학술논문


EBSCO Discovery Service
발행년
-
(예 : 2010-2015)
전자자료 공정이용 안내

우리 대학 도서관에서 구독·제공하는 모든 전자자료(데이터베이스, 전자저널, 전자책 등)는 국내외 저작권법과 출판사와의 라이선스 계약에 따라 엄격하게 보호를 받고 있습니다.
전자자료의 비정상적 이용은 출판사로부터의 경고, 서비스 차단, 손해배상 청구 등 학교 전체에 심각한 불이익을 초래할 수 있으므로, 아래의 공정이용 지침을 반드시 준수해 주시기 바랍니다.

공정이용 지침
  • 전자자료는 개인의 학습·교육·연구 목적의 비영리적 사용에 한하여 이용할 수 있습니다.
  • 합리적인 수준의 다운로드 및 출력만 허용됩니다. (일반적으로 동일 PC에서 동일 출판사의 논문을 1일 30건 이하 다운로드할 것을 권장하며, 출판사별 기준에 따라 다를 수 있습니다.)
  • 출판사에서 제공한 논문의 URL을 수업 관련 웹사이트에 게재할 수 있으나, 출판사 원문 파일 자체를 복제·배포해서는 안 됩니다.
  • 본인의 ID/PW를 타인에게 제공하지 말고, 도용되지 않도록 철저히 관리해 주시기 바랍니다.
불공정 이용 사례
  • 전자적·기계적 수단(다운로딩 프로그램, 웹 크롤러, 로봇, 매크로, RPA 등)을 이용한 대량 다운로드
  • 동일 컴퓨터 또는 동일 IP에서 단시간 내 다수의 원문을 집중적으로 다운로드하거나, 전권(whole issue) 다운로드
  • 저장·출력한 자료를 타인에게 배포하거나 개인 블로그·웹하드 등에 업로드
  • 상업적·영리적 목적으로 자료를 전송·복제·활용
  • ID/PW를 타인에게 양도하거나 타인 계정을 도용하여 이용
  • EndNote, Mendeley 등 서지관리 프로그램의 Find Full Text 기능을 이용한 대량 다운로드
  • 출판사 콘텐츠를 생성형 AI 시스템에서 활용하는 행위(업로드, 개발, 학습, 프로그래밍, 개선 또는 강화 등)
위반 시 제재
  • 출판사에 의한 해당 IP 또는 기관 전체 접속 차단
  • 출판사 배상 요구 시 위반자 개인이 배상 책임 부담
'학술논문' 에서 검색결과 897,716건 | 목록 1~20
Academic Journal
Sherman, Pete / 113 Ill. B.J. 14 (2025) / Illinois Bar Journal, Vol. 113, Issue 5 (May 2025), pp. 14-17
Periodical
Travel Business Review (TBR). February 13, 2026
A Multi-Class Facial Emotion Recognition System for Still Images 1Omage Micheal, 2Fasola Olusanjo & 3Woods Nancy C. 1,3University of Ibadan, Ibadan, Nigeria 2Federal University of Technology, Minna, Nigeria E-mails: 1micheal.omage@gmail.com, 2Sanjo.fasola@futminna.edu.ng, 3Chyn.woods@gmail.com Phone Nos: 1+2348084121913, 2+2348023138034, 3+2348037273291 ABSTRACT Facial expressions serve as a potent and natural means of human communication, conveying a wide range of emotions. In the realm of artificial intelligence and computer vision, recognizing these emotions from facial cues has gained significant importance. Most research focus on detecting a single dominant emotion on any given facial image, although an image could portray several emotions. This work presents a multi-class facial emotion recognition system capable of predicting and quantifying the presence of multiple emotions in a single facial image. The system employs a Convolutional Neural Network (CNN) architecture optimized for multi-label classification across eight emotion classes. The AffectNet dataset, comprising 27,836 images with diverse ages, ethnicities, and real-world settings, was utilized for training the model, to enhance generalization capabilities. The model's performance was evaluated on an impartial benchmark dataset, FER-Plus, containing 35,710 images. The CNN model performed well, with a micro F1-score of 0.7 and a macro F1-score of 0.7 on the test set from AffectNet, and a micro F1-score of 0.7 and a macro F1-score of 0.4 on the FER-Plus dataset. Qualitative analysis demonstrated the model's capability in recognizing blended and subtle facial expressions, providing significant probability distributions across multiple emotion labels. The developed system advances the state-of-the-art in multi-label facial emotion recognition by successfully modeling emotion mixtures, contributing to a deeper understanding of this challenging task and enabling practical applications in affective computing, human-computer interaction, psychological studies, and improving accessibility and inclusivity in emotional computing technologies. Keywords: Multi-Class, Facial Emotion Recognition, Security, System, Still Images, Models, Detection Omage, M. Fasola, O. & Woods, N.C. (2024): A Multi-Class Facial Emotion Recognition System for Still Images. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 3. Pp 67-80. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N3P6
