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检索条件"机构=Advanced Computing and Applications"

共 315 条记录，以下是11-20 订阅

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A CBL Estimation Method for Industrial Customers Based on K-Means and LSTM 11

A CBL Estimation Method for Industrial Customers Based on K-...

引用

11th China International Conference on Electricity Distribution, CICED 2024

作者： Zhang, Wensi Lei, Shuya Liu, Mohan Wei, Renjie Wang, Yishen State Grid Laboratory of Grid Advanced Computing and Applications China Electric Power Research Institute Co. Ltd. Beijing China

ISBN: (纸本)9798350368345

In pursuit of attaining the objective of 'carbon peaking and carbon neutrality', the structure of energy in China has undergone a great transformation. With the rapid development of renewable energy generation, the power grid has been confronted with new challenges. The reliability and flexibility of the grid have been affected. Demand response (DR), as an effective management method, has become a hotspot. Customer baseline load (CBL) serves as the standard for calculating economic compensation in DR. Hence, accurate estimation of CBL is a crucial aspect in ensuring the effective implementation of DR strategies. This paper proposes a CBL estimation method for industrial customers based on the K-means and long short-term memory (LSTM). First, the K-means method is used to cluster the daily load curves of various industries to yield the typical patterns of electricity consumption behaviors. Second, the LSTM neural network is established to estimate CBL. It is trained with historical load data of different industries and clustering results. Finally, the effectiveness of the novel method is validated via a real data set. © 2024 IEEE.

关键词： Sales

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CNN-based Plant Leaf Disease Detection: A Key Solution for Enhancing Agricultural Productivity 3

CNN-based Plant Leaf Disease Detection: A Key Solution for E...

引用

3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Nayak, Deepika Nair, Rekha R Tinababu Kishore, S. Alliance University Department of Computer Science and Engineering Alliance School of Advanced Computing Bengaluru India Department of Computer Applications Karnataka Mangaluru India

ISBN: (纸本)9798350354171

Agriculture plays a pivotal role in our lives and holds significant importance in our economy. Proper management of agricultural practices is essential for maximizing profits in agricultural production. However, many farmers lack expertise in identifying and managing plant leaf diseases, leading to reduced crop yields. Since agricultural productivity directly impacts the profitability of farming, efficient disease detection and management are crucial. To address this issue, Convolutional Neural Networks (CNN) emerge as a viable solution for leaf disease detection and classification. The primary objective of this research is to develop a robust CNN-based system capable of detecting and classifying leaf diseases in various crops such as apple, grape, corn, potato, tomato, and more. In this research, the CNN algorithm was utilised, and the accuracy was 97.58%. The system's application will enable farmers to monitor large fields of crops, facilitating early detection and treatment of diseases. The significance of plant leaf disease detection spans across multiple sectors, including Biological Research and Agriculture Institutes. Detecting diseases promptly can help implement timely medical treatments, thereby mitigating the negative impacts on crop health and productivity. Moreover, such a system can assist in monitoring crop health on a larger scale, benefiting the agricultural sector as a whole. © 2024 IEEE.

关键词： Agribusiness

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Vertex Centric Weighted Edge Index to Accelerate Weighted Graph Traversal 4

Vertex Centric Weighted Edge Index to Accelerate Weighted Gr...

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4th International Conference on Consumer Electronics and Computer Engineering, ICCECE 2024

作者： Lin, Jianchao Ren, Chunhui Liu, Siyan State Grid Smart Grid Research Institute Co. Ltd. State Grid Laboratory of Grid Advanced Computing and Applications Beijing China

ISBN: (纸本)9798350384062

Indexing is an important technique to optimize graph database performance. However, indexing in existing graph databases focuses mainly on property-value based query, and has not drawn much attention on graph traversal. We propose a new indexing technique, which maintains sorted indexing of vertex and weight values of the vertex's adjacent edges. With this proposed vertex centric adjacent weighted edge index, graph traversal algorithms based on weight values like Dijkstra's algorithm and Prim's algorithm have significant performance gains. © 2024 IEEE.

关键词： Graph Databases

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Masked Image Modeling Knowledge Distillation Based on Mutual Learning 24

Masked Image Modeling Knowledge Distillation Based on Mutual...

