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Design of a Low-Cost Wireless Charging Station Based on the Robotic Vision System 20

Design of a Low-Cost Wireless Charging Station Based on the ...

20th IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2023

作者： Cheng, Wen-Yu Cheng, Wen-Chung Ni, Zhen Engeberg, Erik D. Zhong, Xiangnan Florida Atlantic University Department of Ocean and Mechanical Engineering Boca RatonFL33431 United States Florida Atlantic University Department of Electrical Engineering & Computer Science Boca RatonFL33431 United States

ISBN: (纸本)9798350331110

Physical training of autonomous robotic systems are often limited by battery capacity since the intelligent training cycles have to be halted for battery recharge. Many researchers have focused on various solutions of power management for autonomous robots. However, these solutions are usually custom-fitted for the specific applications, and would be difficult to be generalized to other autonomous robotic systems. In this paper, we propose to develop a low-cost wireless charging system based on the on-board vision/camera sensor. This design is capable of autonomous docking for the robot chassis without further external supervision. Specifically, we use the QR codes to label the charging station and establish the robot's camera vision system to recognize the dock. The robot will drive to the station automatically. The low-cost charging station is also designed for wireless charging of the robot. We present the specific designs of charging dock, wireless connection, and battery charging experiments explicitly. This low-cost wireless charging system provides advancements toward a fully autonomous physical training infrastructure for robotic navigation experiments. © 2023 IEEE.

关键词： 3D printing

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Calibrated multiple-output quantile regression with representation learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 923-970页

作者： Shai Feldman Stephen Bates Yaniv Romano Department of Computer Science Technion — Israel Institute of Technology Technion City Haifa Israel Departments of Electrical Engineering and Computer Science and of Statistics University of California Berkeley Berkeley CA Departments of Electrical and Computer Engineering and of Computer Science Technion — Israel Institute of Technology Technion City Haifa Israel

We develop a method to generate predictive regions that cover a multivariate response variable with a user-specified probability. Our work is composed of two components. First, we use a deep generative model to learn a representation of the response that has a unimodal distribution. Existing multiple-output quantile regression approaches are effective in such cases, so we apply them on the learned representation, and then transform the solution to the original space of the response. This process results in a flexible and informative region that can have an arbitrary shape, a property that existing methods lack. Second, we propose an extension of conformal prediction to the multivariate response setting that modifies any method to return sets with a pre-specified coverage level. The desired coverage is theoretically guaranteed in the finite-sample case for any distribution. Experiments conducted on both real and synthetic data show that our method constructs regions that are significantly smaller compared to existing techniques.

关键词： conformal prediction uncertainty quantification quantile regression multiple regression variational auto-encoder

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Adversarial Rap Lyric Generation 4

Adversarial Rap Lyric Generation

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4th International Conference on Natural Language Processing, ICNLP 2022

作者： Chuang, Yun-Yen Hsu, Hung-Min Chang, Ray-I Lee, Hung-Yi Nation Taiwan University Department of Electrical Engineering Taipei Taiwan University of Washington Department of Electrical Computer and Engineering Seattle United States Nation Taiwan University Department of Engineering Science and Ocean Engineering Taipei Taiwan

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

In this paper, we design a novel text generation method called RapGAN for RLG (Rap Lyric Generation). RapGAN proposes two new schemes, PRO (Phrase Roll-Out) and AREGS (Attention Reward at Every Generation Step), to achieve efficient and effective RLG. The conventional text generation methods need to complete the entire-generated sentence in GAN (Generative Adversarial Network) training. RapGAN uses PRO as selective attention to reward only meaningful phrases in every generation step. Due to the varying length of phrases segmented by PRO, we employ an attention-based neural network that can capture the local feature of phrases and the semantics of global sentences as our discriminator. Therefore, we propose AREGS that reward the alignment weights of the attention-based discriminator with feature matching. Our method can provide different weights on different words to improve RLG's diversity, originality and fluency. Experimental results s how that our RapGAN outperforms the state-of-the-art RLG methods on many essential metrics of text generation. We also show the human evaluation to illustrate the relations between human perception and typical NLP metrics in diversity, originality and fluency. Our proposed method can facilitate real-world applications in content open RLG dataset in this paper. © 2022 IEEE.

