Development of Earthquake-Induced Building Damage Estimation Model Based on ALOS/PALSAR Observing the 2007 Peru Earthquake

2013 ◽  
Vol 8 (2) ◽  
pp. 346-355 ◽  
Author(s):  
Masashi Matsuoka ◽  
◽  
Miguel Estrada ◽  
Keyword(s):  
Discriminant Function ◽  
Likelihood Function ◽  
Window Size ◽  
Seismic Intensity ◽  
Building Damage ◽  
Severe Damage ◽  
Estimation Model ◽  
Alos Palsar ◽  
Explanatory Variables ◽  
Before And After

With the aim of developing a model for estimating building damage from synthetic aperture radar (SAR) data in the L-band, which is appropriate for Peru, we propose a regression discriminant function based on field survey data in Pisco, which was seriously damaged in the 2007 Peru earthquake. The proposed function discriminates among damage ranks corresponding to the severe damage ratio of buildings using ALOS/PALSAR imagery of the disaster area before and after the earthquake. By calculating differences in and correlations of backscattering coefficients, which were explanatory variables of the regression discriminant function, we determined an optimum window size capable of estimating the degree of damage more accurately. A normalized likelihood function for the severe damage ratio was developed based on discriminant scores of the regression discriminant function. The distribution of the severe damage ratio was accurately estimated, furthermore, from PALSAR imagery using data integration of the likelihood function with fragility functions in terms of the seismic intensity of the earthquake.

scholarly journals Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data

2021 ◽  
Vol 13 (6) ◽  
pp. 1146
Author(s):  
Yuliang Nie ◽  
Qiming Zeng ◽  
Haizhen Zhang ◽  
Qing Wang
Keyword(s):  
Damage Detection ◽  
Texture Features ◽  
Classification Performance ◽  
Building Damage ◽  
Alos Palsar ◽  
Yushu Earthquake ◽  
Matching Algorithm ◽  
2011 Tohoku Tsunami ◽  
Collapsed Buildings ◽  
Study Sites

Synthetic aperture radar (SAR) is an effective tool in detecting building damage. At present, more and more studies detect building damage using a single post-event fully polarimetric SAR (PolSAR) image, because it permits faster and more convenient damage detection work. However, the existence of non-buildings and obliquely-oriented buildings in disaster areas presents a challenge for obtaining accurate detection results using only post-event PolSAR data. To solve these problems, a new method is proposed in this work to detect completely collapsed buildings using a single post-event full polarization SAR image. The proposed method makes two improvements to building damage detection. First, it provides a more effective solution for non-building area removal in post-event PolSAR images. By selecting and combining three competitive polarization features, the proposed solution can remove most non-building areas effectively, including mountain vegetation and farmland areas, which are easily confused with collapsed buildings. Second, it significantly improves the classification performance of collapsed and standing buildings. A new polarization feature was created specifically for the classification of obliquely-oriented and collapsed buildings via development of the optimization of polarimetric contrast enhancement (OPCE) matching algorithm. Using this developed feature combined with texture features, the proposed method effectively distinguished collapsed and obliquely-oriented buildings, while simultaneously also identifying the affected collapsed buildings in error-prone areas. Experiments were implemented on three PolSAR datasets obtained in fully polarimetric mode: Radarsat-2 PolSAR data from the 2010 Yushu earthquake in China (resolution: 12 m, scale of the study area: ); ALOS PALSAR PolSAR data from the 2011 Tohoku tsunami in Japan (resolution: 23.14 m, scale of the study area: ); and ALOS-2 PolSAR data from the 2016 Kumamoto earthquake in Japan (resolution: 5.1 m, scale of the study area: ). Through the experiments, the proposed method was proven to obtain more than 90% accuracy for built-up area extraction in post-event PolSAR data. The achieved detection accuracies of building damage were 82.3%, 97.4%, and 78.5% in Yushu, Ishinomaki, and Mashiki town study sites, respectively.

