Crystallization Behaviors and Morphology of Novel Poly(octamethylene adipate-cooctamethylene succinate) and Poly(octamethylene adipate)

Xiaojing Li,Zhaobin Qiu 한국고분자학회 2015 Macromolecular Research Vol.23 No.7

Crystallization behaviors and morphology of novel biodegradable poly(octamethylene adipate-cooctamethylene succinate) (POAS) copolymers with different octamethylene succinate (OS) contents and their parent homopolymer poly(octamethylene adipate) (POA) were extensively investigated. Compared to POA, increasing the OS unit does not modify the crystal structures but slightly decreases the crystallinity values of POAS. The glass transition temperature values of POAS are greater than that of POA. Both the nonisothermal crystallization peak temperature and melting point temperature values of POAS decrease gradually with the increment of the OS unit. The overall isothermal melt crystallization rates of POAS decrease with increasing crystallization temperature and the OS content, while the crystallization mechanism does not change. The equilibrium melting point values of POAS are reduced with increasing the OS content, with respect to POA. The nucleation densities of POAS spherulites are reduce significantly; moreover, increasing crystallization temperature and the OS content reduces the spherulitic growth rates of POAS, relative to POA.

Candidate Pruning-Based Differentially Private Frequent Itemsets Mining

Yangyang Xu,Zhaobin Liu,Zhonglian Hu,Zhiyang Li 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.7

Frequent Itemsets Mining(FIM) is a typical data mining task and has gained much attention. Due to the consideration of individual privacy, various studies have been focusing on privacy-preserving FIM problems. Differential privacy has emerged as a promising scheme for protecting individual privacy in data mining against adversaries with arbitrary background knowledge. In this paper, we present an approach to exploring frequent itemsets under rigorous differential privacy model, a recently introduced definition which provides rigorous privacy guarantees in the presence of arbitrary external information. The main idea of differentially privacy FIM is perturbing the support of item which can hide changes caused by absence of any single item. The key observation is that pruning the number of unpromising candidate items can effectively reduce noise added in differential privacy mechanism, which can bring about a better tradeoff between utility and privacy of the result. In order to effectively remove the unpromising items from each candidate set, we use a progressive sampling method to get a super set of frequent items, which is usually much smaller than the original item database. Then the sampled set will be used to shrink candidate set. Extensive experiments on real data sets illustrate that our algorithm can greatly reduce the noise scale injected and output frequent itemsets with high accuracy while satisfying differential privacy.

Differential Privacy via Weighted Sampling Set Cover

Zhonglian Hu,Zhaobin Liu,Yangyang Xu,Zhiyang Li 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.4

Differential privacy is a security guarantee model which widely used in privacy preserving data publishing, but the query result can’t be used in data research directly, especially in high-dimensional datasets. To address this problem, we propose a dimensionality reduction method. The core idea of this method is using a series of low-dimensional datasets to reconstruct a high-dimensional dataset, it improves data availability eventually. The main issue of this method is the reconstruction integrity, so a special sampling via set cover model is proposed in this article, which builds a multidimensional composite marginal tables set as a new middleware in differential privacy model. As a result, any form of disjunctive queries can be answered, and the accuracy of data query is improved. The experiment results also show the effectiveness of our method in practice.

FRChain: A Blockchain-based Flow-Rules-oriented Data Forwarding Security Scheme in SDN

( Weichen Lian ),( Zhaobin Li ),( Chao Guo ),( Zhanzhen Wei ),( Xingyuan Peng ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.1

As the next-generation network architecture, software-defined networking (SDN) has great potential. But how to forward data packets safely is a big challenge today. In SDN, packets are transferred according to flow rules which are made and delivered by the controller. Once flow rules are modified, the packets might be redirected or dropped. According to related research, we believe that the key to forward data flows safely is keeping the consistency of flow rules. However, existing solutions place little emphasis on the safety of flow rules. After summarizing the shortcomings of the existing solutions, we propose FRChain to ensure the security of SDN data forwarding. FRChain is a novel scheme that uses blockchain to secure flow rules in SDN and to detect compromised nodes in the network when the proportion of malicious nodes is less than one-third. The scheme places the flow strategies into blockchain in form of transactions. Once an unmatched flow rule is detected, the system will issue the problem by initiating a vote and possible attacks will be deduced based on the results. To simulate the scheme, we utilize BigchainDB, which has good performance in data processing, to handle transactions. The experimental results show that the scheme is feasible, and the additional overhead for network performance and system performance is less than similar solutions. Overall, FRChain can detect suspicious behaviors and deduce malicious nodes to keep the consistency of flow rules in SDN.

Investigating chip morphology and its characteristics in the high-speed milling of a Ti-6Al-4V thin plate

Lida Zhu,Jijiang Wu,Zhaobin Li,Changfu Liu 대한기계학회 2015 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.29 No.10

Titanium alloy thin-plates have been widely used in the aerospace, automotive and biomedical industries, to name a few. The chipmorphologies and material properties of the thin plate were investigated under different cutting parameters in this paper. First, to furtherunderstand the variability and inherent rules of chip morphology, the characteristics of multi-surface chips (free, back, and cross-sectionsurfaces) were observed and studied by using a scanning electron microscope. The sawtooth frequency and degree of segmentation wereanalyzed through the geometrical characteristics of a serrated chip. Second, variations in the chip microhardness and the wear of the millcutter were measured and investigated under different machining parameters. Results show that the hardenability increases with the increasein cutting speed and the shear band shows higher microhardness than in other parts. In addition, the degree of insert wear decreaseswith proper cutting speed and feed. Some key conclusions and future work about high-speed milling thin-plate Ti-6Al-4V aregiven.

Abnormal Behavior Recognition Based on Spatio-temporal Context

Yuanfeng Yang,Lin Li,Zhaobin Liu,Gang Liu 한국정보처리학회 2020 Journal of information processing systems Vol.16 No.3

This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes whereanomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects’behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial contextof local behavior and the temporal context of global behavior in two different stages. In the first stage of topicmodeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporalcorrelations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation(LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each videoclip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the secondphase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular,an abnormal behavior recognition method was developed based on the learned spatio-temporal context ofbehaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomalyrecognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performedusing the validity of spatio-temporal context learning for local behavior topics and abnormal behaviorrecognition. Furthermore, the performance of the proposed approach in abnormal behavior recognitionimproved effectively and significantly in complex surveillance scenes.

Toxoplasma gondii Induces Apoptosis via Endoplasmic Reticulum Stress-Derived Mitochondrial Pathway in Human Small Intestinal Epithelial Cell-Line

Hao Wang,Chunchao Li,Wei Ye,Zhaobin Pan,Jinhui Sun,Mingzhu Deng,Weiqiang Zhan,Jiaqi Chu 대한기생충학열대의학회 2021 The Korean Journal of Parasitology Vol.59 No.6