Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587807
Miranji Katta, R. Sandanalakshmi
A microcantilever array chip made with Micro-Electro-Mechanical System (MEMS) technology has been demonstrated to develop as a biosensor device. This chip includes four gold-covered and embedded polysilicon wire with microfabricated Si beams. The polysilicon coat serves as a piezoresistor, and changes in resistance due to compressive and tensile forces indicate microcantilever deformation. The relationship between initial resistance and microcantilever deflection demonstrates that this device has a detection range of 0-56kΩ. The investigation of the microcantilever response to biotin immobilisation revealed that resistance change caused by Biotin absorption can be observed and reaches a degree of amount independence at Biotin concentrations higher than 80pg/ml. The results suggested that this device could be developed as a piezoresistive-based microcantilever biosensor.
{"title":"MEMS Piezoresistive Cantilever Fabrication And Characterization","authors":"Miranji Katta, R. Sandanalakshmi","doi":"10.1109/GCAT52182.2021.9587807","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587807","url":null,"abstract":"A microcantilever array chip made with Micro-Electro-Mechanical System (MEMS) technology has been demonstrated to develop as a biosensor device. This chip includes four gold-covered and embedded polysilicon wire with microfabricated Si beams. The polysilicon coat serves as a piezoresistor, and changes in resistance due to compressive and tensile forces indicate microcantilever deformation. The relationship between initial resistance and microcantilever deflection demonstrates that this device has a detection range of 0-56kΩ. The investigation of the microcantilever response to biotin immobilisation revealed that resistance change caused by Biotin absorption can be observed and reaches a degree of amount independence at Biotin concentrations higher than 80pg/ml. The results suggested that this device could be developed as a piezoresistive-based microcantilever biosensor.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"408 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115913217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587518
Vidhi Chhatbar, Mihir Gondhalekar, Shruti Pimple, R. Pawar
We come across different biomedical images. It is difficult to interpret those images as they do not have any description. Image captioning is the process of generating textual description from an image which depends on the object and action in the image. With the advancement in deep learning techniques, we will build models to generate captions for biomedical images. This model will be very useful to accelerate the diagnosis process by telling the abnormalities present in the image. The model will be based on an encoder-decoder framework along with an attention model. The encoder will be using deep CNN to extract image features and the decoder will be using transformers to generate captions. Caption generating involves different complex scenarios starting from collecting the data set, training the model, validating the model, creating trained model to test the image, detecting the image and generating the captions
{"title":"Machine Interpretation of Medical Images Using Deep Learning","authors":"Vidhi Chhatbar, Mihir Gondhalekar, Shruti Pimple, R. Pawar","doi":"10.1109/GCAT52182.2021.9587518","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587518","url":null,"abstract":"We come across different biomedical images. It is difficult to interpret those images as they do not have any description. Image captioning is the process of generating textual description from an image which depends on the object and action in the image. With the advancement in deep learning techniques, we will build models to generate captions for biomedical images. This model will be very useful to accelerate the diagnosis process by telling the abnormalities present in the image. The model will be based on an encoder-decoder framework along with an attention model. The encoder will be using deep CNN to extract image features and the decoder will be using transformers to generate captions. Caption generating involves different complex scenarios starting from collecting the data set, training the model, validating the model, creating trained model to test the image, detecting the image and generating the captions","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124151452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587529
Dilshan Singh Chadha, Kartikey Chaturvedi, M. D. Upadhayay
This work brings an innovative design of a flyswatter shaped antenna for an 8-element linear array with Butler Matrix (BM) as the beamforming network and also proposes four port cross-over. The proposed flyswatter shaped antenna resonates at a frequency of 2.4 GHz. The results of all components (such as quadrature couplers, crossovers, phase shifters) used to realize the design of beam forming network are presented. The proposed cross-over has insertion loss close to 2dB. The 8-element linear array is integrated on FR-4 substrate $left(varepsilon_{mathrm{r}}=4.3 quad text { and } quad text { height }=1.6 quad mathrm{~mm}right)$ with BM based beamforming network to produces eight different beams at $-55^{circ},-36,-21^{circ},-7^{circ}, 55^{circ}, 36,21^{circ}$, and 7°. The reflection coefficients and isolation at respective ports are less than -15 dB at the operating frequency and side lobes of radiation pattern are sufficiently low. This technique finds applications in IEEE 802.11 WLAN, lower frequency bands of 5 G and LTE, and wearable devices.
