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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Chen, Yun-Chi Chao, Tung Phandita, Wimaya Nitya Sun, Tzu-Yu Wang, Huai-Zhi Hsieh, Yi-Hsien Hsu, Le-Yin Lin, Che Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan NTU Data Science Degree Program Taipei Taiwan NTU Department of Electrical Engineering Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (纸本)9798331518622

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients. © 2024 IEEE.

关键词： Diseases

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Objective Sleep Quality Measurement based on Fuzzy Logic and Wearable Device

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Procedia computer Science 2023年 227卷 1153-1162页

作者： Jason A. Wijaya Pauw D. Andersen Nico Surantha Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Department of Electrical Electronic and Communication Engineering Tokyo City University Japan

Sleep is the natural state of relaxation for human being. Sleep quality is an essential yet frequently neglected aspect of sleep in general. Sleep quality is essential because it allows the body to restore itself and prepare for the next day. The standard method for evaluating sleep quality was subjective evaluation. Actigraphy devices, which can measure the sleep cycle, are now widely available. This study developed a method using Fuzzy Logic and an actigraphy device to measure and classify sleep quality. The fuzzy logic method was developed in several stages, which are determining the sleep quality measurement parameters, constructing the fuzzy set for each input variable, and developing the fuzzy rules. To evaluate the proposed fuzzy model, five individuals were invited to participate in the experiment and required to complete the PSQI subjective sleep questionnaire. The evaluation result shows that our proposed Fuzzy model achieves lower error compared to the existing method.

关键词： sleep sleep quality actigraphy artificial intelligences fuzzy logic

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Utilizing Battery Energy Storage Systems to Enhance the Integration of Photovoltaic Systems for Damping Oscillations

Utilizing Battery Energy Storage Systems to Enhance the Inte...

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IEEE Southeastcon

作者： Ilham Akbar Ariesa Budi Zakaria Mayur Basu Eduard Muliadi Suwarno Nanang Hariyanto Graduate Program PLN-ITB PLN Corporate University Auburn USA Electrical and Computer Engineering Auburn University Auburn USA School of Electrical Engineering and Informatics Institut Teknologi Bandung (ITB) Bandung Indonesia

As the integration of Variable Renewable Energy (VRE) generators into power systems increases, there is a potential decrease in overall system reliability, particularly in terms of stability. This research suggests the integration of a Battery Energy Storage System (BESS) into a power system with Variable Renewable Energy sources, such as a utility-scale Photovoltaic (PV) plant, to accelerate the damping of post-fault oscillations following a short-circuit disturbance. The objective is for a Power Oscillation Damping (POD) within the PV plant to address post-fault oscillations within a specific timeframe. The POD in the PV plant is designed to supply additional active and reactive power, responding to the system's demands after a disturbance. The addition of BESS to the system is anticipated to enhance the time required for the system to achieve a stable state, resulting in quicker system stabilization. Simulations conducted through PSCAD software can effectively depict the post-fault conditions of short-circuit disturbances, considering the impact of the integrated BESS.

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Spectral learning of shared dynamics between generalized-linear processes 24

Spectral learning of shared dynamics between generalized-lin...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Lucine L. Oganesian Omid G. Sani Maryam M. Shanechi Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA Ming Hsieh Department of Electrical and Computer Engineering Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los Angeles CA

ISBN: (纸本)9798331314385

Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms.

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Reducing Dependency on Imported Electricity: Utilizing Renewable Energy and Minimizing Fossil Fuel Power Plant Operations in West Kalimantan

Reducing Dependency on Imported Electricity: Utilizing Renew...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Ilham Akbar Ariesa Budi Zakaria Eduard Muljadi Suwarno Nanang Hariyanto Graduate Program PLN-ITB PLN Corporate University Auburn USA Electrical and Computer Engineering Auburn University Auburn USA School of Electrical Engineering and Informatics Institut Teknologi Bandung (ITB) Bandung Indonesia

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This paper discusses West Kalimantan's dependency on imported electricity and fossil fuels and advocates for integrating renewable energy sources, especially solar power. With abundant solar resources and its equatorial location, West Kalimantan presents a significant solar PV generation opportunity. However, its dependence on imported electricity from the ASEAN Grid initiative poses risks to energy security. By promoting solar power as an alternative energy source and reducing reliance on fossil fuels, the paper aims to improve energy independence and sustainability. Integrating solar PV requires assessing frequency stability to ensure system resilience. Moreover, the interconnection between the two grids enhances system resilience and facilitates renewable energy integration. In summary, integrating solar PV power plants into the West Kalimantan system aims to decrease dependence on electricity imports from Sarawak while preserving the system's frequency stability.

