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arXiv 2024年

作者： Shi, Ruohua Pang, Qiufan Ma, Lei Duan, Lingyu Huang, Tiejun Jiang, Tingting National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China State Key Laboratory of Multimedia Information Processing School of Computer Science Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China Peng Cheng Laboratory Shenzhen China

Electron microscopy (EM) imaging offers unparalleled resolution for analyzing neural tissues, crucial for uncovering the intricacies of synaptic connections and neural processes fundamental to understanding behavioral mechanisms. Recently, the foundation models have demonstrated impressive performance across numerous natural and medical image segmentation tasks. However, applying these foundation models to EM segmentation faces significant challenges due to domain disparities. This paper presents ShapeMamba-EM, a specialized fine-tuning method for 3D EM segmentation, which employs adapters for long-range dependency modeling and an encoder for local shape description within the original foundation model. This approach effectively addresses the unique volumetric and morphological complexities of EM data. Tested over a wide range of EM images, covering five segmentation tasks and 10 datasets, ShapeMamba-EM outperforms existing methods, establishing a new standard in EM image segmentation and enhancing the understanding of neural tissue architecture. © 2024, CC BY-NC-SA.

关键词： Tissue

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Dynamic Multi-Objective Optimization Algorithm Guided by Recurrent Neural Network

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Hu, Yaru Ou, Junwei Suganthan, Ponnuthurai Nagaratnam Pedrycz, Witold Wang, Rui Zheng, Jinhua Zou, Juan Song, Yanjie School of Computer Science and School of Cyberspace Security Xiangtan University Xiangtan China College of Engineering KINDI Center for Computing Research Qatar University Doha Qatar Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada College of Systems Engineering National University of Defense Technology Changsha China School of Reliability and Systems Engineering Beihang University Beijing China

In recent years, prediction-based algorithms have attracted much attention for solving dynamic multi-objective optimization problems in the evolutionary computing community. However, this class of algorithms still has potential for further improvements by enhaneing the historical information extraction approach to balance convergence and diversity. In this paper, we propose a dynamic multi-objective optimization algorithm based on a recurrent neural network to balance the population’s convergence and diversity in dynamic environments. The recurrent neural network model in the proposed algorithm employs online learning in order to constantly improve according to the increasing evolutionary information. Meanwhile, differing from most existing prediction-based algorithms, the learning machine is not limited by assumptions, such as linear or nonlinear correlation, when it predicts new solutions for future evolutionary environments. Besides, an auxiliary strategy is performed, which adaptively introduces the random or mutated solutions according to the error losses between the prediction solutions and the optimal solutions in the whole optimization process. The experimental results show that the proposed algorithm is more effective for handling dynamic multi-objective optimization problems than several recent algorithms. IEEE

关键词： Evolutionary algorithms

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A requirement analysis method for the design of the upgradable product-service system 33

A requirement analysis method for the design of the upgradab...

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33rd CIRP Design Conference

作者： Wang, Hanfei Mitake, Yuya Tsutsui, Yusuke Alfarisi, Salman Shimomura, Yoshiki Faculty of Systems Design Tokyo Metropolitan University Asahigaoka 6-6 Hino-shi Tokyo191-0065 Japan Human Augmentation Research Center National Institute of Advanced Industrial Science and Technology Chiba277-0882 Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama719-1197 Japan

For extending the life cycle of products and satisfying the further requirement of customers, product-service system (PSS), an integration of products and services is considered as a promising solution. However, for PSS design, customers' expectations on the purchased product could be changed due to the development of technology and fashion. For this reason, the life of value might be exhausted before the real life of a product. Although there are some existing upgrade plans or maintenance plans for PSS design, it is found to be risky for PSS firms to provide the mentioned plans that are threatened by various uncertain events. Thus, to achieve an upgradable PSS, it is extremely essential to propose a method to identify the requirement for upgradability based on the perspective of customers. To achieve this aim, this paper has developed a method includes four steps. In step one, a set of definitions is provided to clarify the upgradability of PSS. In step 2, this study utilizes a PEST-OT matrix to analyze the driver of the future development. Step3 uses a persona-based explorative scenario building method to identify the characteristic of targeted customer and build a scenario of interaction between customer and PSS in a future scenario. Step 4 provides a requirement transformation method to transform the requirement into receiver state parameter (RSP). After that, the RSP is transformed into detailed product characteristics (PChs) and service characteristics (SChs). To verify the effectiveness, a case study of shared furniture is used. The result of the case study shows this method could work effectively in a Business-to-Customer business. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Life cycle

