检索结果-内蒙古大学图书馆

Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Priyanka Kaushik Patel Saileshchandra Rameshchandra Mitali Kol Ritushree Narayan Abhishek Kumar Gupta R. Rajalakshmi Dept. of Comp. Science Engineering Chandigarh University Chandigarh India Dept. of Humanities & S. Science Madhav University Pindwara India Dept. of Computing & IT Usha Martin University Jharkhand India Dept. of Comp. Eng. & Applications Mangalayatan University Aligarh India Dept. of ECE Dhanalakshmi Srinivasan College Coimbatore India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

This paper introduces an artificial neural Network model that integrates advanced deep learning techniques from computer vision and natural language processing domains. The model focuses on automating the captioning process for images, a crucial task in artificial intelligence. By employing Convolutional neural networks (CNNs) and Long Short-Term Memory networks (LSTMs), the model is trained to optimize the likelihood estimation of descriptive labels corresponding to each image in the dataset. Evaluation of the model’s performance includes both quantitative metrics and qualitative assessment using the state-of-the-art BLEU-1 scoring method.

关键词： Deep learning Measurement Accuracy Computational modeling Feature extraction Transformers Hybrid power systems Convolutional neural networks Machine translation Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Segmentation of Ship Propeller Cavitation Area Using image-Based Anomaly Detection Network

Segmentation of Ship Propeller Cavitation Area Using Image-B...

引用

International image processing, applications and Systems Conference (IPAS)

作者： Wonje Jeong Yong-Jin Shin Soon-Yong Park School of Electronic and Electrical Engineering Kyungpook National University Daegu Republic of Korea Samsung Heavy Industries Co. Ltd. Daejeon Republic of Korea

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Cavitation is the formation of vapor-filled cavities in the liquid. Cavitation in rotating propeller can cause various engineering problems to ships, such as hull vibration, noise, thrust reduction, and propeller corrosion. To address this cavitation problem in designing ship propellers, it needs to analyze the cavitation phenomenon in real propellers. In this study, we introduce a computer vision method to detect and segment cavitation areas in a rotating propeller. The pro-posed method captures the images of a high-speed rotating propeller in a water tunnel and detect the image area of the cavitation. As the first study in the im-age-based automatic cavitation detection, we employ a deep neural network, an anomaly detection method called DDAD (Anomaly Detection with Conditioned Denoising Diffusion Models). In addition, we also use a STN (Spatial Transformer networks) network to compensate the image shaking caused by the vibration from the high-speed propeller and water flow. After the STN compensation, normal and abnormal images are automatically aligned to find cavitation areas more accurately. The proposed method is the first to use an anomaly detection network for ship propeller cavitation detection. Experimental results show that the proposed method can automatically detect the cavitation area without any annotation supervision.

关键词： Vibrations Deep learning Pressure sensors image segmentation Propellers Annotations artificial neural networks Transformers Marine vehicles Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Robust Features in Deep neural networks for Transcoded Speech Recognition DSR and AMR-NB

Robust Features in Deep Neural Networks for Transcoded Speec...

引用

International Conference on image and Signal processing and their applications (ISPA)

作者： Lallouani Bouchakour Mohamed Debyeche Ahmed Krobba Speech Communication and Signal Processing Laboratory Centre for Scientific and Technical Research on Arabic Language Development (CRSTDLA) Université des Sciences et de la Technologie Houari Boumediene (USTHB) Algiers Algeria Speech Communication and Signal Processing Laboratory Université des Sciences et de la Technologie Houari Boumediene (USTHB) Algiers Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Automatic Speech Recognition (ASR) performance in mobile communications degrades significantly if the environment includes many sources of variability, such as when the test environment differs from the training environment and when the acoustical environment includes disturbances like noise, channel distortion, speaker differences, and mobile codecs. In this work, we have used two architectures for speech recognition in mobile networks. The first one is Distributed Speech Recognition based on DSR coding, and the second architecture is based on Adaptive Multi-Rate Narrow-Band (AMR-NB). We propose a novel robust feature extraction (Front-End) technique to improve speech recognition performance in noisy mobile communications. This technique utilizes special parameters such as Gabor features, Power Normalized Spectrum Gabor filter (PNS-Gabor), and Power Standardized Cepstral Coefficients (PNCC). These features take into account psychoacoustic effects like the temporal masking effect and have different distributions of filter banks and filter forms to better model human perception. In the back end, we investigated speech classification systems using Continuous Hidden Markov Models (CHMM) and Deep neural networks (DNN). Based on the results obtained in noisy mobile communications, the proposed features PNS-Gabor and PNCC show significant improvements over conventional acoustic features such as Mel frequency cepstral coefficients (MFCC).

