Pub Date : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073731
Huijie Liu
At present, the signal detection principle of the ultrasonic flowmeter can be roughly divided into propagation velocity difference method, the beam shift method, the Doppler method, correlation method, spatial filtering method and noise method, and hence, the proper selection of the methods will be essential for the success of the modelling. This paper then studies the optimal feedback loop algorithm for the automatic control of ultrasonic gas flowmeter. Firstly, the ultrasonic gas flow measurement principle is introduced, considering that the time difference measurement principle will use the movement time difference of ultrasonic waves in the case of forward flow and reverse flow to measure gas flow. Secondly, realization of the intelligent ultrasonic gas flow measuring system is studied. Here, the Butterworth filters are considered to maintain the useful ultrasonic signal in a proper frequency range by removing high and low frequency noise. Finally, the optimal feedback loop is considered to finalize the model. The proposed experiment will provide the high-speed comparison of signal waveform simulation to show the visualized performance and then, the error comparison analysis in conducted to test the performance under different flow rate.
{"title":"Optimal Feedback Loop Algorithm for Automatic Control of Ultrasonic Gas Flowmeter","authors":"Huijie Liu","doi":"10.1109/ICAIS56108.2023.10073731","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073731","url":null,"abstract":"At present, the signal detection principle of the ultrasonic flowmeter can be roughly divided into propagation velocity difference method, the beam shift method, the Doppler method, correlation method, spatial filtering method and noise method, and hence, the proper selection of the methods will be essential for the success of the modelling. This paper then studies the optimal feedback loop algorithm for the automatic control of ultrasonic gas flowmeter. Firstly, the ultrasonic gas flow measurement principle is introduced, considering that the time difference measurement principle will use the movement time difference of ultrasonic waves in the case of forward flow and reverse flow to measure gas flow. Secondly, realization of the intelligent ultrasonic gas flow measuring system is studied. Here, the Butterworth filters are considered to maintain the useful ultrasonic signal in a proper frequency range by removing high and low frequency noise. Finally, the optimal feedback loop is considered to finalize the model. The proposed experiment will provide the high-speed comparison of signal waveform simulation to show the visualized performance and then, the error comparison analysis in conducted to test the performance under different flow rate.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129025874","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}
In India two wheelers are most common mode of transport to a majority of people because of its’s affordability. Though they are also a major cause of pollution almost 20% of total pollution (which is 10% more than the pollution by cars) and increase in demand and supply gap of oil, which was 6 million barrel per day back in 2020 which is estimated to increase by 14.4 million barrels. All of this can be minimized by simply replacing IC engine vehicles by pure electric or Hybrid electric vehicles for two wheelers. This paper discusses the idea of simulation of HEV two-wheeler using power split to interchange power between the IC engine and motor and provide a balanced output to the
{"title":"Design of Two -Wheeler Hybrid Electric Vehicle using Series Parallel Configuration","authors":"Sandip Shankar Yeole, Sandeep Vasant Kolhe, R. Bibave, Vijay Shivaji Chavan, Bhushan Bhaurao Kadam, Vaibhav Sakharam Bodhe","doi":"10.1109/ICAIS56108.2023.10073870","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073870","url":null,"abstract":"In India two wheelers are most common mode of transport to a majority of people because of its’s affordability. Though they are also a major cause of pollution almost 20% of total pollution (which is 10% more than the pollution by cars) and increase in demand and supply gap of oil, which was 6 million barrel per day back in 2020 which is estimated to increase by 14.4 million barrels. All of this can be minimized by simply replacing IC engine vehicles by pure electric or Hybrid electric vehicles for two wheelers. This paper discusses the idea of simulation of HEV two-wheeler using power split to interchange power between the IC engine and motor and provide a balanced output to the","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130369875","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073830
Sri Teja Chadalawada, Keerthana Mandadi, Jahnavi Machupally, Vippala Indra Sena Reddy, B. T. Reddy, A. V. Praveen Krishna
Systems for cloud storage is growing in popularity. Deduplication, a promising procedure collects only a single replica of recurring data there by reduces cost of cloud storages. Client-side deduplication makes an effort to locate existing deduplication opportunities prior to uploading at the client and avoid using server capacity to upload duplicates of already-existing data. This work, provides mechanism to overcome vulnerabilities and take advantage of client-side deduplication, giving an attacker access to other users' files of any size based on relatively weak hash signatures. More specifically, an attacker who is aware of a file's hash signature can persuade a storage service that it is the rightful owner of the file, at which point the intruder is allowed to acquire the entire file by the server. Proofs-of-ownership (PoWs), that allow a user to effectively prove to a host that the user genuinely owns a file instead of only giving a short summary of it. This work defines the principle of proof-of-ownership under strong security standards and strict efficiency constraints. Then, relying on popular encryption techniques and hash function, this study provides and assess security-related methodologies.
