• Smart Structures and Systems
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• 제31권2호
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• pp.101-111
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• 2023

Structural health monitoring (SHM) covers a range of damage detection strategies for buildings. In real-time, SHM provides a basis for rapid decision making to optimise the speed and economic efficiency of post-event response. Previous work introduced an SHM method based on identifying structural nonlinear hysteretic parameters and their evolution from structural force-deformation hysteresis loops in real-time. This research extends and generalises this method to investigate the impact of a wide range of flag-shaped or pinching shape nonlinear hysteretic response and its impact on the SHM accuracy. A particular focus is plastic stiffness (K_p), where accurate identification of this parameter enables accurate identification of net and total plastic deformation and plastic energy dissipated, all of which are directly related to damage and infrequently assessed in SHM. A sensitivity study using a realistic seismic case study with known ground truth values investigates the impact of hysteresis loop shape, as well as added noise, on SHM accuracy using a suite of 20 ground motions from the PEER database. Monte Carlo analysis over 22,000 simulations with different hysteresis loops and added noise resulted in absolute percentage identification error (median, (IQR)) in K_p of 1.88% (0.79, 4.94)%. Errors were larger where five events (Earthquakes #1, 6, 9, 14) have very large errors over 100% for resulted K_p as an almost entirely linear response yielded only negligible plastic response, increasing identification error. The sensitivity analysis shows accuracy is reduces to within 3% when plastic drift is induced. This method shows clear potential to provide accurate, real-time metrics of non-linear stiffness and deformation to assist rapid damage assessment and decision making, utilising algorithms significantly simpler than previous non-linear structural model-based parameter identification SHM methods.

• The Journal of Asian Finance, Economics and Business
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• 제7권6호
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• pp.9-18
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• 2020

This study seeks to analyze the effect of Non-Performing Loans and Capital Adequacy Ratio on Return on Assets on ten conventional banks listed on the Indonesia Stock Exchange (BEI-IDX). This study uses secondary panel data for 2015-2019 in the form of CAR and NPL values from ten conventional banks listed on the BEI-IDX during the 2020 observation period. The research approach is quantitative descriptive with data analysis methods, namely, linear regression. The testing phase of this study includes: transform value, F-test, T-test and hypothesis test with significancy level sig < 0.05. The results of this study reveal that Non-Performing Loans had a significant negative effect (t = -2,637) (0.011 <0.0) on Return on Assets, while Capital Adequacy Ratio has no significant effect on ROA (0.760 > 0.05). R2 value is 0.128 or 12.8%. It has a significant effect on variables, calling efforts by banks, governments, and authorities monetary of related institutions to maintain the stability of finance. The reduction of Non-Performing Loan impacts on assets and capital adequacy ratio, besides, the normal NPL will control the stability of finance. If a balance is created either in the form of values or amounts of the variables, the reduction in Non-Performing Loans will be controlled.

• 해양환경안전학회지
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• 제18권6호
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• pp.545-550
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• 2012

풍력발전 단지의 설계시 풍력 자원 평가 과정은 필수적인 과정이다. 풍력 자원 평가를 위해 장기풍황(20년)자료를 이용하여야 하지만 장기간 관측하는 것은 어렵기 때문에 예정지의 1년 이상의 관측데이터로 평가를 실시하였다. 예정지의 단기 풍황탑(Met-Mast; Meteorology Mast) 자료를 주변의 장기관측 자료인 자동기상관측(AWS; Automatic Weather Station)데이터를 이용하여 수학적 보간법으로 예정지의 데이터를 장기 데이터로 변환한 것을 MCP(Measure-Correlative-Predict)기법이라 한다. 본 연구에서는 MCP기법 중 선형 회계방법을 적용하였다. 선택된 MCP 회귀 모델식에 따라 제주 북동부 구좌지역의 AWS데이터를 제주 북동부 한동 지역의 Met-mast 데이터에 적용하여 연간 에너지 생산량을 예측 하였다. 예정지의 단기 풍황을 이용하였을 때와 보정된 장기 풍황을 이용하여 때 연간 에너지 생산량을 비교하였다. 그 결과 연간 약 3.6 %의 예측오차를 보였고, 이는 연간 약 271 MW의 에너지 생산량의 차이를 의미한다. 풍력발전기의 생애주기인 20년을 비교 하였을 때 약 5,420 MW의 차이를 나타내었으며, 이는 약 9개월 정도의 에너지 생산량과 비슷한 수준이다. 결과적으로, 제안 된 선형 회귀 MCP 방법을 이용하는 것이 단기관측 자료를 통한 불확식성을 제거하는 합리적인 방법으로 판단된다.