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Study on indoor pollutants emission in Akure, Ondo State, Nigeria

Arab Gulf Journal of Scientific Research

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Arab Gulf Journal of Scientific research 2024年第4期42卷 1643-1663页

作者： Abulude, Francis Olawale Suriano, Domenico Oluwagbayide, Samuel Dare Akinnusotu, Akinyinka Abulude, Ifeoluwa Ayodeji Awogbindin, Emmanuel Environmental and Sustainable Research Group Science and Education Development Institute Akure Nigeria Department of Sustainable Development Research Center of Brindisi ENEA-Italian National Agency for New Technologies Energy and Environment Brindisi Italy Department of Agricultural and Bio-Environmental Engineering Federal Polytechnic Idah Nigeria Department of Science Laboratory Technology Rufus Giwa Polytechnic Owo Nigeria Department of Agricultural Economics Bioeconomy and Food Systems Justus – Liebig University (JLU) Giessen Germany Department of Computer Science Bamidele Olumilua University of Education Science and Technology Ekiti Ikere Nigeria

Purpose: This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO2), ozone (O3), nitrogen dioxide (NO2) and sulfur dioxide (SO2), as well as particulate matter (PM) (PM1, PM... 详细信息

Purpose: This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO₂), ozone (O₃), nitrogen dioxide (NO₂) and sulfur dioxide (SO₂), as well as particulate matter (PM) (PM₁, PM_2.5 and PM₁₀) in Akure, Nigeria, as well as the relationship between the parameters’ concentrations. Design/methodology/approach: The evaluation, which lasted four months, used a low-cost air sensor that was positioned two meters above the ground. All sensor procedures were correctly carried out. Findings: CO₂ (430.34 ppm), NO₂ (93.31 ppb), O₃ (19.94 ppb), SO₂ (40.87 ppb), PM₁ (29.31 µg/m3), PM_2.5 (43.56 µg/m³), PM₁₀ (50.70 µg/m³), temperature (32.4°C) and relative humidity (50.53%) were the average values obtained. The Pearson correlation depicted the relationships between the pollutants and weather factors. With the exception of April, which had significant SO₂ (18%) and low PM₁₀ (49%) contributions, NO₂ and PM₁₀ were the most common pollutants in all of the months. The mean air quality index (AQI) for NO₂indicated that the AQI was “moderate” (51–100). In contrast to SO₂, whose AQI ranged from “moderate” to “very unhealthy,” O₃'s AQI ranged from “good” (50) to “unhealthy” (151–200). Since PM₁, PM_2.5 and PM₁₀ made up the majority of PC1’s contribution, both PM_2.5 and PM₁₀ were deemed “hazardous.” Practical implications: The practical implication of indoor air pollution is long-term health effects, including heart disease, lung cancer and respiratory diseases such as emphysema. Indoor air pollution can also cause long-term damage to people’s nerves, brain, kidneys, liver and other organs. Originality/value: Lack of literature in terms of indoor air qu

关键词： Air quality index Indoor air quality Nigeria Pollutants United States environmental protection agency (USEPA) World health organization (WHO)

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A Survey of Human Action Recognition and Posture Prediction

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Tsinghua Science and technology 2022年第6期27卷 973-1001页

作者： Nan Ma Zhixuan Wu Yiu-ming Cheung Yuchen Guo Yue Gao Jiahong Li Beiyan Jiang Beijing Key Laboratory of Information Service Engineering the College of RoboticsBeijing Union UniversityBeijing 100101China Department of Computer Science Hong Kong Baptist UniversityHong Kong 999077China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China School of Software Tsinghua UniversityBeijing 100084China College of Robotics Beijing Union UniversityBeijing 100101China

Human action recognition and posture prediction aim to recognize and predict respectively the action and postures of persons in *** are both active research topics in computer vision community,which have attracted considerable attention from academia and *** are also the precondition for intelligent interaction and human-computer cooperation,and they help the machine perceive the external *** the past decade,tremendous progress has been made in the field,especially after the emergence of deep learning ***,it is necessary to make a comprehensive review of recent *** this paper,firstly,we attempt to present the background,and then discuss research ***,we introduce datasets,various typical feature representation methods,and explore advanced human action recognition and posture prediction ***,facing the challenges in the field,this paper puts forward the research focus,and introduces the importance of action recognition and posture prediction by taking interactive cognition in self-driving vehicle as an example.

