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2022 6th International Conference on Universal Village (UV)最新文献

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Deep Learning Based Algae Detection Method 基于深度学习的藻类检测方法
Pub Date : 2022-10-22 DOI: 10.1109/UV56588.2022.10185530
Ziye Fang, Shu Jiang, Xiaoyu Du, Zechao Li
The ocean is an important part of the ecosystem and is closely related to our lives. Detecting the status of algae in the ocean contributes to the protection of the marine environment. With the continuous development of target detection technology, small target detection tasks are gradually applied to the task of monitoring marine organisms. We use two-stage cascade RCNN with Res2Net, ResNeSt, CBNet, ConvNeXt and DetectoRS backbone. Secondly, data pre-processing was used with blur, motion blur, MixUp, random rotation and other data enhancements. Then the pseudo label training model is used as a pre-training model. And model ensemble is used to improve the inference results. Finally Post-processing is performed using reduced bbox. We conduct extensive experiments on the dataset and achieve the performance of 0.562.
海洋是生态系统的重要组成部分,与我们的生活息息相关。监测海洋中藻类的状况有助于保护海洋环境。随着目标检测技术的不断发展,小目标检测任务逐渐应用到海洋生物监测任务中。我们使用两级联RCNN与Res2Net, ResNeSt, CBNet, ConvNeXt和DetectoRS主干。其次,对数据进行预处理,对数据进行模糊、运动模糊、MixUp、随机旋转等增强。然后使用伪标签训练模型作为预训练模型。采用模型集成的方法改进推理结果。最后使用简化后的bbox进行后处理。我们在数据集上进行了大量的实验,达到了0.562的性能。
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引用次数: 0
Carpet Defect Detection by Transfer Learning Combing Classification and Semantic Segmentation 结合分类和语义分割的迁移学习地毯缺陷检测
Pub Date : 2022-10-22 DOI: 10.1109/UV56588.2022.10185478
Tianqing Ren, Longfei Zhou, Ke Xu, Yifan Wang, Siyu Wu, Yuliang Gai, Jiazheng Chen, Zhichao Gou
Nowadays, with the development of industrial production technology, defect detection has become an indispensable part of industrial production. However, due to various types of products and defects, it can be extremely difficult to identify and locate those defects precisely and accurately. The current major trend in defect detection is using convolutional neural networks and semantic segmentation techniques to better minimize the error rate of human eye recognition and highly improve efficiency. Our work is based on semantic segmentation method and combines it with transfer learning technique enabling our model to train on a relatively small dataset without compromising the performance, and use CNN to firstly classify input images in order to further reduce the number of images to improve computational efficiency and accuracy. Then through incorporating state-of-the-art semantic segmentation model U-Net++, our model achieves the best performance compared to UNet under transfer learning scenario. We compare our model with the state-of-the-art U-Net. Then we use mIOU and pixel accuracy to measure the models’ performance under two scenarios. Results illustrated that through transfer learning scenario, our model achieves the highest scores over other methods.
如今,随着工业生产技术的发展,缺陷检测已经成为工业生产中不可缺少的一部分。然而,由于各种类型的产品和缺陷,精确和准确地识别和定位这些缺陷可能是极其困难的。目前缺陷检测的主要趋势是使用卷积神经网络和语义分割技术来更好地降低人眼识别的错误率,大大提高效率。我们的工作是基于语义分割方法,并将其与迁移学习技术相结合,使我们的模型能够在不影响性能的情况下在相对较小的数据集上进行训练,并使用CNN对输入图像进行先分类,以进一步减少图像数量,提高计算效率和准确性。然后通过结合最先进的语义分割模型U-Net++,我们的模型在迁移学习场景下达到了相对于UNet的最佳性能。我们将我们的模型与最先进的U-Net进行比较。然后,我们使用mIOU和像素精度来衡量两种场景下模型的性能。结果表明,通过迁移学习场景,我们的模型比其他方法获得了最高的分数。
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引用次数: 0
Primary Hyperhidrosis: A Systematic Review of Current Status and Potential Interventions 原发性多汗症:现状和潜在干预措施的系统回顾
Pub Date : 2022-10-22 DOI: 10.1109/UV56588.2022.10185484
Wenjie Lin, Yajun Fang
Primary hyperhidrosis (PH) is a rare inherited disorder characterized by excessive sweating. It can affect any part of the body, but most commonly affects the axilla, palms of the hands, groin, chest, and soles of the feet. This paper comprehensively overviews current and potential diagnosis and management methods of PH from both medical and engineering perspectives. We also investigate how patients and society can live better with PH by non-invasive medical treatments and propose potential engineering and social interventions.