{"title":"Proving Ownership of Privacy-Protected Cloud Storage Devices","authors":"Sri Teja Chadalawada, Keerthana Mandadi, Jahnavi Machupally, Vippala Indra Sena Reddy, B. T. Reddy, A. V. Praveen Krishna","doi":"10.1109/ICAIS56108.2023.10073830","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073830","url":null,"abstract":"Systems for cloud storage is growing in popularity. Deduplication, a promising procedure collects only a single replica of recurring data there by reduces cost of cloud storages. Client-side deduplication makes an effort to locate existing deduplication opportunities prior to uploading at the client and avoid using server capacity to upload duplicates of already-existing data. This work, provides mechanism to overcome vulnerabilities and take advantage of client-side deduplication, giving an attacker access to other users' files of any size based on relatively weak hash signatures. More specifically, an attacker who is aware of a file's hash signature can persuade a storage service that it is the rightful owner of the file, at which point the intruder is allowed to acquire the entire file by the server. Proofs-of-ownership (PoWs), that allow a user to effectively prove to a host that the user genuinely owns a file instead of only giving a short summary of it. This work defines the principle of proof-of-ownership under strong security standards and strict efficiency constraints. Then, relying on popular encryption techniques and hash function, this study provides and assess security-related methodologies.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127027002","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073797
Liu Zhan
In the recent time, the efficient processing of music (or audio) signal considering the safety and performance has been the research hotspot. This study performs a convergence analysis of music technology from the audio digital watermarking to the denoising algorithm. Traditional music information processing methods are more focused on a single processing perspective, that is, improve the signal quality or signal security guarantee, respectively. The comprehensive tool for the analysis is lacking. Hence, this paper firstly proposes a novel audio digital watermarking algorithm considering the QR decomposition and DWT to construct the efficient scenario. Then, the novel denoising algorithm is studied. The reconstructed signal after hard threshold processing has disadvantages such as the discontinuity, oscillation and distortion. Then, the wavelet analysis model is applied into the algorithm to find the optimal value of threshold. After these 2 major innovations, the whole system is then constructed and implemented with experiment. The experimental data of the stereo modulator calibration method is scientific, and the overall experiment has certain robustness.
{"title":"Convergence Analysis of Music Technology: From Audio Digital Watermarking to Denoising Algorithm","authors":"Liu Zhan","doi":"10.1109/ICAIS56108.2023.10073797","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073797","url":null,"abstract":"In the recent time, the efficient processing of music (or audio) signal considering the safety and performance has been the research hotspot. This study performs a convergence analysis of music technology from the audio digital watermarking to the denoising algorithm. Traditional music information processing methods are more focused on a single processing perspective, that is, improve the signal quality or signal security guarantee, respectively. The comprehensive tool for the analysis is lacking. Hence, this paper firstly proposes a novel audio digital watermarking algorithm considering the QR decomposition and DWT to construct the efficient scenario. Then, the novel denoising algorithm is studied. The reconstructed signal after hard threshold processing has disadvantages such as the discontinuity, oscillation and distortion. Then, the wavelet analysis model is applied into the algorithm to find the optimal value of threshold. After these 2 major innovations, the whole system is then constructed and implemented with experiment. The experimental data of the stereo modulator calibration method is scientific, and the overall experiment has certain robustness.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127032002","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073935
