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Gotowe bibliografie tematyczne / Convolution power

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Gotowa bibliografia na temat „Convolution power”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Convolution power"

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Li, Mingchen, Xuechen Zhang, Yixiao Huang, and Samet Oymak. "On the Power of Convolution-Augmented Transformer." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 18393–402. https://doi.org/10.1609/aaai.v39i17.34024.

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The transformer architecture has catalyzed revolutionary advances in language modeling. However, recent architectural recipes, such as state-space models, have bridged the performance gap. Motivated by this, we examine the benefits of Convolution-Augmented Transformer (CAT) for recall, copying, and length generalization tasks. CAT incorporates convolutional filters in the K/Q/V embeddings of an attention layer. Through CAT, we show that the locality of the convolution synergizes with the global view of the attention. Unlike comparable architectures, such as Mamba or transformer, CAT can provab

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Kim, Hyeonkyu, and Hoyoung Yoo. "Area-Efficient Two-Dimensional Separable Convolution Structure." Journal of Imaging Science and Technology 63, no. 5 (2019): 50404–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2019.63.5.050404.

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Abstract In a recent image processing system, convolution operations play a significant role in manipulating image and extracting features from images. Due to the increase of kernel sizes, the image processing hardware suffers from severe hardware complexity and power consumption. In this article, an area-efficient structure is proposed for two-dimensional separable convolution operations. Since a separable convolution allows to translate a two-dimensional convolution into two one-dimensional convolutions, it is possible to compute row-wise and column-wise convolutions independently. Whereas t

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Akbar, Muhammad Ali, Bo Wang, Samir Brahim Belhaouari, and Amine Bermak. "DROPc-Dynamic Resource Optimization for Convolution Layer." Electronics 14, no. 13 (2025): 2658. https://doi.org/10.3390/electronics14132658.

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The computational complexity of convolutional neural networks (CNNs) becomes challenging for resource-constrained hardware devices. The convolution layer is predominant in the overall CNN architecture, performing the expensive multiplication and accumulation operation. Therefore, designing a hardware-efficient convolution layer will effectively improve the overall performance of a CNN. In this research, we propose a dynamic resource optimization (DROP) approach to improve the power and delay of the convolution layer. The proposed approach controls the computational path in accordance to the in

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Sheng, Wanxing, Keyan Liu, Dongli Jia, Shuo Chen, and Rongheng Lin. "Short-Term Load Forecasting Algorithm Based on LST-TCN in Power Distribution Network." Energies 15, no. 15 (2022): 5584. http://dx.doi.org/10.3390/en15155584.

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In this paper, a neural network model called Long Short-Term Temporal Convolutional Network (LST-TCN) model is proposed for short-term load forecasting. This model refers to the 1-D fully convolution network, causal convolution, and void convolution structure. In the convolution layer, a residual connection layer is added. Additionally, the model makes use of two networks to extract features from long-term data and periodic short-term data, respectively, and fuses the two features to calculate the final predicted value. Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) are

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Zhao, Yulin, Donghui Wang, and Leiou Wang. "Convolution Accelerator Designs Using Fast Algorithms." Algorithms 12, no. 5 (2019): 112. http://dx.doi.org/10.3390/a12050112.

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Convolutional neural networks (CNNs) have achieved great success in image processing. However, the heavy computational burden it imposes makes it difficult for use in embedded applications that have limited power consumption and performance. Although there are many fast convolution algorithms that can reduce the computational complexity, they increase the difficulty of practical implementation. To overcome these difficulties, this paper proposes several convolution accelerator designs using fast algorithms. The designs are based on the field programmable gate array (FPGA) and display a better

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Xiao, Lei. "Construction Technology and Quality Control of Power and Electrical Engineering Based on Convolutional Neural Network." Security and Communication Networks 2021 (December 17, 2021): 1–15. http://dx.doi.org/10.1155/2021/8964532.

