• Structural Monitoring and Maintenance
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• v.2 no.2
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• pp.115-132
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• 2015

This paper presents a new method for seismic vulnerability assessment of buildings with reference to their operational limit state. The importance of this kind of evaluation arises from the civil protection necessity that some buildings, considered strategic for seismic emergency management, should retain their functionality also after a destructive earthquake. The method is based on the identification of experimental modal parameters from ambient vibrations measurements. The knowledge of the experimental modes allows to perform a linear spectral analysis computing the maximum structural drifts of the building caused by an assigned earthquake. Operational condition is then evaluated by comparing the maximum building drifts with the reference value assigned by the Italian Technical Code for the operational limit state. The uncertainty about the actual building seismic frequencies, typically significantly lower than the ambient ones, is explicitly taken into account through a probabilistic approach that allows to define for the building the Operational Index together with the Operational Probability Curve. The method is validated with experimental seismic data from a permanently monitored public building: by comparing the probabilistic prediction and the building experimental drifts, resulting from three weak earthquakes, the reliability of the method is confirmed. Finally an application of the method to a strategic building in Italy is presented: all the procedure, from ambient vibrations measurement, to seismic input definition, up to the computation of the Operational Probability Curve is illustrated.

• Geomechanics and Engineering
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• v.6 no.2
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• pp.99-115
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• 2014

Uncertainties in design variables and design equations have a significant impact on the safety of geotechnical structures like retaining walls and slopes. This paper presents a possible framework for obtaining the partial safety factors based on reliability approach for different random variables affecting the stability of a reinforced concrete cantilever retaining wall and a slope under static loading conditions. Reliability analysis is carried out by Mean First Order Second Moment Method, Point Estimate Method, Monte Carlo Simulation and Response Surface Methodology. A target reliability index ${\beta}$ = 3 is set and partial safety factors for each random variable are calculated based on different coefficient of variations of the random variables. The study shows that although deterministic analysis reveals a safety factor greater than 1.5 which is considered to be safe in conventional approach, reliability analysis indicates quite high failure probability due to variation of soil properties. The results also reveal that a higher factor of safety is required for internal friction angle ${\varphi}$, while almost negligible values of safety factors are required for soil unit weight ${\gamma}$ in case of cantilever retaining wall and soil unit weight ${\gamma}$ and cohesion c in case of slope. Importance of partial safety factors is shown by analyzing two simple geotechnical structures. However, it can be applied for any complex system to achieve economization.

• Architectural research
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• v.21 no.3
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• pp.69-77
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• 2019

Architects are facing increasing risks that result from heightened expectations of benefits and performance when designing green buildings compared to traditional buildings. This study aims to explore the possible risk factors for architects in green building projects in South Korea and assess risk mitigation measures. To attain this goal, 14 risk factors and 12 mitigation measures were determined through an extensive literature review. A questionnaire was administered to architects practicing green building design and criticality index was employed to assess major risk factors and mitigation measures. This study identified 'adoption of new technology and process', 'green building certification results', 'building products and materials', and 'energy saving uncertainty' as the major risk factors of green building projects. Additionally, the questionnaire proposed 'contract indicating each party's role, liability, and limitations clearly', 'utilizing integrated design process', and 'understanding client's goal in green building projects' as the three most effective risk mitigation measures in designing green buildings. There are few studies that focus on architects' perceived risks concerning green building projects; this study contributes to a deeper knowledge and attempts to fill the current literature gap, which would benefit South Korea's green building design practice by aiding in the development of better risk management strategies.

• Geomechanics and Engineering
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• v.24 no.2
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• pp.167-178
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• 2021

Spatial variability is an inherent uncertainty of soil properties. Current reliability analyses generally incorporate random field theory and Monte Carlo simulation (MCS) when dealing with spatial variability, in which the computational efficiency is a significant challenge. This paper proposes a KL-FORM algorithm to improve the computational efficiency. In the proposed KL-FORM, Karhunen-Loeve (KL) expansion is used for discretizing random fields, and first-order reliability method (FORM) is employed for reliability analysis. The KL expansion and FORM can be used in conjunction, through adopting independent standard normal variables in the discretization of KL expansion as the basic variables in the FORM. To illustrate the effectiveness of this KL-FORM, it is applied to a case study of a strip footing in spatially variable unsaturated soil under rainfall, in which the bearing capacity of the footing is computed by numerical simulation. This case study shows that the KL-FORM is accurate and efficient. The parametric analyses suggest that ignoring the spatial variability of the soil may lead to an underestimation of the reliability index of the footing.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.267-275
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• 2021

