• Smart Structures and Systems
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• v.4 no.2
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• pp.195-208
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• 2008

This paper summarizes the experimental vibration-based structural health monitoring study on a historical monument in Jordan. In this work, and within the framework of the European Commission funded project "wide-Range Non-Intrusive Devices Toward Conservation of Historical Monuments in the Mediterranean Area", a seven and a half century old minaret located in Ajloun (73 km north of the capital Amman) is studied. Because of their cultural value, touristic importance and the desire to preserve them for the future, only non-destructive tests were allowed for the experimental investigation of such heritage structures. Therefore, after dimensional measurements and determination of the current state of damage in the selected monument, ambient vibration tests are conducted to measure the accelerations at strategic locations of the system. Output-only modal identification technique is applied to extract the modal parameters such as natural frequencies and mode shapes. A Non-linear version of SAP 2000 computer program is used to develop a three-dimensional finite element model of the minaret. The developed numerical model is then updated according to the modal parameters obtained experimentally by the ambient-vibration test-results and the measured characteristics of old stone and deteriorated mortar. Moreover, a parametric identification method using the N4Sid state space model is employed to model the dynamic behavior of the minaret and to build up a robust, immune and noise tolerant model.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.83-93
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• 2020

This empirical research aims to modeling and improving the forecasting accuracy of the volatility pattern by employing the Saudi Arabia stock market (Tadawul)by studying daily closed price index data from October 2011 to December 2019 with a number of observations being 2048. In order to achieve significant results, this study employs many mathematical functions which are non-linear spectral model Maximum overlapping Discrete Wavelet Transform (MODWT) based on the best localized function (Bl14), autoregressive integrated moving average (ARIMA) model and generalized autoregressive conditional heteroskedasticity (GARCH) models. Therefore, the major findings of this study show that all the previous events during the mentioned period of time will be explained and a new forecasting model will be suggested by combining the best MODWT function (Bl14 function) and the fitted GARCH model. Therefore, the results show that the ability of MODWT in decomposition the stock market data, highlighting the significant events which have the most highly volatile data and improving the forecasting accuracy will be showed based on some mathematical criteria such as Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Root Means Squared Error (RMSE), Akaike information criterion. These results will be implemented using MATLAB software and R- software.

• The Transactions of the Korea Information Processing Society
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• v.4 no.2
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• pp.401-408
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• 1997

Human gets almist all of his knoweledge from the recognition and the accumulation of input patterns,image or sound,the he gets theough his eyes and through his ears.Among these means,his chracter recognition,an ability that allows him to recognize characters and understand their meanings through visual information, is now applied to a pattern recognition system using neural network in computer. Recurrent neural network is one of those models that reuse the output value in neural network learning.Recently many studies try to apply this recurrent neural network to the classification of static patterns like off-line handwritten characters. But most of their efforts are not so drrdtive until now.This stusy suggests a new type of recurrent neural network for an deedctive classification of the static patterns such as off-line handwritten chracters.Using the new J-E(Jordan-Elman)neural network model that enlarges and combines Jordan Model and Elman Model,this new type is better than those of before in recobnizing the static patterms such as figures and handwritten-characters.

• The Journal of the Acoustical Society of Korea
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• v.18 no.3
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• pp.62-67
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• 1999

In this paper, we investigates a reliable model of the Predictive Recurrent Neural Network for the speech recognition. Predictive Neural Networks are modeled by syllable units. For the given input syllable, then a model which gives the minimum prediction error is taken as the recognition result. The Predictive Neural Network which has the structure of recurrent network was composed to give the dynamic feature of the speech pattern into the network. We have compared with the recognition ability of the Recurrent Network proposed by Elman and Jordan. ETRI's SAMDORI has been used for the speech DB. In order to find a reliable model of neural networks, the changes of two recognition rates were compared one another in conditions of: (1) changing prediction order and the number of hidden units: and (2) accumulating previous values with self-loop coefficient in its context. The result shows that the optimum prediction order, the number of hidden units, and self-loop coefficient have differently responded according to the structure of neural network used. However, in general, the Jordan's recurrent network shows relatively higher recognition rate than Elman's. The effects of recognition rate on the self-loop coefficient were variable according to the structures of neural network and their values.

• Journal of Environmental Science International
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• v.4 no.2
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• pp.117-120
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• 1995

The comparision of sunshine duration meter was carried out on the roof of Korea Meteorological Research Institude by comparing Pyrheliometer(Eppley NIP model) to sunshine duration meter(Reflection type) during from Nov. 8, 1989 to Feb. 19, 1990. In the observation Period, daily mean sunshine duration time difference of Jordan sunshine duration meter was recorded 0.47hour and Reflect sunshine duration meter was recorded 0.39hour. More than one hour time difference was observed 15 cases by Jordan sunshine duration meter and 11 cases by Reflect sunshine duration meter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4076-4092
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• 2019

