• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.765-779
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• 2022

This study evaluates the accuracy in identifying the burned area in South Korea using multi-temporal data from Sentinel-2 MSI and Landsat 8/9 OLI. Spectral indices such as the Difference Normalized Burn Ratio (dNBR), Relative Difference Normalized Burn Ratio (RdNBR), and Burned Area Index (BAI) were used to identify the burned area in the March 2022 forest fire in Uljin. Based on the results of six indices, the accuracy to detect the burned area was assessed for four satellites using Sentinel-2 and Landsat 8/9, respectively. Sentinel-2 and Landsat 8/9 produce images every 16 and 10 days, respectively, although it is difficult to acquire clear images due to clouds. Furthermore, using images taken before and after a forest fire to examine the burned area results in a rapid shift because vegetation growth in South Korea began in April, making it difficult to detect. Because Sentinel-2 and Landsat 8/9 images from February to May are based on the same date, this study is able to compare the indices with a relatively high detection accuracy and gets over the temporal resolution limitation. The results of this study are expected to be applied in the development of new indices to detect burned areas and indices that are optimized to detect South Korean forest fires.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.97-105
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• 2023

Recently, with the development of artificial intelligence technology, many technologies for user convenience are being developed. Among them, interest in autonomous vehicles is increasing day by day. Currently, many automobile companies are aiming to commercialize autonomous vehicles. In order to lay the foundation for the government's new and reasonable policy establishment to support commercialization, we tried to analyze changes and perceptions of public opinion through news article data. Therefore, in this paper, 35,891 news article data mentioning terms similar to 'autonomous vehicles' over the past three years were collected and network analyzed. As a result of the analysis, major keywords such as 'autonomous driving', 'AI', 'future', 'Hyundai Motor', 'autonomous driving vehicle', 'automobile', 'industrial', and 'electric vehicle' were derived. In addition, the autonomous vehicle industry is developing into a faster and more diverse platform and service industry by converging with various industries such as semiconductor companies and big tech companies as well as automobile companies and is paying attention to the convergence of industries. To continuously confirm changes and perceptions in public opinion, it is necessary to analyze perceptions through continuous analysis of SNS data or technology trends.

• Korean Journal of Materials Research
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• v.13 no.10
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• pp.635-639
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• 2003

We investigated dry etching of GaAs and AiGaAs in a high density planar inductively coupled plasma system with BCl$_3$and BCl$_3$/Ar gas chemistry. A detailed etch process study of GaAs and ALGaAs was peformed as functions of ICP source power, RIE chuck power and mixing ratio of $BCl_3$ and Ar. Chamber process pressure was fixed at 7.5 mTorr in this study. The ICP source power and RIE chuck power were varied from 0 to 500 W and from 0 to 150 W, respectively. GaAs etch rate increased with the increase of ICP source power and RIE chuck power. It was also found that etch rates of GaAs in $15BCi_3$/5Ar plasmas were relatively high with applied RIE chuck power compared to pure 20 sccm $BCl_3$plasmas. The result was the same as AlGaAs. We expect that high ion-assisted effect in $BCl_3$/Ar plasma increased etch rates of both materials. The GaAs and AlGaAs features etched at 20 sccm $BCl_3$and $15BCl_3$/5Ar with 300 W ICP source power, 100 W RIE chuck power and 7.5 mTorr showed very smooth surfaces(RMS roughness < 2 nm) and excellent sidewall. XPS study on the surfaces of processed GaAs also proved extremely clean surfaces of the materials after dry etching.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.

• Proceedings of the Korean Vacuum Society Conference
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• 1998.02a
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• pp.24-24
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• 1998

