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10th IFAC Triennial Conference on Manufacturing Modelling, Management and Control (MIM)

作者： Liu, Ming Zhao, Yecheng Chu, Feng Zheng, Feifeng Chu, Chengbin Tongji Univ Sch Econ & Management Shanghai Peoples R China Univ Evry Univ Paris Saclay IBISC Evry France Donghua Univ Glorious Sun Sch Business & Management Shanghai Peoples R China Univ Gustave Eiffel ESIEE Paris F-93162 Noisy Le Grand France Lab GRETTIA COSYS F-93162 Noisy Le Grand France

Buses are expected to be punctual, but deviation from the schedule is a common occurrence. Transit signal priority (TSP) and conditional signal priority (CSP) are methods to help the bus perform the schedule better by giving traffic signal priority to the bus. Markov decision process (MDP) is suitable for modeling such sequential decision process. In this article, we point out the Markov property of the system based on the analysis of the bus driving process. We model the process of bus driving with CSP as a Markov decision process. Then, the Deep Q Network (DQN) algorithm is applied to solve this MDP model. To the best of our knowledge, this is the first time that the bus scheduling problem with CSP has been modeled as an MDP model and solved by a learning algorithm. Numerical experiments verify the applicability of the DQN algorithm to solve this MDP model. Copyright (C) 2022 The Authors.

关键词： Bus scheduling smart transportation signal priority Markov decision process artificial intelligence algorithm deep reinforcement learning bus driving process

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Embedded or Integrated, Autonomous Intelligent Monitoring Architecture and Framework for Training Assistance and Automation 6

Embedded or Integrated, Autonomous Intelligent Monitoring Ar...

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6th Annual Conference on Computational Science and Computational intelligence (CSCI)

作者： Wallace, Jeffrey Valdivia, Angelica Rocket Technol Syst LLC Washington DC 20460 USA

ISBN: (纸本)9781728155845

A key element to readiness lies in the ability to provide qualified framers and role player personnel, a challenge in a budget constrained environment. The goal of this effort is to employ practical artificial intelligence (AI) and automation techniques to minimize staffing requirements for training, and to improve the quality and pace of training. This paper outlines implementation experiments and results of these techniques to interoperate multiple AI algorithm types in an architecture and framework to automate certain aspects of training systems. A core capability the ability to integrate into a variety of training systems, from embedded and virtual reality simulations to constructive wargames, via a user-friendly system of APIs.

关键词： training systems automated monitoring machine intervention artificial intelligence algorithm integration

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Research on New Methods of LEO Satellite Networks Routing 6

Research on New Methods of LEO Satellite Networks Routing

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6th International Conference on Information Science and Control Engineering (ICISCE)

作者： Dong, Chaoying Xu, Xin Liu, Aijun Cheng, Fengyi Cai, Biao Chang, Jinhui Army Engn Univ PLA Nanjing Peoples R China

ISBN: (纸本)9781728157122

Satellite communication networks, as an effective complement to terrestrial communication networks, enable global coverage communications. The Low-orbit satellite network has the characteristics of low transmission delay, short operation period and global coverage communication, it has become a popular research direction in satellite communication networks. With the continuous development of digital and intelligent communication in the world, satellite network services continue to expand, and higher requirements are placed on network transmission performance. The traditional single-structure low-orbit satellite network using centralized routing has been unable to meet current needs.. Therefore, with the low-orbit satellite network as the core, people have conducted in-depth research on satellite communication network routing algorithms that meet the requirements of high-quality, high-efficiency and large-capacity communication. Especially in the three hot issues of low-orbit satellite network routing algorithms, a lot of research results have emerged, including: QoS-based artificial intelligence algorithm routing algorithm;multi-layer satellite network routing algorithm;satellite network multipath routing algorithm. Through the analysis and discussion of these problems and achievements, the idea of combining the three algorithms on demand is proposed, and the challenges are analyzed.

