Pub Date : 2022-06-26DOI: 10.24003/emitter.v10i1.661
Prasanth Rao Adiraju, Voore Subba Rao
5G network is the next generation for cellular networks to overcome the challenges and limitations of the 4G network. Cloud Radio Access Network(C-RAN) is providing solutions for cost-efficient and power-efficient solutions for the 5G network. The aim of this paper proposed an energy-efficient C-RAN to minimize the cost of the network by dynamically allocating BBU resources to RRHs as per facing traffic, and also minimize the energy consumption of centralized BBU resources that affect dynamically allocate of RRHs. Particle Swarm Optimization (PSO) algorithm is a Swarm Intelligence algorithm for optimization of mapping between BBU-RRH for resource allocation in C-RAN. The main objective of the paper is as per resource usage in C-RAN the BBU is put in the active or in-active mode to minimize energy consumption in C-RAN of 5G technology. As per our proposed C-RANapplication, the proposed PSO algorithm 90% minimizes energy consumption and maximizes energy efficiency compared with existing work.
{"title":"Dynamically Energy-Efficient Resource Allocation in 5G CRAN Using Intelligence Algorithm","authors":"Prasanth Rao Adiraju, Voore Subba Rao","doi":"10.24003/emitter.v10i1.661","DOIUrl":"https://doi.org/10.24003/emitter.v10i1.661","url":null,"abstract":"5G network is the next generation for cellular networks to overcome the challenges and limitations of the 4G network. Cloud Radio Access Network(C-RAN) is providing solutions for cost-efficient and power-efficient solutions for the 5G network. The aim of this paper proposed an energy-efficient C-RAN to minimize the cost of the network by dynamically allocating BBU resources to RRHs as per facing traffic, and also minimize the energy consumption of centralized BBU resources that affect dynamically allocate of RRHs. Particle Swarm Optimization (PSO) algorithm is a Swarm Intelligence algorithm for optimization of mapping between BBU-RRH for resource allocation in C-RAN. The main objective of the paper is as per resource usage in C-RAN the BBU is put in the active or in-active mode to minimize energy consumption in C-RAN of 5G technology. As per our proposed C-RANapplication, the proposed PSO algorithm 90% minimizes energy consumption and maximizes energy efficiency compared with existing work.","PeriodicalId":40905,"journal":{"name":"EMITTER-International Journal of Engineering Technology","volume":"143 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82063149","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 : 2022-06-24DOI: 10.24003/emitter.v10i1.672
S. G C, V. S. T., Tejas A, Vaishnavi P, Raghunandan Gowda, Panchami Udupa, Spoorthy, S. Reddy, Sudarshan E
Coronavirus disease 2019 popularly known as COVID 19 was first found in Wuhan, China in December 2019. World Health Organization declared Covid 19 as a transmission disease. The symptoms were cough, loss of taste, fever, tiredness, respiratory problem. These symptoms were likely to show within 11 –14 days. The RT-PCR and rapid antigen biochemical tests were done for the detection of COVID 19. In addition to biochemical tests, X-Ray and Computed Tomography (CT) images are used for the minute details of the severity of the disease. To enhance efficiency and accuracy of analysis/detection of COVID images and to reduce of doctors' time for analysis could be addressed through Artificial Intelligence. The dataset from Kaggle was utilized to analyze. The statistical and GLCM features were extracted from CT images for the classification of COVID and NON-COVID instances in this study. CT images were used to extract statistical and GLCM features for categorization. In the proposed/prototype model, we achieved the classification accuracy of 91%, and 94.5% using SVM and Random Forest respectively.
