2021 International Conference on Intelligent Technologies (CONIT)最新文献_第9页

Optimal utilization of UPQC during steady state using evolutionary optimization techniques 基于进化优化技术的UPQC稳态优化利用

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498479

Swati Gade, R. Agrawal

引用次数: 1

Stacked LSTM Recurrent Neural Network: A Deep Learning Approach for Short Term Wind Speed Forecasting 叠置LSTM递归神经网络:一种短期风速预测的深度学习方法

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498314

C. Sowmya, Anu G. Kumar, Sachin Kumar

{"title":"Stacked LSTM Recurrent Neural Network: A Deep Learning Approach for Short Term Wind Speed Forecasting","authors":"C. Sowmya, Anu G. Kumar, Sachin Kumar","doi":"10.1109/CONIT51480.2021.9498314","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498314","url":null,"abstract":"Renewable energy has now become a key to the future. Renewable energy can be harnessed from many sources, one such source is wind. Wind energy is constantly prone to changes hence making it an intermittent or abrupt source of energy. Thus wind forecasting finds multiple applications in various fields such as selecting the sites to construct the wind farm, forecasting the future wind speed, future wind power prediction for deciding the electricity tariffs, for penalty-free bidding process, and for enhancing the power system reliability thus making it an extensive area of research. Forecasting the wind speed will support these applications in having superior outcomes. However, the prediction of wind energy at any given time is still a major challenge. There are many techniques for predicting future wind speed, but considering the accuracy, training pattern, and testing ability, applying Machine Learning is considered as the finest solution. There are various approaches in Machine Learning for forecasting the wind speed, among which Long Short-Term Memory (LSTM) based forecasting is the contemporary method for time series forecasting. In this paper, LSTM is further layered to obtain better accuracy. This paper explores a novel Stacked LSTM based architectures, which can accomplish a better wind forecasting model that can be administered for Maximum Power Point Tracking (MPPT) for finding the optimal wind power output. Comparing with various existing algorithms, a three-layered stacked LSTM is found to have better performance indices.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121808207","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: 2

Classification of Corona Virus Infected Chest X-ray using Deep Convolutional Neural Network 冠状病毒感染胸片的深度卷积神经网络分类

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498366

Nitish Patel, Debasish Pradhan

{"title":"Classification of Corona Virus Infected Chest X-ray using Deep Convolutional Neural Network","authors":"Nitish Patel, Debasish Pradhan","doi":"10.1109/CONIT51480.2021.9498366","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498366","url":null,"abstract":"The coronavirus 2019 is a worldwide pandemic declared by the world health organization (WHO). It starts in China, Wuhan in November 2019 and spread all over the world. As time passed, the detection and clinical treatment of COVID-19 is developed by the researchers. COVID-19 is detected using a reverse transcription-polymerase chain reaction (RT-PCR) test, which is precise but requires two days to complete. Hence, the researchers proposed many classification models, which are mainly based on artificial intelligence. Mainly these classification models are using chest X-ray images for the detection of COVID-19. In this paper, we proposed a deep convolutional neural network model architecture to classify chest X-ray images. We called this model the base model, which is the first train to classify normal and abnormal chest X-ray images. Using the transfer learning technique, we retrained this model for four-classes classification (i.e., Normal, COVID-19, Pneumonia, and Pneumothorax). We obtain 73.9% accuracy for the base model (i.e., binary classification) and 83.2% accuracy for fine-tuned model (i.e., four-classes classification).","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"88 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120905866","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