关键词： Low-orbit satellite Routing Quality of Service artificial intelligence algorithm Mufti-layer satellite network Muftipath Challenge

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Work in Progress: Exploration of Research-study Dual Cycle Mode for Simulation Practice Teaching 7

Work in Progress: Exploration of Research-study Dual Cycle M...

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7th IEEE World Engineering Education Conference (EDUNINE) - Reimaging Engineering - Toward the Next Generation of Engineering Education, Merging Technologies in a Connected World

作者： Li, Hui Wu, Qiang Gu, Zhiqun Gu, Rentao Ji, Yuefeng Beijing Univ Posts & Telecommun Beijing Lab Adv Informat Networks Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conve Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China

ISBN: (纸本)9798350320503

Traditional information course simulation teaching is often based on specific simulation software to simulate and verify some theoretical models in the course knowledge points. Under the constraints of fixed topics and methods, the limited teaching content leads to the limitation of students' innovation ability and practical ability. In view of the above problems, the present paper undertakes the progress of the dual center teaching model, with the help of an extensible online simulation platform, to design the simulation problems of related artificial intelligence algorithms for the actual engineering problems in the communication network, forming a simulation teaching mode of research and learning double cycle through which students simulate cases and apply these algorithms to innovation and entrepreneurship practice.

关键词： Network simulation teaching artificial intelligence algorithm research double cycle innovation and Entrepreneurship Practice

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Application of Hybrid artificial Fish Swarm algorithm Based on Similar Fragments in VRP 10

Application of Hybrid Artificial Fish Swarm Algorithm Based ...

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10th International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR) - Remote Sensing Image Processing, Geographic Information Systems, and Other Applications

作者： Che, Jinnuo Zhou, Kang Zhang, Xueyu Tong, Xin Hou, Lingyun Jia, Shiyu Zhen, Yiting Wuhan Polytech Univ Dept Math & Comp Sci Wuhan 430023 Hubei Peoples R China

ISBN: (数字)9781510617261

ISBN: (纸本)9781510617261;9781510617254

Focused on the issue that the decrease of convergence speed and the precision of calculation at the end of the process in artificial Fish Swaim algorithm(AFSA) and instability of results, a hybrid AFSA based on similar fragments is proposed. Traditional AFSA enjoys a lot of obvious advantages in solving complex optimization problems like Vehicle Routing Problem(VRP). AFSA have a few limitations such as low convergence speed, low precision and instability of results. In this paper, two improvements are introduced. On the one hand, change the definition of the distance for artificial fish, as well as increase vision field of artificial fish, and the problem of speed and precision can be improved when solving VRP. On the other hand, mix artificial bee colony algorithm(ABC) into AFSA - initialize the population of artificial fish by the ABC, and it solves the problem of instability of results in some extend. The experiment results demonstrate that the optimal solution of the hybrid AFSA is easier to approach the optimal solution of the standard database than the other two algorithms. In conclusion, the hybrid algorithm can effectively solve the problem that instability of results and decrease of convergence speed and the precision of calculation at the end of the process.

关键词： VRP ABC AFSA artificial intelligence algorithm

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EXPANDING THE OPERATIONAL RANGE OF UAS WITH AN ONBOARD SUPERVISORY INSTANCE 34

EXPANDING THE OPERATIONAL RANGE OF UAS WITH AN ONBOARD SUPER...

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IEEE/AIAA 34th Digital Avionics Systems Conference (DASC)

作者： Frey, Andreas Hanti, Thomas Univ Appl Sci Ingolstadt Ingolstadt Bavaria Germany