引用

2024 International Conference on Computer Vision and Deep Learning, CVDL 2024

作者： Zhang, Yi Du, Zexu Wei, Wei Chu, Zongbo State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China

ISBN: (纸本)9798400718199

With the rapid improvement of the performance of large models, pre-training a large-scale model and fine-tuning on downstream has become a trend. However, previous knowledge distillation methods mainly concentrates on a specific downstream task. This paper proposes a novel distillation framework Mutual Distillation for Masked Autoencoder Model(MMIM) which transfers knowledge from large-scale pre-trained visual models to provide smaller backbones for downstream tasks, thereby saving time. In our model MMIM, to maintain the advantages of mutual learning, two different student models learn collaboratively to boost both feature representation capabilities simultaneously. At the same time, in order to utilize powerful feature representation capability of pre-trained teacher model, we use the intermediate features of the teacher model to supervise the learning of the student models. Experiments on ImageNet demonstrates the compressed model obtained by our framework possesses powerful feature representation comparable to teacher model. In addition, MMIM also outperforms state-of-the-art knowledge distillation methods which directly distills fine-tuned models. © 2024 ACM.

关键词： Distillation

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Copy-Move Forgery Detection Optimization Using Harmony Search with Clonal Selection Algorithm 6

Copy-Move Forgery Detection Optimization Using Harmony Searc...

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6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Hijazi, Mohd Hanafi Ahmad Josmin, Ezlyn Sherlyn Data Technology and Applications Research Group Creative Advanced Machine Intelligence Research Center Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9798350389692

In today's world, where technology is advancing rapidly, the extensive use of digital images on social media brings up concerns about their authenticity. One common form of deception is image forgery, especially copy-move forgery, where parts of an image are duplicated and inserted elsewhere within the same image. Traditional detection methods, like randomized search algorithms, often struggle with adaptability. This study presents a new approach that uses the Harmony Search with Clonal Selection Algorithm (HSCSA) to improve the detection of copy-move forgeries by fine -tuning the hyperparameters of Convolutional Neural Network (CNN) models. Our method involves preprocessing the CoMoFoD dataset, optimizing essential hyperparameters using HSCSA, and comparing the performance of the optimized model with standard CNN models. The results showed a notable improvement, with accuracy rising from 67.72% to 78.20%. © 2024 IEEE.

关键词： Convolutional neural networks

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Transformer Fault Detection Method Based on Multi-Source Data Fusion of Transformer and RetNet 1

Transformer Fault Detection Method Based on Multi-Source Dat...

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1st International Conference on Artificial Intelligence and Human-Computer Interaction, ArtInHCI 2023

作者： Chang, Ke Gan, Jinrui Xia, Weishang State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing102209 China

ISBN: (纸本)9781643685083

Current fault methods for transformers are all based on a single signal and fail to make full use of fault data. In order to improve the accuracy of state judgment, this paper proposes a Transformer fault detection method based on multi-source data fusion based on Transformer and RetNet, which integrates four fault data: gas in oil, voltage and current, sound and oil temperature. The Transformer fault diagnosis is carried out jointly. Considering the degree of correlation between different data, the four kinds of data are divided into two categories and processed by different models respectively. Transformer is used to focus the data of strong correlation between gas and voltage and current in oil, fuse the characteristic information, and utilize the multi-scale attention mechanism of RetNet. The correlation between sound and oil temperature data is widely mined in different spatio-Temporal ranges. Finally, two models are combined with four data sources to judge transformer *** accuracy of the new method proposed in this paper is 81.36%, which is 2.98% higher than RetNet and 5.03% higher than Transformer. The experimental results show that the method is effective. © 2024 The Authors.

关键词： Failure analysis

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Application of Self-Distillation Framework in Transmission Line Defect Recognition 7

Application of Self-Distillation Framework in Transmission L...

