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Integrated Decision-Making Approach for Automated Diversion Runway Selection and Flight Path Generation

JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2025年第2期22卷 130-144页

作者： Watanabe, Yoko Farges, Jean-Loup Cassaro, Mario Perotto, Filipo Studzinski French Aerosp Lab Dept Informat Proc & Syst ONERA Occitanie France

The automated in-flight airport diversion trajectory generation in case of emergency is, to this day, an open problem and a real need for general and civil aviation. This problem is often addressed by solving three subproblems independently in cascade: i) selection of diversion airport, runway, and approach mode;ii) obstacle-free path planning;and iii) flyable trajectory generation under given aircraft performance. This paper proposes an integrated decision-making approach for subproblems (i) and (ii) by performing multitarget minimum-risk path planning. The N-best runway candidates are first identified based on runway-risk criteria. Then the Monte Carlo tree search algorithm is applied to simultaneously search for obstacle-free paths to these N candidates while minimizing a global risk by aggregating the en route and runway risks. Unlike the cascaded approaches, the proposed integrated approach can evaluate the en route risk more precisely and efficiently and use it in deciding the most suitable diversion runway. Moreover, the planning difficulty is also reflected in the runway selection. The proposed approach is tested in simulation for 1000 randomly generated missions. The results are compared with those of cascaded approaches, demonstrating a higher success rate of feasible path generation in difficult mission configurations.

关键词： General Aviation search algorithm Aircraft Navigation Systems Emergency Landing Path planning Automated Decision Support

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Deduction Game Framework and Information Set Entropy search 6

Deduction Game Framework and Information Set Entropy Search

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6th Annual IEEE Conference on Games (CoG)

作者： Meng, Fandi Lucas, Simon Queen Mary Univ London London England

ISBN: (纸本)9798350350685;9798350350678

We present a game framework tailored for deduction games, enabling structured analysis from the perspective of Shannon entropy variations. Additionally, we introduce a new forward search algorithm, Information Set Entropy search (ISES), which effectively solves many single-player deduction games. The ISES algorithm, augmented with sampling techniques, allows agents to make decisions within controlled computational resources and time constraints. Experimental results on eight games within our framework demonstrate the significant superiority of our method over the Single Observer Information Set Monte Carlo Tree search(SO-ISMCTS) algorithm under limited decision time constraints. The entropy variation of game states in our framework enables explainable decision-making, which can also be used to analyze the appeal of deduction games and provide insights for game designers.

关键词： Information Set Deduction Games Information Entropy Fun of Games Game Design search algorithm

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Toward Rapid and Optimal Strategy for Swarm Conflict: A Computational Game Approach

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第3期60卷 3108-3120页

作者： Zhang, Tao Zhu, Yiji Ma, Dongying Li, Chaoyong Wang, Xiaodong Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Beijing Inst Elect Syst Engn Beijing 100854 Peoples R China

The decision and control problem for swarm operations is crucial for autonomous military conflict management. In this article, the underlying decision and control problem is treated as a noncooperative game problem, in which the underlying target assignment problem is generalized to be a graph-theoretic problem. We introduce an algorithm to seek the desired Nash equilibrium with the help of the parallel maximum weight matching algorithm. Then, we prove that the proposed solution is epsilon-Nash with guaranteed computational efficiency, and is well suited for the swarm conflict. Simulation results verified the effectiveness of the proposed solutions.

关键词： Games Approximation algorithms Computational modeling Military computing Analytical models Nash equilibrium Task analysis Game theory search algorithm swarm operation

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Improving Active Shape Models Performance in Low-Contrast Images Using a KNN-based search algorithm - With Applications In Liver Segmentation

Improving Active Shape Models Performance in Low-Contrast Im...

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Iranian Conference on Biomedical Engineering

作者： Mina Esfandiarkhani Mahdi Delavari Amir Hossein Foruzan Yen-Wei Chen Department of Biomedical Engineering Shahed University Intelligent Image Processing Lab College of Information Science and Engineering Ritsumeikan University

ISBN: (纸本)9781467393522

Active Shape Model (ASM) is considered as a high level image processing algorithm. Typical applications include image segmentation and interpretation. A major challenge in ASMs is to repeatedly move model points towards true boundaries. It is a crucial step in the algorithm which fails in cases of low contrast images. In this paper, we present a new search algorithm for ASM to tackle segmentation of tissues with nearby organs of similar intensities. We train a KNN classifier and employ it to label the region surroundings each mesh point and move the point towards the boundary. Thus, evolution of the initial surface is performed faster in a single step. Evaluation of the proposed method was carried out by Dice and Jaccard similarity measure and accuracy index. The results of segmentation were compared with the results of Active Contour Model and conventional ASM. The Dice (Jaccard) indices are 0.93 (0.87), 0.85 (0.73) and 0.9 (0.76) for our method, conventional ASM and ACM, respectively. Moreover, the accuracy is increased in the proposed method compared to the two other methods.

