Investigate Centralized and Decentralized Information Infrastructure for Future Electricity Market

The power grid is undergoing a transformation from a monopolized control system to a more decentralized one. Distributed renewable energy generation, responsive loads, and distribution automation are posing a new challenge to the traditional centralized control method. To address these challenges, we propose two innovative centralized and decentralized solutions for the information infrastructure of the future electricity market. For the centralized approach, we investigate the applications of an open-source control system platform VOLTTRON in the areas of building control and electric vehicle charging. For the case study, we implement the VOLTTRON platform to solve the economic dispatch (ED) problem. The VOLTTRON platform is used as a central message bus and 16 single-board computers are used to simulate distributed generators and dispatchable loads. For the decentralized approach, we propose an innovative Bitcoin-style distributed transactional model “Bit-Energy” using radically different Internet-of-Things technologies (Blockchain and Ethereum’s smart contract). “Bit-Energy” enables transparent, auditable, and peer-to-peer energy transactions between active market participants. We implement a highly efficient buyer/seller matching algorithm. Case studies demonstrate the accuracy, robustness, effectiveness, and scalability of the proposed Bit-Energy platform under various operating conditions.Master of Science in EngineeringElectrical Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/136613/1/MS Thesis JingweiLuo - 11F.pdfDescription of MS Thesis JingweiLuo - 11F.pdf : Thesi

GROWTH ANALYSIS OF CHINESE LIQUOR LISTED COMPANIES BASED ON PRINCIPAL COMPONENT ANALYSIS

Online shopping has been increasingly popular in recent years due to the spread of the Internet, and the livestream sector has increased. Many traditional companies, such as Chinese liquor enterprises, tried to engage in livestreaming to keep pace with their new customers. The overall perceived value’s impact on user behavior has been examined in social commerce research, but the livestream context has received less attention. This study proposed and empirically tested a theoretical model, considering online word-of-mouth, and investigated the effect of consumer perceived value on livestream purchase intention of Chinese liquor. The findings demonstrate that online word-of-mouth serves as a mediator between the three dimensions of perceived value - functional, emotional, and social value - and livestream purchase intentions of Chinese liquor

Correlations between aesthetic preferences of river and landscape characters

Some landscape characters put great influences on the aesthetic preferences of a river. Finding out these characters will provide for river landscape design and management with explicit keystones. In this paper, 23 sample areas of rivers were selected in Xuzhou, China, and 15 landscape characters of rivers were identified. The photos taken at the sample areas were as stimuli, and undergraduate students were respondents. The results demonstrate that the aesthetic preferences of photos judged one-by-one and judged together receive similar results; the preference scores of deflective views are significantly higher than the ones of opposite views; for urban rivers, “river accessibility” and “number of colours” are reliably positive predictors to aesthetic preferences, “wood diversity index” and “plants on water” are negative ones; for rural rivers, “coverage of riparian vegetation”, “perspective” and “wood diversity index” are reliably positive predictors to aesthetic preferences. First published online: 14 Dec 201

Characterizing and Subsetting Big Data Workloads

Big data benchmark suites must include a diversity of data and workloads to be useful in fairly evaluating big data systems and architectures. However, using truly comprehensive benchmarks poses great challenges for the architecture community. First, we need to thoroughly understand the behaviors of a variety of workloads. Second, our usual simulation-based research methods become prohibitively expensive for big data. As big data is an emerging field, more and more software stacks are being proposed to facilitate the development of big data applications, which aggravates hese challenges. In this paper, we first use Principle Component Analysis (PCA) to identify the most important characteristics from 45 metrics to characterize big data workloads from BigDataBench, a comprehensive big data benchmark suite. Second, we apply a clustering technique to the principle components obtained from the PCA to investigate the similarity among big data workloads, and we verify the importance of including different software stacks for big data benchmarking. Third, we select seven representative big data workloads by removing redundant ones and release the BigDataBench simulation version, which is publicly available from http://prof.ict.ac.cn/BigDataBench/simulatorversion/.Comment: 11 pages, 6 figures, 2014 IEEE International Symposium on Workload Characterizatio