检索结果-内蒙古大学图书馆

IEEE General Meeting Power& Energy Society

作者： Sasha Fung Yufei Tang James VanZwieten Gabriel Alsenas Department of Electrical Engineering and Computer Science Florida Atlantic University Boca Raton FL USA Department of Ocean and Mechanical Engineering Florida Atlantic University Boca Raton FL USA Southeast National Marine Renewable Energy Center Florida Atlantic University Boca Raton FL USA

The large potential for electricity generation powered by ocean currents has gained attention as a viable renewable energy source that can be harnessed by ocean current turbines (OCTs). In this paper, the modeling of an OCT is presented and simulations are executed in the OPAL-RT environment to observe the real-time performance of a 20m rotor diameter OCT under three cases. First, the turbine mechanical torque output is evaluated while operating in realistic conditions simulated using Acoustic Doppler Current Profiler (ADCP) readings that were collected in the Gulf Stream. The second and third cases involve a microgrid-connected OCT system that undergoes a water flow speed change at the turbine input and a load change at the main load respectively. Results exhibit power and frequency stability in grid operations and contribute to demonstrating the effectiveness of the OPAL-RT in marine energy with microgrid simulation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SCORE: A Second-Order Conic Initialization for Range-Aided SLAM

SCORE: A Second-Order Conic Initialization for Range-Aided S...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Alan Papalia Joseph Morales Kevin J. Doherty David M. Rosen John J. Leonard Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology (MIT) Cambridge MA USA Department of Applied Ocean Physics & Engineering Woods Hole Oceanographic Institution Woods Hole MA USA Department of Electrical & Computer Engineering Northeastern University Boston MA USA Department of Math Northeastern University Boston MA USA

We present a novel initialization technique for the range-aided simultaneous localization and mapping (RA-SLAM) problem. In RA-SLAM we consider measurements of point-to-point distances in addition to measurements of rigid transformations to landmark or pose variables. Standard formulations of RA-SLAM approach the problem as non-convex optimization, which requires a good initialization to obtain quality results. The initialization technique proposed here relaxes the RA-SLAM problem to a convex problem which is then solved to determine an initialization for the original, non-convex problem. The relaxation is a second-order cone program (SOCP), which is derived from a quadratically constrained quadratic program (QCQP) formulation of the RA-SLAM problem. As a SOCP, the method is highly scalable. We name this relaxation Second-order COnic RElaxation for RA-SLAM (SCORE). To our knowledge, this work represents the first convex relaxation for RA-SLAM. We present real-world and simulated experiments which show SCORE initialization permits the efficient recovery of quality solutions for a variety of challenging single- and multi-robot RA-SLAM problems with thousands of poses and range measurements.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Natural pigment sensor for solar ultraviolet radiation measurement 14

Natural pigment sensor for solar ultraviolet radiation measu...

引用

Organic and Hybrid Sensors and Bioelectronics XIV 2021

作者： Castillo, D. Sánchez, A. González, J. Chamba, C. Lakshminarayanan, Vasudevan Chemistry Department Universidad Tecnica Particular de Loja Ap. 11-01-608 Loja Ecuador Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science University of Waterloo ON Canada Departments of Physics Electrical and Computer Engineering and Systems Design Engineering University of Waterloo ON Canada

ISBN: (数字)9781510644595

ISBN: (纸本)9781510644588

The ultraviolet (UV) index is an international standard measure of the strength of solar ultraviolet radiation on the earth's surface at a specific place and time. Solar radiation with a high UV index can produce damage to the skin and eye (photoaging and photokeratitis). The levels of UV radiation are commonly detected using silicon-based optoelectronic sensors, which can be expensive. Here we propose a way to measure the UV index using natural organic pigments which fluoresce when exposed to UV radiation. In combination with an optical fiber, we have built a prototype sensor based on the pigment of turmeric or "Curcuma Longa". Curcuma longa fluoresces in the range of 500 to 680 nm when exposed to UV radiation. The system uses a filter to isolate the sunlight UV component. The sensor measures the variation in fluorescence intensity using a light dependent resistor to determine radiation levels and correlate it with the UV index. The sensor has been tested in Loja, Ecuador which is located at the equator (UV levels can reach up to 20.0 at the equator). When compared to a standard commercially available sensor (ML8511/LAPIS Semiconductor) this prototype has an error of ± 2.8%. We will describe the optical design and present measurements made with this novel inexpensive sensor. © 2021 SPIE.

