Load Forecast (LF) is an important task in the planning, control and application of public power systems. Accurate Short Term Load Forecast (STLF) is the premise of safe and economical operation of a power system. In ...
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In this edition of Light People,we are excited to feature *** Dai(Zhejiang University),*** Su(Shanghai Jiao Tong University),and *** Lo(Advanced Micro Foundry Pte Ltd,Singapore),three prominent researchers shaping the...
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In this edition of Light People,we are excited to feature *** Dai(Zhejiang University),*** Su(Shanghai Jiao Tong University),and *** Lo(Advanced Micro Foundry Pte Ltd,Singapore),three prominent researchers shaping the future of silicon *** collaborative work addresses critical issues in silicon photonics,including reducing propagation losses,enlarging the functionalities and enhancing building blocks,integrating efficient laser sources,expanding applications,and pushing the boundaries of optical and electronic *** this interview,we delve into their academic journeys,challenges,and future visions,offering insights into the ongoing evolution of silicon photonics and its potential to transform *** a deeper exploration of their experiences and advice,the full interview is available in the Supplementary material.
Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,...
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Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,area,and time efficiencies of neuromorphic computing *** study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal-oxide-semiconductor(MOS)charge-trapping memory structure—the basic structure for charge-coupled devices(CCD)—and showing its suitability for in-memory light sensing and artificial visual *** memory window of the device increased from 2.8 V to more than 6V when the device was irradiated with optical lights of different wavelengths during the program ***,the charge retention capability of the device at a high temperature(100 ℃)was enhanced from 36 to 64%when exposed to a light wavelength of 400 *** larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al_(2)O_(3)/MoS_(2) interface and in the MoS_(2) layer.A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the *** array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91%*** study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception,adaptive parallel processing networks for in-memory light sensing,and smart CCD cameras with artificial visual perception capabilities.
Recently,we demonstrated the success of a time-synchronized state estimator using deep neural networks(DNNs)for real-time unobservable distribution *** this paper,we provide analytical bounds on the performance of the...
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Recently,we demonstrated the success of a time-synchronized state estimator using deep neural networks(DNNs)for real-time unobservable distribution *** this paper,we provide analytical bounds on the performance of the state estimator as a function of perturbations in the input *** has already been shown that evaluating performance based only on the test dataset might not effectively indicate the ability of a trained DNN to handle input *** such,we analytically verify the robustness and trustworthiness of DNNs to input perturbations by treating them as mixed-integer linear programming(MILP)*** ability of batch normalization in addressing the scalability limitations of the MILP formulation is also *** framework is validated by performing time-synchronized distribution system state estimation for a modified IEEE 34-node system and a real-world large distribution system,both of which are incompletely observed by micro-phasor measurement units.
Semi-supervised learning (SSL) aims to reduce reliance on labeled data. Achieving high performance often requires more complex algorithms, therefore, generic SSL algorithms are less effective when it comes to image cl...
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The rapidly advancing Convolutional Neural Networks(CNNs)have brought about a paradigm shift in various computer vision tasks,while also garnering increasing interest and application in sensor-based Human Activity Rec...
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The rapidly advancing Convolutional Neural Networks(CNNs)have brought about a paradigm shift in various computer vision tasks,while also garnering increasing interest and application in sensor-based Human Activity Recognition(HAR)***,the significant computational demands and memory requirements hinder the practical deployment of deep networks in resource-constrained *** paper introduces a novel network pruning method based on the energy spectral density of data in the frequency domain,which reduces the model’s depth and accelerates activity *** traditional pruning methods that focus on the spatial domain and the importance of filters,this method converts sensor data,such as HAR data,to the frequency domain for *** emphasizes the low-frequency components by calculating their energy spectral density ***,filters that meet the predefined thresholds are retained,and redundant filters are removed,leading to a significant reduction in model size without compromising performance or incurring additional computational ***,the proposed algorithm’s effectiveness is empirically validated on a standard five-layer CNNs backbone *** computational feasibility and data sensitivity of the proposed scheme are thoroughly ***,the classification accuracy on three benchmark HAR datasets UCI-HAR,WISDM,and PAMAP2 reaches 96.20%,98.40%,and 92.38%,***,our strategy achieves a reduction in Floating Point Operations(FLOPs)by 90.73%,93.70%,and 90.74%,respectively,along with a corresponding decrease in memory consumption by 90.53%,93.43%,and 90.05%.
