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CNN-Based Multi-Output and Multi-Task Regression for Supershape Reconstruction from 3D Point Clouds

Informatica (Slovenia) 2025年第5期49卷 37-48页

作者： Remmach, Hassnae Razak, Siti Fatimah Abdul Ullah, Arif Yogarayan, Sumendra Sayeed, Md Shohel Mrhari, Amine Computer Systems Engineering Laboratory Cadi Ayyad University Marrakesh40000 Morocco Faculty of Information Science and Technology Multimedia University Ayer Keroh75450 Malaysia Research in Computer Science Laboratory Faculty of Sciences Ibn Tofail University Kenitra Morocco

In a number of industries, including computer graphics, robotics, and medical imaging, three-dimensional reconstruction is essential. In this research, a CNN-based Multi-output and Multi-Task Regressor with deep learning capabilities is proposed for three-dimensional object reconstruction from 3D point cloud. Our approach is grounded in the original Point Net architecture, which addresses the difficulties associated with convolution when applied to point clouds. Firstly, this paper is modified using a Multi-Output Regressor to accurately recreate Super forms from 3D point clouds. Using this method, we first extract features from the 3D point cloud using Point Net. After that, a Multi-Output Regressor receives these data and uses them to anticipate the Super shape parameters needed to reconstruct the shape. Taking in the data, the Multi-Output Regressor retrieved characteristics from Point Net and simultaneously predicts several outcomes. Second, a Multi-Task Regressor is used to modify the Point Net. The network gains from the capacity to transfer knowledge from one task to another, improving the model's overall performance. The model would forecast the ten parameters needed to create the shape in the case of rebuilding Super shapes. The test findings were better than expected;they are intriguing in terms of prediction accuracy and cost, and they update the result by 80%, which is a good accomplishment for the study. © 2025 Slovene Society Informatika. All rights reserved.

关键词： 3D reconstruction

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Financial Fraud Detection of Listed Companies Based on DNSAE Fusion and Improved Random Forest 7

Financial Fraud Detection of Listed Companies Based on DNSAE...

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7th International Conference on Intelligent information Processing, ICIIP 2022

作者： Huang, Haibin Liu, Yarong Xie, Xiaolan School of Information Science and Engineering Guilin University of Technology China College of Information Science and Engineering Guangxi Key Laboratory of Embedded Technology and Intelligent System China

ISBN: (纸本)9781450396714

The falsification of financial data of listed companies is easy to mislead the market, causing investors to lose their investment and the personal economy is severely damaged. In view of the above problems, this paper proposes a financial fraud detection model of listed companies based on DNSAE fusion and improved random forest to detect the financial fraud of listed companies. The experimental results show that the model has a good detection effect in the financial fraud of listed companies. © 2022 Owner/Author.

关键词： Investments

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BAV-DSS: Blockchain Assisted Verifiable Data Sharing Scheme with Fast Encryption and Outsourced Decryption for IoT 24th

BAV-DSS: Blockchain Assisted Verifiable Data Sharing Scheme ...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Huang, Yao Yu, Zhongyuan Wang, Guijuan Chai, Baobao Zhang, Hongliang Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China School of Information Science and Engineering Lanzhou University Lanzhou730000 China School of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9789819615414

Many sophisticated cloud platforms are available to provide services such as storage and computation for Internet of Things (IoT) applications. However, with the concerns about the semi-trusted nature of cloud platforms, data security and privacy have been gaining broad attention. In this paper, we propose a blockchain-assisted verifiable data sharing scheme (BAV-DSS) for IoT. The BAV-DSS features fine-grained access control, fast encryption, and outsourced decryption with verifiability. Specifically, with the application of ciphertext-policy attribute-based encryption (CP-ABE), flexible access control is enabled. Blockchain is employed to store public keys, generate account addresses, and perform data verification. Furthermore, the selective replayable chosen ciphertext attacks (sRCCA) security of BAV-DSS is proved, and the simulation results indicate that BAV-DSS obtains high efficiency and practicality compared to other schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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IntML-KNN: A Few-Shot Radio Frequency Fingerprint Identification Scheme for Lora Devices

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IEEE Signal Processing Letters 2025年 32卷 2259-2263页

作者： Cao, Xin Tan, Weijie Gao, Qiangqiang Hu, Zhilong Li, Chunguo Guizhou University College of Computer Science and Technology Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Ministry of Industry and Information Technology Laboratory of Cryptograph Application Technology Innovation and Testing Verification Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

