http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Adnan Qayyum Shah (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
A comparative study of linear control strategies on the aerodynamics twin rotor system
Adnan Qayyum Shah,Muhammad Awais,Muhammad Zafar,Ashfaq Ahmed,Muhammad Mudassar,Muhammad Muneer,Memoona Saif,Abdul Razzaq,Seong Ho Jang,김선형,박영권 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.8
This work presents the comparative study among pole-placement (PP), optimalcontrol using output-feedback (OCOF), linear-quadratic regulator (LQR), and PID controllers for the twin rotor multi-input multi-output system (TRMS). The pitch and yaw are key attributes for stabilizing the TRMS MIMO system and control of flight. The main objective of this study is to use these classical controller techniques to monitor the pitch and yaw angles of TRMS and show the result of these techniques. Simulation results depicts the actual performance and reveals how PP outperforms the other techniques.
-
Social Network Analysis of an Online Smoking Cessation Community to Identify Users’ Smoking Status
Adnan Muhammad Shah,Xiangbin Yan,Abdul Qayyum 대한의료정보학회 2021 Healthcare Informatics Research Vol.27 No.2
Objectives: Users share valuable information through online smoking cessation communities (OSCCs), which help peoplemaintain and improve smoking cessation behavior. Although OSCC utilization is common among smokers, limitations existin identifying the smoking status of OSCC users (“quit” vs. “not quit”). Thus, the current study implicitly analyzed user-generatedcontent (UGC) to identify individual users’ smoking status through advanced computational methods and real datafrom an OSCC. Methods: Secondary data analysis was conducted using data from 3,833 users of BcomeAnEX.org. Domainexperts reviewed posts and comments to determine the authors’ smoking status when they wrote them. Seven types of featuresets were extracted from UGC (textual, Doc2Vec, social influence, domain-specific, author-based, and thread-based features,as well as adjacent posts). Results: Introducing novel features boosted smoking status recognition (quit vs. not quit) by 9.3%relative to the use of text-only post features. Furthermore, advanced computational methods outperformed baseline algorithmsacross all models and increased the smoking status prediction performance by up to 12%. Conclusions: The results ofthis study suggest that the current research method provides a valuable platform for researchers involved in online cessationinterventions and furnishes a framework for on-going machine learning applications. The results may help practitioners designa sustainable real-time intervention via personalized post recommendations in OSCCs. A major limitation is that onlyusers’ smoking status was detected. Future research might involve programming machine learning classification methods toidentify abstinence duration using larger datasets.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
