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arXiv 2024年

作者： Iguchi, Yuga Jasra, Ajay Maama, Mohamed Beskos, Alexandros Department of Statistical Science University College London LondonWC1E 6BT United Kingdom School of Data Science The Chinese University of Hong Kong Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we present a new antithetic multilevel Monte Carlo (MLMC) method for the estimation of expectations with respect to laws of diffusion processes that can be elliptic or hypo-elliptic. In particular, we consider the case where one has to resort to time discretization of the diffusion and numerical simulation of such schemes. Motivated by recent developments, we introduce a new MLMC estimator of expectations, which does not require simulation of intractable Lévy areas but has a weak error of order 2 and achieves the optimal computational complexity. We then show how this approach can be used in the context of the filtering problem associated to partially observed diffusions with discrete time observations. We illustrate with numerical simulations that our new approaches provide efficiency gains for several problems relative to some existing methods. © 2024, CC BY.

关键词： Diffusion

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A weighted multivariate fuzzy time series method for multiple output high-dimensional time series forecasting in IoT applications

A weighted multivariate fuzzy time series method for multipl...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Hugo Vinicius Bitencourt Patrícia de Oliveira Lucas Omid Orang Petrônio Silva Frederico Gadelha Guimarães Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Instituto Federal do Norte de Minas Gerais Salinas MG Brazil Department of Computer Science Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Instituto Federal do Norte de Minas Gerais Januária MG Brazil

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

In Internet of Things (IoT) applications, data flows are continuous streams of high-dimensional time series that aggregate various data sources. In this context, decision-making processes frequently encompass multiple factors and criteria that demand forecasting these time series. This paper introduces MO-WMVFTS, a novel multiple-input multiple-output (MIMO) fuzzy time series (FTS) method for tackling this complex scenario. MO-WMVFTS is a hybrid forecasting method that fuses weighted multivariate FTS (WMVFTS) with embedding transformations, designed for IoT applications. To assess the performance of the proposed method, it was applied to the prediction of energy consumption in smart homes and air quality in smart cities. Here, three real-world datasets are used to assess the validity of our proposed approach, and the results obtained are highly competitive when compared to other existing methods.

关键词： Smart buildings Smart cities Soft sensors Time series analysis Smart homes Predictive models Internet of Things Forecasting Streams MIMO communication

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Quality Improvements in the electrical Design of Typical DC-DC Electronic Power Converter for High-Voltage Applications

Quality Improvements in the Electrical Design of Typical DC-...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： King Harold A. Recto Miguel T. Escoto Noel B. Hernandez Department of Electronics Computer and Communications Engineering Ateneo de Manila University Quezon City Philippines Electrical and Electronics Engineering Institute University of the Philippines Diliman Quezon City Philippines Post-doctoral Program in Quality Management of the Graduate School Centro Escolar University Manila Philippines

ISBN: (纸本)9781665491075

The green transition has brought about a worldwide-shift to the use of renewables as alternative energy sources. Because of this, high voltage DC has been a field of interest in power electronics due to its capability in efficiently transmitting power to reach remote areas. Typical DC-DC electronic power converter design has the capability of boosting voltage level to a certain extent but not enough for high-voltage applications. Using the DMAIC Approach of the Six Sigma, quality improvements in the design was proposed to stabilize operating frequency, bring up the output voltage from 100 VDC to 4,000 VDC and provide at least 10% margin of error in the apparent power measured at the output side to offset inherent losses in the electrical design. These proposed quality improvements were sustained by designing a control circuit that would ensure the attainment of the parameters set in the Define Phase of the DMAIC Approach. The result of the study is a practical 12 VDC to 4,000 VDC boost converter design that has incorporated all the proposed quality improvements.

关键词： Renewable energy sources Voltage measurement Power measurement Measurement uncertainty Green products High-voltage techniques Loss measurement

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Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects

arXiv

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arXiv 2022年

作者： Ghassami, AmirEmad Yang, Alan Richardson, David Shpitser, Ilya Tchetgen, Eric Tchetgen Department of Computer Science Johns Hopkins University United States Department of Electrical Engineering Stanford University United States Department of Environmental and Occupational Health Program in Public Health University of California Irvine United States Department of Statistics and Data Science The Wharton School University of Pennsylvania United States

