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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zou, Anni Zhang, Zhuosheng Zhao, Hai Li, Xiaoshan Bian, Minjie Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Technology Innovation Department Shanghai Data Group Co. Ltd. China Shanghai Data Group Co. Ltd. China

ISBN: (纸本)9782493814104

Large language models (LLMs) empowered by chain-of-thought (CoT) prompting have yielded remarkable prowess in reasoning tasks. Nevertheless, current methods predominantly lean on handcrafted or task-specific demonstrations, lack reliable knowledge basis and thus struggle for trustworthy responses in an automated pattern. While recent works endeavor to improve upon one certain aspect, they ignore the importance and necessity of establishing an integrated and interpretable reasoning system. To address these drawbacks and provide a universal solution, we propose AuRoRA: a one-for-all platform for augmented reasoning and refining based on CoT prompting that excels in adaptability, reliability, integrity, and interpretability. The system exhibits superior performances across six reasoning tasks and offers real-time visual analysis, which has pivotal academic and application value in the era of LLMs. The AuRoRA platform is available at https://***/spaces/Anni123/AuRoRA. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Refining

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Position Estimation Method of a Permanent Magnet Synchronous Motor Based on Moving Horizon Estimation EKF algorithm

Position Estimation Method of a Permanent Magnet Synchronous...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Guang-Zhong Cao Hao-Han Zhou Su-Dan Huang Hong-Jin Hu Jiang-biao He Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots Shenzhen University Shenzhen China Department of Electrical and Computer Engineering University of Kentucky Lexington KY USA

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

Extended Kalman filter (EKF) is widely applied in the position estimation algorithm for permanent magnet synchronous motors (PMSMs). However, the estimation accuracy will be degraded, when measurement noise is not O-mean white noise. To solve this problem, this paper proposes an EKF algorithm with moving horizon estimation (MHE) to estimate the rotor position of PMSM more accurately. The proposed MHE EKF algorithm uses the concept of a moving time domain window to estimate the motor operating status by integrating the window information of the $N$ moments. By establishing a cost function and adding random noise to replace the measurement error, the prediction problem is transformed into an optimization problem. Simulation and experiment results show that this algorithm can effectively improve the accuracy of estimation position.

关键词： Accuracy Magnetic separation Estimation Rotors White noise Prediction algorithms Permanent magnet motors

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ACO-GA-Based Optimization to Enhance Global Path Planning for Autonomous Navigation in Grid Environments

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IEEE Transactions on Evolutionary Computation 2025年

作者： Heng, Han Rahiman, Wan Universiti Sains Malaysia Engineering Campus School of Electrical and Electronic Engineering Penang14300 Malaysia Universiti Sains Malaysia Engineering Campus Nibong Tebal Penang14300 Malaysia Daffodil Robotics Laboratory Department of Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh

Exploring path planning techniques for autonomous vehicles to find the safest and quickest routes is fascinating. Among these techniques, generating grid-based maps is notable for simplifying the path planning process, especially in complex environments. This study introduces a novel optimization approach, the modified ant colony optimization and genetic algorithm (MACOGA), which is tailored for path planning in grid environments. MACOGA combines ant colony optimization (ACO) with genetic algorithm (GA) to efficiently navigate grid spaces. First, ACO identifies potential routes within a grid map, and GA optimizes them to find the optimal route. A key feature of MACOGA is its probabilistic prediction mechanism, which improves node selection by integrating the heuristic factors and probabilistic calculations of feasible potential paths, which significantly increases the likelihood of generating feasible initial paths and attains higher success rates in various environmental tests. The enhanced GA in the MACOGA incorporates ACO's pheromones and heuristic factors, and introduces adaptive probability crossover and mutation operations. Additionally, a path reconnection operation ensures favorable length and smoothness. To evaluate MACOGA's effectiveness, six planar models of varying sizes and complexities were studied. Results show MACOGA's superiority in path length and execution time, achieving the shortest path and planning time in several environments. The shortest path can be optimized to yield an improvement of up to 6%. Regarding runtime, even in the complex scenarios, it executes in just 0.3 seconds, outperforming other comparative algorithms. Furthermore, the success rate can reach 100% in relatively complex environments. © 2025 IEEE.

