http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Wi-Fi RSSI Heat Maps Based Indoor Localization System Using Deep Convolutional Neural Networks
Alwin Poulose,Dong Seog Han 한국방송·미디어공학회 2020 한국방송공학회 학술발표대회 논문집 Vol.2020 No.7
An indoor localization system that uses Wi-Fi RSSI signals for localization gives accurate user position results. The conventional Wi-Fi RSSI signal based localization system uses raw RSSI signals from access points (APs) to estimate the user position. However, the RSSI values of a particular location are usually not stable due to the signal propagation in the indoor environments. To reduce the RSSI signal fluctuations, shadow fading, multipath effects and the blockage of Wi-Fi RSSI signals, we propose a Wi-Fi localization system that utilizes the advantages of Wi-Fi RSSI heat maps. The proposed localization system uses a regression model with deep convolutional neural networks (DCNNs) and gives accurate user position results for indoor localization. The experiment results demonstrate the superior performance of the proposed localization system for indoor localization.
Facial Emotion Recognition Using 3D Face Reconstruction
Alwin Poulose,Chinthala Sreya Reddy,Jung Hwan Kim,Dong Seog Han 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
In recent days, autonomous driving systems (ADS) effectively utilize facial emotion recognition (FER) results for safe driving. In FER, the system provides the user emotions such as happy, sad, anger, surprise, disgust, fear, or neutral. These emotions provide helpful information for safe driving and reduce the chances of road accidents. The conventional FER approaches use 2D images as their inputs and classify the user emotions. However, the 2D face images in the conventional FER approaches have limited features for model training. In addition, the features from the 2D face images themselves are not sufficient for accurate emotion classification. To reduce the feature extraction issues in the conventional FER approaches, we propose a 3D face image-based FER approach that uses the 3D face reconstruction technique for converting the 2D face images into 3D face images. The deep convolutional neural networks (DCNNs) used in the proposed FER approach efficiently use the 3D face images as inputs and classify the user emotions with minimum errors. The experiment results show that the proposed 3D face image-based FER approach achieves 99% classification accuracy which is better than the conventional 2D face image-based FER approach.
The Pandemic League of COVID-19: Korea Versus the United States, With Lessons for the Entire World
Alwin Issac,Shine Stephen,Jaison Jacob,Vijay VR,Rakesh Vadakkethil Radhakrishnan,Nadiya Krishnan,Manju Dhandapani 대한예방의학회 2020 예방의학회지 Vol.53 No.4
Coronavirus disease 2019 (COVID-19) is inflicting a brutal blow on humankind, and no corner of the world has been exempted from its wrath. This study analyzes the chief control measures and the distinctive features of the responses implemented by Korea and the United States to contain COVID-19 with the goal of extracting lessons that can be applied globally. Even though both nations reported their index cases on the same day, Korea succeeded in flattening the curve, with 10 752 cases as of April 28, 2020, whereas the outbreak skyrocketed in the United States, which had more than 1 million cases at the same time. The prudent and timely execution of control strategies enabled Korea to tame the spread of the virus, whereas the United States paid a major price for its delay, although it is too early to render a conclusive verdict. Information pertaining to the number of people infected with the virus and measures instituted by the government to control the spread of COVID-19 was retrieved from the United States Centers for Disease Control and Prevention and the Korea Centers for Disease Control and Prevention websites and press releases. Drawing lessons from both nations, it is evident that the resolution to the COVID-19 pandemic lies in the prudent usage of available resources, proactive strategic planning, public participation, transparency
Alwin C. Aguirre 부산외국어대학교 아세안연구원 2017 Suvannabhumi Vol.9 No.1
The paper demonstrates the potential contribution of integrating discursive and affective analytic regimes in framing the study of Southeast Asia. I examine the “emotional possibilities” available to migrants with particular focus on the experience of Filipino domestic helpers in Hong Kong thrown into relief in 2016 by news of maids falling to their deaths while cleaning windows of their employers’ above-ground apartments. First, I situate the study in recent calls for Critical Discourse Studies and Migration Studies to transcend foundational methodologies in their respective fields in order to apprehend formerly disregarded aspects of the human condition, including affect and emotion. I then briefly present the debate in the affective turn in social analysis, which has to do with rethinking the attachment of affect and discourse. My own inquiry is premised on the assertion that emotion is multidimensional. I specifically explore the usefulness of taking emotion as “affective-discursive practice” by focusing on an analysis of the appropriation of the victim role by foreign domestic helper employer groups that could be seen in pertinent news reports of selected online Hong Kong newspapers. In the end, I also emphasize the necessity of reflexivity in projects that take affect as central object of inquiry.