1,088 research outputs found
A Temporal Usage Pattern-based Tag Recommendation Approach
While social tagging can benefit Internet users managing their resources, it suffers the problems such as diverse and/or unchecked vocabulary and unwillingness to tag. Use of freely new tags and/or reuse of frequent tags have degraded coherence of corresponding resources of each tag that further frustrates people in retrieving information due to cognitive dissonance. Tag recommender systems can recommend users the most relevant tags to the resource they intend to annotate, and drastically transfer the tagging process from generation to recognition to reduce user’s cognitive effort and time. Prior research on tag recommendation has addressed the time-dependence issues of tags by applying a time decaying measure to determine the recurrence probability of a tag according to its recency instead of its usage pattern. In response, this study intends to propose the temporal usage pattern-based tag recommendation technique to consider the usage patterns and temporal characteristic of tags for making recommendations
Distributed Optimal Vehicle Grid Integration Strategy with User Behavior Prediction
With the increasing of electric vehicle (EV) adoption in recent years, the
impact of EV charging activities to the power grid becomes more and more
significant. In this article, an optimal scheduling algorithm which combines
smart EV charging and V2G gird service is developed to integrate EVs into power
grid as distributed energy resources, with improved system cost performance.
Specifically, an optimization problem is formulated and solved at each EV
charging station according to control signal from aggregated control center and
user charging behavior prediction by mean estimation and linear regression. The
control center collects distributed optimization results and updates the
control signal, periodically. The iteration continues until it converges to
optimal scheduling. Experimental result shows this algorithm helps fill the
valley and shave the peak in electric load profiles within a microgrid, while
the energy demand of individual driver can be satisfied.Comment: IEEE PES General Meeting 201
Real-Time Bi-directional Electric Vehicle Charging Control with Distribution Grid Implementation
As electric vehicle (EV) adoption is growing year after year, there is no
doubt that EVs will occupy a significant portion of transporting vehicle in the
near future. Although EVs have benefits for environment, large amount of
un-coordinated EV charging will affect the power grid and degrade power
quality. To alleviate negative effects of EV charging load and turn them to
opportunities, a decentralized real-time control algorithm is developed in this
paper to provide optimal scheduling of EV bi-directional charging. To evaluate
the performance of the proposed algorithm, numerical simulation is performed
based on real-world EV user data, and power flow analysis is carried out to
show how the proposed algorithm improve power grid steady state operation. .
The results show that the implementation of proposed algorithm can effectively
coordinate bi-directional charging by 30% peak load shaving, more than 2% of
voltage drop reduction, and 40% transmission line current decrease
Leveling Maintenance Mechanism by Using the Fabry-Perot Interferometer with Machine Learning Technology
This study proposes a method for maintaining parallelism of the optical cavity of a laser interferometer using machine learning. The Fabry-Perot interferometer is utilized as an experimental optical structure in this research due to its advantage of having a brief optical structure. The supervised machine learning method is used to train algorithms to accurately classify and predict the tilt angle of the plane mirror using labeled interference images. Based on the predicted results, stepper motors are fixed on a plane mirror that can automatically adjust the pitch and yaw angles. According to the experimental results, the average correction error and standard deviation in 17-grid classification experiment are 32.38 and 11.21 arcseconds, respectively. In 25-grid classification experiment, the average correction error and standard deviation are 19.44 and 7.86 arcseconds, respectively. The results show that this parallelism maintenance technology has essential for the semiconductor industry and precision positioning technology
Relationship between cortical thickness and neuropsychological performance in normal older adults and those with mild cognitive impairment
Mild cognitive impairment (MCI) has been extensively investigated in recent decades to identify groups with a high risk of dementia and to establish effective prevention methods during this period. Neuropsychological performance and cortical thickness are two important biomarkers used to predict progression from MCI to dementia. This study compares the cortical thickness and neuropsychological performance in people with MCI and cognitively healthy older adults. We further focus on the relationship between cortical thickness and neuropsychological performance in these two groups. Forty-nine participants with MCI and 40 cognitively healthy older adults were recruited. Cortical thickness was analysed with semiautomatic software, Freesurfer. The analysis reveals that the cortical thickness in the left caudal anterior cingulate (p=0.041), lateral occipital (p=0.009) and right superior temporal (p=0.047) areas were significantly thinner in the MCI group after adjustment for age and education. Almost all neuropsychological test results (with the exception of forward digit span) were significantly correlated to cortical thickness in the MCI group after adjustment for age, gender and education. In contrast, only the score on the Category Verbal Fluency Test and the forward digit span were found to have significant inverse correlations to cortical thickness in the control group of cognitively healthy older adults. The study results suggest that cortical thinning in the temporal region reflects the global change in cognition in subjects with MCI and may be useful to predict progression of MCI to Alzheimer's disease. The different pattern in the correlation of cortical thickness to the neuropsychological performance of patients with MCI from the healthy control subjects may be explained by the hypothesis of MCI as a disconnection syndrome
Calibration of Parshall Flumes with Non-Standard Entrance Transitions
The 9-ince and 18-inch Parshall flumes with the throat section installed level with the bottom of an incoming pipe were tested. The measured discharges for given flow depths (free flow) or differences in flow depths (submerged flow) were found to deviate quite significantly fromt he computed standard Parshall flume disharges at both low and high flow rates. New empirical formulats have been developed to take such deviations into account. It is noted that values of the coefficients and exponents contained in the new formulas depend on the throat size of the flume and the slope of the incoming pipe. Calibration curves and tables were prepared for convenient applications of the new formulas
Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks
The traffic classification based on the network applications is one important issue for network management. In this paper, we propose an application-based online and offline traffic classification, based on deep learning mechanisms, over software-defined network (SDN) testbed. The designed deep learning model, resigned in the SDN controller, consists of multilayer perceptron (MLP), convolutional neural network (CNN), and Stacked Auto-Encoder (SAE), in the SDN testbed. We employ an open network traffic dataset with seven most popular applications as the deep learning training and testing datasets. By using the TCPreplay tool, the dataset traffic samples are re-produced and analyzed in our SDN testbed to emulate the online traffic service. The performance analyses, in terms of accuracy, precision, recall, and F1 indicators, are conducted and compared with three deep learning models
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