PURPOSES : To efficiently manage pavements, a systematic pavement management system must be established based on regional characteristics. Suppose that the future conditions of a pavement section can be predicted based on data obtained at present. In this case, a more reasonable road maintenance strategy should be established. Hence, a prediction model of the annual surface distress (SD) change for national highway pavements in Gangwon-do, Korea is developed based on influencing factors. METHODS : To develop the model, pavement performance data and influencing factors were obtained. Exploratory data analysis was performed to analyze the data acquired, and the results show that the data were preprocessed. The variables used for model development were selected via correlation analysis, where variables such as surface distress, international roughness index, daily temperature range, and heat wave days were used. Best subset regression was performed, where the candidate model was selected from all possible subsets based on certain criteria. The final model was selected based on an algorithm developed for rational model selection. The sensitivity of the annual SD change was analyzed based on the variables of the final model. RESULTS : The result of the sensitivity analysis shows that the annual SD change is affected by the variables in the following order: surface distress ˃ heat wave days ˃ daily temperature range ˃ international roughness index. CONCLUSIONS : An annual SD change prediction model is developed by considering the present performance, traffic volume, and climatic conditions. The model can facilitate the establishment of a reasonable road maintenance strategy. The prediction accuracy can be improved by obtaining additional data, such as the construction quality, material properties, and pavement thickness.

In this study, surface roughness and interfacial defect characteristics were analyzed after forming a high-k oxide film on the surface of a prime wafer and a test wafer, to study the possibility of improving the quality of the test wafer. As a result of checking the roughness, the deviation in the test after raising the oxide film was 0.1 nm, which was twice as large as that of the Prime. As a result of current-voltage analysis, Prime after PMA was 1.07 × 10 A/cm2 and Test was 5.61 × 10 A/cm2, which was about 5 times lower than Prime. As a result of analyzing the defects inside the oxide film using the capacitancevoltage characteristic, before PMA Prime showed a higher electrical defect of 0.85 × 1012 cm2 in slow state density and 0.41 × 1013 cm2 in fixed oxide charge. However, after PMA, it was confirmed that Prime had a lower defect of 4.79 × 1011 cm2 in slow state density and 1.33 × 1012 cm2 in fixed oxide charge. The above results confirm the difference in surface roughness and defects between the Test and Prime wafer.

PURPOSES : The surface distress of asphalt pavements is one of the major factors affecting the safety of road users. The aim of this study was to analyze the factors influencing the occurrence of surface distress and statistically predict its annual change to contribute to more reasonable asphalt pavement management using the data periodically collected by the national highway pavement data management system. METHODS : In this study, the factors that were expected to influence the surface distress were determined by reviewing the literature. The normality was secured by changing the forms of the variables to make the distribution of the variables got closer to normal distribution. In addition, min-max normalization was performed to minimize the effect of the unit and magnitude of the candidate independent variables on the dependent variable. The final candidate independent variables were determined by analyzing the correlation between the annual surface distress change and each candidate independent variable. In addition, a prediction model was developed by performing data grouping and multi-regression analysis. RESULTS : An annual surface distress change prediction model was developed using present surface distress, age, and below 0 ℃ days as the independent variables. As a result of sensitivity analysis, the surface distress affected the annual surface distress change the most. The positive correlation between the dependent variable and each independent variable demonstrated engineering and statistical meaningfulness of the prediction model. CONCLUSIONS : The surface distress in the future can be predicted by applying the annual surface distress prediction model to the national highway asphalt pavement sections with survey data. In addition, the prediction model can be applied to the national highway pavement condition index (NHPCI) evaluating the national highway asphalt pavement conditions to be used in the prediction of future NHPCI.

Most structures require high reliability to ensure safety and soundness. The materials used for these structures are not only defective in the manufacturing process and construction process, but also cause generation and progress of defects due to operation of various complex use environments. In order to improve the reliability of the structure, it is very important to detect and estimate the defect size. The method of evaluating these defects without damaging the structure is a non-destructive method. In this paper, an aluminum probe of AC potential drop(ACPD) method is applied to the evaluation of two-dimensional artificial defects in ferromagnetic materials. Since the potential drop of the defect end is larger than that of the sound area, the defect can be detected and its position can be clearly confirmed, and the potential drops are changed according to the depth of the defect. The potential drop ratio (Vjmax/Vs) of the defective area has a large value for the defect. The relationship between the potential drop ratio (Vjmax/Vs) of 10 kHz and the defect depth can reduce the error in predicting the depth.

In this study, the surface passivation process for InP-based quantum dots (QDs) is investigated. Surface coating is performed with poly(methylmethacrylate) (PMMA) and thioglycolic acid. The quantum yield (QY) of a PMMA-coated sample slightly increases by approximately 1.3% relative to that of the as-synthesized InP/ZnS QDs. The QYs of the uncoated and PMMA-coated samples drastically decrease after 16 days because of the high defect state density of the InP-based QDs. PMMA does not have a significant effect on the defect passivation. Thioglycolic acid is investigated in this study for the effective surface passivation of InP-based QDs. Surface passivation with thioglycolic acid is more effective than that with the PMMA coating, and the QY increases from 1.7% to 11.3%. ZnS formed on the surface of the InP QDs and S in thioglycolic acid show strong bonding property. Additionally, the QY is further increased up to 21.0% by the photochemical reaction. Electron–hole pairs are formed by light irradiation and lead to strong bonding between the inorganic and thioglycolic acid sulfur. The surface of the InP core QDs, which does not emit light, is passivated by the irradiated light and emits green light after the photochemical reaction.

Quantum dots (QDs) are capable of controlling the typical emission and absorption wavelengths because of the bandgap widening effect of nanometer-sized particles. These phosphor particles have been used in optical devices, photovoltaic devices, advanced display devices, and several biomedical complexes. In this study, we synthesize ZnSe QDs with controlled surface defects by a heating-up method. The optical properties of the synthesized particles are analyzed using UV-visible and photoluminescence (PL) measurements. Calculations indicate nearly monodisperse particles with a size of about 5.1 nm at 260℃ (full width at half maximum = 27.7 nm). Furthermore, the study results confirm that successful doping is achieved by adding Eu3+ preparing the growth phase of the ZnSe:Eu QDs when heating-up method. Further, we investigate the correlation between the surface defects and the luminescent properties of the QDs.

결함검사는 생산공정에 있어서 상품의 디자인과 함께 매우 중요한 부분으로서, 상품의 경쟁력을 높이는데 필수 불가결한 것이다. 만약, 실시간 결함검출이 상품에 대한 어떤 손상도 없이 할 수 있다면, 품질 및 공정의 효율적 관리와 고비용 인력의 절감을 통하여 생산원가를 줄일 수 있다. 본 논문에서는 철판과 같은 표면에 결함이 있는 경우 필요한 정보만을 추출할 수 있는 3가지 공간필터법에 대하여 제안하였고, 공간필터의 특성을 통하여 결함검출 시스템을 구성하였다. 그리고, 최적의 표면결함 계측용 공간필터법을 개발하기 위하여 결함의 크기와 형태, 광도의 크기 및 외부 광간섭 그리고 슬리트의 개수와 같은 파라메타의 변화에 따른 측정 성능을 비교 및 분석하였다.