Data of various marine environmental factors collected and integrated by the National Center of Ocean Research (NCOR) were used to search for possible statistics relationship with chlorophyll concentration of the Kuroshio and its adjacent waters off eastern Taiwan. The seaWiFS chlorophyll concentration in natural logarithm were used as dependent variable in General Linear Model¡]GLM¡^analysis, followed by least square means (lsmeans) and cluster analysis. Study area ranged from 21.5¡CN and 121¡CE to 26.5¡CN and 125¡CE eastern Taiwan. Data were first assembled, screened, transformed to natural logarithm and reorganized into monthly averages for individual geographical grid points of 10¡¦X 10¡¦. The result of GLM analysis shows that all factors have significant relationship with chlorophyll concentration, more than 20 regression formulae were found with different combination of variable. Results of standard regression analysis show their order of importance as: latitude, depth, longitude, light, sst, east and west current on upper 20 meter(c20EW), north and south current on upper 20 meter(c20NS) and eddy kinetic energy(EKE), respectively. Results of lsmeans listing by latitude and by longitude showed that area with higher chlorophyll concentration are on high-latitude and low-longitude area but not between and area near east Taiwan tend to have high concentration and decreased eastward. Results of cluster analysis indicated that chlorophyll concentration of western longitude, and northern as well as southern latitude are different from other area.

Due to hierarchical sensor networks is capable to elimination extra sensing information and reduce extra communication load, it is remarkably important to increase scalability of network and prolong lifetime of network. In the paper, we focus on relay nodes of two-layered heterogeneous sensor networks. When relay nodes transmit data without scheduling, the collision probability must increase. It will cost too much for energy to re-transmission data and listening channel. To avoid extra energy consumption, we build a grid network according to RSS, and naming IP for every relay node on the grid network. Then, all leaf nodes join a certain cluster and naming IP according to RSS. Through the exclusive IP it schedule mission for TDMA. Only when relay node comes in specific slot, it wakes up to transmit or receive data, the remaining time to sleep and save power consumption. Besides, in order to balance energy consumption of backbone or non-backbone relay nodes and prolong lifetime of network, we proposal three routing protocol (DTS¡BREARBS¡BVIPOS). According to simulation results, VIPOS is the longest lifetime above them.

In recent years, mining data streams has been widely studied. A data streams is a sequence of dynamic, continuous, unbounded and real time data items with a very high data rate that can only be read once. In data mining, clustering is one of use- ful techniques for discovering interesting data in the underlying data objects. The problem of clustering can be defined formally as follows: given n data points in the d- dimensional metric space, partition the data points into k clusters such that the data points within a cluster are more similar to each other than data points in different clusters. In the data streams environment, the difficulties of data streams clustering contain storage overhead, low clustering quality and a low updating efficiency. Cur- rent clustering algorithms can be broadly classified into four categories: partition, hierarchical, density-based and grid-based approaches. The advantage of the grid- based algorithm is that it can handle large databases. Based on the density-based approach, the insertion or deletion of data affects the current clustering only in the neighborhood of this data. Combining the advantages of the grid-based approach and density-based approach, the CDS-Tree algorithm was proposed. Although it can handle large databases, its clustering quality is restricted to the grid partition and the threshold of a dense cell. Therefore, in this thesis, we present a new clustering algo- rithm with high quality, GDense, for data streams. The GDense algorithm has high quality due to two kinds of partition: cells and quadcells, and two kinds of threshold: £_ and (1/4) . Moreover, in our GDense algorithm, in the data insertion part, the 7 cases takes 3 factors about the cell and the quadcell into consideration. In the deletion part, the 10 cases take 5 factors about the cell into consideration. From our simulation results, no matter what condition (including the number of data points, the number of cells, the size of the sliding window, and the threshold of dense cell) is, the clustering purity of our GDense algorithm is always higher than that of the CDS-Tree algorithm. Moreover, we make a comparison of the purity between the our GDense algorithm and the CDS-Tree algorithm with outliers. No matter whether the number of outliers is large or small, the clustering purity of our GDense algorithm is still higher than that of the CDS-Tree and we can improve about 20% the clustering purity as compared to the CDS-Tree algorithm.

