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Articles 61 - 90 of 12009
Full-Text Articles in Engineering
Energy Services Interface (Doe-Psu-0000922-1), Robert B. Bass
Energy Services Interface (Doe-Psu-0000922-1), Robert B. Bass
Electrical and Computer Engineering Faculty Publications and Presentations
This document defines a set of rules known as the Energy Services Interface (ESI), which “establish a bi-directional, service-oriented, logical interface to support secure, trustworthy information exchange between an aggregator and distributed energy resources (DERs). These exchanges facilitate energy interactions between the DERs and the aggregator, thereby allowing the aggregator to provide grid services through dispatch of the DERs.” The ESI serves as an umbrella, ensuring the information exchange between an aggregator and DER owners conforms to expectations: protect privacy, provide security, develop trustworthiness, and ensure interoperability. DERMS developers use the ESI to ensure that information exchange meets these expectations.
Psu Derms Operating Manual And Egot System Reference (Doe-Psu-0000922-7), Tylor Slay, Robert B. Bass
Psu Derms Operating Manual And Egot System Reference (Doe-Psu-0000922-7), Tylor Slay, Robert B. Bass
Electrical and Computer Engineering Faculty Publications and Presentations
This document guides the user of the Portland State University Distributed Energy Resource Management System in configuration and normal operation. For direct access to the underlying code and its usage see the accompanying PSU EGoT System Reference. The system reference outlines all classes and methods used through the Energy Grid of Things system including applications, models, interfaces and the entity component system.
Implementation Profile: Modeling Environment (Doe-Psu-0000922-3), Sean Keene, Midrar Adham, Robert B. Bass
Implementation Profile: Modeling Environment (Doe-Psu-0000922-3), Sean Keene, Midrar Adham, Robert B. Bass
Electrical and Computer Engineering Faculty Publications and Presentations
This implementation profile provides the scope, background, and requirements necessary to implement a Modeling Environment (ME) to test a Distributed Energy Resource (DER) Management System (DERMS). A DERMS is used by an aggregator to dispatch large numbers of DERs in order to provide grid services to a Grid Operator. The ME addresses scalability issues inherent to Hardware-in-the-Loop DERMS simulation; a large number of assets are needed in order to observe effects on the grid from deployment and dispatch of DERs.
Product Specification: Distributed Trust Model System (Doe-Psu-0000922-4), Narmada Sonali Fernando, Abdullah Barghouti, Robert B. Bass, John M. Acken
Product Specification: Distributed Trust Model System (Doe-Psu-0000922-4), Narmada Sonali Fernando, Abdullah Barghouti, Robert B. Bass, John M. Acken
Electrical and Computer Engineering Faculty Publications and Presentations
A Distributed Trust Model (DTM) System is a supervisory component within an energy grid of things. The role of a DTM System is to implement the trust aspects of an energy services interface. The DTM System augments existing security measures by monitoring the communication between the various EGoT System actors and quantifying metrics of trust of each actor.
