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学者姓名：林伟
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通过分析直接内存存取（DMA）控制器的工作原理和主要功耗来源，发现其在空闲状态时依然存在功耗较高的问题，为了解决空闲状态功耗损失问题以及满足DMA控制器实际传输过程中可能出现的暂停需求，提出了一种可暂停的低功耗DMA控制器设计方案。采用自适应时钟控制机制，通过加入时钟门控技术，根据DMA数据传输需求动态调整时钟，使DMA引擎模块功耗降低了62%。针对暂停需求，采用了一种可暂停的控制策略，通过加入暂停指令，实现对DMA传输的实时暂停和恢复，提高了DMA控制器的灵活性。为了保证DMA控制器功能的正确性和完备性，采用基于覆盖率驱动验证（CDV）的验证策略，划分DMA控制器的功能点，针对每个功能点编写测试用例，搭建通用验证方法学（UVM）仿真验证平台，进行大量随机测试和定向测试，给出了测试的结果以及完整的覆盖率分析结果。
Keyword ：
DMA控制器 DMA控制器 低功耗设计 低功耗设计 时钟门控技术 时钟门控技术 暂停指令 暂停指令 覆盖率驱动验证 覆盖率驱动验证 通用验证方法学 通用验证方法学
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| GB/T 7714 | 苏皇滨 , 林伟 , 林伟峰 . 一种可暂停的低功耗DMA控制器设计及验证 [J]. | 电子与封装 , 2024 , 24 (03) : 73-78 . |
| MLA | 苏皇滨 等. "一种可暂停的低功耗DMA控制器设计及验证" . | 电子与封装 24 . 03 (2024) : 73-78 . |
| APA | 苏皇滨 , 林伟 , 林伟峰 . 一种可暂停的低功耗DMA控制器设计及验证 . | 电子与封装 , 2024 , 24 (03) , 73-78 . |
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Recognition and extraction of elements from house plans present significant challenges in the construction, decoration and interior design industries. To address this issue, this paper proposes a wall segmentation system for house plans that integrates deep learning and traditional methods. The system comprises several components, such as image preprocessing, main region extraction, wall segmentation and optimisation of wall smoothing. The study combined the rapidity of the traditional method with the robustness of deep learning to enable the extraction of walls from varied image styles and perform smoothing optimisation. The paper demonstrates that the proposed segmentation technique delivers an 89% mean intersection over union, a 94% detection rate and a 96% recognition accuracy. The research surpasses current findings in the same field. Additionally, when combined with the current house map dataset, the system presents a semantic categorisation dataset featuring 6000 images depicting a range of styles, in addition to a recognition dataset including 4000 images.
Keyword ：
Deep learning Deep learning House plan segmentation House plan segmentation Image preprocessing Image preprocessing Smoothing optimisation Smoothing optimisation
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| GB/T 7714 | Wei, Lin , Lai, Chenghui . Wall segmentation in house plans: fusion of deep learning and traditional methods [J]. | VISUAL COMPUTER , 2023 , 40 (9) : 6015-6031 . |
| MLA | Wei, Lin 等. "Wall segmentation in house plans: fusion of deep learning and traditional methods" . | VISUAL COMPUTER 40 . 9 (2023) : 6015-6031 . |
| APA | Wei, Lin , Lai, Chenghui . Wall segmentation in house plans: fusion of deep learning and traditional methods . | VISUAL COMPUTER , 2023 , 40 (9) , 6015-6031 . |
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The object detection technology holds paramount significance in realizing autonomous driving and AI-assisted driving systems. Swift and precise object detection is crucial for enhancing the safety of autonomous vehicles. However, for in-vehicle edge computing platforms, colossal models fall short of meeting real-time detection requirements, while lightweight models often compromise on detection accuracy. Addressing this issue, this paper proposes an improved real-time object detection algorithm based on YOLOv5.In the proposed method, we combine the One-Shot Aggregation (OSA) concept with the progressive channel compression idea and introduce GSConv to innovatively propose the GSPCA structure. It aims to improve some of the problems exposed by the original C3 structure, so as to enhance the model efficiency. Secondly, we also apply GSConv to the neck network of YOLOv5 and introduce the Content-Aware ReAssembly of Features (CARAFE) upsampling operator in the FPN structure, which utilizes its spatial perception and large receptive field to improve the quality of the upsampling, thus enhancing the feature fusion performance of the network. Experimental results demonstrate that, compared to the baseline, our proposed model achieves the highest improvement of 5.2% in mAP@0.5:0.95 on the PASCAL VOC dataset, KITTI dataset, and SODA10m dataset. Furthermore, the model's parameter count and computational load are slightly less than those of the original model. © 2023 ACM.
