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Topology-Aware-based Traffic Prediction Mechanism for Elastic cognitive Optical Networks

Topology-Aware-based Traffic Prediction Mechanism for Elasti...

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international Wireless Communications and Mobile computing conference, IWCMC

作者： Jianxing Li Fu Wang Haipeng Yao Feng Tian Xiaoli Yin Qi Zhang School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

Elastic cognitive optical network(ECON) embeds artificial intelligence technology into network management to enable resource self-optimization ability, which has aroused the wide interest of researchers. However, realizing precise traffic prediction (TP) in optical networks has been a challenging problem due to channels’ complex variable bandwidth conditions. We provide an ECON architecture and propose a graph-convolutional-network-transformer (GCN-transformer) TP algorithm. The proposed algorithm has been evaluated and compared with the traditional schemes. We build a testbed for the proposed algorithm by OMNET++. The results show a prediction accuracy of 99.74%, which reduces the inaccuracy by 2.03% compared with other typical algorithms.

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An Efficient Mobile Communication Using Energy-Efficient Dynamic cognitive Aware

An Efficient Mobile Communication Using Energy-Efficient Dyn...

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international conference on Advanced computing and Communication Systems (ICACCS)

作者： Sunila Choudhary Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Nowadays, the mobile communication industry is one of the growing areas of information and communication technology. This quick development is because of the steadily expanding number of mobile subscribers, dramatic ascent in cell phones and the breakthrough in mobile application development. Likewise, with the arising 5G advances like the Machine to Machine (M2M) correspondence, mobile data services will be much more popular. A major challenge associated with reducing Energy Efficiency (EE) and maintaining an excellent quality of service. But, at the same time, it leads to high traffic and time delay during the data transmission. To address this problem Energy Efficient Dynamic cognitive Aware (EEDCA) method reduces traffic, and data can transmit efficiently. The proposed EEDCA method is used to analyze path traffic and improves the energy of the node when the sender node sends packet to the target node. Therefore, it is possible to reduce the data loss and the amount of retransmissions and improve the throughput performance, Packet Delivery Ratio (PDR), and improve node energy. The simulation results demonstrate the efficiency of the proposed method compared with the other methods.

关键词： Industries Technological innovation Machine-to-machine communications 5G mobile communication Simulation Quality of service Throughput

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SafeSens - Uncertainty Quantification of complex Perception Systems

SafeSens - Uncertainty Quantification of Complex Perception ...

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international conference on Intelligent Transportation

作者： Iwo Kurzidem Simon Burton Philipp Schleiss Systems Safety Engineering Fraunhofer Institute for Cognitive Systems IKS Munich Germany Research Division Safety Fraunhofer Institute for Cognitive Systems IKS Munich Germany

Safety testing and validation of complex autonomous systems requires a comprehensive and reliable analysis of performance and uncertainty. Especially uncertainty quantification plays a vital part in perception systems operating in open context environments that are neither foreseeable nor deterministic. Therefore, safety assurance based on field tests or corner cases alone is not a feasible option as effort and potential risks are high. Simulations offer a way out. They allow, for example, simulation of potentially hazardous situations, without any real danger, by systematically computing a variety of different (input) parameters quickly. In order to do so, simulations need accurate models to represent the complex system and in particular include uncertainty as inherent property to accurately reflect the interdependence between system components and the environment. We present an approach to creating perception architectures via suitable meta-models to enable a holistic safety analysis to quantify the uncertainties within the system. The models include aleatoric or epistemic uncertainty, dependent on the nature of the approximated component. A showcase of the proposed method highlights, how validation under uncertainty can be used for a camera-based object detection.

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Performance Evaluation of Attention Mechanism and Spiking Neural Networks on sMRI data for Suicide Ideation Assessment

Performance Evaluation of Attention Mechanism and Spiking Ne...

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computing (ICOCO), IEEE international conference on

作者： Corrine Francis Abdulrazak Yahya Saleh Al-Hababi Department of Cognitive Sciences Faculty of Cognitive Sciences and Human Development Universiti Malaysia Sarawak Kota Samarahan Sarawak Malaysia

The coronavirus disease 2019 (COVID-19) pandemic has had a substantial detrimental impact on mental health, especially depression, and this has led to a high incidence of suicidal ideation (SI) around the globe, with the pandemic's post-peak period seeing the highest incidence in young adults. This study aims to propose an effective non-intrusive method for early detection of SI in young adults utilizing depression as a biomarker in structural magnetic resonance imaging. This paper introduces a hybrid machine learning approach utilizing attention mechanisms and spiking neural networks to differentiate between depression patients without SI and healthy controls. The hybrid method successfully completed the classification task after stratified 5-fold cross-validation, achieving test accuracy, sensitivity, specificity, and area under curve of 94%,100%,92%, and 0.96, respectively. The proposed algorithms offer an objective tool for identifying early SI risk in depressed patients without suicidal thoughts, alongside clinical assessment.

