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IEEE Symposium on Network Operations and Management

作者： José Gómez-delaHiz Jaime Galán-Jiménez Dept. of Computer Systems and Telematics Engineering University of Extremadura Cáceres Spain

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

The flexibility and programmability of Software-Defined Networks (SDN) has allowed the research community to propose new Traffic engineering (TE) techniques to improve their performance. Although the installation of heuristic or optimal solutions in the SDN controller allows to obtain good results in network performance, these are based on historical data that may not be updated to actual traffic variations. Moreover, the research community is exploiting the strength of Deep Reinforcement Learning (DRL) and its capability to learn and adapt to the complexities inherent in networks to propose enhanced routing solutions. However, the nature of DRL can cause a performance degradation during the learning process due to the application of exploration when determining the best policy. For this reason, in this work we propose a Multi-Agent DRL (MADRL) based solution that is able to reduce the Maximum Link Utilization (MLU) of SDN networks only considering the local information of the nodes. Each node has a DRL agent and is able to decide the best routing decision for each flow so that the MLU is minimized. The performance evaluation shows that our approach outperforms the classical shortest path rule based on Dijkstra in 8%.

关键词： Performance evaluation Degradation Routing Deep reinforcement learning Complexity theory Software defined networking

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Identification of Cancer Biomarkers for Multi-class Diagnostics through Network Analysis of RNAseq Data of Tumor-Educated Platelets

Identification of Cancer Biomarkers for Multi-class Diagnost...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Toccacieli, Ali Petti, Manuela Sapienza University of Rome MSc Program in Engineering in Computer Science Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9781665468190

Tumor-educated platelets (TEPs) are circulating blood cells with a distinct tumor-driven phenotype and act as carriers and protectors of metastases. To date, some studies have shown that the TEPs transcriptome can be used for cancer diagnostics. The objective of this study is to propose a procedure based on differential gene expression and differential gene co-expression analyses to identify a set of key genes for multi-class cancer diagnostics. To reach this aim, we analyzed RNA-seq data (57736 genes) of 130 subjects, of whom 40 patients with glioblastoma multiforme (GBM), 35 patients with pancreatic adenocarcinoma (PAAD), and 55 healthy donors (HC). We focused our analysis on the subset of differentially expressed genes (DEGs), and we used these genes to build and analyze the differential co-expression networks, identifying the hub genes. With this procedure, we obtained a restricted set of DEGs that maximize the accuracy in classifying patients according to their conditions (GBM, PAAD, or HC). Indeed, we validated our results by comparing the achieved classification accuracy with that resulting from random selections of DEGs and we obtained that genes selected by differential co-expression (DCE) network analysis have greater predictive power than any other set of differentially expressed genes, including using all of them. © 2022 IEEE.

关键词： Platelets

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Enhancing Ship Detection on Satellite Images with Modified DeepLabV3+

Enhancing Ship Detection on Satellite Images with Modified D...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Sharma, Rohit Dahatonde, Shubham Acharjya, Srimanta Kumar Verma, Sourabh Singh Dhaka, Vijaypal Singh Rawat, Ramesh Singh School Of Computing & Intelligent Systems Manipal University Jaipur India C. V Raman Global University Department of Computer Science and engineering Bhubaneswar India Graphic Era deemed to be University Dept. of computer Science and Engg Uttrakhand Deharadun India

ISBN: (纸本)9798350379716

This paper presents a advance approach for ship detection in satellite imagery utilizing a modified DeepLabV3+ architecture, specifically designed to overcome the challenges inherent in such data. The proposed model features an enhanced feature extraction process and a refined atrous spatial pyramid pooling (ASPP) module, which together improve the detection of ships across various sizes and shapes. Comprehensive experiments on publicly available satellite datasets reveal that the modified DeepLabV3+ significantly outperforms existing state-of-the-art methods, achieving an accuracy of 98%. These findings demonstrate the model's robust ability to identify and localize ships in complex maritime settings, offering promising potential for improved maritime situational awareness and operational efficiency. © 2024 IEEE.