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 103-114). Also available in electronic version. Access restricted to campus users.

This Master’s Report outlines graduate research work completed by Akshay Sriprasad, who is supervised by Professor Tom Edgar, in the area of modeling and systems optimization for the smart grid. The scope this report includes the development and validation of strategies to elicit demand response, defined as reduction of peak demand, at the residential level, in conjunction with collaborative research efforts from the Pecan Street Research Institute, a smart grid research consortium based in Austin, TX. The first project outlined is an artificial neural network-­‐based demand forecasting model, initially developed for UT’s campus cooling system and adapted for residential homes. Utilizing this forecasting model, a number of demand response-­‐focused optimization studies are carried out, including optimization of community energy storage for peak shifting, and electric vehicle charging optimization to harness inexpensive night-­‐time Texas wind energy. Community energy storage and electric vehicles are chosen as ideal dynamic charging media due to increased proliferation and focus of Pecan Street Research Institute on critical emerging technologies. As these two technologies involve significant capital investment, an alternative mobile application-­‐based demand response strategy is outlined to complete a comprehensive portfolio of demand response strategies to suit a variety of budgets and capabilities. / text

ITC/USA 2014 Conference Proceedings / The Fiftieth Annual International Telemetering Conference and Technical Exhibition / October 20-23, 2014 / Town and Country Resort & Convention Center, San Diego, CA / This paper describes a device that integrates remote monitoring and control electronics into a commercial off the shelf 120 VAC power distribution strip and surge protector. An integrated microcontroller collects data on power usage from each of four AC outlets, along with two USB ports, and relays this information to a remote location. Using a conventional web browser to generate a graphical user interface, an untrained user can easily visualize their current and past energy usage patterns, and send commands to control individual outlets.

The thesis explores the extent to which interpretive techniques can improve our understanding of the impacts of information systems. Two themes are explored: the capacity of interpretive techniques to evaluate information systems' impacts from within the work context, and the extent to which the process emphasis of the interpretive approach can facilitate learning about information systems and their impacts. The thesis proposes that the impacts of information systems comprise a combination of known or anticipated phenomena and emergent phenomena that cannot be wholly predicted in advance and argues that the value of an information system lies in its effective use, which in turn depends upon its being understood by its users and assimilated into their work. Support for this proposal is provided from the literature and through evaluation of the impacts of the National Automated Fingerprint Identification System (NAFIS) on fingerprint work in England and Wales. Deep insight into NAFIS and fingerprint work are provided by the use of Repertory Grid Analysis (RGA) as a content-free data gathering technique, used within the interpretive framework proposed by Walsham (1993). The data generated by RGA are analysed using the protocols proposed by Thomas and Harri-Augstein (1985) which support what they call "self-organised learning". In this way, RGA is used to facilitate learning through evaluation, enabling participants to learn to benefit from information systems. The RGA process and outcomes are described in detail. The contribution of the RGA data to the interpretive approach is discussed and the results compared with those arising from a more orthodox evaluation ofNAFIS. The results demonstrate that orthodox evaluation techniques underestimate the value of an information system by failing to assess the significance of many of the issues and concerns that emerge as it is assimilated into the work setting. The research is shown to have contributed to the evaluation of NAFIS, directly impacting the work of a wide range of users and managers in the fingerprint, police and wider criminal justice communities. The results are also shown to be generalisable to other organisational settings and therefore having implications for both the information systems and wider evaluation communities.