Nowadays, most users access internet through mobile applications. The common way to authenticate users through websites forms is using passwords; while they are efficient procedures, they are subject to guessed or forgotten and many other problems. Additional multi modal authentication procedures are needed to improve the security. Behavioral authentication is a way to authenticate people based on their typing behavior. It is used as a second factor authentication technique beside the passwords that will strength the authentication effectively. Keystroke dynamic rhythm is one of these behavioral authentication methods. Keystroke dynamics relies on a combination of features that are extracted and processed from typing behavior of users on the touched screen and smart mobile users. This Research presents a novel analysis in the keystroke dynamic authentication field using two features categories: timing and no timing combined features. The proposed model achieved lower error rate of false acceptance rate with 0.1%, false rejection rate with 0.8%, and equal error rate with 0.45%. A comparison in the performance measures is also given for multiple datasets collected in purpose to this research.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.245-254
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• 2021

This paper examines the impact of corporate governance strength on capital structure in an emerging country, namely, Jordan, by constructing a corporate governance score that captures both internal monitoring mechanisms (foreign ownership and institutional ownership) and external monitoring mechanism (audit fees). In addition, this study uses profitability as control variable. This paper uses data of non-financial companies (industrial and services) of 87 listed firms on Amman Stock Exchange (ASE) from 2011 to 2019. Using the random-effects generalized least square (GLS) regression model, the findings reveal that foreign ownership significantly and negatively influences the level leverage, while institutional ownership has a positive and insignificant association with level leverage. Further, audit fees have a positive and strong significant association with level leverage in Jordan. In addition, profitability has a positive and significant association with leverage. These outcomes suggest that foreign ownership should be encouraged in listed companies as it can replace the weakness of other corporate governance mechanisms in Jordan. The outcomes of the current study should be of great interest to regulators and policy-makers. The results, which are robust to a range of alternative proxies and to additional tests, provide new insights into the determinants of level leverage.

• The Journal of Asian Finance, Economics and Business
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• v.10 no.1
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• pp.181-187
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• 2023

The study aimed to study the effect of the inflation rate, the real domestic product, the interbank lending interest rate, and the total deposits on credit facilities in Jordan for 2012-2021 through quarterly data. The study adopted the ARDL model. The study used the time series analysis method, as the study tests the stationarity of the time series. The results showed that the impact of inflation on the total credit facilities was negative. In contrast, the impact of each of the total deposits, real GDP, and the interest rate of interbank loans on the total credit facilities was positive and significant. The study recommended the need for the banking sector in Jordan to develop risk management mechanisms in a way that allows it to adapt to economic cycles and crises by conducting stress tests and developing scenarios that ensure the formation of sufficient provisions to meet emergencies. The study also recommended that the macroeconomic policy should be based on creating a stable macroeconomic environment that allows the efficient employment of resources in all economic sectors in a way that achieves high economic growth rates, which contributes to the promotion of economic recovery and is reflected in income. Hence, individuals have a greater ability to repay loans.

• Computers and Concrete
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• v.27 no.2
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• pp.131-141
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• 2021

This paper aims at investigating the capability of different FRP/concrete interface models to predict the effect of carbon nanotubes on the flexural behavior of RC beams strengthened with CFRP. Three different interfacial bond models are proposed to simulate the adhesion between CFRP composites and concrete, namely: full bond, nonlinear spring element, and cohesive zone model. 3D Nonlinear finite element model is developed then validated using experimental work conducted by the authors in a previous investigation. Cohesive zone model (CZM) has the best agreement with the experimental results in terms of load-deflection response. CZM is the only bond model that accurately predicted the cracks patterns and failure mode of the strengthened RC beams. The FE model is then expanded to predict the effect of bond strength on the flexural capacity of RC beams strengthened with externally bonded CNTs modified CFRP composites using CZM bond model. The results reveal that the flexural capacity of the strengthened beams increases with increasing the bond strength value. However, only 23% and 22% of the CFRP stress and strain capacity; in the case of full bond; can be utilized before failure.

• Computers and Concrete
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• v.21 no.1
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• pp.47-54
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• 2018

Artificial neural network models can be successfully used to simulate the complex behavior of many problems in civil engineering. As compared to conventional computational methods, this popular modeling technique is powerful when the relationship between system parameters is intrinsically nonlinear, or cannot be explicitly identified, as in the case of concrete behavior. In this investigation, an artificial neural network model was developed to assess the residual compressive strength of self-compacted concrete at elevated temperatures ($20-900^{\circ}C$) and various relative humidity conditions (28-99%). A total of 332 experimental datasets, collected from available literature, were used for model calibration and verification. Data used in model development incorporated concrete ingredients, filler and fiber types, and environmental conditions. Based on the feed-forward back propagation algorithm, systematic analyses were performed to improve the accuracy of prediction and determine the most appropriate network topology. Training, testing, and validation results indicated that residual compressive strength of self-compacted concrete, exposed to high temperatures and relative humidity levels, could be estimated precisely with the suggested model. As illustrated by statistical indices, the reliability between experimental and predicted results was excellent. With new ingredients and different environmental conditions, the proposed model is an efficient approach to estimate the residual compressive strength of self-compacted concrete as a substitute for sophisticated laboratory procedures.