本文提出了一种以巴特勒矩阵(BM)作为波束形成网络的八元线性阵列的蜻蜓形天线的创新设计，并提出了四端口交叉。所提出的苍蝇拍形天线谐振频率为2.4 GHz。给出了用于实现波束形成网络设计的所有元件(如正交耦合器、交叉器、移相器)的结果。所提出的交叉具有接近2dB的插入损耗。将8元线性阵列集成在FR-4衬底$left(varepsilon_{mathrm{r}}=4.3 quad text { and } quad text { height }=1.6 quad mathrm{~mm}right)$上，采用基于BM的波束形成网络，在$-55^{circ},-36,-21^{circ},-7^{circ}, 55^{circ}, 36,21^{circ}$和7°处产生8种不同的波束。在工作频率下，各端口的反射系数和隔离度均小于-15 dB，辐射方向图旁瓣足够低。该技术适用于IEEE 802.11 WLAN、5g和LTE的较低频段以及可穿戴设备。
{"title":"Flyswatter Shaped Antenna for 8 Element Beam Forming Network utilizing Butler Matrix","authors":"Dilshan Singh Chadha, Kartikey Chaturvedi, M. D. Upadhayay","doi":"10.1109/GCAT52182.2021.9587529","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587529","url":null,"abstract":"This work brings an innovative design of a flyswatter shaped antenna for an 8-element linear array with Butler Matrix (BM) as the beamforming network and also proposes four port cross-over. The proposed flyswatter shaped antenna resonates at a frequency of 2.4 GHz. The results of all components (such as quadrature couplers, crossovers, phase shifters) used to realize the design of beam forming network are presented. The proposed cross-over has insertion loss close to 2dB. The 8-element linear array is integrated on FR-4 substrate $left(varepsilon_{mathrm{r}}=4.3 quad text { and } quad text { height }=1.6 quad mathrm{~mm}right)$ with BM based beamforming network to produces eight different beams at $-55^{circ},-36,-21^{circ},-7^{circ}, 55^{circ}, 36,21^{circ}$, and 7°. The reflection coefficients and isolation at respective ports are less than -15 dB at the operating frequency and side lobes of radiation pattern are sufficiently low. This technique finds applications in IEEE 802.11 WLAN, lower frequency bands of 5 G and LTE, and wearable devices.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114368111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587648
N. Kaur, Vijay KumarSinha, S. Kang
Autism is neurological disorder in which person is affected with communication and interaction abilities. Lacks of social interaction, repetitive behavior, and stable interest are indication of the autistic child. It essential to identify the autism at very is early stage. CNN plays vital role in health care which requires a process that reduces cost and time. The key objective of proposed paper is to implement convolution neural network algorithms and classify autistic and non-autistic child..In this study, CNN is applied for classification of autistic and non-autistic child. The images of children of age 4 to 11 years were used. About 400 images extracted from pre-defined datasets and were used to train the CNN algorithm using the Google colab framework via Python and Open CV libraries. Using cross validation techniques, The CNN was evaluated. In this sense, our proposed model has achieved a high accuracy rate and robustness for prediction of autistic and non-autistic child. Additionally, the proposed algorithm attains a quick response time. Therefore, we could significantly diminish the time of diagnosis by applying the proposed method and facilitate the diagnosis of ASD in lower cost.