We analyze the spike trains of multiple simultaneously recorded grid cells obtained in di erent conditions, to help determine the role of recurrent network feedback in generating grid responses. An important class of models of grid cell activity is based on low dimensional continuous attractor dynamics arising from recurrent connections within the grid system. A necessary prediction of these models is that the strong recurrent connections force the grid responses of di erent cells to maintain fi xed relative spatial phases over long periods of time, even if the response patterns of each neuron change. The observation that grid cells maintain their relative spatial phase relationships across di erent familiar environments supports the presence of recurrent connections, but does not rule out the possibility that these relationships persist due to feed-forward input. We analyze the stability of pairwise neural correlations for experiments in which the spatial responses of single neurons change over time. The first such experiment involves resizing of a familiar enclosure, with the result that spatial grid responses rescale along the resized dimension. We show that the relative spatial phase of ring between pairs of cells remains stable over time even as the absolute spatial phase of ring in these same cells changes greatly through rescaling. This result is again consistent with recurrent connectivity, but it remains possible that common external sensory cues (e.g. border information arriving from boundary cells) somehow register the rescaled grids of all cells to display the same relative phases as before rescaling. In an attempt to address this, we analyze responses from animals first exposure to novel environments. Grid ring becomes more noisy and the spatial ring pattern expands, then relaxes back to the periodicity seen in familiar enclosures. During the relaxation, external sensory cues are static and thus likely not responsible for the changing grid responses. We show that the constant phase relationships seen across familiar environments are present from first exposure as well. Finally, we illustrate a generative model to predict grid cell spikes. The aim is to obtain the key determinants of grid cell ring, including animal location, velocity modulation, neural adaptation, and recurrent feedback in a Bayesian framework, and thus assess network contributions to grid cell activity. / text

In this report some possible consequences of introducing demand response in the electric power grid are studied. Demand response is a part of the Smart Grid, which is a technology being developed to use our electric power grids more efficiently. Demand response programs aim to move people’s power usage over different times of the day, for example to distribute the power usage more evenly throughout the day or to permit a larger share of renewable, intermittent power sources in the system without making the delivery of electric power less stable.  A distribution system operator (DSO) can encourage customers to shift their power usage between different hours by various tariffs, for example by using time-differentiated or power dependent tariffs.   In this thesis, the change in power losses and possible environmental impact of introducing due to a power shift is studied. Power input curves from a DSO, Sala-Heby Energi AB, are studied and modified to simulate a power shift with an evened out electric power usage. The studies made show that in the best-case scenario, that is a electric power usage evened out to 100% each day, the power losses in the whole grid can be reduced with 2.6%. The environmental study shows that the result varies greatly with what method is chosen to do the calculations. The results are presented in kg CO2-equivalents (CO2e), and depending on method used they can either decrease or increase. The environmental study show that the environmental impact from the power usage is more dependent on the shift in power usage between hours than the decrease in electric power losses.

The installed base of photovoltaic power plants in the United States has roughly doubled every 1 to 2 years between 2008 and 2015. The primary economic drivers of this are government mandates for renewable power, falling prices for all PV system components, 3rd party ownership models, and a generous tariff scheme known as net-metering. Other drivers include a desire for decreasing the environmental impact of electricity generation and a desire for some degree of independence from the local electric utility. The result is that in coming years, PV power will move from being a minor niche to a mainstream source of energy. As additional PV power comes online this will create challenges for the electric grid operators. We examine some problems related to large scale adoption of PV power in the United States. We do this by first discussing questions of reliability and efficiency at the PV system level. We measure the output of a fleet of small PV systems installed at Tucson Electric Power, and we characterize the degradation of those PV systems over several years. We develop methods to predict energy output from PV systems and quantify the impact of negatives such as partial shading, inverter inefficiency and malfunction of bypass diodes. Later we characterize the variability from large PV systems, including fleets of geographically diverse utility scale power plants. We also consider the power and energy requirements needed to smooth those systems, both from the perspective of an individual system and as a fleet. Finally we report on experiments from a utility scale PV plus battery hybrid system deployed near Tucson, Arizona where we characterize the ability of this system to produce smoothly ramping power as well as production of ancillary energy services such as frequency response.