Disaggregating Longer-Term Trends From Seasonal Variations In Measured Pv System Performance, Chibuisi Chinasaokwu Okorieimoh, Brian Norton, Michael Conlon
Disaggregating Longer-Term Trends From Seasonal Variations In Measured Pv System Performance, Chibuisi Chinasaokwu Okorieimoh, Brian Norton, Michael Conlon
Articles
Photovoltaic (PV) systems are widely adopted for renewable energy generation, but their performance is influenced by complex interactions between longer-term trends and seasonal variations. This study aims to remove these factors and provide valuable insights for optimising PV system operation. We employ comprehensive datasets of measured PV system performance over five years, focusing on identifying the distinct contributions of longer-term trends and seasonal effects. To achieve this, we develop a novel analytical framework that combines time series and statistical analytical techniques. By applying this framework to the extensive performance data, we successfully break down the overall PV system output into …
Enhanced Sensitivity And Robustness In An Embeddable Strain Sensor Using Microwave Resonators, Yan Tang, Yizheng Chen, Qi Zhang, Biyao Shi, Jie Huang
Enhanced Sensitivity And Robustness In An Embeddable Strain Sensor Using Microwave Resonators, Yan Tang, Yizheng Chen, Qi Zhang, Biyao Shi, Jie Huang
Electrical and Computer Engineering Faculty Research & Creative Works
This Paper Introduces A Novel, Cost-Effective, And Durable Strain Sensor With Exceptional Sensitivity And Resolution, Utilizing An Open-Ended Hollow Coaxial Cable Resonator (OE-HCCR). The OE-HCCR Is Characterized By Two Reflective Elements: A Metal Post That Connects The Inner And Outer Conductors At The Signal's Entrance, And A Terminal Flange Near The Coaxial Line's End, Establishing A Variable Gap. The Sensor Employs A Paired Anchor Ring In Conjunction With The Terminal Flange To Transduce And Direct Strain. Variations In The Gap Alter The Resonant Frequency By Modulating The Phase Of The Reflection Coefficient At The Cable's Terminus. Initial Calibration Revealed A …
Energy Efficiency In Additive Manufacturing: Condensed Review, Ismail Fidan, Vivekanand Naikwadi, Suhas Alkunte, Roshan Mishra, Khalid Tantawi
Energy Efficiency In Additive Manufacturing: Condensed Review, Ismail Fidan, Vivekanand Naikwadi, Suhas Alkunte, Roshan Mishra, Khalid Tantawi
Engineering Technology Faculty Publications
Today, it is significant that the use of additive manufacturing (AM) has growing in almost every aspect of the daily life. A high number of sectors are adapting and implementing this revolutionary production technology in their domain to increase production volumes, reduce the cost of production, fabricate light weight and complex parts in a short period of time, and respond to the manufacturing needs of customers. It is clear that the AM technologies consume energy to complete the production tasks of each part. Therefore, it is imperative to know the impact of energy efficiency in order to economically and properly …
Urban Flood Extent Segmentation And Evaluation From Real-World Surveillance Camera Images Using Deep Convolutional Neural Network, Yidi Wang, Yawen Shen, Behrouz Salahshour, Mecit Cetin, Khan Iftekharuddin, Navid Tahvildari, Guoping Huang, Devin K. Harris, Kwame Ampofo, Jonathan L. Goodall
Urban Flood Extent Segmentation And Evaluation From Real-World Surveillance Camera Images Using Deep Convolutional Neural Network, Yidi Wang, Yawen Shen, Behrouz Salahshour, Mecit Cetin, Khan Iftekharuddin, Navid Tahvildari, Guoping Huang, Devin K. Harris, Kwame Ampofo, Jonathan L. Goodall
Civil & Environmental Engineering Faculty Publications
This study explores the use of Deep Convolutional Neural Network (DCNN) for semantic segmentation of flood images. Imagery datasets of urban flooding were used to train two DCNN-based models, and camera images were used to test the application of the models with real-world data. Validation results show that both models extracted flood extent with a mean F1-score over 0.9. The factors that affected the performance included still water surface with specular reflection, wet road surface, and low illumination. In testing, reduced visibility during a storm and raindrops on surveillance cameras were major problems that affected the segmentation of flood extent. …
