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A Case Study on Peri-Urban Public Transport Optimisation From an Energy and Environmental Perspective

IET Intelligent Transport systems 2025年第1期19卷

作者： Vasconcelos, André Coelho, Margarida C. Bandeira, Jorge M. Department of Mechanical Engineering Centre for Mechanical Technology and Automation University of Aveiro Aveiro Portugal LASI – Intelligent Systems Associate Laboratory Aveiro Portugal Department of Environment and Planning Centre for Mechanical Technology and Automation University of Aveiro Aveiro Portugal

Peri-urban and rural mobility presents unique challenges due to its reliance on fossil fuels and the lack of comprehensive studies on its externalities. Furthermore, the provision of efficient public transport in peri-urban areas is complicated due to fluctuating demand patterns. In this context, this paper explores the optimisation of a bimodal (bus line and a diesel railway line) public transport corridor in the peri-urban area of a European medium-sized city. A four-stage research methodology is employed: initial system characterisation, energy and environmental performance analysis, operational efficiency assessment, and the development and evaluation of alternative transport strategies. The study concludes that the variability in demand necessitates the implementation of innovative, complementary services that can adapt to changing passenger numbers while optimising existing resources. Findings of the case study indicate that aligning bus types with demand variability and integrating electric buses can lead to substantial reductions in CO2 emissions. The use of minibuses during off-peak hours could achieve a 50% reduction in emissions, while the adoption of a bus rapid transit (BRT) system may result in a 90% decrease compared to conventional diesel trains. The research underlines the potential for innovative service models to utilise existing infrastructure more efficiently. © 2025 The Author(s). IET Intelligent Transport systems published by John Wiley & Sons Ltd on behalf of The Institution of engineering and technology.

关键词： Intermodal transportation

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Short-term Load Prediction Based on Whale Optimized WTCN-BiGRU Hybrid Neural Network 18th

Short-term Load Prediction Based on Whale Optimized WTCN-Bi...

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18th Chinese Intelligent systems Conference, CISC 2022

作者： Gao, Qiang Chen, Jiaqi Ji, Yuehui Liu, Junjie Xu, Liang Zhao, Xuewen The School of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory for Control Theory and Applications in Complicated Industry Systems Tianjin300384 China

ISBN: (纸本)9789811962028

The accuracy of short-term power load prediction provides according to power department companies to make reasonable production scheduling plans and avoid resource waste. In this paper, a Temporal Convolutional Network (TCN) -BiGRU-Attention short period power load forecasting means on the strength of the Whale Optimization Algorithm (WOA) is proposed. TCN is improved by the Morlet mother wavelet basis function. Train network hyperparameters by WOA. The WOA-WTCN- BiGRU-Attention hybrid neural network is formed. Firstly, the high-dimensional features of power load are extracted by the WTCN network. Secondly, the BiGRU network is used to learn the dynamic change rules of high-dimensional characteristics. Ultimately, the Attention mechanism is important to endow homologous proportion to characteristics through mapping weighting and learning parameter matrices to reduce information loss. The proposed method is compared with the prediction results of WTCN-LSTM, WTCN-BiGRU, and TCN-BiGRU-Attention models by taking an open data set from an Australian region as an example. The results show that the proposed method has a more accurate prediction effect. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Open Data

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Research on Eigenvalue of Excitation Signal in Nondestructive Testing of Power Transformer Core 1

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18th Annual Conference of China Electrotechnical Society, ACCES 2023

作者： Qu, Yinghui Jin, Fang Dong, Kaifeng Cai, Shengwei Gong, Yujia Fan, Fan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China China Electric Power Research Institute Co. Ltd. Wuhan430074 China

ISBN: (数字)9789819714285

ISBN: (纸本)9789819714278

Transformer is the most critical equipment in the power system. In recent years, secondary silicon steel sheets have been used in the manufacture of transformer cores, which has caused hidden dangers in the quality of transformers. Currently, the main testing method for transformer core materials is disassembly testing, and there is no effective non-destructive testing method. Because magnetic Buckhausen noise (MBN) signals can reflect the internal defects of ferromagnetic components, this paper uses MBN testing methods to conduct non-destructive testing and analysis the side of transformer core silicon steel sheets. In the study of transformer core MBN signals, it was found that the waveform, frequency, amplitude and other parameters of the excitation signal can affect the eigenvalues of the transformer core MBN signal. The effects of excitation signal waveform, frequency, and amplitude on the characteristic values of MBN signals on the side of transformer core silicon steel sheet were experimentally studied. The research results show that when the excitation signal is a square wave with an amplitude of about 5V, excellent MBN signals can be excited. © Beijing Paike Culture Commu. Co., Ltd. 2024.

