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Parametric Anisotropic(p,q)-Neumann Problems

Acta Mathematicae Applicatae Sinica 2023年第4期39卷 926-942页

作者： Zhen-hai LIU Nikolaos S.PAPAGEORGIOU Center for Applied Mathematics of Guangxi Yulin Normal UniversityYulin 537000China Guangxi Key Laboratory of Universities Optimization Control and Engineering Calculation Guangxi Minzu UniversityNanning 530006China Department of Mathematics National Technical UniversityZografou Campus15780 AthensGreece

We consider a Neumann problem driven by a(p(z), q(z))-Laplacian(anisotropic problem) plus a parametric potential term with λ > 0 being the parameter. The reaction is superlinear but need not satisfy the Ambrosetti-Rabinowitz condition. We prove a bifurcation-type theorem describing the changes in the set of positive solutions as the parameter λ moves on R_(+)=(0,+∞).

关键词： variable exponent spaces superlinear reaction bifurcation-type theorem anisotropic regularity strong comparison positive solutions

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L_(p) Quasi Norm State Estimator for Power Systems

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Journal of Modern Power Systems and Clean Energy 2022年第4期10卷 871-882页

作者： Zhongliang Lyu Hua Wei Xiaoqing Bai Daiyu Xie Le Zhang Peijie Li School of Electrical Engineering Guangxi UniversityNanningChinaand they are also with the Guangxi Key Laboratory of Power System Optimization and Energy TechnologyGuangxi UniversityNanningChin Power Dispatching Control Center of Guangxi Power Grid NanningChina

This paper proposes an L_(p)(0<p<1)quasi norm state estimator for power system static state *** with the existing L1 and L2 norm estimators,the proposed estimator can suppress the bad data more *** robustness of... 详细信息

This paper proposes an L_(p)(0

关键词： Power system bad data quasi norm estimator robustness state estimation

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Who Should I Trust? A Visual Analytics Approach for Comparing Net Load Forecasting Models

Who Should I Trust? A Visual Analytics Approach for Comparin...

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Bhattacharjee, Kaustav Kundu, Soumya Chakraborty, Indrasis Dasgupta, Aritra New Jersey Institute of Technology Department of Data Science United States Pacific Northwest National Laboratory Optimization and Control Group United States Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States

ISBN: (纸本)9798350352528

Net load forecasting is crucial for energy planning and facilitating informed decision-making regarding trade and load distributions. However, evaluating forecasting models' performance against benchmark models remains challenging, thereby impeding experts' trust in the model's performance. In this context, there is a demand for technological interventions that allow scientists to compare models across various timeframes and solar penetration levels. This paper introduces a visual analytics-based application designed to compare the performance of deep-learning-based net load forecasting models with other models for probabilistic net load forecasting. This application employs carefully selected visual analytic interventions, enabling users to discern differences in model performance across different solar penetration levels, dataset resolutions, and hours of the day over multiple months. We also present observations made using our application through a case study, demonstrating the effectiveness of visualizations in aiding scientists in making informed decisions and enhancing trust in net load forecasting models. © 2025 IEEE.

关键词： AI/ML net load forecasting visual analytics

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Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting

Forte: An Interactive Visual Analytic Tool for Trust-Augment...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

ISBN: (纸本)9798350313604

Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature and humidity, remains challenging, particularly for eliciting a high degree of trust in the model outcomes. In this context, there is a growing need for data-driven technological interventions to aid scientists in comprehending how models react to both noisy and clean input variables, thus shedding light on complex behaviors and fostering confidence in the outcomes. In this paper, we present Forte, a visual analytics-based application to explore deep probabilistic net load forecasting models across various input variables and understand the error rates for different scenarios. With carefully designed visual interventions, this web-based interface empowers scientists to derive insights about model performance by simulating diverse scenarios, facilitating an informed decision-making process. We discuss observations made using Forte and demonstrate the effectiveness of visualization techniques to provide valuable insights into the correlation between weather inputs and net load forecasts, ultimately advancing grid capabilities by improving trust in forecasting models. © 2024 IEEE.