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In the context of the Internet era, more and more parties have begun to store, process, and analyze data, but the accompanying question is whether people are reasonable about the data under the impact of massive data, effective and efficient analysis, especially the problems faced in this project. This article aims to study the quality control problems faced by electric power and electrical engineering in the construction process through the use of convolutional neural networks. Under this idea, this article proposes a multilayer convolution method. The experimental results show that the use o

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Park, Eunpyoung, and Jongsu Park. "Low-power Convolution Layer Hardware Design for Convolutional Neural Network Operations." Journal of the Korea Institute of Information and Communication Engineering 28, no. 7 (2024): 887–90. http://dx.doi.org/10.6109/jkiice.2024.28.7.887.

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ALBIN, J. M. P. "A NOTE ON THE CLOSURE OF CONVOLUTION POWER MIXTURES (RANDOM SUMS) OF EXPONENTIAL DISTRIBUTIONS." Journal of the Australian Mathematical Society 84, no. 1 (2008): 1–7. http://dx.doi.org/10.1017/s1446788708000104.

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AbstractWe make a correction to an important result by Cline [D. B. H. Cline, ‘Convolutions of distributions with exponential tails’, J. Austral. Math. Soc. (Series A)43 (1987), 347–365; D. B. H. Cline, ‘Convolutions of distributions with exponential tails: corrigendum’, J. Austral. Math. Soc. (Series A)48 (1990), 152–153] on the closure of the exponential class $\mathcal {L}(\alpha )$ under convolution power mixtures (random summation).

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Comte, F., and V. Genon-Catalot. "Convolution power kernels for density estimation." Journal of Statistical Planning and Inference 142, no. 7 (2012): 1698–715. http://dx.doi.org/10.1016/j.jspi.2012.02.038.

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Liu, Jun, Wei Li, and Zhuang Du. "Combined with the residual and multi-scale method for Chinese thermal power system record text recognition." Thermal Science 23, no. 5 Part A (2019): 2631–40. http://dx.doi.org/10.2298/tsci181128152l.

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Aiming at the problem that the recognition accuracy based on convolutional neural network of thermal power system record text is not high, a method of thermal power system record text recognition based on residual and multi-scale feature combination was proposed and implemented. Combined with the residual, a new network is designed to replace the traditional convolutional neural network and improve the feature extraction ability of the network. The 1 ? 1 convolution core was used to increase the network depth and reduce the parameters instead of the 3 ? 3 convolution core. The network order of

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Rozprawy doktorskie na temat "Convolution power"

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Karimi, Ahmad Maroof. "DATA SCIENCE AND MACHINE LEARNING TO PREDICT DEGRADATION AND POWER OF PHOTOVOLTAIC SYSTEMS: CONVOLUTIONAL AND SPATIOTEMPORAL GRAPH NEURAL NETWORK." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1601082841477951.

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Homeili, Saeid. "Metrological characterisation of Low Power Voltage Transformers by using impulse response analysis." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20998/.

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this thesis presents a new approach in dealing with characterize LPVT and proposes determining the impulse response of LPVT, purposing to find transfer function (h(t)) which contains most electrical characteristics of LPVTs as a dynamic system.

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Sazish, Abdul Naser. "Efficient architectures and power modelling of multiresolution analysis algorithms on FPGA." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/6290.

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In the past two decades, there has been huge amount of interest in Multiresolution Analysis Algorithms (MAAs) and their applications. Processing some of their applications such as medical imaging are computationally intensive, power hungry and requires large amount of memory which cause a high demand for efficient algorithm implementation, low power architecture and acceleration. Recently, some MAAs such as Finite Ridgelet Transform (FRIT) Haar Wavelet Transform (HWT) are became very popular and they are suitable for a number of image processing applications such as detection of line singulari

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Villarreal-Reyes, Salvador. "Convolutional coding schemes with convenient power spectral density characteristics." Thesis, Loughborough University, 2007. https://dspace.lboro.ac.uk/2134/15951.