반도체장비를 포함하는 초고진공장비의 園훌化가 급속히 그리고 절실히 요구되고 있는 것이 현실정이다. 當面해서 실현할 국산진공장비의 대상은 廣範圍하다. 즉, 각종 진공 pump ( (rotary, dry, diffusion, cryo, ion, turbo melecular pump), 진공 chamber, 진공 line, gate valve 를 위 시 한 진공 V머ve, flange, gasket, fl않d야lU, mainpulater 퉁 진공 部品이 다. 진공계 의 핵심 은 適切하고 優良한 진공재료의 선태파 사용이다. 진공장비는 사용자가 원하는 진공도를 원하 는 시간 동안 륨空度를 유지해 주어야 한다. 진공재료 선태의 기준사항은:(1) 기체의 透過성 (2) 薰했훌 (3) 혔體放出특성 - -outgassing과 degassing- (4) 機械的 량훌度 (5) 온도 의존성 (6) 化學톡성 (7) 加I성 및 鎔接 성 (8) 課電특성 (9) 磁氣특성 (10) 高速함子 및 放射線 특성 (11) 經濟성 및 調達생 둥이 다. 우량한 초고진공계재료는 풍부하게 개발되어 왔고, 또 新材料들이 개발되고 있다. 여기에서는 주로 초고진공 내지는 극고진공계의 構造材料, 機能材料, 部品材料 일반파 몇가지 신재료의 특 성에 관해서 記述한다. M Mild SteeHSAE, 1112, 1010, 1020, 1022, etc)., S Stainless SteeHAlSI, 304, 304L, 310, 316, 321, 347): 구조재료, chamber, fl하1ges A Aluminum과 Alloys (1060, 1100, 2014, 4032, 6(뻐1): 구조재료, chamber, flanges, gaskets A AI, Al 떠loy는 SS에 代替하는 역 할올 시 작하고 있다. C Copper, Copper Alloys(C11$\alpha$)0, C26800, C61400, Cl7200): 내장인자, gasket, cryopanel, tubing T Titanium, Ziriconium, Haf띠um 및 Alloys: 특히 Ti은 10n pump 용 getter material 이 외 에 U UHV,XHV용 chamber계로서 관심올 끌고 있다. N Nickel, Nickel Alloys (200, 204, 211, monel, nichrome): 부식 방지 , 전자장치 , 자기 장치 귀 금속(Ag, Au, Pt, Pd, Rh, Ir, Os, Ru): 보조부품, gasket, filament, coating, thermocouple, 접 합부위 T TiC, SiC, zrC, HfC, TaC 둥의 탄화물과, BN, TiN, AlN 동의 질화물, 붕화물이 둥장하고 었 다. 유리: Soda Lime, Borosilicate, Potash Soda Lead: View Port, Chamber envelope C Ceramics: AlZ03, BeO, MgO, zrOz, SiOz, MgOzSiOz, 3Alz032SiOz, Z$textsc{k}$hSiOz S상N4: e electrical, thermal insulators, crucibles, boats, single crystals, sepctr려 windows 저자는 최근 저자들이 발견한 Zr-Ti-Cu-Ni-Be amorphous alloys coated cham뾰r가 radiation p proof로 이용될 수 있는 사실을 점검하고 었다 .. Z.Y. Hua 들은 Cs3Sb를 새로운 photocathode 재료로 보고하고 있다.

• Proceedings of the Korean Vacuum Society Conference
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• 2000.02a
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• pp.148-148
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• 2000

