Large scale of plug-in electric vehicles(PEVs) integration will pose inevitable impacts on the planning and operation of power system in the future.The literature reviews were carried out on PEVs integration on power system including PEVs charging load modeling,simulation and calculation,PEVs' impacts on power system,and control and utilization of PEVs charging and discharging.Firstly,key factors when analyzing the charging load were pointed out.Additionally,the impacts of PEVs integration on generation expansion,grid operation,charging facilities and distribution system planning were discussed.The current research on coordinated charging of PEVs and "vehicle-to-grid(V2G)" operation were reviewed as well.Finally,possible future research areas were presented.

Impacts and Utilization of Electric Vehicles Integration Into Power Systems

With the rise of the robot and cloud computing technology, human-centric healthcare service attracts widely attention in order to meet the great challenges of traditional healthcare, especially the limited medical resources. This paper presents a healthcare system based on cloud computing and robotics, which consists of wireless body area networks, robot, software system and cloud platform. This system is expected to accurately measure user's physiological information for analysis and feedback, which is assisted by the robot integrated with various sensors. In order to improve the practicability of multi-media transmission in the healthcare system, this paper proposes a novel scheme to deliver real-time video contents through an improved UDP-based protocol. Finally, the proposed approach is evaluated with experimental study based on a real testbed.

Robot and cloud-assisted multi-modal healthcare system

Application of Newton-based load flow methods for determining steady-state condition of well and ill-conditioned power systems: A review

Security on mobile devices is becoming increasingly important. HTML5 are widely used to develop mobile applications due to its portability on multi platforms. However it is allowed to mix data and code together in web technology. HTML5-based applications are prone to suffer from code injection attacks that are similar to XSS. In this paper, at first, we introduce a more hidden type of code injection attacks, coding-based attacks. In the new type of code injection attacks, JavaScript code is encoded in a human-unreadable form. Then we use classification algorithms of machine learning to determine whether an app suffers from the code injection attack or not. The experimental result shows that the Precision of our detection method reaches 95.3%. Compare with the other method, our approach improves a lot in detection speed with the precision nearly unchanged. Furthermore, an improved access control model is proposed to mitigate the attack damage. In addition, filters are adopted to remove JavaScript code from data to prevent the attacks. The effectiveness and rationality have been validated through extensive simulations.

Detection and Prevention of Code Injection Attacks on HTML5-Based Apps

For faster access of data or in network bloom filter plays an important part in searching technique. It process data in short amount of time and frequently with probabilistic analysis. Bloom Filter also decreases the cost of analyzing data. Various applications are using this technology for accessing and processing the data. Thus by implementing Bloom's Filter over big data will result into efficient query accessing in big data. In this paper, an approach to implement Locality Sensitive Bloom Filter (LSBF) technique in big data is proposed. To remove the drawbacks of simple hashing technique, the LSBF must be implemented to store data in the bloom filter which will help to search the most approximate result by using the Locality Sensitive Hashing approach.

http://ieeexplore.ieee.org/iel7/7088896/7100186/07100483.pdf

Big data query optimization by using Locality Sensitive Bloom Filter

To boost the performance of massive data processing, solid-state drives (SSDs) have been used as a kind of cache in the Hadoop system. However, most of existing SSD cache management algorithms are ignorant of the characteristics of upper-level applications. In this paper, we propose a novel SSD cache management algorithm called DSA, which can exploit the application-level data similarity to improve the SSD cache performance in Hadoop. Our algorithm takes both temporal similarity and user similarity in querying behaviors into account. We evaluate the effectiveness of our proposed DSA algorithm in a small-scale Hadoop cluster. Our experimental results show that our algorithm can achieve much better performance than other well-known algorithms (e.g., LRU, FIFO). We also clearly point out the underlying tradeoff between cache performance and SSD deployment cost, and identify a number of key factors that affect SSD cache performance. Our findings can provide useful guidelines on how to effectively integrate SSDs into Hadoop.