{"title":"Early detection of ASD Traits in Children using CNN","authors":"N. Kaur, Vijay KumarSinha, S. Kang","doi":"10.1109/GCAT52182.2021.9587648","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587648","url":null,"abstract":"Autism is neurological disorder in which person is affected with communication and interaction abilities. Lacks of social interaction, repetitive behavior, and stable interest are indication of the autistic child. It essential to identify the autism at very is early stage. CNN plays vital role in health care which requires a process that reduces cost and time. The key objective of proposed paper is to implement convolution neural network algorithms and classify autistic and non-autistic child..In this study, CNN is applied for classification of autistic and non-autistic child. The images of children of age 4 to 11 years were used. About 400 images extracted from pre-defined datasets and were used to train the CNN algorithm using the Google colab framework via Python and Open CV libraries. Using cross validation techniques, The CNN was evaluated. In this sense, our proposed model has achieved a high accuracy rate and robustness for prediction of autistic and non-autistic child. Additionally, the proposed algorithm attains a quick response time. Therefore, we could significantly diminish the time of diagnosis by applying the proposed method and facilitate the diagnosis of ASD in lower cost.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114941062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587750
Andrian C. Monroy, Kurt Austin Padilla, Edwin R. Rillera, Jepthah D. Rodriguez, Kenneth Oliver Y. Tindugan, R. Tolentino
In this study, the proponents proposed a mechanism that provides abduction and adduction movements at the MCP joint of index finger and CMC joint of the thumb as well as full actuation in the movements of remaining joints whose human equivalents are capable of fully-independent movement. The system consists of two main digits: the thumb and the index finger. As a rundown, the digits are actuated by several HS35HD Micro Servo Motors, MG996R High-Torque Motor, and PQ-12R Micro Linear Servo. An aspect that can be noticed in the mechanism is the movement in the index PIP joint is actuated by a linear servo whose linear movement translated into rotational movement, with the mechanism allowing the distal phalange of the finger to move dependently of the middle phalange.A series of flex sensors attached on a glove was used to gather finger joint movement data made by the user. Mimicking happens as motors actuate according to the gathered data with the help of Arduino Mega 2560. To compare the angular positions actuated by the motors to that of the movements by the user flex sensors and potentiometer were utilized. The system’s mimicking capability is then evaluated using z-test.
在这项研究中，支持者提出了一种机制，该机制提供了食指MCP关节和拇指CMC关节的外展和内收运动，并在其他关节的运动中完全驱动，而这些关节的人体等效关节能够完全独立运动。该系统由两个主要手指组成:拇指和食指。作为概述，数字由几个HS35HD微伺服电机，MG996R高扭矩电机和PQ-12R微线性伺服驱动。在该机构中可以注意到的一个方面是指指关节的运动是由一个线性伺服驱动的，其线性运动转化为旋转运动，该机构允许手指的远端指骨依赖于中指骨运动。安装在手套上的一系列弯曲传感器用于收集用户手指关节的运动数据。在Arduino Mega 2560的帮助下，根据收集到的数据，电机会进行模拟。为了将电机驱动的角度位置与用户运动的角度位置进行比较，使用了弯曲传感器和电位器。然后使用z检验评估系统的模拟能力。
{"title":"Design and Implementation of Articulated Mimicking Robotic Finger with Abduction and Adduction Movements in the Index MCP Joint and Thumb CMC Joint","authors":"Andrian C. Monroy, Kurt Austin Padilla, Edwin R. Rillera, Jepthah D. Rodriguez, Kenneth Oliver Y. Tindugan, R. Tolentino","doi":"10.1109/GCAT52182.2021.9587750","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587750","url":null,"abstract":"In this study, the proponents proposed a mechanism that provides abduction and adduction movements at the MCP joint of index finger and CMC joint of the thumb as well as full actuation in the movements of remaining joints whose human equivalents are capable of fully-independent movement. The system consists of two main digits: the thumb and the index finger. As a rundown, the digits are actuated by several HS35HD Micro Servo Motors, MG996R High-Torque Motor, and PQ-12R Micro Linear Servo. An aspect that can be noticed in the mechanism is the movement in the index PIP joint is actuated by a linear servo whose linear movement translated into rotational movement, with the mechanism allowing the distal phalange of the finger to move dependently of the middle phalange.A series of flex sensors attached on a glove was used to gather finger joint movement data made by the user. Mimicking happens as motors actuate according to the gathered data with the help of Arduino Mega 2560. To compare the angular positions actuated by the motors to that of the movements by the user flex sensors and potentiometer were utilized. The system’s mimicking capability is then evaluated using z-test.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116011298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587846
M. Srilatha, S. Harini, T. Sushanth
Wireless communication is becoming increasingly important as technology advances. After commercial and public radio, community radio is the third type of broadcasting. Its goal is to give individuals with crucial local news and information for a small community. The proposed system shows how to use LABVIEW and the Universal Software Radio Peripheral to construct community radio using software defined radio (USRP). The major goal is to develop and demonstrate a low-cost, flexible transmitter and receiver using low-cost hardware such as a computer loaded with LABVIEW and a USRP.