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This thesis analyses the effects caused by the introduction of convolutional coding schemes over the power spectral density (PSD) of impulse radio (IR) based ultra wideband (UWB) communication systems. This is an important topic since UWB emissions are expected to comply with spectral masks imposed by various regulatory bodies. The spectral analysis of convolutionally codedlMarkov-driven IR-based UWB signals is carried out by assuming that the data stream at the encoder input is generated by a binary Markov source (BMS). The signal model covers a wide variety of IR-based modulation schemes suc

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Mechmeche, Haïfa. "Méthodologies de simulation de de pré-dimensionnement vibro-acoustique des machines à reluctance variable." Thesis, Ecole centrale de Lille, 2015. http://www.theses.fr/2015ECLI0014.

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Cette thèse de doctorat s'inscrit dans un projet pour le développement du véhicule électrique piloté par la société Renault. Il répond aux prévisions d’exploitation de véhicules électriques pour des déplacements interurbains et urbains afin d’améliorer les aspects environnementaux. L'objectif de nos travaux a été de développer un outil capable de prédire le bruit d'origine électromagnétique produit par des machines à rotor passifs : machine à réluctance variable, sur une large plage de vitesse. Pour cela, le développement d’un modèle vibro-acoustique reposant sur les équations aux dérivées par

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Tomatsopoulos, Billy Vasileios. "Design and implementation of low-power CMOS analogue convolutional decoders using the modified feedback decoding algorithm." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1446263/.

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Convolutional decoders are very important in digital communication systems, especially in applications where very high noise levels are introduced on the information signal by wireless transmission. Examples of such systems are satellite communications, cellular telephony and digital audio broadcasting (DAB). The most commonly employed decoding method so far in convolutional codes has been the Viterbi algorithm (VA), mainly implemented in digital hardware to accommodate large memory requirements. In this thesis an introduction to convolutional codes and basic digital communication systems is g

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Kornfeil, Vojtěch. "Soubor úloh pro kurs Sběr, analýza a zpracování dat." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217707.

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This thesis proposes tasks of exercises for mentioned course and design and creation of automated evaluation system for these exercises. This thesis focuses on discussion and exemplary solutions of possible tasks of each exercise and description of created automated evaluation system. For evaluation program are made tests with chosen special data sets, which will prove it’s functionality in general data sets.

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Jahangiri-Lahekani, M. "Coefficients of powers of subclasses of univalent functions and convolutions of some classes of polynomials and analytic functions." Thesis, University of York, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.356834.

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Hanzálek, Pavel. "Praktické ukázky zpracování signálů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400849.

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The thesis focuses on the issue of signal processing. Using practical examples, it tries to show the use of individual signal processing operations from a practical point of view. For each of the selected signal processing operations, an application is created in MATLAB, including a graphical interface for easier operation. The division of the thesis is such that each chapter is first analyzed from a theoretical point of view, then it is shown using a practical demonstration of what the operation is used in practice. Individual applications are described here, mainly in terms of how they are h

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Lee, Ji Hyun. "Development of a Tool to Assist the Nuclear Power Plant Operator in Declaring a State of Emergency Based on the Use of Dynamic Event Trees and Deep Learning Tools." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1543069550674204.

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Książki na temat "Convolution power"

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Big Data: 4 Manuscripts - Data Analytics for Beginners, Deep Learning with Keras, Analyzing Data with Power BI, Convolutional Neural Networks in Python. CreateSpace Independent Publishing Platform, 2017.

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Części książek na temat "Convolution power"

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Gazi, Orhan. "Energy, Power, Convolution, and Systems." In Principles of Signals and Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17789-7_3.

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Gan, Woon Siong. "Convolution, Correlation, and Power Spectral Density." In Signal Processing and Image Processing for Acoustical Imaging. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-10-5550-8_6.