S. Govindaraju, Rajrupa Metia, P. Girija, K. Baranitharan, M. Indirani, M. R.
The importance of security has skyrocketed alongside the adoption of Internet of Things (IoT) services. Software-defined networking (S DN) provides a means of securely managing IoT devices, which were exposed in a current distributed denial-of-service (DDoS) attack. Many IoT devices unwittingly contributed to the DDoS attack. DDoS attacks, one of the most common types of cyberattack, are particularly pernicious since they can cripple a network’s ability to function and render many of its services inaccessible to users. This research used optimised deep learning-based models to detect DDoS in SDN. At first, the dataset’s normal and DDoS attack traffic characteristics were gathered from the SDN. The models are recommended to be simpler, easier to read, and to have a shorter training period when using an NSL-KDD dataset for feature selection approaches. Real-time DDoS attack detection in SDN is proposed in this research using an Long Short-Term Memory (LS TM) models. High accuracy in classification is achieved by utilising an artificial gorilla troop optimizer to pick the features of NSL-KDD. Using less time and material, the proposed IDS was able to achieve a detection accuracy of 97.59%. These findings provide encouraging evidence that DDoS attack detection in SDN could benefit from the use of deep learning and feature selection techniques, which could significantly reduce processing loads and runtimes.
{"title":"Detection of DDoS Attacks using Artificial Gorilla Troops Optimizer based Deep Learning Model","authors":"S. Govindaraju, Rajrupa Metia, P. Girija, K. Baranitharan, M. Indirani, M. R.","doi":"10.1109/ICAIS56108.2023.10073935","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073935","url":null,"abstract":"The importance of security has skyrocketed alongside the adoption of Internet of Things (IoT) services. Software-defined networking (S DN) provides a means of securely managing IoT devices, which were exposed in a current distributed denial-of-service (DDoS) attack. Many IoT devices unwittingly contributed to the DDoS attack. DDoS attacks, one of the most common types of cyberattack, are particularly pernicious since they can cripple a network’s ability to function and render many of its services inaccessible to users. This research used optimised deep learning-based models to detect DDoS in SDN. At first, the dataset’s normal and DDoS attack traffic characteristics were gathered from the SDN. The models are recommended to be simpler, easier to read, and to have a shorter training period when using an NSL-KDD dataset for feature selection approaches. Real-time DDoS attack detection in SDN is proposed in this research using an Long Short-Term Memory (LS TM) models. High accuracy in classification is achieved by utilising an artificial gorilla troop optimizer to pick the features of NSL-KDD. Using less time and material, the proposed IDS was able to achieve a detection accuracy of 97.59%. These findings provide encouraging evidence that DDoS attack detection in SDN could benefit from the use of deep learning and feature selection techniques, which could significantly reduce processing loads and runtimes.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121294514","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073762
Rahul Kumar Vh, Thamizhamuthu R
Agriculture is the economic backbone of a number of countries. The agriculture sector considerably contributes to the overall GDP of a growing nation like India. Corn (Zea Mays) is one of the principal crops farmed in the nation. It is a significant food source and a critical raw element for several businesses. Plant diseases are a severe setback that all farmers endure. These illnesses lead to a drop in yield, a serious concern since the gap between demand and supply keeps rising. This research describes an architecture that utilizes Image Processing Techniques and Deep Learning. The suggested architecture employs the Non-Local Means method for noise reduction, Unsupervised Wiener filter, and Entropy to accomplish picture pre-processing. It uses Otsu’s Morphology and Canny Edge detection Method for picture segmentation. A histogram of Oriented Gradients is utilized for feature extraction, and Deep Convolutional Neural Network categorizes the illness.
