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Different from representation learning models using deep learning to project original feature space into lower density ones, we propose a feature space learning (FSL) model based on a semi-supervised clustering framework. There are three main contributions in our approach: (1) Inspired by Zipf's law and word bursts, the feature space learning processes not only select trusted unlabeled samples and...
Remaining Useful Life (RUL) prediction plays a critical part in many battery-powered applications. Statistical filter, i.e., particle filter (PF) is widely used to predict RUL with various models as well as its uncertainty representation. However, PF commonly used suffers from the lack of poor adaption of long-term prediction and iterative prediction. This disadvantage may further reduce the RUL estimation...
The increasing penetration of stochastic photovoltaic (PV) generation in electric power systems poses significant challenges to system operators. To ensure reliable operation of power systems, accurate forecasting of PV power production is essential. In this paper, we propose a novel multitime-scale data-driven forecast model to improve the accuracy of short-term PV power production. This model leverages...
CDM-BSC(CRISP-DM applied with Balance Scorecard), which is a new term. CDM-BSC concept is developed from combination of traditional Data Mining Methodology and BSC for performance measurement systems. Balance Scorecard applied to CRISP-DM is a new methodology of improving the performance of Data Mining Process. CRISP-DM plus BS C (Balance Scorecard) is an enhancement of abstractive conception of multidimensional-perspective...
With the development and wide application of the computing technology, performance testing becomes more and more important. Real simulation of the user behavior becomes a concern of the performance testing. The paper introduces the potential model to establish the model of the visit amount which can be used in the performance testing. Firstly, the paper introduces the role of HBase in the search engine...
Multi-dimension data retrieval has been one of the most popular research areas in the field of information retrieval for the last 10 years. The fast growing amounts of multimedia data and the development of the Internet right now need a general access method that offers a fast and effective way on multi-dimension data classification and retrieval. But up to now, no general breakthrough has been achieved...
Studying drivers' route choice behavior under the influence of travel information is important because it provides insight to improve the effect of travel information on traffic environment. This paper mainly aims to study the impact of travel information on travelers' route choice behavior at different departure time. Multinomial logit model (MNL) is used to model travelers' route choice behavior...
One of the most important phases in business-to-business-based electronic construction bid is the process of bidding decision-making module whose operating optimization is considered to be the foundation of implementing the integration of B2B-based bid mode. Strategies facilitating the optimization of E-bidding are demonstrated in this paper: in order to utilize the efficiency and effectiveness both...
Data mining driven fishbone, which is whole a new term, is an enhancement of abstractive conception of multidimensional-data flow of fishbone applied for data mining to optimize the process and structure of data mining. End-to-end DMDF diagram includes complex dataflow and different processing component and improvements for numerous aspects in multiply level. DMDF provides integrated platform and...
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