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Adaptive Backstepping Based Sensor and Actuator Fault Tolerant Control of a Manipulator
Zainab Shahid Awan,Khurram Ali,Jamshed Iqbal,Adeel Mehmood 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.6
The purpose of this research is to propose and design fault tolerant control (FTC) scheme for a robotic manipulator, to increase its reliability and performance in the presence of actuator and sensor faults. To achieve the said objectives, a hybrid control law relying on observer and hardware redundancy-based technique has been formulated in this paper. Non-linear observers are designed to estimate the unknown states. The comparison of actual states and observed states lead to fault identifcation, this is followed by fault tolerance accomplished with redundant sensors. For actuator fault tolerance, fault estimation and controller reconfguration techniques are applied in addition to nominal control law. Fault estimation is based on adaptive back-stepping technique and it is further used to construct actuator fault tolerant control. The proposed method is applied to a six degree of freedom (DOF) robotic manipulator model and the efectiveness of this technique is verifed by LabVIEW simulations. Simulation results witnessed the improved tracking performance in the presence of actuator and sensor failures
Iqra Javaid,Shahid Mehmood,Imran Ahmad,Muhammad Adnan Khan 한국차세대컴퓨팅학회 2023 한국차세대컴퓨팅학회 학술대회 Vol.2023 No.12
According to new WHO statistics, heart disease is the top reason of death worldwide, killing 17.9 million people each year. This is a growing number. One of the most wellknown issues in clinical offices is that no two professionals have the same knowledge and talent when serving their patients. Researchers are utilizing data mining and machine learning techniques to overcome these difficulties by using predictive analytics to anticipate the risk of heart problems. This study examines the accuracy of various machine learning methods, including Logistic Regression, Naive Bayes, Decision Trees, Support Vector Machines, Neural Networks, and Stochastic Gradient Descent in the prediction of heart disease based on various factors and symptoms such as gender, age, chest pain, and blood sugar using appropriate data. The research entails applying a typical data mining approach to accurately uncover relationships between numerous data sources to predict heart disease. These machine learning algorithms take less time and are more accurate at predicting heart illness, which will lower the global convergence of essential life.
Some Biochemical Abnormalities Caused by Cypermethrin in adults of Tribolium castaneum (Herbst.)
ASHFAQ, Muhammad,shahid Mehmood KHALID,Waseem AKRAM,Riaz HUSSAIN,이종진 한국곤충학회 2004 Entomological Research Vol.34 No.1
Biochemical effects of sub lethal doses LC10 and LC20 of cypermethrin were studiedon some enzymes and macromolecule activities of adult beetles of Tribolium castaneum (Herbst.).Cypermethrin caused disturbances in levels of all biochemical components under study. The doseof 0.78 ppm caused abnormalities in α-amylase and FAA by increasing their activities i.e.,45.45% and 21.97% significantly. The higher sub lethal dose of 2.62 ppm disturbed all theparameters (AcP, α-amylase, soluble protein and FAA) except AkP, which was decreased by93.06%. Moreover, sub lethal doses either increased or decreased the levels of all parameters nonsignificantlyexcept AkP and FAA which were effected significantly by 87.92% and 14.29% atlower and higher doses, respectively. In the present studies, cypermethrin significantly enhancedthe activity of AkP in both susceptible and resistant strains of T. castaneum adult beetles whileFAA contents were increased significantly in resistant strain only. The activity of α-amylase wassignificantly lowered in susceptible strain only.
Sentiment Analysis for COVID-19 Vaccine Popularity
( Muhammad Saeed ),( Naeem Ahmed ),( Abid Mehmood ),( Muhammad Aftab ),( Rashid Amin ),( Shahid Kamal ) 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.5
Social media is used for various purposes including entertainment, communication, information search, and voicing their thoughts and concerns about a service, product, or issue. The social media data can be used for information mining and getting insights from it. The World Health Organization has listed COVID-19 as a global epidemic since 2020. People from every aspect of life as well as the entire health system have been severely impacted by this pandemic. Even now, after almost three years of the pandemic declaration, the fear caused by the COVID-19 virus leading to higher depression, stress, and anxiety levels has not been fully overcome. This has also triggered numerous kinds of discussions covering various aspects of the pandemic on the social media platforms. Among these aspects is the part focused on vaccines developed by different countries, their features and the advantages and disadvantages associated with each vaccine. Social media users often share their thoughts about vaccinations and vaccines. This data can be used to determine the popularity levels of vaccines, which can provide the producers with some insight for future decision making about their product. In this article, we used Twitter data for the vaccine popularity detection. We gathered data by scraping tweets about various vaccines from different countries. After that, various machine learning and deep learning models, i.e., naive bayes, decision tree, support vector machines, k-nearest neighbor, and deep neural network are used for sentiment analysis to determine the popularity of each vaccine. The results of experiments show that the proposed deep neural network model outperforms the other models by achieving 97.87% accuracy.