关键词： Fluorescence

来源：评论

学校读者我要写书评

暂无评论

Conformal prediction using conditional histograms 21

Conformal prediction using conditional histograms

引用

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Matteo Sesia Yaniv Romano Department of Data Sciences and Operations University of Southern California Departments of Electrical and Computer Engineering and of Computer Science Technion Israel

ISBN: (纸本)9781713845393

This paper develops a conformal method to compute prediction intervals for non-parametric regression that can automatically adapt to skewed data. Leveraging black-box machine learning algorithms to estimate the conditional distribution of the outcome using histograms, it translates their output into the shortest prediction intervals with approximate conditional coverage. The resulting prediction intervals provably have marginal coverage in finite samples, while asymptotically achieving conditional coverage and optimal length if the black-box model is consistent. Numerical experiments with simulated and real data demonstrate improved performance compared to state-of-the-art alternatives, including conformalized quantile regression and other distributional conformal prediction approaches.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Are convolutional neural networks or transformers more like human vision?

arXiv

引用

arXiv 2021年

作者： Tuli, Shikhar Dasgupta, Ishita Grant, Erin Griffiths, Thomas L. Department of Electrical and Computer Engineering Princeton University DeepMind New York United States Department of Electrical Engineering and Computer Sciences UC Berkeley Departments of Psychology and Computer Science Princeton University

Modern machine learning models for computer vision exceed humans in accuracy on specific visual recognition tasks, notably on datasets like ImageNet. However, high accuracy can be achieved in many ways. The particular decision function found by a machine learning system is determined not only by the data to which the system is exposed, but also the inductive biases of the model, which are typically harder to characterize. In this work, we follow a recent trend of in-depth behavioral analyses of neural network models that go beyond accuracy as an evaluation metric by looking at patterns of errors. Our focus is on comparing a suite of standard Convolutional Neural Networks (CNNs) and a recently-proposed attention-based network, the Vision Transformer (ViT), which relaxes the translation-invariance constraint of CNNs and therefore represents a model with a weaker set of inductive biases. Attention-based networks have previously been shown to achieve higher accuracy than CNNs on vision tasks, and we demonstrate, using new metrics for examining error consistency with more granularity, that their errors are also more consistent with those of humans. These results have implications both for building more human-like vision models, as well as for understanding visual object recognition in humans. © 2021, CC BY.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

ON IDENTIFIABILITY IN TRANSFORMERS 8

ON IDENTIFIABILITY IN TRANSFORMERS

引用

8th International Conference on Learning Representations, ICLR 2020

作者： Brunner, Gino Liu, Yang Pascual, Damián Richter, Oliver Ciaramita, Massimiliano Wattenhofer, Roger Departments of Electrical Engineering and Information Technology Switzerland Computer Science ETH Zurich Switzerland Google Research Zurich Switzerland

In this paper we delve deep in the Transformer architecture by investigating two of its core components: self-attention and contextual embeddings. In particular, we study the identifiability of attention weights and token embeddings, and the aggregation of context into hidden tokens. We show that, for sequences longer than the attention head dimension, attention weights are not identifiable. We propose effective attention as a complementary tool for improving explanatory interpretations based on attention. Furthermore, we show that input tokens retain to a large degree their identity across the model. We also find evidence suggesting that identity information is mainly encoded in the angle of the embeddings and gradually decreases with depth. Finally, we demonstrate strong mixing of input information in the generation of contextual embeddings by means of a novel quantification method based on gradient attribution. Overall, we show that self-attention distributions are not directly interpretable and present tools to better understand and further investigate Transformer models. © 2020 8th International Conference on Learning Representations, ICLR 2020. All rights reserved.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

基于注意力机制的LSTM时变水声信道深度学习预测

引用

哈尔滨工程大学学报（英文版） 2023年第3期22卷 650-658页

作者：朱政亮童峰周跃海张梓峤张福民 National and Local Joint Engineering Research Center for Navigation and Location Service Technology Xiamen UniversityXiamen 361005China College of Ocean and Earth Sciences Xiamen UniversityXiamen 361005China School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlanta 30314USA Cheng Kar-Shun Robotics Institute Hong Kong University of Science and TechnologyHong Kong 00852China

This paper investigates the channel prediction algorithm of the time-varying channels in underwater acoustic(UWA)communication systems using the long short-term memory(LSTM)model with the attention *** is a deep learning model that combines an attention mechanism with LSTM-type models to capture temporal information with different scales from historical UWA *** attention mechanism is used to capture sparsity in the time-delay scales and coherence in the gep-time scale under the LSTM *** soft attention mechanism is introduced before the LSTM to support the model to focus on the features of input sequences and help improve the learning capacity of the proposed *** performance of the proposed model is validated using different simulation time-varying UWA *** with the adaptive channel predictors and the plain LSTM model,the proposed model is better in terms of channel prediction accuracy.

