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Deep reinforcement learning with planning guardrails for building energydemand response

Energy and AI 2023年第1期11卷 12-25页

作者： Doseok Jang Lucas Spangher Selvaprabu Nadarajah Costas Spanos Department of Electrical Engineering and Computer Science University of California at BerkeleyBerkeley94704CAUSA Department of Information and Decision Sciences University of Illinois at ChicagoChicago60607ILUSA

Building energy demand response is projected to be important in decarbonizing energy use. A demand responseprogram that communicates ‘‘artificial’’ hourly price signals to workers as part of a social game has the potentialto elicit energy consumption changes that simultaneously reduce energy costs and emissions. The efficacy ofsuch a program depends on the pricing agent’s ability to learn how workers respond to prices and mitigatethe risk of high energy costs during this learning process. We assess the value of deep reinforcement learning(RL) for mitigating this risk. Specifically, we explore the value of combining: (i) a model-free RL method thatcan learn by posting price signals to workers, (ii) a supervisory ‘‘planning model’’ that provides a syntheticlearning environment, and (iii) a guardrail method that determines whether a price should be posted to realworkers or the planning environment for feedback. In a simulated medium-sized office building, we compareour pricing agent against existing model-free and model-based deep RL agents, and the simpler strategy ofpassing on the time-of-use price signal to workers. We find that our controller eliminates 175,000 US Dollarsin initial investment, decreases by 30% the energy cost, and curbs emissions by 32% compared to energyconsumption under the time-of-use rate. In contrast, the model-free and model-based deep RL benchmarksare unable to overcome initial learning costs. Our results bode well for risk-aware deep RL facilitating thedeployment of building demand response.

关键词： Deep reinforcement learning Risk-aware planning Demand response Energy Decarbonization

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An Improved Webpage Phishing Detection Based on an Ensemble Feature Selection and Multilevel Techniques

An Improved Webpage Phishing Detection Based on an Ensemble ...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Olukoya, Bamidele Musiliu Aderemi, Emmanuel Tunosun Olabisi, Patrick O. Osobukola, Adekunle A. Oluwaseyi, Adedayo-Ajayi Victoria Ogundokun, Roseline Oluwaseun Olaniyi, Olalere Abayomi Olowe, Oluwambo Tolulope Bells University of Technology Computer Engineering Department Ogun State Ota Nigeria Landmark University Department of Computer Science Kwara State Omu-Aran Nigeria Bells University of Technology Electrical/Electronic & Telecommunication Dept Ota Nigeria Graceland Polytechnic Department of Computer Science Kwara State Offa Nigeria

ISBN: (纸本)9798350358155

Over the decades, technology has presented human facets with diverse means of accomplishing complex tasks, especially in the communication domain. One of the risks identified over the decade terrifying network devices and end-users is phishing attacks. Generally, the anti-phishing agents are categorized into conventional and machine learning, but the conventional methods are outdated due to the dynamic nature of the attacks. Furthermore, the machine learning approaches are recognized to be the right antidote to curb the menace, but they involve various steps. One crucial step among the various steps lackadaisically ignored is feature selection and the models are challenged with diverse issues such as overfitting, bias, high computation, and decreased interpretability. To overcome this challenge, this study explored the filter measure methods by applying it as an ensemble feature selection framework to address the complicated issue of feature superfluous, and multilevel models to improve the poor performance of the classical models in the existing studies. The feature obtained by the proposed feature selection and the individual features generated by the filter measures were used. The outcome showed that the features were drastically reduced by the proposed feature selection framework compared to the individual-generated features. However, the models were trained and tested on these four sets of URL features obtained. The results showed that the proposed ensemble feature selection and multilevel mode effectively improved the poor performances of the considered classical models having recorded an accuracy of 98.4% using 7 of the features. © 2024 IEEE.