原发性多汗症(PH)是一种罕见的遗传性疾病,以过度出汗为特征。它可以影响身体的任何部位,但最常见的是腋窝、手掌、腹股沟、胸部和脚底。本文从医学和工程两方面全面综述了酸碱度的诊断和管理方法。我们还研究了患者和社会如何通过非侵入性医学治疗来改善PH,并提出了潜在的工程和社会干预措施。
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引用次数: 0
Intersection Evaluation Using Turning Movement Count Data and SUMO 基于转向运动计数数据和相扑的交叉口评价
Pub Date : 2022-10-22 DOI: 10.1109/UV56588.2022.10185495
Mohammad Shokrolah Shirazi, Hung-Fu Chang, Shiqi Zhang
The turning movement count (TMC) is a salient data source used for design and planning of intersections including sign, and signal installation, timing setup, as well as traffic and capacity analysis. This work presents a typical framework for utilizing the TMC data with simulation of Urban MObility (SUMO) software to mimic realistic traffic scenarios for intersection evaluation and analysis. Due to safety and mobility concerns regarding school campus zones, three intersections around the San Jose State university are selected and their corresponding turning movement data are ported into SUMO for intersection evaluation during peak hours occurred between 8:00 - 9:00 a.m. The traffic parameters extracted from each intersection simulation with realistic scenario are vehicles waiting time, speed and network flow links which imply the effectiveness of utilizing proposed approach for decision making and targeting intersections for signal optimization.
转弯运动计数(TMC)是用于十字路口设计和规划的重要数据源,包括标志,信号安装,定时设置以及交通和容量分析。本文提出了一个典型的框架,利用TMC数据与模拟城市交通(SUMO)软件来模拟真实的交通场景,以进行交叉路口的评估和分析。出于对校园区域安全和机动性的考虑,我们选择了圣何塞州立大学周围的三个十字路口,并将其相应的转弯运动数据移植到SUMO中,以便在上午8点至9点的高峰时段进行十字路口评估。从每个交叉口仿真中提取的交通参数是车辆等待时间、速度和网络流链路,这表明利用该方法进行决策和针对交叉口进行信号优化的有效性。
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引用次数: 0
Improved Handwritten Numeral Recognition on MNIST Dataset with YOLO and LSTM 基于YOLO和LSTM的MNIST数据集手写数字识别改进
Pub Date : 2022-10-22 DOI: 10.1109/UV56588.2022.10185476
Yalin Wen, Wei Ke, Hao Sheng
With the aging of population and the advance of technology, handwritten numeral recognition system is sophisticated and widely used. However, due to the presence of different writing surfaces, postures and other factors, the performance of handwritten numeral recognition is limited. In this paper, we propose a new supervised recurrent neural network, which combines time and space for target location prediction on handwritten datasets. Our method is based on the YOLO framework, and combines a long and short term memory (LSTM) mechanism. Moreover, our method not only locates handwritten images, but also improves the classification accuracy. Extensive comparison with the state-of-the-art methods demonstrates that our method achieves both accuracy and robustness on handwritten datasets. Meanwhile, our method is effective with low computational cost.