关键词： Training Speech codecs Hidden Markov models Filter banks artificial neural networks Mobile communication Noise measurement

来源：评论

学校读者我要写书评

暂无评论

Automatic recognition of emergency vehicles in images from video recorders

Automatic recognition of emergency vehicles in images from v...

引用

Signal processing Algorithms, Architectures, Arrangements and applications (SPA)

作者： Julian Balcerek Paweł Pawłowski Krzysztof Dolny Julia Skotnicka Division of Signal Processing and Electronic Systems Institute of Automatic Control and Robotics Poznan University of Technology Poland

ISBN: (数字)9788362065486

ISBN: (纸本)9798350373806

In this article, a system for automatic recognition, including detection and classification, of emergency vehicles based on images from video recorders is proposed. The system utilizes YOLO v8 artificial neural network. Recognition accuracy tests were conducted on a specially prepared database of frames from real recordings. It includes marked and unmarked police vehicles (which are very difficult to distinguish and typically not included in automatic recognition systems), fire, ambulance, military vehicles and other reference images. The experiments concerned the recognition of vehicle types and the state of emergency lighting. The highest value of total (for all classes) F1 measure is over 86%. In selected classes the F1 reaches 91%, while precision is 94% and sensitivity is 89%. They can be considered as satisfactory results, what indicates the possibility of using the presented system in practice.

关键词： YOLO image recognition Sensitivity Law enforcement Databases Emergency lighting Signal processing algorithms Military vehicles artificial neural networks Recording

来源：评论

学校读者我要写书评

暂无评论

Research on Expression Recognition Algorithm Based on University Students' Mental State

Research on Expression Recognition Algorithm Based on Univer...

引用

image processing and Computer applications (ICIPCA), IEEE International Conference on

作者： Guangli Luo Guangzhou Institute of Science and Technology Guangzhou City China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

With the continuous development of higher education in our country, the number of college students is increasing year by year. There are more and more campus accidents caused by college students' psychological problems, which has aroused people's great attention to college students' psychological problems. Many scholars have conducted in-depth research on college students' psychological problems. To solve students' psychological problems, we must first find out the problems in time. With the improvement of the level of social information, face recognition technology integrates multiple key technologies such as computer vision, artificial intelligence, and deep learning, which has been widely used in different fields of society. However, there are few researches and applications on the mental health status of college students. Therefore, this paper combines the expression recognition technology with the psychological problems of college students, fully excavates the static and dynamic features of dynamic expression behavior through convolutional neural networks, establishes a facial expression recognition model, and conducts comparative experiments through open data sets to verify the accuracy of different algorithms, so as to timely discover the psychological problems of college students. The method of expression recognition is used to control the psychology of college students.

关键词： Emotion recognition Accuracy image recognition Face recognition Heuristic algorithms Mental health Feature extraction Stability analysis Convolutional neural networks Accidents

来源：评论

学校读者我要写书评

暂无评论

image Test Libraries for the In-Field Test of Ultra-Low-Power Devices

Image Test Libraries for the In-Field Test of Ultra-Low-Powe...

