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arXiv 2023年

作者： Ghosh, Shantanu Yu, Ke Batmanghelich, Kayhan Department of Electrical and Computer Engineering Boston University BostonMA United States Intelligent Systems Program University of Pittsburgh PittsburghPA United States

Building generalizable AI models is one of the primary challenges in the healthcare domain. While radiologists rely on generalizable descriptive rules of abnormality, Neural Network (NN) models suffer even with a slight shift in input distribution (e.g., scanner type). Fine-tuning a model to transfer knowledge from one domain to another requires a significant amount of labeled data in the target domain. In this paper, we develop an interpretable model that can be efficiently fine-tuned to an unseen target domain with minimal computational cost. We assume the interpretable component of NN to be approximately domain-invariant. However, interpretable models typically underperform compared to their Blackbox (BB) variants. We start with a BB in the source domain and distill it into a mixture of shallow interpretable models using human-understandable concepts. As each interpretable model covers a subset of data, a mixture of interpretable models achieves comparable performance as BB. Further, we use the pseudo-labeling technique from semi-supervised learning (SSL) to learn the concept classifier in the target domain, followed by fine-tuning the interpretable models in the target domain. We evaluate our model using a real-life large-scale chest-X-ray (CXR) classification dataset. The code is available at: https://github. com/batmanlab/MICCAI-2023-Route-interpret-repeat-CXRs. © 2023, CC BY.

关键词： Large dataset

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Design and Feasibility Study of Wind Powered Hydrogen Refueling Station in Saudi Arabia

Design and Feasibility Study of Wind Powered Hydrogen Refuel...

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International Symposium on Advanced Electrical and Communication Technologies (ISAECT)

作者： Kholud Munshi Raghdan Bokhari Muhammad Khalid Electrical Engineering Department King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

ISBN: (数字)9798331529987

ISBN: (纸本)9798331529994

The transportation sector is one of the leading contributors to global carbon emissions due to its reliance on fossil fuels. Hydrogen, as a non-toxic and clean energy source, presents a promising alternative to fossil fuels. By transitioning to hydrogen-powered vehicles, we can significantly reduce carbon emissions, mitigating greenhouse gas effects and environmental pollution. If sustainable energy sources produce hydrogen and a widespread network of hydrogen refueling stations supports it, this potential becomes especially notable. This study conducted a techno-economic evaluation for a wind-powered hydrogen refueling station in NEOM, Saudi Arabia. The objective was to determine the optimal design for a wind-powered hydrogen refueling station in the city and to assess the project’s economic feasibility and its potential impact on reducing carbon emissions. We designed the station to meet the daily refueling needs of 20 hydrogen vehicles, each with a 5 kg tank capacity. The results show that the cost of producing hydrogen at this facility is approximately $3.75 per kilogram, which is competitive when compared to global hydrogen production costs. Furthermore, the hydrogen refueling station has the potential to reduce CO 2 emissions by 5904 tons and CO emissions by 19.66322 tons over its lifetime. These findings affirm the feasibility of a wind-powered hydrogen refueling station in NEOM and highlight its potential environmental benefits.

关键词： Electric potential Costs Electricity Hydrogen Urban areas Transportation Carbon dioxide Fossil fuels Stakeholders Sustainable development

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Speed Control of Wind Turbines System using Evolutionary Algorithm based Cascaded Controller

Speed Control of Wind Turbines System using Evolutionary Alg...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Ghali Ahmad Muhammad Majid Gulzar Adnan Shakoor Salman Habib Hasnain Ahmad Department of Control & Instrumentation Engineering King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Sustainability Energy Systems (IRC-SES) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Biosystems and Machines (IRC-BMRC) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML) King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Electrical Engineering Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Pakistan

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

In the last decades, wind turbine systems have emerged as one of the sources of electricity, and they are further considered to be part of the renewable energy sources that produce energy with zero carbon emissions. Due to the high demand for electrical energy and the reduction of greenhouse gas emissions, wind turbines have gained massive consideration in previous and current decades. Harvesting that energy from the wind to utilize by the system and producing electrical energy faced many challenges due to different factors, including the non-proper control system of the plant. In this regard, the paper presents control strategies of wind turbine systems using evolutionary algorithm-based cascaded controllers to tackle these issues. The control techniques used in this work are PSO-PID, SMC-PID and GA-PID. The paper described the principles of each control and optimization technique and their application in wind turbine systems. It also compares the results from each and evaluates their effectiveness in enhancing the performance of wind turbines.