Deep learning (DL) is widely used in radio frequency fingerprint identification (RFFI). However, in few-shot case, traditional DL-based RFFI need to construct auxiliary dataset to realize radio frequency fingerprint identification. To address this issue, we propose a few-shot RFFI (FS-RFFI) method based on interpolating metric learning and KNN (IntML-KNN). Specifically, the method first extends the dataset with data augmentation, and CutMix interpolation. Secondly, combining with metric learning to enhance the generalization capacity of the model. Finally, KNN algorithm is designed to realize device classification and detection. The proposed IntML-KNN method is verified on the commercial available LoRa dataset. The experimental results indicate that the proposed scheme exhibits strong classification and generalization performance in FS-RFFI. Meanwhile, the recognition rate of the proposed IntML-KNN reaches 97.00% with only 10% samples. © 1994-2012 IEEE.

关键词： Deep learning

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Multispectral radiation thermometry approach based on feasible region constraints-divide and conquer optimization

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Optics Express 2025年第10期33卷 20736-20753页

作者： Huang, Hairui Zhang, Wei Yu, Peifeng Xu, Jiawen Liu, Xin Gao, Shan Wang, Chao Zhang, Zezhan Niu, Yi Jiang, Jing Clean Energy Materials and Engineering Center School of Electronic Science and Engineering State Key Laboratory of Electronic Thin Film and Integrated Devices University of Electronic Science and Technology of China Chengdu611731 China The State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision Instrument Tsinghua University Beijing100084 China Key Laboratory of Advanced Marine Communication and Information Technology Ministry of Industry and Information Technology School of Information and Communication Engineering Harbin Engineering University Harbin150001 China

Multispectral radiation thermometry is a widely used non-contact temperature measurement method, particularly in extreme environments. However, accurately retrieving the true temperature remains challenging due to the unknown emissivity of the object. This paper proposes a collaborative optimization approach (feasible region constraints-divide and conquer optimization), which combines a feasible domain constraints method (PCR-PSO) with a divide and conquer optimization algorithm (multi-BFGS). Simulation results show that the proposed approach reduces the temperature inversion error from 0.59% to 0.19% compared to the traditional BFGS algorithm. Experimental results obtained with stainless steel samples indicate an average error of less than 0.2% and an average processing time of 0.2 seconds, highlighting the potential for real-time temperature measurement in engineering applications. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Temperature measurement

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A Novel Finite Mixture Model Based on the Generalized t Distributions with Two-Sided Censored Data

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Annals of Data science 2025年第1期12卷 341-379页

作者： Guan, Ruijie Rong, Yaohua Cheng, Weihu Xin, Zhenyu Key Laboratory of Network Information System Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Faculty of Science Beijing University of Technology Beijing100124 China Master of management EBS Universität für Wirtschaft und Recht Oestrich-Winkel65375 Germany

In light of the rapid technological advancements witnessed in recent decades, numerous disciplines have been inundated with voluminous datasets characterized by multimodality, heavy-tailed distributions, and prevalent missing information. Consequently, the task of effectively modeling such intricate data poses a formidable yet indispensable challenge. This paper endeavors to address this challenge by introducing a novel finite mixture model predicated upon the generalized t distribution, tailored specifically to accommodate two-sided censored observations, thereby establishing a foundational framework for modeling this complex data structure. To facilitate parameter estimation within this model, we devise a variant of the EM-type algorithm, amalgamating the profile likelihood approach with the classical Expectation Conditional Maximization algorithm. Notably, this hybridized methodology affords analytical expressions in the E-step and a tractable M-step, thereby substantially enhancing computational expediency and efficiency. Furthermore, we furnish closed-form expressions delineating the observed information matrix, pivotal for approximating the asymptotic covariance matrix of the MLEs within this mixture model. To empirically evaluate the efficacy of the proposed algorithm, a series of simulation studies are conducted, demonstrating promising performance across various artificial datasets. Additionally, the practical applicability of the proposed methodology is elucidated through its deployment on two real-world datasets, thereby underscoring its feasibility and utility in practical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Expectation maximization algorithm

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An Effective Hybrid Jellyfish Search Algorithm for Multi-AGVs Path Planning 12

An Effective Hybrid Jellyfish Search Algorithm for Multi-AGV...