A Novel Physics-Assisted Genetic Algorithm For Decoupling Capacitor Optimization, Li Jiang, Ling Zhang, Shurun Tan, Da Li, Chulsoon Hwang, Jun Fan, Er Ping Li
A Novel Physics-Assisted Genetic Algorithm For Decoupling Capacitor Optimization, Li Jiang, Ling Zhang, Shurun Tan, Da Li, Chulsoon Hwang, Jun Fan, Er Ping Li
Electrical and Computer Engineering Faculty Research & Creative Works
This article proposes a new physics-assisted genetic algorithm (PAGA) for decoupling capacitor (decap) optimization in power distribution networks (PDNs), which is a highly efficient approach to minimizing the number of decaps within an enormous search space. In the proposed PAGA method, the priority of the decap ports is first determined based on their physical loop inductances. Then, an initial solution is quickly obtained by placing decaps sequentially on the port with the highest priority. Subsequently, a GA with prior physical knowledge is developed to find better decap solutions progressively. A port removal scheme that eliminates the low-priority ports and a …
Tutorial: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy
Tutorial: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy
Publications
Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …
Designing High-Performance Identity-Based Quantum Signature Protocol With Strong Security, Sunil Prajapat, Pankaj Kumar, Sandeep Kumar, Ashok Kumar Das, Sachin Shetty, M. Shamim Hossain
Designing High-Performance Identity-Based Quantum Signature Protocol With Strong Security, Sunil Prajapat, Pankaj Kumar, Sandeep Kumar, Ashok Kumar Das, Sachin Shetty, M. Shamim Hossain
VMASC Publications
Due to the rapid advancement of quantum computers, there has been a furious race for quantum technologies in academia and industry. Quantum cryptography is an important tool for achieving security services during quantum communication. Designated verifier signature, a variant of quantum cryptography, is very useful in applications like the Internet of Things (IoT) and auctions. An identity-based quantum-designated verifier signature (QDVS) scheme is suggested in this work. Our protocol features security attributes like eavesdropping, non-repudiation, designated verification, and hiding sources attacks. Additionally, it is protected from attacks on forgery, inter-resending, and impersonation. The proposed scheme benefits from the traditional designated …
K-Perm: Personalized Response Generation Using Dynamic Knowledge Retrieval And Persona-Adaptive Queries, Kanak Raj, Kaushik Roy, Vamshi Bonagiri, Priyanshul Govil, Krishnaprasad Thirunarayan, Raxit Goswami, Manas Gaur
K-Perm: Personalized Response Generation Using Dynamic Knowledge Retrieval And Persona-Adaptive Queries, Kanak Raj, Kaushik Roy, Vamshi Bonagiri, Priyanshul Govil, Krishnaprasad Thirunarayan, Raxit Goswami, Manas Gaur
Publications
Personalizing conversational agents can enhance the quality of conversations and increase user engagement. However, they often lack external knowledge to tend to a user’s persona appropriately. This is particularly crucial for practical applications like mental health support, nutrition planning, culturally sensitive conversations, or reducing toxic behavior in conversational agents. To enhance the relevance and comprehensiveness of personalized responses, we propose using a two-step approach that involves (1) selectively integrating user personas and (2) contextualizing the response with supplementing information from a background knowledge source. We develop K-PERM (Knowledge-guided PErsonalization with Reward Modulation), a dynamic conversational agent that combines these elements. …
Exploring Alternative Approaches To Language Modeling For Learning From Data And Knowledge, Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Amit Sheth
Exploring Alternative Approaches To Language Modeling For Learning From Data And Knowledge, Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Amit Sheth
Publications
Despite their wide applications to language understanding tasks, large language models (LLMs) still face challenges such as hallucinations - the occasional fabrication of information, and alignment issues - the lack of associations with human-curated world models (e.g., intuitive physics or common-sense knowledge). Additionally, the black-box nature of LLMs makes it highly challenging to train them meaningfully in order to achieve a desired behavior. Specifically, the attempt to adjust LLMs’ concept embedding spaces can be highly intractable, which involves analyzing the implicit impact on LLMs’ numerous parameters and the resulting inductive biases. This paper proposes a novel architecture that wraps powerful …
