In this study,a machine learning based method is proposed for creating synthetic eventful phasor measurement unit(PMU)data under time-varying load *** proposed method leverages generative adversarial networks to creat...
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In this study,a machine learning based method is proposed for creating synthetic eventful phasor measurement unit(PMU)data under time-varying load *** proposed method leverages generative adversarial networks to create quasi-steady states for the power system under slowly-varying load conditions and incorporates a framework of neural ordinary differential equations(ODEs)to capture the transient behaviors of the system during voltage oscillation events.A numerical example of a large power grid suggests that this method can create realistic synthetic eventful PMU voltage measurements based on the associated real PMU data without any knowledge of the underlying nonlinear dynamic *** results demonstrate that the synthetic voltage measurements have the key characteristics of real system behavior on distinct time scales.
Recently, several Delta-Sigma modulators (DSMs) with ultra-high quadrature-amplitude-modulation (QAM) order larger than one million, e.g., 1048576 and 4194304 QAM are reported. As different DSM works were implemented ...
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The substring edit error replaces a substring u of x with another string v, where the lengths of u and v are bounded by a given constant k. It encompasses localized insertions, deletions, and substitutions within a wi...
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This paper presents a new capacitor-based multi-level inverter (MLI) configuration for single-phase applications, to reduce the number of switches and sources. Besides reducing the number of switches, the proposed con...
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This paper explores the architecture of the smart grid, emphasizing component modeling and the SGAM layers. It introduces optimized load flow equations and investigates algorithmic enhancements for improved power flow...
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This work presents an Intrusion Prevention System (IPS) called the Embedded Process Prediction Intrusion Prevention System (EPPIPS) to detect cyber-attacks by predicting what harm the attacks could cause to the physic...
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Space-air-ground integrated networks (SAGINs) offer seamless coverage and have emerged as a promising solution for high-speed railway (HSR) communications, which traverse various environments. This paper investigates ...
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In this paper, we introduce a novel class of fast, beam search-based adversarial attack (BEAST) for Language Models (LMs). BEAST employs interpretable parameters, enabling attackers to balance between attack speed, su...
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In this paper, we introduce a novel class of fast, beam search-based adversarial attack (BEAST) for Language Models (LMs). BEAST employs interpretable parameters, enabling attackers to balance between attack speed, success rate, and the readability of adversarial prompts. The computational efficiency of BEAST facilitates us to investigate its applications on LMs for jailbreaking, eliciting hallucinations, and privacy attacks. Our gradient-free targeted attack can jailbreak aligned LMs with high attack success rates within one minute. For instance, BEAST can jailbreak Vicuna-7B-v1.5 under one minute with a success rate of 89% when compared to a gradient-based baseline that takes over an hour to achieve 70% success rate using a single Nvidia RTX A6000 48GB GPU. BEAST can also generate adversarial suffixes for successful jailbreaks that can transfer to unseen prompts and unseen models such as GPT-4-Turbo. Additionally, we discover a unique outcome wherein our untargeted attack induces hallucinations in LM chatbots. Through human evaluations, we find that our untargeted attack causes Vicuna-7B-v1.5 to produce ∼15% more incorrect outputs when compared to LM outputs in the absence of our attack. We also learn that 22% of the time, BEAST causes Vicuna to generate outputs that are not relevant to the original prompt. Further, we use BEAST to generate adversarial prompts in a few seconds that can boost the performance of existing membership inference attacks for LMs. We believe that our fast attack, BEAST, has the potential to accelerate research in LM security and privacy. Copyright 2024 by the author(s)
Background: The concept of pill reminders has been discussed and developed throughout the decade. It varies from cascaded plastic pill boxes to complicated robust dispensers. This proposed smart pill reminding system ...
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Background: The concept of pill reminders has been discussed and developed throughout the decade. It varies from cascaded plastic pill boxes to complicated robust dispensers. This proposed smart pill reminding system based on IoT is being designed by considering ease-to-use and cost-effectiveness. Method: A smart pill reminding system is a system that will alert the patient to take their respective pill at the desired time. It will also track the motion of the patient’s hand while taking the pill and will also display the pill count on an LCD Screen. In case a patient forgets/ignores the reminder provided by the system, the system will automatically display the status on the application that will be installed in the relative/caretaker’s phone and through an email on the patient's relative/caretaker’s email address to take subsequent action. The system will monitor the real-time using an RTC module, and as and when the current time matches the medicines time, it will activate its mechanism, and the patient will have a buffer time to take their medicine. In case a patient does take the medicine in the buffer time provided by the system, then one mechanism of the system will be activated. In another case, if a patient does not take the medicine in the stipulated time, further actions will be initiated by the system to benefit the patient. Results: It was tested and found that out of ten times, the system worked accurately nine times, with calculated accuracy as high as 90%. Initially, the Blynk application will display "Welcome Patient" and "You will be updated". Once the RTC matches the scheduled time to take medicine, the buzzer starts buzzing. If the IR sensor detects the movement of the user’s hand, the LCD will update the pill count, and the pill count is reduced by one. The LCD will also display the message "Medicine Taken". If the IR sensor does not detect the movement of the user’s hand, the LCD will display the same pill count. The LCD will also display the me
The ground state electron density—obtainable using Kohn-Sham Density Functional Theory(KSDFT)simulations—contains a wealth of material information,making its prediction via machine learning(ML)models ***,the computa...
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The ground state electron density—obtainable using Kohn-Sham Density Functional Theory(KSDFT)simulations—contains a wealth of material information,making its prediction via machine learning(ML)models ***,the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation,making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system ***,we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data,while comprehensively sampling systemconfigurations using *** ML models are less reliant on heuristics,and being based on Bayesian neural networks,enable uncertainty *** show that our models incur significantly lower data generation costs while allowing confident—and when verifiable,accurate—predictions for a wide variety of bulk systems well beyond training,including systems with defects,different alloy compositions,and at multi-million-atom ***,such predictions can be carried out using only modest computational resources.
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