关键词： Generative adversarial networks

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Recent advances in microsystem approaches for mechanical characterization of soft biological tissues

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Microsystems & Nanoengineering 2022年第4期8卷 1-16页

作者： Enming Song Ya Huang Ningge Huang Yongfeng Mei Xinge Yu John A.Rogers Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of OptoelectronicsFudan UniversityShanghai 200433China International Institute of Intelligent Nanorobots and Nanosystems Fudan UniversityShanghai 200433China Department of Biomedical Engineering City University of Hong KongHong Kong 999077China Department of Materials Science Fudan UniversityShanghai 200433China Querrey Simpson Institute for Bioelectronics Department of Materials Science and EngineeringDepartments of Biomedical EngineeringNeurological SurgeryChemistryMechanical EngineeringElectrical Engineering and Computer ScienceNorthwestern UniversityEvanstonIL 60208USA

Microsystem technologies for evaluating the mechanical properties of soft biological tissues offer various capabilities relevant to medical research and clinical diagnosis of pathophysiologic *** progress includes(1)the development of tissue-compliant designs that provide minimally invasive interfaces to soft,dynamic biological surfaces and(2)improvements in options for assessments of elastic moduli at spatial scales from cellular resolution to macroscopic areas and across depths from superficial levels to deep *** review summarizes a collection of these technologies,with an emphasis on operational principles,fabrication methods,device designs,integration schemes,and measurement *** core content begins with a discussion of platforms ranging from penetrating filamentary probes and shape-conformal sheets to stretchable arrays of ultrasonic *** sections examine different techniques based on planar microelectromechanical system(MEMS)approaches for biocompatible interfaces to targets that span scales from individual cells to *** highlighted example includes miniature electromechanical devices that allow depth profiling of soft tissue biomechanics across a wide range of *** clinical utility of these technologies is in monitoring changes in tissue properties and in targeting/identifying diseased tissues with distinct variations in *** results suggest future opportunities in engineered systems for biomechanical sensing,spanning a broad scope of applications with relevance to many aspects of health care and biology research.

关键词： advances identifying utility

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Energy Efficient FPGA-Based Binary Transformer Accelerator for Edge Devices

Energy Efficient FPGA-Based Binary Transformer Accelerator f...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Congpeng Du Seok-Bum Ko Hao Zhang Faculty of Information Science and Engineering Ocean University of China China Department of Electrical and Computer Engineering University of Saskatchewan Canada

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Transformer-based large language models have gained much attention recently. Due to their superior performance, they are expected to take the place of conventional deep learning methods in many fields of applications, including edge computing. However, transformer models have even more amount of computations and parameters than convolutional neural networks which makes them challenging to be deployed at resource-constrained edge devices. To tackle this problem, in this paper, an efficient FPGA-based binary transformer accelerator is proposed. Within the proposed architecture, an energy efficient matrix multiplication decomposition method is proposed to reduce the amount of computation. Moreover, an efficient binarized Softmax computation method is also proposed to reduce the memory footprint during Softmax computation. The proposed architecture is implemented on Xilinx Zynq Untrascale+ device and implementation results show that the proposed matrix multiplication decomposition method can reduce up to 78% of computation at runtime. The proposed transformer accelerator can achieve improved throughput and energy efficiency compared to previous transformer accelerator designs.

关键词： Performance evaluation Energy consumption Runtime Memory management Transformers Throughput Energy efficiency

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Accelerating the Use of Classical Infrastructure to Implement Quantum Networks

Accelerating the Use of Classical Infrastructure to Implemen...

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Quantum 2.0, QUANTUM 2022

作者： Broberg, Robert Bassett, Lee Smith, Jonathan University of Pennsylvania Departments of Electrical & Systems Engineering Computer and Information Science 3401 Walnut Street PhiladelphiaPA United States

For emerging quantum networks to coexist with the classical internet, they must conform to existing specifications. We examine the architectural model of the internet, especially physical layer requirements, and we pr...

ISBN: (纸本)9781957171111

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Research on Rice Disease Identification Based on Improved ShuffleNet V2 Network 4

Research on Rice Disease Identification Based on Improved Sh...

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4th International Symposium on computer Technology and Information science, ISCTIS 2024

作者： Chen, Zhihuan Gu, Shenming Yu, Minghui Huang, Zhaoquan Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan310622 China Ningbo University School of Faculty of Electrical Engineering and Computer Science Ningbo315000 China Zhejiang Normal University College of Mathematics and Computer Science Jinhua321000 China

ISBN: (纸本)9798350354560

How to efficiently and accurately identify rice diseases plays a crucial role in rice planting and production. Deploying large convolutional neural networks in practical applications is challenging due to their complex model parameters, while lightweight networks often compromise accuracy to reduce model size. In this study, we introduce a lightweight and precise network called GM-ShuffleNet for rice disease recognition. Building upon the ShuffleNet V2 network, we integrated the Ghost module and MCA attention mechanism, adjusted the network structure and number of channels, and replaced the ReLU activation function with Mish. Through comparative experiments, the accuracy of GM-ShuffleNet algorithm proposed in this paper reaches 96.20%, which is superior to ShuffleNet V2 and other excellent lightweight networks. In addition, in terms of model complexity comparison, the number of model parameters and FLOPs decreased by 82.54% and 77.44% respectively compared with the original network. © 2024 IEEE.

