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intelligent Optimization Under Multiple Factories: Hybrid Flow Shop Scheduling Problem with Blocking Constraints Using an Advanced Iterated Greedy Algorithm

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Complex System Modeling and Simulation 2023年第4期3卷 282-306页

作者： Yong Wang Yuting Wang Yuyan Han Junqing Li Kaizhou Gao Yusuke Nojima School of Computer Science Liaocheng UniversityLiaocheng 252000China School of Computer Science Shandong Normal UniversityJinan 252000China Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China Department of Computer Science and Intelligent Systems Osaka Prefecture UniversityOsaka 599-8531Japan

The distributed hybrid flow shop scheduling problem(DHFSP),which integrates distributed manufacturing models with parallel machines,has gained significant ***,in actual scheduling,some adjacent machines do not have buffers between them,resulting in *** paper focuses on addressing the DHFSP with blocking constraints(DBHFSP)based on the actual production *** solve DBHFSP,we construct a mixed integer linear programming(MILP)model for DBHFSP and validate its correctness using the Gurobi ***,an advanced iterated greedy(AIG)algorithm is designed to minimize the makespan,in which we modify the Nawaz,Enscore,and Ham(NEH)heuristic to solve blocking *** balance the global and local search capabilities of AIG,two effective inter-factory neighborhood search strategies and a swap-based local search strategy are ***,each factory is mutually independent,and the movement within one factory does not affect the *** view of this,we specifically designed a memory-based decoding method for insertion operations to reduce the computation time of the ***,two shaking strategies are incorporated into the algorithm to mitigate premature *** advanced algorithms are used to conduct comparative experiments with AIG on 80 test instances,and experimental results illustrate that the makespan and the relative percentage increase(RPI)obtained by AIG are 1.0%and 86.1%,respectively,better than the comparative algorithms.

关键词： blocking distributed hybrid flow shop neighborhood search iterated greedy algorithm

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LiLMaps: Learnable Implicit Language Maps

arXiv

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arXiv 2025年

作者： Kruzhkov, Evgenii Behnke, Sven Autonomous Intelligent Systems Computer Science Institute VI University of Bonn Germany Autonomous Intelligent Systems Computer Science Institute VI – Intelligent Systems and Robotics Center for Robotics and The Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

One of the current trends in robotics is to employ large language models (LLMs) to provide non-predefined command execution and natural human-robot interaction. It is useful to have an environment map together with its language representation, which can be further utilized by LLMs. Such a comprehensive scene representation enables numerous ways of interaction with the map for autonomously operating robots. In this work, we present an approach that enhances incremental implicit mapping through the integration of vision-language features. Specifically, we (i) propose a decoder optimization technique for implicit language maps which can be used when new objects appear on the scene, and (ii) address the problem of inconsistent vision-language predictions between different viewing positions. Our experiments demonstrate the effectiveness of LiLMaps and solid improvements in performance. © 2025, CC BY-NC-ND.

关键词： Visual languages

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Dynamic Subgoal Based Path Formation and Task Allocation: A NeuroFleets Approach to Scalable Swarm robotics

Dynamic Subgoal Based Path Formation and Task Allocation: A ...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Peter, Robinroy Ratnabala, Lavanya Andrew Charles, Eugene Yugarajah Tsetserukou, Dzmitry Ltd Jaffna Sri Lanka Skolkovo Institute of Science and Technology Intelligent Space Robotics Laboratory Moscow121205 Russia University of Jaffna Department of Computer Science Jaffna Sri Lanka

ISBN: (纸本)9781665410205

This paper addresses the challenges of exploration and navigation in unknown environments from the perspective of evolutionary swarm robotics. A key focus is on path formation, which is essential for enabling cooperative swarm robots to navigate effectively. We designed the task allocation and path formation process based on a finite state machine, ensuring systematic decision-making and efficient state transitions. The approach is decentralized, allowing each robot to make decisions independently based on local information, which enhances scalability and robustness. We present a novel subgoal-based path formation method that establishes paths between locations by leveraging visually connected subgoals. Simulation experiments conducted in the Argos simulator show that this method successfully forms paths in the majority of trials. However, inter-collision (traffic) among numerous robots during path formation can negatively impact performance. To address this issue, we propose a task allocation strategy that uses local communication protocols and light signal-based communication to manage robot deployment. This strategy assesses the distance between points and determines the optimal number of robots needed for the path formation task, thereby reducing unnecessary exploration and traffic congestion. The performance of both the subgoal-based path formation method and the task allocation strategy is evaluated by comparing the path length, time, and resource usage against the A ∗ algorithm. Simulation results demonstrate the effectiveness of our approach, highlighting its scalability, robustness, and fault tolerance. © 2024 IEEE.