{"title":"A Combined Architecture of Image Processing Techniques and Deep Neural Network for the Classification of Corn Plant Diseases","authors":"Rahul Kumar Vh, Thamizhamuthu R","doi":"10.1109/ICAIS56108.2023.10073762","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073762","url":null,"abstract":"Agriculture is the economic backbone of a number of countries. The agriculture sector considerably contributes to the overall GDP of a growing nation like India. Corn (Zea Mays) is one of the principal crops farmed in the nation. It is a significant food source and a critical raw element for several businesses. Plant diseases are a severe setback that all farmers endure. These illnesses lead to a drop in yield, a serious concern since the gap between demand and supply keeps rising. This research describes an architecture that utilizes Image Processing Techniques and Deep Learning. The suggested architecture employs the Non-Local Means method for noise reduction, Unsupervised Wiener filter, and Entropy to accomplish picture pre-processing. It uses Otsu’s Morphology and Canny Edge detection Method for picture segmentation. A histogram of Oriented Gradients is utilized for feature extraction, and Deep Convolutional Neural Network categorizes the illness.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116273822","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073705
Kalaiarasi N, Akshaya Ravichandran, Swathipurna Sahoo
This paper discusses the Cuk and Zeta converter-fed BLDC motors based on power factor correction. The comparative study is based on parameters like torque, stator current, power factor, and rotor speed of both converter-fed BLDC drives. The DC link voltage of the Voltage Source Inverter (VSI) is maintained constant in traditional PFC converter designs. This PFC converter’s primary drawback was complicated control, which necessitates a significant number of sensors. The proposed DC-DC converter follows the voltage follower approach in continuous conduction mode (CCM) to improve the power factor. Also, it helps to reduce switching losses and the stress on power devices. Low-frequency switching is utilized in the VSI to control the speed of the motor by changing the dc bus voltage. The switching loss of VSI is reduced by adopting fundamental frequency switching. This improves the efficiency of the system. Electronic commutation of the BLDC motor helps to eliminate the sensors required for the measurement of DC link voltage and phase currents. The proposed system with proportional-integral (PI) control provides a profitable solution for low-power applications. The performance of converters fed by BLDC motors is studied using MATLAB simulation. The results from Simulink show that the Cuk converter’s performance is better than the Zeta converter. So, the prototype hardware of the suggested model, i.e., the Cuk converter-fed BLDC motor, is implemented.
{"title":"An Efficient Prototype for PV based Water Pumping Solutions","authors":"Kalaiarasi N, Akshaya Ravichandran, Swathipurna Sahoo","doi":"10.1109/ICAIS56108.2023.10073705","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073705","url":null,"abstract":"This paper discusses the Cuk and Zeta converter-fed BLDC motors based on power factor correction. The comparative study is based on parameters like torque, stator current, power factor, and rotor speed of both converter-fed BLDC drives. The DC link voltage of the Voltage Source Inverter (VSI) is maintained constant in traditional PFC converter designs. This PFC converter’s primary drawback was complicated control, which necessitates a significant number of sensors. The proposed DC-DC converter follows the voltage follower approach in continuous conduction mode (CCM) to improve the power factor. Also, it helps to reduce switching losses and the stress on power devices. Low-frequency switching is utilized in the VSI to control the speed of the motor by changing the dc bus voltage. The switching loss of VSI is reduced by adopting fundamental frequency switching. This improves the efficiency of the system. Electronic commutation of the BLDC motor helps to eliminate the sensors required for the measurement of DC link voltage and phase currents. The proposed system with proportional-integral (PI) control provides a profitable solution for low-power applications. The performance of converters fed by BLDC motors is studied using MATLAB simulation. The results from Simulink show that the Cuk converter’s performance is better than the Zeta converter. So, the prototype hardware of the suggested model, i.e., the Cuk converter-fed BLDC motor, is implemented.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121509963","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073858
Sathya D, Chaithra V, Srividya Adiga, Srujana G, P. M
Air-drawing for authentication or gesture recognition is vastly studied such that new methods for the character drawn can be identified with better accuracy and be independent of any hardware components such as gloves to detect the fingertip movement or any wearable sensor or using any external pen. This article surveys how air-writing is done. Some of them include using fingertips as a pen and having the trained CNN model against it using the techniques DNST and Bi-LSTM for the regression and hand gesture identification and classification. This system had an overall accuracy of 88 percentage. Another system solves the air-writing issue by using deep learning architecture, by executing it on 3D space where the next evolution is numerical digits structured into multiple dimensions time-series extracted from a sensor called LMC. In another system, wi-write recognizes hand writing mainly to overcome blurred vision and neurological diseases in people, this will use COTS WiFi but is a device-free handwriting recognition system. In many other systems deep learning models are used and those parameters can be used to achieve higher recognition rate for example 98 percentage above.
