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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Yingrui Zhuang Lin Cheng Kaidi Huang Zijin Li Bo Wang Yongliang Zhao Department of Electrical Engineering Tsinghua University Beijing China Electric Power Research Institute State Grid Beijing Electric Power Company Beijing China Tongzhou Electric Power Supply Company State Grid Beijing Electric Power Company Beijing China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Accurate disaggregation of behind-the-meter (BTM) photovoltaic (PV) generation and load demand (PV-load) is essential for utility operators to make informed decisions (e.g., demand response). Existing methods for BTM PV-load disaggregation commonly rely on the availability of detailed information, such as observations of neighboring end users’ PV generation or load demand and geographical location, physical parameters of the PV panel and weather conditions, or labeled historical disaggregated observations. However, such information is often unavailable in practice due to privacy concerns or high economic costs for monitoring devices. Instead, this paper proposes a privacy-preserving BTM PV-load disaggregation algorithm that only requires the observed net load data based on prior knowledge constrained blind source separation. Specifically, ensemble empirical mode decomposition is first used to enhance the signal dimension of the observed net load, and then prior knowledge constrained independent component analysis is applied to disaggregate PV generation and load demand from the enhanced mixed signal. Furthermore, the demand response potential of the BTM PV-load unit is evaluated based on the disaggregation results. Numerical experiments on a real-world dataset demonstrate the effectiveness and efficiency of the proposed algorithm.

关键词： Privacy Accuracy Empirical mode decomposition Independent component analysis System integration approximation algorithms Demand response Blind source separation Robustness Real-time systems

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Processing and Classification of Covid-19 Audio Data with Optimization-Based Learning Techniques

Processing and Classification of Covid-19 Audio Data with Op...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Yüsra Dağılma Erdal Özbay Computer Engineering Fırat University Elazig Türkiye

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

This study compares and evaluates the performance criteria of the techniques used to identify Covid-19 illness using cough sound signals. The study recommends highperformance methods for future research. A dataset containing cough sounds was used in the study. 12 different feature extraction methods were used to significantly improve the performance. MFCC, Chroma, Mel-spectrogram, Spectral contrast, Tonnetz, SC, Spectral roll off, ZCR, Spectral bandwidth, RMSE, Spectral flatness, and Pitch feature extraction techniques were used to extract important features from sound signals. All feature extractions were combined to obtain a total of 1224 features. After feature extraction, SMOTE algorithm was applied because there was data imbalance. The number of positive classes with 125 data was increased to 960. Negative and positive class values were set to 960. Data imbalance was eliminated with the SMOTE application. Then, Adam optimization was applied together with two different classification methods. The classification was performed by applying Adam optimization to CNN and MLP modeling. As a result of the classification, MLP gave an accuracy value of approximately $92.65 \%$, while CNN gave an accuracy value of approximately $84.93 \%$. It was observed that MLP showed higher and more successful performance than CNN.

关键词： COVID-19 Accuracy Feature extraction approximation algorithms Data processing Classification algorithms Data mining Mel frequency cepstral coefficient Optimization Diseases

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Background-Free Detection of Light-Activatable Perfluorocarbon Nanodroplets

Background-Free Detection of Light-Activatable Perfluorocarb...

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Ultrasonics Symposium (LAUS), IEEE UFFC Latin America

作者： Alejandro Ulate Arce Meybelle Castro Valverde Diego S. Dumani School of Electrical Engineering University of Costa Rica UCR San Pedro Costa Rica

ISBN: (数字)9798350349085

ISBN: (纸本)9798350349092

Perfluorocarbon nanodroplets (PFCnDs) have been studied as an activatable ultrasound contrast agent. During vaporization, light-activatable PFCnDs generate characteristic ultrasound and photoacoustic (US/PA) signals across a sequence of images. As more PFCnDs vaporize in a region of interest, the US contrast increases exponentially while PA contrast decreases similarly. Previous studies have explored background-free imaging algorithms to show the location of the activated PFCnDs, by detecting the slope of exponential signals. In this study, we generate background-free images by identifying exponential trends and signal amplitudes, thus giving the location of PFCnDs and scaling the images by approximate PFCnD concentration. Synthetic US/PA images were generated by simulating a time-series of US/PA images during PFCnD activation events. Tissue background signals (offset) and random noise were also included. The algorithm analyzes intensity values to detect exponential trends using the coefficient of determination $(\mathbf{R}^{\mathbf{2}})$ , followed by image reconstruction. A pixel-wise pre-filtering was performed to account for offset and noise. Then, the $\mathbf{R}^{\mathbf{2}}$ coefficient was calculated to evaluate how each pixel's data fit an exponential behavior. Image reconstruction used the parameters of the fitting function generated, using the estimated amplitude value to scale the concentration image. A Pearson correlation coefficient was used to assess the accuracy of the algorithm by comparing the reconstructed image with the ground truth image. The algorithm was tested under presence of up to 70% noise and 50% signal offsets to assess the potential applicability in realistic tissue environments. In these conditions, the algorithm showed strong performance with high correlation coefficients. The successful reconstruction of background-free images suggests the feasibility of this method for further testing in vivo.