关键词： human action recognition posture prediction computer vision human-computer cooperation interactive cognition

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NumbOD: A Spatial-Frequency Fusion Attack Against Object Detectors

arXiv

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arXiv 2024年

作者： Zhou, Ziqi Li, Bowen Song, Yufei Yu, Zhifei Hu, Shengshan Wan, Wei Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

With the advancement of deep learning, object detectors (ODs) with various architectures have achieved significant success in complex scenarios like autonomous driving. Previous adversarial attacks against ODs have been focused on designing customized attacks targeting their specific structures (e.g., NMS and RPN), yielding some results but simultaneously constraining their scalability. Moreover, most efforts against ODs stem from image-level attacks originally designed for classification tasks, resulting in redundant computations and disturbances in object-irrelevant areas (e.g., background). Consequently, how to design a model-agnostic efficient attack to comprehensively evaluate the vulnerabilities of ODs remains challenging and unresolved. In this paper, we propose NumbOD, a brand-new spatial-frequency fusion attack against various ODs, aimed at disrupting object detection within images. We directly leverage the features output by the OD without relying on its internal structures to craft adversarial examples. Specifically, we first design a dual-track attack target selection strategy to select high-quality bounding boxes from OD outputs for targeting. Subsequently, we employ directional perturbations to shift and compress predicted boxes and change classification results to deceive ODs. Additionally, we focus on manipulating the high-frequency components of images to confuse ODs' attention on critical objects, thereby enhancing the attack efficiency. Our extensive experiments on nine ODs and two datasets show that NumbOD achieves powerful attack performance and high stealthiness. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Hybrid Digital-Analog Semantic Communications

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IEEE Journal on Selected Areas in Communications 2025年

作者： Xie, Huiqiang Qin, Zhijin Han, Zhu Letaief, Khaled B. Jinan University College of Information Science and Technology Guangzhou China Tsinghua University Department of Electronic Engineering Beijing100084 China State Key Laboratory of Space Network and Communications Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China University of Houston Department of Electrical and Computer Engineering Houston United States The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong

Digital and analog semantic communications (SemCom) face inherent limitations such as data security concerns in analog SemCom, as well as leveling-off and cliff-edge effects in digital SemCom. In order to overcome these challenges, we propose a novel SemCom framework and a corresponding system called HDA-DeepSC, which leverages a hybrid digitalanalog approach for multimedia transmission. This is achieved through the introduction of analog-digital allocation and fusion modules. To strike a balance between data rate and distortion, we design new loss functions that take into account long-distance dependencies in the semantic distortion constraint, essential information recovery in the channel distortion constraint, and optimal bit stream generation in the rate constraint. Additionally, we propose denoising diffusion-based signal detection techniques, which involve carefully designed variance schedules and sampling algorithms to refine transmitted signals. Through extensive numerical experiments, we will demonstrate that HDA-DeepSC exhibits robustness to channel variations and is capable of supporting various communication scenarios. Our proposed framework outperforms existing benchmarks in terms of peak signal-to-noise ratio and multi-scale structural similarity, showcasing its superiority in semantic communication quality. © 1983-2012 IEEE.

关键词： Digital communication systems

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A novel design of layered recurrent neural networks for fractional order Caputo-Fabrizio stiff electric circuit models

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Modern Physics Letters B 2024年第2期39卷

作者： Kausar, Aneela Chang, Chuan-Yu Raja, Muhammad Asif Zahoor Shoaib, Muhammad Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin 64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology Yunlin 64002 Taiwan AI Center Yuan Ze University Taoyuan 320 Taiwan

Electrical engineering models often rely on complex circuit configurations that facilitate the dynamic flow of electrically charged particles within a closed conductive network. These circuits serve as essential tools for simulating and analyzing diverse electrical systems and components. This paper introduces a study on nonlinear fractional circuits modeling through the development of a stochastic neuro-computational artificial intelligent-based solver to address mathematical models governing the Fractional order Caputo-Fabrizio stiff electric circuit model (FO-CFSECM) by manipulating the knacks of layered recurrent neural networks (LRNNs) trained with Gradient-based local search algorithm (GLA). In fractional calculus, the Caputo-Fabrizio (CF) fractional order derivative (FOD) emerges as a powerful instrument, binding its capabilities to deliver remarkably accurate solutions for fractional stiff systems. The objective of this work is to exploit the numerical treatment comprehensively for the dynamics of fractal Resistor-Capacitor (RC) and fractal Resistor-Inductor (RL) circuit models by introducing the CF fractional operator. Through the application of artificial intelligence-based soft computing and advanced back-propagative deep neural networks, a deeper understanding of the behavior and distinctive characteristics inherent in these models is sought. The Levenberg-Marquardt optimizer serves as an efficient training GLA tool for learning of LRNNs weights of fractal RL/RC circuit models. The comparative studies on variants of FO-CFSECM demonstrate that LRNNs achieve an impressive mean square error (MSE) ranging from 10^-9 to 10^-19 and absolute error (AE) within 10^-6 to 10^-8. The accuracy, reliability, and efficiency of LRNNs for solving the FO-CFSECM were further validated through MSE, AE, controlling parameters of state transitions, error histograms, and correlation measures. © World Scientific Publishing Company.