Anatomy of Sentiment Analysis of Tweets Using Machine Learning Approach
Misbah Iram,Saif Ur Rehman,Shafaq Shahid,Sayeda Ambreen Mehmood International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.10
Sentiment analysis using social network platforms such as Twitter has achieved tremendous results. Twitter is an online social networking site that contains a rich amount of data. The platform is known as an information channel corresponding to different sites and categories. Tweets are most often publicly accessible with very few limitations and security options available. Twitter also has powerful tools to enhance the utility of Twitter and a powerful search system to make publicly accessible the recently posted tweets by keyword. As popular social media, Twitter has the potential for interconnectivity of information, reviews, updates, and all of which is important to engage the targeted population. In this work, numerous methods that perform a classification of tweet sentiment in Twitter is discussed. There has been a lot of work in the field of sentiment analysis of Twitter data. This study provides a comprehensive analysis of the most standard and widely applicable techniques for opinion mining that are based on machine learning and lexicon-based along with their metrics. The proposed work is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous, and polarized positive, negative or neutral. In order to validate the performance of the proposed framework, an extensive series of experiments has been performed on the real world twitter dataset that alter to show the effectiveness of the proposed framework. This research effort also highlighted the recent challenges in the field of sentiment analysis along with the future scope of the proposed work.
Characterizing Barium Titanate Piezoelectric Material Using the Finite Element Method
Zubair Butt,Shafiq Ur Rahman,Riffat Asim Pasha,Shahid Mehmood,Saqlain Abbas,Hassan Elahi 한국전기전자재료학회 2017 Transactions on Electrical and Electronic Material Vol.18 No.3
The aim of the current research was to develop and present an effective methodology for simulating and analyzingthe electrical and structural properties of piezoelectric material. The finite element method has been used to makeprecise numerical models when dielectric, piezoelectric and mechanical properties are known. The static anddynamic responses of circular ring-shaped barium titanate piezoelectric material have been investigated using thecommercially available finite element software ABAQUS/CAE. To gain insight into the crystal morphology and toevaluate the purity of the material, a microscopic study was conducted using a scanning electron microscope andenergy dispersive x-ray analysis. It is found that the maximum electrical potential of 6.43 V is obtained at a resonancefrequency of 35 Hz by increasing the vibrating load. The results were then compared with the experimentally predicteddata and the results agreed with each other.
Butt, Zubair,Anjum, Zeeshan,Sultan, Amir,Qayyum, Faisal,Khurram Ali, Hafiz Muhammad,Mehmood, Shahid The Korean Institute of Electrical Engineers 2017 Journal of Electrical Engineering & Technology Vol.12 No.2
Piezoelectricity is the capability of a piezoelectric material to change mechanical energy into electrical energy. The determination of electrical and mechanical properties plays a significant role in characterizing the piezoelectric material. The energy losses characteristics of piezoelectric material can be described by mechanical quality factor. In this paper, the output voltage and mechanical quality factor of Lead Zirconate Titanate (PZT-4A) piezoelectric material is determined under various resistance and loading conditions by using the test setup. The commercial FEM software ABAQUS is used to analyze the performance of piezoelectric material under static loading conditions. It is observed that these properties affect the performance of a material particularly in the designing of smart structures. The experimental results are partially compared to the simulation values.
Zubair Butt,Zeeshan Anjum,Amir Sultan,Faisal Qayyum,Hafiz Muhammad Khurram Ali,Shahid Mehmood 대한전기학회 2017 Journal of Electrical Engineering & Technology Vol.12 No.2
Piezoelectricity is the capability of a piezoelectric material to change mechanical energy into electrical energy. The determination of electrical and mechanical properties plays a significant role in characterizing the piezoelectric material. The energy losses characteristics of piezoelectric material can be described by mechanical quality factor. In this paper, the output voltage and mechanical quality factor of Lead Zirconate Titanate (PZT-4A) piezoelectric material is determined under various resistance and loading conditions by using the test setup. The commercial FEM software ABAQUS is used to analyze the performance of piezoelectric material under static loading conditions. It is observed that these properties affect the performance of a material particularly in the designing of smart structures. The experimental results are partially compared to the simulation values.
Characterizing Barium Titanate Piezoelectric Material Using the Finite Element Method
Butt, Zubair,Rahman, Shafiq Ur,Pasha, Riffat Asim,Mehmood, Shahid,Abbas, Saqlain,Elahi, Hassan The Korean Institute of Electrical and Electronic 2017 Transactions on Electrical and Electronic Material Vol.18 No.3
The aim of the current research was to develop and present an effective methodology for simulating and analyzing the electrical and structural properties of piezoelectric material. The finite element method has been used to make precise numerical models when dielectric, piezoelectric and mechanical properties are known. The static and dynamic responses of circular ring-shaped barium titanate piezoelectric material have been investigated using the commercially available finite element software ABAQUS/CAE. To gain insight into the crystal morphology and to evaluate the purity of the material, a microscopic study was conducted using a scanning electron microscope and energy dispersive x-ray analysis. It is found that the maximum electrical potential of 6.43 V is obtained at a resonance frequency of 35 Hz by increasing the vibrating load. The results were then compared with the experimentally predicted data and the results agreed with each other.