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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ifran Rahman Nijhum Dewansh Kaloni Paul Kenny Soumyabrata Dev School of Computer Science University College Dublin Ireland The ADAPT SFI Research Centre Dublin Ireland Birla Institute of Technology And Science Pilani India School of Architecture Planning and Environmental Policy University College Dublin Ireland

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Recent years have seen increased interest in solar as a popular renewable energy source. The adoption of solar energy is directed towards increasing research interest in the incorporation of solar energy estimation and forecasting in the current system. Solar irradiance is the sun’s energy incident on earth, and is dependent on many atmospheric parameters, mainly decided by clouds. The use of a Ground-based sky imaging system is the best for forecasting and estimating for near-real-time temporal spectrum. However, creating efficient solar estimation models for edge devices is still a relatively unexplored field. This paper captures the essence of bringing edge computing on devices for solar estimation on ground-based infrared sky images by proposing a novel knowledge distillation method for enhancing lightweight CNN-regression models. In our study, we leveraged a MobilenetV2-based Teacher model and transferred the knowledge into a simple student model by introducing a sigmoid-based loss in the Knowledge Distillation algorithm. The proposed solution shows an impressive reduction of the Mean Square Error (MSE) of the model from 3015.63 to 2540.67. This research advances the field of solar irradiance estimation by emphasizing the importance of creating efficient and edge device deployable models in this context.

关键词： Solar irradiance Renewable energy sources Computational modeling Image edge detection Clouds Atmospheric modeling Estimation

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The impact of algorithm awareness training on competent interaction with intelligent voice assistants

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Discover Education 2025年第1期4卷 1-22页

作者： Markus, André Baumann, Maximilian Pfister, Jan Hotho, Andreas Carolus, Astrid Wienrich, Carolin Psychology of Intelligent Interactive Systems Institute Human-Computer-Media Julius-Maximilians-University Emil-Fischer-Straße 50 Würzburg 97074 Germany Media Psychology Institute Human-Computer-Media Julius-Maximilians-University Oswald-Külpe-Weg 82 Würzburg 97074 Germany Chair of Data Science (Informatic X) Julius-Maximilians-University Emil-Fischer-Straße 50 Würzburg 97074 Germany

Intelligent Voice Assistants (IVAs) have become integral to many users' daily lives, using advanced algorithms to automate various tasks. Nevertheless, many users do not understand the underlying algorithms and how they work, posing potential risks to the competent and self-determined use of IVAs. This work develops three online training modules to promote algorithm awareness, providing (1) basic knowledge of algorithms, (2) risks posed by algorithms in IVAs, and (3) scientific evidence on algorithm aversion. A total of 110 participants were studied to analyze the training effects on various perception levels relevant to IVAs, including usage perception (attitude, exploration), privacy aspects (trustworthiness, privacy control), persuasion aspects (persuasion knowledge, anthropomorphic perception), and self-determined interaction variables (reflection, indulgence). The results show that the modules increase awareness of IVA algorithms, influence user perceptions (e.g., higher exploration intentions), and promote critical engagement with IVAs (e.g., lower trustworthiness). Moreover, the modules contribute to a higher sense of privacy control, reduce persuasive perceptions of IVAs (e.g., anthropomorphic perception), and promote self-determined interaction (e.g., higher indulgent use). The modules offer a new approach to promoting the competent use of IVAs in society and provide a starting point for further research and educational institutions to increase algorithm awareness for IVAs and other AI-based systems. © The Author(s) 2025.

关键词： Algorithm awareness Digital interaction literacy Human–computer interaction Intelligent voice assistant Privacy Smart speaker

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Efficient 2D Simulators for Deep-Reinforcement-Learning-based Training of Navigation Approaches

Efficient 2D Simulators for Deep-Reinforcement-Learning-base...

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International Conference on Ubiquitous Robots (UR)

作者： Huajian Zeng Linh Kästner Jens Lambrecht The Chair of Robotics Artificial Intelligence and Real-time Systems Technical University Munich (TUM) Germany The Chair of Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

In recent years, Deep Reinforcement Learning (DRL) has emerged as a competitive approach for mobile robot navigation. However, training DRL agents often comes at the cost of difficult and tedious training procedures in which powerful hardware is required to conduct oftentimes long training runs. Especially, for complex environments, this proves to be a major bottleneck for widespread adoption of DRL approaches into industries. In this paper we integrate an efficient 2D simulator into the Arena-Rosnav framework of our previous work as an alternative simulation platform to train and develop DRL agents. Therefore, we utilized the provided API to integrate necessary components into the ecosystem of Arena-Rosnav. We evaluated our simulator by training a DRL agent within that platform and compared the training and navigational performance against the baseline 2D simulator Flatland, which is the default simulating platform of Arena-Rosnav. Results demonstrate that using our Arena2D simulator results in substantially faster training times and in some scenarios better agents. This proves to be an important step towards resource-efficient DRL training, which accelerates training times and improve the development cycle of DRL agents for navigation tasks. We made our simulator openly available at https://***/Arena-Rosnav/arena2d.