Academic Journal
Advances in Multidisciplinary & Scientific Research Journal Publication. 12:67-80
Periodical
Global Banking News (GBN). January 26, 2026
Academic Journal
ZEITSCHRIFT FUR GERONTOLOGIE UND GERIATRIE; JUL 2025, 58 4, 7p.
Academic Journal
Morphology and Physiochemical Characterization of Perfluorosulfonic Acid (PFSA) Membrane forElectrochemical ApplicationMohd Hafiz Md Ali1, Mohammad Noor Jalil1*, Mohamad Nor Amirul Azhar Kamis1,Hamizah Mohd Zaki11School of Chemistry and Environment, Faculty of Applied Science,Universiti Teknologi MARA, Shah Alam, 40000, Selangor MALAYSIA*Corresponding Author: moham423@uitm.edu.myDOI: https://doi.org/10.30880/jst.2025.17.01.011 Article InfoAbstractReceived: 11 September 2024Accepted: 17 May 2025 Available online: 30 June 2025This reportprovides a thorough examination of the surface morphology, chemical content, and physical properties of Perfluorosulfonic Acid (PFSA) membrane which is commonly utilised as a conductive membrane in fuel cells, ionic batteries, and electrolysers. This experimental effort is separated into two parts: morphological characterisation and physicochemical analysis, which aim to improve understanding of the membrane functional qualities. The membrane surface and cross-sectional characteristics were examined at varied magnifications using Field Emission Scanning Electron Microscopy (FESEM) and Energy-Dispersive X-ray Spectroscopy (EDX). The EDX results revealed that the membrane surface is predominantly composed of carbon (C), oxygen (O), manganese (Mn), and nickel (Ni), accounting for 22.82% by mass, whereas the cross-section analysis revealed higher mass percentages of titanium (Ti), sulphur (S), oxygen (O), carbon (C), and fluorine (F), totalling 53.51%. Tensile testing confirmed the membrane's strength, with a tensile stress of 13.71 N/mm² and an elongation at break of 73.73%. Thermal stability and breakdown behaviour were determined using Thermogravimetric Analysis (TGA), which validated this membrane thermal endurance at 422oC degradation temperatures. These findings provide significant understanding into the surface morphology and structural functional features of PFSA membranewhich allow its optimisation for advanced electrochemical applications.KeywordsPerfluorosulfonic acid (PFSA),morphology, chemical, physiochemical1.IntroductionPerfluorosulfonic acid (PFSA) membrane, employed in proton exchange membrane (PEM) fuel cells, demonstrate a distinctive set of properties essential for optimal performance in electrochemical applications[1].This membrane shows high proton conductivity, exceptional chemical and thermal stability, and mechanical strength, rendering it suitable in various applications such as fuel cells, electrolyser and redox flow batteries[2]. PFSA membranes play a crucial role in electrochemical devices, particularly in proton exchange membranes (PEMs) for fuel cells and electrolysers. Instead of that, its nanostructured morphology influences proton conductivity, mechanical strength, and chemical stability[3]. These unique design of PFSA membranes, consisting of hydrophobic polytetrafluoroethylene (PTFE) backbones and hydrophilic sulfonic acid side chains, is key to
Academic Journal
Journal of Science and Technology. 17
검색 결과 제한하기
제한된 항목
[검색어] Also available online
발행연도 제한
-
학술DB(Database Provider)
저널명(출판물, Title)
출판사(Publisher)
자료유형(Source Type)
주제어
언어