关键词： Ant colony optimization

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Optimizing NeRF-based SLAM with Trajectory Smoothness Constraints

arXiv

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arXiv 2024年

作者： He, Yicheng Chen, Guangcheng Zhang, Hong Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

The joint optimization of Neural Radiance Fields (NeRF) and camera trajectories has been widely applied in SLAM tasks due to its superior dense mapping quality and consistency. NeRF-based SLAM learns camera poses using constraints by implicit map representation. A widely observed phenomenon that results from the constraints of this form is jerky and physically unrealistic estimated camera motion, which in turn affects the map quality. To address this deficiency of current NeRF-based SLAM, we propose in this paper TS-SLAM (TS for Trajectory Smoothness). It introduces smoothness constraints on camera trajectories by representing them with uniform cubic B-splines with continuous acceleration that guarantees smooth camera motion. Benefiting from the differentiability and local control properties of B-splines, TS-SLAM can incrementally learn the control points end-to-end using a sliding window paradigm. Additionally, we regularize camera trajectories by exploiting the dynamics prior to further smooth trajectories. Experimental results demonstrate that TS-SLAM achieves superior trajectory accuracy and improves mapping quality versus NeRF-based SLAM that does not employ the above smoothness constraints. © 2024, CC BY.

关键词： Mapping

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Short-Term Prediction of Solar Photovoltaic Power Generation Using a Digital Twin

Short-Term Prediction of Solar Photovoltaic Power Generation...

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Annual North-American Power Symposium

作者： John Yonce Michael Walters Ganesh K. Venayagamoorthy Holcombe Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory Clemson University Clemson SC USA

Large volumes of distributed energy resources (DERs), such as solar photovoltaic (PV) plants are integrated into the power distribution system due to increased awareness of climate change. These DERs introduce variable and uncertain generation sources due to changing weather conditions. This makes operations and controls challenging and complex. To better understand and manage the dynamic nature of solar PV power plants, digital twins (DTs) will be needed. DTs based on artificial intelligence (AI) methods can be applied to replicate the dynamics of PV plants. This study utilizes a popular paradigm of AI - neural networks to create a variety of data-driven DT (DD-DT) prediction models for a 1 MW solar PV plant located at Clemson University in South Carolina, USA. State-of-the-art internet of things (IoT) based real-time measurements are used to develop the DD-DTs. Typical results for short-term PV power prediction for DTs implemented using multilayer perceptron neural networks (MLPNNs) and Elman recurrent neural networks (ERNNs) are presented in this paper.

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Efficient Evaluation of Short-Circuit Faults in Active Distribution Networks

Efficient Evaluation of Short-Circuit Faults in Active Distr...

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International Conference on Power Electronic Drives

作者： Ghanshyam Meena Akhilesh Mathur V. P. Singh Krishna Murari Sukumar Kamalasadan Department of Electrical Engineering Malaviya National Institute of Technology Jaipur Rajasthan India Electrical Engineering and Computer Science Department The University of Toledo Toledo Ohio USA EPIC and Department of Electrical Engineering Power Energy and Intelligent Systems Laboratory University of North Carolina at Charlotte Charlotte NC USA

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

Short-circuit problems in unbalanced radial distribution networks have become more common as a result of distributed generation connectivity and smart grid advancements. An accurate and effective strategy for assessing these faults due to short circuits is presented in this study. In contrast to traditional methods, this approach uses relationship matrices that are created expressly to take use of distribution network's radial characteristics. Through the examination of variations in bus voltages, bus current injections, and branch currents during fault conditions, the suggested method formulates the analysis of various fault types. The accuracy of developed method has been validated on IEEE-123 bus modified test network by comparing the obtained results with PSCAD/EMTDC simulation results.

关键词： Accuracy Simulation Distribution networks Drives Power electronics Distributed power generation Smart grids Circuit faults Active distribution networks

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Direct Truncation and Routh Approximation Assisted Reduced Order Modelling of Islanded Microgrid

Direct Truncation and Routh Approximation Assisted Reduced O...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Richa Chaudhary V. P. Singh Akhilesh Mathur U. K. Yadav Krishna Murari Sukumar Kamalasadan Department of Electrical Engineering Malaviya National Institute of Technology Jaipur Rajasthan India Electrical Engineering and Computer Science Department The University of Toledo Toledo Ohio USA Department of Electrical Engineering Power Energy and Intelligent Systems Laboratory EPIC University of North Carolina at Charlotte Charlotte NC USA

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

Microgrid (MG) networks, with their diverse energy sources and storage systems, represent complex higher-order systems (HOS). Analyzing such networks is challenging due to the uncertainty associated with energy sources like solar and wind power. To address this complexity, reduced-order models (ROMs) are essential, achieved through order reduction techniques. This article presents a method to reduce the eighth-order transfer function of an islanded microgrid to a second-order model. The reduction involves truncation reduction technique (TRT) for the numerator and Routh approximation reduction technique (RART) for the denominator. A comparative analysis is conducted between the original higher-order islanded microgrid system and its ROM. The comparison includes various error indices to determine the effectiveness of the ROM. Graphical representations of impulse, Nichols, step, Nyquist, and Bode responses are provided for both the original system and the ROM to visually compare their performances. This study demonstrates the practical application of mixed reduction techniques to simplify complex microgrid models, making them more manageable for analysis and design purposes.

