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Best Practices and Lessons Learned for Teaching Concrete Materials and Reinforced Concrete 2021

作者： Carroll, J. Chris Behrouzi, Anahid A. Meyer, Karl F. The Department of Civil Computer and Electrical Engineering The School of Science and Engineering Saint Louis University United States California Polytechnic State University United States The Georgia Institute of Technology United States The Department of Civil and Mechanical Engineering The United States Military Academy United States

ISBN: (纸本)9781641952361

Nonrectangular cross sections are a common occurrence in reinforced concrete design typically taking the form of flanged beam sections, circular columns, and square or rectangular columns subject to biaxial bending. Instructors typically introduce the theory behind nonrectangular beams using two-dimensional sketches of flanged sections. Students can struggle to visualize the examples when presented in two dimensions;deciphering the multiple resultant compressive forces and their corresponding moment arms are particularly difficult. This paper presents an overview of nonrectangular beam theory and select active learning methods along with three specific examples used by the authors to teach nonrectangular beams in an undergraduate reinforced concrete design course. The first method is a simple problem-based learning example to dissuade students from "plug and chug" calculations using improper equations;the second method illustrates three cases for a traditional flanged T-beam section using physical three-dimensional models;and the third method uses virtual three-dimensional models to derive the depth of the equivalent stress block and corresponding nominal flexural strength for various cross sections. Each description provides the reader with the best practices to implement the respective technique. Lastly, the authors provide some lessons learned from their past implementations. The overall goal is to provide educators with examples to simplify the presentation of nonrectangular beams theory. © 2023 American Concrete institute. All rights reserved.

关键词： Learning systems

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Study of an Improved Flux Reversal Permanent Magnet Outer-Rotor Motor

Study of an Improved Flux Reversal Permanent Magnet Outer-Ro...

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Iranian Conference on electrical engineering (ICEE)

作者： Mohammad Reza Sarshar Mohammad Amin Jalali Kondelaji Mojtaba Mirsalim Department of Electrical Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology (MIT) Cambridge USA

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

This paper analyzes two proposed flux reversal permanent magnet (FRPM) motors. The main motor, an improved FRPM (I-FRPM), was tested under no-load and full-load conditions. I-FRPM performs a higher torque density and low torque ripple structure. Using finite element methods (FEM), all curves are illustrated and compared to these two structures under fair conditions. This I-FRPM can be used in electric vehicle applications because it suggests a high torque density with minimal ripple motor structure.

关键词： electrical engineering Torque Motors Harmonic analysis Electric vehicles Permanent magnets Finite element analysis

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AdaER: An Adaptive Experience Replay Approach for Continual Lifelong Learning

arXiv

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arXiv 2023年

作者： Li, Xingyu Tang, Bo Li, Haifeng The Department of Electrical and Computer Engineering Mississippi State University Mississippi StateMS39762 United States The Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States The School of Geosciences and fInfo-Physics Central South University Changsha410083 China

Continual lifelong learning is an machine learning framework inspired by human learning, where learners are trained to continuously acquire new knowledge in a sequential manner. However, the non-stationary nature of streaming training data poses a significant challenge known as catastrophic forgetting, which refers to the rapid forgetting of previously learned knowledge when new tasks are introduced. While some approaches, such as experience replay (ER), have been proposed to mitigate this issue, their performance remains limited, particularly in the class-incremental scenario which is considered natural and highly challenging. In this paper, we present a novel algorithm, called adaptive-experience replay (AdaER), to address the challenge of continual lifelong learning. AdaER consists of two stages: memory replay and memory update. In the memory replay stage, AdaER introduces a contextually-cued memory recall (C-CMR) strategy, which selectively replays memories that are most conflicting with the current input data in terms of both data and task. Additionally, AdaER incorporates an entropy-balanced reservoir sampling (E-BRS) strategy to enhance the performance of the memory buffer by maximizing information entropy. To evaluate the effectiveness of AdaER, we conduct experiments on established supervised continual lifelong learning benchmarks, specifically focusing on class-incremental learning scenarios. The results demonstrate that AdaER outperforms existing continual lifelong learning baselines, highlighting its efficacy in mitigating catastrophic forgetting and improving learning performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Study of a Modular Consequent-Pole Flux Reversal Motor with the Synergy of Biased-Flux Effect

Study of a Modular Consequent-Pole Flux Reversal Motor with ...

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electrical Machines and Drives (ICEMD), International Conference on

作者： Mohammad Reza Sarshar Mohammad Afrank Mohammad Amin Jalali Kondelaji Mojtaba Mirsalim Javad Shokrollahi Moghani Department of Electrical Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology (MIT) Cambridge USA

ISBN: (数字)9798331507510

ISBN: (纸本)9798331507527

This paper studies a novel flux reversal permanent magnet (FRPM) motor in which PMs are positioned in consequent-pole and intermodular pattern. The proposed structure applies the synergies of flux reversal and biased flux effects to improve the torque density. The motor is optimized with multi-objective genetic algorithm (GA) to achieve a high torque with low torque ripple. Also, the FRPM is compared with the conventional intermodular PM motor (IMPM) to outline its superiorities. By means of finite element analysis (FEA), the motors are compared under the no-load and full-load conditions, and the results in terms of back-EMF and its harmonic distribution, air-gap flux density distributions, and full-load torque and ripple are analyzed. The FEA results shows that the proposed structure enhances the output torque by 40% compared to the IMPM while reducing the PM volume.