• Near Infrared Analysis
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• 제1권1호
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• pp.27-30
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• 2000

In this study, we investigated the feasibility of non-destructive determination of internal quality factors of Fuji apple fruit using near infrared(NIR) reflectance spectroscopy and developed the calibration models. As the reference methods, refractometer, titration and texture analyzer for sugar content, acidity and firmness were used, respectively. Samples were scanned from 1100∼2500nm with InfraAlyzer 500C spectrometer and SESAME software was used for data analysis. A multiple linear regression(MLR) analysis was performed to develop the calibrations. The correlation coefficient(R) and standard error of prediction(SEP) were as follows; 0.91, 0.41$^{\circ}$Brix for sugar content, 0.90, 0.04% for acidity and 0.84, 0.094 kg for firmness, respectively. This study shows that NIR spectroscopy can be used to evaluate the sugar content acidity and firmness of apple fruit with acceptable accuracy.

• 응용통계연구
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• 제16권2호
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• pp.365-376
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• 2003

본 연구에서는 비정규 시계열 자료에 관한 다양한 회귀모형을 고찰하고, 이들 모형의 선택 기준에 관하여 연구해 보았다. 모형 선택의 기준으로는 AIC (Akaike information criterion), BIC (Baysian information criterion) 그리고 우도비 검정을 확장 적용하였다. 또한, 실제의 Polio 자료분석을 통해 이를 적용해보았다.

• 콘크리트학회논문집
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• 제11권4호
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• pp.157-167
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• 1999

Among several non-destructive testing methods, ultrasonic pulse velocity method and rebound index method have been widely used for the evaluation of concrete strength. However, such methods might not provide accurate estimated results since factors influencing the relationship between strength and either ultrasonic pulse velocity or rebound index are not considered. In this paper, the evaluation method of concrete strength using rod-wave velocity measured by impact-resonance method is proposed. A basic equation is obtained by the linear regression of velocity vs, strength data at specific age and then, aging factor is employed in the equation to consider the difference of the increasing rate between wave velocity and strength. Strengths predicted by the proposed equation agree well with test results. Furthermore, the combined method of rod-wave velocity and rebound index is proposed.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• 제32권3호
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• pp.118-125
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• 2021

Objectives: Childhood adversity is a risk factor for anxiety symptoms, but it affects anxiety symptoms in attention-deficit/hyperactivity disorder (ADHD). The current study aimed to examine the association between childhood adversity and anxiety symptoms in participants with and without ADHD. Methods: Data were obtained from a school-based epidemiological study of 1017 randomly selected children and adolescents. The ADHD and non-ADHD groups were divided using the Diagnostic Interview Schedule for Children Predictive Scale (DPS). The DPS was also used to assess comorbidities such as anxiety and mood disorders. The childhood adversities were assessed using the Early Trauma Inventory Self Report-Short Form, and the anxiety symptoms were assessed using the Screen for Child Anxiety Related Disorders. Linear and logistic regression models were used to investigate the association between childhood adversity and anxiety in the ADHD and non-ADHD groups with adjustments for age and sex. Results: This study found that the ADHD group did not show any significant association between anxiety symptoms and childhood adversities, whereas the non-ADHD group always showed a significant association. In a subgroup analysis of the non-ADHD group, the normal group without any psychiatric disorders assessed with DPS demonstrated a statistically significant association between childhood adversities and anxiety symptoms. These results were consistent with the association between childhood adversities and anxiety disorders assessed using DPS, as shown by logistic regression. Conclusion: The association between anxiety symptoms and childhood adversities statistically disappears in ADHD; ADHD may mask or block the association. Further longitudinal research is necessary to investigate this relationship.

