A Social Motivation-aware Mobility Model for Mobile Opportunistic Networks

( Sen Liu ),( Xiaoming Wang ),( Lichen Zhang ),( Peng Li ),( Yaguang Lin ),( Yunhui Yang ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.8

In mobile opportunistic networks (MONs), human-carried mobile devices such as PDAs and smartphones, with the capability of short range wireless communications, could form various intermittent contacts due to the mobility of humans, and then could use the contact opportunity to communicate with each other. The dynamic changes of the network topology are closely related to the human mobility patterns. In this paper, we propose a social motivation-aware mobility model for MONs, which explains the basic laws of human mobility from the psychological point of view. We analyze and model social motivations of human mobility mainly in terms of expectancy value theory and affiliation motivation. Furthermore, we introduce a new concept of geographic functional cells, which not only incorporates the influence of geographical constraints on human mobility but also simplifies the complicated configuration of simulation areas. Lastly, we validate our model by simulating three real scenarios and comparing it with reality traces and other synthetic traces. The simulation results show that our model has a better match in the performance evaluation when applying social-based forwarding protocols like BUBBULE.

Anomaly Driving Speed Detection and Correction Algorithm based on Quantiles and KNN

Guo Yanling,Liu Lichen,Gao Meng,Gao Lewen 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.3

Driving speed is a key parameter for building the traffic state identification model, its precision directly affects the model reliability and the traffic state identification accuracy. Aiming at the standard normal deviation method’s defects in dealing with the extreme noise data, an anomaly driving speed detection algorithm based on quantiles is proposed, use historical data to establish the exception borders which are used to detect whether an unknown data is abnormal; on the basis of the abnormal data detection, a driving speed prediction algorithm based on improved KNN is proposed, use K-means algorithm to clustering the historical data, and predict the next moment’s speed according to the distance between the data to be predicted and the clusters, the predicted speed can be used to correct the abnormal speed. Experimental results show that the detection rate of the proposed anomaly detection algorithm has improved about 4.25% compared with the standard normal deviation method, and the false detection rate has reduced about 25.51%; the mean relative error of the proposed speed prediction algorithm is 13.69%, it can predict the driving speed well, namely, the anomaly driving speed detection and correction algorithm based on quantiles and KNN is feasible and effective.

Urban Road Traffic State Identification Algorithm Based On Particle Filter and Fuzzy Discrimination

Guo Yanling,Liu Lichen,Gao Meng,Gao Lewen 보안공학연구지원센터 2015 International Journal of Smart Home Vol.9 No.8

Urban road traffic state identification is a key link to realize the intelligent transportation based on the Internet of Vehicles, and accurately positioning vehicles is the foundation to realize the traffic state identification. Aiming at the problem that GPS has signal blind area in positioning vehicles, a vehicle positioning algorithm based on particle filter was proposed, it could improve the traditional algorithm on degradation and large amount of calculations; Based on vehicle positioning, an urban road traffic state identification algorithm based on fuzzy discrimination was proposed, it could comprehensively consider multiple factors’ influence on traffic state. The experiment results show that the improved particle filter algorithm’s mean squared error has increased about 55.437% compared with GPS method, and the traffic state identification algorithm can accurately identify the traffic state of the study area, it can prove that the urban road traffic state identification algorithm based on particle filter and fuzzy discrimination is feasible and effective.

A Regional Forest Tree Layer Biomass Estimation Method Based on Clustering Analysis

Wang Nihong,Gao Meng,Liu Lichen,Gao Lewen 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.1

As an area always contains varies of tree spices or forest types, therefore, when using biomass estimation model based on single tree or forest stand to estimate regional biomass, the modeling workload is big, and the existing models do not adequately reflect the factors that influence the biomass. Aiming at the problems above, this paper proposes a regional forest tree layer biomass estimation method based on clustering analysis, using the forest resources survey data of the study area as the research object, using principal component analysis to extract characteristic factors from 17 indexes, using the improved K-means algorithm to clustering the forest subcompartment, and using support vector regression algorithm to separately build the biomass estimation model based on clusters. The results show that 8 principal components can reflect over 80% information of the original data; the subcompartment of the study area can be divided into 6 classes, the coefficients of each model are ranging from 0.7 to 0.92, the average relative error absolute values of each model are ranging from 11.173% to 23.583%, this method has got a satisfactory accuracy, which can provide a new way for regional biomass estimation.