关键词： Satellite imagery

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Predicting Lifespan of Ground-to-Air Multipath Components in mmWave UAV Channels

arXiv

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arXiv 2025年

作者： Khawaja, Wahab Jacobsen, Rune H. Hussain, Sajid Guvenc, Ismail Dept. Electrical and Computer Engineering Aarhus University Aarhus8000 Denmark Dept. Computer Systems Engineering Mirpur Univ. Science and Technology AJK Mirpur10250 Pakistan

In mobile ground-to-air (GA) propagation channels, the birth and death of multipath components (MPCs) are frequently observed, and the wide-sense stationary uncorrelated scattering (WSSUS) assumption does not always hold. Several methods exist for tracking the birth and death of MPCs, however, to the best of knowledge of authors, there is no existing literature that addresses the prediction of the lifespan of the MPCs in non-WSSUS GA propagation channels. In this work, we consider the GA channel as non-WSSUS and individual MPCs across receiver positions are represented as time series based on the Euclidean distance between channel parameters of the MPCs. These time series representations, referred to as path bins, are analyzed using a semi-Markov chain model. The channel parameter variations and dependencies between path bins are used to predict the lifespan of path bins using weighted sum method, machine learning classifiers, and deep neural networks. For comparison, the birth and death of path bins are also modeled using a Poisson distribution and a Markov chain. Simulation results demonstrate that deep neural networks offer highly accurate predictions for the lifespan (including death) of MPC path bins in the considered GA propagation scenario. © 2025, CC BY.

关键词： Markov chains

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Dynamic Network Optimization with Deep Learning and Deployment of Intelligent Reflecting Surfaces

Dynamic Network Optimization with Deep Learning and Deployme...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Nayeon Jang Jeonghoon Bae Tao Tianyu Gyurin Byun Phuoc-Nguyen Bui Tae-Jin Lee Hyunseung Choo Dept. of AI Systems Engineering Sungkyunkwan University Suwon South Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

The proliferation of 5G technologies has presented challenges such as signal attenuation and high energy consumption. This study proposes a novel approach to enhance wireless networks by integrating Intelligent Reflecting Surfaces (IRS) and Artificial Intelligence (AI) to optimize signal propagation and predict the Received Signal Strength Indicator (RSSI). A new framework combines K-means clustering for IRS deployment with a Temporal Convolutional Network-Gated Recurrent Unit (TCN-GRU) model for signal prediction. Using data from 15,000 Wi-Fi access points in Herefordshire, UK, the results demonstrate significant improvements in signal strength, with the proposed model achieving a Mean Squared Error (MSE) of 37% lower than the existing models, indicating higher prediction accuracy. In addition to enhancing signal quality, the framework achieves throughput increases of 21.26% with single-group IRS and 64.91% with multi-group IRS. These results validate the proposed the potential of the framework to improve performance, making it suitable for next-generation wireless networks.

关键词： Deep learning Wireless networks Predictive models Throughput Data models Received signal strength indicator Artificial intelligence Optimization Next generation networking Wireless fidelity

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Process-Model-Free Design of MPC via a Reference-Model-Based PI Controller for the Gas Turbine 9

Process-Model-Free Design of MPC via a Reference-Model-Based...

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9th International Conference on Electrical engineering, Control and Robotics, EECR 2023

作者： Shi, Gengjin Dai, Chengbo Ma, Miaomiao Li, Donghai Ding, Yanjun Tsinghua University State Key Lab of Power Systems Dept. of Energy & Power Engineering Beijing China North China Electric Power University School of Control & Computer Engineering Beijing China