ISBN: (纸本)9781479989409

Unmanned Air Systems (UAS) are used in a variety of civilian and military applications. Nowadays large scale UASs are mostly operated by a dedicated remote pilot, who also supervises the automated flight phases. Driven by the need to expand the operational range, the next logical step is to raise the autonomy level. Autonomy can enable UAS operation in areas where an online supervision is not possible or the operation of multiple UASs by one operator. Additionally new cooperation concepts are in development, leaving the UAS with a task to solve and at the same time giving it a degree of freedom comparable to a human teammate. Close supervision is not intended or not possible in those cases. In such autonomous missions the UAS uses highly complex artificial intelligence algorithms. The algorithms cannot be tested and qualified due to the nearly unlimited number of internal states. As the use of such non deterministic mission systems leads to safety concerns, monitoring is required throughout the entire flight. Our concept is to transfer the operator's rule based supervisory tasks to on board the aircraft and at the same time enable this supervisory instance to intervene with the appropriate procedure if a hard rule is violated. This ensures a deterministic behavior in case of transient or permanent errors in the mission system.

关键词： aerospace computing artificial intelligence autonomous aerial vehicles control engineering computing knowledge based systems military aircraft mobile robots UAS artificial intelligence algorithm military application nondeterministic mission system onboard supervisory instance operational range expansion rule based supervisory task unmanned air systems Aerospace control Aircraft Automation Knowledge based systems Monitoring Planning Safety

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The Modified Clonal Selection algorithm Applied to the Remote Sensing Image Information Extracting

The Modified Clonal Selection Algorithm Applied to the Remot...

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1st WRI Global Congress on Intelligent Systems (GCIS 2009)

作者： Ling Cheng-xing Zhang Huai-qing Lin-Hui CAF Inst Forest Resource Informat Tech Beijing 100091 Peoples R China CSUFT Res Ctr Forest Remote Sensing & Informat Engn Changsha 410004 Peoples R China

ISBN: (纸本)9780769535715

Clonal Selection algorithm is one of the important artificial intelligence algorithms, which used as a powerful information processing and problem-solving paradigm in both the scientific and engineering fields. However, few studies concern application of CLONALG method in information extracting of satellite image. In this paper, we suggest modified Clonal Selection algorithm (M-CLONALG) improved by niche technology's sharing function and memory calculator technique applying to remote sensing (RS) image information extracting. In order to testify the correctness that M-CLONALG improves the stability of searching results, accuracy of the global optimization, we compare with Maximum Likelihood method, Minimum Distance method and normal CLONALG method. Experimental results confirm that M-CLONALG has self-organizing, self-learning ability, and no limitation to the distribution of training samples from the global data, with complete convergence, can quickly search the best center of classification clustering at high accuracy. Therefore, our method M-CLONALG is superior to other three algorithms in remote sensing image information extracting, and its overall accuracy and Kappa statistic reach 91.7% and 0.89 respectively.

关键词： M-CLONALG CLONALG Remote sensing (RS) Information extracting Classification artificial intelligence algorithm

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Minimizing the total waste in the one-dimensional cutting stock problem with the African buffalo optimization algorithm

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PEERJ 2023年 11卷 e1728页

作者： Montiel-Arrieta, Leonardo Javier Barragan-Vite, Irving Seck-Tuoh-Mora, Juan Carlos Hernandez-Romero, Norberto Gonzalez-Hernandez, Manuel Medina-Marin, Joselito AAIyA ICBI UAEH Mineral De La Reforma Hidalgo Mexico

The one-dimensional cutting-stock problem (1D-CSP) consists of obtaining a set of items of different lengths from stocks of one or different lengths, where the minimization of waste is one of the main objectives to be achieved. This problem arises in several industries like wood, glass, and paper, among others similar. Different approaches have been designed to deal with this problem ranging from exact algorithms to hybrid methods of heuristics or metaheuristics. The African Buffalo Optimization (ABO) algorithm is used in this work to address the 1D-CSP. This algorithm has been recently introduced to solve combinatorial problems such as travel salesman and bin packing problems. A procedure was designed to improve the search by taking advantage of the location of the buffaloes just before it is needed to restart the herd, with the aim of not to losing the advance reached in the search. Different instances from the literature were used to test the algorithm. The results show that the developed method is competitive in waste minimization against other heuristics, metaheuristics, and hybrid approaches.

