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检索条件"任意字段=2023 3rd International Conference on Detection Technology and Intelligence System, DTIS 2023"

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Research and application of distributed new energy active power automatic control strategy 3

Research and application of distributed new energy active po...

引用

3rd IEEE international conference on Digital Twins and Parallel intelligence, DTPI 2023

作者： Zhang, Jiming Wang, Tianyu Yu, Peizhou Zhang, Duxi Wu, Qiuwei State Grid Zhejiang Ningbo Yinzhou Electric Power Co. Ltd. Ningbo China State Grid Zhejiang Electric Power Research Institute Hangzhou China State Grid Ningbo Power Supply Company Ningbo China Tsinghua Shenzhen International Graduate School Shenzhen China

ISBN: (纸本)9798350318470

Active power automatic control is an important technical condition for grid connected operation of generator sets. In order to ensure the safe and reliable operations of wind power, solar energy, and other new energy generation, distributed new energy connected to the power system should be equipped with active power automatic control function. In addition, distributed new energy should complete the performance verification test of active power automatic control technology with the dispatch agency before and after grid connection operation. The article elaborates on the application configuration and performance indicators of the distributed new energy active power automatic control system. The application results indicate that distributed new energy has the ability to control active automatic power generation and can meet the technical requirements for safe and stable operation of the power grid. © 2023 IEEE.

关键词： Wind power

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An Efficient Approach of Drowsiness detection Based on ResNet50 and Xception Architectures 3

An Efficient Approach of Drowsiness Detection Based on ResNe...

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3rd IEEE international conference on Electronic Engineering, ICEEM 2023

作者： El-Nabi, Samy Abd El-Rabaie, El-Sayed M. Emam, Ahmed Ramadan, Khalil F. Faculty of Computer Science and Engineering Dept. of Artificial Intelligence Engineering South Sinai46511 Egypt Menoufia University Faculty of Electronic Engineering Dept. of Electronics and Electrical Communications Menouf32952 Egypt Menoufia University Dept. of Computer Science and Math Faculty of Science Menoufia Egypt

ISBN: (纸本)9798350323511

This paper delves into a critical aspect of road safety by investigating a method to detect drowsiness in images. The accurate utilization of this technology by fatigued drivers holds the potential to prevent numerous fatal accidents, as the proposed models exhibit swift responsiveness in identifying instances of driver drowsiness. Within the realm of eye closure detection, two drowsiness models, namely ResNet50 and Xception, are employed to portray various eye state classifications. Nonetheless, the scarcity of readily available and reliable eye datasets poses a formidable challenge. Leveraging the power of ResNet50, deep facial features are extracted, resulting in an impressive accuracy of 98.06%. Similarly, the utilization of Xception yields a commendable accuracy of 95.69% when evaluated with the MRL dataset. These experimental outcomes firmly establish the higher accuracy and cost-effectiveness of eye closure estimation, thereby underscoring the efficacy of the proposed framework for drowsiness recognition. © 2023 IEEE.

关键词： Cost effectiveness

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Crankshaft defect detection based on machine vision 3

Crankshaft defect detection based on machine vision

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2023 3rd international conference on Artificial intelligence and Industrial technology Applications, AIITA 2023

作者： Guo, Kunxiang Sun, Yuan Shanghai Dian Ji University Shanghai201306 China

As a core component of the Marine engine, the crankshaft crank is required to have a high surface quality to ensure that the ship does not fail during the voyage. After analysing the defect characteristics, aiming at the problems such as the inaccurate detection of single defect feature in the crankshaft bend defect, which leads to poor detection effect, a crankshaft bend defect detection classification method based on machine vision was proposed. The recognition accuracy can reach 97.8%. Compared with the defect identification by texture and geometric features alone, the classification and recognition accuracy can be improved, and the requirements of industrial detection can be better met. © Published under licence by IOP Publishing Ltd.

关键词： Crankshafts

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Epileptic Seizure detection Based on Semi-supervised Generative Adversarial Network 3

Epileptic Seizure Detection Based on Semi-supervised Generat...

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2023 3rd international conference on Artificial intelligence and Industrial technology Applications, AIITA 2023

作者： Liu, Xiaojia School of Information and Electronic Engineering Shandong Technology and Business University China

Since the manual diagnosis of electroencephalograph (EEG) recordings requires a lot of labor and material costs for clinical seizure detection, the annotation for seizure data is of great challenge for seizure detection. To tackle the issue of small samples of epilepsy-labeled data, we propose a semi-supervised generative adversarial network-based seizure detection method. To begin with, a Butterworth filter is used to preprocess the EEG, and the filtered EEG signal is input into the SGAN model. Finally, the output of the SGAN model is subjected to post-processing operations including averaging filtering and threshold comparison, and the discriminative result of whether the tested EEG is a seizure is output. The method achieved an average sensitivity of 90.36%, an average specificity of 93.72%, and an average accuracy of 93.72% in the CHB-MIT EEG dataset. Experiments demonstrate that the semi-supervised generative adversarial network has more accurate classification performance in epilepsy detection. © Published under licence by IOP Publishing Ltd.

