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18th Iberian Conference on Information Systems and Technologies, CISTI 2023

作者： Samuil, Ionela Stancioiu, Loredana Ionica, Andreea Cristina Leba, Monica University of Petrosani Department of Management and Industrial Engineering Petrosani Romania University of Petrosani Engineering and Management Doctoral School Petrosani Romania University of Petrosani Department of System Control and Computer Engineering Petrosani Romania

ISBN: (纸本)9789893347928

In a society that is looking for ways of sustainable development, the use of electric vehicles (EVs) can be an applicable solution in different fields of activity, including the tourism industry. The current research analyzes the possibility of using Buggy EVs in a predominantly agritourism region. In this sense, with the help of STELLA Architect, dynamic modeling software, a simulation is run to determine the potential behavior of tourists who use different types of off-road vehicles and their willingness to become Buggy EV users. Based on the data obtained, an algorithm is developed to size the fleet of Buggy EVs necessary for carrying out the activity, taking into account the most important characteristics of the existing Buggy EV, namely range, charging time and price. The results obtained from the study can be used as a starting point for improving the tourist experience, by integrating the technology we are so connected to. © 2023 ITMA.

关键词： computer software

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Privacy-Preservation for Distributed Quantized k-means Clustering 59

Privacy-Preservation for Distributed Quantized k-means Clust...

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59th Annual Allerton Conference on Communication, control, and Computing, Allerton 2023

作者： Rikos, Apostolos I. Manitara, Nicolas E. Oliva, Gabriele Hadjicostis, Christoforos N. Boston University Division of Systems Engineering Department of Electrical and Computer Engineering BostonMA02215 United States University of Cyprus Department of Electrical and Computer Engineering Nicosia1678 Cyprus Università Campus Bio-Medico di Roma Unit of Automatic Control Department of Engineering Rome00128 Italy

ISBN: (纸本)9798350328141

In this paper, we enhance a distributed version of the well known k-means algorithm with privacy-preservation features. While ensuring that sensitive or confidential information remains undisclosed to unauthorized entities (in our case these entities are curious nodes within the network), we maintain the desirable features of the distributed algorithm: the transmitted values are quantized, which optimizes bandwidth utilization and alleviates communication bottlenecks, and nodes possess the ability to collectively determine when to terminate the algorithm, which enables the conservation of valuable resources. We introduce a novel privacy-preserving protocol that not only preserves the privacy of a node's state, but it also ensures confidentiality about its cluster affiliation (i.e., it does not reveal to curious nodes whether a node participates in the update calculation for a specific centroid value). Moreover, we precisely characterize topological conditions that guarantee privacy preservation for individual nodes. Our distributed algorithm allows the formation of exclusive clusters within a finite time frame on any static and strongly connected directed graph. © 2023 IEEE.

关键词： Directed graphs

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SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification 8

SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced ...

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8th Workshop on Cognitive Aspects of the Lexicon, CogALex 2024

作者： Xia, Yuhan Zhao, Qingqing Long, Yunfei Xu, Ge Wang, Jia School of Computer Science and Electronic Engineering University of Essex United Kingdom Institute of Linguistics Chinese Academy of Social Sciences China College of Computer and Control Engineering Minjiang University China Department of Intelligent Science Xi’an Jiaotong-Liverpool University China

ISBN: (纸本)9782493814456

In traditional research approaches, sensory perception and emotion classification have traditionally been considered separate domains. Yet, the significant influence of sensory experiences on emotional responses is undeniable. The natural language processing (NLP) community has often missed the opportunity to merge sensory knowledge with emotion classification. To address this gap, we propose SensoryT5, a neuro-cognitive approach that integrates sensory information into the T5 (Text-to-Text Transfer Transformer) model, designed specifically for fine-grained emotion classification. This methodology incorporates sensory cues into the T5’s attention mechanism, enabling a harmonious balance between contextual understanding and sensory awareness. The resulting model amplifies the richness of emotional representations. In rigorous tests across various detailed emotion classification datasets, SensoryT5 showcases improved performance, surpassing both the foundational T5 model and current state-of-the-art works. Notably, SensoryT5’s success signifies a pivotal change in the NLP domain, highlighting the potential influence of neuro-cognitive data in refining machine learning models’ emotional sensitivity. © 2024 ELRA Language Resource Association.

关键词： Classification (of information)

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Design of a Mechatronic Endoskeleton Robot for Inspection Tasks: Model Reference Adaptive control (MRAC) for Serpentine Locomotion with Stability Analysis 17

Design of a Mechatronic Endoskeleton Robot for Inspection Ta...

