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arXiv 2020年

作者： Neshat, Mehdi Abbasnejad, Ehsan Heydari, Azim Garcia, Davide Astiaso Nezhad, Meysam Majidi Groppi, Daniele Tjernberg, Lina Bertling Alexander, Bradley Wagner, Markus Optimization and Logistics Group School of Computer Science University of Adelaide Australia Australian Institute for Machine Learning University of Adelaide Australia Sapienza University of Rome Italy Department of Planning Design and Technology of Architecture Sapienza University of Rome Italy School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Reliable wind turbine power prediction is imperative to the planning, scheduling and control of wind energy farms for stable power production. In recent years Machine Learning (ML) methods have been successfully applied in a wide range of domains, including renewable energy. However, due to the challenging nature of power prediction in wind farms, current models are far short of the accuracy required by industry. In this paper, we deploy a composite ML approach-namely a hybrid neuro-evolutionary algorithm-for accurate forecasting of the power output in wind-turbine farms. We use historical data in the supervisory control and data acquisition (SCADA) systems as input to estimate the power output from an onshore wind farm in Sweden. At the beginning stage, the k-means clustering method and an Autoencoder are employed, respectively, to detect and filter noise in the SCADA measurements. Next, with the prior knowledge that the underlying wind patterns are highly non-linear and diverse, we combine a self-adaptive differential evolution (SaDE) algorithm as a hyper-parameter optimizer, and a recurrent neural network (RNN) called Long Short-term memory (LSTM) to model the power curve of a wind turbine in a farm. Two short time forecasting horizons, including ten-minutes ahead and one-hour ahead, are considered in our experiments. We show that our approach outperforms its counterparts. Copyright © 2020, The Authors. All rights reserved.

关键词： Long short-term memory

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Features of the cadmium chalcogenide substrates with surface nanostructure

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Materials Today: Proceedings 2021年 35卷 626-629页

作者： Tetiana Mazur Mykhailo Slyotov Myroslav Mazur Volodymyr Prokopiv Oksana Kinzerska Oleksii Slyotov Department of Solid State Physics and Chemistry/Faculty of Physics and Technology Vasyl Stefanyk Precarpathian National University 57 Shevchenko Str. Ivano-Frankivsk 76019 Ukraine Department of General and Applied Physics/Institute of Architecture Construction and Power Engineering Ivano-Frankisvsk National Technical University of Oil and Gas 15 Karpatska Str. Ivano-Frankivsk 76019 Ukraine Department of Optics/Physical Technical and Computer Science Institute Publishing and Printing Yuriy Fedkovych Chernivtsi National University 2 Kotsubinsky Str. 58012 Chernivtsi Ukraine Department of Physics of Semiconductors and Nanostructures/Physical Technical and Computer Science Institute Yuriy Fedkovych Chernivtsi National University 2 Kotsubinsky Str. 58012 Chernivtsi Ukraine

The effect of single crystalline CdTe, CdSe, CdS substrates normalizing annealing on their optical properties was investigated, and the surface nanostructure formation was determined by the study of optical transmission spectra, photoluminescence and atomic force microscopy (AFM) topograms. Possibility obtaining of intense luminescence in the border region, formed by interband recombination and transitions due to size quantization of the charge carrier energy, is determined.

关键词： Cadmium chalcogenides Normalizing annealing Optical transmission Photoluminescence Size quantization of energy

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The Role of Open-Source LLMs in Shaping the Future of GeoAI

arXiv

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arXiv 2025年

作者： Huang, Xiao Tu, Zhengzhong Ye, Xinyue Goodchild, Michael Department of Environmental Sciences Emory University AtlantaGA30322 United States Department of Computer Science and Engineering Texas A&M University College StationTX77840 United States Department of Landscape Architecture and Urban Planning Center for Geospatial Sciences Applications and Technology Texas A&M University College StationTX77840 United States Department of Geography University of California Santa BarbaraCA93106 United States