关键词： Generative adversarial networks

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detection of Salient Objects in a Video using a Hybrid Neural Network Model 3

Detection of Salient Objects in a Video using a Hybrid Neura...

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3rd international conference on Artificial intelligence and Smart Energy, ICAIS 2023

作者： Indirani, M. Anitha, Cuddapah Goswami, Sohan Baranitharan, K. Govindaraju, S. Mahaveerakannan, R. Hindusthan College of Engineering and Technology Department of It Coimbatore India Mohan Babu University Sree Vidyaniketan Engineering College Department of Computer Science and Systems Engineering Tirupati India Technique Polytechnic Institute Sugandhya Department of Computer Science and Technology West Bengal India Vsb Engineering College Cse Department India Sri Ramakrishna College of Arts and Science Department of Cs Coimbatore India Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665462167

Salient detection is an active and critical area that is designed within the detection of items of a video recording, nonetheless, it attracts elevated interest among scientists. With rising powerful video clip information, the overall performance of saliency item detection techniques is degrading with typical item detection techniques. The problems lie with blurry moving goals, super-fast motion of items as well as dynamic background or background occlusion alteration on foreground areas within the video clip frames. This kind of obstacle leads to bad saliency detection. This paper models a full mastering design to deal with the difficulties, and that works on an advanced framework by merging the thought of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) with firefly Optimization technique for video clip saliency detection. Good utilization of the firefly algorithm together with CRNN is completed for the removal of characteristics by the video clips for item recognition. The primary objective of this newspaper is to present an effective hyperparameter choice framework for Convolution Recurrent Neural Networks (CRNNs) that employ one of the more popular swarm intelligence methods, the firefly algorithm. The suggested technique goals at creating a spatiotemporal design that exploits temporal, local, and spatial restriction cues to attain worldwide SEO. The process of locating the salient items in deep benchmark powerful video recording datasets will be completed by recording the temporal, local, and spatial restriction characteristics with all the CRNN. The CRNN is examined on benchmark datasets from typical video clip salient item detection techniques within the terminology of accuracy and load of Computation. The tests show that the proposed design accomplishes enhanced overall performance compared to some other existing versions which prove to significantly satisfy all the traditional object detection models. © 2023 IEEE.

关键词： Convolution

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Bangladeshi Fresh-Rotten Fruit & Vegetable detection Using Deep Learning Deployment in Effective Application 3

Bangladeshi Fresh-Rotten Fruit & Vegetable Detection Using D...

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3rd IEEE international conference on Computer Communication and Artificial intelligence, CCAI 2023

作者： Hamim, Md Abrar Tahseen, Jeba Hossain, Kazi Md. Istiyak Akter, Nasrin Asha, Umme Fatema Tuj Daffodil International University Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798350335262

Finding rotten fruits and vegetables has been important, especially in the agricultural industry. Computer vision has significant applications in the automation of damaged, freshness detection of fruits and vegetables. In recent decades, the farming sector has discovered computer machine vision and image processing technology to be more and more beneficial, particularly for implementations in quality control by identifying rotten and freshness. Farmers cannot contribute effectively between fresh and rotten fruits, vegetables because this is mainly done by people. People tire out after performing the same task for several days, whereas robots do not. By identifying weaknesses in agricultural product, the study suggested a technique for minimizing human effort and worktime. Vegetables and fruits with defects might affect healthy fruits if they are not identified in time. As an outcome, we put up a methodology to stop rottenness from spreading. The suggested model detects between fresh and decaying fruits and vegetables depending on the input fruit and vegetable photos. In this work, we used six different types of fruits and vegetables like carrot, potato, calabash, cucumber, eggplant, and cauliflower, as well as fruits likes mango, banana, star fruit, jackfruit, guava, and papaya. This study discusses multiple image processing methods for rottenness categorization of fruits and vegetables. A Convolutional Neural Network (CNN), KNN, and SVM are used to gather the features from the data fruit and vegetable photos. On Google and Kaggle datasets, the efficiency of the suggested model is evaluated, and CNN model shows the greatest accuracy which is 95 percent. © 2023 IEEE.

关键词： Vegetables

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Multi-Logic Interactive Model of Virtual Reality Based on Digital Media 3

Multi-Logic Interactive Model of Virtual Reality Based on Di...

引用

3rd international conference on Artificial intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Xu, Qianyan Faculty of Information Engineering Guangzhou International Economics College China

ISBN: (纸本)9781510671485

Online courses in colleges and universities need professional equipment and settings for students to experience the operating environment, but the real equipment is expensive and the operating experimental conditions are limited, which results in the knowledge expression defects of professional courses. The development of virtual simulation laboratory can help the laboratory to realize the practice environment of 3D virtual simulation, which requires the integration of new technologies such as virtual reality into teaching. This paper adopts the latest GL3D Web design technology and Three. js related open source frameworks to design the system. With the help of the virtual laboratory, the whole experimental process and experimental results can be fed back to the virtual laboratory users in real time through three-dimensional visualization, which further improves the effect of experimental teaching and reduces the pressure of experimental work. Through virtual simulation technology, this paper builds a flexible teaching platform to promote the teaching function of controlling virtual equipment with the help of physical control system. © 2023 SPIE.