ISBN: (纸本)9781665491204

Model predictive control (MPC) is a modern advanced control strategy which has great reputation because of its excellent reference tracking performance and the ability to deal with process constraints, time delay and multivariable problems. However, the gas turbine is difficult to model accurately and unmeasurable disturbances are unavoidable, which limits the application of MPC to the gas turbine. To solve these problems, a process-model-free design method for MPC via a reference-model (RM)-based proportional-integral (PI) controller-desired dynamic equational (DDE)-PI controller (MPC-DDE) is proposed in this paper. The DDE-PI provides the design model for the MPC and handle with unmeasurable disturbances while MPC provides a rolling optimized set point for DDE-PI. Simulation results illustrate the superiorities of the proposed hierarchical control strategy in reference tracking, disturbance rejection and robustness, which indicates the promising prospect of practical application of MPC-DDE to the gas turbine. © 2023 IEEE.

关键词： Model predictive control

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Practical Implementation of Q-Learning and Object Detection for Mobile Robot Path Planning

Practical Implementation of Q-Learning and Object Detection ...

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2024 International Conference on Advanced Robotics and Intelligent systems, ARIS 2024

作者： Valcourt, John P. Chandler, Franya M. Avrelus, Chamma Lee, Jou-Yi Güllü, Ali Ihsan Shah, Syed H. Yuan Ze University Dept. of Computer Science and Engineering Taoyuan Taiwan Yuan Ze University International Bachelor Program in Ece Taoyuan Taiwan Yuan Ze University Mechanicai/Industrial Engineering Taoyuan Taiwan Yuan Ze University International Bachelor Program in Engineering Taoyuan Taiwan Gebze Technical University Defense Electronics Engineering Kocaeli Turkey

ISBN: (纸本)9798350362572

Expanding on an earlier study that assessed the performance of a Q-learning approach for solving the path planning problem for mobile robots, this research implemented the RL approach in a real-world setting employing the RoboMas-ter EP Core. The robotic system also included object detection and recognition through the robot's sensors and a pre-trained YOLOv9 model. The robot navigated to predefined target points while avoiding stationary obstacles. The Q-learning algorithm was trained using the Google Colaboratory platform. Experi-ments conducted at various speeds identified an optimal speed and high success rates in obstacle avoidance and target region accuracy were achieved. Additionally, the object detection sys-tem demonstrated strong performance in real-time applications. Despite these successes, challenges such as high friction and multitasking inefficiencies were identified. Future research should address these limitations by enhancing control systems and the robot's multitasking capabilities, as well as using a computer with better processing power to improve overall system performance further. © 2024 IEEE.

关键词： Robot programming

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Low-Latency and Energy-Efficient Task Scheduling for End-Edge-Cloud Collaborative Computing

Low-Latency and Energy-Efficient Task Scheduling for End-Edg...

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

作者： Li, Jingyao Yuan, Haitao Ma, Yaofei Bi, Jing Yang, Jinhong Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Computer Beijing University of Technology Beijing100124 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States

ISBN: (纸本)9781665410205

Mobile edge computing (MEC) is a new paradigm that improves the quality of service compared with traditional cloud computing. In MEC, computational tasks are submitted by numerous end users and are partially offloaded to edge servers or a central cloud. However, the characteristics of tasks are different from each other, and the limited resources of computational nodes are also heterogeneous, which brings great challenges to computation offloading and resource allocation for MEC. This work establishes an end-edge-cloud collaborative computing network, which consists of end devices, edge servers, and a central cloud. Task execution location and CPU running frequency determine the execution time and energy consumption to finish the tasks. Considering the aforementioned factors, a multi-objective constrained optimization problem is formulated. To solve the problem, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with self-adaptive crossover and mutation rates is proposed, which is called Improved NSGA-II with _Self-adaptive Crossover and Mutation (INSCM). The total execution time and energy consumption can be jointly minimized with our proposed INSCM. Numerous experiments are carried out to test the performance of INSCM. Simulation results show that INSCM effectively improves the performance of NSGA-II and surpasses random offloading and NSGA-III, which shows practical use in real-life scenarios. © 2024 IEEE.

