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Communication, Computer Sciences and Engineering (IC3SE), International Conference on

作者： Ajay Shukla Neeraj Kumar Pandey Srabanti Maji Ashok Kumar Roosha Shamoon Computer Science & Engineering Graphic Era Hill University Dehradun India Computer Science & Engineering Graphic Era Deemed to be University Dehradun India School of Computing DIT University Dehradun India Computer Science & Engineering Uttaranchal University Dehradun India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

The graph colouring problem is a NP- class problem explored in the literature due to its numerous applications in real world Engineering problems. Graph colouring problem involves to assign colours in such a fashion that neighbouring nodes have distinct colours. In this paper our objective is to find the number of distinct colours that can be used to colour the vertices in the graph. During the colouring process the objective is to use the least number of colours corresponding to considered graph. In this work we devise a new colouring algorithm using greedy technique by exploring the adjacent vertices which is simple and effective and can be used to compute the graph colouring problem in more enhanced way. The algorithm starts with the calculation of links of each vertex in the graph and then unexplored highest degree vertex will be selected for computation of adjacent nodes for colouring. Finally, we examined the presented algorithm on standard DIMACS graph colouring instances to evaluate the success of the devised algorithm. The result obtained after running the proposed algorithm for several categories of graphs like myciel, mugg, mulsol, david, huck, jean in terms of number of colours is equal to the chromatic number of the graph instances.

关键词： Color approximation algorithms Partitioning algorithms Standards

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Collaborative Graph Exploration with Reduced Pose-SLAM Uncertainty via Submodular Optimization

arXiv

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arXiv 2024年

作者： Bai, Ruofei Yuan, Shenghai Guo, Hongliang Yin, Pengyu Yau, Wei-Yun Xie, Lihua School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Singapore138632 Singapore

This paper considers the collaborative graph exploration problem in GPS-denied environments, where a group of robots are required to cover a graph environment while maintaining reliable pose estimations in collaborative simultaneous localization and mapping (SLAM). Considering both objectives presents challenges for multi-robot pathfinding, as it involves the expensive covariance inference for SLAM uncertainty evaluation, especially considering various combinations of robots’ paths. To reduce the computational complexity, we propose an efficient two-stage strategy where exploration paths are first generated for quick coverage, and then enhanced by adding informative and distance-efficient loop-closing actions, called loop edges, along the paths for reliable pose estimation. We formulate the latter problem as a non-monotone submodular maximization problem by relating SLAM uncertainty with pose graph topology, which (1) facilitates more efficient evaluation of SLAM uncertainty than covariance inference, and (2) allows the application of approximation algorithms in submodular optimization to provide optimality guarantees. We further introduce the ordering heuristics to improve objective values while preserving the optimality bound. Simulation experiments over randomly generated graph environments verify the efficiency of our methods in finding paths for quick coverage and enhanced pose graph reliability, and benchmark the performance of the approximation algorithms and the greedy-based algorithm in the loop edge selection problem. Our implementations will be open-source at https://***/bairuofei/CGE. Copyright © 2024, The Authors. All rights reserved.

关键词： approximation algorithms

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Power Minimization Beamforming Design for Active RIS-Assisted ISAC System

Power Minimization Beamforming Design for Active RIS-Assiste...

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International Conference on Communication Software and Networks, ICCSN

作者： Lei Yin Ming Jin Yaming Li Yinhong Liu Chaodi Chen Huifang Zhang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

ISBN: (数字)9798331506322

ISBN: (纸本)9798331506339

This paper investigates the active reconfigurable intelligent surfaces (RIS)-assisted integrated sensing and communication (ISAC) system, in which a dual-functional base station (BS) simultaneously transmits communication information and senses radar targets. Specifically, we consider a challenging practical scenario where the channel state information (CSI) and the radar reflection coefficient are not fully available. Under this limitation, the communication outage probability and target angle estimation failure probability constraints are introduced to describe the robust beamforming design. We aim to minimize the system’s total power consumption by jointly optimizing the BS transmit beamformers and the active RIS phase-shifting. To address the non-convex optimization problem, we use the BernsteinType Inequality to safely approximate probability constraints. Then, we use the alternate optimization (AO) algorithm and semidefinite relaxation (SDR) technique to solve subproblems iteratively. Numerical results verify the active RIS-assisted ISAC system can significantly alleviate “multiplicative fading” and reduce the power consumption compared to benchmark schemes.

关键词： Power demand Array signal processing Software algorithms Radar Reconfigurable intelligent surfaces Probability Integrated sensing and communication approximation algorithms Power system reliability Optimization

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Task-Oriented Active Learning of Model Preconditions for Inaccurate Dynamics Models

Task-Oriented Active Learning of Model Preconditions for Ina...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Alex LaGrassa Moonyoung Lee Oliver Kroemer Carnegie Mellon University Robotics Institute Pittsburgh PA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

When planning with an inaccurate dynamics model, a practical strategy is to restrict planning to regions of state-action space where the model is accurate: also known as a model precondition. Empirical real-world trajectory data is valuable for defining data-driven model preconditions regard-less of the model form (analytical, simulator, learned, etc…). However, real-world data is often expensive and dangerous to collect. In order to achieve data efficiency, this paper presents an algorithm for actively selecting trajectories to learn a model precondition for an inaccurate pre-specified dynamics model. Our proposed techniques address challenges arising from the sequential nature of trajectories, and potential benefit of prioritizing task-relevant data. The experimental analysis shows how algorithmic properties affect performance in three planning scenarios: icy gridworld, simulated plant watering, and real-world plant watering. Results demonstrate an improvement of approximately 80% after only four real-world trajectories when using our proposed techniques. More material can be found on our project website: https://***/view/active-mde.