Adaptive Replacement Cache Policy in Named Data Networking 命名数据组网中的自适应替换缓存策略

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498489

Prajjwal Singh, Rajneesh Kumar, Saurabh Kannaujia, N. Sarma

{"title":"Adaptive Replacement Cache Policy in Named Data Networking","authors":"Prajjwal Singh, Rajneesh Kumar, Saurabh Kannaujia, N. Sarma","doi":"10.1109/CONIT51480.2021.9498489","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498489","url":null,"abstract":"The traditional IP-based internet architecture is host-oriented and was built over the old ideology of telephony systems. Named Data Networking (NDN), which is based on Content Centric Networking (CCN), is an enhancement, or rather an alternative, to the IP based networking architecture. NDN architecture allows caching of network data packets at the routers to facilitate satisfaction of interests shown by multiple hosts. Therefore, a caching scheme plays a vital role in the network’s performance. Least Recently Used (LRU) and Priority-Based First-In First-Out (FIFO) are cache eviction policies in NDN Forwarding Daemon (NFD). However, both approaches do not give weightage to the frequency of requested data packets during the eviction and are not scan resistant, which could be an important feature in a CCN system. Other policies like Least Recently Frequently Used (LRFU) subsumes LRU and LFU policies but requires tuning parameters and may not always perform best in dynamic network traffic conditions. In this paper, we have implemented the Adaptive Replacement Cache (ARC) Algorithm, which is a scan resistant, self-tuning, and LRU and LFU subsuming cache replacement policy in the ndnSIM simulator and compared the hit rate performance of ARC with the LRU replacement policy. As Content Store size affects the overall performance of NDN, we have proved by simulation that ARC requires smaller Content Store size than LRU. We conducted a simulation study by varying the Grid Topology, Content Store, and Interest Rate size. Simulation results reveal that ARC replacement policy outperforms LRU replacement policy by achieving a 4% higher hit rate. We have also observed that ARC requires a smaller content store size than LRU to reach the 74% hit rate.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122342523","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: 2

Parametric Reconstruction of Photoacoustic Tomographic Imaging using Gaussian Mixture Model and Evolutionary Methods 基于高斯混合模型和进化方法的光声层析成像参数重建

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498439

Bondita Paul, R. Patra

{"title":"Parametric Reconstruction of Photoacoustic Tomographic Imaging using Gaussian Mixture Model and Evolutionary Methods","authors":"Bondita Paul, R. Patra","doi":"10.1109/CONIT51480.2021.9498439","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498439","url":null,"abstract":"Photoacoustic tomographic (PAT) imaging is a non-invasive biomedical imaging methodology based on the photoacoustic effect in the tissue. The basic principle involves that a short pulse laser is used to irradiate the biological tissue where the tissue particles absorb the photon and generate acoustic pressure waves in the sample. The pressure signals are measured by the ultrasonic transducer placed at the multiple locations on the boundary of the sample. Subsequently, the absorbed energy or the initial pressure distribution in the medium is reconstructed from the measured acoustic pressure signals. In this work, to reduce the number of unknowns in the reconstruction problem, an eight component Gaussian mixture model (GMM) is used to parameterize the initial pressure distribution of the tissue medium. In this study, two optimization techniques like Particle swarm optimization (PSO) and Genetic algorithm (GA) have been considered for image reconstruction. Performance of developed algorithms was evaluated using structural similarity index (SSIM) between the reconstructed and the actual images. The obtained values of SSIM of the reconstructed images are 0.9381 for PSO and 0.9357 for GA which shows the accuracy of reconstruction as well as reinforces the potential of the proposed reconstruction algorithms. The proposed algorithm has been illustrated for finger joints like phantom as well.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116480844","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

The Performance Study of MOSFET based MEMS Pressure Sensor with Partially Active Voltage Divider Readout Circuit 基于MOSFET的部分有源分压器读出电路的MEMS压力传感器的性能研究

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498391

Workneh Wolde, Pallavi Gupta

引用次数: 0

Microcontrollers based Motion Analysis and Control of Lower Limb Kinetics for People Suffering with Osteoarthritis 基于微控制器的骨关节炎患者下肢运动分析与控制