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Trajectory Prediction and Multi-Risk-Factors Based Dynamic Collision Prediction Method between Construction Vehicles and Workers in Construction Sites

SSRN

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SSRN 2024年

作者： Gan, Wenxia Gu, Kedi Yang, Ruqin Geng, Jing Wang, Huini Qiu, Canzhi Hu, Xiaodi School of Civil Engineering and Architecture Wuhan Institute of Technology Hubei Wuhan430074 China Hubei Land Surveying and Mapping Institute Hubei Wuhan430074 China School of Computer Science & Technology Beijing Institute of Technology Beijing100081 China

Predicting the trajectory of construction vehicles and workers at construction sites is crucial for preventing collision accidents and enhancing safety. However, current prediction methods based on trajectory prediction often focus on the trajectory of workers or construction vehicles and failing to fully exploit critical risk factors, i.e., proximity, velocity, and hazardous zones etc. Therefore, this paper proposes a collision prediction method for construction vehicles and workers by combining trajectory prediction techniques in computer vision with a multiple risk factors-based collision risk assessment. The effectiveness of the method are evaluated across different construction site scenarios. The transformer-based trajectory prediction model demonstrates superior accuracy compared to classical models, such as social-GAN and social-LSTM, which the average displacement error is 0.53m construction site. The research findings demonstrate that proposed collision prediction method can be used to accurately predicting collision risk level of workers on construction sites. © 2024, The Authors. All rights reserved.

关键词： Trajectories

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Hybrid Global Flexible Power Point Tracking Algorithm for Photovoltaic Systems

Hybrid Global Flexible Power Point Tracking Algorithm for Ph...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Kumaresan, Anusha Tafti, Hossein Dehghani Farivar, Glen G. Pou, Josep Strunz, Kai Niyato, Dusit Nanyang Technological University Energy Research Institute at Ntu Interdisciplinary Graduate Programme Singapore Australia The University of Melbourne Department of Electrical and Electronic Engineering VIC3010 Australia Nanyang Technological University School of Electrical and Electronic Engineering Singapore Technische Universität Chair for Sustainable Electric Networks and Sources of Energy Berlin Germany Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9798350364446

Frequency support in grid-connected photovoltaic (PV) systems is achieved by employing flexible power point tracking (FPPT) algorithms. Subsequently, according to the grid frequency requirements, the PV power is curtailed to a power reference, unlike the traditional method of operating the PV at the maximum power point. In practical cases, the PV arrays are prone to partial shading, and hence, there is a requirement to formulate the FPPT algorithms that operates effectively during such cases. Moreover, fast transient response is a necessity, which implies that the algorithms proposed in this paper is desirable. This paper proposes a hybrid global FPPT (GFPPT) algorithm based on secant and Newton's method, to shift the PV operating point to the required power reference. The Newton's method offers quadratic convergence rate and the secant method guarantees convergence. Hence, a hybrid algorithm consisting of these two methods will provide both fast and guaranteed convergence. Validation through simulation is presented in this paper and the proposed method is compared with the recently presented perturb and observe (P&O) GFPPT algorithm in the literature. © 2024 IEEE.

关键词： Newton-Raphson method

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Population Size in Differential Evolution 1

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7th International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2019, and 5th International Conference on Fuzzy and Neural Computing, FANCCO 2019

作者： Alić, Amina Berkovič, Klemen Bošković, Borko Brest, Janez Faculty of Electrical Engineering and Computer Science Computer Architecture and Languages Laboratory Institute of Computer Science University of Maribor Maribor2000 Slovenia

ISBN: (数字)9783030378387

ISBN: (纸本)9783030378370

In this paper we examined how the population size affects the performance of the differential evolution algorithm. First, we tested the original differential evolution algorithm, and then the improved self-adaptive differential evolution algorithm, on ten benchmark functions, that have been proposed for the CEC 2019 competition. We used six different population sizes. Afterwards, we tested the newly created algorithm with population reinitialization. The results show that the population size affects the algorithm’s efficiency, and that we need to tune it to obtain the best results. In the paper, we demonstrate that the newly created algorithm with reinitialization gives better, or at least comparable, results than the two algorithms without reinitialization. © Springer Nature Switzerland AG 2020.

关键词： Population statistics

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LACC:a hardware and software co-design accelerator for deep neural networks

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High Technology Letters 2021年第1期27卷 62-67页

作者： Yu Yong Zhi Tian Zhou Shengyuan State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190P.R.China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049P.R.China Cambricon Technologies Ltd Beijing 100190P.R.China

With the increasing of data size and model size,deep neural networks(DNNs)show outstanding performance in many artificial intelligence(AI)*** the big model size makes it a challenge for high-performance and low-power running DNN on processors,such as central processing unit(CPU),graphics processing unit(GPU),and tensor processing unit(TPU).This paper proposes a LOGNN data representation of 8 bits and a hardware and software co-design deep neural network accelerator LACC to meet the *** data representation replaces multiply operations to add and shift operations in running *** accelerator achieves higher efficiency than the state-of-the-art DNN accelerators by domain specific arithmetic computing ***,LACC speeds up the performance per watt by 1.5 times,compared to the state-of-the-art DNN accelerators on average.

