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Data Analytics, Computing and Artificial Intelligence (ICDACAI), International Conference on

作者： Xueqiang Sun Junrong Gu Zehui Sun Software Engineering Department Rongcheng College Harbin University of Science and Technology Rongcheng Weihai Shandong China

According to the enterprise data, this paper establishes the cluster analysis model and the hierarchical analysis model based on TOPSIS to help banks analyze their credit risk and make credit decisions. First of all, we preprocess the data, and divide 99 enterprises into six categories based on credit rating, ticket rejection rate and enterprise profit proportion. Secondly, with quota and interest rate as the first level indicators, strength, supply and demand stability, risk and reputation as the second level indicators, the scores of six categories of enterprises under quota and interest rate are determined by subjective and objective weighting method. Then the Credit Strategies of different types of enterprises are determined by the score. Considering the default rate of enterprises, the “0-1“ programming model is established. Finally, the objective evaluation and promotion of the model are carried out.

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Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

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Computer Systems Science & engineering 2023年第10期47卷 873-889页

作者： Mohamed Yacin Sikkandar K.Hemalatha M.Subashree S.Srinivasan Seifedine Kadry Jungeun Kim Keejun Han Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Biomedical Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its *** researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their *** of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography(CT)*** the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical ***,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest *** the epidemic of COVID-19,much research is led on processing the data compared with DL techniques,particularly *** study develops an improved fruit fly optimization with a deep learning-enabled fusion(IFFO-DLEF)model for COVID-19 detection and *** major intention of the IFFO-DLEF model is to investigate the presence or absence of *** do so,the presented IFFODLEF model applies image pre-processing at the initial *** addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature ***,the IFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify *** parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification *** experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches.

关键词： COVID-19 computer vision deep learning image classification fusion model

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A Study of EMG Signals Using QNET Myoelectric Board and LabVIEW software 4

A Study of EMG Signals Using QNET Myoelectric Board and LabV...

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4th International Conference on Applied Automation and Industrial Diagnostics, ICAAID 2022

作者： Alharbi, Hadeel Fahad Patel, Akshet Dasari, Uday Randhawa, Princy University of ha'Il Hail College of Computer Science and Engineering Department of Computer Science and Software Engineering 1234 Saudi Arabia Manipal University Jaipur Department of Mechatronics Engineering Jaipur India Manipal University Jaipur Department of Information Technology Engineering Jaipur India

ISBN: (纸本)9781728189390

This project's aim is to monitor the electrical activity of the user's muscle cells using the QNET Myoelectric Board and NI ELVIS II+ board. An opto-isolated sensor and a grounding strap are included in this electromyograph. The board includes an on-board signal processing and filtering mechanism, as well as a PCI-based data collection system. The board is connected using the LabVIEW application, which stands for Laboratory Virtual Instrument engineering Workbench (LabVIEW). LabVIEW is a graphical programming language and hence a direct connection between the user and the board is possible. The data is acquired, and five assessment criteria are applied to it. Graphs are shown based on time stamps for two participants (male and female), and the analyzed data is compiled to draw crucial conclusions. © 2022 IEEE.

关键词： Machine learning

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Melanoma Moles Detection and Classification Using Cornernet Model

Melanoma Moles Detection and Classification Using Cornernet ...

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2024 1st International Conference on Logistics (ICL)

作者： Marriam Nawaz Tahira Nazir Ali Javed Momina Masood Hussain Dawood Department of Software Engineering University of Engineering and Technology Taxila Pakistan Department of Computing Riphah International University Islamabad Pakistan Skyline University College Sharjah United Arab

ISBN: (数字)9798331518844

ISBN: (纸本)9798331518851

Melanoma is a malicious form of skin cancer and can develop wounds on the entire human body which can lead to the victim's demise at the advanced level. The timely and accurate detection of melanoma moles can prevent the victims from painful treatment and surgeries. However., immense similarity among the healthy and diseased areas and massive variations in the mass, contour., and shades of the melanoma moles complicate the recognition process. Here., we have employed a deep learning (DL) framework named the CornerNet model for the localization and categorization of skin lesions. The Hourglass-oriented CornerNet approach is used for deep features calculations which are later grouped by the one-step object recognizer network of the employed solution. The approach is tested on a standard data sample called the ISIC-2016 database and secured the average mAP and IOU values of 0.916., and 0.921 with an accuracy value of 90.08%. The model testing confirmed that our solution is vigorous for melanoma mole detection and cataloging.

