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10th IEEE Global Conference on Consumer Electronics, GCCE 2021

作者： Muchtar, Kahlil Saddami, Khairun Bintang, Akhyar Cenggoro, Tjeng Wawan Pardamean, Bens Lin, Chih-Yang Ernita, Tia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Bina Nusantara University School of Computer Science Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Yuan-Ze University Department of Electrical And Engineering Taoyuan Taiwan

ISBN: (纸本)9781665436762

In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv3 within the provided ROI. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time. © 2021 IEEE.

关键词： Convolutional neural networks

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Long short-Term memory (LSTM) model-based reinforcement learning for nonlinear mass spring damper system control 7

Long short-Term memory (LSTM) model-based reinforcement lear...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Wijaya, Santo Heryadi, Yaya Arifin, Yulyani Suparta, Wayan Lukas Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta55281 Indonesia Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

The Neural Networks (NN) model which is incorporated in the control system design has been studied, and the results show better performance than the mathematical model approach. However, some studies consider that only offline NN model learning and does not use the online NN model learning directly on the control system. As a result, the controller's performance decreases due to changes in the system environment from time to time. The Reinforcement Learning (RL) method has been investigated intensively, especially Model-based RL (Mb-RL) to predict system dynamics. It has been investigated and performs well in making the system more robust to environmental changes by enabling online learning. This paper proposes online learning of local dynamics using the Mb-RL method by utilizing Long Short-Term Memory (LSTM) model. We consider Model Predictive Control (MPC) scheme as an agent of the Mb-RL method to control the regulatory trajectory objectives with a random shooting policy to search for the minimum objective function. A nonlinear Mass Spring Damper (NMSD) system with parameter-varying linear inertia is used to demonstrate the effectiveness of the proposed method. The simulation results show that the system can effectively control high-oscillating nonlinear systems with good performance. © 2022 Elsevier B.V.. All rights reserved.

关键词： Model predictive control

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PhageBox: An Open Source Digital Microfluidic Extension with Applications for Phage Discovery

TechRxiv

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TechRxiv 2023年

作者： Albin, Dreycey Buecherl, Lukas Kochavi, Eitan Niehaus, Elise Novack, Sasha Uragoda, Shenali Myers, Chris J. Alistar, Mirela Department of Computer Science CU Boulder BoulderCO United States Biomedical Engineering Program CU Boulder United States Department of Electrical Computer and Energy Engineering and Biomedical Engineering Program CU Boulder United States ATLAS Institute The Department of Computer Science CU Boulder BoulderCO United States

Objective: Recent advancements demonstrate the significant role of digital microfluidics in automating laboratory work with DNA and on-site viral testing. However, since commercially available instruments are limited to droplet manipulation, our work addresses the need for accelerated integration of other components, such as temperature control, that can expand the application domain. Methods: We developed PhageBox— an accessible device that can be used as a biochip extension. At hardware level, PhageBox integrates temperature and electromagnetic control modules. At software level, PhageBox is controlled by embedded software containing a unique model for bio-protocol programming, and a graphical user interface for visual device feedback and operation. Results: To evaluate PhageBox’s efficacy for biomedical applications, we performed functional testing. Similarly, we validated the temperature control using thermography, obtaining a range of ±0.2◦C. The electromagnets produced a magnetic force of 15 milliTesla, demonstrating precise immobilization of magnetic beads. We show the potential of PhageBox for bacteriophage research through three initial protocols: a universal framework for PCR, T7 bacteriophage restriction enzyme digestion, and concentrating X174 RF genomic DNA. Conclusion: Our work presents an open-source hardware and software extension for digital microfluidics devices. This extension integrates temperature and electromagnetic modules, demonstrating efficacy in biomedical applications and potential for bacteriophage research. Significance: We developed PhageBox to be accessible: the components are off-the-shelf at a low cost (≤$200), and the hardware designs and software code are open-source. With the long aim of ensuring reproducibility and accelerating collaboration, we also provide a DIY-build document. (GitHub: https://***/Dreycey/PhageBox) © 2023, CC BY-SA.