Causal Event Graph-Guided Language-Based Spatiotemporal Question Answering, Kaushik Roy, Alessandro Oltramari, Yuxin Zi, Chathurangi Shyalika, Vignesh Narayanan, Amit Sheth
Causal Event Graph-Guided Language-Based Spatiotemporal Question Answering, Kaushik Roy, Alessandro Oltramari, Yuxin Zi, Chathurangi Shyalika, Vignesh Narayanan, Amit Sheth
Publications
Large Language Models have excelled at encoding and leveraging language patterns in large text-based corpora for various tasks, including spatiotemporal event-based question answering (QA). However, due to encoding a text-based projection of the world, they have also been shown to lack a fullbodied understanding of such events, e.g., a sense of intuitive physics, and cause-and-effect relationships among events. In this work, we propose using causal event graphs (CEGs) to enhance language understanding of spatiotemporal events in language models, using a novel approach that also provides proofs for the model’s capture of the CEGs. A CEG consists of events denoted by …
A Wavegan Approach For Mmwave-Based Fanet Topology Optimization, Enas Odat, Hakim Ghazzai, Ahmad Alsharoa
A Wavegan Approach For Mmwave-Based Fanet Topology Optimization, Enas Odat, Hakim Ghazzai, Ahmad Alsharoa
Electrical and Computer Engineering Faculty Research & Creative Works
The integration of dynamic Flying Ad hoc Networks (FANETs) and millimeter Wave (mmWave) technology can offer a promising solution for numerous data-intensive applications, as it enables the establishment of a robust flying infrastructure with significant data transmission capabilities. However, to enable effective mmWave communication within this dynamic network, it is essential to precisely align the steerable antennas mounted on Unmanned Aerial Vehicles (UAVs) with their corresponding peer units. Therefore, it is important to design a novel approach that can quickly determine an optimized alignment and network topology. In this paper, we propose a Generative Adversarial Network (GAN)-based approach, called WaveGAN, …
Statistical Eye Diagrams For High-Speed Interconnects Of Packages: A Review, Junyong Park, Donghyun Kim
Statistical Eye Diagrams For High-Speed Interconnects Of Packages: A Review, Junyong Park, Donghyun Kim
Electrical and Computer Engineering Faculty Research & Creative Works
An eye diagram, a critical metric in signal integrity analysis for high-speed interconnects such as packages, interposer, and printed circuit boards (PCBs), is generated by superposition of the received waveform. Obtaining an eye diagram is time-consuming, thus signal integrity analysis is inefficient. This article reviews that have been proposed to overcome this limitation. The statistical eye diagram provides a probability distribution depending on a sampling time and voltage, therefore it can be expanded to other metrics, such as the bit-error rate and shmoo plot. This article introduces previous research on statistical eye diagrams applied to complementary metal-oxide-semiconductors (CMOSs), noise, and …
A Segmentation Approach For Predicting Plane Wave Coupling To Pcb Structures, Shengxuan Xia, James Hunter, Aaron Harmon, Ahmed M. Hassan, Victor Khilkevich, Daryl G. Beetner
A Segmentation Approach For Predicting Plane Wave Coupling To Pcb Structures, Shengxuan Xia, James Hunter, Aaron Harmon, Ahmed M. Hassan, Victor Khilkevich, Daryl G. Beetner
Electrical and Computer Engineering Faculty Research & Creative Works
Evaluating the far-field radio frequency (RF) susceptibility of electronic devices often depends on extensive testing or full wave simulations. These methods are effective when complete system information is available but require substantial time and resources to evaluate a large number of variations in system configurations, where trace routings, integrated circuit (IC) package styles, trace terminations, arrival angle, and polarization of incoming wave, etc., are varied from one configuration to another. The goal of the following article is to develop simulation techniques for studying the statistical characteristics of coupling to typical printed circuit board (PCB) structures. Simulation time can be reduced …
Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha A. Petrenko, Donald C. Wunsch
Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha A. Petrenko, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Understanding the performance and validity of clustering algorithms is both challenging and crucial, particularly when clustering must be done online. Until recently, most validation methods have relied on batch calculation and have required considerable human expertise in their interpretation. Improving real-time performance and interpretability of cluster validation, therefore, continues to be an important theme in unsupervised learning. Building upon previous work on incremental cluster validity indices (iCVIs), this paper introduces the Meta- iCVI as a tool for explainable and concise labeling of partition quality in online clustering. Leveraging a time-series classifier and data-fusion techniques, the Meta- iCVI combines the outputs …
Cascaded Weak Reflector Coaxial Cable Structure For Point And Distributed Large-Strain Sensing, Chen Zhu, Osamah Alsalman, Jie Huang
Cascaded Weak Reflector Coaxial Cable Structure For Point And Distributed Large-Strain Sensing, Chen Zhu, Osamah Alsalman, Jie Huang
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, we present a truly distributed sensing modality based on a cascaded weak reflector coaxial cable structure (CWR-CCS) for large strain measurements. Compared to an optical fiber, a coaxial cable is much more robust and has a larger strain capability to survive harsh conditions, which may enable important applications in structural health monitoring. By drilling serial shallow holes into a commercial flexible coaxial cable perturbing the local impedance along its axial direction, CWRs along the coaxial cable are introduced due to impedance mismatch, forming the CWR-CCS. Gating a certain number of sequential reflectors in the time-domain reflection signal …
Modeling Wideband Radiated Emissions From Pcbs In Shielding Enclosures Based On Single-Plane Phaseless Near-Field Scanning, Zhifei Xiao, Zi An Wang, Li Jun Jiang, Ping Li
Modeling Wideband Radiated Emissions From Pcbs In Shielding Enclosures Based On Single-Plane Phaseless Near-Field Scanning, Zhifei Xiao, Zi An Wang, Li Jun Jiang, Ping Li
Electrical and Computer Engineering Faculty Research & Creative Works
This article presents a wideband phase less source reconstruction method (SRM) for the evaluation of the radiated emissions from printed circuit boards (PCBs) in shielding enclosures. The PCBs are modeled with equivalent dipoles, and the numerical Green's function (NGF) is deployed to establish the relationship between the equivalent source and the input near-field (NF) data, thus the electromagnetic influences of the surrounding environments comprehensively accounted. This method only requires magnitude-only NF scanning over a single plane, thus significantly decreasing the NF measurement difficulty. To remedy the lack of phase information of the NF data, the input NF data are equally …
Tailored Micromagnet Sorting Gate For Simultaneous Multiple Cell Screening In Portable Magnetophoretic Cell-On-Chip Platforms, Jonghwan Yoon, Yumin Kang, Hyeonseol Kim, Abbas Ali, Keonmok Kim, Sri Ramulu Torati, Mi-Young Im, Changyeop Jeon, Byeonghwa Lim, Cheolgi Kim
Tailored Micromagnet Sorting Gate For Simultaneous Multiple Cell Screening In Portable Magnetophoretic Cell-On-Chip Platforms, Jonghwan Yoon, Yumin Kang, Hyeonseol Kim, Abbas Ali, Keonmok Kim, Sri Ramulu Torati, Mi-Young Im, Changyeop Jeon, Byeonghwa Lim, Cheolgi Kim
Bioelectronics Publications
Conventional magnetophoresis techniques for manipulating biocarriers and cells predominantly rely on large-scale electromagnetic systems, which is a major obstacle to the development of portable and miniaturized cell-on-chip platforms. Herein, a novel magnetic engineering approach by tailoring a nanoscale notch on a disk micromagnet using two-step optical and thermal lithography is developed. Versatile manipulations are demonstrated, such as separation and trapping, of carriers and cells by mediating changes in the magnetic domain structure and discontinuous movement of magnetic energy wells around the circumferential edge of the micromagnet caused by a locally fabricated nano-notch in a low magnetic field system. The motion …
