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检索条件"任意字段=Proceedings of the 3rd International Conference on Control and Computer Vision"

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COVID-19 (COV-19) Spreading Diagnoses by Feature Representation Method Through Visual Learning (FVisL-CoV19) 3rd

COVID-19 (COV-19) Spreading Diagnoses by Feature Representat...

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3rd international conference on Information Technology, InCITe 2023

作者： Kakulapati, V. Jayanthiladevi, A. Srinivas University Karnataka Mangaluru India Institute of Computer Science and Information Science Srinivas University Karnataka Mangaluru India

ISBN: (纸本)9789819959969

The use of computer vision and AI methods in medical image assessment has shown impressive success in the analysis and monitoring of respiratory ailment deterioration. To decrease the impact on people and the global economy, the COV-19 epidemic basis on the coronavirus has highlighted the need for artificial intelligence [AI] and disease management. The COV-19 identification approach proposed in this study is completely focused on the depiction of modal functions in pictures. Using standard pre-training features and tiny pattern development during chest CT processing, this approach first extracts modal function characteristics. A regular pooling layer prevents overfitting and reduces training parameters. The representation is trained and fine-tuned;to get the trained model, a machine that learns the scoring standard is utilized. The visible learning modeling approach of Coronavirus 19 (FVisL-CoV19) is used in this study to give a novel multi-layered function feature illustrated representation that is used to examine visible demands, including feature optimum function identification. However, using visual learning of structures improves FVisL accuracy. By employing CoV19’s Chest X-ray images and scans from the reference coranavirus record, the recommended function-based total representations are assessed. For training and testing, we employed 265 (with data-fact augmentation) and 795 COV-19 images, respectively. The FVisL-CoV19 approach demonstrated improved performance overall, using all feature functions in the dataset with an accuracy of 95.22%. The studies show that the kind of technology in this suggested work can analyze the modal statistics of the COV-19 picture more effectively than the standard techniques, and it increases the anticipated impact of appropriately detecting incidents. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Design and Development of a simplified SVPWM based Vector controller for IPMSM with MTPA Strategy 3

Design and Development of a simplified SVPWM based Vector Co...

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3rd IEEE international conference on Energy, Power and Environment - Towards Clean Energy Technologies (ICEPE)

作者： Jena, Manas Ranjan Mohanty, Kanungo Barada Natl Inst Technol Dept Elect Engn Rourkela Odisha India

ISBN: (纸本)9781665425360

This paper presents technique to design current, speed controller using field oriented vector control(FOC) for interior permanent magnet synchronous motor(IPMSM) using a novel simplified SVPWM algorithm. The internal model principle is applied to tune the current loop of ipmsm. To simplified the current loop design procedure, PI controller parameters are directly expressed in terms of machine parameters and desired closed loop bandwidth. Maximum torque per ampere(MTPA) method is used to improve the overall efficiency of motor drive system by injecting demagnetizing current component. A computer simulation shows the validity and effectiveness of proposed algorithm.

关键词： Field oriented control(FOC) Internal model control(IMC) Interior permanent magnet synchronous motor(IPMSM) Maximum torque per ampere(MTPA) Space vector modulation(SVM)

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Deep Learning Approaches for Pneumonia Classification in Healthcare

Deep Learning Approaches for Pneumonia Classification in Hea...

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Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： Sriram G K Karunasagar A Sudharsan R A Malini Department of Computer Science and Business Systems Thiagarajar College of Engineering Madurai India

In the past two decades, there has been a sharp rise in the use of deep learning for medical image processing and analysis. Recent challenges, for instance, the most well-known ImageNet computer vision competition, have almost entirely incorporated deep learning approaches for providing the best result. The concept of Image classification was later extended to Image Segmentation and Object Detection which proved to perform extremely well using state-of-the-art classification algorithms as their backbone architecture. The accuracy of the algorithm and approach has a significant impact on the medical field as there is a constant need for accurate and computationally efficient models. The existing object detection and segmentation approaches need large data for providing accurate results, unlike classification algorithms in which accuracy can be achieved with a relatively smaller amount of data. Hence, for the overall increase of model accuracy, there is a need for image augmentation to be incorporated. In this paper, several deep learning methodologies such as classification, object detection, ensemble, and segmentation for pneumonia classification and detection have been reviewed and an ensemble-based approach for the classification of Pneumonia using chest X-rays has been proposed.