Large Language Models (LLMs) are transforming geospatial artificial intelligence (GeoAI), offering new capabilities in data processing, spatial analysis, and decision support. This paper examines the open-source paradigm's pivotal role in this transformation. While proprietary LLMs offer accessibility, they often limit the customization, interoperability, and transparency vital for specialized geospatial tasks. Conversely, open-source alternatives significantly advance Geographic Information Science (GIScience) by fostering greater adaptability, reproducibility, and community-driven innovation. Open frameworks empower researchers to tailor solutions, integrate cutting-edge methodologies (e.g., reinforcement learning, advanced spatial indexing), and align with FAIR principles. However, the growing reliance on any LLM necessitates careful consideration of security vulnerabilities, ethical risks, and robust governance for AI-generated geospatial outputs. Ongoing debates on accessibility, regulation, and misuse underscore the critical need for responsible AI development strategies. This paper argues that GIScience advances best not through a single model type, but by cultivating a diverse, interoperable ecosystem combining open-source foundations for innovation, bespoke geospatial models, and interdisciplinary collaboration. By critically evaluating the opportunities and challenges of open-source LLMs within the broader GeoAI landscape, this work contributes to a nuanced discourse on leveraging AI to effectively advance spatial research, policy, and decision-making in an equitable, sustainable, and scientifically rigorous manner. © 2025, CC BY-NC-ND.

关键词： Indexing (of information)

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CACAO-X: Contour Assisted Convolutional Neural Networks with Image Explainability and Inference on the Edge

SSRN

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SSRN 2022年

作者： Daher, Ali W. Ferrari, Enrico Verda, Damiano Chible, Hussein Musellic, Marco Caviglia, Daniele D. Department of Electrical Electronic and Telecommunications Engineering and Naval Architecture University of Genoa Via Opera Pia 11 Liguria Genoa16145 Italy Ph.D. School for Sciences and Technology Lebanese University Beirut6573/14 Lebanon Institute of Electronics Computer and Telecommunication Engineering Consiglio Nazionale delle Ricerche Via De Marini 6 Genoa Liguria16149 Italy Rulex Innovation Labs Rulex inc. Via Felice Romani 9 Liguria Genoa16122 Italy

Image classification and interpretability is a basic procedure in modern AI systems as well as being employed in the fields of security, instrumentation, and image classification. Explainable Artificial Intelligence also plays a crucial role in providing machine learning techniques with a white-box output that allows the practitioner to follow or interpret the rules. Therefore, we present CACAO-X which is a framework for ML forecasts that utilizes an algorithm which converts images into time-series and employs CNN, LSTM, and MLP on the original images and converted data in a mixed input approach achieving accurate forecasts on multiple applications. Furthermore, Shapley values are employed on the proposed ML models to extract the local explainable features of such images. These features are again used with the same conversion algorithm to generate time-series and application specific landmarks using the Logic Learning Machine (LLM) to determine the explainable rules. In addition to being generated automatically, explicative labelling is adopted in the dataset which give insight to the operator to which landmarks, positions and angles contribute to the classification of each record. The system is robust as it achieves benchmark results in the gender classification application while remaining competitive in all others where it is used as a reference model for global image explainability. Additionally, the ML inference for every application is performed according to the edge-computing paradigm, by deploying the models on a limited resources Raspberry Pi platform through SSH access. Moreover, a novel callback function has been developed which is able to stop the training of the CACAO-NET model at a stable state based on the level of oscillation around the most recent mean accuracy. © 2022, The Authors. All rights reserved.

关键词： Image classification

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Impact of NBTI on Increasing the Susceptibility of FinFET to Radiation

Impact of NBTI on Increasing the Susceptibility of FinFET to...

引用

Annual International Symposium on Reliability Physics

作者： Frank Sill Torres Hussam Amrouch Jörg Henke Rolf Drechsler Group of Computer Architecture University of Bremen Germany Department of Computer Science Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

This work investigates the interaction between Neg-ative Bias Temperature Instability (NBTI)and radiation effects in 14nm FinFET devices. Due to the complex interaction between traps generated by NBTI and induced charges by strikes of ionizing particles, we opted for a complete physical-based analysis using TCAD mixed-mode simulations. This enables an accurate estimation and then modeling of the duration a circuit requires to recover from a particle strike and, thus, return to correct operation under the effects of NBTI. This a crucial aspect, because the longer the recovery time, the higher the probabilities of a soft-error and that this error remains undetected. Further, our employed setup enables an accurate determination of the critical charge (Q crit ), i. e. the minimum collected charge that results into a faulty transition of a circuit's output node. Our investigation reveals that there is indeed a strong relation between NBTI and the time a circuit remains in faulty state. Consequently, detection schemes must be adapted during circuit's operation to take aging into account in order to avoid that errors remain undetected.

