중한 통역에서 오유분석을 통한 효과적인 한자어지도방법 모색

오춘화(Wu Chunhua) 길림성민족사무위원회 2008 중국조선어문 Vol.156 No.-

Flame retardancy and thermal degradation behavior of red gum wood treated with hydrate magnesium chloride

Yiqiang Wu,Chunhua Yao,Yunchu Hu,Shoulu Yang,Yan Qing,Qinglin Wu 한국공업화학회 2014 Journal of Industrial and Engineering Chemistry Vol.20 No.5

Flame retardancy and thermal degradation of wood treated with magnesium chloride (MgCl2 6H2O) were investigated. Results showed that MgCl2 6H2O decreased flame intensity and heat release rate, and reduced smoke concentration and gas yield. From ambient temperature to 250 ℃, MgCl2 6H2O reduced wood combustibility by gas dilution mechanism. The chemical started to decompose at 350 8C and produced MgOHCl, in which -Cl and -Mg free radicals were generated and intervened the chain reactions of wood combustion. Hydrogen chloride gas generated promoted wood charring. MgCl2 6H2O gradually converted to MgOHCl and MgO compounds at higher temperatures, and MgO suppressed wood combustion by the wall effect mechanism.

User Identification Using Real Environmental Human Computer Interaction Behavior

( Tong Wu ),( Kangfeng Zheng ),( Chunhua Wu ),( Xiujuan Wang ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.6

In this paper, a new user identification method is presented using real environmental human-computer-interaction (HCI) behavior data to improve method usability. User behavior data in this paper are collected continuously without setting experimental scenes such as text length, action number, etc. To illustrate the characteristics of real environmental HCI data, probability density distribution and performance of keyboard and mouse data are analyzed through the random sampling method and Support Vector Machine(SVM) algorithm. Based on the analysis of HCI behavior data in a real environment, the Multiple Kernel Learning (MKL) method is first used for user HCI behavior identification due to the heterogeneity of keyboard and mouse data. All possible kernel methods are compared to determine the MKL algorithm’s parameters to ensure the robustness of the algorithm. Data analysis results show that keyboard data have a narrower range of probability density distribution than mouse data. Keyboard data have better performance with a 1-min time window, while that of mouse data is achieved with a 10-min time window. Finally, experiments using the MKL algorithm with three global polynomial kernels and ten local Gaussian kernels achieve a user identification accuracy of 83.03% in a real environmental HCI dataset, which demonstrates that the proposed method achieves an encouraging performance.

Anatomical Observations of Adventitious Bud Regeneration from Leaf Explants of Ziziphus jujube Mill. ‘Huizao’

Chunhua Ma,Xia Ye,Yanhui Chen,Jiancan Feng,Xiaoli Shang,Jidong Li,Yingxia Wu,Jianbin Hu 한국원예학회 2012 Horticulture, Environment, and Biotechnology Vol.53 No.4

The histological process of adventitious shoot regeneration from the leaf explants of Zizyphus jujuba ‘Huizao’was reported in this study. This is the first report on the detailed histological process of direct shoot regeneration from leaf explants in Z. jujube. Shoot regeneration was obtained from woody plant medium (WPM) supplemented with 2.27 μM thidiazuron (TDZ), 1.07 μM α-naphthalene-acetic acid (NAA) and 2.94 μM silver nitrate (AgNO3) for 10days in the dark followed by 3 weeks at a 14 hours photoperiod. The adventitious buds mostly formed from leaf veins and petioles, and the further histological studies revealed that there were multiple vascular bundles around leaf veins and the adaxial side of explants, and the adventitious buds directly originated from the parenchymatous cells around the vascular bundles without the intervening callus phase. After 3 days of culture, the parenchymatous cells started dividing and meristemoids formed thereafter. The meristematic cells continued division and subsequently gave rise to bud primordia. Well-developed shoot buds through direct organogenesis was achieved after 20 days of culture.

A Facile and Efficient Synthesis of Dronedarone Hydrochloride

Feng Li,Chunhua Jin,Jianwei Zou,Jun Wu 대한화학회 2014 Bulletin of the Korean Chemical Society Vol.35 No.7

A facile and efficient synthesis of dronedarone hydrochloride starting from commercially available 4- nitrophenol is described. This approach features a tandem-type synthesis of 3-carbonylated benzofuran involving cyclization of 2-ethynylphenol followed by CO2 fixation at the 3-position of the benzofuran ring mediated by potassium carbonate without the addition of any transition metal catalyst.

