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Power and Energy Systems engineering (CPESE), International Conference on

作者： Robert Alfie S. Peña Antonio Rafael Castro Erees Queen B. Macabebe Department of Electronics Computer and Communications Engineering Ateneo Energy Research Laboratory School of Science and Engineering Ateneo de Manila University Quezon City Philippines

The need for energy, especially energy from renewable sources, to be distributed and flexible has driven the development of microgrids in recent years. They have been a key technology in achieving the sustainable development goal for energy particularly in rural off-grid areas. However, safe and reliable operation of microgrids remains a challenge given the difficulty in maintaining the physical system due to the lack of technical know-how in the rural localities where they are installed. A monitoring system with power forecasting capability is proposed in this article to aid in the safe and reliable operation of microgrids. The monitoring system features galvanic isolation in both the ac and dc measurement modules protecting users and the monitoring system itself from the high currents of the microgrid power system. The measurements can then be fed into a machine learning algorithm to forecast the power output of the microgrid. Experimental data were presented to validate the measurements and forecasts of the monitoring system. Users can monitor and forecast the power output of a microgrid and conduct maintenance if significant deviations are observed between forecasts and measurements.

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Analysis and Design of An Improved Model Predictive Control for Single-Phase LC-Coupling Hybrid Active Power Filter 48

Analysis and Design of An Improved Model Predictive Control ...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Chan, Pak-Ian Sou, Wai-Kit Lam, Chi-Seng State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macau China Institute of Microelectronics University of Macau Macau China Department of Electrical and Computer Engineering University of Macau Macau China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

In this paper, an improved model predictive control (MPC) is proposed for a single-phase LC-coupling hybrid active power filter (LC-HAPF), which ensures the low steady-state error and fast transient response. Generally, the intrinsic discretized and predictive error of the traditional MPC for the LC-HAPF enlarges the steady-state error. To relax this problem, a signal correction technique is proposed, and therefore reduce the steady-state error. Then, the parameter design of the proposed improved MPC for the LC-HAPF is given based on the stability analysis. Finally, simulated results are provided to verify the effectiveness of the improved MPC compared with the traditional MPC. © 2022 IEEE.

关键词： Model predictive control

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An Efficient Sssp Algorithm on Time-Evolving Graphs with Prediction of Computation Results

SSRN

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SSRN 2023年

作者： Cheng, Yongli Huang, Chuanjie Jiang, Hong Xu, Xianghao Wang, Fang College of Computer and Data Science FuZhou University Fuzhou China Department of Computer Science Engineering University of Texas Arlington United States School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology China

Many applications need to execute Single-Source Shortest Paths (SSSP) algorithm on each snapshot of a time-evolving graph, leading to long waiting times experienced by the users of such applications. However, these applications are often time-sensitive, the delayed computation results can lead to the loss of best decision-making opportunities. To address this problem, in this paper we propose an efficient SSSP algorithm for time-evolving graphs, called V-Grouper. The main idea of V-Grouper is to avoid the redundant computations of the same vertex in different snapshots. Our experimental results over real-world time-evolving graphs show that, due to the high similarity of consecutive snapshots, the computation results of one vertex in neighboring snapshots are equal with a high probability. At the beginning of computation, V-Grouper first divides all the versions of a given vertex in different snapshots into vertex groups, where the computation result of each version is predicted based on the aforementioned insight of neighboring snapshots having equal results. The versions of the vertex in each group have the same predicted computation result. During the computation process for each vertex group, only one version needs to participate in computation, avoiding a large number of redundant computations. Experimental results show that V-Grouper is up to 64.31× faster than the state-of-the-art SSSP algorithm. © 2023, The Authors. All rights reserved.

关键词： Computational efficiency

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Differentiable Optimization Based Time-Varying Control Barrier Functions for Dynamic Obstacle Avoidance

arXiv

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arXiv 2023年

作者： Dai, Bolun Khorrambakht, Rooholla Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

Control barrier functions (CBFs) provide a simple yet effective way for safe control synthesis. Recently, work has been done using differentiable optimization (diffOpt) based methods to systematically construct CBFs for static obstacle avoidance tasks between geometric shapes. In this work, we extend the application of diffOpt CBFs to perform dynamic obstacle avoidance tasks. We show that by using the time-varying CBF (TVCBF) formulation, we can perform obstacle avoidance for dynamic geometric obstacles. Additionally, we show how to extend the TVCBF constraint to consider measurement noise and actuation limits. To demonstrate the efficacy of our proposed approach, we first compare its performance with a model predictive control based method and a circular CBF based method on a simulated dynamic obstacle avoidance task. Then, we demonstrate the performance of our proposed approach in experimental studies using a 7-degree-of-freedom Franka Research 3 robotic manipulator. © 2023, CC BY.

