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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Chun-Yu Wei Jie-Hong R. Jiang Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan and Center for Quantum Science and Engineering National Taiwan University Taipei Taiwan and Physics Division National Center for Theoretical Sciences Taipei Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan and Department of Electrical Engineering National Taiwan University Taipei Taiwan and Center for Quantum Science and Engineering National Taiwan University Taipei Taiwan and Physics Division National Center for Theoretical Sciences Taipei Taiwan

ISBN: (纸本)9798350323481

Exclusive-OR Sum-of-Products expressions (ESOPs) are vital for circuit synthesis of arithmetic functions and emerging technologies. The state-of-the-art ESOP extraction methods are limited in their inefficient exhaustive exploration strategy, significant optimality loss in the divide-and-conquer process, and incapability of handling don't-cares. This work overcomes these limitations by reduced decomposition exploration with a cost estimation and refinement strategy, generally applicable to incompletely specified functions. Experiments show up to 29× (average 12×) runtime and 28× (average 13×) memory-usage improvements with a quality close to the exact optimum obtained by exhaustive exploration for completely-specified functions, and substantial ESOP simplification with don't-cares for incompletely-specified functions.

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Simultaneous Pre- and Free-Assignment Routing for Multiple Redistribution Layers with Irregular Vias 21

Simultaneous Pre- and Free-Assignment Routing for Multiple R...

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Proceedings of the 58th Annual ACM/IEEE Design Automation Conference

作者： Yu-Jie Cai Yang Hsu Yao-Wen Chang Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan and Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (纸本)9781665432740

In modern packaging technology, redistribution layers (RDLs) are often used to redistribute interconnections among multiple chips and between I/O pads and bump pads. For high-density RDL routing, irregular vias, where vias can be placed at arbitrary locations, are adopted to better utilize RDL resources to obtain desired routing solutions. As the problem size increases, however, using irregular vias may suffer from high computation overheads. Moreover, most previous works route pre-assignment (PA) and free-assignment (FA) nets in separate stages, incurring routing resource competition. To remedy these disadvantages, we propose a simultaneous PA and FA routing framework with irregular RDL via planning. In this paper, we first propose a novel partitioning method based on the Voronoi diagram to handle irregular via structures and derive a theoretical upper bound on the number of generated regions. We then propose a chord-based tile model and a net-sequence list to generate non-crossing guides for PA and FA nets on the same routing graph. Finally, we develop a novel geometry-based pattern routing to obtain the final solutions. Experimental results show that our work can achieve 100% routability and an average 30X speedup over the-state-of-the-art work.

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Artificial Intelligence-Based Recommendation System for Detecting and Diagnosing Broken Bars in Induction Motors Under Transient Operation

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IFAC-PapersOnLine 2024年第19期58卷 1156-1161页

作者： Narco A.R. Maciejewski Roberto Z. Freire Anderson L. Szejka Thiago P.M. Bazzo Sofia M.A. Lopes Rogério A. Flauzino Department of Electrical Engineering - DAELT-CT Universidade Tecnológica Federal do Paraná - UTFPR Curitiba PR 80230-90 Brazil Production and Systems Engineering - PPGEPS-PB Energy Systems - PPGSE-CT Industrial and Systems Engineering Graduate Program - PPGEPS Pontifical Catholic University of Parana - PUCPR Curitiba PR 80215-901 Brazil Electrical Engineering - PPGEE-SC São Carlos School of Engineering - EESC Universidade de São Paulo - USP São Carlos SP 13566-590 Brazil

Three-phase induction motors are the main elements for converting electrical energy into mechanical energy and are extensively used in industry. Reducing maintenance costs becomes an incentive for developing systems capable of identifying defects. This research proposes a framework for recommending machine learning algorithms that diagnose and detect broken bar defects in three-phase induction motors under transient operation based on artificial intelligence. Employing experimental data, features were extracted and selected based on current, voltage, and vibration. A protocol of insertion of white noise showed that the proposed framework admitted 80% of noise without losing the predictive capacity based on a multicriteria performance measure.

关键词： condition monitoring fault detection fault classification machine learning maintenance engineering smart systems three-phase induction motor

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Soybean Price Prediction by Using Lstm Models Based on Brazilian Climatic Data

SSRN

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SSRN 2024年

作者： Oliva, Jefferson Tales Martelli, Otávio Pigozzo de Castro Barbosa, Marco Antônio Molin, Viviane Dal Casanova, Dalcimar Teixeira, Marcelo Graduate Program in Electrical Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Department of Academic Informatics Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil Graduate Program in Production and Systems Engineering Federal University of Technology-Paraná PR Pato Branco85503-390 Brazil