关键词： Accuracy Databases Object oriented modeling Surgery Melanoma Wounds Feature extraction Skin Lesions Testing

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Temperature Drift Error Compensation for MEMS Gyroscopes Based on SVR

Temperature Drift Error Compensation for MEMS Gyroscopes Bas...

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Electronic Information engineering and Computer Science (EIECS), 2021 International Conference on

作者： Xin Li Tong Wang Haiquan Lu Keran Zhang Sheng Zhong Hangzai Luo Xi'an Institute of Microelectronics Technology Xi'an China Department of Software Engineering Northwest University of China China

Due to the susceptibility of MEMS devices to temperature variations owing to their micro-mechanical structures, it becomes imperative to perform temperature drift correction to enhance the accuracy of measurement outcomes. Translate to English: When analyzing real experimental data, by contrasting the zero-offset outputs of MEMS at the same temperature and under different temperature variations, it is deduced that the zero-offset output of MEMS is associated with the temperature slope. This study proposes the utilization of input units that encompass temperature variation rate information in lieu of single-valued inputs, and employs the Support Vector Regression (SVR) method to model and forecast the static zero-offset output as a function of temperature variations. Experimental results demonstrate a substantial reduction in the mean squared error (MSE) on the testing set when compared to scenarios where temperature variation rate is not considered, with the MSE decreasing by orders of magnitude.

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A Method of Robot Grasping Based on Reinforcement Learning 3

A Method of Robot Grasping Based on Reinforcement Learning

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2021 3rd International Conference on Robotics, Intelligent Control and Artificial Intelligence, ICRICA 2021

作者： Lu, Liangliang Zhang, Ming He, Dingxin Gu, Qiang Gong, Dongjun Fu, Lei Artificial Intelligence and Automation College Huazhong University of Science and Technology Wuhan China Guyue Home PS-Micro Wuhan China School of Mechanical Engineering Wuhan Vocational College of Software and Engineering Wuhan China

In this paper, we propose a method for robot grasping based on reinforcement learning. A six-degree-of-freedom robot is used to execute the grasping action, and an RGB-D camera is used as a sensing module to collect the information. Different from previous model-based methods, our whole system is data-driven. In order to better grasp a bunch of randomly placed objects, the pushing action is added to spare more space for grasping. The whole process is described as a Markov decision process (MDP). The training is carried out in simulation. Experimental results show that our method can complete the grasping work efficiently, especially in those complicated and difficult scenarios. At the same time, the model can be directly applied in actual grasping tasks which greatly reduce the risk of training in the physical world. © Published under licence by IOP Publishing Ltd.

关键词： Reinforcement learning

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An Output Feedback Game-Theoretic Approach for Defense Against Stealthy GNSS Spoofing Attacks

An Output Feedback Game-Theoretic Approach for Defense Again...

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American Control Conference (ACC)

作者： Surabhi Athalye Filippos Fotiadis Kyriakos G. Vamvoudakis Jérôme Hugues School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Daniel Guggenheim School of Aerospace Engineering Georgia Institute of Technology Atlanta GA USA Carnegie Mellon University/Software Engineering Institute Pittsburgh PA USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

We employ a game-theoretic approach to mitigate stealthy spoofing attacks on an aerial cyber-physical system. The attacker's objective is to drive the vehicle away from its nominal trajectory while misleading the system operator of the adversarial deviation. We characterize this as a dual actuation-deception attack; the former involves corrupting the vehicle's actuating input, and the latter is done by spoofing the measurable output; that is, the acceleration effort and the Global Navigation Satellite System (GNSS) sensor reading, respectively. The attack is designed to be stealthy, implying that its effect on the system's trajectory is indiscernible from that of a naturally occurring disturbance. The defender aims to counter such an attack and steer the aerial vehicle back towards its nominal path. We couple receding horizon control and game-theoretic methods to derive optimal attack and defense policies.

