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A LESO Based Backstepping Controller Considering Coal Seam Hardness for Rotary Speed in Coal Mine Tunnel Drilling Process

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Journal of Systems Science & Complexity 2024年第5期37卷 1789-1808页

作者： CHEN Luefeng LIU Xiao WU Min LU Chengda PEDRYCZ Witold HIROTA Kaoru School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R2G7Canada The Tokyo Institute of Technology Yokohama 226-8502Japan

In the process of coal mine drilling,controlling the rotary speed is important as it determines the efficiency and safety of *** this paper,a linear extended state observer(LESO)based backstepping controller for rotary speed is proposed,which can overcome the impact of changes in coal seam hardness on rotary ***,the influence of coal seam hardness on the drilling rig’s rotary system is considered for the first time,which is reflected in the numerical variation of load torque,and a dynamic model for the design of rotary speed controller is *** an LESO is designed to observe the load torque,and feedforward compensation is carried out to overcome the influence of coal seam *** on the model of the compensated system,a backstepping method is used to design a controller to achieve tracking control of the rotary ***,the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments,the steady-state error of the rotary speed in field is 1 r/min,and the overshoot is reduced to 5.8%.This greatly improves the stability and security,which is exactly what the drilling process requires.

关键词： Backstepping extended state observer hydraulic tunnel drilling machine rotary speed

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A Holistic Assessment Framework for Evaluating Buildings' Green and Digital Readiness Level

A Holistic Assessment Framework for Evaluating Buildings' Gr...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Stylianides, Nearchos Hadjidemetriou, Lenos Charalambous, Charalambos Panayiotou, Christos University of Cyprus Kios Research and Innovation Center of Excellence Department of Electrical and Computer Engineering Nicosia Cyprus

ISBN: (纸本)9789531842976

Smart buildings assessment methodologies are important tools utilized across all stages of transitioning towards a greener, smarter, and energy-efficient building sector. Existing assessment methods either focus only on specific building aspects, or consider buildings with particular technical characteristics, or require explicit technical knowledge by the users to evaluate building performance. Consequently, this paper proposes a holistic and flexible assessment framework that encompasses all facets of a building, including green factor, energy efficiency, occupants' comfort, and digitization levels. The proposed framework does not require strong technical knowhow to perform the assessment and can be generally applied for evaluating different buildings by providing basic information about their main characteristics. The assessment framework was applied to a real commercial building, demonstrating its effective use in evaluating overall building performance and analyzing the score in each individual impact category or domain. In addition, two potential action plans were formulated by utilizing the proposed framework to explore different pathways for enhancing the building's performance. © 2024 IEEE.

关键词： Smart homes

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Increasing the Fairness of Photovoltaic Curtailments for Voltage Management in Distribution Grids

Increasing the Fairness of Photovoltaic Curtailments for Vol...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Kotsonias, Andreas Hadjidemetriou, Lenos Asprou, Markos Panayiotou, Christos G. University of Cyprus Kios Research and Innovation Center of Excellence Department of Electrical and Computer Engineering Nicosia Cyprus

ISBN: (纸本)9789531842976

The increasing penetration of photovoltaic systems (PV) in distribution grids can create significant over-voltage conditions due to intense reverse power flow. In addition to reactive power control, PV curtailments are often employed for voltage regulation to ensure compliance with the regulatory limits. However, reducing the output power of a PV system results to a revenue drop for the owners, raising concerns about fairness. Relevant works often address this issue through penalty terms in the objective function, but most works neglect the trade-off between fairness and total curtailments, i.e., increasing fairness leads to an increase of total curtailments. This paper proposes a novel methodology to enhance the fairness of PV curtailments for voltage regulation while ensuring that the increase in total curtailments is within a specified acceptable limit. In this direction, the Jain's fairness index is used to quantitatively measure fairness, which is incorporated in the optimization process as a second order cone program constraint. An iterative algorithm based on the bisection method is then developed to increase fairness until the allowable total curtailments are reached. Simulation results demonstrate the capability of the proposed method to improve the fairness of PV curtailments, considering different levels of allowable total curtailments. © 2024 IEEE.

