. Benvid

Abstract:

Laser Forming process as a non-contact thermal forming process is widely used to forming and bending of metallic and non-metallic sheets. In this process, according to laser irradiation along a specific path, sheet is bent. One of the most important output parameters in laser forming is bending angle that depends on process parameters such as physical and mechanical properties of materials, laser power, laser travel speed and the number of scan passes. In this paper, Artificial Neural Network and Fuzzy Logic System were used to predict of bending angle in laser forming process. Inputs to these models were laser travel speed and laser power. The comparison between artificial neural network and fuzzy logic models with experimental results has been shown both of these models have high ability to prediction of bending angles with minimum errors.

Keywords: Artificial neural network, bending angle, fuzzy logic, laser forming.

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Authors: Yu Bin, Zhang Yan

Abstract:

The prediction of transmembrane helical segments (TMHs) in membrane proteins is an important field in the bioinformatics research. In this paper, a new method based on discrete wavelet transform (DWT) has been developed to predict the number and location of TMHs in membrane proteins. PDB coded as 1KQG was chosen as an example to describe the prediction of the number and location of TMHs in membrane proteins by using this method. To access the effect of the method, 80 proteins with known 3D-structure from Mptopo database are chosen at random as the test objects (including 325 TMHs), 308 of which can be predicted accurately, the average predicted accuracy is 96.3%. In addition, the above 80 membrane proteins are divided into 13 groups according to their function and type. In particular, the results of the prediction of TMHs of the 13 groups are satisfying.

Keywords: discrete wavelet transform, hydrophobicity, membrane protein, transmembrane helical segments

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Authors: Asmir Gogic, Aljo Mujcic, Sandra Ibric, Nermin Suljanovic

Abstract:

Ubiquity of natural disasters during last few decades have risen serious questions towards the prediction of such events and human safety. Every disaster regardless its proportion has a precursor which is manifested as a disruption of some environmental parameter such as temperature, humidity, pressure, vibrations and etc. In order to anticipate and monitor those changes, in this paper we propose an overall system for disaster prediction and monitoring, based on wireless sensor network (WSN). Furthermore, we introduce a modified and simplified WSN routing protocol built on the top of the trickle routing algorithm. Routing algorithm was deployed using the bluetooth low energy protocol in order to achieve low power consumption. Performance of the WSN network was analyzed using a real life system implementation. Estimates of the WSN parameters such as battery life time, network size and packet delay are determined. Based on the performance of the WSN network, proposed system can be utilized for disaster monitoring and prediction due to its low power profile and mesh routing feature.

Keywords: Bluetooth low energy, disaster prediction, mesh routing protocols, wireless sensor networks.

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Authors: Emad Amar, Tawfik Khattab, Fatma Zada

Abstract:

Predicting earthquakes is an important issue in the study of geography. Accurate prediction of earthquakes can help people to take effective measures to minimize the loss of personal and economic damage, such as large casualties, destruction of buildings and broken of traffic, occurred within a few seconds. United States Geological Survey (USGS) science organization provides reliable scientific information about Earthquake Existed throughout history & the Preliminary database from the National Center Earthquake Information (NEIC) show some useful factors to predict an earthquake in a seismic area like Aleutian Arc in the U.S. state of Alaska. The main advantage of this prediction method that it does not require any assumption, it makes prediction according to the future evolution of the object's time series. The article compares between simulation data result from trained BP and RBF neural network versus actual output result from the system calculations. Therefore, this article focuses on analysis of data relating to real earthquakes. Evaluation results show better accuracy and higher speed by using radial basis functions (RBF) neural network.

Keywords: BP neural network, Prediction, RBF neural network.

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Authors: Gabriele Bedon, Marco Raciti Castelli, Ernesto Benini

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A dynamic stall-corrected Blade Element-Momentum algorithm based on a hybrid polar is validated through the comparison with Sandia experimental measurements on a 5-m diameter wind turbine of Troposkien shape. Different dynamic stall models are evaluated. The numerical predictions obtained using the extended aerodynamic coefficients provided by both Sheldal and Klimas and Raciti Castelli et al. are compared to experimental data, determining the potential of the hybrid database for the numerical prediction of vertical-axis wind turbine performances.

Keywords: Darrieus wind turbine, Blade Element-Momentum Theory, extended airfoil database, hybrid database, Sandia 5-m wind turbine.

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Authors: Radouane Iqdour, Abdelouhab Zeroual

Abstract:

The Multi-Layered Perceptron (MLP) Neural networks have been very successful in a number of signal processing applications. In this work we have studied the possibilities and the met difficulties in the application of the MLP neural networks for the prediction of daily solar radiation data. We have used the Polack-Ribière algorithm for training the neural networks. A comparison, in term of the statistical indicators, with a linear model most used in literature, is also performed, and the obtained results show that the neural networks are more efficient and gave the best results.

Keywords: Daily solar radiation, Prediction, MLP neural networks, linear model

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Authors: Lely A. Luengas, Pedro R. Vizcaya, Giovanni Sánchez

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In this paper, a methodology is presented to obtain the Static Alignment Model for any transtibial amputee person. The proposed methodology starts from experimental data collected on the Hospital Militar Central, Bogotá, Colombia. The effects of transtibial prosthesis malalignment on amputees were measured in terms of joint angles, center of pressure (COP) and weight distribution. Some statistical tools are used to obtain the model parameters. Mathematical predictive models of prosthetic alignment were created. The proposed models are validated in amputees and finding promising results for the prosthesis Static Alignment. Static alignment process is unique to each subject; nevertheless the proposed methodology can be used in each transtibial amputee.

Keywords: Information theory, prediction model, prosthetic alignment, transtibial prosthesis.

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