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OptoElectronics and Communications Conference, OECC

作者： Zhenzhao Yi Jianghai Wo Jiejun Zhang Jianping Yao Institute of Photonics Technology Jinan University Guangzhou China Microwave Photonics Research Laboratory School of Electrical Engineering and Computer Science University of Ottawa Ottawa ON Canada

A time-duration tunable Fourier-domain mode-locked optoelectronic oscillator (FDML-OEO) based on a frequency-shifting loop (FSL) is proposed and experimentally demonstrated. Experimental results show that a linear fre...

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Analyzing the Impact of External Interventions on Water and Energy Demand: Cybersecurity’s Role in Sustainable Development

Analyzing the Impact of External Interventions on Water and ...

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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Daryoush Tavangar Rizi Mohammad Hassan Nazari Ali Maleki Seyed Hossein Hosseinian Maryam Fani G. B. Gharehpetian Department of Physics & Energy Engineering Amirkabir University of Technology Tehran Iran Smart Control Systems Department Niroo Research Institute (NRI) Tehran Iran School of Electrical Engineering & Computer Science Washington State University Pullman Washington Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798350361322

ISBN: (纸本)9798350361339

The nexus of water, food, and energy constitutes a fundamental aspect of sustainable development. Furthermore, the demand for water and energy is becoming more pronounced. Moreover, there is a growing importance placed on studying cyber security within the water-energy nexus. This research investigates the interdependence of these two systems, conceptualizing them as a water-energy nexus and effort has been conducted on optimizing the economic efficiency of electricity and water systems in order to minimize operating expenses. Subsequently, external interventions, such as the simultaneous injection of false data on both water and energy demand, were addressed through an optimization process in GAMS software. The results suggest that the value of the cost objective function remains constant when inaccurate data is introduced, which hinders the accurate estimation of the system by the system operator. in this system, both water and power loads are directly impacted by these interventions. In particular, the power and water load of the system have exhibited changes ranging from 1.5% to 17.99% for electric load and 3.87% to 17.1% for water load, in comparison to the previous state.

关键词： Meters Renewable energy sources Electricity Distance measurement Software computer crime Sustainable development

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Impact of different cable types and positions on CISPR 15 radiated disturbance measurement

Impact of different cable types and positions on CISPR 15 ra...

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IEEE International Symposium on Electromagnetic Compatibility (EMC)

作者： Pedro Machado Neto Cesar Pagan Gustavo Morais Elinaldo Reis Fernando Araujo School of Electrical and Computer Engineering (FEEC) University of Campinas (UNICAMP) Campinas (SP) Brazil Laboratory of Tests (LET) Eldorado Research Institute Campinas (SP) Brazil

This paper deals with an empirical evaluation of variables that may impact radiated emission measurement – mainly for CISPR 15 lamp tests. Edition 9 of CISPR 15 brought modifications that improve reproducibility, the most notable of which are the obligation of using a coupling/decoupling network (CDN) and the reference to the CISPR 16-2-3. The practical tests in this study showed that both the use of CDN and the use of one single cable placement have an impact of up to 74% and 59%, respectively, on the results. Other possible improvements were evaluated. The use of a pre-amplifier and the turntable rotation speed had no impact at all as long as CISPR 16-2-3:2016 setup is used. Tests with cables of different section areas, even when applying the same positioning pattern, resulted in relevant differences (up to 7.26 dB and 6.92 dB at 34 MHz and 200 MHz, respectively). Further studies are currently underway to clarify how the cable acts like an antenna and how to properly consider it in future CISPR 15 versions. The results indicate that, for better reproducibility, CISPR 15 edition 9 must be adopted instead of older editions, although improvements can still be achieved by considering the impact of different cables.

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LRMP: Layer Replication with Mixed Precision for Spatial In-memory DNN Accelerators

arXiv

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arXiv 2023年

作者： Nallathambi, Abinand Bose, Christin David Haensch, Wilfried Raghunathan, Anand School of Electrical and Computer Engineering Purdue University West LafayetteIN United States Material Science Division Argonne National Laboratory ArgonneIL United States

In-memory computing (IMC) with non-volatile memories (NVMs) has emerged as a promising approach to address the rapidly growing computational demands of Deep Neural Networks (DNNs). Mapping DNN layers spatially onto NVM-based IMC accelerators achieves high degrees of parallelism. However, two challenges that arise in this approach are the highly non-uniform distribution of layer processing times and high area requirements. We propose LRMP, a method to jointly apply layer replication and mixed precision quantization to improve the performance of DNNs when mapped to area-constrained NVM-based IMC accelerators. LRMP uses a combination of reinforcement learning and integer linear programming to search the replication-quantization design space using a model that is closely informed by the target hardware architecture. Across five DNN benchmarks, LRMP achieves 2.8-9× latency and 11.8-19× throughput improvement at iso-accuracy. © 2023, CC BY.

