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A Corrective Scheme to Prevent Adverse Dynamic Interaction of Grid-forming Inverters

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A Corrective Scheme to Prevent Adverse Dynamic Interaction o...

Innovative Smart Grid Technologies (ISGT)

作者： Muhammad F. Umar Mohsen Hosseinzadehtaher Mohammad B. Shadmand Hanif Livani Mohammed Ben-Idris Department of Electrical & Computer Engineering Intelligent Power Electronics at Grid Edge (IPEG) Research Laboratory University of Illinois Chicago Department of Electrical and Biomedical Engineering University of Nevada Reno

This paper proposes a control scheme that prevents the adverse dynamic interactions between the heterogeneously controlled grid-forming inverters (GFMI) in power electronics dominated grid (PEDG) towards a resilient self-driving grid. The primary controller of GFMIs in a grid cluster can vary based on their manufacturers such as virtual synchronous generation, droop control, power synchronization control, etc. Therefore, this can introduce heterogeneity among the network of GFMIs in PEDG. Resultantly, during the interconnection of GFMIs that are based on heterogenous primary controller poses various synchronization and dynamic interaction challenges in PEDG. For instance, severe fluctuations in frequency and voltage, high ROCOF, unintended reactive power circulation that poses a threat on the overall transient stability of the PEDG. Therefore, to mitigate these adverse dynamic interactions among the heterogeneously controlled GFMIs, a force enclaved homogenization (FEH) control is proposed in a supervisory level controller. This will autonomously adjust inertia coefficients of the each GFMI to have homogenous transient response and will enforce coherency among the heterogenous DGs. This will prevent the PEDG from the adverse dynamic interactions during an interconnection and load disturbance. Various case studies are presented that validates the effectiveness of the proposed FEH control.

关键词： Transient response Reactive power Fluctuations Dynamics Force Power system stability Inverters

A Review of Brain-Inspired Cognition and Navigation Technology for Mobile Robots

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Cyborg and Bionic Systems 2024年第1期5卷 232-246页

作者： Yanan Bai Shiliang Shao Jin Zhang Xianzhe Zhao Chuxi Fang Ting Wang Yongliang Wang Hai Zhao School of Computer Science and Engineering Northeastern UniversityShenyang 110819China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China Department of Artificial Intelligence University of GroningenGroningen 9747 AGNetherlands

Brain-inspired navigation technologies combine environmental perception,spatial cognition,and target navigation to create a comprehensive navigation research *** have used various sensors to gather environmental data and enhance environmental perception using multimodal information *** spatial cognition,a neural network model is used to simulate the navigation mechanism of the animal brain and to construct an environmental cognition ***,existing models face challenges in achieving high navigation success rate and *** addition,the limited incorporation of navigation mechanisms borrowed from animal brains necessitates further *** the basis of the braininspired navigation process,this paper launched a systematic study on brain-inspired environment perception,brain-inspired spatial cognition,and goal-based navigation in brain-inspired navigation,which provides a new classification of brain-inspired cognition and navigation techniques and a theoretical basis for subsequent experimental *** the future,brain-inspired navigation technology should learn from more perfect brain-inspired mechanisms to improve its generalization ability and be simultaneously applied to large-scale distributed intelligent body cluster *** multidisciplinary nature of braininspired navigation technology presents challenges,and multidisciplinary scholars must cooperate to promote the development of this technology.

关键词： neural network model enhance environmental perception target navigation multimodal information fusionin navigation research systemresearchers spatial cognitiona environmental perceptionspatial cognitionand simulate navigation mechanism

Employing Feature engineering for River Stage Forecasting to Improve Hybrid Model Performance

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Employing Feature Engineering for River Stage Forecasting to...

International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Anna Maria Mihel Špela Pečnik Grega Vrbančič Jonatan Lerga Nino Krvavica Department of Computer Engineering Faculty of Engineering University of Rijeka Rijeka Croatia Intelligent Systems Laboratory Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia Department of Hydrotechnics and Geotechnics Faculty of Civil Engineering University of Rijeka Rijeka Croatia

ISBN: (数字)9789532901382

ISBN: (纸本)9798350354614

Establishing early warning systems and efficient management of water resources in tidal reaches is crucial for achieving adequate flood protection. In tidal reaches, the river stage interacts non-linearly with tides (downstream) and discharge (upstream), making the modeling of river processes extremely complex. Data-driven methods have proven helpful for predicting the river stage parameter. This contrasts with existing numerical approaches, which suffer from several limitations. The aim of this study was to predict the river stage of the upstream part of the tidal river, where the influence of salinity is present during the summer months. Using the CNN-LSTM model trained on historical records of the discharge (station of the variable to be predicted), the river stage of three downstream stations, and the point of interest, the prediction was carried out up to 24 hours in advance. By incorporating feature engineering, we achieved an improvement in model results for the longest horizon, confirmed by standard performance metrics, and considered it efficient and effective for risk mitigation. Introducing feature engineering into the data preprocessing resulted in a predictive performance improvement of 0.98% for the Nash-Sutcliffe Efficiency (NSE) metric, 7.0% for the root mean squared error (RMSE) metric, and 7.25% for the mean absolute error (MAE) metric over the second and third-best scenarios using a spectrogram and time-series data.