关键词： Phishing

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A Robotic-based Remote Control Painting System for Exterior Walls of High-Rise Buildings

A Robotic-based Remote Control Painting System for Exterior ...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Das, Diganta Kundu, Dipanjali Rahman, Anichur Rahman, Muaz Sazzad, Sadia Jahangirnagar University Department of Statistics Dhaka1342 Bangladesh Department of Computer Science Engineering Dhaka1350 Bangladesh Department of Electrical and Electronic Engineering Dhaka1350 Bangladesh

ISBN: (纸本)9798331529765

Exterior painting of high-rise buildings is a challenging task. In our country, as well as in other countries of the world, this task is accomplished manually, which is risky and life-threatening for the workers. Researchers and industry experts are trying to find an automatic and robotic solution for the exterior painting of high-rise building walls. In this paper, we propose a solution to this problem. We design and implement a prototype for automatically painting the building walls' exteriors. A spray mechanism was introduced in the prototype that can move in four different directions (up-down and left-right). All the movements are achieved by using microcontroller-operated servo motors. Further, these components create a scope to upgrade the proposed remote-controlled system to a robotic system in the future. In the presented system, all the operations are controlled remotely from a smartphone interface. Bluetooth technology is used for remote communications. The suggested system is expected to improve productivity with better workplace safety. © 2024 IEEE.

关键词： Tall buildings

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ECG Signal Classification of Cardiovascular Disorder using CWT and DCNN

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Journal of Biomedical Physics and engineering 2025年第1期15卷 77-92页

作者： Rahman, Tawfikur Ahommed, Rasel Deb, Nibedita Das, Utpal Kanti Moniruzzaman, Md. Bhuiyan, Md. Alamgir Sultana, Farzana Kausar, Md. Kamruzzaman Department of Electrical and Electronic Engineering Faculty of Engineering International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh Department of Agriculture International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh Department of Computer Science and Engineering Faculty of Engineering International University of Business Agriculture and Technology Uttara Dhaka 1230 Bangladesh

Background: Cardiovascular Diseases (CVD) requires precise and efficient diagnostic tools. The manual analysis of Electrocardiograms (ECGs) is labor-intensive, necessitating the development of automated methods to enhance diagnostic accuracy and efficiency. Objective: This research aimed to develop an automated ECG classification using Continuous Wavelet Transform (CWT) and Deep Convolutional Neural Network (DCNN), and transform 1D ECG signals into 2D spectrograms using CWT and train a DCNN to accurately detect abnormalities associated with CVD. The DCNN is trained on datasets from PhysioNet and the MIT-BIH arrhythmia dataset. The integrated CWT and DCNN enable simultaneous classification of multiple ECG abnormalities along-side normal signals. Material and Methods: This analytical observational research employed CWT to generate spectrograms from 1D ECG signals, as input to a DCNN trained on di-verse datasets. The model is evaluated using performance metrics, such as precision, specificity, recall, overall accuracy, and F1-score. Results: The proposed algorithm demonstrates remarkable performance metrics with a precision of 100% for normal signals, an average specificity of 100%, an average recall of 97.65%, an average overall accuracy of 98.67%, and an average F1-score of 98.81%. This model achieves an approximate average overall accuracy of 98.67%, highlighting its effectiveness in detecting ***: The integration of CWT and DCNN in ECG classification improves accuracy and classification capabilities, addressing the challenges with manual analy-sis. This algorithm can reduce misdiagnoses in primary care and enhance efficiency in larger medical institutions. By contributing to automated diagnostic tools for cardiovascular disorders, it can significantly improve healthcare practices in the field of CVD detection. © Journal of Biomedical Physics and engineering.

关键词： Wavelet transforms

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Twitching Control for Pneumatic Artificial Muscle Actuators

Twitching Control for Pneumatic Artificial Muscle Actuators

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Bogdan, Piotr A. Smith, Oliver Klein, Frederico B. Gianni, Mario University of Plymouth School of Engineering Computing and Mathematics Faculty of Science and Engineering United Kingdom University of Liverpool School of Electrical Engineering Electronics and Computer Science United Kingdom

ISBN: (纸本)9798350325706

In this work we present the design and development of a twitching control system for pneumatic artificial muscle (PAM) actuators. In this system, a shape memory alloy (SMA) poppet valve is used to control the flow of air pressurizing the inner bladder of a McKibben-based PAM. The valve is driven by pulse width modulation with changing frequency, allowing the PAM to resemble the mammalian muscles twitching. An adjustable passive exhaust orifice is used in place of an exhaust air valve to release the air pressure from the bladder mimicking natural muscle relaxation behaviour. Experiments under isometric and isotonic contractions show that the proposed control system reduces hysteresis of about 72% compared to continuous activation against 15% loss in contraction force with respect to conventional exhaust air valves. © 2023 IEEE.