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Zhou, Honghui, Ruyi Qin, Zihan Liu, Ying Qian, and Xiaoming Ju. "Optimizing Performance of Image Processing Algorithms on GPUs." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_95.

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AbstractThe application of machine learning algorithms in the field of power grid improves the service level of power enterprises and promotes the development of power grid. NVIDIA Volta and Turing GPUs powered by Tensor Cores can accelerate training and learning performance for these algorithms. With Tensor Cores enabled, FP32 and FP16 mixed precision matrix multiplication dramatically accelerates the throughput and reduces AI training times. In order to explore the cause of this phenomenon, we choose a convolutional neural network (CNN), which is widely used in computer vision, as an example

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Mujeeb, Sana, Nadeem Javaid, Hira Gul, Nazia Daood, Shaista Shabbir, and Arooj Arif. "Wind Power Forecasting Based on Efficient Deep Convolution Neural Networks." In Advances on P2P, Parallel, Grid, Cloud and Internet Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33509-0_5.

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Wei, Jianrong, Yilin Liu, Quan Wang, et al. "Encryption Algorithm of Power Equipment Nameplate Based on Quadratic Convolution." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0408-2_59.

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Chothani, Nilesh, Maulik Raichura, and Dharmesh Patel. "Convolution Neural Network and XGBoost-Based Fault Identification in Power Transformer." In Studies in Infrastructure and Control. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3870-4_8.

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Anzalone, Erik, Maurizio Capra, Riccardo Peloso, Maurizio Martina, and Guido Masera. "Low-Power Hardware Accelerator for Sparse Matrix Convolution in Deep Neural Network." In Progresses in Artificial Intelligence and Neural Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5093-5_8.

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Shrinidhi, S., S. Vinuja, R. Lakshmi Prasanna, B. Sumanth, and Navya Mohan. "High Speed and Power Efficient Multiplier and Adder Designs for Linear Convolution." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7753-4_16.

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Gong, Xundong, Fei Hu, Li Jiang, et al. "Extended Kalman Filtering Power System Dynamic State Estimation Based on Time Convolution Networks." In Conference Proceedings of 2022 2nd International Joint Conference on Energy, Electrical and Power Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4334-0_102.

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Khairulzaman, Mohd Ridzuan, and Patrick Goh. "Improving the Reference Impedance for Fast S-Parameter Convolution via an Analytical Method." In 9th International Conference on Robotic, Vision, Signal Processing and Power Applications. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1721-6_11.

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Streszczenia konferencji na temat "Convolution power"

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Tiwari, Aarushi, Julian Gamboa, Tabassom Hamidfar, Xi Shen, Shamima A. Mitu, and Selim M. Shahriar. "Opto-electronic Balanced Joint Transform Correlator as a Convolution Stage for Neural Networks." In Frontiers in Optics. Optica Publishing Group, 2024. https://doi.org/10.1364/fio.2024.jtu4a.55.

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Opto-electronic correlators are able to perform high-speed convolutions with minimal processing and power requirements. Here, we propose the use of a balanced joint transform correlator as a convolution stage for a convolutional neural network.

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Gao, Liming, Ke Zheng, and Tao Liao. "Error prediction for capacitor voltage transformer based on graph convolution and temporal convolution." In 2024 4th International Conference on Energy, Power and Electrical Engineering (EPEE). IEEE, 2024. https://doi.org/10.1109/epee63731.2024.10875391.

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Zhong, Yuhui, Tiangui Yang, Xiaoling Chen, et al. "Dynamic graph convolution with graph sparsification for power load forecasting." In International Symposium on Artificial Intelligence Innovations (ISAII 2025), edited by Xin Xu and Weihua Ou. SPIE, 2025. https://doi.org/10.1117/12.3075131.