scholarly journals Causal-Effect Analysis using Bayesian LiNGAM Comparing with Correlation Analysis in Function Point Metrics and Effort

2018 ◽  
Vol 3 (2) ◽  
pp. 90-112 ◽  
Author(s):  
Masanari Kondo ◽  
Osamu Mizuno ◽  
Eun-Hye Choi
Keyword(s):  
Causal Effect ◽  
Task Assignment ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Estimation Model ◽  
Effect Analysis ◽  
Explanatory Variables ◽  
Function Point ◽  
New Findings ◽  
Non Gaussian

Software effort estimation is a critical task for successful software development, which is necessary for appropriately managing software task assignment and schedule and consequently producing high quality software. Function Point (FP) metrics are commonly used for software effort estimation. To build a good effort estimation model, independent explanatory variables corresponding to FP metrics are required to avoid a multicollinearity problem. For this reason, previous studies have tackled analyzing correlation relationships between FP metrics. However, previous results on the relationships have some inconsistencies. To obtain evidences for such inconsistent results and achieve more effective effort estimation, we propose a novel analysis, which investigates causal-effect relationships between FP metrics and effort. We use an advanced linear non-Gaussian acyclic model called BayesLiNGAM for our causal-effect analysis, and compare the correlation relationships with the causal-effect relationships between FP metrics. In this paper, we report several new findings including the most effective FP metric for effort estimation investigated by our analysis using two datasets.

scholarly journals Monitoring antimicrobial resistance trends in commensal Escherichia coli from livestock, the Netherlands, 1998 to 2016

2019 ◽  
Vol 24 (25) ◽  
Author(s):  
Ayla Hesp ◽  
Kees Veldman ◽  
Jeanet van der Goot ◽  
Dik Mevius ◽  
Gerdien van Schaik
Keyword(s):  
Escherichia Coli ◽  
Antimicrobial Resistance ◽  
Monitoring Data ◽  
Resistant Isolate ◽  
Similar Data ◽  
Explanatory Variables ◽  
E Coli ◽  
Veal Calves ◽  
Before And After ◽  
Using Data

Background Monitoring of antimicrobial resistance (AMR) in animals is essential for public health surveillance. To enhance interpretation of monitoring data, evaluation and optimisation of AMR trend analysis is needed. Aims To quantify and evaluate trends in AMR in commensal Escherichia coli, using data from the Dutch national AMR monitoring programme in livestock (1998–2016). Methods Faecal samples were collected at slaughter from broilers, pigs and veal calves. Minimum inhibitory concentration values were obtained by broth microdilution for E. coli for 15 antimicrobials of eight antimicrobial classes. A Poisson regression model was applied to resistant isolate counts, with explanatory variables representing time before and after 2009 (reference year); for veal calves, sampling changed from 2012 represented by an extra explanatory variable. Results Resistant counts increased significantly from 1998-2009 in broilers and pigs, except for tetracyclines and sulfamethoxazole in broilers and chloramphenicol and aminoglycosides in pigs. Since 2009, resistant counts decreased for all antimicrobials in broilers and for all but the phenicols in pigs. In veal calves, for most antimicrobials no significant decrease in resistant counts could be determined for 2009–16, except for sulfamethoxazole and nalidixic acid. Within animal species, antimicrobial-specific trends were similar. Conclusions Using Dutch monitoring data from 1998-2016, this study quantified AMR trends in broilers and slaughter pigs and showed significant trend changes in the reference year 2009. We showed that monitoring in commensal E. coli is useful to quantify trends and detect trend changes in AMR. This model is applicable to similar data from other European countries.

scholarly journals ESTIMATION OF DAMAGED AREAS DUE TO THE 2010 CHILE EARTHQUAKE AND TSUNAMI USING SAR IMAGERY OF ALOS/PALSAR

