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iGreenhouse : A Case Study for Connecting Physical Devices into Mobile Social Networks
Jiajin Zhang,Lichang Chen,Xiaobo Cai,Quan Gao 보안공학연구지원센터 2015 International Journal of Smart Home Vol.9 No.6
Social networks have become extremely popular in recent years. A new requirement of connecting physical devices into social networks is emerging gradually. In this paper, we propose a case study to explore the effectiveness of connecting devices into mobile social networks for status monitoring and controlling. In our work, we developed iGreenhouse, a greenhouse can post its environment information automatically to the most popular mobile social networks Wechat in China, and Wechat users are able to remotely control the devices in the greenhouse easily. Experiment results show that our proposed approach is feasible, cost-effective and flexible. Therefore our solution can provide an alternative ubiquitous platform for monitoring and controlling of connected devices.
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A Security Assessment Framework and Selection Method for Outsourcing Cloud Service
Xiaochen Liu,Chunhe Xia,Jiajin Cao,Jinghua Gao,Zhao Wei 보안공학연구지원센터 2014 International Journal of Security and Its Applicat Vol.8 No.6
Cloud services have become state of the art of resource sharing and interoperability among different service providers. The federated cloud seek to reduce costs and maximize efficiency, provide flexible and reliable services composed of various external cloud service and internal services, but this would introduce a risk of security due to outsourcing. For organization which provide an integrity cloud service must rely on SLA to illustrate the capacity of risk and performance, the various providers involved in the federations or services compositions should effectively distribute the organizational responsibilities and SLA. To solve the fears and deal with the threats associated with outsourcing, new method for selection of outsourcing cloud service providers(CSP) based on assessment of security and performance is urgently needed. This paper presents an approach on how to select the proper CSP to guarantee organizational SLA, which quantify the SLA terms and calculate a security weight, the organization can choose the appropriate CSP based on security weight, meanwhile minimize the business cost. This paper make contribution on modeling the capacity of the CSP, considering the business cost and historical performance. The proposed approach is validated through case study that shows selected CSP through our approach can best meet the organizational security and cost needs.
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Impact of the August Asian–Pacific Oscillation on Autumn Precipitation in Central Eastern China
Zouxing Lin,Jiajin Zhu,Wei Hua,Guangzhou Fan 한국기상학회 2021 Asia-Pacific Journal of Atmospheric Sciences Vol.57 No.2
The August Asian–Pacific Oscillation (APO) plays an important role in the variability of autumn (September ~ October mean) precipitation in central eastern China (CEC). Using observational and reanalysis data, the impact of the August APO on autumn CEC precipitation from 1960 to 2016 was studied. The statistical result showed that August APO is closely linked to the autumn precipitation anomalies in CEC with a significant positive correlation (r = 0.45). Further analysis revealed that when the APO is strong, the strengthened East Asian trough and the North Pacific high / vertical shear occur in the mid-lower / upper troposphere, resulting in anomalous southerly along the East Asia coast, which are favorable for strengthening the anomalous convergence and upward movement of moist warm air from the northwestern Pacific and arid cold air from the north China, introducing more precipitation, but that this configuration became much diminished during weak APO years. The possible mechanism can be explained as the thermal effect in the mid-upper troposphere can last from August until autumn, and the corresponding concurrent thermal effect would lead to anomalies in both atmospheric circulation and precipitation. Additionally, though an evidently negative relationship between preceding Niño indices and autumn CEC precipitation was revealed, the August APO induced changes in autumn CEC precipitation is greater than those of Niño indices, whether interannual or interdecadal changes, further indicating that the APO is an effective signal for precipitation prediction in CEC.
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Lin Lin,Xiaohuan Wu,Jiajin Qi,Hongxin Ci 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.1
Power quality (PQ) disturbances recognition is the foundation of power quality analysis and improvement. In order to improve the classification accuracy and efficiency, a new classification approach based on modified Fourier neural networks (FNN) and Hyperbolic S-transform (HST) was designed for PQ disturbances classification. HST has better a time-frequency resolution than S-transform. The features extracted from HST results compose the input vectors of classifier. The DFP emendatory Quasi-Newton method is used to improve the learning ability of FNN and avoid local minimum problem. Three modified FNNs were used to construct a classifier with the structure of decision tree. Six types of disturbances with different noise ratio were simulated to test the classification ability of the new approach. Simulation results show that the new classifier has better classification accuracy than other classifiers based on BP neural networks and Fourier neural networks. The new approach is effective.
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An Efficient Parallel Top-k Similarity Join for Massive Multidimensional Data Using Spark
Dehua Chen,Changgan Shen,Jieying Feng,Jiajin Le 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.3
Top-k similarity join has been used in a wide range of applications that require calculating the most top-k similar pairs of data records in a given database. However, the time performance will be a challenging problem, as an increasing trend of applications that need to process massive data. Obviously, finding the top-k pairs in such vast amounts of data with traditional methods is awkward. In this paper, we propose the RDD-based algorithm to perform the top-k similarity join for massive multidimensional data over a large cluster built with commodity machines using Spark. The RDD- based algorithm consists of four steps, which loads a set of multidimensional records stored in HDFS and finally output an ordered list of top-k closest pairs into HDFS. Firstly, we develop an efficient distance function based on LSH(Locality Sensitive Hashing) to improve the efficiency in pairwise similarity comparison. Secondly, to minimize the amount of data during the RDD running- time, we split conceptually all pairs of LSH signatures into partitions. Moreover, we exploit a serial computation strategy to calculate all top-k closest pairs in parallel. Finally, all the local top-k pairs sorted by their Hamming distances will contribute to the global top-k pairs. In this paper, the performance evaluation between Spark and Hadoop confirms the effectiveness and scalability of our RDD-based algorithm.
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