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Fluid and kinetic modeling of runaway electron generation from tritium beta decay and Compton scattering 50

Fluid and kinetic modeling of runaway electron generation fr...

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50th EPS Conference on Plasma Physics, EPS 2024

作者： Ekmark, I. Hoppe, M. Fülöp, T. Tinguely, R.A. Sweeney, R. Jansson, P. Antonsson, L. Vallhagen, O. Pusztai, I. Department of Physics Chalmers University of Technology Göteborg Sweden Department of Electrical Engineering KTH Royal Inst. Technology Stockholm Sweden Plasma Science and Fusion Center Massachusetts Inst. Technology CambridgeMA United States Commonwealth Fusion Systems DevensMA United States Department of Computer Science and Engineering Chalmers Univ. Technology Göteborg Sweden

ISBN: (纸本)9798331305239

We derived kinetic sources of energetic electrons generated by tritium beta decay and Compton scattering and compared them to the corresponding fluid RE generation rates [2]. In the SPARC example considered, using kinetic (as opposed to fluid) sources yielded 25 % higher RE current. The reason for this is that energetic electrons generated by the kinetic sources with p c can run away since collisions for these electrons are kinetically resolved. © 2024 50th EPS Conference on Plasma Physics, EPS 2024. All rights reserved.

关键词： Compton scattering

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Analysis and Prediction of Location based Criminal Behaviors Through Machine Learning

Analysis and Prediction of Location based Criminal Behaviors...

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2022 International Conference on Edge Computing and Applications, ICECAA 2022

作者： Pandya, Darshanaben Dipakkumar Amarawat, Geetanjali Jadeja, Abhijeetsinh Degadwala, Sheshang Vyas, Dhairya Visnagar India Faculty of Computer Engineering Swaminarayan University Kalol India Computer Engineering Department Sigma Inst. of Engineering Gujarat Vadodara India Computer Science and Engineering The Maharaja Sayajirao University of Baroda Gujarat Vadodara India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Criminal Behaviors may be predicted, identified, and prevented via data mining. The research of criminal activity and its features is known as criminology. To be more accurate, crime analysis entails investigating and detecting crimes and their connections to convict. Because of the vast number of criminal record and intricacy of the linkages between different types of data, criminology is an excellent place to use data mining techniques. The first stage in doing more research is to identify criminal traits. This study offered an overview of current work in the field of crime prevention or prediction using data mining and machine learning technologies. For the system to learn, a year's worth of crime data is fed into it from a reliable Indian internet source that focuses on murder, kidnapping and abduction as well as dacoits and burglars and rape. To anticipate crime rates in different states, a regression model is created using Indian statistics, which provides data on a wide range of crimes over the last few years. A variety of Machine Learning techniques, including unsupervised, semi-supervised, and supervised learning, are used to improve the accuracy of crime predictions. Local police stations will benefit from this effort with regards to crime avoidance. The results may help policymakers better understand crime prediction and prevention. © 2022 IEEE.

关键词： Crime

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A Single-Carrier Modulation Strategy for the Reduction of the DC-Link Voltage Ripple of a 5-Level Multilevel DC-Link Inverter 50

A Single-Carrier Modulation Strategy for the Reduction of th...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Kahwa, Almachius Ebot, Besong John Fujimoto, Yasutaka Mwasilu, Francis Ojo, Joseph Olorunfemi Dept. of Electrical Engineering University of Dar es Salaam Dar es Salaam Tanzania United Republic of Inst. of Multidisciplinary Science Yokohama National University Yokohama Japan Dept. of Physics Electrical and Computer Engineering Yokohama National University Yokohama Japan Dept. of Electrical and Computer Engineering Tennessee Technological University TN United States

ISBN: (纸本)9781665464543

This paper presents a simple single-carrier PWM (SC-PWM) method based on the multi-modulation technique and using the redundant switching states of the 5-level hybrid multilevel dc-link inverter to balance the dc-link voltage. The proposed SC-PWM reduces the dc-link capacitor-voltage ripple, thereby reducing the voltage stress on the switches and improving the reliability of the inverter by eliminating all the low-fundamental frequency voltage oscillations in the capacitor voltage in the full range of power factors. Additionally, the proposed modulation strategy achieves a 2-fold reduction in the size of the output filter while reducing the total harmonic distortion of the output voltage and current. The feasibility of the proposed scheme over other modulation techniques is confirmed in a comparative analysis. Finally, the steady-state performances of the SC-PWM strategy are demonstrated in simulations and experiments. © 2024 IEEE.

关键词： Capacitor bank

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Toward EEG-Based Brain State Recognition for Personalized Neuromodulation 15

Toward EEG-Based Brain State Recognition for Personalized Ne...

