http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Visuomotor anomalies in achiasmatic mice expressing a transfer-defective Vax1 mutant
Min Kwang Wook,Kim Namsuk,Lee Jae Hoon,Sung Younghoon,Kim Museong,Lee Eun Jung,Kim Jong-Myeong,Kim Jae-Hyun,Lee Jaeyoung,Cho Wonjin,Yang Jee Myung,Kim Nury,Kim Jaehoon,Lee C. Justin,Park Young-Gyun,Le 생화학분자생물학회 2023 Experimental and molecular medicine Vol.55 No.-
In binocular animals that exhibit stereoscopic visual responses, the axons of retinal ganglion cells (RGCs) connect to brain areas bilaterally by forming a commissure called the optic chiasm (OC). Ventral anterior homeobox 1 (Vax1) contributes to the formation of the OC, acting endogenously in optic pathway cells and exogenously in growing RGC axons. Here, we generated Vax1AA/AA mice expressing the Vax1AA mutant, which is incapable of intercellular transfer. We found that RGC axons cannot take up Vax1AA protein from the Vax1AA/AA mouse optic stalk (OS) and grow slowly to arrive at the hypothalamus at a late stage. The RGC axons of Vax1AA/AA mice connect exclusively to ipsilateral brain areas after failing to access the midline, resulting in reduced visual acuity and abnormal oculomotor responses. Overall, our study provides physiological evidence for the necessity of intercellular transfer of Vax1 and the importance of the bilateral RGC axon projection in proper visuomotor responses.
김무성(Museong Kim),김남규(Namgyu Kim) 한국컴퓨터정보학회 2021 韓國컴퓨터情報學會論文誌 Vol.26 No.1
최근 딥 러닝(Deep Learning) 분석에 이질적인 데이터를 함께 사용하는 멀티모달(Multi-modal) 딥러닝 기술이 많이 활용되고 있으며, 특히 텍스트로부터 자동으로 이미지를 생성해내는 Text to Image 합성에 관한 연구가 활발하게 수행되고 있다. 이미지 합성을 위한 딥러닝 학습은 방대한 양의 이미지와 이미지를 설명하는 텍스트의 쌍으로 구성된 데이터를 필요로 하므로, 소량의 데이터로부터 다량의 데이터를 생성하기 위한 데이터 증강 기법이 고안되어 왔다. 텍스트 데이터 증강의 경우 유의어 대체에 기반을 둔 기법들이 다수 사용되고 있지만, 이들 기법은 명사 단어의 유의어 대체 시 이미지의 내용과 상이한 텍스트를 생성할 가능성이 있다는 한계를 갖는다. 따라서 본 연구에서는 단어가 갖는 품사별 특징을 활용하는 텍스트 데이터 증강 방안, 즉 일부 품사에 대해 단어 계층 정보를 활용하여 단어를 대체하는 방안을 제시하였다. 또한 제안 방법론의 성능을 평가하기 위해 MSCOCO 데이터를 사용하여 실험을 수행하여 결과를 제시하였다. Recently, multi-modal deep learning techniques that combine heterogeneous data for deep learning analysis have been utilized a lot. In particular, studies on the synthesis of Text to Image that automatically generate images from text are being actively conducted. Deep learning for image synthesis requires a vast amount of data consisting of pairs of images and text describing the image. Therefore, various data augmentation techniques have been devised to generate a large amount of data from small data. A number of text augmentation techniques based on synonym replacement have been proposed so far. However, these techniques have a common limitation in that there is a possibility of generating a incorrect text from the content of an image when replacing the synonym for a noun word. In this study, we propose a text augmentation method to replace words using word hierarchy information for noun words. Additionally, we performed experiments using MSCOCO data in order to evaluate the performance of the proposed methodology.
다중 레이블 분류의 정확도 향상을 위한 스킵 연결 오토인코더 기반 레이블 임베딩 방법론
김무성(Museong Kim),김남규(Namgyu Kim) 한국지능정보시스템학회 2021 지능정보연구 Vol.27 No.3
Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology we
김무성(MuSeong Kim),이훈상(HoonSang Lee),황원태(Wontae Hwang) 한국가시화정보학회 2019 한국가시화정보학회지 Vol.17 No.2
Impingement cooling utilizing synthetic jets is emerging as a popular cooling technique because of its high local cooling efficiency. The interaction between the vortex structure of the synthetic jet and the surface is crucial in understanding the mechanism of this technique. In this study, the impinging vortex structure and its advection are investigated by experiments with jet-to-surface spacing 2 ≤ H/D ≤ 7, and synthetic jet Reynolds number 5120 ≤ Re ≤ 9050. Using phase-locked particle image velocimetry, ensemble averaged (phase averaged) flow fields are obtained, and vortex identification and quantification techniques are applied. The shape, trajectory, and intensity change of the vortex are assessed. A sharp decline in the vortex intensity and the occurrence of a counter-rotating vortex at the impingement point are observed.
Ko, Chang-Yong,Kim, Sol-Bi,Kim, Jong Kwon,Chang, Yunhee,Kim, Shinki,Ryu, Jeicheong,Mun, Museong Korean Society for Precision Engineering 2014 International Journal of Precision Engineering and Vol.15 No.12
This study aimed to investigate kinetics and kinematics of the ankle joint during level ground, ramp, and stair ambulations using different types of adaptive ankle feet (AAFs), Proprio-Foot<TEX>$^{TM}$</TEX> (<TEX>$\ddot{O}$</TEX>ssur, IcEland), <TEX>$\acute{e}$</TEX>lan foot (Endolite, USA), and Echelon foot (Endolite, USA). A transtibial amputee was asked to walk on a level ground, a ramp with a <TEX>$7^{\circ}$</TEX> slope, and a stair of height 15 cm. The ankle angle and symmetry index (SI) based on the symmetry of the external work performed were measured for AAFs and ambulation. The single support time and stance phase during ambulation were higher for AAFs than the fixed ankle foot. During level ambulation, dorsiflexion increased for all AAFs. During slope ascent ambulation, dorsiflexion increased for the <TEX>$\acute{e}$</TEX>lan foot and Proprio-Foot<TEX>$^{TM}$</TEX>, as well as during the swing phase of the Echelon foot. During slope descent ambulation, the maximum dorsiflexion decreased for the Proprio-Foot<TEX>$^{TM}$</TEX>. During stair ascent, the <TEX>$\acute{e}$</TEX>lanElan foot and Proprio-Foot<TEX>$^{TM}$</TEX> feet improved the dorsiflexion. During stair descent, all AAFs improved the dorsiflextion. Furthermore, SI increased for most of the adaptive ankle feet for all terrain conditions. These results indicate that AAFs can be used to improve the kinetics and kinematics of the ankle on the involved side.