关键词： Nondestructive examination

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Multi-parameter load sensing pump model simulation and flow rate characteristics research

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Chinese Journal of Aeronautics 2022年第12期35卷 296-308页

作者： ZongXia JIAO Zhenyu WANG Xiaochao LIU HuJiang WANG Pengyuan QI Weizhi QIAO School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100083China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027China Research Institute for Frontier Science Beihang UniversityBeijing100083China Science and Technology on Aircraft Control Laboratory Beihang UniversityBeijing 100083China Science and Technology on Vehicle Transmission Laboratory China North Vehicle Research InstituteBeijing 100072China Ningbo Institute of Technology Beihang UniversityNingbo 315800China

Load sensing pumps have been widely used in diverse hydraulic *** show that structural parameters have undeniable impacts on the characteristics and efficiency of the load sensing *** main purpose of this article is to study the influence of load sensing pump structure parameters on flow *** the present study,a nonlinear multi-parameter model is proposed for this type of *** this model,different parameters,including spool clearance,spool covering amount,internal leakage are considered to reflect the displacement adjustment process of the load sensing ***,a frequency sweep method is proposed to analyze the frequency domain of the nonlinear mathematical *** experiment rig was built to study the influence of key structural parameters on the dynamic follow-up characteristics of the pump *** obtained results show that the diameter of the orifice d can significantly affect the working characteristics of the *** is found that a large diameter of the orifice d can improve the phase following ability of the system,while a small diameter of the orifice d can reduce the bypass flow rate and increase the amplitude following *** paper provides a new consideration to study the dynamic follow-up characteristics of the load sensing pump.

关键词： Flow rate characteristics Frequency analysis Hydraulic Load sensing pump Nonlinear

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ES-MSCNN: An Echo State Multi-Scale Convolutional Neural Network for Multi-Step Prediction of Wastewater Quality Variables

ES-MSCNN: An Echo State Multi-Scale Convolutional Neural Net...

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Robotics, control and automation engineering (RCAE), International Conference on

作者： Yue Sun Yiqi Liu Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science & Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

Given the limitations of hardware sensors together with strong nonlinearities and nexus between variables in wastewater treatment processes (WWTPs), the measurement of key quality variables is usually inaccurate or even impossible. Moreover, due to the short data sampling period, the single-output single-step prediction model cannot meet the actual demand. Therefore, this paper proposes a new soft sensor, termed as the Echo State Multi-Scale Convolutional Neural Network (ES-MSCNN), designed for accurate multivariate multi-step prediction. ES-MSCNN first generates an echo state representation (ESR) containing temporal information through an echo state network (ESN). Subsequently, a convolutional neural network (CNN) is employed to avoid the shallowness of ESN and further extract high-dimensional feature information from the data. Besides, a multi-scale learning strategy is used in the convolutional module to capture multi-timescale features and improve the multivariate prediction accuracy. Validation on the benchmark simulation model 2 platform and comparison with several models indicate that the proposed ES-MSCNN has higher accuracy and stronger robustness in multi-step prediction tasks with multi-input and multi-output.

关键词： Accuracy Soft sensors Predictive models Feature extraction Prediction algorithms Robustness Convolutional neural networks Wastewater Wastewater treatment Robots

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Consensus of High-Order Multi-Agent systems with Binary-Valued Communications and Switching Topologies

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IEEE Transactions on control of Network systems 2024年

作者： An, Ru Wang, Ying Zhao, Yanlong Zhang, Ji-Feng Chinese Academy of Sciences Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden Zhongyuan University of Technology School of Automation and Electrical Engineering Henan Province Zhengzhou450007 China

This paper studies the consensus problem of high-order multi-agent systems (MASs) with binary-valued communications and switching topologies. To tackle the challenge of unknown states caused by binary-valued communications, this paper constructs an estimation-based consensus algorithm. First, a recursive projection identification algorithm is presented to estimate the neighbors' states dynamically. Then, based on these estimates, a consensus law is designed. By constructing and analyzing two combined Lyapunov functions about estimation error and state error, this paper establishes their relation, to overcome the difficulty resulting from the coupling of the estimation and control and less information due to switching topologies. Under the condition of jointly connected topologies, it is proven that by properly selecting the step coefficient, the estimates of states can converge to the true states with a convergence rate as the reciprocal of the recursion times. Besides, the MAS is proved to achieve weak consensus and the consensus rate is also established as the reciprocal of the recursion times. Finally, a simulation example is given to validate the algorithm. © 2024 IEEE.

关键词： Consensus algorithm

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Accelerated Degradation Modeling of Power MOSFET Under Multiple Stresses 12

Accelerated Degradation Modeling of Power MOSFET Under Multi...