Transcriptional profiling of mouse cavernous pericytes under high-glucose conditions: Implications for diabetic angiopathy

윤국남,Jitao Wu,Yuanshan Cui,Chunhua Li,Lei Shi,Zhen-Li Gao,서준규,류지간,Hai-Rong Jin 대한비뇨의학회 2021 Investigative and Clinical Urology Vol.62 No.1

Purpose: Penile erection requires integrative interactions between vascular endothelial cells, pericytes, smooth muscle cells, and autonomic nerves. Furthermore, the importance of the role played by pericytes in the pathogenesis of angiopathy has only recently been appreciated. However, global gene expression in pericytes in diabetes mellitus-induced erectile dysfunction (DMED) remains unclear. We aimed to identify potential target genes related to DMED in mouse cavernous pericytes (MCPs). Materials and Methods: Mouse cavernous tissue was allowed to settle under gravity in collagen I-coated dishes, and sprouted cells were subcultivated for experiments. To imitate diabetic conditions, MCPs were treated with normal-glucose (NG, 5 mM) or high-glucose (HG, 30 mM) media for 3 days. Microarray technology was used to evaluate gene expression profiles, and RT-PCR was used to validate sequencing data. Histological examinations and Western blot were used to validate final selected target genes related to DMED. Results: Decreased tube formation and increased apoptosis were detected in MCPs exposed to the HG condition. As shown by microarray analysis, the gene expression profiles of MCPs exposed to the NG or HG condition differed. A total of 2,523 genes with significantly altered expression were classified into 15 major gene categories. After further screening based on gene expression and RT-PCR and histologic results, we found that Hebp1 gene expression was significantly diminished under the HG condition and in DM mice. Conclusions: This gene profiling study provides new potential targets responsible for diabetes in MCPs. Validation studies suggest that Hebp1 may be a suitable biomarker for DMED.

Feature Selection to Mine Joint Features from High-dimension Space for Android Malware Detection

( Yanping Xu ),( Chunhua Wu ),( Kangfeng Zheng ),( Xinxin Niu ),( Tianling Lu ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.9

Android is now the most popular smartphone platform and remains rapid growth. There are huge number of sensitive privacy information stored in Android devices. Kinds of methods have been proposed to detect Android malicious applications and protect the privacy information. In this work, we focus on extracting the fine-grained features to maximize the information of Android malware detection, and selecting the least joint features to minimize the number of features. Firstly, permissions and APIs, not only from Android permissions and SDK APIs but also from the developer-defined permissions and third-party library APIs, are extracted as features from the decompiled source codes. Secondly, feature selection methods, including information gain (IG), regularization and particle swarm optimization (PSO) algorithms, are used to analyze and utilize the correlation between the features to eliminate the redundant data, reduce the feature dimension and mine the useful joint features. Furthermore, regularization and PSO are integrated to create a new joint feature mining method. Experiment results show that the joint feature mining method can utilize the advantages of regularization and PSO, and ensure good performance and efficiency for Android malware detection.

A Framework of Granular Computing Clustering Algorithms

Hongbing Liu,Chunhua Liu,Chang-an Wu 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.12

A framework of granular computing clustering algorithms is proposed in the paper. Firstly, granules are represented as the normal forms, such the diamond granule in 2-domensional space and hyperdiamond granule in N-dimensional space, sphere granule in 2-dimensional space and hypersphere granule in N-dimensional space. Secondly, operations between two granules are designed to realize the transformation between two spaces with different granularities. Thirdly, the threshold of granularity is used to control the join process between two granules. The performance of granular computing algorithms is evaluated by the experimental results on the data sets selected from machine learning repository.

Fuzzy Cube Granule Structure for Image Segmentation

Hongbing Liu,Chunhua Liu,Chang-an Wu,Jun Huang 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.6

Fuzzy Cube Granule Structure (FCGS) for image segmentation is proposed in the paper. Firstly, the atomic cube granule is represented as the vector including the YCbCr values of pixel of color image and radii 0. Secondly, the join operation between two cube granules is designed to obtain the larger cube granule. Thirdly, the FCGS is formed by the fuzzy inclusion measure defined by join operation and the user-defined granularity threshold . Global Consistency Error (GCE), Variation of Information (VI), Rand Index (RI) are used to evaluate the segmentations. Images selected from BSD300 are used to verify the feasibility of FCGS.

A Nature-inspired Multiple Kernel Extreme Learning Machine Model for Intrusion Detection

( Yanping Shen ),( Kangfeng Zheng ),( Chunhua Wu ),( Yixian Yang ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.2

The application of machine learning (ML) in intrusion detection has attracted much attention with the rapid growth of information security threat. As an efficient multi-label classifier, kernel extreme learning machine (KELM) has been gradually used in intrusion detection system. However, the performance of KELM heavily relies on the kernel selection. In this paper, a novel multiple kernel extreme learning machine (MKELM) model combining the ReliefF with nature-inspired methods is proposed for intrusion detection. The MKELM is designed to estimate whether the attack is carried out and the ReliefF is used as a preprocessor of MKELM to select appropriate features. In addition, the nature-inspired methods whose fitness functions are defined based on the kernel alignment are employed to build the optimal composite kernel in the MKELM. The KDD99, NSL and Kyoto datasets are used to evaluate the performance of the model. The experimental results indicate that the optimal composite kernel function can be determined by using any heuristic optimization method, including PSO, GA, GWO, BA and DE. Since the filter-based feature selection method is combined with the multiple kernel learning approach independent of the classifier, the proposed model can have a good performance while saving a lot of training time.