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Underwater SLAM Implementation for a Bionic Robotic Dolphin

Underwater SLAM Implementation for a Bionic Robotic Dolphin

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IEEE international conference on Real-time computing and Robotics (RCAR)

作者： Shaoxuan Ma Jian Wang Shuaizheng Yan Zhengxing Wu Junzhi Yu Laboratory of Cognitive and Decision Intelligence for Complex System Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Mechanics and Engineering Science BIC-ESAT College of Engineering State Key Laboratory for Turbulence and Complex System Peking University

This paper aims to implement a simultaneous localization and mapping (SLAM) algorithm for a bionic robotic dolphin in underwater simulation environment. Through combining UWSim with ROS, an underwater simulation environment is constructed through importing various environmental models including coral, tropical fish, gullies, and so on. Thereafter, as an autonomous platform for underwater SLAM, a bionic robotic dolphin with its dynamic model is particularly built in the environment to perform dolphin-like swimming. Based on the sensor information from an onboard camera and an inertial measurement unit, the VINS-Mono algorithm is employed and implemented by the robotic dolphin to position itself underwater. Extensive simulations are conducted to validate the effectiveness of the employed SLAM algorithm in various dolphin-like motions of the robotic dolphin, such as linear, circular, and spiral motions. The obtained results provide valuable guidance for the underwater SLAM research.

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Design of a Novel Autonomous Docking System With a Supporting Robotic Fish

Design of a Novel Autonomous Docking System With a Supportin...

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IEEE international conference on Real-time computing and Robotics (RCAR)

作者： Huijie Dong Chunyang Zhao Tiezhu Qiao Jian Wang Di Chen Yan Meng Junzhi Yu Ministry of Education Key Laboratory of Advanced Transducers and Intelligent Control System Taiyuan University of Technology Taiyuan China Institute of Automation Laboratory of Cognitive and Decision Intelligence for Complex System Chinese Academy of Sciences Beijing China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China College of Electronics and Information Engineering Guangdong Ocean University Zhanjiang China

Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel autonomous docking system centered on a designed supporting robotic fish named ‘CourierFish’. Specifically, CourierFish is capable of docking with a surface dock station for supplying itself and docking with a seafloor platform for supporting equipment in the platform. A visual navigation scheme integrating LED and ArUco markers is presented for accurate localization. The control approach for docking motion is also illustrated. Simulations and aquatic experiments are performed to verify the feasibility of the proposed docking system. The obtained results lay a solid foundation for the construction of various underwater equipment and robot networks.

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2nd international conference on Advanced Network Technologies and Intelligent computing, ANTIC 2022

2nd International Conference on Advanced Network Technologie...

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2nd international conference on Advanced Network Technologies and Intelligent computing, ANTIC 2022

ISBN: (纸本)9783031281792

The proceedings contain 79 papers. The special focus in this conference is on Advanced Network Technologies and Intelligent computing. The topics include: Effective Link Prediction in complex Networks Using Differential Evolution Based Extreme Gradient Boosting Algorithm;Hybrid Partitioning for Embedded and Distributed CNNs Inference on Edge Devices;Application of Abelian Mechanism on UFS-ACM for Risk Analysis;support Systems and Technological Capabilities in Digital Transformation According to the Nature of the Company;an Improved Approach of Image Steganography Based on Least Significant Bit Technique for Secure Communication in Cloud;enhanced Horse Optimization Algorithm Based Intelligent Query Optimization in Crowdsourcing Systems;design of Energy Efficient IoT-Based Smart Street Light System;an Optimized Low-Cost 4 × 4 Multiplier Circuit Design Using Reversible Logic Gates;an Improved Resource Allocation Process Using Modified Max-Min Algorithm for Dynamic Cloud;design of an Energy Aware Cluster-Based Routing Scheme to Minimize Energy Consumption in Wireless Sensor Networks;a Load Threshold Allocation Approach: Tackling Blackouts Through Brownout;Predictive Analysis of Ambient Environment for Urban N Cultivation Using IOT Based Hydroponic System;blockchain-Aided Keyword Search over Encrypted data in Cloud;Impact of Selfish Nodes on the Performance of AODV and DSR Routing Protocols;a Deadlock-Free and Adaptive Prime Perspective Turn Model for 3D-Mesh Based Network-on-Chips;Effect of Channel Slot Time on Performance of IEEE 802.15.6-Based Medical Body Area Network;coviBand - Wearable Social Distancing and Contact Tracing Device Using Signal Strength Indicator Metrics;Average Time Based PRoPHET Routing Protocol for Opportunistic Networks;analysis and Implementation of Microservices Using Docker;automated Energy Modeling Framework for Microcontroller-Based Edge computing Nodes.