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7th IEEE advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Yang, He Shi, Cuncun Lin, Long State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing102209 China

ISBN: (纸本)9798350339161

Drones have become very common in inspections of transmission lines across the country, but the images and videos captured by drones still require manual processing, which is time-consuming and labor-intensive, not intelligent enough to meet real-time requirements, and the response is relatively passive;currently in the power sector artificial intelligence has been used to process drone footage, but it has not yet reached the level of human experts. In recent years, methods to improve model performance have focused on conceiving new network structures and improving the quantity and quality of sample sets. Both methods are costly. This paper proposes a training framework for the model based on self-distillation. Without changing the model structure or changing the data set, you can learn the knowledge of your own network to improve model performance and use smaller models under the same performance requirements. Based on this framework, we implemented a self-distillation system on ResNet and verified the framework by comparing original training and self-distillation training. Of course, this framework is also applicable to other networks. Experiments show that the self-distillation method can significantly improve the accuracy of recognition. © 2024 IEEE.

关键词： Electric lines

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Online Fine-tuning Method for Power Grid Artificial Intelligence Model Based on Cloud-Edge Collaboration 3

Online Fine-tuning Method for Power Grid Artificial Intellig...

引用

3rd International Conference on Robotics, Automation and Intelligent Control, ICRAIC 2023

作者： Du, Zexu Zhang, Guoliang Zhang, Yi Wei, Wei Chu, Zongbo State Grid Smart Grid Research Institute Co.ltd State Grid Laboratory of Grid Advanced Computing and Applications Beijing China

ISBN: (纸本)9798350360813

As artificial intelligence methods play an increasingly important role in the digital transformation of power grids, when applied in power grid scenarios, these methods face problems such as the inability of models to adaptively deploy data distribution changes, slow response speed, inability to ensure data security on the side, limited computing power on the side, and uneven distribution of computing resources. This method proposes a novel fine-tuning framework based on cloud-edge collaboration to address these issues. This method utilizes reinforcement learning to segment the model, discarding unreliable samples collected on-site to fine-tune the model's fine-tuning parameters. Finally, validation was conducted on the application scenario of detecting violations in the power grid, and the experimental results show the effectiveness of our method. © 2023 IEEE.

关键词： Reinforcement learning

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AXIM: A Programmable Simulation Environment for FPGA Kernels with AXI Interfaces 7

AXIM: A Programmable Simulation Environment for FPGA Kernels...

引用

7th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Lin, Long Shi, Cuncun Yang, He State Grid Smart Grid Research Institute Co. Ltd State Grid Laboratory of Grid Advanced Computing and Applications Beijing China

ISBN: (纸本)9798350334197

In the recent decade, the development of FPGA kernels raises its level of abstraction from RTL to HLS C or OpenCL. And so are the simulation and debugging software suites. However, it makes some existing high-quality RTL simulators incompatible to use. Therefore, we propose AXIM, a programmable simulation environment that integrates existing RTL simulators for FPGA kernels with interfaces at a high-level abstraction via AXI buses. AXIM consists three modules, including AXIM hardware driver, AXIM runtime library, and AXIM debugger. Experimental results show that AXIM runs faster than the one integrated with Xilinx Vitis. Besides, another experiment demonstrates that visualization analysis of DRAM's activities generated by AXIM can help developers optimize the design of kernels. As a side product, we also identify an inconsistent behavior in hardware emulation in Xilinx Vitis 2019.2 about data transfer using AXI interfaces. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A Method for Defects Inspection of Power Transmission Lines Based on DETA and Integrated Improvement Algorithm 11

A Method for Defects Inspection of Power Transmission Lines ...

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11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Wang, Jin Wang, Bo Wu, Peng Zhang, Guoliang Chen, Jiangqi Zhang, Xi State Grid Smart Grid Research Institute Co.Ltd State Grid Laboratory of Grid Advanced Computing and Applications Beijing China

ISBN: (纸本)9798350333664

Collecting images through drone and helicopter aerial photography is one of the main methods for transmission line inspection. Currently, the popular intelligent transmission defects recognition is mainly based on deep neural network object detection algorithms. Due to the outstanding performance in various public datasets and rapid development of DETR algorithms, there is a promising application prospect in the field of transmission inspection. This article proposes a transmission line inspection model based on DETA, which is optimized by integrated improvement algorithm, which inlcudes SNIP sampling algorithm, multi-scale inference strategy, and prior knowledge based box-selection algorithm. Experiments have shown that the method proposed in this article has the advantages of accurate defect localization, high detection accuracy, and strong scalability. © 2023 IEEE.

关键词： Deep Learning defect recognition DETA DETR object detection transmission inspection

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