{"title":"A Machine learning Classification approach for detection of Covid 19 using CT images","authors":"S. G C, V. S. T., Tejas A, Vaishnavi P, Raghunandan Gowda, Panchami Udupa, Spoorthy, S. Reddy, Sudarshan E","doi":"10.24003/emitter.v10i1.672","DOIUrl":"https://doi.org/10.24003/emitter.v10i1.672","url":null,"abstract":"Coronavirus disease 2019 popularly known as COVID 19 was first found in Wuhan, China in December 2019. World Health Organization declared Covid 19 as a transmission disease. The symptoms were cough, loss of taste, fever, tiredness, respiratory problem. These symptoms were likely to show within 11 –14 days. The RT-PCR and rapid antigen biochemical tests were done for the detection of COVID 19. In addition to biochemical tests, X-Ray and Computed Tomography (CT) images are used for the minute details of the severity of the disease. To enhance efficiency and accuracy of analysis/detection of COVID images and to reduce of doctors' time for analysis could be addressed through Artificial Intelligence. The dataset from Kaggle was utilized to analyze. The statistical and GLCM features were extracted from CT images for the classification of COVID and NON-COVID instances in this study. CT images were used to extract statistical and GLCM features for categorization. In the proposed/prototype model, we achieved the classification accuracy of 91%, and 94.5% using SVM and Random Forest respectively.","PeriodicalId":40905,"journal":{"name":"EMITTER-International Journal of Engineering Technology","volume":"os-16 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87193944","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 : 2022-06-24DOI: 10.24003/emitter.v10i1.694
Andi Kurniawan Nugroho, Dinar Mutiara Kusumo Nugraheni, Terawan Agus Putranto, I Ketut Eddy Purnama, Mauridhi Hery Purnomo
When the blood flow to the arteries in brain is blocked, its known as Ischemic stroke or blockage stroke. Ischemic stroke can occur due to the formation of blood clots in other parts of the body. Plaque buildup in arteries, on the other hand, can cause blockages because if it ruptures, it can form blood clots. The b-1000 Diffusion Weighted (DW) Magnetic Resonance Imaging (MRI) image was used in a general examination to obtain an image of the part of the brain that had a stroke. In this study, classifications used several variations of layer convolution to obtain high accuracy and high computational consumption using b-1000 Diffusion Weighted (DW) MR in ischemic stroke types: acute, sub-acute and chronic. Ischemic stroke was classified using five variants of the Convolutional Neural Network (CNN) architectural design, i.e., CNN1–CNN5. The test results show that the CNN5 architectural design provides the best ischemic stroke classification compared to other architectural designs tested, with an accuracy of 99.861%, precision 99.862%, recall 99.861, and F1-score 99.861%.
{"title":"Classification of Ischemic Stroke with Convolutional Neural Network (CNN) approach on b-1000 Diffusion-Weighted (DW) MRI","authors":"Andi Kurniawan Nugroho, Dinar Mutiara Kusumo Nugraheni, Terawan Agus Putranto, I Ketut Eddy Purnama, Mauridhi Hery Purnomo","doi":"10.24003/emitter.v10i1.694","DOIUrl":"https://doi.org/10.24003/emitter.v10i1.694","url":null,"abstract":"When the blood flow to the arteries in brain is blocked, its known as Ischemic stroke or blockage stroke. Ischemic stroke can occur due to the formation of blood clots in other parts of the body. Plaque buildup in arteries, on the other hand, can cause blockages because if it ruptures, it can form blood clots. The b-1000 Diffusion Weighted (DW) Magnetic Resonance Imaging (MRI) image was used in a general examination to obtain an image of the part of the brain that had a stroke. In this study, classifications used several variations of layer convolution to obtain high accuracy and high computational consumption using b-1000 Diffusion Weighted (DW) MR in ischemic stroke types: acute, sub-acute and chronic. Ischemic stroke was classified using five variants of the Convolutional Neural Network (CNN) architectural design, i.e., CNN1–CNN5. The test results show that the CNN5 architectural design provides the best ischemic stroke classification compared to other architectural designs tested, with an accuracy of 99.861%, precision 99.862%, recall 99.861, and F1-score 99.861%.","PeriodicalId":40905,"journal":{"name":"EMITTER-International Journal of Engineering Technology","volume":"7 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83212994","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 : 2022-06-20DOI: 10.24003/emitter.v10i1.704
Alfan Rizaldy Pratama Pratama, Bima Sena Bayu Dewantara, Dewi Mutiara Sari, Dadet Pramadihanto
One of the most commonly faced tasks in industrial robots is bin picking. Much work has been done in this related topic is about grasping and picking an object from the piled bin but ignoring the recognition step in their pipeline. In this paper, a recognition pipeline for industrial bin picking is proposed. Begin with obtaining point cloud data from different manner of stacking objects there are well separated, well piled, and arbitrary piled. Then followed by segmentation using Density-based Spatial Clustering Application with Noise (DBSCAN) to obtain individual object data. The systems then use Convolutional Neural Network (CNN) that consume raw point cloud data. Performance of the segmentation reaches an impressive result in separating objects and network is evaluated under the varying style of stacking objects and give the result with average Accuracy, Recall, Precision, and F1-Score on 98.72%, 95.45%, 99.39%, and 97.33% respectively. Then the obtained model can be used for multiple objects recognition in one scene.