关键词： Decision making

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Resonant-Superlinear Nonhomogeneous Dirichlet Problems

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Acta Mathematica Sinica,English Series 2023年第11期39卷 2091-2116页

作者： Zhen Hai LIU Nikolaos S.PAPAGEORGIOU Center for Applied Mathematics of Guangxi Yulin Normal UniversityYulin 537000P.R.China Guangxi Key Laboratory of Universities Optimization Control and Engineering Calculation College of Mathematics and PhysicsGuangxi Minzu UniversityNanning 530006P.R.China Department of Mathematics National Technical UniversityZografou Campus15780 AthensGreece

We consider a Dirichlet nonlinear equation driven by the(p,2)-Laplacian and with a reaction having the competing effects of a parametric asymmetric superlinear term and a resonant *** show that for all small values of... 详细信息

关键词： Asymmetric reaction resonance superlinear term nonlinear regularity comparison principle critical groups solutions with sign information

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From Concept to Standard: The Trajectory of CubeSat Space Protocol Standardization 1st

From Concept to Standard: The Trajectory of CubeSat Space Pr...

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1st International Conference on Electrical Systems and Smart Technologies, ICESST 2024

作者： Belali, Fadwa Bah, Slimane El Wafi, Ilyas Daghouri, Amina Guennoun, Zouhair SCRT Smart Communication Research Team EMI Mohammed V University in Rabat Rabat Morocco STIS Research Center Research Team "Energy Optimization Diagnosis and Control" ENSAM of Rabat Mohammed V University in Rabat Rabat Morocco University Center for Research in Space Technologies EMI Mohammed V University in Rabat Rabat Morocco

ISBN: (纸本)9783031867040

The CubeSat Space Protocol (CSP) has proven to be an effective and efficient communication protocol specifically designed for distributed embedded systems within small networks such as those found in nanosatellites. The lack of standardisation in this protocol has raised concerns among space engineers about interoperability, error rates and reliability. Despite these concerns, CSP has been widely adopted and integrated into several major projects. This paper highlights the critical need for standardisation of CSP, with a focus on robust modelling and verification methodologies for data transmission. The study provides an in-depth analysis of CSP, emphasising its client-server architecture through finite-state machine modelling. In particular, advanced verification techniques using the Promela language are employed to provide a high level of reliability for the protocol by rigorously examining packet transmission. The results confirm the effectiveness of CSP and highlight the need for standardisation, allowing CubeSat developers to implement the protocol with greater confidence and efficiency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Signaling

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Integrating a Real Time Operating-System Layer to a Memory-Constrained Electrical Power System for a Nanosatellite 1st

Integrating a Real Time Operating-System Layer to a Memory-C...

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1st International Conference on Electrical Systems and Smart Technologies, ICESST 2024

作者： Essetty, Abdellah Daghouri, Amina Bah, Slimane Guennoun, Zouhair SCRT Smart Communication Research Team EMI Mohammed V University in Rabat Rabat Morocco STIS Research Center Research Team "Energy Optimization Diagnosis and Control" ENSAM of Rabat Mohammed V University in Rabat Rabat Morocco University Center for Research in Space Technologies EMI Mohammed V University in Rabat Rabat Morocco

ISBN: (纸本)9783031867040

In the realm of space applications, software reliability plays a vital role in defining the success or failure of a mission. For Nanosatellite missions, software is typically developed on demand, tailored to meet the specific needs and constraints of the system. These constraints often necessitate a minimalistic approach to software design to ensure safety and secure operation. However, the software must also be sufficiently complex to achieve all mission objectives. While the use of general purpose operating systems like Linux has been increasing in the past years, commercial off-the-shelf real-time operating systems (RTOS) are still preferred due to their deterministic nature and the ease of development and debugging. This paper explores the integration of a FreeRTOS layer into an MSP430-based Electrical Power System for a nanosatellite. The integration involves adding telemetry collection and housekeeping tasks to enhance subsystem integration and responsiveness with other subsystems without putting pressure in the system resources. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Linux

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Indoor Localization for an Autonomous Model Car: A Marker-Based Multi-Sensor Fusion Framework

Indoor Localization for an Autonomous Model Car: A Marker-Ba...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Xibo Li Shruti Patel David Stronzek-Pfeifer Christof Büskens working group Optimization and Optimal Control Center for Industrial Mathematics University of Bremen Bremen Germany

