在本文中,我们分析了具有基于视觉导航的无人机(UAV)的时间延迟动力学对控制器设计的影响。时间延迟是网络物理系统中不可避免的现象,并且对无人机的控制器设计和轨迹产生具有重要意义。时间延迟对无人机动态的影响随着基于视力较慢的导航堆栈的使用而增加。我们表明,文献中的现有模型不包括时间延迟,不适合控制器调整,因为一个微不足道的解决方案始终存在错误的解决方案。我们确定的微不足道的解决方案表明,使用无限控制器的利益来实现最佳性能,这与实际发现相矛盾。我们通过引入无人机的新型非线性时间延迟模型来避免这种缺点,然后获得与每个UAV控制回路相对应的一组线性解耦模型。分析了角度和高度动力学的线性时间延迟模型的成本函数,与无延迟模型相反,我们显示了有限的最佳控制器参数的存在。由于使用了时间延迟模型,我们在实验上表明,所提出的模型准确地表示系统稳定性限制。由于时间延迟的考虑,我们使用基于视觉探视的无人机(VO)导航,在跟踪峰值速度为2.09 m/s的lemsistate轨迹时,我们实现了RMSE 5.01 cm的跟踪结果,这与最新-艺术。
translated by 谷歌翻译
This article presents a novel review of Active SLAM (A-SLAM) research conducted in the last decade. We discuss the formulation, application, and methodology applied in A-SLAM for trajectory generation and control action selection using information theory based approaches. Our extensive qualitative and quantitative analysis highlights the approaches, scenarios, configurations, types of robots, sensor types, dataset usage, and path planning approaches of A-SLAM research. We conclude by presenting the limitations and proposing future research possibilities. We believe that this survey will be helpful to researchers in understanding the various methods and techniques applied to A-SLAM formulation.
translated by 谷歌翻译
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.
translated by 谷歌翻译
Accurate recognition of food items along with quality assessment is of paramount importance in the agricultural industry. Such automated systems can speed up the wheel of the food processing sector and save tons of manual labor. In this connection, the recent advancement of Deep learning-based architectures has introduced a wide variety of solutions offering remarkable performance in several classification tasks. In this work, we have exploited the concept of Densely Connected Convolutional Neural Networks (DenseNets) for fruit quality assessment. The feature propagation towards the deeper layers has enabled the network to tackle the vanishing gradient problems and ensured the reuse of features to learn meaningful insights. Evaluating on a dataset of 19,526 images containing six fruits having three quality grades for each, the proposed pipeline achieved a remarkable accuracy of 99.67%. The robustness of the model was further tested for fruit classification and quality assessment tasks where the model produced a similar performance, which makes it suitable for real-life applications.
translated by 谷歌翻译
In this paper, deep-learning-based approaches namely fine-tuning of pretrained convolutional neural networks (VGG16 and VGG19), and end-to-end training of a developed CNN model, have been used in order to classify X-Ray images into four different classes that include COVID-19, normal, opacity and pneumonia cases. A dataset containing more than 20,000 X-ray scans was retrieved from Kaggle and used in this experiment. A two-stage classification approach was implemented to be compared to the one-shot classification approach. Our hypothesis was that a two-stage model will be able to achieve better performance than a one-shot model. Our results show otherwise as VGG16 achieved 95% accuracy using one-shot approach over 5-fold of training. Future work will focus on a more robust implementation of the two-stage classification model Covid-TSC. The main improvement will be allowing data to flow from the output of stage-1 to the input of stage-2, where stage-1 and stage-2 models are VGG16 models fine-tuned on the Covid-19 dataset.
translated by 谷歌翻译
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.
translated by 谷歌翻译
数据驱动的优化和基于机器学习的无线电访问网络的性能诊断不仅需要源于基本数据源的性质,而且还归因于复杂的时空关系以及由于用户移动性和不同流量模式而引起的单元格之间的相互依赖性。我们讨论如何使用多元分析来研究这些配置和性能管理数据集以及在关键性能指标方面识别细胞之间的关系。为此,我们利用了基于规范相关分析(CCA)的新框架,这不仅是降低维度的高效方法,而且还用于分析跨不同多元数据集的关系。作为一个案例研究,我们讨论了基于商业蜂窝网络中细胞关闭的节能用例,在该案例中,我们将CCA应用于分析容量细胞关闭对同一部门覆盖电池KPI的影响。来自LTE网络的数据用于分析示例案例。我们得出的结论是,CCA是一种可行的方法,用于识别网络计划和配置数据之间的关键关系,还可以动态绩效数据,为诸如降低维度降低,绩效分析和性能诊断的根本原因分析等努力铺平道路。
translated by 谷歌翻译
作物疾病是对粮食安全的主要威胁,其快速识别对于防止产量损失很重要。由于缺乏必要的基础设施,因此很难迅速识别这些疾病。计算机视觉的最新进展和智能手机渗透的渗透为智能手机辅助疾病识别铺平了道路。大多数植物疾病在植物的叶面结构上留下了特定的文物。这项研究于2020年在巴基斯坦拉合尔工程技术大学计算机科学与工程系进行,以检查基于叶片的植物疾病识别。这项研究为叶面疾病鉴定提供了基于神经网络的深度解决方案,并纳入了图像质量评估,以选择执行识别所需质量的图像,并将其命名为农业病理学家(AGRO PATH)。新手摄影师的捕获图像可能包含噪音,缺乏结构和模糊,从而导致诊断失败或不准确。此外,Agropath模型具有99.42%的叶面疾病鉴定精度。拟议的添加对于在农业领域的叶面疾病鉴定的应用特别有用。
translated by 谷歌翻译
Starcraft II多代理挑战(SMAC)被创建为合作多代理增强学习(MARL)的具有挑战性的基准问题。 SMAC专注于星际争霸微管理的问题,并假设每个单元都由独立行动并仅具有本地信息的学习代理人单独控制;假定通过分散执行(CTDE)进行集中培训。为了在SMAC中表现良好,MARL算法必须处理多机构信贷分配和联合行动评估的双重问题。本文介绍了一种新的体系结构Transmix,这是一个基于变压器的联合行动值混合网络,与其他最先进的合作MARL解决方案相比,我们显示出高效且可扩展的。 Transmix利用变形金刚学习更丰富的混合功能的能力来结合代理的个人价值函数。它与以前的SMAC场景上的工作相当,并且在困难场景上胜过其他技术,以及被高斯噪音损坏的场景以模拟战争的雾。
translated by 谷歌翻译
肿瘤浸润淋巴细胞(TIL)的定量已被证明是乳腺癌患者预后的独立预测因子。通常,病理学家对含有tils的基质区域的比例进行估计,以获得TILS评分。乳腺癌(Tiger)挑战中肿瘤浸润淋巴细胞旨在评估计算机生成的TILS评分的预后意义,以预测作为COX比例风险模型的一部分的存活率。在这一挑战中,作为Tiager团队,我们已经开发了一种算法,以将肿瘤与基质与基质进行第一部分,然后将肿瘤散装区域用于TILS检测。最后,我们使用这些输出来生成每种情况的TILS分数。在初步测试中,我们的方法达到了肿瘤 - 细胞瘤的加权骰子评分为0.791,而淋巴细胞检测的FROC得分为0.572。为了预测生存,我们的模型达到了0.719的C索引。这些结果在老虎挑战的初步测试排行榜中获得了第一名。
translated by 谷歌翻译