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Conference of Recent Trends and Applications of Soft Computing in engineering, RTASCE 2024

作者： Abood, Layla H. Al-Hilali, Aqeel Ali Galeb, Basim Saber, Ali Ali Bashar, Mustafa Department of Control and Systems Engineering University of Technology Baghdad Iraq Medical Instrumentation Engineering Department Al-Farahidi University Baghdad Iraq Computer Technician Engineering Al Hikma University College Baghdad Iraq Cybersecurity Technology Engineering Department Al-Kitab University Kirkuk Iraq Medical Instrumentation Engineering Department Uruk University Baghdad Iraq

ISBN: (纸本)9783031828805

The potential of optical wireless communication (OWC) systems for high-speed data transfer, especially over extended distances, is being investigated more and more. However, problems including signal attenuation, dispersion, and noise pose a serious challenge to these systems’ effectiveness as transmission lengths increase. This research examines how an OWC system built using OptiSystem software is affected by different transmission lengths, ranging from 1000 to 3000 km. The results of the investigation show that the Quality Factor (QF) and the Bit Error Rate (BER) have a significant negative connection, with more signal distortion and attenuation occurring at greater distances. While rising noise power—due to attenuation and compensatory noise from optical amplifiers—further degrades signal quality by reducing the Signal-To-Noise Ratio (SNR) and Optical Signal-To-Noise Ratio (OSNR), the eye diagram's narrowing indicates increased pulse broadening and intersymbol interference. Furthermore, timing irregularities, as measured by RMS jitter and peak-to-peak jitter, became larger at greater distances, indicating the detrimental effects of attenuation on signal accuracy. The exponential increase in BER logarithm with distance highlights the increasing error rates. These results highlight the serious problems with long-distance transmission and the need for creative solutions. To further enhance the performance and dependability of high-speed OWC systems over long distances, future research should concentrate on creating sophisticated dispersion compensation algorithms, improving noise control tactics, and investigating adaptive modulation schemes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Timing jitter

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Pervasive Indoor User Identification Leveraging Mobile Single Station Localization

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IEEE Internet of Things Journal 2025年第11期12卷 15224-15237页

作者： Nie, Wendi Liu, Zexing Wang, Xiaoyang Duan, Yaoxin Lam, Kam-Yiu Liu, Kai Ng, Joseph K. Y. Xue, Chun Jason Gui, Guan Ministry of Education Key Laboratory of Industrial Internet of Things and Networked Control China Chongqing University of Posts and Telecommunications School of Automation/School of Industrial Internet Chongqing40065 China Institute of Industrial Internet Chongqing401120 China City University of Hong Kong Computer Science Department Hong Kong Chongqing University College of Computer Science Chongqing400030 China Hong Kong Baptist University Computer Science Department Hong Kong Mohamed Bin Zayed University of Artificial Intelligence Department of Computer Science Abu Dhabi United Arab Emirates Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing210003 China

The utilization of Wi-Fi-based technology for pervasive indoor user identification has gained prominence due to its cost-effective nature and compatibility with user devices. Previous works proposed capturing the media access control (MAC) address emitted from a user's device and using Information Elements (IE)-based MAC de-randomization methods to mitigate the impairment caused by random MAC. However, IE types of different Wi-Fi devices are not consistently differentiated, leading to identification errors in IE-based methods. Additionally, typical Wi-Fi fingerprinting approaches require densely pre-deployed Wi-Fi stations, contradicting the principle of pervasive localization. To address these challenges, we propose the mobile single station-based user identification (***) technique, which leverages Wi-Fi mobile single stations for pervasive indoor user identification. *** includes mobile single station localization (MSL) and MAC de-randomization based on users' spatio-temporal location and IE information (***). MSL can be implemented on a standard mobile Wi-Fi station without extensive pre-deployment. *** performs MAC de-randomization using the LIC algorithm to identify users with random MAC addresses. Experimental results demonstrate that *** outperforms previous IE-based user identification methods and multi-station localization techniques. MSL achieves a localization error of 1.15 meters which is better than multi-station with 12 APs of 1.40 meters. *** demonstrates an identification accuracy of 95.24% which is better than AIMAC of 85.48%. © 2014 IEEE.

关键词： Medium access control

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CNFPRNet: Innovative Fusion of GPT-2 Language Model and Natural Language Generation Model for Personalized Educational Recommendations

CNFPRNet: Innovative Fusion of GPT-2 Language Model and Natu...