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15th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2022

作者： Chang, Yu-Cheng Chao, Pin-Hsuan Chen, Sin-Horng Wei, Chun-Shu NYCU Dept. of Computer Science Taiwan Inst. of Bme NYCU Taiwan Dept. of Electronics and Electrical Engineering Taiwan

ISBN: (纸本)9781665464994

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive antidepressant neuromodulation therapy for treatment-resistant depression (TRD). However, the remission rate of patients remains unsatisfactory possibly due to the suboptimal configuration of conventional rTMS protocol. This work aims to design a close-loop TMS system and validate the practicability of brain-state-dependent stimulation based on real-time monitoring of electroencephalogram (EEG). We propose a novel method of phase estimation to enhance the precision of EEG phase-triggered firing of TMS. Our implementation supports subsequent studies on personalized brain-state-dependent neuromodulation for clinical applications. © 2022 IEEE.

关键词： Electroencephalography

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DDoS Detection at Fog Layer in Internet of Things

DDoS Detection at Fog Layer in Internet of Things

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2022 International Conference on Edge Computing and Applications, ICECAA 2022

作者： Dave, Shrinal Degadwala, Sheshang Vyas, Dhairya G.H Patel College of Engineering and Technology Gujarat India Computer Engineering Department Sigma Inst. of Engineering Gujarat Vadodara India Computer Science and Engineering The Maharaja Sayajirao University of Baroda Gujarat Vadodara India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

The Internet of Things (IoT) is a quite recent technology thathas emerged in recent years. It enables the progression of a diverse wide variety of new venture application options (e.g., wearables, smarthomes, smart cities, etc.). However, there are issues with IoT security measures that must be handled in order to maintain a secure connection. In recent years, distributed denial of service (DDOS) attacks has emerged as significant danger to the Internet of Things network. DDoS attacks deplete the resources available to IoTend devices, such as storage and network bandwidth, resulting in difficulties with IoT applications. This study's goal is to develop an effective DDoS detection framework for IoTenvironments in the fog layer. The framework uses two types of techniques to deal with both known and unknown attacks. They are signature-based and machine learning-based anomaly detection techniques. Fog computing enables the mitigation framework to be resource efficient. © 2022 IEEE.

关键词： Internet of things

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An Ultra-Low Power (86 nW) Low-Voltage (0.6 V) Self-Biased inst.umentation Amplifier for Bio-Medical Applications

An Ultra-Low Power (86 nW) Low-Voltage (0.6 V) Self-Biased I...

引用

2022 IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2022

作者： Mukherjee, Koyel Pandit, Soumya Pal, Rajat Kumar University of Calcutta Inst. of Radio Physics and Electronics Kolkata India University of Calcutta Dept. of Computer Science and Engineering Kolkata India

ISBN: (纸本)9781665472050

In this article, an operational transconductance amplifier (OTA)-based self-biased, ultra-low power inst.umentation amplifier is proposed. The design is performed in SCL 0.18 μm technology under 0.6 V power supply. The total current drawn from the supply is 144 nA, resulting in ultra-low power consumption of 86 nW. The entire circuit is self-biased by an in-built current reference circuit. The inst.umentation amplifier produces a fixed gain of 61 dB with unity gain bandwidth of 4.1 KHz and input RMS noise voltage of 2.01 μVrms. The amplifier is aimed for bio-medical applications and is capable of amplifying a signal as low as 0.5 μV with an ultra-low frequency of 1 Hz also, serving the range of even the weakest possible bio-potential signal quite efficiently. © 2022 IEEE.

关键词： Operational amplifiers

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Bayesian optimization of disruption scenarios with fluid-kinetic models 49

Bayesian optimization of disruption scenarios with fluid-kin...

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49th EPS Conference on Plasma Physics, EPS 2023

作者： Ekmark, I. Pusztai, I. Hoppe, M. Jansson, P. Fülöp, T. Department of Physics Chalmers Univ. Technology Göteborg Sweden Department of Electrical Engineering KTH Royal Inst. Technology Stockholm Sweden Department of Computer Science and Engineering Chalmers Univ. Technology Göteborg Sweden

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Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry 38

Private Algorithms for Stochastic Saddle Points and Variatio...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bassily, Raef Guzmán, Cristóbal Menart, Michael The Ohio State University United States Inst. for Mathematical and Comput. Eng. Fac. de Matemáticas Esc. de Ingeniería Pontificia Universidad Católica de Chile Chile Department of Computer Science & Engineering The Ohio State University United States Department of Computer Science University of Toronto Vector Institute Canada