{"title":"Density-based Clustering for 3D Stacked Pipe Object Recognition using Directly-given Point Cloud Data on Convolutional Neural Network","authors":"Alfan Rizaldy Pratama Pratama, Bima Sena Bayu Dewantara, Dewi Mutiara Sari, Dadet Pramadihanto","doi":"10.24003/emitter.v10i1.704","DOIUrl":"https://doi.org/10.24003/emitter.v10i1.704","url":null,"abstract":"One of the most commonly faced tasks in industrial robots is bin picking. Much work has been done in this related topic is about grasping and picking an object from the piled bin but ignoring the recognition step in their pipeline. In this paper, a recognition pipeline for industrial bin picking is proposed. Begin with obtaining point cloud data from different manner of stacking objects there are well separated, well piled, and arbitrary piled. Then followed by segmentation using Density-based Spatial Clustering Application with Noise (DBSCAN) to obtain individual object data. The systems then use Convolutional Neural Network (CNN) that consume raw point cloud data. Performance of the segmentation reaches an impressive result in separating objects and network is evaluated under the varying style of stacking objects and give the result with average Accuracy, Recall, Precision, and F1-Score on 98.72%, 95.45%, 99.39%, and 97.33% respectively. Then the obtained model can be used for multiple objects recognition in one scene.","PeriodicalId":40905,"journal":{"name":"EMITTER-International Journal of Engineering Technology","volume":"30 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89217454","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 : 2022-06-20DOI: 10.24003/emitter.v10i1.650
Sujanth Roy J, G Lakshminarayanan
Polar codes have recently emerged as an error-correcting code and have become popular owing to their capacity-achieving nature. Polar code based communication system primarily consists of two parts, including Polar Encoder and Decoder. Successive Cancellation Decoder is one of the methods used in the decoding process. The Successive Cancellation Decoder is a recursive structure built with the building block called Processing Element. This article proposes a low power, area-efficient architecture for the Successive Cancellation Decoder for polar codes. Successive Cancellation Decoder with code length 1024 and code rate 0.5 was designed in Verilog HDL and implemented using 45-nm CMOS technology. The proposed work focuses on developing an area-efficient Successive Cancellation Decoder architecture by presenting a new Processing Element architecture. The proposed architecture has produced about 35% lesser area with a 12% reduced gate count. Moreover, power is also reduced by 50%. A substantial reduction in the latency and improvement in the Technology Scaled Normalized Throughput value was observed.
{"title":"Low Power, Area Efficient Architecture for Successive Cancellation Decoder","authors":"Sujanth Roy J, G Lakshminarayanan","doi":"10.24003/emitter.v10i1.650","DOIUrl":"https://doi.org/10.24003/emitter.v10i1.650","url":null,"abstract":"Polar codes have recently emerged as an error-correcting code and have become popular owing to their capacity-achieving nature. Polar code based communication system primarily consists of two parts, including Polar Encoder and Decoder. Successive Cancellation Decoder is one of the methods used in the decoding process. The Successive Cancellation Decoder is a recursive structure built with the building block called Processing Element. This article proposes a low power, area-efficient architecture for the Successive Cancellation Decoder for polar codes. Successive Cancellation Decoder with code length 1024 and code rate 0.5 was designed in Verilog HDL and implemented using 45-nm CMOS technology. The proposed work focuses on developing an area-efficient Successive Cancellation Decoder architecture by presenting a new Processing Element architecture. The proposed architecture has produced about 35% lesser area with a 12% reduced gate count. Moreover, power is also reduced by 50%. A substantial reduction in the latency and improvement in the Technology Scaled Normalized Throughput value was observed.","PeriodicalId":40905,"journal":{"name":"EMITTER-International Journal of Engineering Technology","volume":"48 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88017526","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 : 2022-06-20DOI: 10.24003/emitter.v10i1.695
Nada Fitrieyatul Hikmah, T. A. Sardjono, Windy Deftia Mertiana, Nabila Puspita Firdi, Diana Purwitasari
Breast cancer is the leading cause of cancer death in women. The early phase of breast cancer is asymptomatic, without any signs or symptoms. The earlier breast cancer can be detected, the greater chance of cure. Early detection using screening mammography is a common step for detecting the presence of breast cancer. Many studies of computer-based using breast cancer detection have been done previously. However, the detection process for craniocaudal (CC) view and mediolateral oblique (MLO) view angles were done separately. This study aims to improve the detection performance for breast cancer diagnosis with CC and MLO view analysis. An image processing framework for multi-view screening was used to improve the diagnostic results rather than single-view. Image enhancement, segmentation, and feature extraction are all part of the framework provided in this study. The stages of image quality improvement are very important because the contrast of mammographic images is relatively low, so it often overlaps between cancer tissue and normal tissue. Texture-based segmentation utilizing the first-order local entropy approach was used to segment the images. The value of the radius and the region of probable cancer were calculated using the findings of feature extraction. The results of this study show the accuracy of breast cancer detection using CC and MLO views were 88.0% and 80.5% respectively. The proposed framework was useful in the diagnosis of breast cancer, that the detection results and features help clinicians in making treatment.