{"title":"Community Radio Using USRP 2920","authors":"M. Srilatha, S. Harini, T. Sushanth","doi":"10.1109/GCAT52182.2021.9587846","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587846","url":null,"abstract":"Wireless communication is becoming increasingly important as technology advances. After commercial and public radio, community radio is the third type of broadcasting. Its goal is to give individuals with crucial local news and information for a small community. The proposed system shows how to use LABVIEW and the Universal Software Radio Peripheral to construct community radio using software defined radio (USRP). The major goal is to develop and demonstrate a low-cost, flexible transmitter and receiver using low-cost hardware such as a computer loaded with LABVIEW and a USRP.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"309 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123673544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587840
Sivaprasad Naru, Munisekhar Sadu
This paper gives a summary of design and hardware development of consistent and vigorous space borne power supply unit for synthetic aperture radars with the variation of input voltage of 24V to 38V. The available battery raw bus voltage in satellite is 28V DC nominal. This power supply unit can use for various radio frequency (Exciter and Receiver) and digital circuits (processor). The power supply unit (PSU) consists of two converters in which the converter-1 consist of single output of 15V/0.6A and converter-2 consists of three outputs of +5.3V/12A, +5.4V/4.5A & -5V/0.6A. The total output power of PSU is 100.4W with the input voltage variation of 24V to 38V DC (28V DC nominal) at an operating frequency of 140 KHz. The power supply unit is implemented by using single switch Forward Converter Topology with saturable inductor and Low Drop out (LDO) as post regulators is used to get the required regulated outputs. The PSU has been designed and developed with bias feedback using Voltage mode feed forward control technique.
{"title":"Design and Development of Space Borne Multiple Output DC DC Converter with Bias Feedback Using Voltage Feedforward Control Technique","authors":"Sivaprasad Naru, Munisekhar Sadu","doi":"10.1109/GCAT52182.2021.9587840","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587840","url":null,"abstract":"This paper gives a summary of design and hardware development of consistent and vigorous space borne power supply unit for synthetic aperture radars with the variation of input voltage of 24V to 38V. The available battery raw bus voltage in satellite is 28V DC nominal. This power supply unit can use for various radio frequency (Exciter and Receiver) and digital circuits (processor). The power supply unit (PSU) consists of two converters in which the converter-1 consist of single output of 15V/0.6A and converter-2 consists of three outputs of +5.3V/12A, +5.4V/4.5A & -5V/0.6A. The total output power of PSU is 100.4W with the input voltage variation of 24V to 38V DC (28V DC nominal) at an operating frequency of 140 KHz. The power supply unit is implemented by using single switch Forward Converter Topology with saturable inductor and Low Drop out (LDO) as post regulators is used to get the required regulated outputs. The PSU has been designed and developed with bias feedback using Voltage mode feed forward control technique.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121925191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Micro Expressions are those involuntary muscular movements of the facial muscles produced in response to a stimulus. They are short-lived expressions that last for anywhere between 0.04 to 0.2 seconds and are extremely subtle in their amplitude. Given their fleeting and elusive nature, it becomes almost impossible to detect these expressions through the naked eye. Recent developments in Deep Learning models have shown great success in efficiently identifying and analyzing Micro Expressions. In this paper, various models have been implemented on the SAMM dataset. The models studied are namely– VGG16, ResNet50, MobileNet, InceptionV3, and Xception. The experimental results have helped us carefully analyze the various metrics related to the models and compare them with each other to ascertain which one outperformed the others and is best suited for real-world applications. The MobileNet model has surpassed all other models in terms of its efficiency with respect to the domain of this paper. It has been able to describe and understand all the information that can be found in the various Micro Expressions.