关键词： Uncertainty Industry applications Transfer functions Microgrids Wind power generation Reduced order systems Complexity theory Time-domain analysis Read only memory Energy storage

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PROSPECT: Precision Robot Spectroscopy Exploration and Characterization Tool

PROSPECT: Precision Robot Spectroscopy Exploration and Chara...

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

作者： Nathaniel Hanson Gary Lvov Vedant Rautela Samuel Hibbard Ethan Holand Charles DiMarzio Taşkın Padır Institute for Experiential Robotics Lincoln Laboratory Massachusetts Institute of Technology Lexington Massachusetts USA Robotics Institute Carnegie Mellon University Pittsburgh Pennsylvania USA Electrical and Computer Engineering Department Northeastern University Boston Massachusetts USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Near Infrared (NIR) spectroscopy is widely used in industrial quality control and automation to test the purity and grade of items. In this research, we propose a novel sensorized end effector and acquisition strategy to capture spectral signatures from objects and register them with a 3D point cloud. Our methodology first takes a 3D scan of an object generated by a time-of-flight depth camera and decomposes the object into a series of planned viewpoints covering the surface. We generate motion plans for a robot manipulator and end-effector to visit these viewpoints while maintaining a fixed distance and surface normal. This process is enabled by the spherical motion of the end-effector and ensures maximal spectral signal quality. By continuously acquiring surface reflectance values as the end-effector scans the target object, the autonomous system develops a four-dimensional model of the target object: position in an R 3 coordinate frame, and a reflectance vector denoting the associated spectral signature. We demonstrate this system in building spectral-spatial object profiles of increasingly complex geometries. We show the proposed system and spectral acquisition planning produce more consistent spectral signals than naïve point scanning strategies. Our work represents a significant step towards high-resolution spectral-spatial sensor fusion for automated quality assessment.

关键词： Reflectivity Point cloud compression Geometry Spectroscopy Three-dimensional displays Robot kinematics Robot sensing systems End effectors Planning Surface treatment

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RTAGrasp: Learning Task-Oriented Grasping from Human Videos via Retrieval, Transfer, and Alignment

arXiv

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arXiv 2024年

作者： Dong, Wenlong Huang, Dehao Liu, Jiangshan Tang, Chao Zhang, Hong Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

Task-oriented grasping (TOG) is crucial for robots to accomplish manipulation tasks, requiring the determination of TOG positions and directions. Existing methods either rely on costly manual TOG annotations or only extract coarse grasping positions or regions from human demonstrations, limiting their practicality in real-world applications. To address these limitations, we introduce RTAGrasp, a Retrieval, Transfer, and Alignment framework inspired by human grasping strategies. Specifically, our approach first effortlessly constructs a robot memory from human grasping demonstration videos, extracting both TOG position and direction constraints. Then, given a task instruction and a visual observation of the target object, RTAGrasp retrieves the most similar human grasping experience from its memory and leverages semantic matching capabilities of vision foundation models to transfer the TOG constraints to the target object in a training-free manner. Finally, RTAGrasp aligns the transferred TOG constraints with the robot’s action for execution. Evaluations on the public TOG benchmark, TaskGrasp dataset, show the competitive performance of RTAGrasp on both seen and unseen object categories compared to existing baseline methods. Real-world experiments further validate its effectiveness on a robotic arm. Our code, appendix, and video are available at https: //***/view/rtagrasp/home. © 2024, CC BY.

关键词： Robotic arms

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A Generic Load Flow Solution for Droop-Controlled AC Microgrids Considering Electric Vehicle Loads

A Generic Load Flow Solution for Droop-Controlled AC Microgr...

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International Conference on Power Electronic Drives

作者： Ghanshyam Meena Akhilesh Mathur V. P. Singh Krishna Murari Sukumar Kamalasadan Department of Electrical Engineering Malaviya National Institute of Technology Jaipur Rajasthan India Electrical Engineering and Computer Science Department The University of Toledo Toledo Ohio USA Department of Electrical Engineering Power Energy and Intelligent Systems Laboratory EPIC University of North Carolina at Charlotte Charlotte NC USA

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

Microgrids (MGs) have become more unpredictable due to integration of renewable generation sources. Several methods are used to solve load flow problems in MGs. Load flow analysis is a complex problem for islanded MGs due to the absence of slack bus and dependency of active power generation of droop-controlled DGs on frequency. In this paper, a Modified Newton Raphson (MNR) approach is used to perform load flow analysis of isolated MGs considering various types of electric vehicle (EV) loads. The presented method has been validated on 6-bus and 38-bus test systems of the islanded microgrid to give an accurate and simple approach for computing the load flow solutions.

关键词： Renewable energy sources Microgrids Drives Electric vehicles Software Power electronics Distributed power generation Planning MATLAB Load flow analysis

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