Stochastic Optimization with Importance Sampling

Uniform sampling of training data has been commonly used in traditional stochastic optimization algorithms such as Proximal Stochastic Gradient Descent (prox-SGD) and Proximal Stochastic Dual Coordinate Ascent (prox-SDCA). Although uniform sampling can guarantee that the sampled stochastic quantity is an unbiased estimate of the corresponding true quantity, the resulting estimator may have a rather high variance, which negatively affects the convergence of the underlying optimization procedure. In this paper we study stochastic optimization with importance sampling, which improves the convergence rate by reducing the stochastic variance. Specifically, we study prox-SGD (actually, stochastic mirror descent) with importance sampling and prox-SDCA with importance sampling. For prox-SGD, instead of adopting uniform sampling throughout the training process, the proposed algorithm employs importance sampling to minimize the variance of the stochastic gradient. For prox-SDCA, the proposed importance sampling scheme aims to achieve higher expected dual value at each dual coordinate ascent step. We provide extensive theoretical analysis to show that the convergence rates with the proposed importance sampling methods can be significantly improved under suitable conditions both for prox-SGD and for prox-SDCA. Experiments are provided to verify the theoretical analysis.Comment: 29 page

An investigation into catalysts to improve the low temperature performance of an SCR

Selective catalytic reduction with NH3 is considered as one of the most effective technologies controlling NOx emission. Metal Fe based catalysts were used in the investigation to improve the low temperature performance of NOx conversion. The temperature range studied was between 150 degrees C and 350 degrees C with the interval of 50 degrees C. The honeycomb catalysts were prepared by an impregnation method. The study also included characterization of catalysts by BET, XRD, H2-TPR, SEM and XPS methods. It is found an increase in metal Fe content from 2 to 6 % wt. offers an improvement in the catalytic performance. However, a further increment in Fe content will result in a decrease in its performance. More than 90 % NOx conversion rate could be achieved over the Fe-based honeycomb catalyst at a low temperature by doping with Ni and Zr metal with different weights. Among all the catalysts studied, the mixed metal catalyst of Fe-Ni-Zr is found the most potential one, not only because of its higher NOx conversion rate at a low temperature, but also because of its wider operation temperature window. The effect of gas hourly space velocity (GHSV) was also investigated in the study and results show as GHSV increases that reduction of NOx is decreased

Wireless Sensor System for Monitoring and Control

With the fast development of wireless sensor network (WSN) technology, a large number of applications have been widely used over the past few years. As a matter of fact, wireless monitoring and control system is unavoidable one of the applications that consist of WSN nodes. A generic, modular and stackable WSN node, named UWASA Node has been developed by the University of Vaasa and Aalto University lately. Besides, SurfNet node, developed by Seinäjoki University of Applied Science, is designed as low-power consumption, high-data rate, small and powerful sensor node that is suitable to implement the monitoring and control tasks under multiple conditions. In this work, a wireless sensor system for monitoring and control is integrated and developed by one UWASA Node, one Linux board, and SurfNet nodes. Firstly, the basics of WSN including IEEE 802.15.4 and ZigBee standard are introduced. Secondly, a new design and development of the hardware and software for the wireless sensor system is explained in detail. After that, several experiments are performed to verify the system performance due to the limited computational and power source of the sensor nodes in the WSN. In one word, this developed wireless sensor system provides a wireless solution for remote monitoring and control of the deployed environment.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Catch-up cycle: A general equilibrium framework

Certain stylized facts are common among successful economic latecomers: an inverse U-shaped gross domestic product and capital per capita growth rate, high growth rates during the catch-up period, and rapid structural changes. This paper, for the first time, proposes a general equilibrium framework to document the catch-up cycle that a successful latecomer is likely to experience. We argue that technology adoption and imitation, and the diminishing marginal returns to capital are the two driving forces of the catch-up cycle. The technological gap and speed/efficiency of technological catching-up are two fundamental factors for successful catching-up. This paper concludes with a case study for the People's Republic of China and sheds light on the different policy choices in various stages of the catch-up cycle

Spectral Compressive Sensing with Model Selection

The performance of existing approaches to the recovery of frequency-sparse signals from compressed measurements is limited by the coherence of required sparsity dictionaries and the discretization of frequency parameter space. In this paper, we adopt a parametric joint recovery-estimation method based on model selection in spectral compressive sensing. Numerical experiments show that our approach outperforms most state-of-the-art spectral CS recovery approaches in fidelity, tolerance to noise and computation efficiency.Comment: 5 pages, 2 figures, 1 table, published in ICASSP 201

An investigation into machine pattern recognition based on time-frequency image feature extraction using a support vector machine

In this article, a new method of pattern recognition for machine working conditions is presented that is based on time-frequency image (TFI) feature extraction and support vector machines (SVMs). In this study, the Hilbert time-frequency spectrum (HTFS) is used to construct TFIs because of its good performance in non-stationary and non-linear signal analysis. Cyclostationarity signal analysis is a pre-processing method for improving the performance of the HTFS in the construction of TFIs. Feature extraction for TFIs is investigated in detail to construct a feature vector for pattern recognition. Gravity centre and information entropy of TFIs are used to construct the feature vector for pattern recognition. SVMs are used for different working conditions classification by the constructed feature vector because of its powerful performance even for small samples. In the end, rolling bearing pattern recognition is used as an example to testify the effectiveness of this method. According to the result analysis, it can be concluded that this method will contribute to the development of preventative maintenance

An investigation into catalysts to improve low temperature performance in the selective catalytic reduction of NO with NH3

Selective catalytic reduction with NH3 is considered one of the most effective technologies controlling NOx emission. Metal Fe-based catalysts were used in the investigation to improve low temperature performance of NOx conversion. The temperature range studied was betweet 15 degrees C and 350 degrees C in increments of 50 degrees C. The honeycomb catalysts were prepared by an impregnation method. The study also included characterisation of catalysts by BET, XRD, H2-TPR and XPS methods. It was found that an increase in metal Fe content from 2 to 6% wt offered an improvement in the catalytic performance. However, a further increase in Fe content resulted in a decrease in its performance. More than 90% NOx conversion rate could be achieved over the Fe-based honeycomb catalyst at a low temperature by doping with different weights of Ni and Zr metals. Amongst all the catalysts studied, the mixed metal catalyst of Fe-Ni-Zr was the one with most potential. This was because of its higher NOx conversion rate at a low temperature and also because of its wider operating temperature window. The effect of gas hourly space velocity (GHSV) was also investigated and the results showed that as GHSV increased, the reduction of NOx decreased