{"title":"Comparative Analysis of Micro Expression Recognition using Deep Learning and Transfer Learning","authors":"Rahil Kadakia, Parth Kalkotwar, Pruthav Jhaveri, Rahul Patanwadia, Kriti Srivastava","doi":"10.1109/GCAT52182.2021.9587731","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587731","url":null,"abstract":"Micro Expressions are those involuntary muscular movements of the facial muscles produced in response to a stimulus. They are short-lived expressions that last for anywhere between 0.04 to 0.2 seconds and are extremely subtle in their amplitude. Given their fleeting and elusive nature, it becomes almost impossible to detect these expressions through the naked eye. Recent developments in Deep Learning models have shown great success in efficiently identifying and analyzing Micro Expressions. In this paper, various models have been implemented on the SAMM dataset. The models studied are namely– VGG16, ResNet50, MobileNet, InceptionV3, and Xception. The experimental results have helped us carefully analyze the various metrics related to the models and compare them with each other to ascertain which one outperformed the others and is best suited for real-world applications. The MobileNet model has surpassed all other models in terms of its efficiency with respect to the domain of this paper. It has been able to describe and understand all the information that can be found in the various Micro Expressions.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"47 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120881308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587500
L. Raj, L. Arun
A Voltage Source Inverter (VSI) when connected to a weak AC grid exhibits instability problems due to the unfavourable interactions between the Phase Locked Loop (PLL) and inner current controller of the VSI. In this paper a voltage modulated direct power control technique is used to integrate a Permanent Magnet Synchronous Generatr (PMSG) based wind energy system to a weak AC grid. This control technique does not need a PLL for its operation. The perturb and observe algorithm based maximum power point tracking is used to extract maximum power from wind. The performance of the system is evaluated using simulation studies done in MATLAB/Simulink. The effects of change in wind speed and sag in grid voltage are also analyzed. The results show that the controller is able to follow the active and reactive power commands with permissible THD in the current injected to grid.
{"title":"A Voltage Modulated Direct Power Controlled Wind Energy System Connected to a Weak AC Grid with Maximum Power Point Tracking","authors":"L. Raj, L. Arun","doi":"10.1109/GCAT52182.2021.9587500","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587500","url":null,"abstract":"A Voltage Source Inverter (VSI) when connected to a weak AC grid exhibits instability problems due to the unfavourable interactions between the Phase Locked Loop (PLL) and inner current controller of the VSI. In this paper a voltage modulated direct power control technique is used to integrate a Permanent Magnet Synchronous Generatr (PMSG) based wind energy system to a weak AC grid. This control technique does not need a PLL for its operation. The perturb and observe algorithm based maximum power point tracking is used to extract maximum power from wind. The performance of the system is evaluated using simulation studies done in MATLAB/Simulink. The effects of change in wind speed and sag in grid voltage are also analyzed. The results show that the controller is able to follow the active and reactive power commands with permissible THD in the current injected to grid.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125796201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1109/GCAT52182.2021.9587647
Ayush Patel, Alankar Uniyal, Ritesh Dhanare
The region of the United Kingdom has seen a substantial rise in the number of accidents in recent times. Having car insurance in your arsenal acts as a savior in times of financial crisis. In this paper, car accidents occurring in a specific region are analyzed and the insurance policy which is best suitable for the consumer is recommended. Here the car accidents are categorized into different groups of ages and accidents happening on the different days of the week are shown using matplotlib and seaborn libraries.
{"title":"U.K. Car Accident and Insurance Predictor using Machine Learning","authors":"Ayush Patel, Alankar Uniyal, Ritesh Dhanare","doi":"10.1109/GCAT52182.2021.9587647","DOIUrl":"https://doi.org/10.1109/GCAT52182.2021.9587647","url":null,"abstract":"The region of the United Kingdom has seen a substantial rise in the number of accidents in recent times. Having car insurance in your arsenal acts as a savior in times of financial crisis. In this paper, car accidents occurring in a specific region are analyzed and the insurance policy which is best suitable for the consumer is recommended. Here the car accidents are categorized into different groups of ages and accidents happening on the different days of the week are shown using matplotlib and seaborn libraries.","PeriodicalId":436231,"journal":{"name":"2021 2nd Global Conference for Advancement in Technology (GCAT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125839775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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