Residual Continual Learning

We propose a novel continual learning method called Residual Continual Learning (ResCL). Our method can prevent the catastrophic forgetting phenomenon in sequential learning of multiple tasks, without any source task information except the original network. ResCL reparameterizes network parameters by linearly combining each layer of the original network and a fine-tuned network; therefore, the size of the network does not increase at all. To apply the proposed method to general convolutional neural networks, the effects of batch normalization layers are also considered. By utilizing residual-learning-like reparameterization and a special weight decay loss, the trade-off between source and target performance is effectively controlled. The proposed method exhibits state-of-the-art performance in various continual learning scenarios.Comment: AAAI 202

Continual Learning with Extended Kronecker-factored Approximate Curvature

We propose a quadratic penalty method for continual learning of neural networks that contain batch normalization (BN) layers. The Hessian of a loss function represents the curvature of the quadratic penalty function, and a Kronecker-factored approximate curvature (K-FAC) is used widely to practically compute the Hessian of a neural network. However, the approximation is not valid if there is dependence between examples, typically caused by BN layers in deep network architectures. We extend the K-FAC method so that the inter-example relations are taken into account and the Hessian of deep neural networks can be properly approximated under practical assumptions. We also propose a method of weight merging and reparameterization to properly handle statistical parameters of BN, which plays a critical role for continual learning with BN, and a method that selects hyperparameters without source task data. Our method shows better performance than baselines in the permuted MNIST task with BN layers and in sequential learning from the ImageNet classification task to fine-grained classification tasks with ResNet-50, without any explicit or implicit use of source task data for hyperparameter selection.Comment: CVPR 202

CNT Foam-Embedded Micro Gas Preconcentrator for Low-Concentration Ethane Measurements

Breath analysis has become increasingly important as a noninvasive process for the clinical diagnosis of patients suffering from various diseases. Many commercial gas preconcentration instruments are already being used to overcome the detection limits of commercial gas sensors for gas concentrations which are as low as ~100 ppb in exhaled breath. However, commercial instruments are large and expensive, and they require high power consumption and intensive maintenance. In the proposed study, a micro gas preconcentrator (μ-PC) filled with a carbon nanotube (CNT) foam as an adsorbing material was designed and fabricated for the detection of low-concentration ethane, which is known to be one of the most important biomarkers related to chronic obstructive pulmonary disease (COPD) and asthma. A comparison of the performance of two gas-adsorbing materials, i.e., the proposed CNT foam and commercial adsorbing material, was performed using the developed μ-PC. The experimental results showed that the synthesized CNT foam performs better than a commercial adsorbing material owing to its lower pressure drop and greater preconcentration efficiency in the μ-PC. The present results show that the application of CNT foam-embedded μ-PC in portable breath analysis systems holds great promise

Development of Open-Tubular-Type Micro Gas Chromatography Column with Bump Structures

Gas chromatography (GC) is the chemical analysis technique most widely used to separate and identify gas components, and it has been extensively applied in various gas analysis fields such as non-invasive medical diagnoses, indoor air quality monitoring, and outdoor environmental monitoring. Micro-electro-mechanical systems (MEMS)-based GC columns are essential for miniaturizing an integrated gas analysis system (Micro GC system). This study reports an open-tubular-type micro GC (μ-GC) column with internal bump structures (bump structure μ-GC column) that substantially increase the interaction between the gas mixture and a stationary phase. The developed bump structure μ-GC column, which was fabricated on a 2 cm × 2 cm μ-GC chip and coated with a non-polar stationary phase, is 1.5 m-long, 150 μm-wide, and 400 μm-deep. It has an internal microfluidic channel in which the bumps, which are 150 μm diameter half-circles, are alternatingly disposed to face each other on the surface of the microchannel. The fabricated bump structure μ-GC column yielded a height-equivalent-to-a-theoretical-plate (HETP) of 0.009 cm (11,110 plates/m) at an optimal carrier gas velocity of 17 cm/s. The mechanically robust bump structure μ-GC column proposed in this study achieved higher separation efficiency than a commercially available GC column and a typical μ-GC column with internal post structures classified as a semi-packed-type column. The experimental results demonstrate that the developed bump structure μ-GC column can separate a gas mixture completely, with excellent separation resolution for formaldehyde, benzene, toluene, ethylbenzene, and xylene mixture, under programmed operating temperatures

A Transfer Learning Architecture Based on a Support Vector Machine for Histopathology Image Classification

Recently, digital pathology is an essential application for clinical practice and medical research. Due to the lack of large annotated datasets, the deep transfer learning technique is often used to classify histopathology images. A softmax classifier is often used to perform classification tasks. Besides, a Support Vector Machine (SVM) classifier is also popularly employed, especially for binary classification problems. Accurately determining the category of the histopathology images is vital for the diagnosis of diseases. In this paper, the conventional softmax classifier and the SVM classifier-based transfer learning approach are evaluated to classify histopathology cancer images in a binary breast cancer dataset and a multiclass lung and colon cancer dataset. In order to achieve better classification accuracy, a methodology that attaches SVM classifier to the fully-connected (FC) layer of the softmax-based transfer learning model is proposed. The proposed architecture involves a first step training the newly added FC layer on the target dataset using the softmax-based model and a second step training the SVM classifier with the newly trained FC layer. Cross-validation is used to ensure no bias for the evaluation of the performance of the models. Experimental results reveal that the conventional SVM classifier-based model is the least accurate on either binary or multiclass cancer datasets. The conventional softmax-based model shows moderate classification accuracy, while the proposed synthetic architecture achieves the best classification accuracy