关键词： Torque Motors Harmonic analysis Permanent magnets Topology Finite element analysis Scattering parameters Torque measurement Optimization Genetic algorithms

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Analysis of a Toothed Consequent-Pole Flux Reversal Motor with Assisting Permanent Magnets

Analysis of a Toothed Consequent-Pole Flux Reversal Motor wi...

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electrical Machines and Drives (ICEMD), International Conference on

作者： Mohammad Reza Sarshar Jamshid Kavianpour Mohammad Amin Jalali Kondelaji Mojtaba Mirsalim Amir Khorsandi Department of Electrical Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology (MIT) Cambridge USA

ISBN: (数字)9798331507510

ISBN: (纸本)9798331507527

This article studies three new toothed consequent-pole flux-reversal permanent magnet (FRPM) motors, each with specific combined PM arrays. The PM arrays comprise the biased-flux (BF) and flux-reversal (FR) effects. The FR effect is added mainly to boost the output torque production, and applying a magnetic-equivalent circuit (MEC) shows that the BF effect lowers the saturation risk in the stator core. Also, using finite element analysis (FEA), the main proposed motor generates the sinusoidal back-EMF waveform with an RMS value of 11.3V. Additionally, the full-load results show that the proposed motor produces 1.76N.m torque with 3.5% ripple. Furthermore, the flux density distribution of motors shows the importance of the BF effect in overload performances and verifies the analytical MEC results.

关键词： Stator cores Torque Windings Production Motors Permanent magnets Finite element analysis Torque measurement Optimization Saturation magnetization

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A Hybrid Model and Learning-Based Force Estimation Framework for Surgical Robots

arXiv

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arXiv 2024年

作者： Yang, Hao Zhou, Haoying Fischer, Gregory S. Wu, Jie Ying The Department of Computer Science Vanderbilt University TN37212 United States The Department of Robotics Engineering Worcester Polytechnic Institute MA01609 United States

Haptic feedback to the surgeon during robotic surgery would enable safer and more immersive surgeries but estimating tissue interaction forces at the tips of robotically controlled surgical instruments has proven challenging. Few existing surgical robots can measure interaction forces directly and the additional sensor may limit the life of instruments. We present a hybrid model and learning-based framework for force estimation for the Patient Side Manipulators (PSM) of a da Vinci Research Kit (dVRK). The model-based component identifies the dynamic parameters of the robot and estimates free-space joint torque, while the learning-based component compensates for environmental factors, such as the additional torque caused by trocar interaction between the PSM instrument and the patient's body wall. We evaluate our method in an abdominal phantom and achieve an error in force estimation of under 10% normalized root-mean-squared error. We show that by using a model-based method to perform dynamics identification, we reduce reliance on the training data covering the entire workspace. Although originally developed for the dVRK, the proposed method is a generalizable framework for other compliant surgical robots. The code is available at https://***/vu-maple-lab/dvrk_force_estimation. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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Efficient Pretraining and Finetuning of Quantized LLMs with Low-Rank Structure

Efficient Pretraining and Finetuning of Quantized LLMs with ...

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International Conference on Distributed Computing Systems

作者： Xiao-Yang Liu Jie Zhang Guoxuan Wang Weiqin Tong Anwar Walid Department of Computer Science Rensselaer Polytechnic Institute Department of Electrical Engineering Columbia University School of Computer Engineering and Science Shanghai University Department of Computer Science Whiting School of Engineering Johns Hopkins University

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Large language models (LLMs) are computationally intensive. The computation workload and the memory footprint grow quadratically with the dimension (layer width). Most of LLMs' parameters come from the linear layers of the transformer structure and are highly redundant. These linear layers contribute more than 80% of the computation workload and 99% of the model size. To pretrain and finetune LLMs efficiently, there are three major challenges to address: 1) reducing redundancy of the linear layers; 2) reducing GPU memory footprint; 3) improving GPU utilization when using distributed training. Prior methods, such as LoRA and QLoRA, utilized low-rank structure and quantization to reduce the number of trainable parameters and model size, respectively. However, the resulting model still consumes a large amount of GPU memory. In this paper, we present high-performance GPU-based methods for both pretraining and finetuning quantized LLMs with low-rank structures. We replace a single linear layer in the transformer structure with two narrower linear layers, significantly reducing the number of parameters by several orders of magnitude. By quantizing the pretrained parameters into low precision (8-bit and 4-bit), the memory consumption of the resulting model is further reduced. Compared with existing LLMs, our methods achieve a speedup of 1.3x and a model compression ratio of 2.64 x for pretraining without accuracy drop. For finetuning, our methods achieve an average accuracy score increase of 6.3 and 24.0 in general tasks and financial tasks, respectively, and GPU memory consumption is reduced by 6.3x. The sizes of our models are smaller than 0.59 GB, allowing inference on a smartphone.