关键词： Digital microfluidics

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Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2023年第5期20卷 3267-3277页

作者： Tu, Deng-Yao Lin, Peng-Chan Chou, Hsin-Hung Shen, Meng-Ru Hsieh, Sun-Yuan National Cheng Kung University Master Degree Program on Artificial Intelligence Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Informatics Department of Oncology Department of Genomic Medicine National Cheng Kung University Hospital College of Medicine Department of Computer Science and Information Engineering Tainan City70101 Taiwan National Chi Nan University Department of Computer Science and Information Engineering Nantou County54561 Taiwan National Cheng Kung University Graduate Institute of Clinical Medicine Department of Obstetrics and Gynecology Department of Pharmacology National Cheng Kung University Hospital College of Medicine Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models International Center for the Scientific Development of Shrimp Aquaculture Department of Computer Science and Information Engineering Tainan City70101 Taiwan

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models. © 2004-2012 IEEE.

关键词： Tumors

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Unbiased and Multilevel Methods for a Class of Diffusions Partially Observed via Marked Point Processes

arXiv

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arXiv 2023年

作者： Alvarez, Miguel Jasra, Ajay Ruzayqat, Hamza Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

In this article we consider the filtering problem associated to partially observed diffusions, with observations following a marked point process. In the model, the data form a point process with observation times that have its intensity driven by a diffusion, with the associated marks also depending upon the diffusion process. We assume that one must resort to time-discretizing the diffusion process and develop particle and multilevel particle filters to recursively approximate the filter. In particular, we prove that our multilevel particle filter can achieve a mean square error (MSE) of O(∈2) (∈ > 0 and arbitrary) with a cost of O(∈-2.5) versus using a particle filter which has a cost of O(∈-3) to achieve the same MSE. We then show how this methodology can be extended to give unbiased (that is with no time-discretization error) estimators of the filter, which are proved to have finite variance and with high-probability have finite cost. Finally, we extend our methodology to the problem of online static-parameter estimation. Key words: Unbiased Methods, Multilevel Monte Carlo, Non-Linear Filtering, Point Processes, Parameter *** Codes 60G55, 60G35, 62M20, 62F30 © 2023, CC BY.

关键词： Diffusion

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A Graph Neural Network Multi-Task Learning-Based Approach for Detection and Localization of Cyberattacks in Smart Grids

A Graph Neural Network Multi-Task Learning-Based Approach fo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Abdulrahman Takiddin Rachad Atat Muhammad Ismail Katherine Davis Erchin Serpedin Department of Electrical & Computer Engineering Texas A&M University College Station TX USA Electrical & Computer Engineering Program Texas A&M University at Qatar Doha Qatar Department of Computer Science Tennessee Technological University Cookeville TN USA

False data injection attacks (FDIAs) on smart power grids’ measurement data present a threat to system stability. When malicious entities launch cyberattacks to manipulate the measurement data, different grid components will be affected, which leads to failures. For effective attack mitigation, two tasks are required: determining the status of the system (normal operation/under attack) and localizing the attacked bus/power substation. Existing mitigation techniques carry out these tasks separately and offer limited detection performance. In this paper, we propose a multi-task learning-based approach that performs both tasks simultaneously using a graph neural network (GNN) with stacked convolutional Chebyshev graph layers. Our results show that the proposed model presents superior system status identification and attack localization abilities with detection rates of 98.5−100% and 99 − 100%, respectively, presenting improvements of 5 − 30% compared to benchmarks.

关键词： Location awareness Convolution Power system stability Chebyshev approximation Benchmark testing Multitasking Graph neural networks

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Bayesian Parameter Inference for Partially Observed Stochastic Volterra Equations

arXiv

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arXiv 2023年

作者： Jasra, Ajay Ruzayqat, Hamza Wu, Amin Applied Mathematics & Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

In this article we consider Bayesian parameter inference for a type of partially observed stochastic Volterra equation (SVE). SVEs are found in many areas such as physics and mathematical finance. In the latter field they can be used to represent long memory in unobserved volatility processes. In many cases of practical interest, SVEs must be time-discretized and then parameter inference is based upon the posterior associated to this time-discretized process. Based upon recent studies on time-discretization of SVEs (e.g. [24]) we use Euler-Maruyama methods for the afore-mentioned discretization. We then show how multilevel Markov chain Monte Carlo (MCMC) methods [19] can be applied in this context. In the examples we study, we give a proof that shows that the cost to achieve a 4 mean square error (MSE) of O(ϵ2), ϵ > 0, is O(ϵ− 4/2H+1 ), where H is the Hurst parameter. If one uses a single level MCMC method then the cost is O(ϵ−2(2H+3)/ 2H+1 ) to achieve the same MSE. We illustrate these results in the context of state-space and stochastic volatility models, with the latter applied to real *** Codes 62F15, 62M20, 60J10, 60J22, 65C40 © 2023, CC BY.