引用

Latin American Test Workshop, LATW

作者： Antonio Porsia Giacomo Perlo Annachiara Ruospo Ernesto Sanchez Politecnico di Torino DAUIN Turin Italy

ISBN: (数字)9781665477635

ISBN: (纸本)9781665477642

In recent years, research and technology advancements have driven exponential growth in the adoption of artificial Intelligence (AI)-based systems, even in safety-critical contexts such as autonomous driving and healthcare applications. The joint effort of academia and industry has yielded techniques and standards with the objective of ensuring the safe operation of AI-based technology. In the specific context of Convolutional neural networks (CNNs) running on GPUs, image Test Libraries (ITLs) have been proposed as an effective method for performing on-line functional testing of GPU multipliers. This is achieved by launching the inference of a set of test images containing a set of ATPG-generated functional test patterns. However, while the demand for computational power for DNN models is constantly increasing, another branch of Machine Learning (ML) research, namely TinyML, focuses on minimizing the computational requirements of DNN models in order to bring AI capabilities to edge devices, whose constraints on power usage, memory space and processing power do not allow for the deployment of conventional DNN models. This research work aims to adapt the ITL technique to CNNs running on ultra-low-power edge hardware, while also overcoming some limitations of GPU ITLs. Experimental results demonstrate that a single test image generated using the proposed method is capable of detecting 96.01% of stuck-at faults occurring in the 32-bit integer multiplier of a RISC-V-based ultra-low-power System-on-Chip executing a quantized CNN.

关键词： Adaptation models Computational modeling image edge detection Tiny machine learning neural networks Graphics processing units Libraries System-on-chip Convolutional neural networks Testing

来源：评论

学校读者我要写书评

暂无评论

Reconfigurable field-programmable gate array-based on-chip learning neuromorphic digital implementation for nonlinear function approximation

引用

INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND applications 2021年第8期49卷 2425-2435页

作者： Gholami, Morteza Farsa, Edris Zaman Karimi, Gholamreza Razi Univ Elect Engn Dept Fac Engn Kermanshah Iran Islamic Azad Univ Dept Comp Engn Sanandaj Branch Sanandaj Iran

Hardware implementations of spiking neural networks, which are known as neuromorphic architectures, provide an explicit understanding of brain performance. As a result, biological features of the brain may well inspire the next generation of computers and electronic systems used in such areas as signal processing, image processing, function approximation, and pattern recognition. Approximating nonlinear functions has many uses in computer science and applied mathematics. The sigmoid is the most universal activation function in neural networks by which the relationship between biological and artificial neurons is defined. It is a suitable option for predicting the probability of anything from 0 to 1 as output. In this paper, a spiking neural network using Izhikevich neurons and a gradient descent learning algorithm are propounded to approximate the sigmoid and other nonlinear functions. The flexibility of the spiking network is demonstrated by showing the average relative errors in the approximation process. A time- and cost-efficient digital neuromorphic implementation on the base of on-chip learning method for approximating the sigmoid function is also discussed. The paper reports the results of the hardware synthesis and the spiking network's physical implementation on a field-programmable gate array. The maximum frequency and throughput of the implemented network were 83.209 MHz and 9.86 Mb/s, respectively.

关键词： digital hardware field-programmable gate array (FPGA) hardware implementation neuromorphic on-chip learning spiking neural networks (SNNs)

来源：评论

学校读者我要写书评

暂无评论

FMSNet: Underwater image Restoration by Learning from a Synthesized Dataset 30th

FMSNet: Underwater Image Restoration by Learning from a Synt...

引用

30th International Conference on artificial neural networks (ICANN)

作者： Yin, Xiangyu Liu, Xiaohong Liu, Huan Guilin Univ Elect Technol Guilin Peoples R China McMaster Univ Hamilton ON Canada

ISBN: (纸本)9783030863654;9783030863647

Underwater images suffer from various degradation, which can significantly lower the visual quality and the accuracy of subsequent applications. Moreover, the artificial light source tends to invalidate many image restoration algorithms. In this paper, an underwater image restoration (UIR) method using a novel Convolutional neural Network (CNN) architecture and a synthesized underwater dataset is proposed. We discuss the reason for the over enhancement that exists in current UIR methods and revise the underwater image formation model (IFM) to alleviate the problem. With the revised IFM, we proposed an underwater image synthesizing method that can create a realistic underwater dataset. In order to effectively conduct end-to-end supervised learning, we design a network based on the characteristics of image restoration tasks, namely FMSNet. Different from existing networks, the decomposition and fusion operation in FMSNet can process the feature maps more efficiently and improve the contrast more prominently. The UIR method built by FMSNet can directly recover the degraded underwater images without the need of any pre-processing and post-processing. The experimental results indicate that FMSNet performs favorably against the widely used network architectures and our UIR method can outperform the state-of-the-art methods on both qualitative and quantitative evaluations. Comparing with the original underwater images, the experiments carried out by subsequent mission shows that 285% more feature points can be detected in the restored images by using our method.