关键词： Negative bias temperature instability Thermal variables control Inverters Ions Circuit faults FinFETs Load modeling

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Cacao-X: Contour Assisted Convolutional Neural Networks with Image Explainability and Inference on the Edge

SSRN

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SSRN 2022年

作者： Daher, Ali W. Muselli, Marco Caviglia, Daniele D. Ferrari, Enrico Verda, Damiano Chible, Hussein Department of Electrical Electronic and Telecommunications Engineering and Naval Architecture University of Genoa Via Opera Pia 11 Liguria Genoa16145 Italy Ph.D. School for Sciences and Technology Lebanese University Beirut6573/14 Lebanon Institute of Electronics Computer and Telecommunication Engineering Consiglio Nazionale delle Ricerche Via De Marini 6 Genoa Liguria16149 Italy Rulex Innovation Labs Rulex inc. Via Felice Romani 9 Liguria Genoa16122 Italy

关键词： Image classification

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When creators meet the metaverse: A survey on computational arts

arXiv

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arXiv 2021年

作者： Lee, Lik-Hang Lin, Zijun Hu, Rui Gong, Zhengya Kumar, Abhishek Li, Tangyao Li, Sijia Hui, Pan Augmented Reality and Media Lab KAIST Korea Republic of Department of Economics University College London United Kingdom Computational Media & Arts The Hong Kong University of Science and Technology Hong Kong Center for Ubiquitous Computing University of Oulu Finland Department of Computer Science University of Helsinki Finland School of Mathematical Sciences Queen Mary University of London United Kingdom Department of Architecture Inha University Korea Republic of

The metaverse, enormous virtual-physical cyberspace, has brought unprecedented opportunities for artists to blend every corner of our physical surroundings with digital creativity. This article conducts a comprehensive survey on computational arts, in which seven critical topics are relevant to the metaverse, describing novel artworks in blended virtual-physical realities. The topics first cover the building elements for the metaverse, e.g., virtual scenes and characters, auditory, textual elements. Next, several remarkable types of novel creations in the expanded horizons of metaverse cyberspace have been reflected, such as immersive arts, robotic arts, and other user-centric approaches fuelling contemporary creative outputs. Finally, we propose several research agendas: democratising computational arts, digital privacy, and safety for metaverse artists, ownership recognition for digital artworks, technological challenges, and so on. The survey also serves as introductory material for artists and metaverse technologists to begin creations in the realm of surrealistic cyberspace. © 2021, CC BY-NC-SA.

关键词： Surveys

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Using the Model of a Functional Rationalizer of Consumer Behavior in Recommendation Systems for Managing the Transport Activity of the Urban Population 23

Using the Model of a Functional Rationalizer of Consumer Beh...

引用

23rd International Conference on Soft Computing and Measurements, SCM 2020

作者： Seliverstov, Yaroslav A. Seliverstov, Svytoslav A. Podoprigora, Nikolay V. Starichenkov, Alexey L. Naryshkin, Roman S. Solomenko Institute of Transport Problems Russian Academy of Sciences Institute of Computer Science and Technology Peter the Great St.Petersburg Polytechnic University Laboratory of Intelligent Transport Systems St. Petersburg Russia Saint Petersburg State University of Architecture and Civil Engineering Department of Ground Transportation Technological Machines St. Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Department of Ship Control Systems St. Petersburg Russia Saint Petersburg State University Department of Programming Technology St. Petersburg Russia

ISBN: (纸本)9781728196923

An overview of existing urban software mobile applications of the transport and economic direction is given. A model of a functional rationalizer of consumer behavior is being built. The software model of the functional rationalizer of consumer behavior (SM FRCB) was developed for the first time. PMFRCB allows the user to expand the number of his preferred needs (occupations, activities) during the daily cycle through intelligent recommendations. Also, SM FRCB allows you to optimize needs (activities, activities) at the cost and rate of their use. The SM FRCB is made in Python_3 using the libraries matplotlib, collections, numpy, pyplot, mplot3d. The analysis of consumer behavior before and after using the functional rationalizer model is carried out. The graphical results of a structural analysis of the costs of old and new consumer behavior, analysis of consumer behavior within the boundaries of the theory of possibilities and the model of transport behavior of a resident before and after applying the functional rationalizer model are presented. The prospects of further improving the SM FRCB are considered. © 2020 IEEE.