关键词： Renewable energy sources Wind energy Velocity control Carbon dioxide Control systems Wind turbines Steady-state

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Age Feature Enhanced Neural Network for RUL Estimation of Power Electronic Devices

Age Feature Enhanced Neural Network for RUL Estimation of Po...

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International Conference on Prognostics and Health Management, PHM

作者： Zhonghai Lu Rui Shi Chao Guo Mingrui Liu Department of Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden Embedded Systems Master Program KTH Royal Institute of Technology Stockholm Sweden

Like other deep learning problems, critical features are critical to enable effective estimation of Remaining Useful Lifetime (RUL) for power electronic devices using Neural Networks (NNs). However, these critical features are often indirectly obtained after data pre-processing, complicated either in form (high dimension) or in computation (computation-intensive pre-processing). In the paper, we suggest adding a simple direct feature, age, into the NN based RUL estimation technique. The rationale for incorporating this feature is that the device lifetime is a sum of past time (age) plus RUL. Thus it has a strong correlation to RUL. In our experiments using accelerated aging tests, we show that the new age feature enhanced Recurrent Neural Network (RNN) model can significantly improve estimation accuracy and shorten training convergence time. It also outperforms a state-of-the-art RNN model using derived time-domain statistical features.

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Design and Characterization of a Configurable Current Conveyor Circuit

Design and Characterization of a Configurable Current Convey...

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International Conference on Microelectronics, ICM

作者： Muneer A. Al Absi Ahmed Reda Mohamed Department of Electrical Engineering King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Interdisciplinary Research Center for Communications Systems and Sensing King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia

ISBN: (数字)9798350379396

ISBN: (纸本)9798350379402

This paper presents a new design of a high-precision and configurable second-generation current conveyor circuit for biomedical applications. It is composed of a Miller-compensated trans-conductance amplifier (OTA), feedback network (FBN), and feedforward network (FFN)-based class AB amplifier. In TSMC 180 nm, the circuit is designed and simulated with a power supply of 1.8V. Moreover, it occupies a relatively small silicon area of 27 μm × 195 μm and consumes a power of $73.3\ \mu \mathrm{W}$ . The post-layout simulation results indicate the voltage gain reached 0.731 mdB with a bandwidth of 2.8 MHz and the current gain is 23.5 dB with a bandwidth of 24.2 MHz. 0.7 mdB and $25\ \mu \text{dB}$ , respectively, are the deviations in voltage gain and current gain. In addition, the proposed conveyor has a low input impedance of $101\ \Omega$ and a high output impedance of up to $81.6\ \mathrm{M}\Omega$ . Process-voltage-temperature (PVT) and Monte-Carlo (MC) variations have been used to guarantee consistent performance in the manufacturing process. Furthermore, the comparison table shows that this work is superior to prior arts.

关键词： Art Monte Carlo methods Power supplies Simulation Voltage Bandwidth Silicon Microelectronics Impedance Gain

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A novel framework for human leukocyte antigen (HLA) genotyping using probe capture-based targeted next-generation sequencing and computational analysis

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Computational and Structural Biotechnology Journal 2024年 23卷 1562-1571页

作者： Lai, Sheng-Kai Luo, Allen Chilun Chiu, I-Hsuan Chuang, Hui-Wen Chou, Ting-Hsuan Hung, Tsung-Kai Hsu, Jacob Shujui Chen, Chien-Yu Yang, Wei-Shiung Yang, Ya-Chien Chen, Pei-Lung Genome and Systems Biology Degree Program Academia Sinica and National Taiwan University Taipei Taiwan Department of Medical Genetics National Taiwan University Hospital Taipei Taiwan Graduate Institute of Medical Genomics and Proteomics College of Medicine National Taiwan University Taipei Taiwan Department of Biomechatronics Engineering National Taiwan University Taipei Taiwan Graduate Institute of Clinical Medicine College of Medicine National Taiwan University Taipei Taiwan Division of Endocrinology and Metabolism Department of Internal Medicine National Taiwan University Hospital Taipei Taiwan Department of Clinical Laboratory Sciences and Medical Biotechnology College of Medicine National Taiwan University Taipei Taiwan Department of Laboratory Medicine National Taiwan University Hospital Taipei Taiwan