关键词： Cutting stock problem Combinatorial optimization African buffalo optimization Meta-heuristic techniques artificial intelligence algorithm Swarm intelligence

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An improved heuristic mechanism ant colony optimization algorithm for solving path planning

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KNOWLEDGE-BASED SYSTEMS 2023年第1期271卷

作者： Liu, Chao Wu, Lei Xiao, Wensheng Li, Guangxin Xu, Dengpan Guo, Jingjing Li, Wentao China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China China Univ Petr East China Natl Engn Res Ctr Offshore Geophys & Explorat Equi Qingdao 266580 Peoples R China Nanyang Technol Univ Maritime Inst NTU Sch Civil & Environm Engn Singapore 639798 Singapore Macao Polytech Univ Fac Appl Sci Taipa 999078 Macao Peoples R China CNOOC Safety & Technol Serv Co Ltd Tianjin 300450 Peoples R China

With the development of artificial intelligence algorithms, researchers are attracted to intelligent path planning due to its broad applications and potential development. The ant colony optimization (ACO) algorithm is one of the most widely used methods to solve path planning. However, the traditional ACO has some shortcomings such as low search efficiency, easy stagnation, etc. In this study, a novel variant of ACO named improved heuristic mechanism ACO (IHMACO) is proposed. The IHMACO contains four improved mechanisms including adaptive pheromone concentration setting, heuristic mechanism with directional judgment, improved pseudo-random transfer strategy, and dynamic adjustment of the pheromone evaporation rate. In detail, the adaptive pheromone concentration setting and heuristic mechanism with directional judgment are presented to enhance the purposiveness and reduce turn times of planned path. The improved pseudo-random transfer strategy and dynamic adjustment of the pheromone evaporation rate are introduced to enhance search efficiency and global search ability, further avoiding falling into local optimum. Subsequently, a series of experiments are conducted to test effectiveness of the four mechanisms and verify the performance of the presented IHMACO. Compared with 15 existing approaches for solving path planning, including nine variants of ACO and six commonly used deterministic search algorithms. The experimental results indicate that the relative improvement percentages of the proposed IHMACO in terms of the path turn times are 33.33%, 83.33%, 35.29%, 38.46%, and 38.46% respectively, demonstrating the superiority of IHMACO in terms of the availability and high-efficiency.

关键词： Path planning artificial intelligence algorithm Ant colony optimization algorithm Heuristic mechanism Pheromone evaporation

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Experimental investigation and predictive modeling of shear performance for concrete-encased steel beams using artificial neural networks

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MATERIALS AND STRUCTURES 2023年第8期56卷 141-141页

作者： Wang, Jun Cui, Menglin Northeast Forestry Univ Sch Civil Engn Harbin 150000 Peoples R China

This manuscript employs a highly efficient artificial intelligence (AI) technique for machine learning (ML) through artificial neural networks (ANNs) and introduces a novel numerical predictive model capable of accurately forecasting the shear capacity of concrete-encased steel (CES) beams. The research begins by conducting shear tests on nine CES beams with high-strength steel, which addresses a significant gap in the shear performance of high-strength steel in CES beams. Subsequently, a comprehensive database of CES beam shear tests is established to train and validate the ANN model. The database consists of 242 sets of test data, compiled from published literature, encompassing a wide range of geometrical and material properties. A sensitivity analysis of the proposed model is then performed using a Pearson chi-square test to determine the relative importance of each input parameter on shear strength. Furthermore, a thorough examination is conducted to assess the impact of each parameter. Finally, the proposed predictive model is compared against current design codes, including ANSI/AISC 360-16 (USA, North America), BS EN 1994-1-1:2004 (Europe), and JGJ 138-2016 (China, Asia). The comparison reveals that ML technology exhibits higher accuracy and robustness in predicting shear bearing capacity.

关键词： Composite beams Shear strength Prediction model artificial intelligence algorithm artificial neural network Machine learning

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