关键词： Photovoltaic systems Renewable energy sources Fault tolerance Electricity Power system stability Fossil fuels Security Sustainable development Thermal stability Research and development

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Power Oscillation Damping of a Wind Turbine Generation with Battery Energy to Smoothen Power System Stability

Power Oscillation Damping of a Wind Turbine Generation with ...

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IEEE Southeastcon

作者： Ariesa Budi Zakaria Ilham Akbar Mayur Basu Eduard Muljadi Suwano Nanang Hariyanto Graduate Program PLN-ITB PLN Corporate University Auburn USA Electrical and Computer Engineering Auburn University Auburn USA School of Electrical Engineering and Informatics Institut Teknologi Bandung (ITB) Bandung Indonesia

Integrating wind turbine generation (WTG) into the power grid presents challenges due to their variable and unpredictable output, creating instability. The intermittent nature of wind power and the reduced inertia of wind turbines pose significant challenges to maintaining power system stability, especially during grid faults and transient events. Intermittency can impact the electrical grid's stability and requires additional grid balancing and energy storage measures. This study proposes a Power Oscillation Damping (POD) control scheme integrated with battery energy to enhance WTG stability following grid faults. The proposed scheme combines a POD controller for WTG and a battery controller, demonstrating faster oscillation damping than a WTG without batteries. During damping control, the scheme prioritizes active power control of POD, utilizing the wind turbine's kinetic energy. Simultaneously, the reactive power control of POD adjusts based on the synchronous generator's rotational speed to maintain voltage stability. Feedback for both control loops is derived from the nearest synchronous generator, ensuring coordinated and responsive control. Adding a battery into the control scheme helps to smooth out power fluctuations and enhance stability. Therefore, it is possible to compensate for the intermittency of wind power and voltage drop after short-circuit events through various measures, such as advanced control systems and battery power. The effectiveness of this proposed control scheme, with the added integration of Battery Energy, is validated through the PSCAD simulator involving a short-circuit fault in a two-area power system. Results demonstrate a notable improvement in power system stability, particularly after grid faults.

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AGV Detection in Industrial Environments through computer Vision

AGV Detection in Industrial Environments through Computer Vi...

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IEEE Latin American Robotics Symposium, LARS

作者： Wilson Eduardo Barioni Igor Pardal Latini André Lazzaretti Marco Teixeira Flavio Neves Lucia Valeria Ramos De Arruda Automation and Advanced Control System Laboratory (LASCA) Graduate Program in Electrical and Computer Engineering (CPGEI) Universidade Tecnologica Federal do Parana (UTFPR) Curitiba Parana Brazil

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

Automated Guided Vehicles (AGVs) are usual in industrial settings, with an increasing economic impact on processes. They move through numerous environments inside factories, commonly navigating long distances, while performing several activities. The constant detection of such vehicles, especially in cases of maintenance and safety, is a main issue in an industry setting. Usually, this information could be provided by a supervisory system, but many applications are not so large as to make such a system viable. Thus, a solution via machine learning and computer vision is developed in this work, by using simple cameras, such as the factories' security cameras. As the industrial environment is a scenario with a lot of variation and noise, the Transfer Learning technique is used to improve the training step of the developed AGV detection system. Finally, a database with 1067 images is used to build and validate the model, achieving a result greater than 80% of F1-score for various confidence values.

关键词： Training computer vision Remotely guided vehicles Computational modeling Transfer learning Cameras Production facilities

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Daily Peak Load Forecast of Banda Aceh City Using Adaptive Neuro Fuzzy Inference System (ANFIS) Method

Daily Peak Load Forecast of Banda Aceh City Using Adaptive N...

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computer System, Information Technology, and electrical engineering (COSITE), International Conference on

作者： Heldi Tindra Ira Devi Sara Ramzi Adriman Graduate program of Electrical Engineering Universitas Syiah Kuala Banda Aceh Aceh Dept. of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Aceh

electrical energy has become a fundamental need for society to achieve economic and technical efficiency. To meet the demand for electrical energy, the thing that is done is Electric Load Forecast. In this study, we developed a daily peak load forecast model for Banda Aceh City by considering data on temperature, humidity, and today’s electricity load data at peak hours. Forecasts are made using artificial intelligence, namely, the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. Software used Matlab R2015a to create a daily peak load forecast model based on the neuro-fuzzy designer toolbox. The ANFIS model developed is a variation of triangular, trapezium, and Gaussian membership function types, with each membership function equipped with 3 and 4 variable fuzzy sets. This study uses the MAPE instrument to measure the accuracy of the developed ANFIS model. The results obtained through simulations that have been carried out, all ANFIS Models produce MAPE values below 10%. This indicates that the developed ANFIS Model is very appropriate to be used for Daily Peak Load Forecast in Banda Aceh.

