2022 Smart Technologies, Communication and Robotics (STCR)最新文献_第2页

Analysis of Artificial Intelligence based Forecasting Techniques for Renewable Wind Power Generation 基于人工智能的可再生风力发电预测技术分析

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009141

Jarabala Ranga, T. Arun Srinivas, Santosh Kumar, Harishchander Anandaram, P. Kulkarni, M. Amina Begum

{"title":"Analysis of Artificial Intelligence based Forecasting Techniques for Renewable Wind Power Generation","authors":"Jarabala Ranga, T. Arun Srinivas, Santosh Kumar, Harishchander Anandaram, P. Kulkarni, M. Amina Begum","doi":"10.1109/STCR55312.2022.10009141","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009141","url":null,"abstract":"Wind is considered as the renewable energy resource which consists of high state of efficiency with low pollution. The accurate level of forecasting can reduce the minimal range of losses and risk in the unrealizable factor. High energy of wind powers are defined where it comprises with the challenges in the power systems and other variability in generating the power. One of the key factors of the electricity supply is wind forecasting. It implies with the several improvements where many literatures have initially developed new technologies to forecast the wind power. A different range of forecast are been developed and are been categorized according to the expected production. These are indicated using the power productivity potential over the state of time interval. In this research paper, an overview of the different technologies used in the wind power forecasting are been discussed. This paper mainly focuses upon the research work of different literature review and their principles with the practical development. Based upon the categories, the futuristic development of each wind forecasting are been directed accordingly.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"33 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114014007","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}

引用次数: 0

Convolutional Neural Network based Human Emotion Recognition System: A Deep Learning Approach 基于卷积神经网络的人类情感识别系统:一种深度学习方法

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009123

S. Depuru, A. Nandam, S. Sivanantham, K. Amala, V. Akshaya, M. Saktivel

{"title":"Convolutional Neural Network based Human Emotion Recognition System: A Deep Learning Approach","authors":"S. Depuru, A. Nandam, S. Sivanantham, K. Amala, V. Akshaya, M. Saktivel","doi":"10.1109/STCR55312.2022.10009123","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009123","url":null,"abstract":"Recent research focuses towards Expression recognition. Variety of applications is now available ranging from security cameras to detecting emotions. Facial recognition is an important activity in emotion detection Convolutional Neural Networks (CNN) are used for facial recognition. Images are taken as input and facial expressions are produced as outcome like Happy, Sad, Disgust, Angry, Fear, Surprise and neutral. In this paper, we propose an Artificial Intelligence (AI) which recognizes the facial emotions using the different layers in the CNN. Thorough examination of deep Face Expression Recognizer (FER), including datasets and methods that shed light on these underlying difficulties. First, the FER scheme, which includes pertinent background information, is implemented for seeking advice for each level. The dataset used for experimentation is FER challenge dataset available in kaggle repository. The implementation environment includes keras, tensorflow, cv2 python packages. The results include the comparison of accuracy of emotion detection between training and testing phase. The average accuracy achieved was 84.50%.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127718703","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}

引用次数: 12

UAV and Vehicular Communication with Hardware Impairments in Multiple RIS System 多RIS系统中存在硬件缺陷的无人机与车载通信

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009609

K. M., D. D., C. K, Venkateshkumar U, S. V, P. l

引用次数: 0

Design and Control of Soft Pneumatic Actuator with Embedded Flexion Sensor 嵌入式柔性气动执行器的设计与控制

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009151

S. Martínez, Simon Valencia, Alejandro Toro-Ossaba, J. David Núñez, Juan C. Tejada, Santiago Rúa, A. López-González

{"title":"Design and Control of Soft Pneumatic Actuator with Embedded Flexion Sensor","authors":"S. Martínez, Simon Valencia, Alejandro Toro-Ossaba, J. David Núñez, Juan C. Tejada, Santiago Rúa, A. López-González","doi":"10.1109/STCR55312.2022.10009151","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009151","url":null,"abstract":"Soft robotics has been widely explored in the past decade due to their ability to adapt to different environments and interact softly with external objects. This paper presents the design, manufacture, model identification and control of a low cost soft robotic pneumatic actuator with sensing capabilities, the proposed actuator has potential application in prosthetic and orthotic devices. Initially, the design and fabrication of the actuator is presented, including the design parameters and manufacturing techniques. The system was modeled using system identification techniques allowing to implement a PI controller to control the actuator deformation. In addition, the design and fabrication of a soft bending sensor, based on a variable resistance material, was presented. The sensor was used as feedback element for the control system and allowed to control the deformation of the actuator. The soft actuator only requires 0.4 bar of pressure to achieve maximum deformation; the controlled actuator achieved a settling time of 4.5 s and an steady state error of 2 %.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131398065","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}