This study investigates the relationship between cultural distance and entry mode choice, where the foreign investor firm and the host country are both from emergent economies. Within this framework, research is limited and the issue is whether companies, regardless of their specific situations, have the same strategy when they meet a high degree of uncertainty in the host environment. In this study, we focused on the influence of informal institutional factors: cultural distance, that has been extensively analyzed in international business, measured by Kogut and Singh index and defined according to Hofstede, Globe Project and Schwartz approaches. The general trend derived from prior research proves that when a company from a developed country is involved; overall more enthusiasm is shown for wholly-owned subsidiaries rather than joint venture. This result still stands validated for corporations from this emergent economy area. Our analysis of a sample of 163 FDI in the Kingdom of Saudi Arabia (KSA) using logistic binary regression model reveals that the foreign firms prefer to establish wholly-owned subsidiaries in the host country over entering into a joint venture with a local firm, taking into consideration the large cultural distance.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.29-36
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• 2022

In deep learning classification tasks, most models frequently assume that all labels are available for the training datasets. As such strategies to learn new concepts from unlabeled datasets are scarce. In fingerprint classification tasks, most of the fingerprint datasets are labelled using the subject/individual and fingerprint datasets labelled with finger type classes are scarce. In this paper, authors have developed approaches of classifying fingerprint images using the majorly known fingerprint classes. Our study provides a flexible method to learn new classes of fingerprints. Our classifier model combines both the clustering technique and use of deep learning to cluster and hence label the fingerprint images into appropriate classes. The K means clustering strategy explores the label uncertainty and high-density regions from unlabeled data to be clustered. Using similarity index, five clusters are created. Deep learning is then used to train a model using a publicly known fingerprint dataset with known finger class types. A prediction technique is then employed to predict the classes of the clusters from the trained model. Our proposed model is better and has less computational costs in learning new classes and hence significantly saving on labelling costs of fingerprint images.

• The Journal of Economics, Marketing and Management
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• v.10 no.5
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• pp.1-6
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• 2022

Purpose: To overcome the question that depends too much on expert's subjective judgment in traditional risk identification, this paper structure the multilevel generalized fuzzy comprehensive evaluation mathematics model of the risk identification of project, to research the risk identification of the project. Research design, data and methodology: This paper constructs the multilevel generalized fuzzy comprehensive evaluation mathematics model. Through iterative algorithm of AHP analysis, make sure the important degree of the sub project in risk analysis, then combine expert's subjective judgment with objective quantitative analysis, and distinguish the risk through identification models. Meanwhile, the concrete method of multilevel generalized fuzzy comprehensive evaluation is probed. Using the index weights to analyse project risks is discussed in detail. Results: The improved fuzzy comprehensive evaluation algorithm is proposed in the paper, at first the method of fuzzy sets core is used to optimize the fuzzy relation matrix. It improves the capability of the algorithm. Then, the method of entropy weight is used to establish weight vectors. This makes the computation process fair and open. And thereby, the uncertainty of the evaluation result brought by the subjectivity can be avoided effectively and the evaluation result becomes more objective and more reasonable. Conclusions: In this paper, we use an improved fuzzy comprehensive evaluation method to evaluate a railroad engineering project risk. It can give a more reliable result for a reference of decision making.

• East Asian Economic Review
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• v.27 no.1
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• pp.33-60
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• 2023

This paper analyzes the entire distribution of stock market returns/volatility in five emerging markets (ASEAN5) and figures out the conditional distribution of the CHI_EPU index. The aim is to examine the impact of CHI_EPU on the stock returns/volatility density of ASEAN5 markets. It also examined whether changes in CHI_EPU explain returns at higher or lower points (abnormal returns). This paper models the behaviour of stock returns from March 2011 to June 2018 using a non-parametric conditional density estimation approach. The results indicate that CHI_EPU diminishes stock returns and augments volatility in ASEAN5 markets, except for Malaysia, where it affects stock returns positively. The possible reason for this positive impact is that EPU is not the leading factor reducing Malaysian stock returns; but, other forces, such as dependency on other countries' stock markets and global factors, may have a positive impact on stock returns (Bachmann and Bayer, 2013). Thus, the risk of simultaneous investment in Chinese and ASEAN5 stock markets, except Malaysia, is high. Further, the degree of this influence intensifies at extreme high/low intervals (positive/negative tails). The findings of this study have significant implications for investors, policymakers, market agents, and analysts of ASEAN5.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.134-134
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• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2314-2333
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• 2023

It is significant to predict the performance degradation of complex electromechanical systems. Among the existing performance degradation prediction models, belief rule base (BRB) is a model that deal with quantitative data and qualitative information with uncertainty. However, when analyzing dynamic systems where observable indicators change frequently over time and working conditions, the traditional belief rule base (BRB) can not adapt to frequent changes in working conditions, such as the prediction of aeroengine performance degradation considering working condition. For the sake of settling this problem, this paper puts forward a new hidden belief rule base (HBRB) prediction method, in which the performance of aeroengines is regarded as hidden behavior, and operating conditions are used as observable indicators of the HBRB model to describe the hidden behavior to solve the problem of performance degradation prediction under different times and operating conditions. The performance degradation prediction case study of turbofan aeroengine simulation experiments proves the advantages of HBRB model, and the results testify the effectiveness and practicability of this method. Furthermore, it is compared with other advanced forecasting methods. The results testify this model can generate better predictions in aspects of accuracy and interpretability.