关键词： Correlation coefficient In vivo Ultrasonic imaging Noise Imaging Market research approximation algorithms

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OFDM Signal Processing Based On Tone Reservation and Active Constellation Extension

OFDM Signal Processing Based On Tone Reservation and Active ...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Fengyuan Guo Yujie Zhou Xinjian Xu Jianhua Ge School of Communication Engineering Xidian University Xi 'an China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

A significant disadvantage of Orthogonal Frequency Division Multiplexing (OFDM) systems is the high Peak Average Power Ratio (PAPR). Among the various PAPR reduction techniques, probabilistic methods are widely favored due to their non-distortion characteristics. These methods primarily disrupt the phase coherence between subcarriers by incorporating additional data. This paper proposes a combined algorithm named Least Squares approximation - Tone Reservation - Active Constellation Extension - Bunded Distortion (LSA-TR-ACE-BD) algorithm, which combines the Least Squares approximation - Tone Reservation (LSA-TR) algorithm with the Least Squares approximation - Active Constellation Extension - Bunded Distortion (LSA-ACE-BD) algorithm. The simulation results demonstrate that the LSA-TR-ACE-BD algorithm can reduce the PAPR to about 5.6dB with low complexity and low Bit Error Rate (BER), indicating its significant practical application potential in engineering.

关键词： Least squares approximations Simulation Bit error rate Signal processing algorithms Peak to average power ratio Coherence approximation algorithms Distortion Probabilistic logic Complexity theory

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Speed-robust scheduling revisited

arXiv

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arXiv 2024年

作者： Minařík, Josef Sgall, Jiří Computer Science Institute of Charles Univ. Faculty of Mathematics and Physics Prague Czech Republic

Speed-robust scheduling is the following two-stage problem of scheduling n jobs on m uniformly related machines. In the first stage, the algorithm receives the value of m and the processing times of n jobs;it has to partition the jobs into b groups called bags. In the second stage, the machine speeds are revealed and the bags are assigned to the machines, i.e., the algorithm produces a schedule where all the jobs in the same bag are assigned to the same machine. The objective is to minimize the makespan (the length of the schedule). The algorithm is compared to the optimal schedule and it is called ρ-robust, if its makespan is always at most ρ times the optimal one. Our main result is an improved bound for equal-size jobs for b = m. We give an upper bound of 1.6. This improves previous bound of 1.8 and it is almost tight in the light of previous lower bound of 1.58. Second, for infinitesimally small jobs, we give tight upper and lower bounds for the case when b ≥ m. This generalizes and simplifies the previous bounds for b = m. Finally, we introduce a new special case with relatively small jobs for which we give an algorithm whose robustness is close to that of infinitesimal jobs and thus gives better than 2-robust for a large class of inputs. © 2024, CC BY.

关键词： approximation algorithms

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Distributing Quantum Circuits with Minimum Circuit Execution Time over Quantum Networks

Distributing Quantum Circuits with Minimum Circuit Execution...

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Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Ranjani G. Sundaram Himanshu Gupta C. R. Ramakrishnan Stony Brook University NY

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Present quantum computers are constrained by limited qubit capacity and restricted physical connectivity, leading to challenges in large-scale quantum computations. Distributing quantum computations across a network of quantum computers is a promising way to circumvent these challenges and facilitate large quantum computations. However, distributed quantum computations require entanglements (to execute remote gates) which can incur significant generation latency and, thus, lead to decoherence of qubits. In this work, we consider the problem of distributing quantum circuits across a quantum network to minimize the execution time. The problem entails mapping the circuit qubits to network memories, including within each computer since limited connectivity within computers can affect the circuit execution time. We provide two-step solutions for the above problem: In the first step, we allocate qubits to memories to minimize the estimated execution time; for this step, we design an efficient algorithm based on an approximation algorithm for the max-quadratic-assignment problem. In the second step, we determine an efficient execution scheme, including generating required entanglements with minimum latency under the network re-source and decoherence constraints; for this step, we develop two algorithms with appropriate performance guarantees under certain settings or assumptions. We consider multiple protocols for executing remote gates, viz., telegates and cat-entanglements. With extensive simulations over NetSquid, a quantum network simulator, we demonstrate the effectiveness of our developed techniques and show that they outperform a scheme based on prior work by up to 95%.

关键词： Computers Quantum entanglement Heuristic algorithms Qubit Memory management Logic gates approximation algorithms Quantum networks Resource management Quantum circuit

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Estimation of State of Charge and State of Health in Aged Lithium-Ion Battery Performance: An Extended Kalman Filter Approach for Predictive Modelling

Estimation of State of Charge and State of Health in Aged Li...