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6th International Conference on computer Science and Educational Informatization, CSEI 2024

作者： Li, Chao Xu, Zixuan Jing, Weipeng College of Computer and Control Engineering Northeast Forestry University Harbin150040 China

ISBN: (纸本)9789819637379

In this paper, we propose a novel AI-based model that combines a GPT-2 language model with a natural language generation component to generate Customized Narrative Fragments for Personalized Recommendations(CNFPR) in education. Our approach builds on the existing literature on AI-based learning content generation and learning pathway augmentation, but introduces a new component that leverages the power of GPT-2 and an innovative Conceptual Definition Extractor to generate high-quality natural language text. By integrating this component into our model, we aim to create a more personalized and engaging learning experience for students, one that is tailored to their individual needs and preferences. Experimental results show that compared with the Zero-shot evaluation with GPT-2, our CNFPRNet model demonstrates a 23.5% increase in ROUGE scores. This improves the accuracy of recommended content and enables teaching resources to better meet the individual needs of users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Report generators

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FlashMix: Fast Map-Free LiDAR Localization via Feature Mixing and Contrastive-Constrained Accelerated Training

FlashMix: Fast Map-Free LiDAR Localization via Feature Mixin...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Raktim Gautam Goswami Naman Patel Prashanth Krishnamurthy Farshad Khorrami Department of Electrical and Computer Engineering Control/Robotics Research Laboratory (CRRL) NYU Tandon School of Engineering Brooklyn NY USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Map-free LiDAR localization systems accurately localize within known environments by predicting sensor position and orientation directly from raw point clouds, eliminating the need for large maps and descriptors. However, their long training times hinder rapid adaptation to new environments. To address this, we propose FlashMix, which uses a frozen, scene-agnostic backbone to extract local point descriptors, aggregated with an MLP mixer to predict sensor pose. A buffer of local descriptors is used to accelerate training by orders of magnitude, combined with metric learning or contrastive loss regularization of aggregated descriptors to improve performance and convergence. We evaluate FlashMix on various LiDAR localization benchmarks, examining different regularizations and aggregators, and demonstrating its effectiveness for rapid and accurate LiDAR localization in real-world scenarios. The code is available at https://***/raktimgg/FlashMix.

关键词： Location awareness Training Measurement Point cloud compression Laser radar Accuracy Pose estimation Benchmark testing Mixers Convergence

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Split Cost Function based Single-Horizon Finite-Set Model Predictive control for Boost Converter with Loading Current Estimation

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Tatari, Fatemeh Rezayof Bizhani, Hamed Iwanski, Grzegorz Warsaw University of Technology Faculty of Electrical Engineering Warsaw Poland Kielce University of Technology Faculty of Electrical Engineering Automatic Control and Computer Science Kielce Poland

This paper presents a robust single-horizon finite-set model predictive control (FS-MPC) method for controlling the output voltage of boost converters. Unlike the long-horizon FS-MPC approach, which addresses the non-minimum phase (NMP) behavior of the converter by extending the prediction horizon (thereby increasing computational effort), the proposed method ensures stable voltage regulation by incorporating inductor current regulation into the cost function. To achieve this, the desired inductor current is calculated based on input-output power balance theory. Unlike recent studies that require an additional sensor to measure the loading current, this paper develops a loading current estimation approach to eliminate the need for extra current sensors, thereby enhancing the robustness of the proposed FS-MPC method. Additionally, over-current protection, which is typically absent in conventional FS-MPC used for direct voltage regulation, is also incorporated into the proposed method. The conventional cost function is also modified to tackle the NMP behavior more effectively. The performance of the proposed method in regulating the output voltage is evaluated through simulation results. Finally, its effectiveness across a wide range of output power and various load characteristics, including resistive, constant power, and constant current loads, is validated via experimental results. The results demonstrate that the proposed method is not only able to eliminate the overshoot and undershoot, but can also improve the settling time by 41% compared to a well-tuned conventional cascaded PI controller. In addition, the proposed method can reduce the computational burden by 84% compared to the conventional long-horizon FS-MPC approach. © 2013 IEEE.

关键词： Cost functions

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Switching event-triggered self-adjusting prescribed performance force control of electrohydraulic load simulator

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ISA Transactions 2025年

作者： Li, Jiafeng Ji, Ruihang Yan, Hao Shan, Jiefeng Chen, Zanwei School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore

This paper addresses the problem of switching event-triggered self-adjusting prescribed performance force control of electro-hydraulic load simulator (EHLS). A reduced-order fuzzy speed state observer for speed estimation is designed and a self-adjusting performance boundary is devised to mitigate the commutation jitter problem. The switching event-triggered mechanism dynamically adjusts the triggering conditions to optimize resource utilization. To reduce the complexity of the traditional inverse stepping control, the dynamic surface technique is adopted. The results demonstrate that the proposed control method guarantees the boundedness of the closed-loop system states and confines the tracking error to a small range near the equilibrium point within a finite time horizon. Finally, the effectiveness of the proposed method is demonstrated through experimentation. © 2025 International Society of Automation

关键词： Closed loop control systems

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Conditional Feature Generative Adversarial Network for Fault Diagnosis of Axial Piston Pump

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IEEE Sensors Journal 2025年第11期25卷 20907-20919页

作者： Chen, Dong-Ning Hu, Dong-Bo Wang, Hao-Wen Xian, Qing-Gui Yao, Cheng-Yu Deng, Ran-Yang Yanshan University School of Mechanical Engineering Qinhuangdao066004 China Yanshan University Hebei Key Laboratory of Industrial Computer Control Engineering Qinhuangdao066004 China