关键词： Model predictive control

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Nonparametric Sparse Online Learning of the Koopman Operator

arXiv

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arXiv 2024年

作者： Hou, Boya Sanjari, Sina Dahlin, Nathan Koppel, Alec Bose, Subhonmesh Carl R. Woese Institute for Genomic Biology University of Illinois Urbana-Champaign United States Department of Mathematics and Computer Science Royal Military College of Canada Canada Department of Electrical and Computer Engineering University at Albany SUNY United States Artificial Intelligence Research JP Morgan Chase & Co United States Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign United States

The Koopman operator provides a powerful framework for representing the dynamics of general nonlinear dynamical systems. Data-driven techniques to learn the Koopman operator typically assume that the chosen function space is closed under system dynamics. In this paper, we study the Koopman operator via its action on the reproducing kernel Hilbert space (RKHS), and explore the mis-specified scenario where the dynamics may escape the chosen function space. We relate the Koopman operator to the conditional mean embeddings (CME) operator and then present an operator stochastic approximation algorithm to learn the Koopman operator iteratively with control over the complexity of the representation. We provide both asymptotic and finite-time last-iterate guarantees of the online sparse learning algorithm with trajectory-based sampling with an analysis that is substantially more involved than that for finite-dimensional stochastic approximation. Numerical examples confirm the effectiveness of the proposed algorithm. © 2024, CC BY-NC-ND.

关键词： Dynamical systems

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When Sign Language Meets Semantic Communications 35

When Sign Language Meets Semantic Communications

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Kouvakis, Vasileios Trevlakis, Stylianos E. Boulogeorgos, Alexandros-Apostolos A. Tsiftsis, Theodoros Singh, Keshav Qi, Nan InnoCube P.C. Department of Research and Development 17th Noemvriou 79 Thessaloniki55535 Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece University of Thessaly Department of Informatics & Telecommunications Lamia35100 Greece Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing210016 China

ISBN: (纸本)9798350362244

This paper presents an American sign language (ASL) semantic communications scheme. The scheme consists of a semantic encoder that leverages a convolutional neural network to effectively utilize the ASL alphabet. The encoded information is transmitted with the 24-QAM quadrature amplitude modulation (QAM). Additionally, this paper introduces a dataset that involves the overlaying of red-green-blue landmarks and key-points onto the acquired images, thereby augmenting the depiction of hand posture. The quantification of the proposed system's training, testing, and communication performance is accomplished through numerical results, which serve to emphasize the attainable benefits and stimulate meaningful discussions. © 2024 IEEE.

关键词： Quadrature amplitude modulation

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CLASSIFICATION OF LUNG CANCER SUBTYPES ON CT IMAGES WITH SYNTHETIC PATHOLOGICAL PRIORS

arXiv

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arXiv 2023年

作者： Zhu, Wentao Jin, Yuan Ma, Gege Chen, Geng Egger, Jan Zhang, Shaoting Metaxas, Dimitris N. Research Center for Healthcare Data Science Zhejiang Lab Hangzhou311121 China School of Computer Science and Engineering Northwestern Polytechnical University Shaanxi Xi’an710072 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Shanghai Artificial Intelligence Laboratory Shanghai200120 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States

The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements. In this paper, we propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on computed tomography (CT) images. Inspired by studies stating that cross-scale associations exist in the image patterns between the same case’s CT images and its pathological images, we innovatively developed a pathological feature synthetic module (PFSM), which quantitatively maps cross-modality associations through deep neural networks, to derive the "gold standard" information contained in the corresponding pathological images from CT images. Additionally, we designed a radiological feature extraction module (RFEM) to directly acquire CT image information and integrated it with the pathological priors under an effective feature fusion framework, enabling the entire classification model to generate more indicative and specific pathologically related features and eventually output more accurate predictions. The superiority of the proposed model lies in its ability to self-generate hybrid features that contain multi-modality image information based on a single-modality input. To evaluate the effectiveness, adaptability, and generalization ability of our model, we performed extensive experiments on a large-scale multi-center dataset (i.e., 829 cases from three hospitals) to compare our model and a series of state-of-the-art (SOTA) classification models. The experimental results demonstrated the superiority of our model for lung cancer subtypes classification with significant accuracy improvements in terms of accuracy (ACC), area under the curve (AUC), and F1 score. © 2023, CC0.