关键词： Mean square error

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Performance Analysis of Economic Model Predictive Control on a Solar-Powered Direct Contact Membrane Distillation System

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IFAC-PapersOnLine 2022年第33期55卷 13-19页

作者： Hichem Bessafa Ghania Khodja Chakir Messaoud Taous-Meriem Laleg-Kirati Electrical and Computer Engineering Program. Computer Electrical and Mathematical Science and Engineering (CEMSE) division. King Abdullah University of Science and Technology (KAUST) Ecole Polytechnique of Algiers Algeria National Institute for Research in Digital Science and Technology (INRIA) Paris-Saclay France

This paper proposes an economic model predictive control (EMPC) design for a Direct Contact Membrane Distillation powered by a solar collector system which aims at enhancing its economical performances. A differential algebraic equations-based model is used for the design of the EMPC control. Moreover, a nonlinear observer is developed for the estimation of the unmeasured state. A neural network is proposed to predict the unknown solar irradiance for future horizon where a solar model provides temperature predictions. The proposed control design has been validated in simulation using data provided by a partial differential equation-based model mimicking the real plant.

关键词： Direct contact membrane distillation Economic model predictive control differential algebraic equations nonlinear observer Long-short term memory neural network

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Modular Self-Lock Origami: design, modeling, and simulation to improve the performance of a rotational joint

arXiv

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arXiv 2023年

作者： Zare, Samira Spaeth, Alex Suresh, Sandya Teodorescu, Mircea Department of Electrical and Computer Engineering University of California Santa Cruz United States SIP program University of California Santa Cruz United States Department of Computer Science and Engineering United States

Origami structures have been widely explored in robotics due to their many potential advantages. Origami robots can be very compact, as well as cheap and efficient to produce. In particular, they can be constructed in a flat format using modern manufacturing techniques. Rotational motion is essential for robotics, and a variety of origami rotational joints have been proposed in the literature. However, few of these are even approximately flat-foldable. One potential enabler of flat origami rotational joints is the inclusion of lightweight pneumatic pouches which actuate the origami’s folds;however, pouch actuators only enable a relatively small amount of rotational displacement. The previously proposed Four-Vertex Origami is a flat-foldable structure which provides an angular multiplier for a pouch actuator, but suffers from a degenerate state. This paper presents a novel rigid origami, the Self-Lock Origami, which eliminates this degeneracy by slightly relaxing the assumption of flat-foldability. This joint is analysed in terms of a trade-off between the angular multiplier and the mechanical advantage. Furthermore, the Self-Lock Origami is a modular joint which can be connected to similar or different joints to produce complex movements for various applications;three different manipulator designs are introduced as a proof of concept. Copyright © 2023, The Authors. All rights reserved.

关键词： Manipulators

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Resilience of Data-Driven Cyberattack Detection Systems in Smart Power Grids

Resilience of Data-Driven Cyberattack Detection Systems in S...

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European Signal Processing Conference (EUSIPCO)

作者： Abdulrahman Takiddin Rachad Atat Hayat Mbayed Muhammad Ismail Erchin Serpedin Department of Electrical and Computer Engineering Florida State University Tallahassee FL USA Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Center for Advanced Power Systems Florida State University Tallahassee FL USA Department of Computer Science Tennessee Technological University Cookeville TN USA Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Data communications within the smart power grid components are susceptible to cyberattacks due to the inter-connected nature of the grid and reliance on communication networks. Such cyberattacks can exploit the integrity of the exchanged data and result in operational instability. Existing data-driven cyberattack detection systems (CDSs) are proposed in the literature but their effectiveness is only verified against one type of cyberattacks. In reality, a smart grid system could encounter more than one attack type at once. Thus, in this paper, we investigate the resilience of state-of-the-art data-driven CDSs against replay false data injection, adversarial evasion, and adversarial data poisoning attacks on a realistic IEEE 118-bus system model. It turns out that a convolutional recurrent graph autoencoder-based CDS offers an attack detection rate of 96 – 97.5%, which outperforms other machine learning and deep learning-based data-driven CDSs by 16 – 54% since it captures the recurrent and spatial aspects of the data without being trained on attack data.

关键词： Deep learning Europe Feature extraction Graph neural networks Data models Smart grids Reliability computer crime Unsupervised learning Resilience

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