关键词： deep neural network(DNN) domain specific accelerator domain specific data type

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A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection

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computers, Materials & Continua 2022年第10期73卷 719-733页

作者： Jie Chen Chengsheng Yuan Chen Cui Zhihua Xia Xingming Sun Thangarajah Akilan School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310053China Jiangsu Yuchi Blockchain Research Institute Nanjing210044China Department of Software Engineering Lakehead UniversityThunder BayON P7B 5E1Canada

Fingerprint identification systems have been widely deployed in many occasions of our daily ***,together with many advantages,they are still vulnerable to the presentation attack(PA)by some counterfeit *** address challenges from PA,fingerprint liveness detection(FLD)technology has been proposed and gradually attracted people’s *** vast majority of the FLD methods directly employ convolutional neural network(CNN),and rarely pay attention to the problem of overparameterization and over-fitting of models,resulting in large calculation force of model deployment and poor model *** at filling this gap,this paper designs a lightweight multi-scale convolutional neural network method,and further proposes a novel hybrid spatial pyramid pooling block to extract abundant features,so that the number of model parameters is greatly reduced,and support multi-scale true/fake fingerprint ***,the representation self-challenge(RSC)method is used to train the model,and the attention mechanism is also adopted for optimization during execution,which alleviates the problem of model over-fitting and enhances generalization of detection ***,experimental results on two publicly benchmarks:LivDet2011 and LivDet2013 sets,show that our method achieves outstanding detection results for blind materials and *** size of the model parameters is only 548 KB,and the average detection error of cross-sensors and cross-materials are 15.22 and 1 respectively,reaching the highest level currently available.

关键词： FLD lightweight multi-scale RSC blind materials

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Nonparametric Control Koopman Operators

arXiv

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arXiv 2024年

作者： Bevanda, Petar Driessen, Bas Iacob, Lucian Cristian Sosnowski, Stefan Tóth, Roland Hirche, Sandra Chair of Information-oriented Control TU München Germany Control Systems Group TU/e Eindhoven Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

This paper presents a novel Koopman (composition) operator representation framework for control systems in reproducing kernel Hilbert spaces (RKHSs) that is free of explicit dictionary or input parametrizations. By establishing fundamental equivalences between different model representations, we are able to close the gap of control system operator learning and infinite-dimensional regression, enabling various empirical estimators and the connection to well-understood learning theory in RKHSs under one unified framework. As a consequence, our proposed framework allows for arbitrary accurate finite-rank approximations in infinite-dimensional spaces and leads to finite-dimensional predictors without apriori restrictions to a finite span of functions or inputs. To enable applications to high-dimensional control systems, we improve the scalability of our proposed control Koopman operator estimates by utilizing sketching techniques. Numerical experiments demonstrate superior prediction accuracy compared to bilinear EDMD, especially in high dimensions. Finally, we show that our learned models are readily interfaced with linear-parameter-varying techniques for model predictive control. Copyright © 2024, The Authors. All rights reserved.

关键词： Predictive control systems

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Microwave assisted pyrolysis of biomass feedstock fundamentals and the effect of process parameters - A Review 4

Microwave assisted pyrolysis of biomass feedstock fundamenta...

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4th International Conference on Emerging Electrical Energy, Electronics and Computing, ICE4CT 2022

作者： Hamzah, Husam Talib Veluru, Sridevi Hasan, Hikmat Mohammed Abed, Hayder Yousif Mahdi, Husam Salah Hamdullah, Noor Abdulqader Tukarambai, M. Poiba, Venkata Rao Srikanth, R. Department of Chemical Engineering College of Engineering Andhra University Visakhapatnam India Department of Electrical Engineering Faculty of Engineering Ferdowsi University of Mashhad Mashhad Iran Department of Electrical Engineering University of Technology Baghdad Iraq Department of Computer Science and System Engineering College of Engineering Andhra University Visakhapatnam India Electrical and Computer Engineering School of Engineering and Architecture Altinbas University Istanbul Turkey Department of Chemical Engineering Anil Neerukonda Institute of Technology and Sciences Sangivalasa India

Globally, biomass usage as a supply of non-depletable resources materials used in the production of energy at their rawest state is an issue. Pyrolysis is a method of thermally treating biomass that as a consequence in the formation of liquid, products, both solid and gaseous. Elevated heating is required to transform the complicated composition of the biomass's underlying structure matrix into usable products. Heating with a microwave oven has the potential to become a competitive option. to more traditional methods of heating. Owing to its ease of operation and rapid heating rate, it has recently been widely employed in pyrolysis. The goals of this research are to found the principles of MW-assisted pyrolysis action and to examine some critical issues characteristics of operation that have an influence on the yield of a product. This procedure appears to be governed by a number of operational factors for instance, microwave power and temperature degree, the addition of microwave absorbers and their concentration, initial relative humidity, primary sweep gas flow rate/time spent in residence. © Published under licence by IOP Publishing Ltd.

关键词： Biomass

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