The evolution of the electrical grid from its early centralized structure to today’s advanced "smart grid" reflects significant technological progress. Early grids, designed for simple power delivery from l...
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The evolution of the electrical grid from its early centralized structure to today’s advanced "smart grid" reflects significant technological progress. Early grids, designed for simple power delivery from large plants to consumers, faced challenges in efficiency, reliability, and scalability. Over time, the grid has transformed into a decentralized network driven by innovative technologies, particularly artificial intelligence (AI). AI has become instrumental in enhancing efficiency, security, and resilience by enabling real-time data analysis, predictive maintenance, demand-response optimization, and automated fault detection, thereby improving overall operational efficiency. This paper examines the evolution of the electrical grid, tracing its transition from early limitations to the methodologies adopted in present smart grids for addressing those challenges. Current smart grids leverage AI to optimize energy management, predict faults, and seamlessly integrate electric vehicles (EVs), reducing transmission losses and improving performance. However, these advancements are not without limitations. Present grids remain vulnerable to cyberattacks, necessitating the adoption of more robust methodologies and advanced technologies for future grids. Looking forward, emerging technologies such as Digital Twin (DT) models, the Internet of Energy (IoE), and decentralized grid management are set to redefine grid architectures. These advanced technologies enable real-time simulations, adaptive control, and enhanced human–machine collaboration, supporting dynamic energy distribution and proactive risk management. Integrating AI with advanced energy storage, renewable resources, and adaptive access control mechanisms will ensure future grids are resilient, sustainable, and responsive to growing energy demands. This study emphasizes AI’s transformative role in addressing the challenges of the early grid, enhancing the capabilities of the present smart grid, and shaping a secure
The Sultanate of Oman has been dealing with a severe renewable energy issue for the past few decades,and the government has struggled to find a *** addition,Oman’s strategy for converting power generation to sources ...
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The Sultanate of Oman has been dealing with a severe renewable energy issue for the past few decades,and the government has struggled to find a *** addition,Oman’s strategy for converting power generation to sources of renewable energy includes a goal of 60 percent of national energy demands being met by renewables by 2040,including solar and wind ***,the use of small-scale energy from wind devices has been on the rise in recent *** upward trend is attributed to advancements in wind turbine technology,which have lowered the cost of energy from *** calculate the internal and external factors that affect the small-scale energy of wind technologies,the study used a fuzzy analytical hierarchy process technique for order of preference by similarity to an ideal *** a result,in the decision model,four criteria,seventeen sub-criteria,and three resources of renewable energy were calculated as options from the viewpoint of the Sultanate of *** research is based on an examination of statistics on energy produced by wind turbines at various locations in the Sultanate of ***,six distinct miniature wind turbines were investigated for four different *** outcomes of this study indicate that the tiny wind turbine has a lot of potential in the Sultanate of Oman for applications such as homes,schools,college campuses,irrigation,greenhouses,communities,and small *** government should also use renewable energy resources to help with the renewable energy issue and make sure that the country has enough renewable energy for its long-term growth.
The component aging has become a significant concern worldwide,and the frequent failures pose a serious threat to the reliability of modern power *** light of this issue,this paper presents a power system reliability ...
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The component aging has become a significant concern worldwide,and the frequent failures pose a serious threat to the reliability of modern power *** light of this issue,this paper presents a power system reliability evaluation method based on sequential Monte Carlo simulation(SMCS)to quantify system reliability considering multiple failure modes of ***,a three-state component reliability model is established to explicitly describe the state transition process of the component subject to both aging failure and random failure *** this model,the impact of each failure mode is decoupled and characterized as the combination of two state duration variables,which are separately modeled using specific probability ***,SMCS is used to integrate the three-state component reliability model for state transition sequence generation and system reliability ***,various reliability metrics,including the probability of load curtailment(PLC),expected frequency of load curtailment(EFLC),and expected energy not supplied(EENS),can be *** ensure the applicability of the proposed method,Hash table grouping and the maximum feasible load level judgment techniques are jointly adopted to enhance its computational *** studies are conducted on different aging scenarios to illustrate and validate the effectiveness and practicality of the proposed method.
Electroencephalogram (EEG) reveals the brain dynamics that are of a great value in clinical applications, e.g., epilepsy diagnosis, cognitive status, and other disorders. Intracranial Electroencephalogram (iEEG) is en...
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