In this work, we conduct a systematic study of stochastic saddle point problems (SSP) and stochastic variational inequalities (SVI) under the constraint of (ϵ, δ)differential privacy (DP) in both Euclidean and non-Euclidean setups. We first consider Lipschitz convex-concave SSPs in the p/q setup, p, q ∈ [1, 2]. That is, we consider the case where the primal problem has an p-setup (i.e., the primal parameter is constrained to an p bounded domain and the loss is p-Lipschitz with respect to the primal parameter) and the dual problem has an q setup. Here, we obtain a bound of O͠(Equation presented) on the strong SP-gap, where n is the number of samples and d is the dimension. This rate is nearly optimal for any p, q ∈ [1, 2]. Without additional assumptions, such as smoothness or linearity requirements, prior work under DP has only obtained this rate when p = q = 2 (i.e., only in the Euclidean setup). Further, existing algorithms have each only been shown to work for specific settings of p and q and under certain assumptions on the loss and the feasible set, whereas we provide a general algorithm for DP SSPs whenever p, q ∈ [1, 2]. Our result is obtained via a novel analysis of the recursive regularization algorithm. In particular, we develop new tools for analyzing generalization, which may be of independent interest. Next, we turn our attention towards SVIs with a monotone, bounded and Lipschitz operator and consider p-setups, p ∈ [1, 2]. Here, we provide the first analysis which obtains a bound on the strong VI-gap of (Equation presented). For p − 1 = Ω(1), this rate is near optimal due to existing lower bounds. To obtain this result, we develop a modified version of recursive regularization. Our analysis builds on the techniques we develop for SSPs as well as employing additional novel components which handle difficulties arising from adapting the recursive regularization framework to SVIs. © 2024 Neural information processing systems foundation. All rights reserved.

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Public-data Assisted Private Stochastic Optimization: Power and Limitations 38

Public-data Assisted Private Stochastic Optimization: Power ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Ullah, Enayat Menart, Michael Bassily, Raef Guzmán, Cristóbal Arora, Raman Meta United States Department of Computer Science & Engineering The Ohio State University United States Department of Computer Science University of Toronto Vector Institute Canada The Ohio State University United States Inst. for Mathematical and Comput. Eng. Fac. de Matemáticas Esc. de Ingeniería Pontificia Universidad Católica de Chile Chile Department of Computer Science Johns Hopkins University United States

We study the limits and capability of public-data assisted differentially private (PA-DP) algorithms. Specifically, we focus on the problem of stochastic convex optimization (SCO) with either labeled or unlabeled public data. For complete/labeled public data, we show that any (Ε, δ)-PA-DP has excess risk Ω˜(min {1/√npub, 1/√n + √d/nΕ}), where d is the dimension, npub is the number of public samples, npriv is the number of private samples, and n = npub + npriv. These lower bounds are established via our new lower bounds for PA-DP mean estimation, which are of a similar form. Up to constant factors, these lower bounds show that the simple strategy of either treating all data as private or discarding the private data, is optimal. We also study PA-DP supervised learning with unlabeled public samples. In contrast to our previous result, we here show novel methods for leveraging public data in private supervised learning. For generalized linear models (GLM) with unlabeled public data, we show an efficient algorithm which, given Õ(nprivΕ) unlabeled public samples, achieves the dimension independent rate Õ(1/√npriv + 1/√nprivΕ). We develop new lower bounds for this setting which shows that this rate cannot be improved with more public samples, and any fewer public samples leads to a worse rate. Finally, we provide extensions of this result to general hypothesis classes with finite fat-shattering dimension with applications to neural networks and non-Euclidean geometries. © 2024 Neural information processing systems foundation. All rights reserved.

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LIVES: XAI-based Secure UAV Communication Framework for Essential Items Delivery

LIVES: XAI-based Secure UAV Communication Framework for Esse...

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Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Vidhi Ruparelia Sneh Shah Lakshin Pathak Rajesh Gupta Sudeep Tanwar Sahil Sawhney Ankur Gupta Department of Electronics and Communication Engineering Inst. of Technology Nirma University Ahmedabad India Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Department of Computer Science & Engineering Model Institute of Engineering & Technology Jammu & Kashmir India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

The application of Unmanned Aerial Vehicles (UAVs) for securely delivering essential items during extreme conditions includes numerous challenges, particularly in ensuring identification of potential threats. To solve this challenge, we propose a strong framework that makes use of Boosting algorithms, with a particular focus on XGBoost to enhance attack classification in UAV-based delivery systems. The XGBoost model achieved an accuracy of 93%, a precision of 0.9312, a recall of 0.9311, and an F1 score of 0.9283 , which makes it a highly suitable model for security of UAVs. Explainable AI (XAI) is used to further enhance classification including SHAP and LIME to identify the most influential features, such as the barometer and battery levels. Our framework provides a comprehensive solution for secure UAV-based deliveries. The experimental results proves the effectiveness of the proposed model which shows its potential for real-life applications.

关键词： Accuracy Explainable AI Autonomous aerial vehicles Boosting Data engineering Barometers Classification algorithms Batteries Security

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