关键词： Deep neural networks

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Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection

Binary Bleed: Fast Distributed and Parallel Method for Autom...

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Ryan Barron Maksim E. Eren Manish Bhattarai Ismael Boureima Cynthia Matuszek Boian S. Alexandrov Theoretical Division Los Alamos National Laboratory Los Alamos USA Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Maryland USA Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos USA

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

In several Machine Learning (ML) clustering and dimensionality reduction approaches, such as nonnegative matrix factorization (NMF), RESCAL, and K-Means clustering, users must select a hyper-parameter $k$ to define the number of clusters or components that yield an ideal separation of samples or clean clusters. This selection, while difficult, is crucial to avoid overfitting or underfitting the data. Several ML applications use scoring methods (e.g., Silhouette and Davies Boulding scores) to evaluate the cluster pattern stability for a specific $k$ . The score is calculated for different trials over a range of $k$ , and the ideal $k$ is heuristically selected as the value before the model starts overfitting, indicated by a drop or increase in the score resembling an elbow curve plot. While the grid-search method can be used to accurately find a good $k$ value, visiting a range of $k$ can become time-consuming and computationally resource-intensive. In this paper, we introduce the Binary Bleed method based on binary search, which significantly reduces the $k$ search space for these grid-search ML algorithms by truncating the target $k$ values from the search space using a heuristic with thresholding over the scores. Binary Bleed is designed to work with single-node serial, single-node multi-processing, and distributed computing resources. In our experiments, we demonstrate the reduced search space gain over a naive sequential search of the ideal $k$ and the accuracy of the Binary Bleed in identifying the correct $k$ for NMFk, K-Means pyDNMFk, and pyDRESCALk with Silhouette and Davies Boulding scores. We make our implementation of Binary Bleed for the NMF algorithm available on GitHub.

关键词： Instruction sets Computational modeling Heuristic algorithms Power system stability Stability analysis Elbow Distributed computing Optimization Overfitting Software development management

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Unsupervised Recognition of Unknown Objects for Open-World Object Detection

arXiv

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arXiv 2023年

作者： Fang, Ruohuan Pang, Guansong Zhou, Lei Bai, Xiao Zheng, Jin School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University China School of Computing and Information Systems Singapore Management University Singapore School of Computer Science and Artificial Intelligence Wuhan University of Technology China

Open-World Object Detection (OWOD) extends object detection problem to a realistic and dynamic scenario, where a detection model is required to be capable of detecting both known and unknown objects and incrementally learning newly introduced knowledge. Current OWOD models, such as ORE and OW-DETR, focus on pseudo-labeling regions with high objectness scores as unknowns, whose performance relies heavily on the supervision of known objects. While they can detect the unknowns that exhibit similar features to the known objects, they suffer from a severe label bias problem that they tend to detect all regions (including unknown object regions) that are dissimilar to the known objects as part of the background. To eliminate the label bias, this paper proposes a novel approach that learns an unsupervised discriminative model to recognize true unknown objects from raw pseudo labels generated by unsupervised region proposal methods. The resulting model can be further refined by a classification-free self-training method which iteratively extends pseudo unknown objects to the unlabeled regions. Experimental results show that our method 1) significantly outperforms the prior SOTA in detecting unknown objects while maintaining competitive performance of detecting known object classes on the MS COCO dataset, and 2) achieves better generalization ability on the LVIS and Objects365 datasets. Code is available at https://***/frh23333/mepu-owod. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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A Distributed Magnetostatic Resonator

arXiv

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arXiv 2024年

作者： Devitt, Connor Tiwari, Sudhanshu Bhave, Sunil A. Wang, Renyuan The OxideMEMS Lab Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States FAST Labs BAE Systems Inc. NashuaNH03060 United States

This work reports the design, fabrication, and characterization of coupling-enhanced magnetostatic forward volume wave resonators with significant spur suppression. The fabrication is based on surface micro-machining of yttrium iron garnet (YIG) film on a gadolinium gallium garnet (GGG) substrate with thick gold transducers. A distributed resonator is used to excite forward volume waves in YIG to realize a frequency dependent coupling boost. Fabricated devices at 18 GHz and 7 GHz show coupling coefficients as high as 13% and quality factors above 1000. Higher-order magnetostatic mode suppression is experimentally demonstrated through a combination of transducer and YIG geometry design. An edge-coupling filter topology is proposed and simulated which utilizes this novel distributed magnetostatic resonator. © 2024, CC BY.

关键词： Resonators

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The $R_{\text{ON}}-V_{\text{BK}}$ Relationship in $\beta$-Ga2O3 Lateral MESFETs Determined Using Physics-Based TCAD Simulation

The $R_{\text{ON}}-V_{\text{BK}}$ Relationship in $\beta$-Ga...