{"title":"An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images","authors":"Nada Fitrieyatul Hikmah, T. A. Sardjono, Windy Deftia Mertiana, Nabila Puspita Firdi, Diana Purwitasari","doi":"10.24003/emitter.v10i1.695","DOIUrl":"https://doi.org/10.24003/emitter.v10i1.695","url":null,"abstract":"Breast cancer is the leading cause of cancer death in women. The early phase of breast cancer is asymptomatic, without any signs or symptoms. The earlier breast cancer can be detected, the greater chance of cure. Early detection using screening mammography is a common step for detecting the presence of breast cancer. Many studies of computer-based using breast cancer detection have been done previously. However, the detection process for craniocaudal (CC) view and mediolateral oblique (MLO) view angles were done separately. This study aims to improve the detection performance for breast cancer diagnosis with CC and MLO view analysis. An image processing framework for multi-view screening was used to improve the diagnostic results rather than single-view. Image enhancement, segmentation, and feature extraction are all part of the framework provided in this study. The stages of image quality improvement are very important because the contrast of mammographic images is relatively low, so it often overlaps between cancer tissue and normal tissue. Texture-based segmentation utilizing the first-order local entropy approach was used to segment the images. The value of the radius and the region of probable cancer were calculated using the findings of feature extraction. The results of this study show the accuracy of breast cancer detection using CC and MLO views were 88.0% and 80.5% respectively. The proposed framework was useful in the diagnosis of breast cancer, that the detection results and features help clinicians in making treatment.","PeriodicalId":40905,"journal":{"name":"EMITTER-International Journal of Engineering Technology","volume":"26 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74565569","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 : 2022-06-20DOI: 10.24003/emitter.v10i1.690
Febby Ronaldo, Amang Sudarsono, D. Pramadihanto
Drone technology is considered the most effective solution for the improvement of various industrial fields. As a delivery service, drones need a secure communication system that is also able to manage all of the information data in real-time. However, because the data transmission process occurs in a wireless network, data will be sent over a channel that is more unstable and vulnerable to attack. Thus, this research, purposes a Forward Prediction Scheduling-based Stream Control Transmission Protocol (FPS-SCTP) scheme that is implemented on drone data transmission system. This scheme supports piggybacking, multi-streaming, and Late Messages Filter (LMF) which will improve the real-time transmission process in IEEE 802.11 wireless network. Meanwhile, on the cybersecurity aspect, this scheme provides the embedded option feature to enable the encryption mechanism using AES and the digital signatures mechanism using ECDSA. The results show that the FPS-SCTP scheme has better network performance than the default SCTP, and provides full security services with low computation time. This research contributes to providing a communication protocol scheme that is suitable for use on the internet of drones’ environment, both in real-time and reliable security levels.
