본 연구는 전산화단층촬영에서 간 질환의 자동 인식으로 질감특징분석(texture feature analysis. TFA) 알고리즘을 제안하고자 하였으며, 간세포암(Hepatocellular carcinoma. HCC)에 대한 컴퓨터보조진단(computer-aided diagnosis.CAD) 시스템을 설계하고, 제안하는 각 알고리즘의 성능을 평가하고자 하였다. HCC 영상에서 분석영역(40×40 픽셀)을 설정하고 각 부분영상에 통계적 특징을 이용한 6가지 TFA 파라메터(평균 밝기, 평균 대조도, 평탄도, 왜곡도, 균일도, 엔트로피)비교하여 간세포암 인식률(recognition rate)을 구하였다. 결과적으로 TFA는 간세포암 인식률을 나타내는 척도로 유의함을 알 수 있었으며 6가지 파라메터에서 균일도가 가장 인식률이 높았으며 평균 대조도, 평탄도, 왜곡도가 비교적 높았고 평균 밝기와 엔트로피는 상대적으로 낮은 인식률을 나타내었다. 이와 관련하여 높은 인식률을 보인 알고리즘(최대 97.14%, 최소 82.86%)을 간세포암 영상의 병변을 판별하여 임상의 조기 진단을 보조하여 치료를시행한다면 진단의 효율성이 높아 질 것으로 판단되었으며, 향후 효율적이고 정량적인 분석을 추가함으로써 질병인식의 일반화에 대한 기준 연구가 필요 할 것으로 사료되었다.

Catalytic activities of V2O5/TiO2 catalyst were investigated under reaction conditions such as reaction temperature, catalyst size, inlet concentration and space velocity. A 1,2-dichlorobenzene(1,2-DCB) concentrations were measured in front and after of the heated V2O5/TiO2 catalyst bed, and conversion efficiency of 1,2-DCB was determined from it's concentration difference. The conversion of 1,2-DCB using a pellet type catalyst in the bench-scale reactor was lower than that with the powder type used in the micro flow-scale reactor. However, when the pellet size was halved, the conversion was similar to that with the powder type catalyst. The highest conversion was shown with an inlet concentration of 100 ppmv, but when the concentration was higher or lower than 100 ppmv, the conversion was found to decrease. Complete conversion was obtained when the GHSV was maintained at below 10,000 h-1, even at the relatively low temperature of 250°C. Water vapor inhibited the conversion of 1,2-DCB, which was suspected to be due to the competitive adsorption between the reactant and water for active sites.

In this study, the fundamental experiments were performed for catalytic oxidation of NO (50 ppm) on MnO2 in the presence of ozone. The experiments were carried out at various catalytic temperatures (30-120℃) and ozone concentrations (50-150 ppm) to investigate the behavior of NO oxidation. The honeycomb type MnO2 catalyst was rectangular with a cell density of 300 cells per squuare inch. Due to O3 injection, NO reacted with O3 to form NO2, which was adsorbed at the MnO2 surface. The excessive ozone was decomposed to O* onto the MnO2 catalyst bed, and then that O* was reacted with NO2 to form NO3-. It was found that the optimal O3/NO ratio for catalytic oxidation of NO on MnO2 was 2.0, and the NO removal efficiency on MnO2 was 83% at 30℃. As a result, NO was converted mainly to NO3-.