关键词： Traffic congestion

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Autonomous 3D Reconstruction with Small Unmanned Aerial Vehicle (UAV) using Structure from Motion: Practical Aspects

Autonomous 3D Reconstruction with Small Unmanned Aerial Vehi...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Rayon, Nathan Stevens, Raymond Sevil, Hakki Erhan Department of Computer Science University of West Florida FL32514 United States Department of Electrical and Computer Engineering University of West Florida FL32514 United States Department of Intelligent Systems & Robotics University of West Florida FL32514 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper explores the practical considerations and challenges involved in achieving autonomous 3D reconstruction utilizing small Unmanned Aerial Vehicles (UAVs) through the framework of Structure from Motion (SFM). The utilization of small UAVs equipped with lightweight cameras presents a promising resource for cost-effective and rapid 3D mapping in various domains such as disaster response, infrastructure inspection, and search and rescue. In this study, the critical practical aspects of this process, including height level for taking images, UAV selection, number of images, image acquisition strategies, and computational time. The effectiveness and limitations of autonomous 3D reconstruction with small UAVs through practical insights are summarized, with discussions on potential advancements and areas for future research. This paper serves as a guide for researchers interested in utilizing small UAVs for autonomous 3D reconstruction tasks applications. © 2025, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Towards Conscious Service Robots

arXiv

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arXiv 2025年

作者： Behnke, Sven Autonomous Intelligent Systems Computer Science Institute VI – Intelligent Systems and Robotics Center for Robotics the Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

Deep learning’s success in perception, natural language processing, etc. inspires hopes for advancements in autonomous robotics. However, real-world robotics face challenges like variability, high-dimensional state spaces, non-linear dependencies, and partial observability. A key issue is non-stationarity of robots, environments, and tasks, leading to performance drops with out-of-distribution data. Unlike current machine learning models, humans adapt quickly to changes and new tasks due to a cognitive architecture that enables systematic generalization and meta-cognition. Human brain’s System 1 handles routine tasks unconsciously, while System 2 manages complex tasks consciously, facilitating flexible problem-solving and self-monitoring. For robots to achieve human-like learning and reasoning, they need to integrate causal models, working memory, planning, and metacognitive processing. By incorporating human cognition insights, the next generation of service robots will handle novel situations and monitor themselves to avoid risks and mitigate errors. Copyright © 2025, The Authors. All rights reserved.

关键词： Problem solving

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Classification and Comprehension of Software Requirements Using Ensemble Learning

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computers, Materials & Continua 2024年第8期80卷 2839-2855页

作者： Jalil Abbas Arshad Ahmad Syed Muqsit Shaheed Rubia Fatima Sajid Shah Mohammad Elaffendi Gauhar Ali School of Computer Science and Technology Anhui UniversityHefei230039China School of Computing Sciences Pak Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science and IT University of LahoreLahore55150Pakistan Department of Computer Science Emerson UniversityPunjabMultan60000Pakistan EIAS(Emerging Intelligent Autonomous Systems)Data Science Lab Prince Sultan UniversityRiyad12435Saudi Arabia

The software development process mostly depends on accurately identifying both essential and optional ***,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional *** address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human ***,existing techniques often struggle with complex instructions and large-scale *** our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer *** results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT *** datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse *** findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.

关键词： Ensemble learning machine learning non-functional requirements requirement engineering accuracy sliding window

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Person Segmentation and Action Classification for Multi-Channel Hemisphere Field of View LiDAR Sensors

Person Segmentation and Action Classification for Multi-Chan...

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IEEE/SICE International Symposium on System Integration

作者： Svetlana Seliunina Artem Otelepko Raphael Memmesheimer Sven Behnke Autonomous Intelligent Systems Group Computer Science Institute VI – Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence and Center for Robotics University of Bonn

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

Robots need to perceive persons in their surroundings for safety and to interact with them. In this paper, we present a person segmentation and action classification approach that operates on 3D scans of hemisphere field of view LiDAR sensors. We recorded a data set with an Ouster OSDome-64 sensor consisting of scenes where persons perform three different actions and annotated it. We propose a method based on a MaskDINO model to detect and segment persons and to recognize their actions from combined spherical projected multi-channel representations of the LiDAR data with an additional positional encoding. Our approach demonstrates good performance for the person segmentation task and further performs well for the estimation of the person action states walking, waving, and sitting. An ablation study provides insights about the individual channel contributions for the person segmentation task. The trained models, code and dataset are made publicly available.