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Device Research Conference

作者： S. Ahmed A. Islam D. Dryden K. Liddy N. Hendricks N. Moser K. Chabak A. Green School of Electrical Computer and Biomedical Engineering Southern Illinois University Carbondale IL USA Air Force Research Laboratory Sensors Directorate Dayton OH USA

$\beta$ -Ga 2 O 3 transistor promises a large breakdown voltage ( $V_{\text{BK}}$ ) at a particular on-resistance ( $R_{\text{ON}}$ ) that can result in large power figure-of-merit ( $\text{PFoM}=V_{BK}{}^{2}/R_{ON, sp}$ ; where $R_{ON, sp}$ is the area normalized specific $R_{\text{ON}}$ ) for the device [1]. Theoretically calculated PFoM [2] is valid only for vertical transistors, however, is routinely used to compare with the measured PFoM in both lateral and vertical transistors. In particular, transport in lateral transistors has smaller current flow cross-section and is significantly influenced by the 2-D electro-statistics, which is ignored during the measurement and theoretical comparison. In this paper, we present a physics-based TCAD model to determine the $R_{ON}-V_{BK}$ relationship in lateral MESFETs. The model uses atomistically defined carrier transport parameters and is benchmarked with I-V data measured in similar devices [3]. The model also uses a refined impact ionization model to simulate the intrinsic breakdown in devices, determines the theoretical limit for $R_{ON}-V_{BK}$ in lateral devices, and hence highlights the importance of considering two-dimensional electrostatics and extrinsic breakdown pathways for understanding the variation of $V_{BK}$ in lateral devices.

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Using Large Language Models to Mitigate Human-Induced Bias in SMS Spam: An Empirical Approach

Using Large Language Models to Mitigate Human-Induced Bias i...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Gerard Shu Fuhnwi Matthew Revelle Bradley Whitaker Clemente Izurieta Gianforte School of Computing Montana State University Bozeman MT USA Electrical & Computer Engineering Montana State University Bozeman MT USA Gianforte School of Computing Montana State University Pacific Northwest National Laboratory Idaho National Laboratory Bozeman MT USA

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

Short Message Service (SMS) is a widely used text messaging feature on both basic and smartphones. SMS spam detection is a crucial task. Traditional machine learning approaches often struggle in this domain due to their reliance on manually crafted features, such as keyword detection, which can result in overly simplistic patterns and misclassification of more complex messages. With this shortcoming, these models can amplify human-induced biases if the training data contains inconsistent labeling or subjective interpretations, leading to unfair treatment of specific keywords or contexts. Conversely, advanced LLMs present effective approaches to addressing such issues, as they can more accurately capture linguistic patterns, contextual nuances, and textual ambiguities than traditional models, representing a substantial advancement in improving label accuracy. This paper proposes utilizing LLMs to address humaninduced labeling bias in spam detection and applying different prompt design methods to guide the process. In text classification, we surveyed two leading-edge LLMs, ChatGPT and Gemini, and evaluated them on the English SMS spam dataset source from UC Irvine’s Machine Learning Repository. We explored the highest-performing prompt designs using approaches like in-context learning. The findings indicate that in-context techniques for prompting improve model effectiveness by reducing human-induced (contextual) labeling bias in SMS spam detection with a Balanced Accuracy of 82% $\mathbf{97 \%}$ and an Equal Opportunity Difference (EOD) of precisely zero, indicating LLMs’ trustworthiness (fairness) in reducing this bias compared to traditional machine learning approaches. Our results also suggested that expanding the sample size can decrease LLMs’ ability to reduce human-induced labeling bias in spam detection. In general, this study provides information on the strengths and limitations of LLMs and suggestions for methods to minimize human-induced labeling bias in sp

关键词： Accuracy Large language models Text categorization Training data Machine learning Linguistics Message services Labeling Smart phones Context modeling

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Distributed Optimal Load Frequency Control with Stochastic Wind Power Generation

Distributed Optimal Load Frequency Control with Stochastic W...

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2021 European Control Conference, ECC 2021

作者： Silani, Amirreza Cucuzzella, Michele Scherpen, Jacquelien M. A. Javad Yazdanpanah, Mohammad University of Groningen Jan C. Willems Center For Systems and Control ENTEG Faculty of Science and Engineering Groningen Netherlands University of Tehran Control Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Tehran Iran Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (纸本)9789463842365

Motivated by the inadequacy of conventional control methods for power networks with a large share of renewable generation, in this paper we study the (stochastic) passivity property of wind turbines based on the Doubly Fed Induction Generator (DFIG). Differently from the majority of the results in the literature, where renewable generation is ignored or assumed to be constant, we model wind power generation as a stochastic process, where wind speed is described by a class of stochastic differential equations. Then, we design a distributed control scheme that achieves load frequency control and economic dispatch, ensuring the stochastic stability of the controlled network. © 2021 EUCA.

关键词： Random processes

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