A conditional feature generative adversarial network (CFGAN) for small sample data augmentation was proposed in this paper to address the issue of the scarcity of fault samples in axial piston pump fault diagnosis. With one-dimensional data as input, a feature extractor was constructed in this method to extract features, bringing about an original deep feature dataset. Subsequently, this dataset was employed to train the generation model for enhancing the training efficiency. Additionally, the Wasserstein distance was introduced to improve the stability of the model training. Conditional labels were added to the inputs of the generator and discriminator to guide the model's training and generate high-quality feature datasets. Afterward, the effectiveness of the proposed data generation method was verified through the data obtained from the Paderborn Bearing Dataset and an axial piston pump fault simulation testbed. The fault diagnosis accuracy of the augmented dataset was significantly improved compared to the non-augmented dataset. Furthermore, a multi-scale convolutional neural network was established and utilized different-sized convolutional kernels to extract and fuse multi-scale information from the samples. This approach enabled the capture of more comprehensive fault state features, contributing to enhanced diagnostic accuracy. The experimental data of the axial piston pump validate that the diagnostic accuracy with fused multi-sensor signals was higher compared to a single sensor. © 2001-2012 IEEE.

关键词： Generative adversarial networks

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Semantic-aware dynamic scene tracking and reconstruction for mobile robots leveraging visual SLAM

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Journal of Supercomputing 2025年第7期81卷 1-39页

作者： Liu, Tianwei Luo, Jingwen School of Information Science and Technology Yunnan Normal University 768 Juxian Street Yunnan Kunming650500 China Engineering Research Center of Computer Vision and Intelligent Control Technology Department of Education of Yunnan Province 768 Juxian Street Yunnan Kunming650500 China

Mobile robots operating in dynamic environments often encounter challenges due to interference from moving objects, compromising the performance of their SLAM systems. To address this issue, we propose a depth camera based semantic-aware visual SLAM approach for mobile robots, aimed at dynamic scene tracking and reconstruction. By leveraging a lightweight Yolact++ network, we segment and detect objects in the scene, eliminate dynamic features through prior knowledge and epipolar constraints, and utilize high-confidence static features for robot’s pose estimation. Subsequently, the overall similarity between images is evaluated based on the similarity of the matched semantic object regions between images to select high-quality candidate keyframes, effectively enhancing the precision of loop closure detection. Furthermore, we employ Delta Generalized Labeled Multi-Bernoulli (Delta-GLMB) filtering to track the masks of prior dynamic objects provided by Yolact++. A global semantic octree map is then constructed, integrating both the semantic knowledge of the static scene and the tracking information of dynamic targets, which in turn realizes the tracking & reconstruction of the dynamic scene. This allows mobile robots to mimic human behavior and cognitive abilities, not merely to construct a map of unknown environments more efficiently, but also to track the state of moving targets in the field-of-view(FoV) in real-time, enabling self-localization and navigation functions. Experimental results demonstrate the effectiveness of our approach in improving the robustness, accuracy, and adaptability of mobile robots in dynamic environments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Mobile robots

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Crowdsourcing Task Recommendation Method Based on Heterogeneous Graph Feature Anomaly Detection 18th

Crowdsourcing Task Recommendation Method Based on Heterogen...

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18th China Conference on Wireless Sensor Networks, CWSN 2024

作者： Wei, Kai Pan, Qingxian Yu, Song Fan, Zequn Li, Jinru School of Computer and Control Engineering Yantai University Yantai264005 China School of Computer and Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9789819621859

The crowdsourcing task recommendation method addresses worker selection overload by intelligently matching workers with tasks. However, existing research often overlooks the inherent characteristics of workers and tasks, the long-term behavioral preferences of workers, and abnormal information about workers and tasks, which impacts the accuracy of recommendations. To address these issues, we propose a novel crowdsourcing task recommendation method based on anomaly detection using multi-attribute heterogeneous graph features. This method first establishes the connection relationships between crowdsourcing workers and tasks to construct a worker-task heterogeneous graph. DeepWalk generates initial node features, and the message-passing mechanism in the graph neural network is employed to explore the feature information of workers and task nodes. During message transmission, the graph convolutional autoencoder drives the detection of worker and task feature information. Finally, Bi-GRU captures the long-term behavioral preferences of workers, and link prediction constructs links based on node similarity to complete the recommendation of sensing tasks. We evaluate nine real datasets, and the experimental results demonstrate that the proposed method outperforms the baseline method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural networks

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Community Membership Hiding via Gradient-based Optimization

arXiv

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arXiv 2025年

作者： Silvestri, Matteo Gabrielli, Edoardo Silvestri, Fabrizio Tolomei, Gabriele Department of Computer Science Sapienza University of Rome Rome Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

We tackle the problem of community membership hiding, which involves strategically altering a network’s structure to obscure a target node’s membership in a specific community identified by a detection algorithm. We reformulate the original discrete counterfactual graph objective as a differentiable constrained optimization task. To solve this, we propose ∇- CMH, a gradient-based method that modifies the network’s structure within the feasible bounds for an individual target node, effectively concealing its membership. Experimental results across multiple datasets and community detection algorithms show that our approach surpasses existing baselines, offering a better balance between accuracy and computational efficiency. © 2025, CC BY.

关键词： Optimization algorithms

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