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Department of Management Engineeringclos

Ordinal-imbalanced data classification through data reduction by singular value decomposing truncation

In the real world, multi-class ordinal data classification problems occur frequently. Most ordinal classifiers were constructed assuming that a class distribution is balanced, but most ordinal data have skewed class distributions. The class imbalance degrades the performance of traditional learning. Many papers address the difficulty of the class imbalance but pay little attention to the imbalance arising in ordinary class data. So, we analyze the imbalance issue of ordinal data. This paper introduces a matrix factorization method of preprocessing algorithm called singular value decomposition (SVD) truncation for ordinal classification. It has a role of noise reduction which is an effective method for the imbalance issue. Also, the proposed method diminishes an overlapping area that also has a positive effect on dealing the imbalance. Furthermore, the suggested preprocess algorithm does not modify class distributions. It complements the weaknesses of existing sampling methods such as loss information and over-fitting. We used the wkNN algorithm for ordinal classification after the proposed preprocessing technique for experiments. Experimental results on actual ordinal data verify the usefulness of the methodology. ?? 2019 IISE Annual Conference and Expo 2019. All rights reserved

Sensor drift compensation for gas mixture classification in batch experiments

Sensor drift in batch experiments is a well-known problem in mixed gas classification. In batch experiments, gas sensors can be easily affected by environmental covariates that hinder mixed gas classification. To address this problem, we propose a novel end-to-end deep learning model comprising a drift-compensation module and classification module. Utilizing the nonlinear relationship between sensor readings and environmental covariates, the drift-compensation module corrects the drifted sensor readings in batch experiments by minimizing a scatteredness-based fitness function. The corrected values are then fed into the classification module. To train the proposed model, which involves optimizing two different objectives simultaneously, the hypernetwork-based optimization approach with the stochastic gradient descent is employed. We validated the effectiveness of the proposed method for mixed gas classification using synthetic and real gas mixture data collected from the UCI machine learning repository

Self energy recycling techniques for MIMO wireless communication systems

In this paper, we study self energy recycling techniques for point-to-point multiple-input multiple-output systems where a full-duplex transmitter with multiple antennas communicates with a multi-antenna receiver. Due to the full-duplex nature, the transmitter receives a signal transmitted by itself through a loop-back channel. Then, the energy of the signal is harvested and stored in an energy storage. Assuming time-slotted systems, we propose a new communication protocol in which the harvested energy at the transmitter is recycled for future data transmissions to the receiver. Under this setup, we present a transmit covariance matrix optimization method in order to maximize the sum rate performance for two different cases. First, for a perfect channel state information (CSI) case, the globally optimal algorithm for the sum rate maximization problem is proposed. Next, for an imperfect CSI case, we provide a robust covariance matrix optimization approach where the worst-case sum rate performance can be maximized. Numerical results demonstrate that the proposed methods offer a significant performance gain over conventional schemes

Sparse Depth-Guided Image Enhancement Using Incremental GP with Informative Point Selection

We propose an online dehazing method with sparse depth priors using an incremental Gaussian Process (iGP). Conventional approaches focus on achieving single image dehazing by using multiple channels. In many robotics platforms, range measurements are directly available, except in a sparse form. This paper exploits direct and possibly sparse depth data in order to achieve efficient and effective dehazing that works for both color and grayscale images. The proposed algorithm is not limited to the channel information and works equally well for both color and gray images. However, efficient depth map estimations (from sparse depth priors) are additionally required. This paper focuses on a highly sparse depth prior for online dehazing. For efficient dehazing, we adopted iGP for incremental depth map estimation and dehazing. Incremental selection of the depth prior was conducted in an information-theoretic way by evaluating mutual information (MI) and other information-based metrics. As per updates, only the most informative depth prior was added, and haze-free images were reconstructed from the atmospheric scattering model with incrementally estimated depth. The proposed method was validated using different scenarios, color images under synthetic fog, real color, and grayscale haze indoors, outdoors, and underwater scenes

SERS-based immunoassay of tumor marker VEGF using DNA aptamers and silica-encapsulated hollow gold nanospheres

A novel SERS-based sandwich immunoassay using DNA aptamers, silica-encapsulated hollow gold nanospheres (SEHGNs) and a gold-patterned microarray was developed for sensitive detection of VEGF (vascular endothelial growth factor) angiogenesis protein markers. Here, a DNA aptamer conjugated to SEHGN was used as a highly reproducible SERS-encoding nanoprobe, and a hybrid microarray including hydrophilic gold wells and other hydrophobic areas was used as a SERS substrate. Target specific DNA aptamers that fold into a G-quadruplex structure were used as a target recognition unit instead of VEGF antibodies. The detection sensitivity was increased by 2 or 3 orders of magnitude over the conventional ELISA method. In particular, the dynamic concentration range was 3 or 4 orders of magnitude greater than that of conventional ELISA. The results demonstrate that this sensing strategy using DNA aptamers is a powerful platform for the design of novel immune-sensors with high performance. In particular, SERS-based detection using SEHGNs provides great promise for highly sensitive biomarker sensing with unprecedented advantages.A novel SERS-based sandwich immunoassay using DNA aptamers, silica-encapsulated hollow gold nanospheres (SEHGNs) and a gold-patterned microarray was developed for sensitive detection of VEGF (vascular endothelial growth factor) angiogenesis protein markers. Here, a DNA aptamer conjugated to SEHGN was used as a highly reproducible SERS-encoding nanoprobe, and a hybrid microarray including hydrophilic gold wells and other hydrophobic areas was used as a SERS substrate. Target specific DNA aptamers that fold into a G-quadruplex structure were used as a target recognition unit instead of VEGF antibodies. The detection sensitivity was increased by 2 or 3 orders of magnitude over the conventional ELISA method. In particular, the dynamic concentration range was 3 or 4 orders of magnitude greater than that of conventional ELISA. The results demonstrate that this sensing strategy using DNA aptamers is a powerful platform for the design of novel immune-sensors with high performance. In particular, SERS-based detection using SEHGNs provides great promise for highly sensitive biomarker sensing with unprecedented advantages