引用次数: 0

Performance Analysis of Inbuilt Hearing Aid using Signal Enhancement by Deep Learning 基于深度学习信号增强的内置助听器性能分析

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009425

Dinesh Kumar J R, Shri Dharshana S, G. C, P. K, Shamitha M, Reveetha A

{"title":"Performance Analysis of Inbuilt Hearing Aid using Signal Enhancement by Deep Learning","authors":"Dinesh Kumar J R, Shri Dharshana S, G. C, P. K, Shamitha M, Reveetha A","doi":"10.1109/STCR55312.2022.10009425","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009425","url":null,"abstract":"Audio Signal processing is a method that uses intensive algorithms that are applied to audio signals. Audio signals are in the form of both analog and digital signals and they are the typical representation of sound. The frequency of audio ranges from 20Hz to 20,000 Hz, and 20Hz is the lower limit of our ears and 20,000Hz is the upper limit of our ears. The process of audio signal processing gives the desired audio by removing the unwanted noise from the speech signal. This process balances the time and frequency range. This process also aims on commutative methods by altering sounds and removes echo, unwanted noise and over modulation. Recent literatures focus on removal of noise from the audio signal. We are dealing with enhancing the quality of speech. Speech consists of various noises such as stationaries noises and non-stationary noises. Several strategies are proposed which are based on Deep learning and Deep Neural Networks to overcome this problem. The main goal of the paper is improvement in the quality of speech signals that are corrupted by noise. This will enhance the performance of digital hearing aid using Deep Neural Networks before it delivers to the needy people and also to measure and analyze the emotion of speech.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128227596","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}

引用次数: 0

Design and Simulation of Object Detection Based Autonomous Trash Collector Bot 基于目标检测的自主垃圾收集机器人设计与仿真

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009636

B. R, Maheswari K T, B. M, Sangeethkumar C

{"title":"Design and Simulation of Object Detection Based Autonomous Trash Collector Bot","authors":"B. R, Maheswari K T, B. M, Sangeethkumar C","doi":"10.1109/STCR55312.2022.10009636","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009636","url":null,"abstract":"Nowadays, due to the increased population, usage of public places becomes very high. However, garbage is left uncleaned in most public areas. There is no constant survey of the area at a given time so that the waste gets accumulated. The most crowded places like malls and parks remains unclean. This affects not only the environment but also the mindset of the people visiting the places, so the proposed idea provides a great solution to these problems through the following steps. It continuously monitors a given area and detects the presence of garbage in that given area. The live image is then calibrated to detect the distance and to operate the arm to pick up the trash. The picked trash is then thrown in the collector attached at the back of the bot and released when it is full, on the established disposal area. This will help to clean the environment in a drastic way and also in attracting the people to visit the area. The bot will help in reducing the burden of human beings and also provides 24/7 maintenance to the predefined area. For a much larger area using this bot will be the most commercially feasible, the bot will not only be most effective for the above scenario but it will be most commercially effective as well as a viable option as well. This is the main objective for making a bot that does the above function. In this paper, a software has been developed using python with which various items can be identified and can be segregated as trash and not a trash item. The autonomous trash collector robot has been designed using sold works and Roboanalyzer softwares and the results are analyzed.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"304 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132042474","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}

引用次数: 0

Lung Cancer Detection using 3D-Convolution Neural Network 三维卷积神经网络检测肺癌

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009146

S. V, Poongodi C, S. G, S. S, S. V

引用次数: 1

File Security using Image-based Encryption (FSUIE) 使用基于映像的加密(FSUIE)实现文件安全

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009103

Mohammed Sekhi, M. Ilyas

引用次数: 0

Automated Essay Scoring System with Grammar Score Analysis 自动作文评分系统与语法评分分析

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009053

Aniket Ajit Tambe, Manasi Kulkarni

引用次数: 0

Analyzing Haar and dB2 with Compensatory GMM Classifier for Epilepsy Detection 基于补偿GMM分类器的Haar和dB2癫痫检测分析

2022 Smart Technologies, Communication and Robotics (STCR) Pub Date : 2022-12-10 DOI: 10.1109/STCR55312.2022.10009556

G. C, Gowri Shankar M, H. Rajaguru, Priyanka G S, A. T

引用次数: 0