关键词： Deformable models Analytical models Heuristic algorithms Estimation approximation algorithms Data models Trajectory

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Supervised Learning Approaches for Vibration Analysis and Anomaly Detection in Rotating Machines

Supervised Learning Approaches for Vibration Analysis and An...

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Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE), International Conference on

作者： Syed Zulfiqar Ali Syed Hasnat Ali Anees Javeed Ihsan Ullah Sufyan Ali Memon Sajjad Manzoor Dept. of Electrical and Computer Engineering CUI Abbottabad Campus Abbottabad Pakistan Dept. of Defence Systems Engineering Sejong University Seoul Republic of Korea Dept. of Electrical Engineering MUST Mirpur AJK Pakistan

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

Safety and reliability are crucial for modern systems and technologies. This paper presents an approach for real-time anomaly detection in rotating machines using vibration analysis and machine learning algorithms for predictive maintenance. Rotating machines such as motors, engines, gearboxes and turbines are widely used systems and faults in these systems can result in complete shutdowns. Preventing unexpected downtime while ensuring equipment reliability and safety is essential. In this context, anomaly detection plays a vital role in monitoring the healthy operation of rotating machines. A test-bed was designed to generate datasets for both balanced (healthy) and unbalanced (faulty) motor conditions. The experimental setup consists of a three-phase induction motor, a signal acquisition system (Raspberry Pi), a vibration sensor (MPU6050) and a coupling system capable of adjusting motor balance. Supervised machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF) were applied to classify and detect anomalies in the collected datasets. A grid search algorithm was employed for hyperparameter tuning of the SVM and RF models. The proposed technique achieved a maximum accuracy of approximately 98% using SVM and 100% using the RF algorithm leveraging nine optimally selected temporal features.

关键词： Support vector machines Vibrations Radio frequency Machine learning algorithms approximation algorithms Vectors Safety Classification algorithms Reliability Anomaly detection

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Improved Computation Time of Electronic Ring Oscillator Networks Compared to a Popular Classical Optimization Algorithm for the Max-Cut Problem

Improved Computation Time of Electronic Ring Oscillator Netw...

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Sanyam Singhal Nakul Aggarwal Debanjan Bhowmik Department of Physics Indian Institute of Technology Bombay Mumbai India Department of Physics and Astronomy Rutgers University Piscataway USA Department of Electrical Engineering Indian Institute of Technology Bombay Mumbai India

ISBN: (数字)9798350377200

ISBN: (纸本)9798350377217

Time required to solve combinatorial optimization problems like Max-Cut increases exponentially with problem size. Hence, approximation algorithms like Goemans-Williamson (GW), implemented on conventional digital computers, are used to solve them approximately. However, analog systems like oscillator neural networks (ONN) (also called oscillator Ising machines (OIM)) have emerged as an alternative way to solve such problems. An electronic ring-oscillator circuit is a cheap and scalable way of implementing such an ONN. Here, we design and simulate such a ring-oscillator-based ONN with all-to-all connectivity. Consequently, we show that computation time, or time to solution (TTS), of our ONN is much lower than that of GW for Max-Cut solved on graphs of three types (Mobius ladder, random cubic, and Erdos Renyi), with the number of nodes varying from 10 to 80 at gaps of 10. In fact, we show that for ring-oscillator-based ONN, TTS almost remains constant irrespective of graph size (unlike GW, where TTS grows quadratically with graph size), while accuracy numbers of ONN and GW are comparable. This makes electronic ring oscillators attractive for solving optimization problems very fast.

关键词： Ring oscillators Digital computers Accuracy Computational modeling Neural networks SPICE approximation algorithms Hardware Optimization Electronic circuits

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Blind Detection of Non-cooperative Communication Signals Based on Feature Dimensionality Reduction

Blind Detection of Non-cooperative Communication Signals Bas...

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International Conference on Communications, Circuits and Systems (ICCCAS)

作者： Xiaoyu Wang Bangning Zhang Zhibo Chen Daoxing Guo College of Communication Engineering Army Engineering University of PLA Nanjing China

ISBN: (数字)9798350386271

ISBN: (纸本)9798350386288

Due to the complexities of channel environments and the diversity of signal types, blind detection of communication signals in a non-cooperative communication environment remains a challenging problem. To alleviate these issues, we proposes a blind detection method for communication signals based on feature dimensionality reduction. Initially, multimodulus algorithm (MMA) is employed to eliminate the effects of the channel and a short time Fourier transform (STFT) is applied to generate a time-frequency representation. Furthermore, a combination of convolutional neural network (CNN) and linear discriminant analysis (LDA) is utilized to extract common features that distinguish multiple types of signals from noise. This approach significantly enhances the algorithm's ability to extract features and improves detection rates. Experimental results demonstrate that, in a Rayleigh channel environment, with a false alarm rate of 10 % and a signal-to-noise ratio of -10dB, the proposed algorithm achieves a detection rate of 94 %, which represents a notable improvement of approximately 10% compared to the STFT-CNN algorithm.