Global navigation satellite systems readily provide accurate position information when localizing a robot outdoors. However, an analogous standard solution does not exist yet for mobile robots operating indoors. This paper presents an integrated framework for indoor localization and experimental validation of an autonomous driving system based on an advanced driver-assistance system (ADAS) model car. The global pose of the model car is obtained by fusing information from fiducial markers, inertial sensors and wheel odometry. In order to achieve robust localization, we investigate and compare two extensions to the Extended Kalman Filter; first with adaptive noise tuning and second with $\chi^{2}-\mathbf{testing}$ for measurement outlier detection. An efficient and low-cost ground truth measurement method using a single LiDAR sensor is also proposed to validate the results. The performance of the localization algorithms is tested on a complete autonomous driving system with trajectory planning and model predictive control.

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Novel Cooperative Particle Swarm optimization with Interactive Learning Strategy 1

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wang, Qiliang Liu, Yiheng Xu, Hao Zhang, Pinggai Wu, Qinlin Wei, Lisheng Anhui Key Laboratory of Electric Drive and Control School of Electrical Engineering Anhui Polytechnic University Anhui Wuhu241000 China Industrial Process Control Optimization and Automation Engineering Research Center School of Electronic Engineering Chaohu University Anhui Chaohu238024 China School of Computer Science and Artificial Intelligence Chaohu University Anhui Chaohu238024 China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

Particle Swarm optimization (PSO) is an optimization method grounded in swarm intelligence, designed to mimic the social behaviors of bird flocks during hunting. By sharing information about their positions, the entire swarm can explore and search diverse areas within a given space. This method features rapid convergence, minimal parameters, and straightforward implementation, making it a potent and efficient metaheuristic technique. Nevertheless, conventional PSO algorithms, despite their simplicity and efficiency, often become trapped in local optima when tackling complex multimodal problems, which limits their global search capability. To mitigate these limitations, a new Cooperative Particle Swarm optimization algorithm with Interactive Learning (CPSOIL) is introduced. This approach incorporates an interactive learning strategy, allowing particles to learn from their neighbors by perceiving the surrounding particles during updates. Moreover, a mechanism has been added to modify the particle velocity update when the algorithm encounters local optima, thereby preserving population diversity, enhancing the likelihood of escaping local optima, and improving algorithm accuracy. Comparisons with other leading algorithms on the CEC2005 benchmark functions indicate that, in most cases, the enhanced algorithm outperforms its counterparts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Particle swarm optimization (PSO)

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Industrial Smoke Images Generation Network Based on Bidirectional Recurrent Network

Industrial Smoke Images Generation Network Based on Bidirect...

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第43届中国控制会议

作者： Kai Chen Zheng LV Qiao Sun Jun Zhao Wei Wang Key Laboratory of Intelligent Control and Optimization for Industrial Equipment (Dalian University of Technology) Ministry of Education Electric Operations Control Center State Grid Dalian Power Supply Company

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The monitoring of smoke emissions serves as a crucial reference for guiding operations in the steel industry. However,due to the scarcity of industrial smoke datasets, most deep learning methods encounter difficulties when addressing this *** address the issue of limited smoke data, this paper proposes a novel data augmentation method called the bidirectional recurrent generative network. This algorithm combines the concepts of recurrent generative networks and Laplacian pyramid networks to learn smoke generation from smoke-free blast furnaces and eliminate smoke from smoke blast furnaces. In order to overcome the limitation of recurrent generative networks in accurately depicting target contours, this paper introduces contour feature vectors, which provide precise characterization of the target contours through constraint-based feature vectors. Furthermore, to alleviate the difficulty of generation, the Laplacian pyramid model is introduced, which reduces parameters and learning time by learning from low-resolution images. To validate the effectiveness of the proposed algorithm, extensive ablation experiments and comparative trials are conducted on four different smoke image datasets from industrial scenarios with limited quantities. The results demonstrate significant improvements achieved by this paper's algorithm. Compared to other generation methods, the proposed algorithm exhibits superior performance in smoke generation tasks, offering an effective solution to address the challenges of scarce industrial smoke data and the training difficulties faced by related deep learning algorithms.

关键词： Bidirectional Recurrent Generative Network Data Augmentation Smoke Generation Laplacian Pyramid

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