Keyword ：
Autonomous vehicles Autonomous vehicles Object detection Object detection Object recognition Object recognition Signal detection Signal detection Signal sampling Signal sampling
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| GB/T 7714 | Wang, Jiansong , Lin, Wei . YOLOGP: A YOLOv5-Based Lightweight Network for Efficient Vehicle Detection in Autonomous Driving Scenarios [C] . 2023 : 98-105 . |
| MLA | Wang, Jiansong 等. "YOLOGP: A YOLOv5-Based Lightweight Network for Efficient Vehicle Detection in Autonomous Driving Scenarios" . (2023) : 98-105 . |
| APA | Wang, Jiansong , Lin, Wei . YOLOGP: A YOLOv5-Based Lightweight Network for Efficient Vehicle Detection in Autonomous Driving Scenarios . (2023) : 98-105 . |
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Smoking is a common behavior in daily life, but smoking in public places not only affects public health and the health of others, but also may cause accidents such as fires. Therefore, detecting and recognizing smoking behavior is of great importance. Aiming at the problem that most current target detection networks are large in volume, have many parameters, and are difficult to deploy on low-performance device terminals, the article proposes a lightweight improved YOLOv5 smoking behavior detection algorithm model. This model replaces the backbone with a lightweight MobileNetv3 neural network, and at the same time absorbs and references the design ideas of ShuffleNet to improve the width of the neck-head part, making it smaller and more suitable for deployment on low-configuration devices. The experimental data show that after improvement, the new network has 80% less parameters and 63% less inference time, and when deployed on low-configuration device (i3-4000m 2.4ghz 2c4t), its inference time (50ms) can be reduced by half compared to the original network (120ms), and meet the requirement of near real-time. Therefore, the improved network can reduce network parameters while ensuring accuracy, and can achieve near real-time performance on low-configuration devices. © 2023 SPIE.
Keyword ：
deep learning deep learning lightweight network lightweight network object detecting object detecting YOLO YOLO
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| GB/T 7714 | Liu, H. , Lin, W. . Smoking Behavior Recognition Algorithm Based on Lightweight Object Detection Network [未知]. |
| MLA | Liu, H. 等. "Smoking Behavior Recognition Algorithm Based on Lightweight Object Detection Network" [未知]. |
| APA | Liu, H. , Lin, W. . Smoking Behavior Recognition Algorithm Based on Lightweight Object Detection Network [未知]. |
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Floating garbage on sea surface has always been a key issue in the long-term research of environmental pollution. In order to effectively solve the problem of marine garbage pollution, this paper conducts in-depth research on the existing VGG16 network model and proposes an improved lightweight VGG network model. Instead of the fully connected layer, our model uses the global average pooling layer to reduce the number of network parameters, and adds a residual module to the convolution module to improve the accuracy of the model. The experimental results show that the accuracy of the improved lightweight VGG network model is as high as 97.8% in the self-built sea surface waste data set. Compared with the traditional VGG16 network model, the number of parameters is reduced by 98.5% and the calculation amount is reduced by 78.5%, achieving the goal of rapid and accurate classification. © 2023 SPIE.
Keyword ：
Marine pollution Marine pollution Surface waters Surface waters
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| GB/T 7714 | Lin, Weiming , Lin, Wei . Research on sea surface garbage classification algorithm based on improved VGG network [C] . 2023 . |
| MLA | Lin, Weiming 等. "Research on sea surface garbage classification algorithm based on improved VGG network" . (2023) . |
| APA | Lin, Weiming , Lin, Wei . Research on sea surface garbage classification algorithm based on improved VGG network . (2023) . |
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With the increasing number of augmented reality apps for houses in recent years, home modeling is essential to complete a 3D reconstruction via identifying the primary features of the house based on a 2D floorplan. Due to the dispersed wall arrangement in 2D floor layouts and the abundant interference information surrounding varied thicknesses, existing segmentation methods mainly rely on image morphology or use deep learning models in other fields like Unet. However, these schemes do not solve poor robust performance problems. In this paper, we propose an Reflect Strip Pooling Unet (RSP-Unet) to enhance the segmentation capabilities of the network for strip features. Specifically, we utilize reflect strip pooling to replace the maximum pooling step and reduce feature loss during the downsampling in the Unet network. More importantly, the proposed module is also integrated with the SE (Squeeze-and- Excitation) mechanism to interact with input from several channels, lessen model overfitting, and increase model robustness. Finally, our extensive experience shows that the results on the self-built floorplan dataset demonstrate that the mean Intersection Over Union(mIOU) is increased by 8.34% and the Dice coefficient is increased by 8.78% compared with the original Unet model. © 2023, John Wiley and Sons Inc. All rights reserved.