Online Monitoring of Power Converter Degradation Using Deep Neural Network

Power semiconductor devices in the power converters used for motor drives are susceptible to wear-out and failure, especially when operated in harsh environments. Therefore, detection of degradation of power devices is crucial for ensuring the reliable performance of power converters. In this paper, a deep learning approach for online classification of the health states of the snubber resistors in the Insulated Gate Bipolar Transistors (IGBTs) in a three-phase Brushless DC (BLDC) motor drive is proposed. The method can locate one out of the six IGBTs experiencing a snubber resistor degradation problem by measuring the voltage waveforms of the three shunt resistors using voltage sensors. The range of the degradation of the snubber resistors for successful classification is also investigated. The off-the-shelf deep Convolutional Neural Network (CNN) architecture ResNet50 is used for transfer learning to determine which snubber resistor has degraded. The dataset for evaluating the above classification scheme of IGBT degradation is obtained by measuring the shunt voltage waveforms with varying snubber resistance and reference current. Then, the three-phase voltage waveforms are converted into greyscale images and RGB spectrogram images, which are later fed into the deep CNN. Experiments are carried out on the greyscale image dataset and the spectrogram image dataset using four-fold cross-validation. The results show that the proposed scheme can classify seven classes (one class for normal condition and six classes for abnormal condition in one of the six IGBTs in a three-phase BLDC drive) with over 95% average accuracy within a specific range of snubber resistance. Using grayscale images and using spectrogram-based RGB images yields similar accuracy

Patch-Wise Attention Network for Monocular Depth Estimation

In computer vision, monocular depth estimation is the problem of obtaining a high-quality depth map from a two-dimensional image. This map provides information on three-dimensional scene geometry, which is necessary for various applications in academia and industry, such as robotics and autonomous driving. Recent studies based on convolutional neural networks achieved impressive results for this task. However, most previous studies did not consider the relationships between the neighboring pixels in a local area of the scene. To overcome the drawbacks of existing methods, we propose a patch-wise attention method for focusing on each local area. After extracting patches from an input feature map, our module generates attention maps for each local patch, using two attention modules for each patch along the channel and spatial dimensions. Subsequently, the attention maps return to their initial positions and merge into one attention feature. Our method is straightforward but effective. The experimental results on two challenging datasets, KITTI and NYU Depth V2, demonstrate that the proposed method achieves significant performance. Furthermore, our method outperforms other state-of-the-art methods on the KITTI depth estimation benchmark

Human-Robot Interface to Operate Robotic Systems via Muscle Synergy-Based Kinodynamic Information Transfer

When a human performs a given specific task, it has been known that the central nervous system controls modularized muscle group, which is called muscle synergy. For human-robot interface design problem, therefore, the muscle synergy can be utilized to reduce the dimensionality of control signal as well as the complexity of classifying human posture and motion. In this paper, we propose an approach to design a human-robot interface which enables a human operator to transfer a kinodynamic control command to robotic systems. A key feature of the proposed approach is that the muscle synergy and corresponding activation curve are employed to calculate a force generated by a tool at the robot end effector. A test bed for experiments consisted of two armband type surface electromyography sensors, an RGB-d camera, and a Kinova Gen2 robotic manipulator to verify the proposed approach. The result showed that both force and position commands could be successfully transferred to the robotic manipulator via our muscle synergy-based kinodynamic interface.Comment: 4 pages, 8 figures, 1 table, in Proceedings of the 19th Conference on Ubiquitous Robots(UR), July 4-6, 2022, Jeju, Kore

Highly Increased Flow-Induced Power Generation on Plasmonically Carbonized Single-Walled Carbon Nanotube

We generate networks and carbonization between individualized single-walled carbon nanotubes (SWCNTs) by an optimized plasmonic heating process using a halogen lamp to improve electrical properties for flow-induced energy harvesting. These properties were characterized by Raman spectra, a field-emission-scanning probe, transmission electron microscopy, atomic force microscopy and thermographic camera. The electrical sheet resistance of carbonized SWCNTs was decreased to 2.71 kΩ/□, 2.5 times smaller than normal-SWCNTs. We demonstrated flow-induced voltage generation on SWCNTs at various ion concentrations of NaCl. The generated voltage and current for the carbonized-SWCNTs were 9.5 and 23.5 times larger than for the normal-SWCNTs, respectively, based on the electron dragging mechanism

Spatio-spectral 4D coherent ranging using a flutter-wavelength-swept laser

Abstract Coherent light detection and ranging (LiDAR), particularly the frequency-modulated continuous-wave LiDAR, is a robust optical imaging technology for measuring long-range distance and velocity in three dimensions (3D). We propose a spatio-spectral coherent LiDAR based on a unique wavelength-swept laser to enable both axial coherent ranging and lateral spatio-spectral beam scanning simultaneously. Instead of the conventional unidirectional wavelength-swept laser, a flutter-wavelength-swept laser (FWSL) successfully decoupled bidirectional wavelength modulation and continuous wavelength sweep, which overcame the measurable distance limited by the sampling process. The decoupled operation in FWSL enabled sequential sampling of flutter-wavelength modulation across its wide spectral bandwidth of 160 nm and, thus, allowed simultaneous distance and velocity measurement over an extended measurable distance. Herein, complete four-dimensional (4D) imaging, combining real-time 3D distance and velocity measurements, was implemented by solid-state beam scanning. An acousto-optic scanner was synchronized to facilitate the other lateral beam scanning, resulting in an optimized solid-state coherent LiDAR system. The proposed spatio-spectral coherent LiDAR system achieved high-resolution coherent ranging over long distances and real-time 4D imaging with a frame rate of 10 Hz, even in challenging environments