随着人口老龄化和技术的进步,手写体数字识别系统日趋成熟,应用越来越广泛。然而,由于不同书写表面、姿势等因素的存在,手写数字识别的性能受到了限制。本文提出了一种新的监督递归神经网络,将时间和空间相结合,用于手写数据集的目标位置预测。该方法基于YOLO框架,结合了长短期记忆(LSTM)机制。此外,我们的方法不仅可以定位手写图像,还可以提高分类精度。与最先进的方法进行广泛的比较表明,我们的方法在手写数据集上实现了准确性和鲁棒性。同时,该方法具有计算成本低、效率高的特点。
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引用次数: 0
MSDT: Masked Language Model Scoring Defense in Text Domain 文本域掩码语言模型评分防御
Pub Date : 2022-10-22 DOI: 10.1109/UV56588.2022.10185524
Jaechul Roh, Minhao Cheng, Yajun Fang
Pre-trained language models allowed us to process downstream tasks with the help of fine-tuning, which aids the model to achieve fairly high accuracy in various Natural Language Processing (NLP) tasks. Such easily-downloaded language models from various websites empowered the public users as well as some major institutions to give a momentum to their real-life application. However, it was recently proven that models become extremely vulnerable when they are backdoor attacked with trigger-inserted poisoned datasets by malicious users. The attackers then redistribute the victim models to the public to attract other users to use them, where the models tend to misclassify when certain triggers are detected within the training sample. In this paper, we will introduce a novel improved textual backdoor defense method, named MSDT, that outperforms the current existing defensive algorithms in specific datasets. The experimental results illustrate that our method can be effective and constructive in terms of defending against backdoor attack in text domain.
预训练的语言模型允许我们在微调的帮助下处理下游任务,这有助于模型在各种自然语言处理(NLP)任务中达到相当高的精度。这些易于从各种网站下载的语言模型使公众用户和一些主要机构能够推动它们在现实生活中的应用。然而,最近证明,当模型受到恶意用户通过触发插入的有毒数据集的后门攻击时,它们会变得非常脆弱。然后,攻击者将受害模型重新分发给公众,以吸引其他用户使用它们,当在训练样本中检测到某些触发因素时,模型往往会进行错误分类。在本文中,我们将介绍一种新的改进的文本后门防御方法,称为MSDT,它在特定数据集上优于现有的防御算法。实验结果表明,该方法在文本域防御后门攻击方面是有效的。
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引用次数: 1
Vaccine Prediction and Distribution Model under the New Situation in China Based on Informer 基于Informer的中国新形势下疫苗预测与分布模型
Pub Date : 2022-10-22 DOI: 10.1109/UV56588.2022.10185527
Yupeng Niu, Jiaqi Xu, Jingxuan Tan
In this paper, a comprehensive prediction model of daily vaccination in China was established by using Informer long sequence prediction model. For the first time, we established a comprehensive prediction model considering the number of nearby residents, transportation convenience, number of medical personnel, vaccine storage and transportation costs.
本文采用Informer长序列预测模型,建立了中国日疫苗接种的综合预测模型。首次建立了考虑附近居民数量、交通便利性、医护人员数量、疫苗储存和运输成本的综合预测模型。
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引用次数: 0
Towards Effective Microalgae Object Detection Solutions to IEEE UV 2022 “Vision Meets Alage” Object Detection Challenge 面向IEEE UV 2022“视觉与藻类相遇”目标检测挑战的有效微藻目标检测解决方案
Pub Date : 2022-10-22 DOI: 10.1109/UV56588.2022.10185487
Yunchen Zhang, Wei Zeng, Fan Yang
This technical report introduces our solution for microalgae object detection in IEEE UV 2022 Vision Meets Alage Object Detection Challenge. The purpose of this challenge is to employ computer vision to more effectively analyze population change in ocean microalgae species. Therefore, we performed a comprehensive analysis of the distribution of the microalgae dataset and designed a customized training strategy for the task. In order to better identify the categories and coordinates of microalgae in microscopic images, we propose CBSwin-Cascade RCNN++ as a strong baseline for microalgae detection. Our final submission the results, which achieves 56.13 in mAP 0.5:0.95 on a single model, and obtains 57.09 in mAP 0.5:0.95 with the ensembled models.