关键词： Reflectivity Adaptation models Laser radar Three-dimensional displays System integration Robot sensing systems Encoding Sensor systems Sensors Safety

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Training Variational Autoencoders for Population Synthesis in Public Health with Missing Data

Training Variational Autoencoders for Population Synthesis i...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Blackthorn, Nathan Mahyari, Andrew Arash Srinivasan, Ashok University of West Florida Department of Computer Science PensacolaFL United States PensacolaFL United States University of West Florida Department of Intelligent Systems and Robotics PensacolaFL United States

ISBN: (纸本)9798350362480

The importance of social determinants of health in shaping equitable public health policies is gaining increasing recognition. Emerging data sources, such as mobility and social media data, are becoming key to public health models. However, privacy concerns often limit access to these sensitive data, as even anonymized datasets are vulnerable to deductive disclosure. A promising solution to this challenge is the use of synthetic populations, which not only safeguard privacy but also enable the exploration of various "what-if"scenarios. Most existing studies on synthetic populations assume full access to complete datasets for training. However, in many public health applications, such as census data, this assumption is unrealistic due to incomplete responses, especially regarding sensitive questions like wealth or education. This paper introduces a novel approach for training Variational AutoEncoders (VAEs) with incomplete data, without resorting to missing value imputation. Instead, the VAEs are trained solely on the observed data. Using the 2019 PUMS dataset for Florida, we successfully train VAEs to generate diverse and flexible synthetic populations. By comparing marginal distributions and utilizing t-SNE for analysis, the results highlight the effectiveness of this method in addressing missing data challenges. This work demonstrates the potential of VAEs in generating synthetic populations for health research, even when complete datasets are unavailable, thereby offering a robust solution to advance public health studies while preserving data privacy. © 2024 IEEE.

关键词： Differential privacy

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Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

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computer systems science & Engineering 2023年第9期46卷 2741-2754页

作者： Sana Zahir Rafi Ullah Khan Mohib Ullah Muhammad Ishaq Naqqash Dilshad Amin Ullah Mi Young Lee Institute of Computer Sciences and Information Technology(ICS/IT) The University of AgriculturePeshawar25130Khyber PakhtunkhwaPakistan Department of Convergence Engineering for Intelligent Drone Sejong UniversitySeoul05006Korea Collaborative Robotics and Intelligent Systems(CoRIS)Institute Oregon State UniversityORUSA Department of Software Sejong UniversitySeoul05006Korea

The analysis of overcrowded areas is essential for flow monitoring,assembly control,and *** counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary *** crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test *** this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd *** proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training *** decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive *** benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.

关键词： Artificial intelligence deep learning crowd counting scene understanding

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Shape effect of prodrug nanoassemblies on treatment efficacy of cancer therapy

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Nano Research 2025年第2期18卷 616-626页

作者： Fengxiang Liu Rongzheng Liu Xiaoyuan Fan Xia Wang Kaiyuan Wang Hainan Zhao Hao Ye Shunzhe Zheng Xiao Kuang Yinxian Yang Haotian Zhang Qiming Kan Zhonggui He Jian Chen Jin Sun Department of Pharmaceutics Wuya College of InnovationShenyang Pharmaceutical UniversityShenyang 110016China School of Pharmacy Shenyang Pharmaceutical UniversityShenyang 110016China Nephrology department The First Affiliated Hospital of Jinzhou Medical UniversityJinzhou 121000China Multi-Scale Robotics Lab(MSRL) Institute of Robotics&Intelligent Systems(IRIS)ETH Zurich 8092Switzerland School of Life Science and Biopharmaceutics Shenyang Pharmaceutical UniversityShenyang 110016China Joint International Research Laboratory of Intelligent Drug Delivery Systems Ministry of EducationShenyang 110016China School of Pharmacy Shanghai Jiao Tong UniversityShanghai 200240China

The tumor microenvironment-sensitive prodrug-based nanoparticles(NPs)have emerged as a promising drug delivery system(DDS).The shape of these particles plays a crucial role in their in vivo ***,non-spherical organic NPs are rarely reported due to the inherent flexibility and variability of organic ***,we fabricate reduction-sensitive prodrug NPs and explore the impact of their morphology properties on their in vivo *** are self-assembled into spherical NPs with distearoyl phosphoethanolamine-PEG2000(DSPE-PEG2k),or into rod-shaped NPs with D-a-tocopherol polyethylene glycol 2000 succinate(TPGS2k)due to the stronger binding *** comparison with spherical NPs,the endocytosis of rod-shaped NPs predominantly relies on caveolae-mediated pathways rather than clathrin-mediated ones,potentially avoiding degradation by ***,the rod-shaped NPs exhibit prolonged circulation time,increased tumor accumulation,and enhanced antitumor *** current findings reveal the significant effect of particle shape on the behavior of prodrug NPs and introduce a novel paradigm for high-efficacy cancer therapy of prodrug NPs.

关键词： camptothecin self-assembly prodrug-based nanoparticles nanorod cancer therapy

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