Gnss Software Defined Radio: History, Current Developments, And Standardization Efforts, Thomas Pany, Dennis Akos, Javier Arribas, M. Zahidul H. Bhuiyan, Pau Closas, Fabio Dovis, Ignacio Fernandez-Hernandez, Carles Fernandez-Prades, Sanjeev Gunawardena, Todd Humphreys, Zaher M. Kassas, Jose A. Lopez Salcedo, Mario Nicola, Mario L. Psiaki, Alexander Rugamer, Yong-Jin Song, Jong-Hoon Won
Gnss Software Defined Radio: History, Current Developments, And Standardization Efforts, Thomas Pany, Dennis Akos, Javier Arribas, M. Zahidul H. Bhuiyan, Pau Closas, Fabio Dovis, Ignacio Fernandez-Hernandez, Carles Fernandez-Prades, Sanjeev Gunawardena, Todd Humphreys, Zaher M. Kassas, Jose A. Lopez Salcedo, Mario Nicola, Mario L. Psiaki, Alexander Rugamer, Yong-Jin Song, Jong-Hoon Won
Faculty Publications
Taking the work conducted by the global navigation satellite system (GNSS) software-defined radio (SDR) working group during the last decade as a seed, this contribution summarizes, for the first time, the history of GNSS SDR development. This report highlights selected SDR implementations and achievements that are available to the public or that influenced the general development of SDR. Aspects related to the standardization process of intermediate-frequency sample data and metadata are discussed, and an update of the Institute of Navigation SDR Standard is proposed. This work focuses on GNSS SDR implementations in general-purpose processors and leaves aside developments conducted on …
Complete Solution Of The Lady In The Lake Scenario, Alexander Von Moll, Meir Pachter
Complete Solution Of The Lady In The Lake Scenario, Alexander Von Moll, Meir Pachter
Faculty Publications
In the Lady in the Lake scenario, a mobile agent, L, is pitted against an agent, M, who is constrained to move along the perimeter of a circle. L is assumed to begin inside the circle and wishes to escape to the perimeter with some finite angular separation from M at the perimeter. This scenario has, in the past, been formulated as a zero-sum differential game wherein L seeks to maximize terminal separation and M seeks to minimize it. Its solution is well-known. However, there is a large portion of the state space for which the canonical solution does not …
An Analysis Of Precision: Occlusion And Perspective Geometry’S Role In 6d Pose Estimation, Jeffrey Choate, Derek Worth, Scott Nykl, Clark N. Taylor, Brett J. Borghetti, Christine M. Schubert Kabban
An Analysis Of Precision: Occlusion And Perspective Geometry’S Role In 6d Pose Estimation, Jeffrey Choate, Derek Worth, Scott Nykl, Clark N. Taylor, Brett J. Borghetti, Christine M. Schubert Kabban
Faculty Publications
Achieving precise 6 degrees of freedom (6D) pose estimation of rigid objects from color images is a critical challenge with wide-ranging applications in robotics and close-contact aircraft operations. This study investigates key techniques in the application of YOLOv5 object detection convolutional neural network (CNN) for 6D pose localization of aircraft using only color imagery. Traditional object detection labeling methods suffer from inaccuracies due to perspective geometry and being limited to visible key points. This research demonstrates that with precise labeling, a CNN can predict object features with near-pixel accuracy, effectively learning the distinct appearance of the object due to perspective …
Time-Resolved Electromagnetic Near-Field Scanning: Dual Sparse Sampling In Time And Space, Yanming Zhang, Peifeng Ma, Lijun Jiang, Steven Gao
Time-Resolved Electromagnetic Near-Field Scanning: Dual Sparse Sampling In Time And Space, Yanming Zhang, Peifeng Ma, Lijun Jiang, Steven Gao
Electrical and Computer Engineering Faculty Research & Creative Works
Time-resolved electromagnetic near-field scanning plays a pivotal role in antenna measurement and unraveling complex electromagnetic interference and compatibility issues. However, the rapid acquisition of high-resolution spatio–temporal data remains challenging due to physical constraints, such as moving the probe position and allowing sufficient time for sampling. This article presents a novel hybrid approach combining kriging for sparse spatial measurement, compressed sensing (cs) for sparse temporal sampling, and dynamic mode decomposition (dmd) for comprehensive analysis of the dual-sparse sampling electromagnetic near-field data. We leverage cs to optimize sparse sampling in the time domain and latin hypercube sampling to guide the probe position …