关键词： Measurement Weather forecasting Predictive models Wavelet analysis Data models Rivers Numerical models Forecasting Spectrogram Water resources

Data-Efficient Control Barrier Function Refinement

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arXiv 2023年

作者： Dai, Bolun Huang, Heming Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

Control barrier functions (CBFs) have been widely used for synthesizing controllers in safety-critical applications. When used as a safety filter, it provides a simple and computationally efficient way to obtain safe controls from a possibly unsafe performance controller. Despite its conceptual simplicity, constructing a valid CBF is well known to be challenging, especially for high-relative degree systems under nonconvex constraints. Recently, work has been done to learn a valid CBF from data based on a handcrafted CBF (HCBF). Even though the HCBF gives a good initialization point, it still requires a large amount of data to train the CBF network. In this work, we propose a new method to learn more efficiently from the collected data through a novel prioritized data sampling strategy. A priority score is computed from the loss value of each data point. Then, a probability distribution based on the priority score of the data points is used to sample data and update the learned CBF. Using our proposed approach, we can learn a valid CBF that recovers a larger portion of the true safe set using a smaller amount of data. The effectiveness of our method is demonstrated in simulation on a unicycle and a two-link arm. © 2023, CC BY.

关键词： Controllers

Differentiable Optimization Based Time-Varying Control Barrier Functions for Dynamic Obstacle Avoidance

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arXiv 2023年

作者： Dai, Bolun Khorrambakht, Rooholla Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

Control barrier functions (CBFs) provide a simple yet effective way for safe control synthesis. Recently, work has been done using differentiable optimization (diffOpt) based methods to systematically construct CBFs for static obstacle avoidance tasks between geometric shapes. In this work, we extend the application of diffOpt CBFs to perform dynamic obstacle avoidance tasks. We show that by using the time-varying CBF (TVCBF) formulation, we can perform obstacle avoidance for dynamic geometric obstacles. Additionally, we show how to extend the TVCBF constraint to consider measurement noise and actuation limits. To demonstrate the efficacy of our proposed approach, we first compare its performance with a model predictive control based method and a circular CBF based method on a simulated dynamic obstacle avoidance task. Then, we demonstrate the performance of our proposed approach in experimental studies using a 7-degree-of-freedom Franka Research 3 robotic manipulator. © 2023, CC BY.

关键词： Model predictive control

DBF-Net: A Dual-Branch Network with Feature Fusion for Ultrasound Image Segmentation

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arXiv 2024年

作者： Xu, Guoping Wu, Xiaming Liao, Wentao Wu, Xinglong Huang, Qing Li, Chang Hubei Key Laboratory of Intelligent Robotics School of Computer Science and Engineering Wuhan Institute of Technology Hubei Wuhan430205 China Department of Biomedical Engineering Hefei University of Technology Anhui Hefei230009 China

Objective: Accurately segmenting lesions in ultrasound images remains a challenging task due to the inherent ambiguity in distinguishing boundaries between lesions and adjacent tissues. Although deep learning has made significant progress in accurate ultrasound image segmentation, there is still a lack of attention to the quality of boundary segmentation and its relationship with the body. As a result, the current accuracy of ultrasound image segmentation still has room for improvement in enhancing the precision of lesion delineation. We aim to improve the overall segmentation accuracy and the quality of boundary segmentation by exploring the relationship between the body and the boundary in ultrasound images. Methods: We introduce a dual-branch structure based on a deep neural network that allows the model to learn the relationship between the body and boundary parts under supervision, leading to an improvement in overall segmentation accuracy and boundary segmentation quality. Additionally, we propose a novel feature fusion module aimed at facilitating the integration and interaction of body and boundary information. Based on the dual-branch structure and feature fusion strategy, we have designed UBBS-Net for ultrasound image segmentation. Results: The proposed approach is evaluated on three challenging public ultrasound image datasets. Our experimental results demonstrate the superiority of the proposed method compared to existing state-of-the-art methods. Specifically, we achieve a Dice Similarity Coefficient of 81.05±10.44% for breast cancer segmentation, 76.41±5.52% for brachial plexus nerves segmentation, and 87.75±4.18% for infantile hemangioma segmentation on BUSI, UNS and UHES datasets, respectively. Conclusions: We propose UBBS-Net, a novel network for ultrasound image segmentation that combines body and boundary information with a proposed feature fusion module. Our method outperforms existing approaches on three challenging public datasets, demonstratin