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17th International Conference on Developments in eSystems engineering, DeSE 2024

作者： Al Mhdawi, Ammar K. Wright, Nick Younis, Mohammed I. Humaidi, Amjad J. School of Engineering and Sustainable Development De Montfort University LeicesterLE19BH United Kingdom Department of Electrical and Electronic Engineering Newcastle University Newcastle United Kingdom Department of Computer Engineering University of Baghdad Iraq Department of Control and Systems Engineering University of Technology Baghdad Iraq

ISBN: (纸本)9798350368697

An advantage of bio-inspired robots is the versatility of their locomotion on a wide range of terrains that conventional robots are not able to traverse. The snake-like robot, which is a mechanism designed to move in the manner of a biological snake, is an example of a bio-inspired platform. This paper aims to implement and validate, using real and simulation tests on flat terrain, a snake-like endoskeleton robot that permits serpentine movement with adaptive control capabilities. The prototype mechatronic robot was comprised of eleven segments that were 3D printed sequentially. A simulation using Matlab’s Multibody Mechanics tool determines the necessary torques for each motor. In this simulation, a parameterized virtual model of the robot is created, where rectilinear gaits are programmed. In accordance with simulation and experimental results, the robot undertakes different times to reach the goal trajectory or end points depending on the frequency, angular position, and wave length (duty cycle). As a result, there is a high degree of similarity between the simulation tests and those conducted with the prototype endoskeleton. Furthermore, the robot is equipped with machine vision capabilities that allow it to detect faults within oil and gas pipes for the purpose of inspection and maintenance. Furthermore, a Model Reference Adaptive control (MRAC) method based on Lyapunov stability analysis and MIT rules is proposed. The theoretical analysis and numerical simulation show that the designed trajectory tracking control law can make the multi-joint snake-like robot track the trajectory of the front joint when the robot encounters disturbances and stabilize the angular position and velocity of the first two head joints when disturbances occur. © 2024 IEEE.

关键词： Model reference adaptive control

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Optimizing the LSTM Model for Electricity Price Forecasting Using the MSADBO Algorithm

Optimizing the LSTM Model for Electricity Price Forecasting ...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Ahmadvand, Fatemeh Imani, Mahmood Hosseini Andani, Hamid Taheri Andani, Mansour Taheri Politecnico di Torino Department of Control and Computer Engineering Torino Italy Torino Italy Isfahan University of Technology Department of Electronic Engineering Isfahan Iran School of Manufacturing Systems and Networks Arizona State University TempeAZ United States

ISBN: (纸本)9798331541125

Achieving accurate electricity price prediction is essential for market participants aiming to maximize their profits. In this respect, forecasting wholesale electricity market price plays a pivotal role. The advanced nonlinear modeling capabilities of deep learning networks have shown notable success in forecasting electricity prices. However, designing such networks, particularly selecting their optimal hyperparameters, still remains a big challenge. This paper presents a Modified Sine Algorithm Dung Beetle Optimization algorithm (MSADBO) as an improved Dung Beetle Optimization (DBO) algorithm to optimize the hyperparameters of the Long Short-Term Memory (LSTM) neural network to handle the electricity price forecasting in the Italian wholesale market. After optimizing the three most important hyperparameters of the LSTM using MSADBO, the efficiency of the model has shown a drastic improvement. Comparisons with conventional and machine learning-based approaches show that the LSTM-MSADBO model is highly precise in predictions with increased robustness and effectively captures volatility in the electricity market price. © 2025 IEEE.

关键词： Long short-term memory

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Spiker+: a framework for the generation of efficient Spiking Neural Networks FPGA accelerators for inference at the edge

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IEEE Transactions on Emerging Topics in Computing 2024年

作者： Carpegna, Alessio Savino, Alessandro Carlo, Stefano Di Department of Control and Computer Engineering of Politecnico di Torino Torino10129 Italy

Including Artificial Neural Networks (ANNs) in embedded systems at the edge allows applications to exploit Artificial Intelligence (AI) capabilities directly within devices operating at the network periphery, facilitating real-time decision-making. Especially critical in domains such as autonomous vehicles, industrial automation, and healthcare, the use of ANNs can enable these systems to process substantial data volumes locally, thereby reducing latency and power consumption. Moreover, it enhances privacy and security by containing sensitive data within the confines of the edge device. The adoption of Spiking Neural Networks (SNNs) in these environments offers a promising computing paradigm, mimicking the behavior of biological neurons and efficiently handling dynamic, time-sensitive data. However, deploying efficient SNNs in resource-constrained edge environments requires hardware accelerators, such as solutions based on Field Programmable Gate Arrays (FPGAs), that provide high parallelism and reconfigurability. This paper introduces Spiker+, a comprehensive framework for generating efficient, low-power, and low-area customized SNNs accelerators on FPGAs for inference at the edge. Spiker+ presents a configurable multi-layer hardware SNN architecture, a library of highly efficient neuron architectures, and a design framework, enabling the development of complex neural network accelerators with few lines of Python code. Spiker+ is tested on three benchmark datasets, the MNIST, the Spiking Heidelberg Dataset (SHD) and the AudioMNIST. On the MNIST, it demonstrates competitive performance compared to state-of-the-art SNN accelerators. It outperforms them in terms of resource allocation, with a requirement of 7,612 logic cells and 18 Block RAMs (BRAMs), which makes it fit in very small FPGAs, and power consumption, draining only 180mW for a complete inference on an input image. The latency is comparable to the ones observed in the state-of-the-art, with 780μs/img. To th

关键词： High level synthesis

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Streamlining the New Product Introduction by Integrating the Escalation Process 18th

Streamlining the New Product Introduction by Integrating the...