关键词： Deep learning Image segmentation Pulmonary diseases Computational modeling Object detection Medical services Classification algorithms

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Bone Image Analysis For Early Detection of Osteoporosis

Bone Image Analysis For Early Detection of Osteoporosis

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： B.S.S.Kiran Rekha A.Madhumitha Reddy Ch. Vaishnavi A. Sreethiskesh Chunduri Madhu Babu Dept. of Computer Science Engineering B V Raju Institute of Technology Narsapur Medak Telangana India

Numerous studies believe that bone mineral density (BMD) is the only technique that fundamentally reveals bone strength, despite the fact that the latter provides important information regarding the risk of osteoporotic fracture. Analyzing macro- and micro-structural characteristics quantitatively may improve beyond simply estimating bone density. Bone structural information can be obtained using non-interfering, non-carnage procedures. According to many research, bone mineral densitometry (BMD), which also provides important information on osteoporosis, primarily measures bone strength. Numerous works in the field of photo-domain computer vision have introduced efficient strategies that benefit from different viewpoints on the same instance in other domains such as self-supervised and semi-supervised learning. In order to tackle these problems we are using a simple yet efficient method termed as multi-view ct network or mvctnet along with multimodal data fusing technique called Early Fusion. Utilizing cutting-edge methods and technologies will improve image analysis in multi-view CT scanning for osteoporosis detection, increasing the precision and effectiveness of osteoporosis detection.

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Research on the Mechanism and Impact Effect of Intelligent Pressure control Dry Powder Fire Extinguishing Based on Intelligent Fire Fighting Devices 3

Research on the Mechanism and Impact Effect of Intelligent P...

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3rd international conference on Artificial Intelligence, Human-computer Interaction and Robotics, AIHCIR 2024

作者： Chu, Shengli Wang, Siyang Jin, Shuli Xiong, Linsen Liu, Yixin Gan, Yifan Zheng, Wenpei CNPC Research Institute of Safety & Environment Technology Key Laboratory of Oil & Gas Storage and Transportation Safety Risk Prevention Ministry of Emergency Management Beijing China College of Safety and Ocean Engineering University of China University of Petroleum Beijing Key Laboratory of Oil and Gas Safety and Emergency Technology Ministry of Emergency Management Beijing China

ISBN: (纸本)9798331534035

In response to the high demand for intelligent fire extinguishing robots in various industries and the rapid development of research and design of robot fire extinguishing modules, a pressure controlled dry powder fire extinguishing experimental platform based on fire extinguishing technology has been developed. The experimental platform includes an experimental bench module, a dry powder automatic release module, a fire source simulation module, and a data acquisition module. By replacing different types of combustibles, adjusting the nitrogen driving pressure, adjusting the nozzle spray height and angle, adjusting the dry powder filling amount, adjusting the inclination angle of the dry powder tank, adjusting the position of the dry powder inlet and outlet, and adjusting the length of the built-in conduit, it is possible to simulate the controlled pressure dry powder fire extinguishing effect in different locations and scales of fires. Realize the intelligence and standardization of such experiments. Through on-site testing and application, it was found that the best fire extinguishing effect is achieved when the nitrogen drive is 0.3MPa, the dry powder pipe diameter is 8mm, the nozzle is horn shaped, and the dry powder loading ratio is 1. © 2024 IEEE.

关键词： Fire extinguishers

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The Liquid Level control of a Three-Tank Water Tank is Based On Beetle-Particle Swarm Algorithm

The Liquid Level Control of a Three-Tank Water Tank is Based...

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international conference on Artificial Intelligence and computer Information Technology (AICIT)

作者： Jian Chen Zicheng Li Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan China

ISBN: (数字)9798350365443

ISBN: (纸本)9798350365450

Aiming at the uncertainty factors such as large inertia, large delay and nonlinearity in the three-tank water level control system, an intelligent control algorithm of Beetle antennae-particle swarm optimization was proposed. Firstly, on the basis of establishing the mathematical model of the three-capacity water tank, the BAS-PSO algorithm is designed, which increases the ability of individual to learn the surrounding information independently on the basis of maintaining the group optimization search and learning the global optimal, and continuously iterates the optimization until the optimal solution is found. Secondly, 5 examples of benchmark test functions are selected for performance testing to evaluate the local search ability and global development ability of the algorithm. Finally, the simulation results show that compared with the traditional intelligent control algorithm, the BAS-PSO algorithm can effectively improve the speed and robustness of the system.

关键词： Water Uncertainty Simulation Storage management Robustness Level control Particle swarm optimization Optimization Antennas Intelligent control

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Prosformer: Accurate Surface Reconstruction for Sparse Profilometer Measurement with Transformer 3

Prosformer: Accurate Surface Reconstruction for Sparse Profi...