This work uses machine learning to verify whether the Brazilian climate influences international soybean price prediction. For this, climatic data, dollar quotes, inflation, and annual soybean production were collected from different data sources. Then, the collected data were pre-processed and grouped by Brazilian state. Afterward, data from the five most productive Brazilian states are chosen, such as Mato Grosso (MT), Mato Grosso do Sul (MS), Goiás (GO), Paraná (PR), and Rio Grande do Sul (RS). Afterward, predictive models are built using the long short-term memory (LSTM) method, which is applied in each chosen state dataset considering two different settings: with and without climatic data. Thus, two models were built for each Brazilian state, totaling ten predictors. In the experimental evaluation, when trained with climatic data, the PR model achieved the lowest values of root mean squared error (RMSE) and mean absolute error (MAE). Regarding predictors trained without climate data, the RS and PR obtained the lowest values of RMSE and MAE, respectively. After conducting statistical hypothesis tests, no significant difference was found among models, stating, with 95% certainty, that, in this study, the Brazilian climatic data did not influence the model performance for predicting soybean prices. On the other hand, one of the main contributions of work is conducting an unprecedented study about soybean price prediction using Brazilian climatic data. © 2024, The Authors. All rights reserved.

关键词： Long short-term memory

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Review ofPractical Psychometrics: A Guide for Test Users

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Structural Equation Modeling: A Multidisciplinary Journal 2024年第5期31卷 932-934页

作者： Musyaffa’ Ahmad Muhammad Faisal a Graduate Program Department of Electrical Engineering and Information Technology Faculty of Engineering Universitas Gadjah Mada Jl. Grafika No. 2 UGM Campus Yogyakarta55281 Indonesia b Master of Mathematics Education Program Faculty of Mathematics and Natural Sciences Yogyakarta State University Jl. Colombo 1 Yogyakarta55281 Indonesia

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A Zero Harmonic Distortion Grid-Forming Converter for Islanded Microgrids

A Zero Harmonic Distortion Grid-Forming Converter for Island...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Gabriel V. Ramos Thiago M. Parreiras Dener A. de L. Brandão Sidelmo M. Silva Braz de J. C. Filho Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Department of Electrical Engineering Centro Federal de Educacão Tecnológica de Minas Gerais Belo Horizonte Brazil Department of Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil

The world has been experiencing a fast change in the production and use of electrical energy. The electric power system is fast changing from the conventional centralized generation to a more distributed generation (DG) with renewable energy sources installed near the consumers. The evolution of DG paves the way for the concept of grid-forming converters improving voltage and frequency stability in microgrids (MGs) and islanded applications, due to the growth of inverter-based resources. In islanded MG, power quality is a concern due to the presence of disturbing loads and possible resonance issues encountered with multiple electronically interfaced distributed energy resources (DERs) and loads. The present work proposes the application of the Zero Harmonic Distortion Converter (ZHD) as a grid-forming converter due to its inherently sinusoidal voltage source characteristic without the need of capacitive filtering elements that negatively impact in costs, efficiency, size and can be a source of resonances issues. These features are achieved by the proper combination of harmonic cancellation in a three-winding transformer and harmonic elimination using the Selective Harmonic Elimination Pulse Width Modulation (SHE PWM). Simulation results show the ZHD converter setting voltage and frequency to an islanded microgrid, delivering sinusoidal voltage waveforms even in the presence of nonlinear loads. The converter is able to maintain the voltage distortion below the IEEE 519 limits by respecting the current harmonic limits imposed by the same standard.

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Superlattice HfO2-ZrO2 based Ferro-Stack HfZrO2 FeFETs: Homogeneous-Domain Merits Ultra-Low Error, Low programming Voltage 4 V and Robust Endurance 109 cycles for Multibit NVM

Superlattice HfO2-ZrO2 based Ferro-Stack HfZrO2 FeFETs: Homo...

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International Electron Devices Meeting (IEDM)

作者： C.-Y. Liao Z.-F. Lou C.-Y. Lin A. Senapati R. Karmakar K.-Y. Hsiang Z.-X. Li W.-C. Ray J.-Y. Lee P.-H. Chen F.-S. Chang H.-H. Tseng C.-C. Wang J.-H. Tsai Y.-T. Tang S. T. Chang C. W. Liu S. Maikap M. H. Lee NTNU Institute and Undergraduate Program of Electro-Optical Engineering Taipei Taiwan Department of Electronic Engineering CGU Taoyuan Taiwan NYCU Institute of Electronics Hsinchu Taiwan Graduate School of Advance Technology NTU Taipei Taiwan Department of Electrical Engineering NTNU Taipei Taiwan Department of Electrical Engineering NCU Taoyuan Taiwan Department of Electrical Engineering NCHU Taichung Taiwan

ISBN: (纸本)9781665489607

Superlattice (SL) HfO 2 -ZrO 2 with physical thickness of 5 nm and low phase fraction ratio 0.101:1 of monoclinic-phase (m-phase) to orthorhombic-phase (o-phase) investigated by geometrical phase analysis (GPA) is demonstrated. The homogeneous and congruous of SL-HfZrO 2 (HZO) with sufficient ferroelectric-domain is integrated as ferro-stack FeFETs for multibit NVM with low |V PG/ER | = 4 V, ultra-low error rate = 7.5×10 -16 , record high 2-bit endurance for 10 9 cycles, and stable data retention > 10 4 s. The device-to-device (D2D) variation of nanoscale 3D FeFETs is also improved with the proposed SL-HZO. The superlattice technique for FE-HZO is a promising concept with elevating the coherence of domain access due to high o-phase toward emerging memory applications.