关键词： Mobile edge computing

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Spatial Distances and Knowledge Acquisition Strategy for Patenting by Indonesian Academic Inventors 5

Spatial Distances and Knowledge Acquisition Strategy for Pat...

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5th International Conference on Advancement in Data Science, E-Learning and Information systems, ICADEIS 2023

作者： Arimbawa, I Wayan Agus Wedashwara, Wirarama Gelana, Kassahun Fauzi, Rahmat Iskandar, Ade Rahhmat University of Mataram Dept. of Informatics Engineering Mataram Indonesia Seoul National University Technology Management Economics and Policy Program Seoul Korea Republic of Telkom University School of Industrial and Systems Engineering Indonesia

ISBN: (纸本)9798350303414

Knowledge acquisition is important for innovation and adaptation of organizations including universities. New knowledge and capabilities can be acquired from inside or outside of the institution. There are large body of literature discuss the interaction of universities and the other Triple-helix model's agents, but rarely found and discuss intra helices interactions. This research explores the characteristics of Indonesian academic inventors' knowledge acquisition strategy for patenting in Indonesia. There are two points of view of this research: firstly, the study discusses the knowledge acquisition strategy of Indonesian universities in regard of the knowledge diversity among two collaborating universities and secondly, how is the magnitude of knowledge gaps between two entities influencing the decision for inhouse or out of university *** the data from Sinta, knowledge diversity index is measured using Shannon entropy index of the academic inventor' expertise, university's department, and university's division. The research recognition's gap between two collaborating inventors is measured using the difference of their Scopus' h-indices. The findings show some interesting facts of how academic inventors strategize their collaboration patterns. First, interuniversity collaborations for patenting reveal more diversity on the academic inventors' knowledge than those are in intra-university collaborations. Second, collaborations for patenting with different universities expect better knowledge spill-overs than collaborations within a university. But, this knowledge spill-over only significant at individual level but not at institutional level. It seems that individual recognition is more important than institutional recognition when choosing a collaboration partner. © 2023 IEEE.

关键词： Knowledge acquisition

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Advanced Tool Learning and Selection System (ATLASS): A Closed-Loop Framework Using LLM

arXiv

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arXiv 2025年

作者： Haque, Mohd Ariful Williams, Justin Siddique, Sunzida Islam, Md. Hujaifa Ali, Hasmot Gupta, Kishor Datta George, Roy Dept. of Cyber Physical Systems Clark Atlanta University United States Dept. of Computer Science and Engineering Daffodil International University Bangladesh Dept. of Computer Science and Engineering Ahsanullah University of Science and Technology Bangladesh

The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions within the scope of pre-existing tools created by experts. To address this problem, we propose ATLASS, an advanced tool learning and selection system designed as a closed-loop framework. It enables the LLM to solve problems by dynamically generating external tools on demand. In this framework, agents play a crucial role in orchestrating tool selection, execution, and refinement, ensuring adaptive problem-solving capabilities. The operation of ATLASS follows three phases: The first phase, Understanding Tool Requirements, involves the Agents determining whether tools are required and specifying their functionality;the second phase, Tool Retrieval/Generation, involves the Agents retrieving or generating tools based on their availability;and the third phase, Task Solving, involves combining all the component tools necessary to complete the initial task. The Tool Dataset stores the generated tools, ensuring reusability and minimizing inference cost. Current LLM-based tool generation systems have difficulty creating complex tools that need APIs or external packages. In ATLASS, we solve the problem by automatically setting up the environment, fetching relevant API documentation online, and using a Python interpreter to create a reliable, versatile tool that works in a wider range of situations. OpenAI GPT-4.0 is used as the LLM agent, and safety and ethical concerns are handled through human feedback before executing generated code. By addressing the limitations of predefined toolsets and enhancing adaptability, ATLASS serves as a real-world solution that empowers users with dynamically generated tools for complex problem-solving. © 2025, CC BY-SA.

关键词： Reusability

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