关键词： Smart city

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Improving the prediction accuracy of cyanobacterial blooms using a hybrid model integrating dimensionality reduction and deep learning

Water Biology and Security

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Water Biology and Security 2025年

作者： Chen, Jiwei Hou, Zhongbao Li, Yunxi Li, Jiexian Li, Lin School of Naval Architecture and Ocean Engineering Huazhong University of Science and Technology Wuhan 430074 China Department of Public Health Fu Jen Catholic University Taiwan 242062 China Khoury College of Computer Sciences Northeastern University Vancouver V6B1Z3 Canada Institute of Hydrobiology Chinese Academy of Sciences Wuhan 430072 China

Cyanobacteria Harmful Blooms have negatively impacted human socio-economic activities and the natural ecological environment. Predicting cyanobacterial blooms requires the application of advanced analytical models powered by artificial intelligence to accurately model bloom development. In this study, we developed a hybrid model that integrates the Temporal Convolution Network (TCN) with the Long Short-Term Memory Network (LSTM) to achieve highly accurate predictions of cyanobacterial bloom events. We used Principal Component Analysis (PCA) during the preprocessing phase to reduce data dimensionality of model input variables and enhance computational efficiency. We used a data set from the Darling River in Australia to conduct a comparative analysis with traditional deep learning models and comprehensively assess the proposed model performance. We also investigated the application of the correlation coefficient matrix method in variable selection. The results demonstrate that the proposed model outperforms conventional methods in predicting cyanobacterial blooms. When benchmarked against the Recurrent Neural Network (RNN) model, our model demonstrates a superior ability to capture the intricacies of time series data. Specifically, it achieves a 79.5 % reduction in Mean Square Error (MSE) and a 197 % increase in the Coefficient of Determination (R²). Furthermore, comparative experiments were carried out across several prediction time steps, indicating that the model achieves optimal performance in minimizing both the MSE and Mean Absolute Error (MAE) when predicting one day ahead. Notably, the error distribution is most condensed at this time step, outperforming models with prediction horizons ranging from two to seven days. We also undertook a thorough analysis of the significance of each principal component in model predictions. The results highlight that principal component 5 and principal component 2 exhibit the most substantial contribution to the p

关键词： Cyanobacteria bloom Deep learning Long short-term memory network Prediction Principal component analysis Temporal convolution network

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ZETA: a library for Zonotope-based EsTimation and fAult diagnosis of discrete-time systems

arXiv

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arXiv 2025年

作者： Rego, Brenner S. Scott, Joseph K. Raimondo, Davide M. Terra, Marco H. Raffo, Guilherme V. Department of Electrical and Computer Engineering University of São Paulo SP São Carlos13566-590 Brazil Department of Chemical and Biomolecular Engineering Georgia Institute of Technology 311 Ferst Dr. AtlantaGA30318 United States Department of Engineering and Architecture University of Trieste Trieste34127 Italy Department of Electronics Engineering Graduate Program in Electrical Engineering Federal University of Minas Gerais MG Belo Horizonte31270-901 Brazil

This paper introduces ZETA, a new MATLAB library for Zonotope-based EsTimation and fAult diagnosis of discrete-time systems. It features user-friendly implementations of set representations based on zonotopes, namely zonotopes, constrained zonotopes, and line zonotopes, in addition to a basic implementation of interval arithmetic. This library has capabilities starting from the basic set operations with these sets, including propagations through nonlinear functions using various approximation methods. The features of ZETA allow for reachability analysis and state estimation of discrete-time linear, nonlinear, and descriptor systems, in addition to active fault diagnosis of linear systems. Efficient order reduction methods are also implemented for the respective set representations. Some examples are presented in order to illustrate the functionalities of the new library. © 2025, CC BY.

关键词： Digital arithmetic

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