关键词： Caputo-Fabrizio electric circuit model integrated circuits optimization RC circuits Recurrent neural network RL circuits soft computing stiff model

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Sensitivity studies and optimization of an impedance-based biosensor for point-of-care applications

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Biosensors and Bioelectronics: X 2024年 18卷

作者： Jiang, Tianxiang Ye, Xuanjie Tian, Zuyuan Shaheen, Mohamed Khorshed, Ahmed A. Feng, Yiwei Li, Bingxuan Zhang, Yusheng Wang, Xihua Chen, Jie Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 1H9 Canada National Engineering Research Center for Biomaterials College of Biomedical Engineering Sichuan University Sichuan Chengdu610064 China Academy for Engineering & Technology Fudan University Shanghai200433 China

In this study, we examined the relationship between the sensitivity of interdigitated electrode (IDE) impedimetric biosensors and the gap between the IDEs. Our aim is to find an optimal design to maximize sensitivity. A three-dimensional COMSOL model was constructed for determining the effects of electrode gap, width, and height on impedance sensitivity, revealing a singular linear correlation with the inner gap. Considering both the simulation results and fabrication processes, we have developed three IDE prototype chips with electrode gaps of 3 μm, 4 μm, and 5 μm, respectively. For empirical validation, human anti-SARS-CoV-2 monoclonal antibody (mAb) was utilized, with immobilization of the SARS-CoV-2 spike protein on the chip's surface for mAb capture. This interaction, further amplified by Protein G conjugation, induced shifts in the impedance spectrum. The sensitivity of each prototype chip was evaluated across mAb concentrations ranging from 50 ng/mL to 500 ng/mL. The 3 μm configuration emerged as the most sensitive, demonstrating the ability to detect mAb concentrations as low as 50 ng/mL, a threshold unattainable by the other designs. This outcome underscores the critical influence of reduced inter-electrode gap on enhancing biosensor detection limits. The findings from this investigation offer a foundational approach for advancing biosensor sensitivity via electrode geometric optimization, with broad potential applications extending beyond COVID-19 diagnostics to a wide spectrum of clinical and research contexts. © 2024 The Authors

关键词： Electrochemical impedance spectroscopy

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A Deep Belief Networks Intrusion Detection Method Based on Generative Adversarial Networks

A Deep Belief Networks Intrusion Detection Method Based on G...

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Artificial Intelligence, Networking and Information technology (AINIT), International Seminar on

作者： Yi Wei Yingxiong Nong Zhe Li Jian Pan Zhenyu Yang Zezhang Zheng Ningjiang Chen China Tobacco Guangxi Industrial Co. Ltd Nanning China School of Computer Electronics and Information Guangxi University Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Key Laboratory of Parallel Distributed and Intelligent Computing School of Computer Electronics and Information Guangxi University (Guangxi University) Nanning China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

In response to the substantial threat that Internet attacks pose to data center network security, researchers have proposed several deep learning-based methods for detecting network intrusions. However, while algorithms are constantly improving in terms of accuracy, their stability in the face of insufficient attack samples is a major obstacle. To solve the issues of insufficient attack samples and low detection accuracy in network intrusion detection, this paper proposes a deep confidence network intrusion detection method G-DBN based on GAN. The model is based on the malicious sample extension of the generative adversarial network, and it can produce adversarial samples using malicious network flows as original samples. Furthermore, this paper uses deep belief network technology to create and assess the efficacy of the G-DBN model in detecting network attacks, comparing it to standard DBN models and other network intrusion detection techniques. Experimental results show that compared to the standard three-layer DBN method, the G-DBN method described in this paper improves the detection accuracy of attack samples by 6.46% and better meets the performance requirements of current practical applications.

关键词： Seminars Learning systems Data centers Accuracy Network intrusion detection Network security Generative adversarial networks

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