Human leukocyte antigen (HLA) genes play pivotal roles in numerous immunological applications. Given the immense number of polymorphisms, achieving accurate high-throughput HLA typing remains challenging. This study aimed to harness the human pan-genome reference consortium (HPRC) resources as a potential benchmark for HLA reference materials. We meticulously annotated specific four field-resolution alleles for 11 HLA genes (HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, -DRB1, -DRB3, -DRB4 and -DRB5) from 44 high-quality HPRC personal genome assemblies. For sequencing, we crafted HLA-specific probes and conducted capture-based targeted sequencing of the genomic DNA of the HPRC cohort, ensuring focused and comprehensive coverage of the HLA region of interest. We used publicly available short-read whole-genome sequencing (WGS) data from identical samples to offer a comparative perspective. To decipher the vast amount of sequencing data, we employed seven distinct software tools: OptiType, HLA-VBseq, HISAT genotype, SpecHLA, T1K, QzType, and DRAGEN. Each tool offers unique capabilities and algorithms for HLA genotyping, allowing comprehensive analysis and validation of the results. We then compared these results with benchmarks derived from personal genome assemblies. Our findings present a comprehensive four-field-resolution HLA allele annotation for 44 HPRC samples. Significantly, our innovative targeted next-generation sequencing (NGS) approach for HLA genes showed superior accuracy compared with conventional short-read WGS. An integrated analysis involving QzType, T1K, and DRAGEN was developed, achieving 100% accuracy for all 11 HLA genes. In conclusion, our study highlighted the combination of targeted short-read sequencing and astute computational analysis as a robust approach for HLA genotyping. Furthermore, the HPRC cohort has emerged as a valuable assembly-based reference in this realm. © 2024 The Authors

关键词： Probes

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Deep Learning-Based Residual Useful Lifetime Prediction for Assets With Uncertain Failure Modes

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Journal of Computing and Information Science in engineering 2025年第4期25卷 041002页

作者： Su, Yuqi Fang, Xiaolei Operations Research Graduate Program North Carolina State University 915 Partners Way Raleigh NC 27607 Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University 915 Partners Way Raleigh NC 27607

Industrial prognostics focuses on utilizing degradation signals to forecast and continually update the residual useful life of complex engineering systems. However, existing prognostic models for systems with multiple failure modes face several challenges in real-world applications, including overlapping degradation signals from multiple components, the presence of unlabeled historical data, and the similarity of signals across different failure modes. To tackle these issues, this research introduces two prognostic models that integrate the mixture (log)-location-scale distribution with deep learning. This integration facilitates the modeling of overlapping degradation signals, eliminates the need for explicit failure mode identification, and utilizes deep learning to capture complex nonlinear relationships between degradation signals and residual useful lifetimes. Numerical studies validate the superior performance of these proposed models compared to existing methods.

关键词： Failure mechanisms mixture (log)-location-scale distribution Failure Weibull distribution multiple failure modes Signals Service life (Equipment) prognostics data analytics Deep learning machine learning for engineering applications engineering systems and industry applications industrial Internet of things big data and analytics

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Design of Adaptive-SMC based Multifunctional Inverter under Smart Grid Initiative for Grid Tied PV System

Design of Adaptive-SMC based Multifunctional Inverter under ...