2016 ◽  
Vol III-8 ◽  
pp. 35-42
Author(s):  
Pertiwi Jaya Ni Made ◽  
Fusanori Miura ◽  
A. Besse Rimba
Keyword(s):  
Large Scale ◽  
Aerial Photographs ◽  
Alos Palsar ◽  
Aster Gdem ◽  
Tsunami Damage ◽  
Before And After ◽  
Chile Earthquake ◽  
Multi Temporal ◽  
Sar Imagery ◽  
Inundation Map

A large-scale earthquake and tsunami affect thousands of people and cause serious damages worldwide every year. Quick observation of the disaster damage is extremely important for planning effective rescue operations. In the past, acquiring damage information was limited to only field surveys or using aerial photographs. In the last decade, space-borne images were used in many disaster researches, such as tsunami damage detection. In this study, SAR data of ALOS/PALSAR satellite images were used to estimate tsunami damage in the form of inundation areas in Talcahuano, the area near the epicentre of the 2010 Chile earthquake. The image processing consisted of three stages, i.e. pre-processing, analysis processing, and post-processing. It was conducted using multi-temporal images before and after the disaster. In the analysis processing, inundation areas were extracted through the masking processing. It consisted of water masking using a high-resolution optical image of ALOS/AVNIR-2 and elevation masking which built upon the inundation height using DEM image of ASTER-GDEM. The area result was 8.77 Km<sup>2</sup>. It showed a good result and corresponded to the inundation map of Talcahuano. Future study in another area is needed in order to strengthen the estimation processing method.

A Study on Estimation Model of Incidence Factor of the Thermal Bridge Using In-Situ Measurement Infrared Thermography

2021 ◽  
Author(s):  
Eunho Kang ◽  
Hyomoon Lee ◽  
Dongsu Kim ◽  
Jongho Yoon
Keyword(s):  
Infrared Thermography ◽  
Coefficient Of Variation ◽  
Performance Indicator ◽  
Likelihood Function ◽  
Estimation Method ◽  
Estimation Model ◽  
Thermal Bridge ◽  
Convergence Results ◽  
Bridge Performance

Abstract Practical thermal bridge performance indicators (ITBs) of existing buildings may differ from calculated thermal bridge performance derived theoretically due to actual construction conditions, such as effect of irregular shapes and aging. To fill this gap in a practical manner, more realistic quantitative evaluation of thermal bridge at on-site needs to be considered to identify thermal behaviors throughout exterior walls and thus improve overall insulation performance of buildings. In this paper, the model of a thermal bridge performance indicator is developed based on an in-situ Infrared thermography method, and a case study is then carried out to evaluate thermal performance of an existing exterior wall using the developed model. For the estimation method in this study, the form of the likelihood function is used with the Bayesian method to constantly reflect the measured data. Subsequently, the coefficient of variation is applied to analyze required times for the assumed convergence. Results from the measurement for three days show that thermal bridge under the measurement has more heat losses, including 1.14 times, when compared to the non-thermal bridge. In addition, the results present that it takes about 40 hours to reach 1% of the variation coefficient. Comparison of the ITB estimated at coefficient of variation 1% (40 hours point) with the ITB estimated at end-of-experiment (72 hours point) results in 0.9% of a relative error.

Pay-performance sensitivity and corporate governance mechanisms: evidence from Tunisia

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Meriem Ghrab ◽  
Marjène Gana ◽  
Mejda Dakhlaoui
Keyword(s):  
Corporate Governance ◽  
Firm Performance ◽  
Pay For Performance ◽  
Simultaneous Equation ◽  
Executive Pay ◽  
Estimation Model ◽  
Content Type ◽  
Explanatory Variables ◽  
Performance Sensitivity ◽  
Ceo Pay

