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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Nakajima, Kazuma Matsuura, Takafumi Kimura, Takayuki Graduate School of Electronics Information and Media Engineering Nippon Institute of Technology 4-1 Gakuendai Miyashiro Saitama Minami-Saitama345-8501 Japan Department of Data Science Faculty of Advanced Engineering Nippon Institute of Technology 4-1 Gakuendai Miyashiro Saitama Minami-Saitama345-8501 Japan Department of Electrical Electronics and Communication Engineering Faculty of Fundamental Engineering Nippon Institute of Technology 4-1 Gakuendai Miyashiro Saitama Minami-Saitama345-8501 Japan

ISBN: (纸本)9789819615346

Transport and logistics operated by unmanned aerial vehicles, commonly known as drones, have attracted attention in recent years for their potential to revolutionize the transport industries. For example, Amazon was the first to use drones to deliver goods. Several distribution companies have since been working on similar services. To find effective delivery routes by using vehicles and drones, a flying sidekick traveling salesman problem has firstly been formulated. This problem constructs a delivery route by using a single drone and a single vehicle. In further formulation of this problem, vehicle routing problem with drones (VRPD), in which several numbers of drones and vehicles deliver goods to customers, has been formulated. We have already proposed a solution search method based on chaotic neurodynamics for VRPD. In this article, to further achieve effective search capability for VRPD, we change neural codings of chaotic neural networks according to the combinations of neighborhood operations. Experimental results show that our proposed method exhibits better objective function values than those by the conventional chaotic search method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Drones

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Multi-Dimensional Spectrum Prediction Method Based on Efficient Adaptive Broad Learning

Multi-Dimensional Spectrum Prediction Method Based on Effici...

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IEEE Conference on Wireless Communications and Networking

作者： Niancong Ji Shufei Wang Yibin Zhang Yang Peng Tomoaki Otsuki Dusit Niyato Guan Gui College of Telecommunications and Information Engineering NJUPT Nanjing China Faculty of Science and Technology Keio University Kanagawa Japan College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Multi-dimensional spectrum prediction is essential for spectrum sharing and dynamic spectrum access (DSA), tack-ling spectrum scarcity and improving wireless communication. Traditional methods often use machine learning (ML), which requires manual feature extraction, or deep learning (DL), which demands high computational resources. This paper proposes a lightweight multi-dimensional spectrum prediction model using an adaptive broad learning network (ABLN). The model employs a sliding window to preprocess data and establishes input layers using randomly generated feature and enhancement nodes. The weights of broad learning are determined by solving the pseudo-inverse, and the structure is incrementally extended without retraining, reducing computational complexity. An adaptive node increment module optimizes hyperparameters efficiently. Experimental results demonstrate that ABLN reduces computational overhead while maintaining robust prediction performance across various scenarios.

关键词： Wireless communication Adaptation models Adaptive systems Computational modeling Dynamic spectrum access Training data Manuals Predictive models data models Robustness

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Predict+Optimize Problem in Renewable Energy Scheduling

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IEEE Access 2025年 13卷 60064-60087页

作者： Bergmeir, Christoph de Nijs, Frits Genov, Evgenii Sriramulu, Abishek Abolghasemi, Mahdi Bean, Richard Betts, John Bui, Quang Dinh, Nam Trong Einecke, Nils Esmaeilbeigi, Rasul Ferraro, Scott Galketiya, Priya Glasgow, Robert Godahewa, Rakshitha Kang, Yanfei Limmer, Steffen Magdalena, Luis Montero-Manso, Pablo Peralta, Daniel Kumar, Yogesh Pipada Sunil Rosales-Pérez, Alejandro Ruddick, Julian Stratigakos, Akylas Stuckey, Peter Tack, Guido Triguero, Isaac Yuan, Rui DaSCI Andalusian Institute in Data Science and Computational Intelligence Granada18071 Spain University of Granada Department of Computer Science and Artificial Intelligence Granada18071 Spain Monash University Department of Data Science and Artificial Intelligence MelbourneVIC3800 Australia Vrije Universiteit Brussel EVERGi MOBI Brussels1050 Belgium Queensland University of Technology Faculty of Science School of Mathematical Sciences BrisbaneQLD4000 Australia The University of Queensland Centre for Energy Data Innovation School of Information Technology and Electrical Engineering BrisbaneQLD4072 Australia The University of Adelaide School of Electrical and Electronics Engineering AdelaideSA5005 Australia Honda Research Institute Europe GmbH Offenbach63073 Germany Deakin University School of Information Technology MelbourneVIC3125 Australia Monash University Building and Property Division MelbourneVIC3800 Australia Beihang University School of Economics and Management Beijing100191 China Universidad Politécnica de Madrid E.T.S. Ingenieros Informáticos Madrid28660 Spain The University of Sydney Disciple of Business Analytics SydneyNSW2050 Australia Centro de Investigación en Matemáticas Department of Computer Science Monterrey66629 Mexico Mines Paris Sophia Antipolis06904 France Laboratory School of Computer Science NottinghamNG8 1BB United Kingdom

Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are critical factors. This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling, focusing on forecasting renewable production and demand and optimizing energy cost. The competition attracted 49 participants in total. The top-ranked method employed stochastic optimization using LightGBM ensembles, and achieved at least a 2% reduction in energy costs compared to deterministic approaches, demonstrating that the most accurate point forecast does not necessarily guarantee the best performance in downstream optimization. The published data and problem setting establish a benchmark for further research into integrated forecasting-optimization methods for energy systems, highlighting the importance of considering forecast uncertainty in optimization models to achieve cost-effective and reliable energy management. The novelty of this work lies in its comprehensive evaluation of Predict+Optimize methodologies applied to a real-world renewable energy scheduling problem, providing insights into the scalability, generalizability, and effectiveness of the proposed solutions. Potential applications extend beyond energy systems to any domain requiring integrated forecasting and optimization, such as supply chain management, transportation planning, and financial portfolio optimization. © 2013 IEEE.

关键词： Optimization Forecasting Renewable energy sources Benchmark testing Australia Scheduling Production Costs Artificial neural networks Stochastic processes

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Shared Encoding and Representation of Tool-Mediated and Direct Touch Via Superposition Mechanisms

Shared Encoding and Representation of Tool-Mediated and Dire...

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Computational Intelligence in Artificial Life and Cooperative Intelligent Systems (ALIFE-CIS), IEEE Symposium on

作者： Haruna Kasashima Wataru Noguchi Yasumasa Tamura Masahito Yamamoto Hiroyuki Iizuka Graduate School of Information Science and Technology Hokkaido University Japan Education and Research Center for Mathematical and Data Science Hokkaido University Japan Faculty of Information Science and Technology Hokkaido University Japan Center for Human Nature Artificial Intelligence and Neuroscience Hokkaido University Japan

ISBN: (数字)9798331508371

ISBN: (纸本)9798331508388

This study proposes a multimodal predictive neural network model that simulates tool-mediated object perception, addressing the neurologically observed phenomenon where subjective experiences during tool use are similar to direct tactile perception. To explain this phenomenon, we apply a superposition mechanism that processes experiences from both tool use and non-tool use scenarios indistinguishably. Our model processes visual, tactile, and proprioceptive inputs from a simulated robotic arm using an encoder-decoder architecture with a shared Long Short-Term Memory (LSTM) module. Trained on long-term sensorimotor data collected from robot movements during tool use and non-tool use, the network demonstrates the ability to predict visual and tactile sensations even when certain sensory inputs are masked. Notably, our results reveal that tactile stimuli in similar situations are processed uniformly, regardless of tool use. When the model's internal state during tool use is employed to simulate non-tool experiences, it predicts as if the tool were an extension of the arm itself. This suggests that the model has incorporated the tool as an integral part of its body representation, mirroring the phenomenon of tool embodiment observed in humans.

关键词： Deep learning Visualization Adaptation models Propioception Predictive models Robot sensing systems Manipulators Encoding Intelligent systems Long short term memory

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Design of a Dust Cleaning Prototype for Photovoltaic Generators 4th

Design of a Dust Cleaning Prototype for Photovoltaic Generat...

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4th International Conference on Electronic Engineering and Renewable Energy Systems, ICEERE 2024

作者： Latreche, M. Medjahed, B. Mellit, A. Rabhi, H. Hajji, B. Faculty of Science and Technology University of Jijel Algiers Algeria Laboratory of Modelization Information and Systems University of Picardie Jules Verne 3 Rue Saint Leu Amiens Cedex 180039 France Embedded System and Data Processing Laboratory Renewable Energy National School of Applied Sciences Mohamed First University Oujda Morocco

ISBN: (纸本)9789819799749

Solar panels are vulnerable to accumulated dust and sand, especially in desert regions, which significantly decreases their efficiency. Therefore, the necessity of developing modern cleaning systems is a crucial task. Various types of cleaning systems have been proposed in the literature, such as electrostatic, ultrasonic, and robotic systems. However, the best methods are the ones guarantee as much efficiency as possible at the lowest cost. In this work, we propose a simple automatic system based on a rotating brush. The system is composed of two parts linked together by Bluetooth modules. The first one is a dust sensor, its main task is to send a signal to the second part, which is the cleaner, in case of detecting dirt or sand. The feasibility of the proposed prototype has been experimentally demonstrated. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cleaner production

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Enhanced multi-objective scheduling for heterogeneous computing platforms using hybrid MOEAD with SA and TS-guided initialization strategies

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Journal of Supercomputing 2025年第7期81卷 1-62页