关键词： Dimensionality reduction Time-frequency analysis Fourier transforms Rayleigh channels Feature extraction approximation algorithms Linear discriminant analysis

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Approximate Axis-Aligned Nearest Neighbor

Approximate Axis-Aligned Nearest Neighbor

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International Computer Science and Engineering Conference (ICSEC)

作者： Yodthong Rodkaew Warin Wattanapornprom Digital Game Simulation Faculty of Science and Technology University of the Thai Chamber of Commerce Bangkok Thailand Department of Mathematics Faculty of Science King Mongkut’s University of Technology Bangkok Thailand

ISBN: (数字)9798350366860

ISBN: (纸本)9798350366877

The nearest neighbor problem is a fundamental challenge in computer and data science, essential for tasks ranging from classification to clustering and beyond. As data dimensionality increases, traditional methods like brute-force search and structure-oriented algorithms such as k-D trees become computationally infeasible. This paper introduces the Approximate Axis-Aligned Nearest Neighbor (AAANN) algorithm, designed to efficiently address the nearest neighbor problem in both low and high-dimensional spaces. The algorithm leverages axis-aligned projections to achieve superior performance for dimensions less than 10, outperforming traditional methods. For higher dimensions, the Taxicab algorithm is recommended due to its robustness and efficiency. Extensive experimental evaluations demonstrate the effectiveness of AAANN, with potential applications in classification searches, ray-tracing, path planning, collision detection, and n-body simulations. The AAANN algorithm represents a significant advancement in the field, offering a scalable and efficient solution to the nearest neighbor problem across a wide range of dimensions.

关键词： Computational modeling Biological system modeling Clustering algorithms Ray tracing Data science approximation algorithms Path planning Classification algorithms Collision avoidance Robots

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A Fast ADMM Framework for Training Deep Neural Networks Without Gradients

A Fast ADMM Framework for Training Deep Neural Networks With...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xueting Wen Yongmei Lei School of Computer Engineering and Science Shanghai University Shanghai China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Stochastic gradient descent (SGD) and its many variants are widely used algorithms for training deep neural networks (DNN). However, SGD has some unavoidable drawbacks, including vanishing gradients, great sensitivity to inputs, and lack of theoretical guarantees. To address these drawbacks, the Alternating Direction Method of Multipliers (ADMM) has been proposed as an effective alternative to gradient-based methods with gradient-free properties. It has been successfully used to train deep neural networks. Nevertheless, existing ADMM-based approaches face several challenges, including slow convergence, complex update processes, and being single-task oriented. In this paper, we propose a novel optimization framework for deep learning via fast ADMM (fdlADMM) to address these drawbacks. The fdlADMM algorithm combines the benefits of the fast ADMM method with the characteristics of the DNN structure to enhance model convergence speed and improve performance. This algorithm introduces novel ideas and methods to the domain of deep learning. Additionally, the algorithm introduces a novel update process and utilizes a quadratic approximation to efficiently solve the sub-problem, thereby simplifying the update process and reducing computation time. We conduct extensive experiments on two distinct tasks, namely image classification and node classification, utilizing four benchmark datasets. The goal is to validate the effectiveness and efficiency of the proposed fdlADMM algorithm across varied domains and tasks. The results show that the fdlADMM algorithm converges significantly faster, achieves better accuracy, and maintains a very competitive training speed compared to other state-of-the-art ADMM-based optimizers.

关键词： Training Deep learning Accuracy Artificial neural networks Computer architecture approximation algorithms Convex functions

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A General Method Approximating the Convex Hull for Single-Unit Commitment Problems

A General Method Approximating the Convex Hull for Single-Un...

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IEEE General Meeting Power& Energy Society

作者： Xinfei Yan Haiwang Zhong Yanhong Liu Zekuan Yu Department of Electrical Engineering Tsinghua University Beijing China

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

With the increasing penetration of renewable energy resources, it becomes time-consuming to optimize the unit commitment of power systems incorporating massive heterogeneous resources. A typical acceleration approach concentrates on formulating the convex hulls of single-unit commitment (1UC) problems. However, existing researches on acquiring the convex hull are limited to specific units, and the derivation of the convex hull is complex. In this paper, a general method is proposed to approximate the convex hulls of all types of resources with the cutting planes around the optimal solution. After solving the 1UC problem, cutting planes are obtained to form the approximated convex hull and then accelerate the following solving processes on the 1UC problem. Simulation results show that the proposed method can accelerate the solution of the 1UC problem and the Larangian-relaxation-based decomposing algorithm for unit commitment. The generality and efficiency of the proposed method facilitate its application in industrial practice.

关键词： Renewable energy sources Accuracy Simulation approximation algorithms Power systems

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