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Permutation Sum-Capacity of Binary Adder Multiple-Access Channels

Permutation Sum-Capacity of Binary Adder Multiple-Access Cha...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Lu, William Makur, Anuran Purdue University Department of Computer Science West LafayetteIN47907 United States Purdue University School of Electrical and Computer Engineering West LafayetteIN47907 United States

ISBN: (纸本)9781665475549

Propelled by recent advances in the study of point-to-point permutation channels, which stem from communication networks and biological communications applications, we analyze the permutation binary adder multiple-access channel (PAMAC) in this work. The PAMAC network model consists of d senders communicating with a single receiver through a standard binary adder multiple-access channel followed by a random permutation block that shuffles the output codeword of the multiple-access channel. We formally define an appropriate notion of permutation sum-capacity Cpsum for this model, and then establish that Cpsum = d/2. To derive an achievability bound we construct d randomized encoders where input codewords are i.i.d. samples from Bernoulli distributions with carefully chosen parameters. These parameters can be perceived as roots of the probability generating function of the distribution over the output alphabet after the PAMAC's addition operation. Our achievability proof crucially uses eigenvalue perturbation results to provide quantitative estimates on the stability of the roots, which allows us to analyze decoding performance. This argument also yields an inner bound on the permutation capacity region of the PAMAC model. Finally, we also obtain a converse bound on Cpsum matching our achievability result. © 2023 IEEE.

关键词： Adders

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Forecasting Groundwater Depletion in Quetta Sub-Basin using Remote Sensing Data: Deep Learning Approach 17

Forecasting Groundwater Depletion in Quetta Sub-Basin using ...

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17th International Conference on Open Source Systems and Technologies, ICOSST 2023

作者： Khan, Pamir Umer, Rahila Fatima Muhammad, Jan Rauf, Zahid Ghafoor, Hifsa Computer Science Baluchistan University of IT Engineering and Management Science Quetta Quetta87300 Pakistan Balochistan University of IT Engineering & Management Sciences Quetta Department of Computer Science Quetta87300 Pakistan Balochistan University of IT Engineering & Management Sciences Quetta Department of Computer Engineering Quetta87300 Pakistan Balochistan University of IT Engineering & Management Sciences Quetta Department of Electrical Engineering Quetta87300 Pakistan

ISBN: (纸本)9798350381320

Groundwater resource is the largest freshwater storage resource, and it is also the largest storage for most human usage via domestic, industrial, and agricultural water supply and has maintained global food and water security. Therefore, it's important to sustain the supply and demand to balance the huge population. Pakistan is one of the largest groundwater consumers in the region of South Asia, which extracts roughly 60 billion m3, and Pakistan's groundwater table is dropping a meter per year. Groundwater has been over-exploited for drinking and agriculture. Balochistan is the country's largest province by size but the smallest by population;the situation is worse than in other regions of the country because groundwater level (GWL) is declining at a pace of 2 to 3 m per year. The primary goal of this research is to anticipate GWL changes and depletion, as well as to evaluate various machine learning techniques in predicting the best deep learning models for forecasting GWL changes utilizing GRACE and GLDAS data in the Quetta District of Balochistan. Deep learning models are used to predict aquifers' water-balancing capacities. The proposed approach would use data modeling with groundwater levels and meteorological factors as training parameters. By combining advanced groundwater modeling techniques and various datasets, the study aims to provide insights and implications for the entire research area. The ultimate goal is to develop effective management measures and ensure a sustainable supply of groundwater to support the needs of the large population in Balochistan, Pakistan, and the region of South Asia as a whole. © 2023 IEEE.

关键词： Aquifers

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RFFI Protocols Using Antenna Mutual Coupling and Power Amplifier Nonlinear Memory Effects

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IEEE Communications Letters 2025年第6期29卷 1250-1254页

作者： Li, Yuepei Podilchak, Symon K. Zhang, Junqing Cotton, Simon L. Ratnarajah, Tharmalingam Ding, Yuan Institute of Digital Communications School of Engineering The University of Edinburgh EdinburghEH8 9YL United Kingdom University of Liverpool Department of Electrical Engineering and Electronic LiverpoolL69 7ZX United Kingdom Queen’s University Belfast Department of Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom College of Engineering San Diego State University Electrical and Computer Engineering San DiegoCA92182 United States Heriot-Watt University EdinburghEH14 4AS United Kingdom

