Dynamics and Stability Analysis of IPMSM Position Sensorless Control for xEV Drive System

芝浦工業大学2019年

The Study on Effects of Foreign Ownership on Innovation

In developing countries, government actively promotes foreign investment in order to adapt the new and latest technology. This leads to greater R&D activities, thus this creates knowledge and technology spillover. In this paper, we look at Korea where the R&D has been the main factor of rapid growth. We study the effects of foreign ownership on technological performance by looking at 756 R&D intensive Korean firms from 1999 to 2009. We look the number of applied and registered patents are dependent variables (as a technological performance) and observe statistically significant and positive correlation with foreign ownership due to three main reasons: (a) knowledge and technology spillover, (b) relatively more risk-taking investment behavior of institutional investors, and (c) cherry-picking strategy of investing in firms that perform well. Furthermore, we also observe the R&D expenditure has a strong and positive correlation with the number of applied and registered patents, and R&D expenditure could serve as a proxy variable for technologically advanced industries. Lastly, we observe that the coefficients increase for applied and registered patents for different technology index sub-groups

Robust Evaluation of Diffusion-Based Adversarial Purification

We question the current evaluation practice on diffusion-based purification methods. Diffusion-based purification methods aim to remove adversarial effects from an input data point at test time. The approach gains increasing attention as an alternative to adversarial training due to the disentangling between training and testing. Well-known white-box attacks are often employed to measure the robustness of the purification. However, it is unknown whether these attacks are the most effective for the diffusion-based purification since the attacks are often tailored for adversarial training. We analyze the current practices and provide a new guideline for measuring the robustness of purification methods against adversarial attacks. Based on our analysis, we further propose a new purification strategy improving robustness compared to the current diffusion-based purification methods.Comment: Accepted by ICCV 2023, Oral presentatio

GM-VAE: Representation Learning with VAE on Gaussian Manifold

We propose a Gaussian manifold variational auto-encoder (GM-VAE) whose latent space consists of a set of diagonal Gaussian distributions. It is known that the set of the diagonal Gaussian distributions with the Fisher information metric forms a product hyperbolic space, which we call a Gaussian manifold. To learn the VAE endowed with the Gaussian manifold, we first propose a pseudo Gaussian manifold normal distribution based on the Kullback-Leibler divergence, a local approximation of the squared Fisher-Rao distance, to define a density over the latent space. With the newly proposed distribution, we introduce geometric transformations at the last and the first of the encoder and the decoder of VAE, respectively to help the transition between the Euclidean and Gaussian manifolds. Through the empirical experiments, we show competitive generalization performance of GM-VAE against other variants of hyperbolic- and Euclidean-VAEs. Our model achieves strong numerical stability, which is a common limitation reported with previous hyperbolic-VAEs.Comment: 17 pages, 7 figure

Performance analysis on 'new growth engine action plan' in Korea

Thesis(Master) --KDI School:Master of Public Policy,2017While actively fostering new growth engine as the next generation technology all over the world, Korea government also has been striving for economic development by promoting new growth engine industries by carrying out public R&D support since 2009. This study conducts performance analysis on ‘New Growth Engine Action Plan’ by concentrating on new growth engine firms’ growth in quantitative and qualitative aspects. It uses average sales growth rate and average added value growth rate analysis on Korean firms in 30 new growth engine sectors. First, this study finds that the average sales growth rate in most new growth engine sectors keeps decreasing after the policy implementation while the average added value growth rate sharply increases in the short-term, but decreases soon with a large deviation in the intermediate-term. This pattern accords with Schumpeterian theory Mark I, which explains that firms are hard to generate healthy revenue during the process of innovating the new technologies. In addition, the policy effect is most effective when the new growth engine sectors are in the early 1st stages of technology introduction by fully absorbing the R&D support. ‘New Growth Engine Action Plan’ can be regarded as a successful policy since it has clearly supported in improving the firm’s innovation capacity in qualitative aspect despite of the short-term effect.I. Introduction II. Literature Review III. Research Method IV. Results V. ConclusionmasterpublishedDongwoo LEE

EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost

Graph-based models have become increasingly important in various domains, but the limited size and diversity of existing graph datasets often limit their performance. To address this issue, we propose EPIC (Edit Path Interpolation via learnable Cost), a novel interpolation-based method for augmenting graph datasets. Our approach leverages graph edit distance to generate new graphs that are similar to the original ones but exhibit some variation in their structures. To achieve this, we learn the graph edit distance through a comparison of labeled graphs and utilize this knowledge to create graph edit paths between pairs of original graphs. With randomly sampled graphs from a graph edit path, we enrich the training set to enhance the generalization capability of classification models. We demonstrate the effectiveness of our approach on several benchmark datasets and show that it outperforms existing augmentation methods in graph classification tasks

Hybrid cell line development system utilizing site-specific integration and methotrexate- mediated gene amplification in Chinese Hamster Ovary cells

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Combinatorial Discovery of Irradiation Damage Tolerant Nano-structured W-based alloys

One of the challenges in fusion reactors is the discovery of plasma facing materials capable of withstanding extreme conditions, such as radiation damage and high heat flux. Development of fusion materials can be a daunting task since vast combinations of microstructures and compositions need to be explored, each of which requires trial-and-error based irradiation experiments and materials characterizations. Here, we utilize combinatorial experiments that allow rapid and systematic characterizations of composition-microstructure dependent irradiation damage behaviors of nanostructured tungsten alloys. The combinatorial materials library of W-Re-Ta alloys was synthesized, followed by the high-throughput experiments for probing irradiation damages to the mechanical, thermal, and structural properties of the alloys. This highly efficient technique allows rapid identification of composition ranges with excellent damage tolerance. We find that the distribution of implanted He clusters can be significantly altered by the addition of Ta and Re, which play a critical role in determining property changes upon irradiation