{"title":"Secure Real-time Data Transmission for Drone Delivery Services using Forward Prediction Scheduling SCTP","authors":"Febby Ronaldo, Amang Sudarsono, D. Pramadihanto","doi":"10.24003/emitter.v10i1.690","DOIUrl":"https://doi.org/10.24003/emitter.v10i1.690","url":null,"abstract":"Drone technology is considered the most effective solution for the improvement of various industrial fields. As a delivery service, drones need a secure communication system that is also able to manage all of the information data in real-time. However, because the data transmission process occurs in a wireless network, data will be sent over a channel that is more unstable and vulnerable to attack. Thus, this research, purposes a Forward Prediction Scheduling-based Stream Control Transmission Protocol (FPS-SCTP) scheme that is implemented on drone data transmission system. This scheme supports piggybacking, multi-streaming, and Late Messages Filter (LMF) which will improve the real-time transmission process in IEEE 802.11 wireless network. Meanwhile, on the cybersecurity aspect, this scheme provides the embedded option feature to enable the encryption mechanism using AES and the digital signatures mechanism using ECDSA. The results show that the FPS-SCTP scheme has better network performance than the default SCTP, and provides full security services with low computation time. This research contributes to providing a communication protocol scheme that is suitable for use on the internet of drones’ environment, both in real-time and reliable security levels.","PeriodicalId":40905,"journal":{"name":"EMITTER-International Journal of Engineering Technology","volume":"45 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83328491","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 : 2022-06-12DOI: 10.24003/emitter.v10i1.637
V. Gopala, T. Anil Kumar, D. Krishna, Ch. Srinivasa Rao, Shashank Kumar, Sudipto Poddar
In this paper, Rapid Control Prototyping (RCP) of five-level Modular Multilevel Converter (MMC) based Induction Motor (IM) drive performance is observed with different switching frequencies. The Semikron based MMC Stacks with two half-bridge each are tested with the switching logic generated by phase and level shifted based Sinusoidal Pulse Width Modulation (SPWM) technique. The switching logic is generated by the Typhoon Hardware in Loop (HIL) 402. The disadvantages of Multilevel Converter like not so good output quality, less modularity, not scalable and high voltage and current rating demand for the power semiconductor switches can be overcome by using MMC. In this work, the IM drive is fed by MMC and the experimentally the performance is observed. The performance of the Induction Motor in terms of speed is observed with different switching frequencies of 2.5kHz, 5kHz, 7.5kHz, 10kHz, 12.5kHz and the results are tabulated in terms of Total Harmonic Distortion (THD) of input voltage and current to the Induction Motor Drive. The complete model is developed using Typhoon HIL 2021.2 Version Real-Time Simulation Software.
本文采用快速控制原型法(RCP)对基于五电平模块化多电平变换器(MMC)的感应电机(IM)在不同开关频率下的驱动性能进行了研究。采用基于相位和电平移位的正弦脉宽调制(SPWM)技术产生的开关逻辑，对基于赛米控的两个半桥MMC堆叠进行了测试。切换逻辑由Typhoon Hardware in Loop (HIL) 402生成。多电平变换器的输出质量差、模块化程度低、不可扩展以及对功率半导体开关的电压和额定电流要求高等缺点可以通过MMC来克服。本文采用MMC作为驱动源，并对其性能进行了实验观察。观察了感应电机在2.5kHz、5kHz、7.5kHz、10kHz、12.5kHz不同开关频率下的速度性能，并将结果以感应电机驱动器输入电压和电流的总谐波失真(THD)表出来。利用台风HIL 2021.2版实时仿真软件开发了完整的模型。
{"title":"Rapid Control Prototyping of Five-Level MMC based Induction Motor Drive with different Switching Frequencies","authors":"V. Gopala, T. Anil Kumar, D. Krishna, Ch. Srinivasa Rao, Shashank Kumar, Sudipto Poddar","doi":"10.24003/emitter.v10i1.637","DOIUrl":"https://doi.org/10.24003/emitter.v10i1.637","url":null,"abstract":"In this paper, Rapid Control Prototyping (RCP) of five-level Modular Multilevel Converter (MMC) based Induction Motor (IM) drive performance is observed with different switching frequencies. The Semikron based MMC Stacks with two half-bridge each are tested with the switching logic generated by phase and level shifted based Sinusoidal Pulse Width Modulation (SPWM) technique. The switching logic is generated by the Typhoon Hardware in Loop (HIL) 402. The disadvantages of Multilevel Converter like not so good output quality, less modularity, not scalable and high voltage and current rating demand for the power semiconductor switches can be overcome by using MMC. In this work, the IM drive is fed by MMC and the experimentally the performance is observed. The performance of the Induction Motor in terms of speed is observed with different switching frequencies of 2.5kHz, 5kHz, 7.5kHz, 10kHz, 12.5kHz and the results are tabulated in terms of Total Harmonic Distortion (THD) of input voltage and current to the Induction Motor Drive. The complete model is developed using Typhoon HIL 2021.2 Version Real-Time Simulation Software.","PeriodicalId":40905,"journal":{"name":"EMITTER-International Journal of Engineering Technology","volume":"1 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73859097","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 : 2022-04-26DOI: 10.24003/emitter.v10i1.628