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12th International Conference on Intelligent Control and information Processing, ICICIP 2024

作者： Chen, Xiaojie Wu, Qingtao Zhao, Xuhui Yao, Shan Chen, Xi Zhang, Mingchuan School of Information Engineering Henan University of Science and Technology Luoyang China School of Information Engineering Henan University of Science and Technology Longmen Laboratory Luoyang China 360 Digital Security Technology Group Co. Ltd Longmen Laboratory Beijing China

ISBN: (纸本)9798350308020

This paper considers a path planning problem of multiple automated guided vehicles (AGVs) in a production workshop with many varieties and small batches. To meet the logistics needs of the workshop, multi-AGVs material distribution and finished product recovery are involved at multiple task points. To solve this problem, we first present a mathematical model to optimize the handling cost under the constraints of AGV load and task priority. Secondly, we propose a hybrid jellyfish search (HJS) algorithm, where Lévy flight (LF) and differential evolution (DE) are into the jellyfish search (JS) algorithm by leveraging their advantages. Finally, we conduct various experiments to verify the performance of the proposed algorithm. The results show that HJS is more effective than other multi-AGV path planning algorithms in the AGV transportation distance and the operation of workshops, respectively. © 2024 IEEE.

关键词： Motion planning

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Optimization of optical convolution kernel of optoelectronic hybrid convolution neural network

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Optoelectronics Letters 2022年第3期18卷 181-186页

作者： XU Xiaofeng ZHU Lianqing ZHUANG Wei ZHANG Dongliang LU Lidan YUAN Pei School of Electro-Optical Engineering Changchun University of Science and TechnologyJilin 130022China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology UniversityBeijing 100192China Beijing Laboratory of Optical Fiber Sensing and System Beijing Information Science and Technology UniversityBeijing 100016China

To enhance the optical computation’s utilization efficiency, we develop an optimization method for optical convolution kernel in the optoelectronic hybrid convolution neural network(OHCNN). To comply with the actual calculation process, the convolution kernel is expanded from single-channel to two-channel, containing positive and negative weights. The Fashion-MNIST dataset is used to test the network architecture’s accuracy, and the accuracy is improved by 7.5% with the optimized optical convolution kernel. The energy efficiency ratio(EER) of two-channel network is 46.7% higher than that of the single-channel network, and it is 2.53 times of that of traditional electronic products.

关键词： convolution kernel weights

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Joint Optimization of Latency and Energy Consumption for Mobile Edge Computing Based Proximity Detection in Road Networks

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China Communications 2022年第4期19卷 274-290页

作者： Tongyu Zhao Yaqiong Liu Guochu Shou Xinwei Yao School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing 100876China School of Computer Science and Technology Zhejiang University of TechnologyChina

In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

关键词： proximity detection mobile edge computing road networks constrained multiobjective optimization

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Superior triethylamine-sensing properties based on SnO2 hollow nanospheres synthesized via one-step process

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Progress in Natural science:Materials International 2024年第4期34卷 682-689页

作者： Mingyue Xu Shu Yan Zia Ur Rehman Jianhua Hou Liangyun Zhai Shengjie Zhu Chuanbao Cao Wanfeng Xie Junyu Zhong Jun Yang State Key Laboratory of Bio-fibers and Eco-textiles School of Electronics and Information College of Materials Science and Engineering College of Chemistry and Chemical Engineering Qingdao University College of Environmental Science and Engineering Yangzhou University State Key Laboratory of Chemical Safety SINOPEC Research Institute of Safety Engineering Co. Ltd. National Registration Center for Chemicals Ministry of Emergency Management Research Center of Materials Science Beijing Institute of Technology State Key Laboratory of Multiphase Complex Systems Institute of Process Engineering Chinese Academy of Sciences

Due to serious harm of triethylamine（TEA） to environmental safety and human health, it is significant to synthesize gas-sensitive materials with high performance for TEA detection. However, it is still a challenge to achieve high-sensitivity detection of TEA at low temperature for a sensor synthesized through an economical and efficient method. In this work, hollow-structured SnO2（HS-SnO2） nanospheres have been fabricated by a facile, low-cost hydrothermal method in one step, which exhibit superior TEA-sensing properties, including not only ultrahigh response（127.75） for 100 ppm TEA, good selectivity, but also fast response and recovery time（17/28 s）, low detection threshold（1 ppm） and robust stability at a relatively low optimum operational temperature of 225°*** excellent gas-sensitizing performances are ascribed to porous hollow structures with rich oxygen vacancies that provide abundant active sites for raising O2adsorption and reaction of TEA and oxygen species. This work offers an effective and economical strategy for fabricating high-performance TEA sensors for industrial applications.

关键词： Hollow-structured SnO2 nanospheres TEA High response Gas sensors

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