• Bulletin of the Korean Chemical Society
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• v.23 no.8
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• pp.1149-1153
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• 2002

CH4/CO2 dry reforming was carried out to make syn gas on the Ni/Al2O3 catalysts calcined at different temperatures. The Ni/Al2O3 (850 $^{\circ}C)$ catalyst gave good activity and stability w hereas the Ni/Al2O3 $(450^{\circ}C)$ catalyst showed lower activity and stability. The NiO/Al2O3 catalyst calcined at $850^{\circ}C$ for 16 h (Ni/Al2O3 $(850^{\circ}C))$ formed the spinel structure of nickel aluminate, which was confirmed by TPR. The carbon formation rate on the Ni/Al2O3 $(850^{\circ}C)$ catalyst was very low till 20 h, and then steeply increased with reaction time without decreasing the activity for CH4 reforming. The Ni/Al2O3 $(450^{\circ}C)$ catalyst showed high carbon formation rate at the initial reaction time and then, the rate nearly stopped with continuous decreasing the activity for CH4 reforming. Even though the amount of carbon deposition on the Ni/Al2O3 $(850^{\circ}C)$ catalyst was higher than that on the Ni/Al2O3 $(450^{\circ}C)$ catalyst, the activity for CH4ing was also high, which could be attributed to the different type of the carbon formed on the catalyst surface.

• BMB Reports
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• v.38 no.3
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• pp.354-359
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• 2005

Open reading frame 29 (ha29) is a gene specific for Helicoverpa armigera single-nucleocapsid nucleopolyhedrovirus (HearSNPV). Sequence analyses showed that the transcription factor Tfb2 motif, bromodomain and Half-A-TPR (HAT) repeat were present at aa 66-82, 4-76, 55-90 of the Ha29 protein respectively. The product of Ha29 was detected in HearSNPV-infected HzAM1 cells at 3 h post-infection. Western blot analysis using a polyclonal antibody produced by immunizing a rabbit with purified GST-Ha29 fusion protein indicates that Ha29 is an early gene. The size of Ha29 product in infected HzAM1 cells was about 25 kDa, which was larger than the presumed size of 20.4 kDa. Tunicamycin treatment of HearSNPV-infected HzAM1 cells suggested that the Ha29 protein is N-glycosylated. Fluorescent confocal laser scanning microscope examination, and Western blot analysis of purified budded virus (BVs), occlusion-derived virus (ODVs), cell nuclear and cytoplasmic fraction, showed that the Ha29 protein was localized in the nucleus. Our results suggested that ha29 of HearSNPV encodes a non-structurally functional protein that may be associated with virus gene transcription in Helicoverpa hosts.

• Transactions of the Korean hydrogen and new energy society
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• v.21 no.5
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• pp.412-418
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• 2010

Steam reforming of n-hexadecane, a major component of diesel over Ni-based hydrotalcite-like catalyst was carried out at $900^{\circ}C$ at atmospheric pressure with space velocity of $10,000h^{-1}$ and feed molar ratio of steam/carbon=3.0. Ni-based hydrotalcite catalyst was prepared by a solid phase crystallization (spc) method and characterized by $N_2$-physisorption, CO chemisorption, TPR., XRD, and TEM techniques. It was found that spc Ni/MgAl catalyst showed higher catalytic stability and inhibition of carbon formation than Ni/$\gamma-Al_2O_3$ catalyst under the tested conditions. The results suggest that the modified spc-Ni/MgAl catalyst after optimization may be applied for the SR reaction of diesel.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.494-496
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• 2017

본 논문에서는 기존의 회전근개 건 파열 추출 방법을 개선하기 위하여 초음파 영상에서 환자 정보를 제거하여 ROI 영역을 추출한다. 추출된 ROI 영역에서 명암 대비를 강조하기 위해 기존의 사다리꼴 형태의 퍼지 스트레칭 기법에서 소속 함수를 개선한 퍼지 스트레칭 기법을 적용하여 힘줄과 연골 영역을 효과적으로 강조한다. 강조된 ROI 영역에서 Max-Min 이진화와 8방향 윤곽선 추적 기법 및 Monoton Cubic Spline 기법을 적용한 후에 라벨링 기법을 적용하여 힘줄 및 연골 영역을 추출한다. 추출된 힘줄과 연골 영역을 이용하여 회전근개 영역을 추출한다. 추출한 회전근개 영역에 SOM 기반 양자화 기법을 적용하여 회전근개 건 파열 영역을 추출한다. 제안된 회전근개 건 파열 영역 추출 방법을 다양한 초음파 회전근개 건 파열 영상을 대상으로 실험한 결과, 제안된 회전근개 건 파열 영역이 기존의 추출 방법보다 TPR 값이 증가되어 회전근개 건 파열 분석에 효과적인 것을 확인할 수 있었다.