关键词： Long short-term memory(LSTM) Attention mechanism Underwater acoustic communication Underwater acoustic channel Channel prediction

来源：评论

学校读者我要写书评

暂无评论

ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution

arXiv

引用

arXiv 2023年

作者： Niu, Axi Trung, Pham Xuan Zhang, Kang Sun, Jinqiu Zhu, Yu Kweon, In So Zhang, Yanning School of Computer Science Northwestern Polytechnical University Xi’an710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710072 China School of Electrical Engineering Korea Advanced Institute of Science and Technology Korea Republic of School of Astronautics Northwestern Polytechnical University Xi’an710072 China

Diffusion models have gained significant popularity in the field of image-to-image translation. Previous efforts applying diffusion models to image super-resolution (SR) have demonstrated that iteratively refining pure Gaussian noise using a U-Net architecture trained on denoising at various noise levels can yield satisfactory high-resolution images from low-resolution inputs. However, this iterative refinement process comes with the drawback of low inference speed, which strongly limits its applications. To speed up inference and further enhance the performance, our research revisits diffusion models in image super-resolution and proposes a straightforward yet significant diffusion model-based super-resolution method called ACDMSR (accelerated conditional diffusion model for image super-resolution). Specifically, our method adapts the standard diffusion model to perform super-resolution through a deterministic iterative denoising process. Our study also highlights the effectiveness of using a pre-trained SR model to provide the conditional image of the given low-resolution (LR) image to achieve superior high-resolution results. We demonstrate that our method surpasses previous attempts in qualitative and quantitative results through extensive experiments conducted on benchmark datasets such as Set5, Set14, Urban100, BSD100, and Manga109. Moreover, our approach generates more visually realistic counterparts for low-resolution images, emphasizing its effectiveness in practical scenarios. © 2023, CC BY.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Estimating Total Treatment Effect in Randomized Experiments with Unknown Network Structure

arXiv

引用

arXiv 2022年

作者： Yu, Christina Lee Airoldi, Edoardo M. Borgs, Christian Chayes, Jennifer T. School of Operations Research and Information Engineering Cornell University IthacaNY United States Department of Statistics Operations and Data Science Fox School of Business Temple University PhiladelphiaPA United States Department of Electrical Engineering and Computer Science UC Berkeley BerkeleyCA United States Departments of Electrical Engineering and Computer Science Statistics Mathematics The School of Information UC Berkeley BerkeleyCA United States

Randomized experiments are widely used to estimate the causal effects of a proposed treatment in many areas of science, from medicine and healthcare to the physical and biological sciences, from the social sciences to engineering, to public policy and to the technology industry at large. Here, we consider situations where classical methods for estimating the total treatment effect on a target population are considerably biased due to confounding network effects, i.e., the fact that the treatment of an individual may impact their neighbors’ outcomes, an issue referred to as network interference or as non-individualized treatment response. A key challenge in these situations, is that the network is often unknown, and difficult, or costly, to measure. In this paper, we characterize the limitations in estimating the total treatment effect without knowledge of the network that drives interference, assuming a potential outcomes model with heterogeneous additive network effects. This model encompasses a broad class of network interference sources, including spillover, peer effects, and contagion. Within this framework, we show that, surprisingly, given access to average historical baseline measurements prior to the experiment, we can develop a simple estimator and efficient randomized design that outputs an unbiased estimate with low variance. Our solution does not require knowledge of the underlying network structure, and it comes with statistical guarantees for a broad class of models. We believe our results are poised to impact current randomized experimentation strategies due to its ease of interpretation and implementation, alongside its provable theoretical insights under heterogeneous network effects. Copyright © 2022, The Authors. All rights reserved.

关键词： Heterogeneous networks

来源：评论

学校读者我要写书评

暂无评论

AED: An anytime evolutionary DCOP algorithm 19

AED: An anytime evolutionary DCOP algorithm

引用

19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020

作者： Mahmud, Saaduddin Choudhury, Moumita Mosaddek Khan, Md. Tran-Thanh, Long Jennings, Nicholas R. Department of Computer Science and Engineering University of Dhaka Bangladesh School of Electronics and Computer Science University of Southampton United Kingdom Departments of Computing and Electrical and Electronic Engineering Imperial College London United Kingdom

ISBN: (纸本)9781450375184

Evolutionary optimization is a generic population-based metaheuristic that can be adapted to solve a wide variety of optimization problems and has proven very effective for combinatorial optimization problems. However, the potential of this metaheuristic has not been utilized in Distributed Constraint Optimization Problems (DCOPs), a well-known class of combinatorial optimization problems prevalent in Multi-Agent Systems. In this paper, we present a novel population-based algorithm, Anytime Evolutionary DCOP (AED), that uses evolutionary optimization to solve DCOPs. In AED, the agents cooperatively construct an initial set of random solutions and gradually improve them through a new mechanism that considers an optimistic approximation of local benefits. Moreover, we present a new anytime update mechanism for AED that identifies the best among a distributed set of candidate solutions and notifies all the agents when a new best is found. In our theoretical analysis, we prove that AED is anytime. Finally, we present empirical results indicating AED outperforms the state-of-the-art DCOP algorithms in terms of solution quality. © 2020 International Foundation for Autonomous.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：