Latent Filling: Latent Space Data Augmentation for Zero-shot Speech Synthesis

Previous works in zero-shot text-to-speech (ZS-TTS) have attempted to enhance its systems by enlarging the training data through crowd-sourcing or augmenting existing speech data. However, the use of low-quality data has led to a decline in the overall system performance. To avoid such degradation, instead of directly augmenting the input data, we propose a latent filling (LF) method that adopts simple but effective latent space data augmentation in the speaker embedding space of the ZS-TTS system. By incorporating a consistency loss, LF can be seamlessly integrated into existing ZS-TTS systems without the need for additional training stages. Experimental results show that LF significantly improves speaker similarity while preserving speech quality.Comment: Accepted to ICASSP 202

An Empirical Study on L2 Accents of Cross-lingual Text-to-Speech Systems via Vowel Space

With the recent developments in cross-lingual Text-to-Speech (TTS) systems, L2 (second-language, or foreign) accent problems arise. Moreover, running a subjective evaluation for such cross-lingual TTS systems is troublesome. The vowel space analysis, which is often utilized to explore various aspects of language including L2 accents, is a great alternative analysis tool. In this study, we apply the vowel space analysis method to explore L2 accents of cross-lingual TTS systems. Through the vowel space analysis, we observe the three followings: a) a parallel architecture (Glow-TTS) is less L2-accented than an auto-regressive one (Tacotron); b) L2 accents are more dominant in non-shared vowels in a language pair; and c) L2 accents of cross-lingual TTS systems share some phenomena with those of human L2 learners. Our findings imply that it is necessary for TTS systems to handle each language pair differently, depending on their linguistic characteristics such as non-shared vowels. They also hint that we can further incorporate linguistics knowledge in developing cross-lingual TTS systems.Comment: Submitted to ICASSP 202

Microspinning: Local Surface Mixing via Rotation of Magnetic Microparticles for Efficient Small-Volume Bioassays

The need for high-throughput screening has led to the miniaturization of the reaction volume of the chamber in bioassays. As the reactor gets smaller, surface tension dominates the gravitational or inertial force, and mixing efficiency decreases in small-scale reactions. Because passive mixing by simple diffusion in tens of microliter-scale volumes takes a long time, active mixing is needed. Here, we report an efficient micromixing method using magnetically rotating microparticles with patterned magnetization induced by magnetic nanoparticle chains. Because the microparticles have magnetization patterning due to fabrication with magnetic nanoparticle chains, the microparticles can rotate along the external rotating magnetic field, causing micromixing. We validated the reaction efficiency by comparing this micromixing method with other mixing methods such as simple diffusion and the use of a rocking shaker at various working volumes. This method has the potential to be widely utilized in suspension assay technology as an efficient mixing strategy

Flexible and Stretchable Temperature Sensors with High Sensitivity

Since the rapid growth of smart technology for the last several years, the trend of technology is focused on smarter, and more human wearable devices. Following this trend, the development of a flexible, stretchable, very accurate, and temperature sensitive sensor is attracting much attention. Temperature sensitive sensors are not only limited in a certain area, but also are widely used in many different fields such as aerospace, nuclear, mechanical, chemical, medical, food and agriculture industries [1]. Recently, in accordance with the advances in materials for stretchable electronics, researchers have pioneered new fields of applications, especially in systems involving intimate integration with the human skin [2]. For this research, I have worked with two different types of material to build high thermal coefficient resistant (TCR) sensors for precisely and continuously monitoring temperature change of human skin; gold-doped silicon and a carbon nanotube (CNT)/PDMS mixture.U of I OnlySenior thesis not recommended for open acces

An Intelligent Process to Estimate the Nonlinear Behaviors of an Elasto-Plastic Steel Coil Damper Using Artificial Neural Networks

This study developed a nonlinear behavior prediction model for elasto-plastic steel coil dampers (SCDs) using artificial neural networks (ANN). To train the ANN, first, the input and output data of the behavior of the elasto-plastic SCD was prepared. This study utilized the design parameters and load–displacement curves of the SCD to train the ANN. The elasto-plastic load–displacement curve of the SCD was obtained from simulation results using an ANSYS workbench. The design parameters (wire diameter, internal diameter, number of active windings, yield strength) of the SCD were defined as the input patterns, while the yield deformation, first stiffness, and second stiffness were output patterns. During learning of the neural network model, 60 datasets of the SCD were used as the learning pattern, and the remaining 21 were used to verify the model. Although this study used a small number of learning patterns, the ANN predicted accurate results for yield displacement, first stiffness, and second stiffness. In this study, the ANN was found to perform very well, predicting the nonlinear response of the SCD, compared with the values obtained from a finite element analysis program

An Intelligent Process to Estimate the Nonlinear Behaviors of an Elasto-Plastic Steel Coil Damper Using Artificial Neural Networks