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3rd international conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Ren, Jieji Guo, Qianyu Ren, Mingjun Zhang, Wenqiang Sun, Lijian Zhu, Limin School of Mechanical Engineering Shanghai Jiao Tong University China School of Computer Science Fudan University China AI Center Zhejiang Lab China

ISBN: (纸本)9781665492812

Surface micro-structure measurement is significant for precision manufacturing. However, existing stylus profilometer is inefficient, and sparse line-scan measurement can't support accurate surface description. To improve the reconstruction performance, we propose a high-accurate reconstruction method with sparse line-scan measurement based on attention mechanism. We first arrange the sparse-line measurement in the 2D matrix and crop as patch region. Then we utilize transformer to construct semantic relationships between patches and assign new weights to each patch to accurately model the structural relationships of target region and perform feature extraction, where the self-attention can enhance the description of local details while cross-attention will interact with global information. Finally, a fully connected network as a decoder is adopted to reconstruct accurate surface details with complete geometric representation. We refer to this model as Prosformer. Furthermore, we simulate a larger-scale surface micro-structure dataset to drive the training process and measure micro-structures to valid Prosformer. Experiments show proposed method can effectively restore complex surface details. © 2022 IEEE.

关键词： neural networks profilometer surface measurement

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An Innovative High-Frequency Wireless Communication Technique Inspired by Multi-Frequency Carrier Signals 3

An Innovative High-Frequency Wireless Communication Techniqu...

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3rd international conference on computer Communication and the Internet, ICCCI 2021

作者： Pal, Prashnatita Sahana, Bikash Chandra Poray, Jayanta National Institute of Technology Department of Electronics and Communication Engineering Patna India Techno India University Department of Computer Science and Engineering West Bengal Kolkata India

ISBN: (纸本)9781728176185

Modern intelligent broadcasting technologies using Internet-of-Things (IoT) and Wireless Communication mediums are playing an important role to connect multiple heterogeneous devices. This fulfills the requirement to achieve advanced and robust communication systems. This paper describes a data transmission technique using a high-frequency generator reflex klystron. Here, the high-frequency multi-level shift keying techniques are generated with the help of a single reflex klystron, namely M-ary High-Frequency Shift Keying (MHFSK). The reflex klystron plays the primary role to generate multiple independent high-frequency signals. In this scheme, the high-frequency carrier signal is selected through a sequence of binary bits which control the reflector voltage. As a result, this modulation system creates multiple high frequencies which act as an important parameter of this proposed multi-frequency modulation scheme. Here, the main goal is to improve the overall efficiency compared to other binary digital modulation systems. This new concept has been verified through an experimental framework. © 2021 IEEE.

关键词： Internet of things

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Waysides Inspection Using Wayside Processing Imaging and Deep Learning

Waysides Inspection Using Wayside Processing Imaging and Dee...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： R. Thillaikkarasi M. Mohamed Yaseen R. Rameshbabu Radha Prabhakaran R. Kesavan Jose Anand A. Department of ECE Salem College of Engineering and Technology Salem Tamil Nadu India Department of ECE KCG College of Technology Chennai Tamil Nadu India Department of ECE VSB Engineering College Puduvoyal Tamil Nadu India Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Chennai Tamil Nadu India

Advances in deep learning techniques have made computer vision tasks more accurate and faster by relying on convolutional neural networks and more powerful hardware. In industry, automatic inspection supported by these methods is capable of offering constant maintenance and avoiding accidents on railways. Thus, this work proposes the application of deep learning and image processing methods to perform the automatic inspection of wheel sets in train cars. More specifically, the size of the wheel and the thickness of the bandage are measured, in addition to locating the bearings' fixation screws. The constructed neural network performs semantic segmentation on photographs provided by the mining company Vale. Using a U-Net architecture, with ResNet50 as a backbone, the network was able to reach 92.50% in mIoU and 97.52% in mPA, metrics adopted to evaluate this proposal. The post-processing step recovered the screws and improved the evaluation metrics, indicating the success of the proposed inspection.

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Face Detection with Landmark using YOLOv8

Face Detection with Landmark using YOLOv8

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Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET), international conference on

作者： Nehith Sai Vemulapalli Pruthvi Paladugula Guggilam Sai Prabhat S Abhishek Anjali T Dept. of Computer Science and Engineering Amrita School of Computing Amritapuri India

The advent of deep learning combined with computer vision has brought forth unparalleled advancements in facial detection and landmark identification. One pivotal player in this transformation has been the YOLO (You Only Look Once) series, setting new milestones in object detection methodologies. Our research is centered on harnessing the YOLOv8 model to optimize face detection processes. We incorporate the OpenCV library for image processing, enhancing detection fidelity through adjustable parameters like confidence and intersection over union (IoU) thresholds. A standout feature of our methodology is its innate ability to adjust to diverse image proportions. This is achieved by innovatively resizing and padding input images, which not only maintains consistency in detection but also augments accuracy. The proposed technique not only demarcates the face but also pinpoints facial landmarks, thus offering a comprehensive spatial map for each detected face. Preliminary results on benchmark datasets underscore the model's dual advantage of speed and precision. Our approach promises not only improved face detection but also paves the way for its amalgamation into expansive facial recognition and analytic platforms.

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