关键词： Training Accuracy Quantization (signal) Computational modeling Memory management Redundancy Graphics processing units

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AdaER: An Adaptive Experience Replay Approach for Continual Lifelong Learning

arXiv

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arXiv 2023年

作者： Li, Xingyu Tang, Bo Li, Haifeng Department of Electrical and Computer Engineering Mississippi State University Mississippi StateMS39762 United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States School of Geosciences and fInfo-Physics Central South University Changsha410083 China

关键词： Machine learning

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An Efficient Authenticated Elliptic Curve Cryptography Scheme for Multicore Wireless Sensor Networks

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IEEE Access 2023年 11卷 1306-1323页

作者： Oladipupo, Esau Taiwo Abikoye, Oluwakemi Christiana Imoize, Agbotiname Lucky Awotunde, Joseph Bamidele Chang, Ting-Yi Lee, Cheng-Chi Do, Dinh-Thuan The Federal Polytechnic Bida Department of Computer Science Bida912211 Nigeria University of Ilorin Faculty of Information and Communication Sciences Department of Computer Science Ilorin240003 Nigeria University of Lagos Faculty of Engineering Department of Electrical and Electronics Engineering Akoka Lagos100213 Nigeria Institute of Digital Communication Ruhr University Department of Electrical Engineering and Information Technology Bochum44801 Germany National Changhua University of Education Department of Industrial Education and Technology Changhua City500 Taiwan Fu Jen Catholic University Research and Development Center for Physical Education Health and Information Technology Department of Library and Information Science New Taipei City24205 Taiwan Asia University Department of Computer Science and Information Engineering Taichung City41354 Taiwan University of Mount Union School of Engineering AllianceOH44601 United States

The need to ensure the longevity of Wireless Sensor Networks (WSNs) and secure their communication has spurred various researchers to come up with various WSN models. Prime among the methods for extending the life span of WSNs is the clustering of Wireless Sensors (WS), which reduces the workload of WS and thereby reduces its power consumption. However, a drastic reduction in the power consumption of the sensors when multicore sensors are used in combination with sensors clustering has not been well explored. Therefore, this work proposes a WSN model that employs clustering of multicore WS. The existing Elliptic Curve Cryptographic (ECC) algorithm is optimized for parallel execution of the encryption/decryption processes and security against primitive attacks. The Elliptic Curve Diffie-Helman (ECDH) was used for the key exchange algorithm, and the Elliptic Curve Digital Signature Algorithm (ECDSA) was used to authenticate the communicating nodes. Security analysis of the model and comparative performance analysis with the existing ones were demonstrated. The security analysis results reveal that the proposed model meets the security requirements and resists various security attacks. Additionally, the projected model is scalable, energy-conservative, and supports data freshness. The results of comparative performance analysis show that the proposed WSN model can efficiently leverage multiprocessors and/or many cores for quicker execution and conserves power usage. © 2013 IEEE.

关键词： Electric power utilization

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When Zero-Trust Meets Federated Learning

When Zero-Trust Meets Federated Learning

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Xinran Wang, Dan Zhu, Yifei Chen, Weilong Chang, Zheng Han, Zhu The Hong Kong Polytechnic University Department of Computing Hong Kong Shanghai Jiao Tong University UM-SJTU Joint Institute Shanghai China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Jyväskylä Faculty of Information Technology Jyväskylä Finland University of Houston Department of Electrical and Computer Engineering HoustonTX United States Kyung Hee University Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350351255

Nowadays, Federated Learning (FL) has emerged as a promising and critical machine learning scheme to protect data privacy and reduce communication overhead. As the scale and connectivity expand in the FL system, enhancing the model's robustness against security threats from malicious clients grows ever more critical. An effective defensive solution involves selecting benign clients appropriately, thereby mitigating the vulnerability of the FL system to malicious attacks. However, clients exhibit varying behaviors over time, which complicates the task of accurately modeling their future trustworthiness. Moreover, blindly trusting clients with high trust values poses risks, given the potential for severe losses from betrayal. To tackle these problems, we propose a zero-trust policy in FL aimed at establishing continuous trust in each client while maintaining skepticism towards potential betrayal attacks. Specifically, we develop a Dirichlet-based trust evaluation technique to enable a comprehensive selection of trustworthy participants. This technique leverages the posterior distribution to estimate clients' trust values from their evolving behavior records over time. Then, we anticipate potential betrayal from a selected client and formulate a min-max optimization problem to minimize the worst-case betrayal loss, thereby boosting the system's betrayalaware robustness. Next, we convert this problem into a convex optimization problem and utilize the interior point method for resolution. We conduct extensive simulations to validate the efficacy of our proposed zero-trust policy in accurately assessing trust and enhancing the model's robustness to betrayal. © 2024 IEEE.

关键词： Federated learning

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