关键词： Convolutional neural networks

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Quantum Fourier Transform Using Dynamic Circuits

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Physical Review Letters 2024年第15期133卷 150602页

作者： Elisa Bäumer Vinay Tripathi Alireza Seif Daniel Lidar Derek S. Wang IBM Quantum Department of Physics & Astronomy and Center for Quantum Information Science & Technology Departments of Electrical & Computer Engineering Chemistry Physics & Astronomy and Center for Quantum Information Science & Technology

In dynamic quantum circuits, classical information from midcircuit measurements is fed forward during circuit execution. This emerging capability of quantum computers confers numerous advantages that can enable more efficient and powerful protocols by drastically reducing the resource requirements for certain core algorithmic primitives. In particular, in the case of the n-qubit quantum Fourier transform followed immediately by measurement, the scaling of resource requirements is reduced from O(n2) two-qubit gates in an all-to-all connectivity in the standard unitary formulation to O(n) midcircuit measurements in its dynamic counterpart without any connectivity constraints. Here, we demonstrate the advantage of dynamic quantum circuits for the quantum Fourier transform on IBM’s superconducting quantum hardware with certified process fidelities of >50% on up to 16 qubits and >1% on up to 37 qubits, exceeding previous reports across all quantum computing platforms. These results are enabled by our contribution of an efficient method for certifying the process fidelity, as well as of a dynamical decoupling protocol for error suppression during midcircuit measurements and feed forward within a dynamic quantum circuit that we call “feed-forward-compensated dynamical decoupling.” Our results demonstrate the advantages of leveraging dynamic circuits in optimizing the compilation of quantum algorithms.

关键词： Measurement-based quantum computing Quantum algorithms Quantum benchmarking Quantum circuits

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Interpretable Fault Detection Approach with Deep Neural Networks to Industrial Applications

Interpretable Fault Detection Approach with Deep Neural Netw...

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2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023

作者： Kakavandi, Fatemeh Han, Peihua De Reus, Roger Larsen, Peter Gorm Zhang, Houxiang Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark Norwegian University of Science and Technology Department of Ocean Operations and Civil Engineering Alesund Norway Device Manufacturing Development Department 2540 Novo Nordisk A/S Hillerod Denmark

ISBN: (纸本)9798350347074

Different explainable techniques have been introduced to overcome the challenges in complex machine learning models, such as uncertainty and lack of interpretability in sensitive processes. This paper presents an interpretable deep-leaning-based fault detection approach for two separate but relatively sensitive use cases. The first use case includes a vessel engine that aims to replicate a real-life ferry crossing. Furthermore, the second use case is an industrial, medical device assembly line that mounts and engages different product components. In this approach, first, we investigate two deep-learning models that can classify the samples as normal and abnormal. Then different explainable algorithms are studied to explain the prediction outcome for both models. Furthermore, the quantitative and qualitative evaluations of these methods are also carried on. Ultimately the deep learning model with the best-performing explainable algorithm is chosen as the final interpretable fault detector. However, depending on the use case, diverse classifiers and explainable techniques should be selected. For example, for the fault detection of the medical device assembly, the DeepLiftShap algorithm is most aligned with the expert knowledge and therefore has higher qualitative results. On the other hand, the Occlusion algorithm has lower sensitivity, and therefore, higher quantitative results. Consequently, choosing the final explainable algorithm compromises the qualitative and quantitative performance of the method. © 2023 IEEE.

关键词： Fault detection

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A fast and slightly robust covariance estimator

arXiv

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arXiv 2025年

作者： Duchi, John Haque, Saminul Kuditipudi, Rohith Departments of Statistics Stanford University United States Departments of Electrical Engineering Stanford University United States Departments of Computer Science Stanford University United States

Let (formula presented) from a distribution P with mean zero and covariance Σ. Given a dataset X such that (formula presented), we are interested in finding an efficient estimator Σˆ that achieves (formula presented). We focus on the low contamination regime Ε = o(1/√d). In this regime, prior work required either Ω(d3/2) samples or runtime that is exponential in d. We present an algorithm that, for subgaussian data, has near-linear sample complexity n = Ω͂(d) and runtime (formula presented), where ω is the matrix multiplication exponent. We also show that this algorithm works for heavy-tailed data with near-linear sample complexity, but in a smaller regime of Ε. Concurrent to our work, Diakonikolas et al. [2024] give Sum-of-Squares estimators that achieve similar sample complexity but with large polynomial runtime. © 2025, CC BY.

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