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

Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms

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

A segmentation and classification scheme for single tooth in MicroCT images based on 3D level set and k-means+.

In order to reduce the greenhouse gas emission and limit the rise in global temperature, the trend in automotive industry is changing rapidly and most of the manufacturers are moving towards the electrification of vehicles. Computational intelligence and machine learning play a very important role in the field of electric vehicles (EVs) due to the necessity of automatic control in battery charging and port accessibility. Due to the limited ranges of EVs, they have to be charged periodically during their travels and its charging will take more time. As the number of EVs increases, suitable charging infrastructure having many charging stations and co‐ordination of scheduling the charging vehicles from charging stations are necessary. As charging stations have less number of fast charging ports, accessing these fast charging ports needs proper planning. The major challenge of an EV is to identify the charging station with a fast charging port which is on route to the destination with minimum waiting time. This article deals with the application of machine learning in selecting a charging station with available fast charging port and minimum waiting time.

Machine learning‐based charge scheduling of electric vehicles with minimum waiting time

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

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

In software development process, the last step is usually the Graphic User Interface(GUI) test, which is part of the final user experience (UE) test. Traditionally, there exist some GUI test tools in the market, such as Abbot Java GUI Test Framework and Pounder, in which testers pre-configure in the script all desired actions and instructions for the computer, nonetheless requiring too much of invariance of GUI environment; and they require reconfiguration in case of GUI changes, therefore still to be done mostly manually and hard for non-programmer testers to. Consequently, we proposed GUI tests by image recognition to automate the last process; we managed to innovate upon current algorithms such as SIFT and Random Fern, from which we develop the new algorithm scheme retrieving most efficient feature and dispelling inefficient part of each algorithm. Computers then apply the algorithm, to search for target patterns themselves and take subsequent actions such as manual mouse, keyboard and screen I/O automatically to test the GUI without any manual instructions. Test results showed that the proposed approach can accelerate GUI test largely compared to current benchmarks.

Automatic GUI test by using SIFT matching

Visualization plays an important role in the analysis of grid circuit topological structure. The traditional line visualization method based on geographical position usually contains such problems that the nodes density distribution is uneven and the lines are too complex to understand. Some other methods completely neglect the real geographic location information. In this paper, we propose a new visualization method by adjustment of the nodes' location and optimization of drawing lines. Initially, the scheduling substation node is designed as the central node, the other nodes are divides into different groups by k-means++ cluster algorithm. Next, the location of the nodes is adjusted and optimized after clustering, and the data density is balanced. Finally, the nodes are connected with the colored lines of ring or radial structure. Experimental results show that our visualization method can more clearly show the topological structure of the current power transmission lines and retain the main geographical location information.

Dan Wu

Papers

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

Cloud computing in electric vehicles charging control and dispatch optimization

Automatic GUI test by using SIFT matching

A Novel Visualization Method of Power Transmission Lines