作者： Saad, Abla Abdel-Raouf, Osama Hadhoud, Mohy Kafafy, Ahmed Machine Intelligence Department Faculty of Artificial Intelligence Menoufia University Menoufia Shibīn al-Kawm Egypt Information Technology Department Faculty of Computers & Information Menoufia University Menoufia Shibīn al-Kawm Egypt Data Science Department Faculty of Artificial Intelligence Menoufia University Menoufia Shibīn al-Kawm Egypt

Rapid growth in data and computational demands has driven the widespread adoption of heterogeneous computing systems that combine different processing units, such as CPUs, GPUs, and FPGAs. Efficient scheduling of tasks in these systems is essential to optimize resource utilization, reduce power consumption, and meet performance requirements. However, the complexities of managing multiple tasks, diverse machine architectures, and conflicting objectives make scheduling in these environments an NP-hard problem. This study reformulates the traditional scheduling model as a multi-objective optimization problem focusing on minimizing makespan and maximizing parallelization across heterogeneous systems. Multi-Objective Evolutionary Algorithms based on Decomposition (MOEA/D) have effectively solved these complex scheduling challenges. MOEA/D offers a flexible and powerful approach for handling multi-objective scheduling tasks in a heterogeneous computing environment. This paper explores how MOEA/D can be combined with other metaheuristics to further improve task scheduling and balance competing performance objectives. The effectiveness of the approach is demonstrated through 28 validation scenarios. The algorithm’s performance is evaluated using four scientific workflows: structured as (FFT and Montage) and unstructured as (Molecular-A and Molecular-B). Performance evaluation is performed using appropriate metrics, specifically hypervolume and inverted generational distance plus, to assess the quality of the solutions. Experimental results demonstrate that the GRASP-MOEA/D hybrid approach outperforms all competitors in structured workflows, achieving the best schedule by minimizing makespan and maximizing resource utilization within a reasonable runtime. For unstructured workflows, GSA-MOEA/D achieves the best performance in 75% of cases, while SA-MOEA/D is the best in 37.5% of cases. However, GSA-MOEA/D and SA-MOEA/D face significant computational overhead as workflow size

关键词： Multiobjective optimization

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Analysis of Device-To-Device Communication in Iot System 1st

Analysis of Device-To-Device Communication in Iot System

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Nawaz, Usman Rehman, Faisal Ullah, Kainat Azmat Raza, Usman Ahmed Sharif, Hanan Rafique, Furqan Ali, Abid Ashiq, Masood Iqbal, Junaid Department of Computer Science Lahore Leads University Lahore Pakistan Department of Statistics and Data Science University of Mianwali Mianwali Pakistan Faculty of Information Technology University of Central Punjab Lahore Pakistan

ISBN: (纸本)9783031776199

Peer-to-Peer communication is based on an important feature of communication which is Device-to-Device (D2D) communication. IoT is a computational device that is equipped with accurate identifiers and the capacity to speak data across a network without the assistance of a human. D2D communication is referred to as "Direct verbal exchange among the taking part gadgets without the use of the centralized infrastructure". This research study examines the advantages and barriers of D2D communication as a means of meeting the IoT's requirements. Many companies and quality management organizations have demonstrated a strong interest in implementing the D2D approach in wireless networks. The thought of working without the necessary control of centralized supervision and making Wi-Fi networks wider as well as energy saving can be made possible with D2D communication in IoT. These devices’ technical limitations and the connection's support limitations provide an upward push to numerous problems that must be addressed. Some of the IoT studies Electricity production, connectivity, security, scenario technologies, and so forth are all issues. We cognizance’s on troubles that in the IoT there is an influence in D2D communication surroundings. We also discovered some important stumbling blocks to the success of smart D2D speech communication in the Internet of Things. We talked about how to deal with these difficult situations. This research explains the idea of a tool to tool verbal exchange and IoT system and defines its predominant techniques and applications imparting a subsequent era therefore as procedure strategy to the demanding situations. To boost research and development in the domains IoT problems have been studied. Every cited research work's contribution and limitations are discussed as well as any potential problem statement and open rely. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Wi-Fi

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APPLYING TEXT MINING TO ANALYZE HUMAN QUESTION ASKING IN CREATIVITY RESEARCH

arXiv

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arXiv 2025年

作者： Wróblewska, Anna Korbin, Marceli Kenett, Yoed N. Dan, Daniel Ganzha, Maria Paprzycki, Marcin Faculty of Mathematics and Information Sciences Warsaw University of Technology Warsaw Poland Faculty of Data and Decision Sciences Technion - Israel Institute of Technology Haifa Israel School of Applied Data Science Modul University Vienna Austria Systems Research Institute Polish Academy of Sciences Warsaw Poland