• Journal of Information Processing Systems
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• v.10 no.2
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• pp.314-333
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• 2014

When analyzing default predictions in real estate companies, the number of non-defaulted cases always greatly exceeds the defaulted ones, which creates the two-class imbalance problem. This lowers the ability of prediction models to distinguish the default sample. In order to avoid this sample selection bias and to improve the prediction model, this paper applies a minority sample generation approach to create new minority samples. The logistic regression, support vector machine (SVM) classification, and neural network (NN) classification use an imbalanced dataset. They were used as benchmarks with a single prediction model that used a balanced dataset corrected by the minority samples generation approach. Instead of using prediction-oriented tests and the overall accuracy, the true positive rate (TPR), the true negative rate (TNR), G-mean, and F-score are used to measure the performance of default prediction models for imbalanced dataset. In this paper, we describe an empirical experiment that used a sampling of 14 default and 315 non-default listed real estate companies in China and report that most results using single prediction models with a balanced dataset generated better results than an imbalanced dataset.

• Journal of Industrial and Engineering Chemistry
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• v.68
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• pp.69-78
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• 2018

A study has been carried out of the regenerability of a commercial Ni catalyst used in the steam reforming of the volatiles from biomass pyrolysis (gases and bio-oil), determining the evolution of the reaction indices (conversion, product yields and $H_2$ production) in successive reaction-regeneration cycles. The causes of catalyst deactivation (coke deposition and Ni sintering) have been ascertained characterizing the deactivated and regenerated catalysts by TPO, TEM, TPR and XRD. Catalyst activity is not fully recovered by coke combustion in the first cycles due to the irreversible deactivation by Ni sintering, but the catalyst reaches a pseudo-stable state beyond the fourth cycle, reproducing its behaviour in subsequent cycles.

• Korean Journal of Chemical Engineering
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• v.35 no.12
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• pp.2365-2378
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• 2018

We investigated the correlation between vanadium surface density and VOx structure species in the selective catalytic reduction of NOx by $NH_3$. The properties of the $VOx/TiO_2$ catalysts were investigated using physicochemical measurements, including BET, XRD, Raman spectroscopy, FE-TEM, UV-visible DRS, $NH_3-TPD$, $H_2-TPR$, $O_2-On/Off$. Catalysts were prepared using the wet impregnation method by supporting 1.0-3.0 wt% vanadium on $TiO_2$ thermally treated at various calcination temperatures. Through the above analysis, we found that VOx surface density was $3.4VOx/nm^2$, and the optimal V loading amounts were 2.0-2.5 wt% and the specific surface area was $65-80m^2/g$. In addition, it was confirmed that the optimal VOx surface density and formation of vanadium structure species correlated with the reaction activity depending on the V loading amounts and the specific surface area size.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.389-396
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• 2022

Smartphones are growing more susceptible as technology develops because they contain sensitive data that offers a severe security risk if it falls into the wrong hands. The Android OS includes permissions as a crucial component for safeguarding user privacy and confidentiality. On the other hand, mobile malware continues to struggle with permission misuse. Although permission-based detection is frequently utilized, the significant false alarm rates brought on by the permission-based issue are thought to make it inadequate. The present detection method has a high incidence of false alarms, which reduces its ability to identify permission-based attacks. By using permission features with intent, this research attempted to improve permission-based detection. However, it creates an excessive number of features and increases the likelihood of false alarms. In order to generate the optimal number of features created and boost the quality of features chosen, this research developed an intersection feature approach. Performance was assessed using metrics including accuracy, TPR, TNR, and FPR. The most important characteristics were chosen using the Correlation Feature Selection, and the malicious program was categorized using SVM and naive Bayes. The Intersection Feature Technique, according to the findings, reduces characteristics from 486 to 17, has a 97 percent accuracy rate, and produces 0.1 percent false alarms.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.350-358
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• 2021

In modern years, the performance of the students is analysed with lot of difficulties, which is a very important problem in all the academic institutions. The main idea of this paper is to analyze and evaluate the academic performance of the college students with bipolar disorder by applying data mining classification algorithms using Jupiter Notebook, python tool. This tool has been generally used as a decision-making tool in terms of academic performance of the students. The various classifiers could be logistic regression, random forest classifier gini, random forest classifier entropy, decision tree classifier, K-Neighbours classifier, Ada Boost classifier, Extra Tree Classifier, GaussianNB, BernoulliNB are used. The results of such classification model deals with 13 measures like Accuracy, Precision, Recall, F1 Measure, Sensitivity, Specificity, R Squared, Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, TPR, TNR, FPR and FNR. Therefore, conclusion could be reached that the Decision Tree Classifier is better than that of different algorithms.