지금까지 반도체 표면에 대한 연구는 주로 (1000, (111) 표면 등 낮은 밀러 지표를 가진 표면에 대해 이루어져 왔다. 이에 반해 밀러 지표가 높은 Si 면은 불안정하고, 가열하면 다른 표면, 즉 지표가 낮은 면으로 재배열하는 경향이 있는 것으로 알려져 있는데 아직 이들 높은 밀러 지표를 가진 표면에 대한 연구는 미미한 상태이다. 그러나, Si(113)면은 밀러 지표가 높으면서도 안정하기 때문에 Si(113)의 구조를 정확하게 알 수 있다면 밀러 지표가 낮은 Si 표면이 안정한 이유를 이해할 수 있을 것이다. 따라서 본 연구에서는 TOF-CAICISS 장치(Time of Flight - CoAxial Impact Collision Ion Scattering Spectroscopy) 장비와 RHEED(Reflection High Energy Electron Diffrction)를 이용하여 Si(113) 표면의 구조와 Si(113) 표면의 온도에 따른 구조 변화를 관찰하였다. TOF-CAICISS 실험결과를 보면 (3$\times$2)에서 (3$\times$1)으로 상변환하면서 Si(113) 표면에 오각형을 이루는 dimer 원자들과 adatom 원자들간의 높이차가 작아짐을 알 수 있다. RHEED 실험결과와 전산 모사 결과로부터 상온에서 Si(113)(3$\times$2) 구조를 가지다가 45$0^{\circ}C$~50$0^{\circ}C$에서 Si(113) (3$\times$1) 구조로 상변환한다는 것을 알 수 있다. 그러나, 아직 상전이 메카니즘은 명확하게 밝혀지지 않았다. 실험결과를 전산 모사와 비교함으로써 Si(113) 표면에 [33]방향으로 이온빔을 입사시켰을 경우 dabrowski 모델과 Ranke AI 모델이 적합하지 않다는 것을 알 수 있다./TEX>, shower head의 온도는 $65^{\circ}C$로 설정하였다. 증착된 Cu 박막은 SEM, XRD, AFM를 통해 제작된 박막의 특성을 비교.분석하였다. 초기 plasma 처리를 한 경우에는 그림 1에서와 같이 현저히 증가한 초기 구리 입자들이 관측되었으며, 이는 도상 표면에 활성화된 catalytic site의 증가에 기인한다고 보여진다. 이러한 특성은 Cu films의 성장률을 향상시키고, 또한 voids를 줄여 전기적 성질 및 surface morphology를 향상시키는 것으로 나타났다. 결과 필름의 잔류 응력과 biaxial elastic modulus는 필름의 두께가 감소함에 따라 감소하는 경향을 나타냈으며, 같은 두께의 필름인 경우, 식각 깊이에 따른 biaxial elastic modulus 의 변화를 통해 최적의 식각 깊이를 알 수 있었다.도의 값을 나타내었으며 X-선 회절 data로부터 분석한 박막의 변형은 증온도에 따라 7.2%에서 0.04%로 감소하였고 이 이경향은 유전손실은 감소경향과 일치하였다.는 현저하게 향상되었다. 그 원인은 SB power의 인가에 의해 활성화된 precursor 분자들이 큰 에너지를 가지고 기판에 유입되어 치밀한 박막이 형성되었기 때문으로 사료된다.을수 있었다.보았다.다.다양한 기능을 가진 신소재 제조에 있다. 또한 경제적인 측면에서도 고부가 가치의 제품 개발에 따른 새로운 수요 창출과 수익률 향상, 기존의 기능성 안료를 나노(nano)화하여 나노 입자를 제조, 기존의 기능성 안료에 대한 비용 절감 효과등을 유도 할 수 있다. 역시 기술적인 측면에서도 특수소재 개발에 있어 최적의 나노 입자 제어기술 개발 및 나노입자를 기능성 소재로 사용하여 새로운 제품의 제조와 고압 기상 분사기술의 최적화에 의한 기능성 나노 입자 제조 기술을 확립하고 2차 오염 발생원인 유기계 항균제를 무기계 항균제로 대체할 수 있다.

• Journal of Navigation and Port Research
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• v.45 no.6
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• pp.333-345
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• 2021

Amid the rapidly changing logistics environment and demand changes in the post-corona-19 era, the importance of the cold chain logistics sector is being highlighted. The scope of cold chain is not limited to food, but is expanding to various fields such as pharmaceuticals, semiconductors, and flowers. The demand on the storage and transportation of corona vaccines is rapidly increasing. The rapid increase in domestic low-temperature facility construction and renovation may lead to the saturation of the cold chain related industry in the future and slow growth. In preparation for this, it is necessary to accumulate infrastructure know-how using IT technologies, and to consider entering into the UN procurement market as a potential niche market, by taking advantage of Korea's recent global status. The demand for cold chain in the UN procurement market is increasing mainly in underdeveloped countries, and it is expected to continue to grow. In this paper, the capabilities of domestic cold chain related companies were analyzed, domestic and overseas cold chain logistics market trends and overseas market entry status were investigated. An in-depth survey was conducted to present strategies for domestic cold chain logistics related companies to enter the UN procurement market.

• Information Systems Review
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• v.25 no.4
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• pp.183-204
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• 2023

Due to the COVID-19 pandemic, lots of occasions need to be held in online environment. This is the reason why "Metaverse" gets lots of attention in 2021. A number of companies made announcements on Metaverse, and this situation also boomed stock market. This paper investigates the relationship between Metaverse initiatives and business value of the firm (i.e., stock prices). We examine this relationship by using event study method with Lexis-Nexis News data from 2019 to 2021. The results indicate that Metaverse initiatives significantly impact positive influence on firm's value. In the technological perspective, technical factors affect more positive market returns, including Metaverse enablers (e.g., NFT, VR devices, digital twin) and common infrastructure (e.g., semiconductor, AI, cloud), and especially virtual environment was emphasized. Additionally, in the strategical perspective, radical innovation (e.g., pivoting, acquisition) impact more positive market return rather than incremental innovation (e.g., partnership, investment). Also, firms from non-service industries can achieve benefits from Metaverse initiatives rather than service industry in some degree.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.99-107
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• 2024

Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.