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498337

Shivam Kumar, Chirag Manghani, Ruban Nersisson

{"title":"Microcontrollers based Motion Analysis and Control of Lower Limb Kinetics for People Suffering with Osteoarthritis","authors":"Shivam Kumar, Chirag Manghani, Ruban Nersisson","doi":"10.1109/CONIT51480.2021.9498337","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498337","url":null,"abstract":"Knee injuries, leg amputation or arthritis have always been the biggest concern amongst the surgeons and different department of medicines. So far the advancements being done in this arena have proved to be futile, sometimes the condition of the concerned patient becomes too complex due to the pain or any injury that he is tend to remove that part from his body which eventually costs him to spend plenty for the surgery. The knee related problems and amputation can be resolved either through surgery or prescription but for the case of arthritis the patient is confined to live in pain for many years as the complete cure for the same has not been in the medicine department so far. In this study we have attempted to propose a model of leg which allows the patient to move the lower leg autonomously and the speed can be controlled to attain that relaxed range with potentiometers. The whole model has been segregated into different parts which eventually lead to deep analysis of the forces and other parameter acting on the prosthetics at the time of transition from one position to another. Keeping in mind the case of surgery and other methods, this leg tend to perform tasks which do not require any surgery or serious implantation.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127192780","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

SRM Torque Ripple Reduction Using Grey Wolf and Teaching and Learning Based optimization in Hysteresis Control 基于灰狼和基于教与学的磁滞控制优化的SRM转矩纹波减小

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498374

Mrityunjay K Jha, Nikhil Seth, Nikhil Tyagi, Sikandar Ali Khan

引用次数: 3

ASIC Implementation and Analysis of Logic BIST Controller for Ripple Carry Adder at Different Technology 不同工艺下纹波进位加法器逻辑BIST控制器的ASIC实现与分析

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498398

S. Umarani, M. Rathod

{"title":"ASIC Implementation and Analysis of Logic BIST Controller for Ripple Carry Adder at Different Technology","authors":"S. Umarani, M. Rathod","doi":"10.1109/CONIT51480.2021.9498398","DOIUrl":"https://doi.org/10.1109/CONIT51480.2021.9498398","url":null,"abstract":"Logic-Built-In-Self-Test (LBIST) is an architectural methodology that tests the Circuit Under Test (CUT) by itself. An Application Specific Integrated Circuit (ASIC) LBIST method is proposed which generates patterns of weighted pseudorandom tests for the CUT. A technique used for testing combinational blocks, sequential blocks, memories, adders, and other embedded logic blocks is Built In Self Test (BIST). The technique entails to produce the test patterns, given into the circuits that is being tested, and then check the response. LBIST allow testing at fast paced and fault coverage is high. The circuit operates in standard or test phase depending by the test data to the controller. In this paper, we describe an implementing of LBIST controller for a combinational logic ripple carry adder by utilization of Xilinx ISE and ASIC flow in Verilog using cadence tools like genus, innovus in 45nm and 180nm library technology. We proposed operation of the testing of CUT can be stop at any point. It helps us to suspend the generation of the signature, in the test sequence at any desired stage. In this instance, the LBIST circuit is considered to provide logic for keeping and an element of generating Signature. Implemented in such a way that it is functioning the basic operation of CUT if it is fault free. In this LBIST scheme considered two cases, one is without fault in circuit under test other is with fault in circuit under test. We have done physical implementation and analysis of LBIST in 45nm and 180nm technology for power, timing, area and we will observe the effect of technology on the performance of LBIST.","PeriodicalId":426131,"journal":{"name":"2021 International Conference on Intelligent Technologies (CONIT)","volume":"17 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132624842","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: 1

Conversational AI: Chatbots 会话AI:聊天机器人

2021 International Conference on Intelligent Technologies (CONIT) Pub Date : 2021-06-25 DOI: 10.1109/CONIT51480.2021.9498508

Siddhant Meshram, Namit Naik, Megha Vr, Tanmay More, S. Kharche

引用次数: 7