This study developed a nonlinear behavior prediction model for elasto-plastic steel coil dampers (SCDs) using artificial neural networks (ANN). To train the ANN, first, the input and output data of the behavior of the elasto-plastic SCD was prepared. This study utilized the design parameters and load–displacement curves of the SCD to train the ANN. The elasto-plastic load–displacement curve of the SCD was obtained from simulation results using an ANSYS workbench. The design parameters (wire diameter, internal diameter, number of active windings, yield strength) of the SCD were defined as the input patterns, while the yield deformation, first stiffness, and second stiffness were output patterns. During learning of the neural network model, 60 datasets of the SCD were used as the learning pattern, and the remaining 21 were used to verify the model. Although this study used a small number of learning patterns, the ANN predicted accurate results for yield displacement, first stiffness, and second stiffness. In this study, the ANN was found to perform very well, predicting the nonlinear response of the SCD, compared with the values obtained from a finite element analysis program

FLUIDIC SELF-ASSEMBLY TRANSFER TECHNOLOGY FOR MICRO-LED DISPLAY

In this paper, we present a demonstration of high yield Fluidic Self-Assembly (FSA) technology to transfer gallium nitride (GaN) microchips which can be used for Micro-LED display. The low melting point alloy on the substrate and the metal electrode of the chip were assembled in heated solution by a simple shaking motion. More than 19,000 blue GaN microchips with 45um in diameter were precisely assembled at 99.90% yield within 1 min. At the chip sizes below 50um, this dramatic improvement in assembly yield was achieved by using a new assembly solution and chip designs. This low cost and fast demonstration has proven that FSA is a suitable mass transfer technology which is applicable to Micro-LED display after some further development.N

Application of Tuned Mass Damper to Mitigation of the Seismic Responses of Electrical Equipment in Nuclear Power Plants

A tuned mass damper (TMD) was developed for mitigating the seismic responses of electrical equipment inside nuclear power plants (NPPs), in particular, the response of an electrical cabinet. A shaking table test was performed, and the frequency and damping ratio were extracted, to confirm the dynamics of the cabinet. Electrical cabinets with and without TMDs were modeled while using SAP2000 software (Version 20, Computers and Structures, NY, USA) that was based on the results. TMDs were designed while using an optimization method and the equations of Den Hartog, Warburton, and Sadek. The numerical models were verified while using the shaking table test results. A sinusoidal sweep wave was applied as input to identify the vibration characteristics of the electrical cabinet over a wide frequency range. Applying various seismic loads that were adjusted to meet the RG 1.60 design response spectrum of 0.3 g then validated the control performance of the TMD. The minimum and maximum response spectrum reduction rates of the designed TMDs were 44.7% and 62.9%, respectively. Further, the amplification factor of the electrical cabinet with the TMD was decreased by 53%, on average, with the proposed optimization method. In conclusion, TMDs can be considered to be an effective way of enhancing the seismic performance of the electrical equipment inside NPPs

Vibration Control of Nuclear Power Plant Piping System Using Stockbridge Damper under Earthquakes

Generally the piping system of a nuclear power plant (NPP) has to be designed for normal loads such as dead weight, internal pressure, temperature, and accidental loads such as earthquake. In the proposed paper, effect of Stockbridge damper to mitigate the response of piping system of NPP subjected to earthquake is studied. Finite element analysis of piping system with and without Stockbridge damper using commercial software SAP2000 is performed. Vertical and horizontal components of earthquakes such as El Centro, California, and Northridge are used in the piping analysis. A sine sweep wave is also used to investigate the control effects on the piping system under wide frequency range. It is found that the proposed Stockbridge damper can reduce the seismic response of piping system subjected to earthquake loading

Textile-fiber-embedded multiluminescent devices: A new approach to soft display systems

In the recent remarkable advances in soft electronic systems, light-emitting functions play a prominent role. In particular, polymer composite systems with embedded luminescent particles have attracted considerable attention as a luminescent component owing to their flexibility and simple fabrication. However, most flexible composite-based electroluminescent (EL) devices have coplanar structures, requiring mechanically compliant electrodes with high transmittance, durability, and stable electrical conductivity. This is a limitation for systems designed for providing superior flexible characteristics without loss of luminescence. Here, we introduce a novel EL device architecture—a durable/flexible textile-fiber-embedded polydimethylsiloxane and zinc sulfide (PDMS + ZnS) composite, driven by an in-plane electric field, which eliminates the requirement for high transmittance. On applying an AC voltage, light is radially emitted from the ZnS particles surrounding the fibers, originating from the radially distributed electric/optical fields; the rolling and stretching flexibilities are maintained during this process. The device also exhibits strong EL intensities in a thick emitting layer—a parameter on which EL and mechanoluminescent (ML) intensities in coplanar structures are dependent. This is because the electric field is applied between in-plane fibers. Using this smart design, simultaneously high EL and ML intensities can be simply achieved by embedding fibers in strong ML-emitting PDMS + ZnS. We also present a patterned device controlled by different fiber embedding depths, utilizing the vertical and in-plane electric fields. This application may provide a basis for the development of emerging soft display systems that require high luminescence as well as flexibility in the light-emitting components. © 2019 Elsevier Ltd1