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12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2022

作者： Ye, Xuerong Hu, Yifan Feng, Rui Chen, Cen Zhai, Guofu Tang, Loon Ching School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Shanghai Aerospace Control Technology Institute Shanghai China Department of Industrial and Systems Engineering & Management National University of Singapore Singapore

ISBN: (纸本)9781839538360

Power MOSFET is one of the most critical components in power electronic converters. However, it often performs a lower reliability index under multiple stresses. Conventional single-stress accelerated degradation test (ADT) and corresponding statistical inference are unable to evaluate its reliability accurately. In this article, a new accelerated degradation model is designed by combining multi-stress accelerated model and the generalized Wiener process model. This comprehensive model not only establishes the relationship between drift coefficient and multiple stresses but also take the nonlinearity and uncertainties existing in degradation process into consideration. Subsequently, an ADT with temperature, electrical and vibration stresses for power MOSFET is carried out to obtain the degradation data under different stress levels, which is employed for unknown parameter estimation. Comparative analysis demonstrates that the proposed model can provide a more accurate and reasonable reliability assessment result. © 2022 IEEE.

关键词： Power MOSFET

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Advances in inter-edge transfer learning with self-curriculum-labeling adaptive learning and lightweight attention

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Computers and Electrical engineering 2024年 116卷

作者： Petchhan, Jirayu Su, Shun-Feng Intelligent Systems and Control Laboratory Department of Electrical Engineering National Taiwan University of Science and Technology Taiwan Taipei City Taiwan

The ongoing realization of a transition towards increased reliance on edge computing is evident. This shift not only underscores the importance of interdependence in knowledge sharing but also contributes to a reduction in adaptation and transmission time through cloud computing directly, along with decreased computational demands compared to edge-to-edge computing. A persistent challenge in knowledge transfer stems from the shift in dependence from high-performance computing systems to compact models, making the adjustment cumbersome. Even with cross-edge knowledge transfer, pre-processing remains essential to facilitate seamless adaptation—a necessity that endures despite the escalating volume of data. Consequently, the system tends to become overburdened with instance filtering instead. To address this issue, we present an optimization for small-to-small inter-edge transfer learning of a comparable size. This is achieved through a self-supervised domain-adaptive protocol, utilizing self-target curriculum-based pseudo-labeling creation, new statistical alignment, and lightweight attention. In the multi-view verification using public visual DA datasets, we demonstrate a remarkable improvement of 73.6 (+4.2 %) in Top-1 performance. Our results consistently outperform other methods, particularly when using the latent feature input technique. This advantage is most noticeable at the 10-th layer for latent feature input. Crucially, our approach incurs the modest increase in computational demands, adding just 100 K-units of parameters and MFLOPS of computational power. Furthermore, through model enhancement via acceleration and compression, we achieve significant compression—up to 6.28 times faster at FP32 and 7.18 times faster at FP16 runtimes—resulting in an increased FPS to 124.9 and 142.9, respectively. This demonstration effectively illustrates the feasibility of advancing inter-edge transfer learning with our approach. © 2024 Elsevier Ltd

关键词： Curricula

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Sampled-Data Output Voltage Practical Tracking for Uninterruptible Power Supply systems with Transmission Delays

Sampled-Data Output Voltage Practical Tracking for Uninterru...

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Complex systems and Intelligent Science (CSIS-IAC), International Annual Conference on

作者： Mengqi Jia Wei Liu Key Laboratory of Autonomous Systems and Networked Control of Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798331504755

ISBN: (纸本)9798331504762

This paper aims at addressing output voltage practi-cal tracking problem for uninterruptible power supply systems by devising an aperiodic sampled-data control scheme. We consider the presence of transmission delay in the input channel. We demonstrate that the problem can be solved by a predictor-based sampled-data dynamic output feedback control law. Moreover, the tracking error can converge to a prescribed neighborhood of the origin.

关键词： Regulation Delays Voltage control Complex systems Uninterruptible power systems Output feedback

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Distributed Least-Squares Optimization Solvers with Differential Privacy

Distributed Least-Squares Optimization Solvers with Differen...

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American control Conference (ACC)

作者： Weijia Liu Lei Wang Fanghong Guo Zhengguang Wu Hongye Su Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China Department of Automation Zhejiang University of Technology Hangzhou China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper studies the distributed least-squares optimization problem with differential privacy requirement of local cost functions, for which two differentially private distributed solvers are proposed. The first is established on the distributed gradient tracking algorithm, by appropriately perturbing the initial values and parameters that contain the privacy-sensitive data with Gaussian and truncated Laplacian noises, respectively. Rigorous proofs are established to show the achievable trade-off between the (E, 8)-differential privacy and the computation accuracy. The second solver is established on the combination of the distributed shuffling mechanism and the average consensus algorithm, which enables each agent to obtain a noisy version of parameters characterizing the global gradient. As a result, the least-squares optimization problem can be eventually solved by each agent locally in such a way that any given (E, 8)-differential privacy requirement can be preserved while the solution may be computed with the accuracy independent of the network size, which makes the latter more suitable for large-scale distributed least-squares problems. Numerical simulations are presented to show the effectiveness of both solvers.

关键词： Differential privacy Privacy Accuracy Laplace equations Gaussian noise Distributed databases Cost function

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