Supramolecular Honeycomb and Columnar Assemblies Formed by Self-Assembly of Coil-Rod-Coil Molecules with a Conjugated Rod Segment

Li Chen,Ke-Li Zhong,Long Yi Jin,Zhegang Huang,Libin Liu,Linda S. Hirst 한국고분자학회 2010 Macromolecular Research Vol.18 No.8

Rod-coil molecules with conjugated rod segments have a strong tendency to self-organize into a variety of supramolecular structures in the bulk state. This paper reports the synthesis and self-assembly behavior of coilrod-coil molecules consisting of three biphenyls linked through a vinylene unit as a conjugated rod segment and poly(ethylene oxide) (PEO) with a degree of polymerization of 12 and 17 coil segments. The self-assembling behavior of these materials, which was investigated by differential scanning calorimetry (DSC) and X-ray diffraction (XRD), revealed these molecules to spontaneously self-organize into a 3-D hexagonally perforated layer (HPL) structure and a 2-D rectangular or a oblique columnar structure in the crystalline and liquid crystalline phases, respectively.

Mimicking the pattern formation of fruits and leaves using gel materials

Li Chen,Yang Zhang,Somsak Swaddiwudhipong,Zishun Liu 국제구조공학회 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.50 No.5

Gel materials have recently gained more attention due to its unique capability of large andreversible volumetric changes. This study explores the possibility of mimicking the pattern formation ofcertain natural fruits during their growing process and leaves during drying processes through the swellingand de-swelling of gel materials. This will hopefully provide certain technical explanations on themorphology of fruits and plants. We adopt the inhomogeneous field gel theory to predict the deformationconfigurations of gel structures to describe the morphology of natural fruits and plants. The growingprocesses of apple and capsicum are simulated by imposing appropriate boundary conditions and fieldloading via varying the chemical potential from their immature to mature stages. The drying processes ofthree types of leaves with different vein structures are also investigated. The simulations lead to promisingresults and demonstrate that pattern formation of fruits and plants may be described from mechanicalperspective by the behavior of gel materials based on the inhomogeneous field theory.

Enhanced Protein Production by Sorbitol Co-feeding with Methanol in Recombinant Pichia pastoris Strains

Li Chen,Ali Mohsin,Ju Chu,Ying-ping Zhuang,Yamei Liu,Mei-Jin Guo 한국생물공학회 2017 Biotechnology and Bioprocess Engineering Vol.22 No.6

Pichia pastoris strains carrying 1, 6, 12, and 18 copies of the porcine insulin precursor (PIP) gene, were employed to investigate the effects of sorbitol co-feeding with methanol on the physiology of the strains. Multicopy clones of the methylotrophic yeast were generated to vary the PIP gene dosage and recombinant proteins. Elevated gene dosage increased levels of the recombinant PIP protein when methanol served as the sole carbon and energy source i.e., an increase of 1.9% for a strain carrying 1 copy, 42.6% for a strain carrying 6 copies, 34.7% for a strain carrying 12 copies and 80.9% for a strain carrying 18 copies, respectively (using sorbitol co-feeding with methanol during the induction phase). However, it had no significant influence on a lower gene dosage strain (1 copy), but this approach affirmed enhancement in cell growth and PIP production for higher gene dosage strain (6, 12, and 18 copies) via using sorbitol co-feeding with methanol. Additionally, the co-feeding strategy could hold vital importance for recombinant protein production by a multi-copy P. pastoris system.

Effective Following Patterns Mining Scheme for the Movements of Objects

Li Chen,Lianggui Liu,Bingxian Chen,Huiling Jia,Yu Zhang 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.6

Technological advances of location aware devices allow us to locate and track the moving objects accurately and in more complex environments. In order to derive valuable knowledge embedded in data from databases, many domestic and foreign scholars have done a lot of work. In this paper, we review the progress of patterns mining mainly in frequent pattern, periodic pattern and following pattern. And we provide a general perspective for studies on different patterns mining by reviewing and comparing the methods and algorithms in detail, providing a quick understanding of research to the worker and giving effective following patterns mining scheme for the movements of objects. The study on patterns mining in movements not only has a direct bearing on human life but also play an important part in environment protection, traffic and transportation, privacy and security and so on. So, the rest of this paper will present these specific applications and analyzed it in theory.

An Efficient and Privacy-Preserving Semantic Multi-Keyword Ranked Search over Encrypted Cloud Data

Li Chen,Xingming Sun,Zhihua Xia,Qi Liu 보안공학연구지원센터 2014 International Journal of Security and Its Applicat Vol.8 No.2