关键词： Digital storage

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Facial Shape Analysis and Accessory Recommendation: A Human-Centric AI Approach 3

Facial Shape Analysis and Accessory Recommendation: A Human-...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Komatireddy, Sanjay Reddy Meghana, Karnam Gude, Venkataramaiah Ramesh, G. Gokaraju Rangaraju Institute of Engineering and Technology Computer Science and Engineering Hyderabad India GP Technologies LLC United States

ISBN: (纸本)9798350343632

In this digital world, the fusion of Artificial intelligence (AI) and Human-Computer Interaction (HCI) opens new avenues for enhancing user experiences through personalization. This research centers on the realm of facial shape analysis and its applications in providing tailored accessory recommendations, harnessing AI techniques such as Machine Learning (ML) algorithms to offer customized suggestions. The primary objective is to create an intelligent system adept at recognizing facial shapes and delivering accessory recommendations that harmonize seamlessly with an individual's unique facial characteristics. To accomplish this, advanced Artificial intelligence (AI) algorithms specialized in facial shape recognition and analysis have been utilized. These algorithms include K-Means, DBSCAN, and Agglomerative Clustering, each playing a crucial role in the process. The system's recommendations are founded on a comprehensive analysis that combines both quantitative data and qualitative insights. The findings underscore the effectiveness of AI-driven facial shape analysis in personalizing accessory recommendations. This study highlights AI's capacity to elevate user experiences and showcases the intersection of technology and fashion within HCI. As a contribution to the evolving field of AI and HCI, this research emphasizes the importance of user centered solutions. Moreover, the results act as a foundational pillar for future research in personalized recommendations and user interfaces, effectively bridging the gap between AI advancements and human-centric design. © 2023 IEEE.

关键词： Accessory Recommendation Artificial intelligence Facial Shape Analysis Human-Computer Interaction User-Centric Solutions Virtual Try-on

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detection of Pneumonia Cases from X-ray Chest Images using Deep Learning Based on Transfer Learning CNN and Hyperparameter Optimization 3

Detection of Pneumonia Cases from X-ray Chest Images using D...

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3rd international conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Agrawal, Sonal Agrawal, Pragati Maulana Azad National Institute of Technology Computer Science and Engineering M.P. Bhopal India

ISBN: (纸本)9798350300710

Pneumonia is a viral infection affecting many people, especially in underdeveloped and impoverished nations where contaminated, crowded, and unhygienic living conditions are common and inadequate healthcare infrastructures. Recognizing pneumonia immediately is a challenging step that can increase survival odds and allow for early-stage treatment. The successful construction of prediction models makes use of the artificial intelligence discipline of deep learning. There are many approaches to identifying pneumonia, including CT scans, pulse oximetry, and many others, but X-ray tomography is the most popular method. However, reviewing chest X-rays (CXR) is difficult and vulnerable to subjectivity variations. Using x-ray chest images, this study suggests a novel deep learning-based architecture for the quick diagnosis of covid-19 and pneumonia cases. As our basic model, we use the CNN transfer learning models VGG16, ResNet50, and InceptionV3. To adjust the hyperparameters of our model, we use random search optimization approach. © 2023 IEEE.

关键词： Computerized tomography

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A Spot Welding Spatter Monitoring system Based on Machine Vision 3

A Spot Welding Spatter Monitoring System Based on Machine Vi...

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3rd international conference on Electrical Engineering and Mechatronics technology, ICEEMT 2023

作者： Wang, Xiaodong Zhu, Zhengqiang Peng, Hongyu Wang, Yongqiang Tangshan University College of Artificial Intelligence Hebei Tangshan China Nanchang University School of Advanced Manufacturing Jiangxi Nanchang China

ISBN: (纸本)9798350303698

A new vision monitoring method for resistance spot welding with high speed industrial camera is presented, and by this method the dynamic procedure of spot welding electrodes touching can be inspected. The pressure and current influence for resistance spot welding spatter can be distinguished by a serial of continuous images with millisecond time interval. The other spatter phenomena caused by welding pole wearing or fixing obliquely can also be distinguished by vision images. By a series of experiments, this machine vision system is be built by C++ language, which can detect the resistance spot welding spatter occurring and distinguish the spattering possible reasons. In conclusion, the machine vision monitoring system built in this paper can capture thousands of frames at one welding duty cycle, and then find the key frame with significantly welding spatter features, subsequently calculate the amount of spatter and distinguish the classification. The running testing of this system shows that this machine vision system run stably and can timely find the spot welding spatter problems in time. © 2023 IEEE.

关键词： Spot welding

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