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Comparative Analysis between Three-Leg Interleaved and Conventional Bidirectional Converters Applied in Battery of Wind Power-Battery Unit Supplying an Islanded Microgrid

Comparative Analysis between Three-Leg Interleaved and Conve...

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IEEE General Meeting Power& Energy Society

作者： André C. Do Nascimento Giusep M. S. Ribeiro Lindemberg R. de Lima Pedro A. de Alcântara Luana C. S. Soares Luciano S. Barros Camila M. V. Barros Electrical Engineering Graduate Program Federal Institute of Pará Belém Brazil Electrical Engineering Graduate Program Federal University of Paraíba João Pessoa Brazil Mathematics and Computational Modeling Graduate Program Federal University of Paraíba João Pessoa Brazil Computer Systems Department Federal University of Paraíba João Pessoa Brazil

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Battery energy storage systems (BESS) are crucial for microgrids, enhancing dynamic performance and mitigating uncertainties inherent to isolated environments characterized by intermittent power generation. When microgrid is supplied by wind power in connection with BESS, the bidirectional DC-DC converter associated with BESS assumes a critical responsibility—ensuring the control of a wind power supplied DC-link. This permits the Voltage Source Inverter (VSI) to synthesize consistent voltages for the connected AC load. However, challenges emerge, particularly in arduous stress on switches of the bidirectional converter attributed to elevated current ripple. This stress does not only prejudice the overall system’s functioning but can also potentially leading it to shutdown in case of switch failure. Traditionally, the conventional buck-boost converter topology has been the go-to choice for this application, nevertheless, exploring novel converter topologies, such as interleaved bidirectional DC-DC converter (IBDC) has garnered attention, promising enhanced performance. This paper’s goal is to conduct a comparative analysis of the operation of these converters in an islanded microgrid supplied by a single Permanent Magnet Synchronous Generators (PMSG) small-scale wind turbine with a BESS system. Load variation with Maximum Power Point Tracking (MPPT) operation of the PMSG is performed during MATLAB/Simulink environment simulations. The results suggest significant advantages for the interleaved three-phase converter, such as reduced battery charging current ripple, switch disturbance, and voltage ripple in the DC-link.

关键词： Maximum power point trackers Magnetic separation Microgrids DC-DC power converters Wind power generation Permanent magnets Batteries Topology Wind turbines Stress

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BAQET: BRAM-aware Quantization for Efficient Transformer Inference via Stream-based Architecture on an FPGA 25

BAQET: BRAM-aware Quantization for Efficient Transformer Inf...

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Proceedings of the 2025 ACM/SIGDA International Symposium on Field programmable Gate Arrays

作者： Ling-Chi Yang Chi-Jui Chen Trung Le Bo-Cheng Lai Scott Hauck Shih-Chieh Hsu Institute of Electronics National Yang Ming Chiao Tung University Hsinchu Taiwan Graduate Degree Program of College of Electrical and Computer Engineering in National Yang Ming Chiao Tung University Hsinchu Taiwan Electrical and Computer Engineering in University of Washington Seattle USA Electrical and Computer Engineering University of Washington Seattle USA Physics University of Washington Seattle USA

ISBN: (纸本)9798400713965

FPGAs are a compelling substrate for supporting machine learning inference. Tools such as High-Level Synthesis and hls4ml can shorten the development cycle for deploying ML algorithms on FPGAs, but can struggle to handle the large on-chip storage needed for many of these models. In particular the high BRAM usage found in many of these flows can cause Place & Route failures during synthesis. In this paper we propose using a Simulated-Annealing based flow to perform BRAM-aware quantization. This approach trades off inference accuracy with BRAM usage, to provide a high-quality inference engine that still meets on-chip resource constraints. We demonstrate this flow for Transformer-based machine learning algorithms, which include Flash Attention in a Stream-based Dataflow architecture. Our system imposes minimal accuracy drops, yet can reduce BRAM usage by 20%-50%, and improve power efficiency by 264%-812% compared to existing Transformer-based accelerators on FPGAs

关键词： efficient transformer

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