关键词： Active Shape Models search algorithm KNN Classification Liver Segmentation

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Automatic near-optimal generation of software test data for critical paths

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JOURNAL OF SUPERCOMPUTING 2025年第5期81卷 1-34页

作者： Abdi, Mina Mardukhi, Farhad Razi Univ Dept Comp Engn & Informat Technol Kermanshah Iran

Software testing is a critical phase in software development that ensures the identification and resolution of faults. Automating test data generation can significantly reduce costs and improve efficiency. While genetic algorithms and particle swarm optimization (PSO) are widely used in this domain, recent research has introduced alternative approaches with enhanced performance. This paper presents an improved version of the Improved Lyrebird Optimization algorithm (ILOA) for automated test data generation, aiming to enhance its exploration and exploitation capabilities. A novel fitness function is introduced to optimize the search process and improve test case generation. The proposed method is evaluated on multiple benchmark programs, and the experimental results demonstrate that ILOA outperforms existing state-of-the-art algorithms in terms of the number of fitness evaluations, execution time, test generation efficiency, coverage rate, and success rate. These findings highlight the effectiveness of ILOA in improving test data generation for software testing.

关键词： Software testing Test data generation search algorithm Path testing Lyrebird optimization algorithm

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Classical Artificial Neural Network Training Using Quantum Walks as a search Procedure

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IEEE TRANSACTIONS ON COMPUTERS 2022年第2期71卷 378-389页

作者： de Souza, Luciano S. de Carvalho, Jonathan H. A. Ferreira, Tiago A. E. Univ Fed Rural Pernambuco Dept Estat & Informat BR-52171900 Recife PE Brazil

This article proposes a computational procedure that applies a quantum algorithm to train classical artificial neural networks. The goal of the procedure is to apply quantum walk as a search algorithm in a complete graph to find all synaptic weights of a classical artificial neural network. Each vertex of this complete graph represents a possible synaptic weight set in the w-dimensional search space, where w is the number of weights of the neural network. To know the number of iterations required a priorito obtain the solutions is one of the main advantages of the procedure. Another advantage is that the proposed method does not stagnate in local minimums. Thus, it is possible to use the quantum walk search procedure as an alternative to the backpropagation algorithm. The proposed method was employed fora XOR problem to prove the proposed concept. To solve this problem, the proposed method trained a classical artificial neural network with nine weights. However, the procedure can find solutions for any number of dimensions. The results achieved demonstrate the viability of the proposal, contributing to machine learning and quantum computing researches.

关键词： Artificial neural networks training quantum computing quantum walk search algorithm

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Review of research progress and development trend of digital image correlation

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MULTIDISCIPLINE MODELING IN MATERIALS AND STRUCTURES 2024年第1期20卷 81-114页

作者： He, Xindang Zhou, Run Liu, Zheyuan Yang, Suliang Chen, Ke Li, Lei Northwestern Polytech Univ Sch Mech Civil Engn & Architecture Dept Engn Mech Xian Peoples R China AVIC Aircraft Strength Res Inst Xian Peoples R China Northwestern Polytech Univ Sch Mech Civil Engn & Architecture Xian Peoples R China Northwestern Polytech Univ Dept Engn Mech Xian Peoples R China

Purpose - The purpose of this paper is to provide a comprehensive review of a non-contact full-field optical measurement technique known as digital image correlation (DIC).Design/methodology/approach - The approach of this review paper is to introduce the research pertaining to DIC. It comprehensively covers crucial facets including its principles, historical development, core challenges, current research status and practical applications. Additionally, it delves into unresolved issues and outlines future research *** - The findings of this review encompass essential aspects of DIC, including core issues like the subpixel registration algorithm, camera calibration, measurement of surface deformation in 3D complex structures and applications in ultra-high-temperature settings. Additionally, the review presents the prevailing strategies for addressing these challenges, the most recent advancements in DIC applications across quasi-static, dynamic, ultra-high-temperature, large-scale and micro-scale engineering domains, along with key directions for future research ***/value - This review holds a substantial value as it furnishes a comprehensive and in-depth introduction to DIC, while also spotlighting its prospective applications.