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18th International Conference on Interdisciplinarity in engineering, INTER-ENG 2024

作者： Zgirdea, Mihai Ionica, Andreea Cristina Leba, Monica Engineering and Management Doctoral School University of Petrosani 20 Universitatii Petrosani332006 Romania Management and Industrial Engineering Department University of Petrosani 20 Universitatii Petrosani332006 Romania Computer and System Control Engineering Department University of Petrosani 20 Universitatii Petrosani332006 Romania

ISBN: (纸本)9783031813771

In the dynamic landscape of modern business, companies face the challenge of rapidly introducing new products to meet consumer demands. This research aims to propel NPI efficiency, focusing on the automotive industry, by enhancing cooperative effectiveness and streamlining processes. The overarch- ing objective is to expedite high-quality product with the focus of introducing a controlled escalation process to facilitate swift corrections and improvements throughout each phase of NPI. To realize these objectives, a comprehensive NPI system has been developed. Integration of an escalation process into this sys- tem enables hierarchical escalation of concerns, blocking points, and new pro- cesses, addressing common project delays. The proposed software stands out for its unique integration of the escalation process, ensuring that all organizational members are promptly informed of any deviations using a dynamic project status feature, adapting as the project progresses. The integrated escalation process not only addresses anomalies promptly but also quantifies the time taken to restore normalcy or implement corrections, thereby fostering a more responsive and effi- cient NPI framework. The paper is organized into sections comprising a literature review on NPI methodologies, the integration of an escalation process into the NPI framework, a case study illustrating the implementation of the escalation process in an existing NPI methodology, and the subsequent results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Automotive Industry Collaborative Teamwork controlled Escalation Process Dynamic NPI Software New Product Introduction (NPI)

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Zero-phase CARIMA Filtering and Application in Wind-storage System Sizing and Power Dispatch Optimization

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CSEE Journal of Power and Energy Systems 2024年第6期10卷 2283-2295页

作者： Wei Wang Peng Chen Guorui Ren Jizhen Liu Fang Fang Zhe Chen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Department of Energy Technology Aalborg University9220 AalborgDenmark

Hybrid energy storage system(HESS)is an effective way to mitigate wind power fluctuations on multi-time scale,and can improve influence of large-scale grid-connected wind power on stability and reliability of power system.A novel methodology named zero-phase controlled auto-regressive integrated moving-average(CARIMA)filter is proposed to integrate HESS to smooth wind power ***,a design method for zero-phase CARIMA filter is provided,and then used to determine grid-connected power for a wind storage system and size *** reasons,direct current(DC)component caused by energy storage efficiency and grid-connected power delay caused by phase shift,for causing superfluous energy storage configuration are *** addition,a nonlinear programming scheduling strategy considering battery degradation is *** imbalance caused by efficiency difference during dynamic adjustment of energy storage output power is ***,thermostatically controlled loads(TCLs)are integrated in sizing and scheduling HESS to reduce energy storage demand and improve operating conditions of energy ***,effectiveness of the proposed strategy is verified by a case study.

关键词： CARIMA demand response energy storage optimization wind power zero-phase

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Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 231-239页

作者： Yahui Liu Bin Tian Yisheng Lv Lingxi Li Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 IEEE the Transportation and Autonomous Systems Institute(TASI)and the Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University Indianapolis(IUPUI)Indianapolis 46202 USA

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://***/yahuiliu99/PointC onT.

关键词： Content-based Transformer deep learning feature aggregator local attention point cloud classification

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LLCoach: Generating Robot Soccer Plans Using Multi-role Large Language Models

LLCoach: Generating Robot Soccer Plans Using Multi-role Larg...

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27th RoboCup International Symposium, 2024

作者： Brienza, M. Musumeci, E. Suriani, V. Affinita, D. Pennisi, A. Nardi, D. Bloisi, D.D. Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy School of Engineering University of Basilicata Potenza Italy Department of International Humanities and Social Sciences International University of Rome Rome Italy

ISBN: (纸本)9783031858581

The deployment of robots into human scenarios necessitates advanced planning strategies, particularly when we ask robots to operate in dynamic, unstructured environments. RoboCup offers the chance to deploy robots in one of those scenarios, a human-shaped game represented by a soccer match. In such scenarios, robots must operate using predefined behaviors that can fail in unpredictable conditions. This paper introduces a novel application of Large Language Models (LLMs) to address the challenge of generating actionable plans in such settings, specifically within the context of the RoboCup Standard Platform League (SPL) competitions where robots are required to autonomously execute soccer strategies that emerge from the interactions of individual agents. In particular, we propose a multi-role approach leveraging the capabilities of LLMs to generate and refine plans for a robotic soccer team. The potential of the proposed method is demonstrated through an experimental evaluation, carried out simulating multiple matches where robots with AI-generated plans play against robots running human-built code. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Anthropomorphic robots

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