关键词： Ultrasonic imaging

On Shaping Gain of Multidimensional Constellation in Linear and Nonlinear Optical Fiber Channel

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arXiv 2024年

作者： Chen, Bin Liang, Zhiwei Lei, Yi Deng, JingXin Li, Shen Liga, Gabriele School of Computer Science and Information Engineering Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province China Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden Department of Electrical and Computer Engineering Université Laval Québec Canada Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands

Utilizing the multi-dimensional (MD) space for constellation shaping has been proven to be an effective approach for achieving shaping gains. Despite there exists a variety of MD modulation formats tailored for specific optical transmission scenarios, there remains a notable absence of a dependable comparison method for efficiently and promptly re-evaluating their performance in arbitrary transmission systems. In this paper, we introduce an analytical nonlinear interference (NLI) power model-based shaping gain estimation method to enable a fast performance evaluation of various MD modulation formats in coherent dual-polarization (DP) optical transmission system. In order to extend the applicability of this method to a broader set of modulation formats, we extend the established NLI model to take the 4D joint distribution into account and thus able to analyze the complex interactions of non-iid signaling in DP systems. With the help of the NLI model, we conduct a comprehensive analysis of the state-of-the-art modulation formats and investigate their actual shaping gains in two types of optical fiber communication scenarios (multi-span and single-span). The numerical simulation shows that for arbitrary modulation formats, the NLI power and relative shaping gains in terms of signal-to-noise ratio can be more accurately estimated by capturing the statistics of MD symbols. Furthermore, the proposed method further validates the effectiveness of the reported NLI-tolerant modulation format in the literature, which reveals that the linear shaping gains and modulation-dependent NLI should be jointly considered for nonlinearity mitigation. © 2024, CC BY-NC-SA.

关键词： Optical fibers

A Graph-Based Self-Calibration Technique for Cable-Driven Robots with Sagging Cable

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A Graph-Based Self-Calibration Technique for Cable-Driven Ro...

IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： M. R. Dindarloo A. S. Mirjalili S. A. Khalilpour R. Khorrambakht Stephan Weiss H. D. Taghirad Advanced Robotics and Automated Systems (ARAS) Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Tandon School of Engineering Control/Robotics Research Laboratory New York University Brooklyn NY Department of Smart Systems Technologies University of Klagenfurt Austria

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The efficient operation of large-scale Cable-Driven Parallel Robots (CDPRs) relies on precise calibration of kinematic parameters and the simplicity of the calibration process. This paper presents a graph-based self-calibration framework that explicitly addresses cable sag effects and facilitates the calibration procedure for large-scale CDPRs by only relying on internal sensors. A unified factor graph is proposed, incorporating a catenary cable model to capture cable sagging. The factor graph iteratively refines kinematic parameters, including anchor point locations and initial cable length, by considering jointly onboard sensor data and the robot’s kineto-static model. The applicability and accuracy of the proposed technique are demonstrated through Finite Element (FE) simulations, on both large and small-scale CDPRs subjected to significant initialization perturbations.

关键词： Meters Accuracy Perturbation methods Kinematics Robot sensing systems End effectors Software Calibration Sensors Finite element analysis

Blockchain in Environmental Sustainability Measures: a Survey

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arXiv 2024年

作者： Vladucu, Maria-Victoria Wu, Hailun Medina, Jorge Salehin, Khondaker M. Dong, Ziqian Rojas-Cessa, Roberto The Network and Innovation Laboratory Department of Electrical and Computer Engineering New York Institute of Technology New YorkNY10023 United States Department of Computer Science University of Portland PortlandOR97203 United States The Networking and Intelligent Systems Laboratory Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Real and effective regulation of contributions to greenhouse gas emissions and pollutants requires unbiased and truthful monitoring. Blockchain has emerged not only as an approach that provides verifiable economical interactions but also as a mechanism to keep the measurement, monitoring, incentivation of environmental conservationist practices and enforcement of policy. Here, we present a survey of areas in what blockchain has been considered as a response to concerns on keeping an accurate recording of environmental practices to monitor levels of pollution and management of environmental practices. We classify the applications of blockchain into different segments of concerns, such as greenhouse gas emissions, solid waste, water, plastics, food waste, and circular economy, and show the objectives for the addressed concerns. We also classify the different blockchains and the explored and designed properties as identified for the proposed solutions. At the end, we provide a discussion about the niches and challenges that remain for future research. © 2024, CC BY-NC-ND.

关键词： Circular economy