Exploiting application-level similarity to improve SSD cache performance in Hadoop

The MapReduce processing framework is unaware of the property of underlying datasets For ordered datasets (eg, time-series data), in which records have been already sorted, MapReduce still performs unnecessary sorting operations during its execution It directly results in a significant increase of execution time, as sorting a large volume of data is time-consuming In this paper, we propose a bloom filter-based approach to improve the performance of MapReduce when processing ordered datasets In our approach, all records are stored in a set of bloom filters after the Mapping phase and data queries can be efficiently processed by checking the bloom filters Due to the high querying efficiency of bloom filters, we can achieve significant performance gain in the Reducing phase We conduct a series of experiments to evaluate the effectiveness of our proposed bloom filter-based approach Our experimental results show that our approach can achieve 2x speedup in terms of query processing performance, and reduce the CPU/memory utilization in the meanwhile Moreover, we also evaluate the scalability of our proposed approach when processing multiple queries, and observe that the speedup can be further improved with the increasing number of queries

A Bloom Filter-Based Approach for Efficient Mapreduce Query Processing on Ordered Datasets

Charging of large-scale electric vehicles will have a significant impact on the grid so that their charging schedule needs to be optimized. The traditional centralized scheduling method requires high performance in information exchange and computing. Based on the analysis of the features of electric vehicle charging load and the research of cloud computing, we propose a cloud computing based schedule platform for optimal charging of electric vehicles. In addition, the function and implementation of the three basic modules of the platform (the data collection module, the cloud computing center and the control center) are exposed. The platform performs well in optimal scheduling for electric vehicles. In addition, the platform can also make full use of the computing resource and reduce the communication congestion.

Cloud computing in electric vehicles charging control and dispatch optimization

The invention relates to a hybrid storage system based on multidimensional data similarity and a data management method thereof. The hybrid storage system is composed of an upper layer memory unit (3), a middle layer cache unit (2), a lower layer storage unit (1) and a control unit (4). The upper layer memory unit (3) is used for storing system data and data index tables which are frequently accessed in all storage layers. The middle layer cache unit (2) is used for storing part of data which are frequently accessed, and the lower layer storage unit (1) is used for storing all data sets. The control unit (4) is used for being connected with the upper layer memory unit (3), the middle layer cache unit (2) and the lower layer storage unit (1) through I/O ports to constitute the hybrid storage system of a multi-layer storage structure. The invention further discloses the data management method of the hybrid storage system. According to the hybrid storage system based on multidimensional data similarity and the data management method thereof, the accessed speed can be improved, corresponding data can be quickly searched and sought in the accessing process, data access delay is reduced, the data processing efficiency can further be improved, and a request throughput capacity is improved.

Hybrid storage system based on multidimensional data similarity and data management method

The utility model relates to a hybrid storage system based on multidimensional data similarity. The system comprises an upper-layer memory unit (3), a middle-layer caching unit (2), a lower-layer storage unit (1) and a control unit (4). The upper-layer memory unit (3) is used for storing system data and data index tables always being accessed in each storage layer. The middle-layer caching unit (2) is used for storing a part of data which are often accessed. The lower-layer storage unit (1) is used for storing all datasets. The control unit (4) is connected with the upper-layer memory unit (3), the middle-layer caching unit (2) and the lower-layer storage unit (1) through I/O ports to form the hybrid storage system of a multi-layer storage structure. Compared with single HDD storage systems, the hybrid storage system has better data processing performance; meanwhile, by means of the multi-layer storage structure, improvement of performance of the storage system can be achieved at low economic cost; the utilization rate of a cache in the hybrid storage system is improved, and data reading and writing performance is improved through the cache.

Zhijian Chen

Papers

Exploiting application-level similarity to improve SSD cache performance in Hadoop

A Bloom Filter-Based Approach for Efficient Mapreduce Query Processing on Ordered Datasets

Cloud computing in electric vehicles charging control and dispatch optimization

Hybrid storage system based on multidimensional data similarity and data management method

Hybrid storage system based on multidimensional data similarity