关键词： Digital image correlation Deformation measurement technology Ultra-high temperature environment Speckle search algorithm Camera calibration

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A novel Bagged-CNN architecture for short-term wind power forecasting

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2024年第12期21卷 2712-2723页

作者： Mulewa, Sachin Parmar, Azan De, Ashoke Indian Inst Technol Kanpur Dept Sustainable Energy Engn Kanpur India Indian Inst Technol Kanpur Dept Aerosp Engn Kanpur India Indian Inst Technol Kanpur Dept Aerosp Engn Kanpur 208016 India

Wind power forecasting plays a pivotal role in ensuring the dependable integration of wind energy into power grids. This research introduces a novel Bagged-CNN architecture tailored for short-term wind power forecasting. This architecture harnesses the combined strengths of the Bagging ensemble technique and Convolutional Neural Networks (CNN) to enhance prediction accuracy and robustness. Our approach demonstrates superior forecasting capabilities by exploiting the spatial and temporal features captured by CNNs. To validate our proposed architecture, we have conducted a comprehensive evaluation across three distinct wind farms, each characterized by unique geographical and climatic conditions. We systematically compared the performance of our Bagged-CNN model against several standalone machine-learning algorithms commonly employed in wind power forecasting. The hyperparameters of our Bagged-CNN model are meticulously optimized using the GRID search algorithm, which systematically explores a predefined parameter space. Notably, our predictions shed light on the substantial impact of training data period length on wind power prediction accuracy. Our research underscores the potential of coupling Bagging and CNN techniques for short-term wind power forecasting. The Bagged-CNN architecture, as proposed, serves as a robust framework for capturing intricate spatial and temporal patterns in wind power data, thereby facilitating more precise and dependable predictions.

关键词： Wind power forecasting ML CNN Bagged-CNN search algorithm

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A Peak-search algorithm for Load-Pull Optimization of Power-Added Efficiency and Adjacent-Channel Power Ratio

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2014年第8期62卷 1772-1783页

作者： Martin, Joshua Baylis, Charles Cohen, Lawrence de Graaf, Jean Marks, Robert J., II Baylor Univ Dept Elect & Comp Engn Waco TX 76798 USA Naval Res Lab Div Radar Washington DC 20375 USA

Power amplifiers in modern wireless systems must meet increasingly stringent spectral constraints while still operating with high power efficiency. A new peak-search algorithm is demonstrated to find the Pareto optimum of power-added efficiency (PAE) while producing an adjacent channel power ratio (ACPR) value under a specified maximum. A peak-search is first performed for the maximum PAE, and then a small-step steepest descent walk in the ACPR is taken from the PAE maximum toward the ACPR minimum until ACPR requirements are met. This approach reasonably approximates the Pareto optimum for the device. Simulation and measurement data are presented for searches from multiple starting points, and good correspondence is obtained for the Pareto optimum reflection coefficient, as well as the optimum PAE and ACPR values. This measurement-based algorithm is expected to find useful application in real-time adaptive radio and radar transmitters, as well as in bench-top measurements for amplifier design.

关键词： Adjacent channel power ratio (ACPR) gradient load-pull power-added efficiency (PAE) search algorithm steepest ascent

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Real-time prediction of ship motion based on improved empirical mode composition and dynamic residual neural network

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OCEAN ENGINEERING 2024年 292卷

作者： Gao, Nan Chuang, Zhenju Hu, Ankang Dalian Maritime Univ Sch Naval Architecture & Ocean Engn 1 Linghai St Dalian 116026 Peoples R China

Ship motion attitude data have strong random and non-stationary characteristics under severe sea states. Real-time and accurate prediction of the ship's motion attitude can significantly improve the safety of navigation. Therefore, this article proposes a prediction model based on the improved empirical mode decomposition (IEMD) and the dynamic residual recurrent neural network with bidirectional structure and time pattern attention mechanism (TPA-Bi-DRRNN), and proposes a new algorithm: dynamic adaptive beetle swarm antennae search (DABSAS) algorithm to optimise the initial weight and threshold of the prediction model. The input data are adaptively composed into multiple intrinsic mode functions (IMFs) containing their frequency characteristics using IEMD, and a prediction is made for each IMF using TPA-Bi-DRRNN. The model structure can be regulated in real time according to the sliding window. The destroyer DTMB5415 is used as an example to conduct a performance test of the hybrid prediction model and DABSAS. The results show that no mode mixing occurred in the IMFs, the noise in each IMF was significantly reduced, thus dramatically reducing the difficulty of using the input data for prediction;and the optimization performance of DABSAS in applying the TPA-Bi-DRRNN is much better than that of the traditional algorithms. Compared with other models, the difference in the predictive accuracy of TPA-Bi-DRRNN under each condition is the smallest, suggestings that it has extremely high robustness and will always be able to maintain much higher accuracy than other models over a long period, thus meeting the needs for real-time accurate prediction of ship motion attitude.

关键词： The improved empirical mode composition Ship motion Dynamic bidirectional residual recurrent neural network Dynamic adaptive beetle swarm antennae search algorithm Time pattern attrition

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