• 한국산업보건학회지
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• 제32권3호
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• pp.221-230
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• 2022

Objectives: The aim of this study is to evaluate the association between shiftwork and skeletal muscle mass index in a single university health check-up. Methods: We used data from 98,227 workers who answered in a special interview on health check-up at a local university hospital from 2014 to 2020. Pearson correlation analysis was conducted for comparing the association between skeletal muscle mass index and demographic and hematological variables in shiftwork and non-shiftwork groups. Mixed linear model analysis after controlling demographic and hematological variables was used to analyze the difference of skeletal muscle mass index between groups at every visit for seven years. Results: In linear regression analysis, the variables most significantly correlated with skeletal muscle index in both groups were shiftwork(p=0.049), BMI(p<0.001), hypertension(p=0.024), platelet(p<0.001), total protein (p<0.001), AST(p=0.028), ALT(p=0.003), ALP(p<0.001), total cholesterol(p=0.002), triglyceride(p=0.019), BUN (p=0.001), creatinine(p<0.001), and uric acid(p=0.002). After the adjustment for demographic and hematologic variables, the skeletal muscle mass index at every visit was decreased both in the shiftwork group and non-shiftwork group. The slope of the shiftwork group was -0.240 and non-shiftwork group -0.149, showing a significant difference (p<0.001). Conclusions: In the shiftwork group, the skeletal muscle mass index showed a tendency to decrease markedly over time compared to the non-shiftwork group. It is presumed that shift workers' skeletal muscle health was adversely affected by changes in the biological clock due to changes in wake-up and sleep patterns, and changes in food intake.

• 한국환경보건학회지
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• 제42권4호
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• pp.280-292
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• 2016

Objectives: The objective of this study was to build COD regression models for the Han River and evaluate water quality. Methods: Water quality data sets for the dry season (as of January) during a four-year period (2012-2015) were collected from the database of the Han River automatic water quality monitoring stations. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR) were used to build five-descriptor COD models. Multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA) are useful tools for extracting meaningful information. Results: The $r^2$ of the best COD models provided significant high values (> 0.8) between 2012 and 2015. Total organic carbon (TOC) was a surrogate indicator for COD (as COD/TOC) with high reliability ($r^2=0.63$ in 2012, $r^2=0.75$ for 2013, $r^2=0.79$ for 2014 and $r^2=0.85$ for 2015). The ratios of COD/TOC were calculated as 2.08 in 2012, 1.79 in 2013, 1.52 and 1.45 in 2015, indicating that biodegradability in the water body of the Han River was being sustained, thereby further improving water quality. The BOD/COD ratio supported these findings. The cluster analysis revealed higher annual levels of microorganisms and phosphorous at stations along the Hangang-Seoul and Hantangang areas. Nevertheless, the overall water quality over the last four years showed an observable trend toward continuous improvement. These findings also suggest that non-point pollution control strategies should consider the influence of upstreams and downstreams to protect water quality in the Han River. Conclusion: This data analysis procedure provided an efficient and comprehensive tool to interpret complex water quality data matrices. Results from a trend analysis provided much important information about sources and parameters for Han River water quality management.

• 한국대기환경학회지
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• 제19권6호
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• pp.701-717
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• 2003

The objective of this study was to extensively estimate the air quality trends of the study area by surveying con-centration trends in months or seasons, after analyzing the mass concentration of PM-10 samples and the inorganic lements, ion, and total carbon in PM-10. Also, the study introduced to apply the PMF (Positive Matrix Factoriza-tion) model that is useful when absence of the source profile. Thus the model was thought to be suitable in Korea that often has few information about pollution sources. After obtaining results from the PMF modeling, the existing sources at the study area were qualitatively identified The PM-10 particles collected on quartz fiber filters by a PM-10 high-vol air sampler for 3 years (Mar. 1999∼Dec.2001) in Kyung Hee University. The 25 chemical species (Al, Mn, Ti, V, Cr, Fe, Ni, Cu, Zn, As, Se, Cd, Ba, Ce, Pb, Si, N $a^{#}$, N $H_4$$^{+}$, $K^{+}$, $Mg^{2+}$, $Ca^{2+}$, C $l^{[-10]}$ , N $O_3$$^{[-10]}$ , S $O_4$$^{2-}$, TC) were analyzed by ICP-AES, IC, and EA after executing proper pre - treatments of each sample filter. The PMF model was intensively applied to estimate the quantitative contribution of air pollution sources based on the chemical information (128 samples and 25 chemical species). Through a case study of the PMF modeling for the PM-10 aerosols. the total of 11 factors were determined. The multiple linear regression analysis between the observed PM-10 mass concentration and the estimated G matrix had been performed following the FPEAK test. Finally the regression analysis provided source profiles (scaled F matrix). So, 11 sources were qualitatively identified, such as secondary aerosol related source, soil related source, waste incineration source, field burning source, fossil fuel combustion source, industry related source, motor vehicle source, oil/coal combustion source, non-ferrous metal source, and aged sea- salt source, respectively.ively.y.