关键词： DC distribution systems

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Efficient Global Neural Architecture Search

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SN computer Science 2025年第3期6卷 1-12页

作者： Siddiqui, Shahid Kyrkou, Christos Theocharides, Theocharis Electrical and Computer Engineering Department University of Cyprus Nicosia Cyprus KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

Neural architecture search (NAS) has shown promise towards automating neural network design for a given task, but it is computationally demanding due to training costs associated with evaluating a large number of architectures to find the optimal one. To speed up NAS, recent works limit the search to network building blocks (modular search) instead of searching the entire architecture (global search), approximate candidates’ performance evaluation in lieu of complete training, and use gradient descent rather than naturally suitable discrete optimization approaches. However, modular search does not determine network’s macro architecture i.e. depth and width, demanding manual trial and error post-search, hence lacking automation. In this work, we revisit NAS and design a navigable, yet architecturally diverse, macro-micro search space. In addition, to determine relative rankings of candidates, existing methods employ consistent approximations across entire search spaces, whereas different networks may not be fairly comparable under one training protocol. Hence, we propose an architecture-aware approximation with variable training schemes for different networks. Moreover, we develop an efficient search strategy by disjoining macro-micro network design that yields competitive architectures in terms of both accuracy and size. Our proposed framework achieves a new state-of-the-art on EMNIST and KMNIST, while being highly competitive on the CIFAR-10, CIFAR-100, and FashionMNIST datasets and being 2-4 times faster than the fastest global search methods. Lastly, we demonstrate the transferability of our framework to real-world computer vision problems by discovering competitive architectures for face recognition applications. © The Author(s) 2025.

关键词： AutoML Convolutional neural networks Deep learning Image classification Neural architecture search

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Near-Field Beam Training with Analog Extremely Large True-Time-Delay Arrays: (Invited Paper) 58

Near-Field Beam Training with Analog Extremely Large True-Ti...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Pehlivan, Ibrahim Ilter, Mehmet C. Valkama, Mikko Cabric, Danijela University of California Electrical and Computer Engineering Department Los Angeles United States Tampere Wireless Research Center Tampere University Department of Electrical Engineering Finland

ISBN: (纸本)9798350354058

As wireless systems have started to use millimeter-wave (mmWave) frequencies to exploit the abundant bandwidth, large antenna arrays with high beamforming gain have become necessary for compensating severe path loss. However, deploying large number of antennas extends the near-field (NF) region where effective beamforming requires the knowledge of not only the user equipment (UE)'s direction, but also distance. In this paper, we present a fast NF beam training method for analog extremely large (XL) true-time-delay (TTD) arrays using only a single orthogonal frequency-division multiplexing (OFDM) pilot symbol. In the proposed virtual sub-array sparse rainbow (VSSR) algorithm, we virtually partition the antenna array into smaller aperture sub-arrays so that a UE falls in the far-field region of each sub-array. By exploiting properties of the rainbow beamforming, we obtain near-orthogonal sub-array measurements such that UE directions with respect to each sub-array can be estimated with a sparse recovery algorithm. Then, these sub-array referenced UE directions are combined to estimate UE's location. Our simulation results demonstrate that the proposed method enables near-optimal beam training using only a single OFDM symbol, achieving an average of at least the 95% of the optimal array gain under various channel conditions. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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GRU combined model based on multi-objective optimization for short-term residential load forecasting

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10423-10440页

作者： Yi, Lingzhi Peng, Xinlong Fan, Chaodong Wang, Yahui Li, Yunfan Liu, Jiangyong College of Automation and Electronic Engineering Xiangtan University Hunan Engineering Research Center of Multi-energy Cooperative Control Technology Hunan Xiangtan China School of Computer Science and Technology Hainan University Hainan China College of Electrical and Information Engineering Hunan University Changsha China

Reliable and accurate short-term forecasting of residential load plays an important role in DSM. However, the high uncertainty inherent in single-user loads makes them difficult to forecast accurately. Various traditional methods have been used to address the problem of residential load forecasting. A single load forecast model in the traditional method does not allow for comprehensive learning of data characteristics for residential loads, and utilizing RNNs faces the problem of long-term memory with vanishing or exploding gradients in backpropagation. Therefore, a gated GRU combined model based on multi-objective optimization is proposed to improve the short-term residential load forecasting accuracy in this paper. In order to demonstrate the effectiveness, GRUCC-MOP is first experimentally tested with the unimproved model to verify the model performance and forecasting effectiveness. Secondly the method is evaluated experimentally with other excellent forecasting methods: models such as DBN, LSTM, GRU, EMD-DBN and EMD-MODBN. By comparing simulation experiments, the proposed GRU combined model can get better results in terms of MAPE on January, April, July, and November load data, so this proposed method has better performance than other research methods in short-term residential load forecasting. © 2024 - IOS Press. All rights reserved.