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IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)

作者： Jayakara Babu Kondru Y.P. Obulesu School of Electrical Engineering Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798350387193

ISBN: (纸本)9798350387209

This study emphasizes the increasing significanceof lithium-ion technology in both consumer devices and electriccars, citing characteristics such as high energy density, longevity, and lack of memory effect. In this setting, it is critical to accurately determine factors such as state of charge (SoC) and State of Health to develop precise battery models. The study describes an improved algorithm for predicting SoC in cylindrical lithium cells, using MATLAB/Simulink and experimental data. A second-order mathematical model. Thevenin circuit was designed to replicate battery operation and the Lookup Table method in Simulink is used to identify internal parameters. The major goal is to improve the accuracy of SoC and SoH estimates by approximating the most accurate values of internal parameters using the Extended KalmanFilter(EKF) algorithm's advantages. This paper advances the understanding and efficiency of lithium-ion battery systems in various applications.

关键词： Lithium-ion batteries Accuracy Software packages Prediction algorithms Lithium approximation algorithms Mathematical models State of charge Kalman filters MATLAB

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Exploring Approximate Adders for an Energy-Efficient Pre-Processing Pan-Tompkins Algorithm VLSI Design

Exploring Approximate Adders for an Energy-Efficient Pre-Pro...

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Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Léo Ribeiro Morgana M. A. Rosa Rafael Soares Eduardo Costa Graduate Program on Computing Federal University of Pelotas (UFPel) Pelotas Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil

ISBN: (数字)9798350391695

ISBN: (纸本)9798350391701

This work explores a set of approximate adders (COPY adder, error-tolerant adder - ETA, lower-part OR adder - LOA, truncation - Trunc, and approximate parallel prefix adder - AxPPA) to enhance a VLSI design for ECG signal preprocessing based on Pan-Tompkins algorithm (AxPTA) to improve energy efficiency and QRS complex detection. Validation with the MIT-BIH database confirms that the AxPTA design maintains excellent sensitivity and positive predictivity with reduced energy consumption and circuit area. The AxPTA with AxPPA achieves a 10.22% reduction in circuit size and saves 85.06% energy, with an acceptable 1.32% precision deviation. These results suggest that the AxPPA significantly boosts energy efficiency in the AxPTA design for ECG analysis, supporting the development of portable devices for continuous heart monitoring and cardiovascular disease prevention.

关键词： Energy consumption Sensitivity Databases Energy conservation Electrocardiography Very large scale integration approximation algorithms Energy efficiency Adders System analysis and design

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Fuzzy-Differential Coevolution for Many Objective Optimization

Fuzzy-Differential Coevolution for Many Objective Optimizati...

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Advances in Modern Age Technologies for Health and Engineering Science (AMATHE), International Conference on

作者： Selina Khoirom Pratyusha Rakshit Department of Electronics and Telecommunication Engineering Jadavpur University India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

The paper proposes a novel coevolution-based many- objective optimization (MaOO) algorithm to enjoy the implicit parallelism of the evolutionary algorithm. The proposed MaOO handles individual objectives in parallel using differential evolution (DE) algorithm. After achieving convergence, a group of good-quality solutions is judiciously selected from each DE population, dealing with a specific objective. Next, the groups are combined to form a union set. Finally, a fuzzy membership- induced rank measure is devised to identify the best-ranked equally good members from the union set indicating the approximate Pareto optimal solutions to the given MaOO problem. The proposed MaOO algorithm, referred to as fuzzy- differential coevolution (FDC) is compared with three state-of-the-art techniques. Experiments undertaken reveal that FDC outperforms its contenders with respect to the traditional performance metrics.

关键词： Measurement Machine learning algorithms Sociology Machine learning Pareto optimization Parallel processing approximation algorithms

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Research on Adaptive Signal Optimization Algorithm of Intelligent Decision Support Enterprise Based on Deep Learning

Research on Adaptive Signal Optimization Algorithm of Intell...

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Sensors, Electronics and Computer Engineering (ICSECE), IEEE International Conference on

作者： Junyu Yu School of Management Shanghai University Shanghai China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

In order to solve the problems such as low efficiency of cloud manufacturing resource allocation and unreasonable selection of manufacturing services, this project intends to study the resource-oriented cloud manufacturing resource allocation strategy based on user demand. By introducing the methods of data warehouse, online analysis and processing, and data mining, the functional model of ERP data analysis is established. The dual-track mechanism of case base and model base is applied to the process design of model intelligent decision support system (IDSS) and the corresponding algorithm is proposed. A multi-index optimization modeling method for cloud manufacturing based on time-expense-quality-of-service and customer satisfaction was constructed. In this project, Chebyshev filter is combined with second-order vibration to solve the problem of early convergence of particle swarm. The variation factor is added to the subjective weight to reflect the contrast strength of the target data. The numerical simulation shows that the new algorithm has a wide range of solutions and good convergence performance, and also proves the correctness of the proposed theory and decision method.

关键词： Vibrations Optimization methods Filtering algorithms Chebyshev approximation approximation algorithms Filtering theory Manufacturing Sensors Resource management Convergence

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