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Nazar Kashif,Jaffery Mujtaba Hussain,Shakir Imran,Nazar Atif,Raza Rizwan 한국물리학회 2022 Current Applied Physics Vol.40 No.-
This paper demonstrates the mathematical and MATLAB simulation model of 1 KW (28.8Vdc) PEM fuel cell system with boost convertor and RL load to analyze the yield behavior in accordance to control the hydrogen fuel utilization. Two cases have been designed to evaluate the performance of this model. In the first case, fuel cell parameters are examined with and without a fuel regulator that controls the hydrogen fuel rate while in the second case, the operating temperature of a fuel cell stack is varied to observe the impact on the system. PEM fuel cell based power systems can become an alternate choice in the transportation sector to overcome contamination concerns, especially in South Asia where the environmental issues are at peak. The purpose of this work is to introduce such environmentally friendly system of transportation in South Asia, especially in Pakistan and this stack model can be used as a prototype for developing FC based motorbike as currently no practical models have been tested in this region. Therefore, this model has unique advantages over the existing in the literature.
Aslam, Muhammad,Ahmad, Rizwan,Yasin, Muhammad,Khan, Asim Laeeq,Shahid, Muhammad Kashif,Hossain, Shakhawat,Khan, Zakir,Jamil, Farrukh,Rafiq, Sikander,Bilad, Muhammad Roil,Kim, Jeonghwan,Kumar, Gopalakr Elsevier 2018 Bioresource technology Vol.269 No.-
<P><B>Abstract</B></P> <P>Biohydrogen as one of the most appealing energy vector for the future represents attractive avenue in alternative energy research. Recently, variety of biohydrogen production pathways has been suggested to improve the key features of the process. Nevertheless, researches are still needed to overcome remaining barriers to practical applications such as low yields and production rates. Considering practicality aspects, this review emphasized on anaerobic membrane bioreactors (AnMBRs) for biological hydrogen production. Recent advances and emerging issues associated with biohydrogen generation in AnMBR technology are critically discussed. Several techniques are highlighted that are aimed at overcoming these barriers. Moreover, environmental and economical potentials along with future research perspectives are addressed to drive biohydrogen technology towards practicality and economical-feasibility.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Anaerobic membrane bioreactor technology for biohydrogen production is overviewed. </LI> <LI> Enhancement of biohydrogen yield and generation rates via various strategies is discussed. </LI> <LI> Techno-economic and environmental impacts of this approach are addressed. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data
Moiz Ahmed,Nadeem Mehmood,Adnan Nadeem,Amir Mehmood,Kashif Rizwan 대한의료정보학회 2017 Healthcare Informatics Research Vol.23 No.3
Objectives: Falling in the elderly is considered a major cause of death. In recent years, ambient and wireless sensor platforms have been extensively used in developed countries for the detection of falls in the elderly. However, we believe extra efforts are required to address this issue in developing countries, such as Pakistan, where most deaths due to falls are not even reported. Considering this, in this paper, we propose a fall detection system prototype that s based on the classification on real time shimmer sensor data. Methods: We first developed a data set, ‘SMotion’ of certain postures that could lead to falls in the elderly by using a body area network of Shimmer sensors and categorized the items in this data set into age and weight groups. We developed a feature selection and classification system using three classifiers, namely, support vector machine (SVM), Knearest neighbor (KNN), and neural network (NN). Finally, a prototype was fabricated to generate alerts to caregivers, health experts, or emergency services in case of fall. Results: To evaluate the proposed system, SVM, KNN, and NN were used. The results of this study identified KNN as the most accurate classifier with maximum accuracy of 96% for age groups and 93% for weight groups. Conclusions: In this paper, a classification-based fall detection system is proposed. For this purpose, the SMotion data set was developed and categorized into two groups (age and weight groups). The proposed fall detection system for the elderly is implemented through a body area sensor network using third-generation sensors. The evaluation results demonstrate the reasonable performance of the proposed fall detection prototype system in the tested scenarios.