Purpose The purpose of this study is to analyze the CEO compensation sensitivity to firm performance, termed as the pay-for-performance sensitivity (PPS) in the Tunisian context and to test the robustness of this relationship when corporate governance (CG) mechanisms are considered. Design/methodology/approach The consideration of past executive pay as one of the explanatory variables makes this estimation model a dynamic one. Furthermore, to avoid the problem of endogeneity, this study uses the system-GMM estimator developed by Blundell and Bond (1998). For robustness check, this study aims to use a simultaneous equation approach (three-stage least squares [3SLS]) to estimate the link between performance and CEO pay with a set of CG mechanisms to control for possible simultaneous interdependencies. Findings Using a sample of 336 firm-years from Tunisia over the 2009–2015 periods, this study finds strong evidence that the pay-performance relationship is insignificant and negative, and it becomes more negative or remains insignificant after introducing CG mechanisms consistently with the managerial power approach. The findings are robust to the use of alternative performance measures. This study provides new empirical evidence that CEOs of Tunisian firms abuse extracting rents independently of firm performance. Originality/value This study contributes to the unexamined research on PPS in a frontier market. This study also shows the ineffectiveness of the Tunisian CG structure and thus recommends for the legislator to impose a mandatory CG guide.

Delay-Based Admission Control to Sustain QoS in a Managed IEEE 802.11 Wireless LANs

2012 ◽  
pp. 638-660
Author(s):  
A. Ksentini ◽  
A. Nafaa
Keyword(s):  
Admission Control ◽  
Window Size ◽  
Estimation Model ◽  
Delay Sensitive ◽  
Network Utilization ◽  
Contention Window Size ◽  
802.11 Wireless Lans ◽  
Distributed Admission Control ◽  
Time Basis ◽  
Adaptation Scheme

In this chapter, we present a delay-sensitive MAC adaptation scheme combined with an admission control mechanism. The proposed solution is based on thorough analysis of the trade-off existing between high network utilization and achieving bounded QoS metrics in operated 802.11-based networks. First, we derive an accurate delay estimation model to adjust the contention window size in real-time basis by considering key net-work factors, MAC queue dynamics, and application-level QoS requirements. Second, we use the abovementioned delay-based CW size adaptation scheme to derive a fully distributed admission control model that provides protection for existing flows in terms of QoS guarantees.

scholarly journals Exploring Twitter for CSR Disclosure: Influence of CEO and Firm Characteristics in Latin American Companies

2018 ◽  
Vol 10 (8) ◽  
pp. 2617 ◽  
Author(s):  
Yuli Suárez-Rico ◽  
Mauricio Gómez-Villegas ◽  
María García-Benau
Keyword(s):  
Regression Analysis ◽  
Latin American ◽  
Chief Executive ◽  
Multivariate Regression Analysis ◽  
Explanatory Variables ◽  
The Pacific ◽  
Synthetic Index ◽  
Before And After ◽  
Csr Disclosure ◽  
Corporate Social

Social networks, which are characterised by accessibility and interactivity, offer great potential for dialogue between companies and stakeholders, for example as platforms for publishing information on aspects of corporate social responsibility (CSR). In this paper, we use a synthetic index to analyse levels of CSR disclosure via Twitter, and identify explanatory variables of this disclosure by studying the demographic characteristics of the Chief Executive Officer (CEO) and of the company. This synthetic index was based on data for 93 companies located in the four countries of the Pacific Alliance (Chile, Colombia, Mexico, and Peru), using categories based on the 2016 Global Reporting Initave (GRI) Standards. The tweets were compiled during a period of two months in 2017, immediately before and after the publication of each CSR report. The synthetic index was taken as the dependent variable and used as the basis for multivariate regression analysis to identify the relationship between the level of CSR disclosure on Twitter and the characteristics of the firm and its CEO. The results obtained show that firms operating in environmentally-sensitive industries present higher levels of CSR disclosure on Twitter than those in other sectors. By country of origin, the Colombian and Chilean companies offered higher levels of disclosure than those in Mexico and Peru. The regression analysis revealed a positive relationship between the firm operating in a sensitive industry and its level of CSR disclosure on Twitter, and an inverse relationship between the latter variable and the tenure of the CEO.

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