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Oracle Character Recognition Based on Attention Enhancement and Multi-level Feature Fusion 27th

Oracle Character Recognition Based on Attention Enhancement...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Han, Zhiwang Yadikar, Nurbiya Xu, Xuebin Aysa, Alimjan Ubul, Kurban School of Computer Science and Technology Xinjiang University Urumqi830046 China Xinjiang Multilingual Information Technology Key Laboratory Xinjiang University Urumqi830046 China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Xinjiang University Urumqi830046 China

ISBN: (纸本)9783031781186

Oracle bone characters represent the earliest inscriptions in China. Recognizing and deciphering these characters is significant. Despite some progress made by recent methods, their recognition accuracy remains limited by two major issues: 1) how to focus on characters features within complex background noise images, and 2) how to effectively fuse shallow detail information with deep semantic information. To address these issues, we propose a novel deep learning model called Character Feature Enhancement Network (CFE-Net). The model consists of two key components: Character Feature Enhancement (CFE) and Adaptive Multi-level Classifier Fusion (AMCF). Specifically, CFE utilizes the Spatial Focus Attention Module (SFAM) to focus on extracting foreground character features and suppressing background noise, thereby significantly enhancing high-level semantic representation capabilities. AMCF, on the other hand, achieves multi-level feature fusion by adaptively fusing the outputs of different classifiers, effectively avoiding information loss or interference that simple fusion strategies might cause. We evaluated the CFE-Net on two rubbing oracle bone characters benchmark datasets, OBC306 and Oracle-MNIST. The experimental results demonstrate that CFE-Net significantly outperforms several existing methods in terms of Top-1 accuracy, establishing it as the new state-of-the-art. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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2nd international conference on Advanced Network Technologies and Intelligent computing, ANTIC 2022

2nd International Conference on Advanced Network Technologie...

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2nd international conference on Advanced Network Technologies and Intelligent computing, ANTIC 2022

ISBN: (纸本)9783031281822

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Advances in machine learning for the innovation economy: in the shadow of war 10

Advances in machine learning for the innovation economy: in ...

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10th international conference on Monitoring, Modeling and Management of Emergent Economy, M3E2-MLPEED 2022

作者： Danylchuk, Hanna B. Semerikov, Serhiy O. The Bohdan Khmelnytsky National University of Cherkasy 81 Shevchenko Blvd. Cherkasy18031 Ukraine Kryvyi Rih State Pedagogical University 54 Gagarin Ave. Kryvyi Rih50086 Ukraine Kryvyi Rih National University 11 Vitalii Matusevych Str. Kryvyi Rih50027 Ukraine Institute for Digitalisation of Education of the NAES of Ukraine 9 M. Berlynskoho Str. Kyiv04060 Ukraine Zhytomyr Polytechnic State University 103 Chudnivsyka Str. Zhytomyr10005 Ukraine Academy of Cognitive and Natural Sciences 54 Gagarin Ave. Kryvyi Rih50086 Ukraine

This preface introduces the selected and revised papers presented at the 10th international conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2022), held online in Ukraine, on November 17-18, 2022. The conference aimed to bring together researchers, practitioners, and students from various fields to exchange ideas, share experiences, and discuss challenges and opportunities in applying computational intelligence and data science for the innovation economy. The innovation economy is a term that describes the emerging paradigm of economic development that is driven by knowledge, creativity, and innovation. It requires new approaches and methods for solving complex problems, discovering new opportunities, and creating value in various domains of science, business, and society. Computational intelligence and data science are two key disciplines that can provide such approaches and methods by exploiting the power of data, algorithms, models, and systems to enable intelligent decision making, learning, adaptation, optimization, and discovery. The papers in this proceedings cover a wide range of topics related to computational intelligence and data science for the innovation economy. They include theoretical foundations, novel techniques, and innovative applications. The papers were selected and revised based on the feedback from the program committee members and reviewers who ensured their high quality. We would like to thank all the authors who submitted their papers to M3E2 2022. We also appreciate the keynote speakers who shared their insights and visions on the current trends and future directions of computational intelligence and data science for the innovation economy. We acknowledge the support of our sponsors, partners, and organizers who made this conference possible despite the challenging circumstances caused by the ongoing war in Ukraine. Finally, we thank all the participants who attended the conference online and contributed to its su

关键词： Neural networks

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