关键词： Multiobjective optimization

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Machine Learning for Smart Agriculture: A Comprehensive Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2568-2588页

作者： Mahmood, M. Rezwanul Matin, Mohammad Abdul Goudos, Sotirios K. Karagiannidis, George North South University Department of Electrical and Computer Engineering Dhaka1229 Bangladesh Aristotle University of Thessaloniki ELEDIA@AUTH Department of Physics Thessaloniki54124 Greece Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece Cyber Security Systems and Applied AI Research Center Beirut1102 2801 Lebanon

As communication technologies and equipment evolve, smart assets become smarter. The agricultural industry is also evolving in line with the implementation of modern communication protocols, intelligent sensors, and equipment. This evolution is enabling large-scale agricultural production processes to operate independently, thus, securing the food supply chain for an ever-growing population. Data processing for such a system with multiple heterogeneous sources requires proper management for effective agricultural operations. Recognizing the advantages of machine learning (ML) in performing large-scale data processing, researchers are investigating the implementation of ML to design an effective intelligent agricultural architecture. The aim of this article is to provide a thorough analysis of the state-of-the-art in smart agriculture, open challenges, and guidelines for the development of further enhanced smart agriculture systems. Specifically, we describe how ML is used to create intelligent agricultural systems supported by state-of-the-art technology. © 2020 IEEE.

关键词： Weed control

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Higher-order properties and extensions for indirect MRAC and APPC of linear systems

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Science China(Information Sciences) 2024年第4期67卷 218-238页

作者： Yanjun ZHANG Zhipeng ZHANG Jian SUN Lei WANG Kangkang ZHANG National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology School of Automation Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center College of Control Science and Engineering Zhejiang University Department of Electrical and Electronic Engineering Imperial College London KIOS Research and Innovation Center of Excellence University of Cyprus

Recently, a reference derived some new higher-order output tracking properties for direct model reference adaptive control（MRAC） of linear time-invariant（LTI） systems: limt→∞ e（i）（t） = 0, i = 1,..., n*-1, where n*and e（i）（t） denote the relative degree of the system and the i-th derivative of the output tracking error, respectively. However, a naturally arising question involves whether indirect adaptive control（including indirect MRAC and indirect adaptive pole placement control） of LTI systems still has higher-order tracking properties. Such properties have not been reported in the literature. Therefore, this paper provides an affirmative answer to this question. Such higher-order tracking properties are new discoveries since they hold without any additional design conditions and, in particular, without the persistent excitation condition. Given the higher-order properties, a new adaptive control system is developed with stronger tracking features.（1） It can track a reference signal with any order derivatives being unknown.（2） It has higher-order exponential or practical output tracking properties.（3） Finally, it is different from the usual MRAC system, whose reference signal's derivatives up to the n*order are assumed to be known. Finally, two simulation examples are provided to verify the theoretical results obtained in this paper.

关键词： higher-order tracking indirect adaptive control model reference adaptive control adaptive pole placement control

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A Survey of Cyber Attacks on Cyber Physical Systems:Recent Advances and Challenges

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 784-800页

作者： Wenli Duo MengChu Zhou Abdullah Abusorrah Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China IEEE Department of Electrical and Computer Engineering K.A.CARE Energy Research and Innovation Center Department of Electrical and Computer Engineering K.A.CARE Energy Research and Innovation Center and Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz University

A cyber physical system(CPS)is a complex system that integrates sensing,computation,control and networking into physical processes and objects over *** plays a key role in modern industry since it connects physical and cyber *** order to meet ever-changing industrial requirements,its structures and functions are constantly ***,new security issues have arisen.A ubiquitous problem is the fact that cyber attacks can cause significant damage to industrial systems,and thus has gained increasing attention from researchers and *** paper presents a survey of state-of-the-art results of cyber attacks on cyber physical ***,as typical system models are employed to study these systems,time-driven and event-driven systems are ***,recent advances on three types of attacks,i.e.,those on availability,integrity,and confidentiality are *** particular,the detailed studies on availability and integrity attacks are introduced from the perspective of attackers and ***,both attack and defense strategies are discussed based on different system *** challenges and open issues are indicated to guide future research and inspire the further exploration of this increasingly important area.

关键词： Attack detection attack strategy cyber attack cyber physical system(CPS) secure control

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Dynamic Task Offloading in Connected Vehicles: Leveraging a Graph Neural Networks Approach for Multi-hop Search

Dynamic Task Offloading in Connected Vehicles: Leveraging a ...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Malik, Asad Waqar Khan, Samee U. Mississippi State University National Strategic Planning and Analysis Research Center Department of Electrical and Computer Engineering United States Mississippi State University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350367942

The vehicular edge computing model provides computational support to nearby vehicles requiring low-latency computation. However, existing algorithms typically only consider immediate neighbor nodes as potential vehicles for computation offloading. In this study, we address this limitation by modeling the task offloading problem using a Graph Neural Network approach. This approach incorporates dynamic properties of vehicles and edges, facilitating a more effective selection of vehicles for task offloading. By leveraging message passing and aggregate feature techniques, our proposed approach expands the search space for source vehicles, thereby distributing the workload across a wider spectrum, particularly benefiting vehicles that frequently generate computational tasks. Evaluation of our proposed system demonstrates significant improvements in the utilization factor of available vehicles, a reduction in queuing states, and an overall system efficiency of 92%. © 2024 IEEE.

关键词： Computation offloading

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