A Robust Demand Regulation Strategy For Ders In A Single-Controllable Active Distribution Network, Shah Fahad, Arman Goudarzi, Rui Bo, Muhammad Waseem, Rashid Al-Ammari, Atif Iqbal
A Robust Demand Regulation Strategy For Ders In A Single-Controllable Active Distribution Network, Shah Fahad, Arman Goudarzi, Rui Bo, Muhammad Waseem, Rashid Al-Ammari, Atif Iqbal
Electrical and Computer Engineering Faculty Research & Creative Works
Over the past decade, pq regulation schemes for a single-controllable active distribution network (adn) using coordination among a network of virtual synchronous generators (vsgs) have been proposed. However, considering the variable nature of intermittent renewable energy sources (iress), coupling a cluster of iress with the point of common coupling (pcc) of adn could inflict transient issues for the power management of the whole adn. To counter these challenges, the proposed study has three main objectives: 1) to propose a modified mathematical model that represents the apparent resistance-reactance at the pcc of adn in relation to the pq coordination among the …
Robust Denoising And Densenet Classification Framework For Plant Disease Detection, Kevin Zhou, Dimah Dera
Robust Denoising And Densenet Classification Framework For Plant Disease Detection, Kevin Zhou, Dimah Dera
Electrical and Computer Engineering Faculty Publications and Presentations
Plant disease is one of many obstacles encountered in the field of agriculture. Machine learning models have been used to classify and detect diseases among plants by analyzing and extracting features from plant images. However, a common problem for many models is that they are trained on clean laboratory images and do not exemplify real conditions where noise can be present. In addition, the emergence of adversarial noise that can mislead models into wrong predictions poses a severe challenge to developing preserved models against noisy environments. In this paper, we propose an end-to-end robust plant disease detection framework that combines …
H-Nobs: Achieving Certified Fairness And Robustness In Distributed Learning On Heterogeneous Datasets, Guanqiang Zhou, Ping Xu, Yue Wang, Zhi Tian
H-Nobs: Achieving Certified Fairness And Robustness In Distributed Learning On Heterogeneous Datasets, Guanqiang Zhou, Ping Xu, Yue Wang, Zhi Tian
Electrical and Computer Engineering Faculty Publications and Presentations
Fairness and robustness are two important goals in the design of modern distributed learning systems. Despite a few prior works attempting to achieve both fairness and robustness, some key aspects of this direction remain underexplored. In this paper, we try to answer three largely unnoticed and unaddressed questions that are of paramount significance to this topic: (i) What makes jointly satisfying fairness and robustness difficult? (ii) Is it possible to establish theoretical guarantee for the dual property of fairness and robustness? (iii) How much does fairness have to sacrifice at the expense of robustness being incorporated into the system? To …
A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari
A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari
Computer Science Faculty Publications
Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental …
A Chinese Power Text Classification Algorithm Based On Deep Active Learning, Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
A Chinese Power Text Classification Algorithm Based On Deep Active Learning, Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Computer Science Faculty Publications
The construction of knowledge graph is beneficial for grid production, electrical safety protection, fault diagnosis and traceability in an observable and controllable way. Highly-precision text classification algorithm is crucial to build a professional knowledge graph in power system. Unfortunately, there are a large number of poorly described and specialized texts in the power business system, and the amount of data containing valid labels in these texts is low. This will bring great challenges to improve the precision of text classification models. To offset the gap, we propose a classification algorithm for Chinese text in the power system based on deep …