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Narahari, Sujatha Canavoy, K. Haripriya, M. Srinath, and Ch Jasmitha. "Colour Image Noise Removal using Convolution Neural Network." In 2024 IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE). IEEE, 2024. http://dx.doi.org/10.1109/icspcre62303.2024.10675291.

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Zhao, Ning, Jingyue Xu, and Gang Zhou. "Turbine Data cleansing based on conditional depth convolution generation confrontation." In 2024 6th International Conference on Energy, Power and Grid (ICEPG). IEEE, 2024. https://doi.org/10.1109/icepg63230.2024.10775622.

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Geng, Zhiheng. "Point Cloud Object Classification based on Dynamic Relation Shape Convolution." In 2024 International Conference on Artificial Intelligence and Power Systems (AIPS). IEEE, 2024. http://dx.doi.org/10.1109/aips64124.2024.00039.

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Mehta, Kamal Kishore, and Kalpana Meena. "Hyper Parameter Optimization and Solar Power Forecasting using Convolution Neural Networks." In 2024 IEEE 5th India Council International Subsections Conference (INDISCON). IEEE, 2024. http://dx.doi.org/10.1109/indiscon62179.2024.10744399.

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Wang, Zian, Yue Yin, Chenxiao Jin, Ruoyao Zhang, Zhilin Gao, and Yihan Huang. "Photovoltaic System Segmentation Algorithm via Transformer and Multi-Scale Convolution." In 2025 8th International Conference on Energy, Electrical and Power Engineering (CEEPE). IEEE, 2025. https://doi.org/10.1109/ceepe64987.2025.11034235.

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Cao, Zhiqiang, Jincheng Yang, Wenxia Zhang, Xiaokui Zang, Mingyang Liu, and Huachang Wang. "Capacitor Voltage Transformer Error Prediction Method Based on Fusion Convolution Module." In 2024 6th International Conference on Energy, Power and Grid (ICEPG). IEEE, 2024. https://doi.org/10.1109/icepg63230.2024.10775656.

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Shen, Yu-Long, Qinyu Huang, and Tao Jin. "Electricity Theft Detection Based on Multi-Scale Feature Fusion Parallel Convolution Model." In 2024 3rd Asia Power and Electrical Technology Conference (APET). IEEE, 2024. https://doi.org/10.1109/apet63768.2024.10882723.

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Raporty organizacyjne na temat "Convolution power"

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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Kendall Niles, Ken Pathak, and Joe Tom. Widened attention-enhanced atrous convolutional network for efficient embedded vision applications under resource constraints. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49459.

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Onboard image analysis enables real-time autonomous capabilities for unmanned platforms including aerial, ground, and aquatic drones. Performing classification on embedded systems, rather than transmitting data, allows rapid perception and decision-making critical for time-sensitive applications such as search and rescue, hazardous environment exploration, and military operations. To fully capitalize on these systems’ potential, specialized deep learning solutions are needed that balance accuracy and computational efficiency for time-sensitive inference. This article introduces the widened att

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Lee, Michael, Michael D’Arcy, and Gail Vaucher. In-situ Atmospheric Intelligence for Hybrid Power Grids: Volume 6 (Convolutional Neural Networks for Whole Sky Imager Data Analysis). DEVCOM Analysis Center, 2022. http://dx.doi.org/10.21236/ad1171285.

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Navarro, Adoracion, and Jethro El Camara. Mapping the Energy Sector Issues in the Philippines. Philippine Institute for Development Studies, 2024. http://dx.doi.org/10.62986/dp2023.50.

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Given the frequent observation that the Philippine energy sector is complex, this study provides a structured review of issues in the sector. The structured review method is usually employed to systematically introduce readers to the complexities of a broad topic, such as the convolutions of a problem, the intricacies of a theme or sector, or the conjectures of a new trend or technology. The study starts with a discussion of the physical flows of energy via a Sankey diagram of energy flows in the Philippines and then proceeds to describe the upstream oil, gas, and coal industry, the downstream

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