{"title":"Systematic Review on on-Air Hand Doodle System for the Purpose of Authentication","authors":"Sathya D, Chaithra V, Srividya Adiga, Srujana G, P. M","doi":"10.1109/ICAIS56108.2023.10073858","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073858","url":null,"abstract":"Air-drawing for authentication or gesture recognition is vastly studied such that new methods for the character drawn can be identified with better accuracy and be independent of any hardware components such as gloves to detect the fingertip movement or any wearable sensor or using any external pen. This article surveys how air-writing is done. Some of them include using fingertips as a pen and having the trained CNN model against it using the techniques DNST and Bi-LSTM for the regression and hand gesture identification and classification. This system had an overall accuracy of 88 percentage. Another system solves the air-writing issue by using deep learning architecture, by executing it on 3D space where the next evolution is numerical digits structured into multiple dimensions time-series extracted from a sensor called LMC. In another system, wi-write recognizes hand writing mainly to overcome blurred vision and neurological diseases in people, this will use COTS WiFi but is a device-free handwriting recognition system. In many other systems deep learning models are used and those parameters can be used to achieve higher recognition rate for example 98 percentage above.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121568755","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073700
Rushali L. Kapse, V. Chandrakar
The main aim of this article is to determine the effective control signal for the function of damping oscillation by using Multilayer Feed Forward Network(MLFN) based SVC. MLFN based SVC is designed to achieve reduce settling time of response of different parameters during large and sudden disturbance in multimachine power system. The comparative analysis of proposed ANN based SVC with PI based SVC using MATLAB environment for testing and validation.
{"title":"ANN Based Static Var Compensator For Improved Power System Security","authors":"Rushali L. Kapse, V. Chandrakar","doi":"10.1109/ICAIS56108.2023.10073700","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073700","url":null,"abstract":"The main aim of this article is to determine the effective control signal for the function of damping oscillation by using Multilayer Feed Forward Network(MLFN) based SVC. MLFN based SVC is designed to achieve reduce settling time of response of different parameters during large and sudden disturbance in multimachine power system. The comparative analysis of proposed ANN based SVC with PI based SVC using MATLAB environment for testing and validation.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"58 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113986759","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 : 2023-02-02DOI: 10.1109/ICAIS56108.2023.10073670
K. R, K. Pitchai
This paper presents a method of attacking proof of work consensus based on selfish mining. The current mitigation strategies for the blockchain network's egotistical mining are not self-sufficient after a certain number of generations. Additionally, these solutions do not address the network nodes' cooperative and defector behavior. Additionally, more blocks from self-centered nodes are added to the blockchain in this development. This study analyzes to what extent these risks may affect cryptocurrency extraction. Minority mining pools keep some blocks private by deviating from the original mining protocol. An attacking collection aims to increase revenue by wasting other miners' computing power. By adopting a novel approach in this study. To determine whether such attacks are profitable. Using the interaction between pools in this model, mining strategies can be derived using game theory. By analyzing the relative revenue rather than the monetary award, this model simulates the game for a Bitcoin blockchain. This illustrates the usefulness of considering the cost of a strategy when discussing the potential outcomes of selfish mining strategies. The author highlights scenarios where the system might be compromised based on the way the parameters are set up in the game.
{"title":"Modeling and Simulation of Selfish Mining Attacks in Blockchain Network using Evolutionary Game Theory","authors":"K. R, K. Pitchai","doi":"10.1109/ICAIS56108.2023.10073670","DOIUrl":"https://doi.org/10.1109/ICAIS56108.2023.10073670","url":null,"abstract":"This paper presents a method of attacking proof of work consensus based on selfish mining. The current mitigation strategies for the blockchain network's egotistical mining are not self-sufficient after a certain number of generations. Additionally, these solutions do not address the network nodes' cooperative and defector behavior. Additionally, more blocks from self-centered nodes are added to the blockchain in this development. This study analyzes to what extent these risks may affect cryptocurrency extraction. Minority mining pools keep some blocks private by deviating from the original mining protocol. An attacking collection aims to increase revenue by wasting other miners' computing power. By adopting a novel approach in this study. To determine whether such attacks are profitable. Using the interaction between pools in this model, mining strategies can be derived using game theory. By analyzing the relative revenue rather than the monetary award, this model simulates the game for a Bitcoin blockchain. This illustrates the usefulness of considering the cost of a strategy when discussing the potential outcomes of selfish mining strategies. The author highlights scenarios where the system might be compromised based on the way the parameters are set up in the game.","PeriodicalId":164345,"journal":{"name":"2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115943882","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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