We consider the task of identifying and estimating the causal effect of a treatment variable on a long-term outcome variable using data from an observational domain and an experimental domain. The observational domain is subject to unobserved confounding. Furthermore, subjects in the experiment are only followed for a short period of time;hence, long-term effects of treatment are unobserved but short-term effects will be observed. Therefore, data from neither domain alone suffices for causal inference about the effect of the treatment on the long-term outcome, and must be pooled in a principled way, instead. Athey et al. (2020) proposed a method for systematically combining such data for identifying the downstream causal effect in view. Their approach is based on the assumptions of internal and external validity of the experimental data, and an extra novel assumption called latent unconfoundedness. In this paper, we first review their proposed approach, and then we propose three alternative approaches for data fusion for the purpose of identifying and estimating average treatment effect as well as the effect of treatment on the treated. Our first approach is based on assuming equi-confounding bias for the short-term and long-term outcomes. Our second approach is based on a relaxed version of the equi-confounding bias assumption, where we assume the existence of an observed confounder such that the short-term and long-term potential outcome variables have the same partial additive association with that confounder. Our third approach is based on the proximal causal inference framework, in which we assume the existence of an extra variable in the system which is a proxy of the latent confounder of the treatment-outcome relation. We propose influence function-based estimation strategies for each of our data fusion frameworks and study the robustness properties of the proposed estimators. © 2022, CC BY.

关键词： Data fusion

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Bayesian quantum phase estimation with fixed photon states

arXiv

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arXiv 2023年

作者： Zhou, Boyu Guha, Saikat Gagatsos, Christos N. Department of Physics The University of Arizona 1118 E 4th St TucsonAZ85721 United States Department of Electrical and Computer Engineering University of Maryland 2410 A.V. Williams Building College ParkMD20742 United States Wyant College of Optical Sciences The University of Arizona 1630 E University Blvd TucsonAZ85721 United States Department of Electrical and Computer Engineering The University of Arizona 1230 E Speedway Blvd TucsonAZ85721 United States Program in Applied Mathematics The University of Arizona 617 N. Santa Rita TucsonAZ85721 United States

We consider a two-mode bosonic state with fixed photon number n ∈ N, whose upper and lower modes pick up a phase and − respectively. We compute the optimal Fock coefficients of the input state, such that the mean square error (MSE) for estimating is minimized while the minimum MSE is always attainable by a measurement. Our setting is Bayesian, i.e., we consider to be a random variable that follows a prior probability distribution function (PDF). Initially, we consider the flat prior PDF and we discuss the well-known fact that the MSE is not an informative tool for estimating a phase when the variance of the prior PDF is large. Therefore, we move on to study truncated versions of the flat prior in both single-shot and adaptive approaches. For our adaptive technique we consider n = 1 and truncated prior PDFs. Each subsequent step utilizes as prior PDF the posterior probability of the previous step and at the same time we update the optimal state and optimal measurement. Copyright © 2023, The Authors. All rights reserved.

关键词： Distribution functions

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Metamaterial assisted speckle illumination nanoscopy for high-resolution biological imaging

Metamaterial assisted speckle illumination nanoscopy for hig...

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Novel Techniques in Microscopy, NTM 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021

作者： Zhao, Junxiang Lee, Yeon Ui Ma, Qian Khorashad, Larousse Khosravi Posner, Clara Li, Guangru Wisna, Bimananda G.M. Burns, Zachary Zhang, Jin Liu, Zhaowei Department of Electrical and Computer Engineering University of California San Diego 9500 Gilman Drive San diegoCA92093 United States Department of Pharmacology University of California San Diego 9500 Gilman Drive San diegoCA92093 United States Material Science and Engineering Program University of California San Diego 9500 Gilman Drive San diegoCA92093 United States

We developed a wide field of view super resolution microscopy technology, i.e. speckle metamaterial-assisted illumination nanoscopy (speckle-MAIN), showing biological imaging with resolution down to 40nm. © 2021 ... 详细信息

ISBN: (纸本)9781557528209

关键词： Metamaterials

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GNN-Based Point Cloud Maps Feature Extraction and Residual Feature Fusion for 3D Object Detection

GNN-Based Point Cloud Maps Feature Extraction and Residual F...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wei-Hsiang Liao Chieh-Chih Wang Wen-Chieh Lin Graduate Degree Program of Artificial Intelligence National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Institute of Multimedia Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

LiDAR detection of long-range vehicles is challenging because very few and sparse points are measured in long distances and vehicles with similar shapes of targets could lead to false positives easily. To tackle these challenges, taking the environment information (HD maps) into account could be beneficial to predetermine where targets are more or less likely to appear. Compared with semantic maps, HD maps formed by point clouds provide much richer information from surrounding static objects and scenes. In this work, we construct a GNN-based feature extraction of point cloud maps to increase the receptive fields of learning map features. Our work is based on PVRCNN, the state-of-the-art LiDAR object detection method. With point-wise and voxel-wise features obtained from PVRCNN, residual feature fusion is proposed to fuse the features from PVRCNN and the map features from GNN. Our approach is evaluated on NuScenes dataset. It achieves a 24.78% average precision improvement for long-range objects at 40–50 meters, the farthest areas with ground truth annotation. Our approach also has a 4.22% reduction of false positives in the entire sensing areas.