关键词： Classification (of information)

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Deep Space Network Scheduling via Mixed-Integer Linear Programming

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IEEE Access 2021年 9卷 39985-39994页

作者： Sabol, Alex Alimo, Ryan Kamangar, Farhad Madani, Ramtin Computer Science and Engineering Department The University of Texas at Arlington ArlingtonTX United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Electrical Engineering Department The University of Texas at Arlington ArlingtonTX United States

NASA's Deep Space Network (DSN) is a globally-spanning communications network responsible for supporting the interplanetary spacecraft missions of NASA and other international users. The DSN is a highly utilized asset, and the large demand for its' services makes the assignment of DSN resources a daunting computational problem. In this paper we study the DSN scheduling problem, which is the problem of assigning the DSN's limited resources to its users within a given time horizon. The DSN scheduling problem is oversubscribed, meaning that only a subset of the activities can be scheduled, and network operators must decide which activities to exclude from the schedule. We first formulate this challenging scheduling task as a Mixed-Integer Linear Programming (MILP) optimization problem. Next, we develop a sequential algorithm which solves the resulting MILP formulation to produce valid schedules for large-scale instances of the DSN scheduling problem. We use real world DSN data from week 44 of 2016 in order to evaluate our algorithm's performance. We find that given a fixed run time, our algorithm outperforms a simple implementation of our MILP model, generating a feasible schedule in which 17% more activities are scheduled by the algorithm than by the simple implementation. We design a non-MILP based heuristic to further validate our results. We find that our algorithm also outperforms this heuristic, scheduling 8% more activities and 20% more tracking time than the best results achieved by the non-MILP implementation. © 2013 IEEE.

关键词： Scheduling

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Dual Illumination Image Enhancement Using Automated MSRCR and Illumination Estimation

Dual Illumination Image Enhancement Using Automated MSRCR an...

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IEEE International Conference on Artificial Intelligence in engineering and Technology (IICAIET)

作者： Allysa Kate Brillantes Edwin Sybingco Argel Bandala Robert Kerwin Billones Alexis Fillone Elmer Dadios Department of Electronics and Computer Engineering Manila Philippines Tntelligent Systems Laboratory Malate Manila Philippines Department of Manufacturing Engineering and Management Malate Manila Philippines Department of Civil Engineering Malate Manila Philippines

Images taken in low-light environments like nighttime or cloudy weather are not conducive to monitoring and fail to fulfill the requirements of applications such as image processing and computer vision. This paper focuses on improving a dual illumination image enhancement algorithm to achieve better image quality and reduce the processing time of producing an enhanced image. The underexposed correction image was produced using Automated Multiscale Retinex Color Restoration while the overexposed correction image was produced using illumination estimation, the corrected images were combined to generate a better-quality image. The system was assessed using the metrics: BRISQUE, NIQE, MDM, and CEIQ, and achieved a BRISQUE value of 18.628, NIQE value of 1.744, MDM value of 0.968, and CEIQ value of 3.615.

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ULTRA FLEXIBLE MACROPOROUS MICRONEEDLES FOR SUSTAINED DELIVERY OF (2R,6R)-HYDROXYNORKETAMINE

ULTRA FLEXIBLE MACROPOROUS MICRONEEDLES FOR SUSTAINED DELIVE...

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2022 Solid-State Sensors, Actuators and Microsystems Workshop, Hilton Head 2022

作者： Das, Riddha Sadeqi, Aydin Browne, Caroline A. Lucki, Irwin Sonkusale, Sameer Department of Electrical and Computer Engineering Tufts University United States Nano Lab. Advanced Technology Laboratory Tufts University United States Uniformed Services University of Health Sciences United States

ISBN: (纸本)9781940470047

Transdermal delivery using microneedles provides an alternative method to locally administer drug molecules with controlled release rate. In this study, our goal was to develop an ultra-flexible macro porous patch with hard microneedles that penetrate the skin. We demonstrated encapsulation of a non-hallucinogenic derivative of a dissociative anesthetic pharmaceutical, which is approved as a therapy for treatment resistant depression. The microneedles are made from biocompatible photo-curable resin. Solid formulation of this drug is encapsulated in the resin, which ensures high drug loading capacity. A flexible substrate is used as the backing to ensure skin conformability. The encapsulated drug molecules are released by diffusion in contact with interstitial fluid. Our findings demonstrate uniform encapsulation of the drug molecules into the resin and sufficient robustness of the microneedles to withstand the skin penetration forces. © 2022 TRF.

关键词： Actuators

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