This letter presents a radio frequency fingerprinting identification (RFFI) protocol in wireless links with multi-antenna array transmitters. Multi-antenna systems are widely used in wireless communication systems for diversity and/or multiplexing. The mutual coupling (MC) effects arising from electromagnetic interactions between adjacent array elements can influence the RF characteristics of the transmitter and eventually the performance of the established links. In this letter, a novel RFF strategy is proposed to expand the differences in the RFF characteristics among wireless devices from the same vendor, with the goal of massively improving RFF classification accuracy in low to medium signal-to-noise ratio (SNR) channel conditions. Experimental results show that when classifying six power amplifiers (PAs) from the same vendor, 21% to 62% average classification accuracy improvement can be achieved by enlarging the RFF feature differences arising from the PA nonlinearity arising from the array coupling. © 1997-2012 IEEE.

关键词： Power amplifiers

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FPGA-Accelerated Password Cracking

FPGA-Accelerated Password Cracking

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2023 Congress in computer science, computer engineering, and Applied Computing, CSCE 2023

作者： Britten, Eric Gofman, Mikhail Bai, Yu California State University Fullerton Department of Computer Science Fullerton United States California State University Fullerton Department of Electrical and Computer Engineering Fullerton United States

ISBN: (纸本)9798350327595

Passwords are a common way of securing systems and applications from unauthorized access. However, passwords can be vulnerable to attackers who try to crack them by using random guesses, common patterns (e.g., password topologies), dictionary words, or leaked passwords. In this paper, we propose a hardware-accelerated password cracking method that leverages field-programmable gate array (FPGA) technology to recover passwords hashed with the sha512crypt algorithm. This algorithm is widely used on Linux systems. Our approach focuses on fast development to simulate a casual attacker who wants to boost their password cracking performance by using the speed and parallelism of FPGAs, which can outperform traditional CPUs and GPUs. Using C++ high-level language (a technique called high-level synthesis), we created a hardware device (i.e., a core) that runs the sha512crypt algorithm on the Zynq Z-7020 CPU-FPGA hybrid chip. We then tested the password cracking speed of our core (passwords/sec) and compared it to the AMD Ryzen 9 and Apple M1 Max CPUs. Based on the results of one core and two parallel sha512crypt cores on our chip, the maximum that could fit, we estimated that an FPGA chip with more than 8 sha512crypt cores and the same parameters as our chip could crack 10-character passwords at 360 passwords/sec, which is faster than a single-threaded sha512crypt on either CPU. We also projected that with 15 cores, we could achieve a speed of 675 passwords/sec, which is twice as fast as either of the CPUs. We think this work is a useful addition to the research on cybersecurity applications of FPGAs, as few works have tried to break sha512crypt on the FPGA. In the future, we will implement our design on modern FPGA chips that can accommodate more than 10 cores, optimize our designs and evaluation methodology, and perform a more extensive evaluation. © 2023 IEEE.

关键词： cybersecurity field-programmable gate array FPGA password cracking passwords Security

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Comparative Study of Enactment Scrutiny on Sheltered DSSS Multiuser Recognition Under Near- Far Atmosphere 3

Comparative Study of Enactment Scrutiny on Sheltered DSSS Mu...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Manna, Tanajit Mohankumar, N. Banerjee, Dhrubojyoti Mukherjee, Shibabrata Bhattacharjee, Bidrohi Maity, Heranmoy Pailan College of Management & Technology Electronics & Communication Engineering Kolkata India Computer Science & Engineering Puna India Pailan College of Management & Technology Electrical & Electronics Engineering Kolkata India

ISBN: (纸本)9798350374643

The study offers a workable, low-cost solution to the near-far delinquent in CDMA systems by enhancing communication in Direct Sequence Spread Spectrum (DSSS) multiuser systems as opposed to single users. The existing telecommunications system's hear ability problem, which requires improvement, has several security applications. This study uses the Multi Operator Finding Decorelator (MUD-Decorrelator) & MUD-Decision Feedback approach to offer a comparison analysis of Bit Error Rate (BER) vs. Signal to Noise Ratio in near-far situations. A MUD-Decision Feedback system is used to alter the modulated signal's BER rather than relying on a single user and a MUD-Decorelator. The communicated and established Power Spectral Densities (PSD) of mutually systems show no change in the signal characteristics. © 2024 IEEE.

关键词： Bit error rate

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