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Nonlocal Scatterer for Compact Wave-Based Analog Computing

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Physical Review Letters 2022年第7期128卷 073201-073201页

作者： Heedong Goh Andrea Alù Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10016 USA

Analog computing based on wave interactions with metamaterials has been raising significant interest as a low-energy, ultrafast platform to process large amounts of data. Engineered materials can be tailored to impart mathematical operations of choice on the spatial distribution of the impinging signals, but they also require extended footprints and precise large-area fabrication, which may hinder their practical applicability. Here we show that the nonlocal response of a compact scatterer can be engineered to impart operations of choice on arbitrary impinging waves, and even to solve integro-differential equations, whose solution is observed in the scattered fields. The lack of strongly resonant phenomena makes the response robust, and the compact nature opens to scalability and cascading of these processes, paving the way to efficient, compact analog computers based on engineered microstructures.

关键词： Light propagation, transmission & absorption Mie scattering Scattering theory

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Development of Support Vector Machine Method to Forecast Dengue-Endemic Areas

Development of Support Vector Machine Method to Forecast Den...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Hetty Meileni Ermatita Abdiansah Nyayu Latifah Husni Desmawati Doctoral Program in Engineering Science Universitas Sriwijaya (Politeknik Negeri Sriwijaya) Palembang Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Artificial Intelligence Research Development (AIRD) Universitas Sriwijaya Palembang Indonesia Department of Electrical Engineering Politeknik Negeri Sriwijaya Palembang Palembang Indonesia Faculty of Health Science Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia

The cases of dengue hemorrhagic fever (DHF) in Indonesia have increased significantly since 2020. Data shown by the Central Statistics Agency, for example, in South Sumatra Province, there were 6,348 cases of DHF during that period. To suppress the case number of DHF, forecasting areas that are susceptible to this disease is urgently needed. This research aims to develop a machine learning method Support Vector Machine (SVM). The data used include area data, population density data, weather data, and dengue fever case data. The development of the SVM model is carried out using a machine learning method that combines regional data, population density data, weather data, and dengue case data. In the end, using the SVM method can be used to forecast dengue-endemic areas, so that the government and society can take further action to reduce the negative impact of DHF.

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Loss tangent fluctuations due to two-level systems in superconducting microwave resonators

arXiv

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arXiv 2024年

作者： Vallières, André Russell, Megan E. You, Xinyuan Garcia-Wetten, David A. Goronzy, Dominic P. Walker, Mitchell J. Bedzyk, Michael J. Hersam, Mark C. Romanenko, Alexander Lu, Yao Grassellino, Anna Koch, Jens McRae, Corey Rae H. Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Center for Applied Physics and Superconducting Technologies Northwestern University EvanstonIL60208 United States Electrical Computing and Energy Engineering Department University of Colorado BoulderCO80309 United States Department of Physics University of Colorado BoulderCO80309 United States National Institute of Standards and Technology BoulderCO80305 United States

Superconducting microwave resonators are critical to quantum computing and sensing technologies. Additionally, they are common proxies for superconducting qubits when determining the effects of performance-limiting loss mechanisms such as from two-level systems (TLS). The extraction of these loss mechanisms is often performed by measuring the internal quality factor Qi as a function of power or temperature. In this work, we investigate large temporal fluctuations of Qi at low powers over periods of 12 to 16 hours (relative standard deviation σQi/Qi = 13%). These fluctuations are ubiquitous across multiple resonators, chips and cooldowns. We are able to attribute these fluctuations to variations in the TLS loss tangent due to two main indicators. First, measured fluctuations decrease as power and temperature increase. Second, for interleaved measurements, we observe correlations between low- and medium-power Qi fluctuations and an absence of correlations with high-power fluctuations. Agreement with the TLS loss tangent mean is obtained by performing measurements over a time span of a few hours. We hypothesize that, in addition to decoherence due to coupling to individual near-resonant TLS, superconducting qubits are affected by these observed TLS loss tangent fluctuations. © 2024, CC BY.

关键词： Qubits

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