Keyword ：
house map house map image segmentation image segmentation neural network neural network target detection target detection
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| GB/T 7714 | Lin, W. , Xiao, X. , Lang, T. et al. Deep Learning-based System for Processing Complex Floorplans [未知]. |
| MLA | Lin, W. et al. "Deep Learning-based System for Processing Complex Floorplans" [未知]. |
| APA | Lin, W. , Xiao, X. , Lang, T. , Wang, J. , Li, J. . Deep Learning-based System for Processing Complex Floorplans [未知]. |
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设计一种具有高速率、低功耗的基带控制电路，同时提出一种灵活的数据帧结构，并对其一次性的收发操作进行了仿真验证。基带控制电路主要由总线控制模块、组帧模块、解帧模块以及核心控制单元组成，可实现串行外设接口（Serial Peripheral Interface,SPI）通信以及多种收发模式间的切换，达到有效降低功耗的目的。
Keyword ：
串行外设接口(SPI)通信 串行外设接口(SPI)通信 基带控制器 基带控制器 数据帧 数据帧 无线收发机 无线收发机
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| GB/T 7714 | 任兴福 , 林伟 . 一种低成本短距无线收发机基带电路的设计 [J]. | 电视技术 , 2022 , 46 (03) : 83-86 . |
| MLA | 任兴福 et al. "一种低成本短距无线收发机基带电路的设计" . | 电视技术 46 . 03 (2022) : 83-86 . |
| APA | 任兴福 , 林伟 . 一种低成本短距无线收发机基带电路的设计 . | 电视技术 , 2022 , 46 (03) , 83-86 . |
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Correcting historical corpora in digital version is a crucial task for the historical research, however, scan quality, book layout, visual character similarity can affect the quality of the recognizing. OCR is at the forefront of digitization projects for cultural heritage preservation. The main task is to identify characters from their visual form into their textual representation. In this paper, we propose a model combining recurrent neutral network(RNN) and deep convolutional network(DCNN) to correct OCR transcription errors. The experiment on a historical book corpus in German language shows that the model is very robust in capturing diverse OCR transcription errors greatly. © Published under licence by IOP Publishing Ltd.
Keyword ：
Convolutional neural networks Convolutional neural networks Historic preservation Historic preservation Recurrent neural networks Recurrent neural networks
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| GB/T 7714 | Lin, Wei , Lin, Zhaoquan . Historical Corpora Correlation based on RNN and DCNN [C] . 2021 . |
| MLA | Lin, Wei et al. "Historical Corpora Correlation based on RNN and DCNN" . (2021) . |
| APA | Lin, Wei , Lin, Zhaoquan . Historical Corpora Correlation based on RNN and DCNN . (2021) . |
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针对当前PID控制器参数难以整定和成本功耗过高的现状,以板球系统为例设计了基于嵌入式设备的自适应PID系统.文章首先改进PID算法并对系统进行模型分析,基于该简化模型提出了一种基于参数实时更新的神经网络来提高控制精度,降低控制时间.将该控制网络简化后,移植到嵌入式设备上实现了PID自适应控制.最后,用对比实验验证了该系统的可行性.
Keyword ：
PID控制 PID控制 嵌入式设备 嵌入式设备 板球系统 板球系统 模型分析 模型分析 神经网络 神经网络
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| GB/T 7714 | 黄国伟 , 林伟 . 基于嵌入式设备的自适应PID控制系统设计 [J]. | 电气开关 , 2020 , 58 (1) : 21-25 . |
| MLA | 黄国伟 et al. "基于嵌入式设备的自适应PID控制系统设计" . | 电气开关 58 . 1 (2020) : 21-25 . |
| APA | 黄国伟 , 林伟 . 基于嵌入式设备的自适应PID控制系统设计 . | 电气开关 , 2020 , 58 (1) , 21-25 . |
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针对传统透皮给药医疗仪功耗高、功能单一等缺点,在透皮给药原理的基础上,设计并实现了一种低功耗透皮给药电疗仪.以低功耗STC89 C52和Small_RTOS51为电疗仪的硬、软件平台,设计了电致孔、电离子导入和超声波的发生电路,并将三种脉冲电波联合运用,增加了药物促渗效果.实验结果表明,该系统性能优良,操作简单,功耗较低,能有效促进药物的透皮吸收,具有较强的应用价值.
Keyword ：
STC89C52 STC89C52 低功耗 低功耗 电疗仪 电疗仪 透皮给药 透皮给药
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| GB/T 7714 | 付卫华 , 林伟 . 低功耗透皮给药电疗仪的设计与实现 [J]. | 电气开关 , 2019 , 57 (5) : 81-84 . |
| MLA | 付卫华 et al. "低功耗透皮给药电疗仪的设计与实现" . | 电气开关 57 . 5 (2019) : 81-84 . |
| APA | 付卫华 , 林伟 . 低功耗透皮给药电疗仪的设计与实现 . | 电气开关 , 2019 , 57 (5) , 81-84 . |
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