本技术报告介绍了我们在IEEE UV 2022视觉与藻类物体检测挑战赛中微藻物体检测的解决方案。这项挑战的目的是利用计算机视觉更有效地分析海洋微藻物种的种群变化。因此,我们对微藻数据集的分布进行了全面分析,并针对该任务设计了定制化的训练策略。为了更好地识别微藻在显微图像中的类别和坐标,我们提出了CBSwin-Cascade RCNN++作为微藻检测的强基线。我们最终提交了结果,在单个模型上mAP 0.5:0.95得到56.13,在集成模型上mAP 0.5:0.95得到57.09。
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引用次数: 0
A Novel Adaptive Signal Timing Control Approach for Signalized Intersections 一种新的信号交叉口自适应信号配时控制方法
Pub Date : 2022-10-22 DOI: 10.1109/UV56588.2022.10185472
Fan He, Longfei Zhou, Siyu Wu, Haoliang Liu, Zehang Li, Ke Xu, Yuliang Gai, Fei Teng, Pengfei Liu
Vehicular traffic congestion is a severe global problem, leading to a range of issues such as increased travel times, increased fuel consumption, and increased pollutant emissions. The signal timing of traffic lights is one of the major factors that we can change to reduce traffic congestion at signalized intersections. Most traffic lights used in real life are hard-coded which means the fixed timing is applied for traffic control. In these hard-coded signalized intersection models, we do not have much to do to deal with real-time congestion, especially for large traffic volumes. In this study, we propose an adaptive signal timing control approach to reduce traffic congestion according to real-time traffic flow situations. In this novel approach, the signal timing can be changed over time based on real-time information about traffic flows. The Eclipse SUMO is used to simulate traffic conditions at real-world intersections to optimize road traffic light control and reduce real-time traffic delays for signalized intersections. Simulation results show that the proposed method obtains better performance than typical traffic light timing control strategies.
车辆交通拥堵是一个严重的全球性问题,导致了一系列问题,如旅行时间增加,燃料消耗增加,污染物排放增加。交通信号灯的信号配时是我们可以改变的主要因素之一,以减少信号交叉口的交通拥堵。现实生活中使用的交通灯大多是硬编码的,即采用固定的定时方式进行交通控制。在这些硬编码信号交叉口模型中,我们不需要做太多的事情来处理实时拥堵,特别是对于大交通量。在本研究中,我们提出一种自适应信号配时控制方法,以因应实时交通流情况,减少交通拥塞。在这种新颖的方法中,信号定时可以根据交通流量的实时信息随时间改变。Eclipse SUMO用于模拟现实世界十字路口的交通状况,以优化道路交通灯控制,减少信号交叉口的实时交通延迟。仿真结果表明,该方法比典型的交通灯定时控制策略具有更好的控制性能。
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引用次数: 0
Research and Development of Intelligent Tests and a Process Design System for Complex and Precision Parts of Electronic Products 电子产品复杂精密零件智能测试与工艺设计系统的研究与开发
Pub Date : 2022-10-22 DOI: 10.1109/UV56588.2022.10185477
Ling Chen, Yaman Wang, Yuchen Long, Zengfeng Duan, Yanyan Li
Complex precision parts of electronic products are essential to defense information technology equipment and the manufacturing industry. The workshop testing process for electronic products is crucial to ensuring their quality is qualified. Due to its multi-breed, multi-batch, and complex structure, its experimental process design is challenged by more and more indicators and complex processes. Currently, the process of detecting complex electronic products still adopts manual process document design, which is inefficient and inconsistent, and it is difficult to guarantee accuracy by manual experience. Therefore, a new intelligent test process method is designed to complete the process design. The method first automatically extracts test indicators and related parameters from the imported unstructured technical files or sop files, then automatically matches the test indicators with the test table, then automatically fills the test parameters under each index, then clusters and outputs XML test procedures for each indicator. Moreover, the key technology of each process is studied, the intelligent test process system for complex electronic products is developed, and the application of one model of microwave component products in a military industry enterprise is used as an example. The test program generated by the system can be directly used for subsequent workshop machine execution.
电子产品的复杂精密部件对国防信息技术设备和制造业至关重要。电子产品的车间检测过程是保证电子产品质量合格的关键。由于其多品种、多批次、结构复杂,实验工艺设计受到越来越多指标和复杂工艺的挑战。目前,复杂电子产品的检测过程仍采用手工流程文档设计,效率低下且不一致,难以通过手工体验来保证准确性。为此,设计了一种新的智能测试工艺方法来完成工艺设计。该方法首先从导入的非结构化技术文件或sop文件中自动提取测试指标和相关参数,然后将测试指标与测试表自动匹配,然后自动填充每个指标下的测试参数,然后对每个指标进行聚类并输出XML测试程序。研究了各过程的关键技术,开发了复杂电子产品智能测试过程系统,并以某军工企业微波元件产品模型为例进行了应用。系统生成的测试程序可直接用于后续的车间机器执行。
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引用次数: 0
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2022 6th International Conference on Universal Village (UV)
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