关键词： neural network image restoration CNN architecture image formation model

来源：评论

学校读者我要写书评

暂无评论

Crop Disease and Pest Detection using Convolutional neural networks (CNN)

Crop Disease and Pest Detection using Convolutional Neural N...

引用

image processing and Capsule networks (ICIPCN), International Conference on

作者： S Kalaimanivel K France Department of Computer Applications 22295014 Hindustan institute of technology and science Chennai India Department of Computer Applications Hindustan institute of technology and science Chennai India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Agriculture is often known as the art and science of nurturing soil. It involves preparing plants and animals for use in products. Agriculture is the process of growing crops and rearing animals for human consumption, fiber production, and other reasons. It is one of the oldest and most important human activities, laying the groundwork for food production and billions of people’s lives throughout the world. As technology advances, additional capabilities for crop protection and disease prevention become accessible. artificial Intelligence (AI) and Machine Learning (ML) algorithms capture features such as crop and soil monitoring, crop maturity detection, autonomous weeding, intelligent crop spraying, pest and disease detection, and more. This study suggests a novel technique for automated crop disease identification by utilizing Convolutional neural networks (CNNs) from the field of computer vision. By performing a thorough testing and validation on separate test sets, the proposed methodology outperforms other existing methods in terms of accuracy. The Plant Village collection, maintained by the Centers for Disease Control and Prevention (CDC), includes damaged plant leaf photos and labels. The proposed method has achieved an accuracy of about 99.6%.

关键词： Training Accuracy Prevention and mitigation Crops Production Soil Loss measurement

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Automated Detection of Lung Cancer from Medical Imaging Data 1

Deep Learning for Automated Detection of Lung Cancer from Me...

引用

1st International Conference on artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Devarajan, Harshitha Raghavan Balasubramanian, Sivasubramanian Kumar Swarnkar, Suman Kumar, Purushottam Jallepalli, Venkateswara Rao Millennium Management Llc New York United States University of Washington Department of Data Science Seattle United States Shri Shankaracharya Institute of Professional Management and Technology Dept of Computer Science and Engineering Raipur India Amity University Department of Computer Science and Cyber Security Jharkhand India School of Pharmaceutical Sciences Lovely Professional University Department of Pharmacology Jalandhar India

ISBN: (纸本)9798350330915

Early and accurate detection of lung cancer is essential for improving patient outcomes and reducing mortality rates. On a range of medical image processing applications in recent years, deep learning techniques have demonstrated exceptional effectiveness. This study used medical imaging data, specifically CT scans and chest X-rays., to conduct a thorough research into the use of deep learning for automated lung cancer diagnosis. The early and accurate detection of lung cancer plays a pivotal role in improving patient outcomes and reducing mortality rates. In recent years, deep learning techniques have demonstrated remarkable performance in various medical image analysis tasks. This research paper presents a comprehensive study on the application of deep learning for the automated detection of lung cancer from medical imaging data, primarily focusing on chest X-rays and computed tomography (CT) scans. We explore different deep learning architectures, data preprocessing techniques, and training strategies to enhance the accuracy and efficiency of lung cancer detection. Through rigorous experimentation and validation on diverse datasets, we showcase the potential of deep learning models in identifying lung cancer During its initial phases. The findings highlight the significance of implementing effective data augmentation techniques., transfer learning, and model interpretability in achieving reliable and clinically applicable outcomes. This study contributes to the ongoing efforts in harnessing the power of deep learning for effective lung cancer diagnosis and establishes a foundation for further advancements in the field of medical image analysis. © 2023 IEEE.

关键词： automated diagnosis chest X-rays computed tomography data preprocessing deep learning deep neural networks early detection lung cancer detection medical imaging model interpretability

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：