Creativity relates to the ability to generate novel and effective ideas in the areas of interest. How are such creative ideas generated? One possible mechanism that supports creative ideation and is gaining increased empirical attention is by asking questions. Question asking is a likely cognitive mechanism that allows defining problems, facilitating creative problem solving [15]. However, much is unknown about the exact role of questions in creativity. This work presents an attempt to apply text mining methods to measure the cognitive potential of questions, taking into account, among others, (a) question type, (b) question complexity, and (c) the content of the answer. This contribution summarizes the history of question mining as a part of creativity research, along with the natural language processing methods deemed useful or helpful in the study. In addition, a novel approach is proposed, implemented, and applied to five datasets. The experimental results obtained are comprehensively analyzed, suggesting that natural language processing has a role to play in creative research. Copyright © 2025, The Authors. All rights reserved.

关键词： Text mining

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Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Lu, Mengkang Wu, Yicheng Xia, Yong Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Xi’an710072 China The University of Adelaide Australian Institute for Machine Learning AdelaideSA5000 Australia Monash University Faculty of Information Technology Department of Data Science and AI Australia

The scarcity of annotations has become a significant obstacle in training powerful deep-learning models for medical image segmentation, limiting their clinical application. To overcome this, semi-supervised learning that leverages abundant unlabeled data is highly desirable to enhance model training. However, most existing works still focus on specific medical tasks and underestimate the potential of learning across diverse tasks and datasets. In this paper, we propose a Versatile Semi-supervised framework (VerSemi) to present a new perspective that integrates various SSL tasks into a unified model with an extensive label space, exploiting more unlabeled data for semi-supervised medical image segmentation. Specifically, we introduce a dynamic task-prompted design to segment various targets from different datasets. Next, this unified model is used to identify the foreground regions from all labeled data, capturing cross-dataset semantics. Particularly, we create a synthetic task with a CutMix strategy to augment foreground targets within the expanded label space. To effectively utilize unlabeled data, we introduce a consistency constraint that aligns aggregated predictions from various tasks with those from the synthetic task, further guiding the model to accurately segment foreground regions during training. We evaluated our VerSemi framework against seven established SSL methods on four public benchmarking datasets. Our results suggest that VerSemi consistently outperforms all competing methods, beating the second-best method with a 2.69% average Dice gain on four datasets and setting a new state of the art for semi-supervised medical image segmentation. Code is available at https://***/maxwell0027/VerSemi. © 1982-2012 IEEE.

关键词： Self-supervised learning

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Vibration analysis of elastically supported sandwich beam with damaged core and functionally graded face sheets

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Steel and Composite Structures 2025年第1期54卷 71-83页

作者： Wang, Lulu Mohsin, Hasan F. Bains, Pardeep Singh Sharma, Rohit Bohlooli, Nasrin College of Mechanical and Electrical Engineering Guangdong University of Science and Technology Guangdong Dongguan523083 China Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Baghdad10011 Iraq Computer Technical Engineering Department College of Technical Engineering The Islamic University Najaf54001 Iraq University Karnataka Bengaluru560069 India Department of Mechanical Engineering Vivekananda Global University Rajasthan Jaipur303012 India School of Engineering and Technology Shobhit University Uttar Pradesh Gangoh247341 India Department of Mechanical Engineering Arka Jain University Jharkhand Jamshedpur831001 India Nabi Data Science & Computational Intelligence Research Co. Tehran Iran

This paper presents the influence of carbon nanotubes (CNTs) waviness, aspect ratio and damaged core on the vibrational behavior of functionally graded nanocomposite sandwich beams resting on two-parameter elastic foundations. The distributions of CNTs are considered functionally graded (FG) or uniform along the thickness of upper and bottom layers of the sandwich beam and their mechanical properties are estimated by an extended rule of mixture. In this study, the classical theory concerning the mechanical efficiency of a matrix embedding finite length fibers has been modified by introducing the tube-to-tube random contact, which explicitly accounts for the progressive reduction of the tubes’ effective aspect ratio as the filler content increases. A damage model is introduced to provide an analytical description of an irreversible rheological process that causes the decay of the mechanical properties, in terms of engineering constants. An isotropic damage is considered for the core of the sandwich beams. The equations of motion are derived based on Timoshenko beam theory and employing Hamilton’s principle. The problem is modeled using a semi-analytical approach composed of generalized differential quadrature method (GDQM) and series solution adopted to solve the equations of motion. Detailed parametric studies are carried out to investigate the mechanical behavior of these multi-layered structures depending on the damage features, through-the-thickness distribution and boundary conditions. Also, the effects of carbon nanotubes (CNTs) waviness, CNT aspect ratio, Winkler foundation modulus, shear elastic foundation modulus and geometrical conditions on the vibrational behavior of the sandwich structure are studied. © 2025 Techno-Press, Ltd.

关键词： Aspect ratio

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