Usman Habib Khan, Muhammad Naeem Ahmed Khan, Aamir, Mehmood Mirza, Muhammad Akram, Shariqa Fakhar, Shumaila, Hussain, Irfan Ahmed Magsi, R. A. Wagan
Testing of mobile applications (apps) has its quirks as numerous events are required to be tested. Mobile apps testing, being an evolving domain, carries certain challenges that should be accounted for in the overall testing process. Since smartphone apps are moderate in size so we consider that model-based testing (MBT) using state machines and statecharts could be a promising option for ensuring maximum coverage and completeness of test cases. Using model-based testing approach, we can automate the tedious phase of test case generation, which not only saves time of the overall testing process but also minimizes defects and ensures maximum test case coverage and completeness. In this paper, we explore and model the most critical modules of the mobile app for generating test cases to ascertain the efficiency and impact of using model-based testing. Test cases for the targeted model of the application under test were generated on a real device. The experimental results indicate that our framework reduced the time required to execute all the generated test cases by 50%. Experimental setup and results are reported herein.
{"title":"Automating Test Case Generation for Android Applications using Model-based Testing","authors":"Usman Habib Khan, Muhammad Naeem Ahmed Khan, Aamir, Mehmood Mirza, Muhammad Akram, Shariqa Fakhar, Shumaila, Hussain, Irfan Ahmed Magsi, R. A. Wagan","doi":"10.24003/emitter.v10i1.628","DOIUrl":"https://doi.org/10.24003/emitter.v10i1.628","url":null,"abstract":"Testing of mobile applications (apps) has its quirks as numerous events are required to be tested. Mobile apps testing, being an evolving domain, carries certain challenges that should be accounted for in the overall testing process. Since smartphone apps are moderate in size so we consider that model-based testing (MBT) using state machines and statecharts could be a promising option for ensuring maximum coverage and completeness of test cases. Using model-based testing approach, we can automate the tedious phase of test case generation, which not only saves time of the overall testing process but also minimizes defects and ensures maximum test case coverage and completeness. In this paper, we explore and model the most critical modules of the mobile app for generating test cases to ascertain the efficiency and impact of using model-based testing. Test cases for the targeted model of the application under test were generated on a real device. The experimental results indicate that our framework reduced the time required to execute all the generated test cases by 50%. Experimental setup and results are reported herein.","PeriodicalId":40905,"journal":{"name":"EMITTER-International Journal of Engineering Technology","volume":"57 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91100324","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 : 2022-04-26DOI: 10.24003/emitter.v10i1.679
A. Wibawa, Arni Muarifah Amri, Arbintoro Mas, Syahrul Iman
Opening job vacancies using the Internet will receive many applications quickly. Manually filtering resumes takes a lot of time and incurs huge costs. In addition, this manual screening process tends to be inaccurate due to fatigue conditions and fails in obtaining the right candidate for the job. This paper proposed a solution to automatically generate the most suitable candidate from the application document. In this study, 126 application documents from a private company were used for the experiment. The documents consist of 41 documents for Human Resource and Development (HRD) staff, 42 documents for IT (Data Developer), and 43 documents for the Marketing position. Text Processing is implemented to extract relevant information such as skills, education, experiences from the unstructured resumes and summarize each application. A specific dictionary for each vacancy is generated based on terms used in each profession. Two methods are implemented and compared to match and score the application document, namely Document Vector and N-gram analysis. The highest the score obtained by one document, the highest the possibility of application to be accepted. The two methods’ results are then validated by the real selection process by the company. The highest accuracy was achieved by the N-Gram method in IT vacancy with 87,5%, while the Document Vector showed 75% accuracy. For Marketing staff vacancy, both methods achieved the same accuracy as 78%. In HRD staff vacancy, the N-Gram method showed 68%, while Document Vector showed 74%. In conclusion, overall the N-gram method showed slightly better accuracy compared to the Document Vector method.
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