关键词： Voltage fluctuations Three-dimensional displays Nonvolatile memory Superlattices Switches programming Nanoscale devices

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CRISP - DM Process in Smart Building Management

CRISP - DM Process in Smart Building Management

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International Conf on electrical and Electronic engineering (ICEEE)

作者： Mahir Dursun Moldir Sabyrzhan Dept. of Electrical and Electronics Engineering Faculty of Technology Gazi University Ankara Türkiye Department of Computer Science Graduate School of Informatics Gazi University Ankara Türkiye

ISBN: (数字)9798350362541

ISBN: (纸本)9798350362558

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. This paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. The developed model will predict energy consumption in classrooms and laboratories based on weather conditions. The study is being conducted in two stages. The first stage describes how to collect data using electronic devices such as Internet of Things (IoT) controllers of Smart Building Management Systems. The second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM).

关键词： Heating systems Smart buildings Laboratories Energy conservation Decision making Process control Data models Planning Internet of Things Data mining

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Intelligent Omni-Surfaces (IOSs) For Full-dimensional Wireless Coverage Extension: IOS Orientation and Location Optimization

Intelligent Omni-Surfaces (IOSs) For Full-dimensional Wirele...

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IEEE International Conference on Communications in China (ICCC)

作者： Menghan Li Shuhao Zeng Hongliang Zhang Boya Di Xi Weng Lingyang Song School of Electronics Engineering and Computer Science Peking University Beijing China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China Department of Electrical and Computer Engineering Princeton University NJ USA School of Electronics Peking University Beijing China Guanghua School of Management Peking University Beijing China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

The intelligent omni-surface (IOS), a type of metasurface which can simultaneously reflect and refract incident signals, is a promising technology for full-dimensional cell coverage extension. Existing works only explore the IOS configuration optimization for coverage extension given the IOS location, while how to deploy the IOS to maximize the cell coverage has not been investigated. In this paper, we aim to maximize the coverage of an IOS-assisted network by optimizing the deployment of the IOS. The placement optimization for metasurfaces in existing works mainly focuses on the reflective metasurface, which can not be applied here directly since the reflection and refraction of the IOS are coupled. To cope with this problem, we consider a downlink IOS-assisted multiple-input-single-output (MISO) system. The cell coverage of the IOS-assisted system is first analyzed, based on which we formulate a coverage maximization problem and solve it by jointly optimizing the IOS orientation and relative position between the IOS and the BS based on the projected gradient descent method. Numerical results verify the effectiveness of the proposed algorithm.

关键词： Wireless communication Simulation MISO communication Directive antennas Metasurfaces Downlink Reflector antennas

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Detection of Lactate in Human Sweat Via Surface-Modified, Screen-Printed Carbon Electrodes

SSRN

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SSRN 2023年

作者： Kumar, Nitish Lin, Yi-Jie Huang, Yu-Chiao Liao, Yu-Te Lin, Shu-Ping Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung40227 Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University Hsinchu300093 Taiwan

Real-time and continuous monitoring of lactate levels in sweat has been used as an indicator of physiological information to evaluate exercise outcomes and sports performance. We developed an optimal enzyme-based biosensor to detect the concentrations of lactate in different fluids (i.e., a buffer solution and human sweat). The surface of the screen-printed carbon electrode (SPCE) was first treated with oxygen plasma and then surface-modified by lactate dehydrogenase (LDH). The optimal sensing surface of the LDH-modified SPCE was identified by Fourier transform infrared spectroscopy and electron spectroscopy for chemical analysis. After connecting the LDH-modified SPCE to a benchtop E4980A precision LCR meter, our results showed that the recorded response was a function of the lactate concentration with a wide dynamic range of 0.1–100 mM (R2 = 0.95) and a limit of detection of 0.1 mM which was unachievable without using redox species A state-of-the-art electrochemical impedance spectroscopy (EIS) chip was developed to integrate the LDH-modified SPCE for a portable bioelectronic platform in the detection of lactate in human sweat. We believe the optimal sensing surface can improve the sensitivity of lactate sensing in a portable bioelectronic EIS platform for early diagnosis or real-time monitoring during different physical activities. © 2023, The Authors. All rights reserved.

关键词： Electrochemical impedance spectroscopy

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