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Australasian Universities Power engineering Conference, AUPEC

作者： Kamran Zeb Muhammad Khalid Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Electrical Engineering Department KFUPM and IRC-SES KFUPM Dhahran Saudi Arabia

ISBN: (数字)9798350377941

ISBN: (纸本)9798350377958

Smart Inverters (SI) are essential for controlling and integrating distributed generation (DG) into the power grid, offering advanced functionalities. This paper successfully presents an in-depth design and analysis of a 5 kVA Single Phase Voltage-Source Smart Inverter (SPV-SSI) under the Smart Grid Initiative (SGI) of the U.S. department of Energy. SPV-SSI combined functions include powering local loads, injecting energy into the grid, controlling the voltage at the Point of Common Coupling (PCC) during voltage sags or faults, storing energy in a bank of lead acid batteries, powering utility loads up to the inverter’s rated capacity, and real-time decision-making capability through advance metering. Further, the complete design of SI in a dq-synchronous reference frame with current and voltage control loops, bidirectional DC-DC Buck-Boost converter, IEEE standard 1547 islanding and recloser, and Static Synchronous Compensator (STATCOM) functions are included in this paper. In addition, the designed Adaptive Sliding Mode Controllers (ASMC) has robust, stable, and superior performance compared with the PI controller.

关键词： Lead acid batteries PI control Simulation Power quality Decision making Inverters Stability analysis Robustness Real-time systems Smart grids

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Synthetic helicoidal rotation of a quasielectrostatic ring resonator for ultrabroadband magnet-free nonreciprocity

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Physical Review Applied 2024年第6期22卷 064020-064020页

作者： Aravind Nagulu Ahmed Mekawy Mykhailo Tymchenko Dimitrios Sounas Harish Krishnaswamy Andrea Alù Department of Electrical and Systems Engineering Washington University in St. Louis St. Louis Missouri 63130 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Department of Electrical Engineering Wayne State University Detroit Michigan 48202 USA Department of Electrical Engineering Columbia University New York New York 10027 USA Physics Program Graduate Center City University of New York New York New York 10016 USA

Suitably tailored forms of spatiotemporal modulation in electronic circuit networks have been recently employed to overcome fundamental challenges in modern electronic systems, including breaking reciprocity, squeezing the footprint of high-Q resonators, and overcoming the delay-bandwidth limit. Rotating patterns of temporal modulations have been used to synthesize angular momentum, which replaces magnetic bias to break reciprocity in integrated circuits. However, this approach is limited by trade-offs between modulation speed, footprint, and bandwidth of operation. Rotating switching patterns in commutated capacitor networks also enables compact filters and quasielectrostatic wave propagation, overcoming the delay-bandwidth limit. In this paper, we combine these mechanisms in an integrated-circuit ring that synthetically rotates in two dimensions, realizing an effective helicoidal motion that provides ultrabroadband quasielectrostatic nonreciprocal responses fitting within a theoretically infinitesimal size. We also analyze the impact of modulation signal noise on time-modulated nonreciprocal components and unveil the role of a dynamic noise mechanism based on which the noise level increases in the presence of a strong signal passing through the component, along with methods to mitigate this effect. We experimentally verify these principles in a three-port integrated circulator based on a 65-nm CMOS process that operates from dc to 1 GHz with a miniaturization factor of 2 × 106.

关键词： Metamaterials Non-reciprocal transmission Photonics Topolectrical circuits

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The Effect of Different Design and Operational Parameters on the Performance of Can-Type Combustors 3

The Effect of Different Design and Operational Parameters on...

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3rd Novel Intelligent and Leading Emerging Sciences Conference, NILES 2021

作者： Boraey, Mohammed A. Kareem, Gamal Abdel Hameed, Hesham Abdel Salmawy, Hafez El Zagazig University Mechanical Power Engineering Department Zagazig Egypt Smart Engineering Systems Research Center Nile University Gize Egypt Mechanical Engineering Program Nile University Gize Egypt Marine units and maintenance Department PetroGulf Misr Cairo Egypt

ISBN: (纸本)9781665421577

Numerical simulation of the non-premixed combustion of methane air mixture in a gas turbine can- type swirl combustor is conducted. The study objective is to examine the effect of different design and operational parameters on the combustor performance and its emissions. The investigated parameters are the primary air flow rate, the swirl ratio between the secondary and the primary air and the fuel to the primary air swirl ratio. Several indicators are used to evaluate the combustion performance and emissions which include average exit temperature of the chamber, pattern factor, NOx and CO emission indices, combustion efficiency and the pressure drop. The results of the study demonstrate the importance of good tuning of the design and operating parameters for better performance and reduced emissions. © 2021 IEEE.

关键词： Combustors

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