关键词： Accelerometers Global navigation satellite system Cyber-physical systems Trajectory Output feedback

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Research on the Dynamical Behavior of Sand/Steel Composite Structures Under Confined Explosion

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Journal of Pressure Vessel technology 2025年第4期147卷

作者： Zhou, Dezheng Li, Xiaojie Liu, Zhenfeng Yan, Honghao College of Science Heilongjiang University of Science and Technology Harbin150022 China School of Mechanics and Aerospace Engineering State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipmen Dalian University of Technology Dalian116024 China School of Mechanics and Aerospace Engineering Dalian University of Technology Dalian116024 China

Previous research has shown that using sand as an explosive barrier material to design super-large explosion containment vessels is the most ideal. Therefore, it is essential to analyze the dynamic strain variation of the flat cover on the explosion containment vessel under different sand thicknesses by experimental method. First, by observing the history curves of strain collected by the experiments, it is found that the overall dynamic strain in 1000 ms consists of an abruptly rising phase and a gradually decreasing vibration recovery phase. By comparing the strain histories, it is found that when the sand thickness is 450 mm, the peak strains and the quasi-static residual strains of the 8 mm and 6 mm flat cover are weakened dramatically. Therefore, covering the cap with sand can significantly undermine the dynamic strain on the container closure. With the gradual increase of the sand thickness, the peak strain and the quasi-static residual strain of the flat cover show a linear decay law, and the slope of the linear decay is not affected by the thickness of the flat cover. Finally, according to the experiments and numerical analyses, the most effective range of sand/steel thickness ratio is determined to be 50-75, which can be accurately determined according to the actual working conditions. Sand can effectively absorb the transmitted energy of stress waves, and the sand/steel composite anti-explosion structure can effectively resist the impact of confined explosion. Copyright © 2025 by ASME.

关键词： Container closures

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SHEDAD: SNN-ENHANCED DISTRICT HEATING ANOMALY DETECTION FOR URBAN SUBSTATIONS

arXiv

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arXiv 2024年

作者： van Dreven, Jonne Cheddad, Abbas Alawadi, Sadi Ghazi, Ahmad Nauman Al Koussa, Jad Vanhoudt, Dirk Department of Computer Science Blekinge Institute of Technology Karlskrona Sweden Department of Software Engineering Blekinge Institute of Technology Karlskrona Sweden Mol Belgium EnergyVille Genk Belgium

District Heating (DH) systems are essential for energy-efficient urban heating. However, despite the advancements in automated fault detection and diagnosis (FDD), DH still faces challenges in operational faults that impact efficiency. This study introduces the Shared Nearest Neighbor Enhanced District Heating Anomaly Detection (SHEDAD) approach, designed to approximate the DH network topology and allow for local anomaly detection without disclosing sensitive information, such as substation locations. The approach leverages a multi-adaptive k-Nearest Neighbor (k-NN) graph to improve the initial neighborhood creation. Moreover, it introduces a merging technique that reduces noise and eliminates trivial edges. We use the Median Absolute Deviation (MAD) and modified z-scores to flag anomalous substations. The results reveal that SHEDAD outperforms traditional clustering methods, achieving significantly lower intra-cluster variance and distance. Additionally, SHEDAD effectively isolates and identifies two distinct categories of anomalies: supply temperatures and substation performance. We identified 30 anomalous substations and reached a sensitivity of approximately 65% and specificity of approximately 97%. By focusing on this subset of poorperforming substations in the network, SHEDAD enables more targeted and effective maintenance interventions, which can reduce energy usage while optimizing network performance. © 2024, CC BY.

关键词： Nearest neighbor search

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A Logical Pattern Memory Pre-trained Model for Entailment Tree Generation

arXiv

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arXiv 2024年

作者： Yuan, Li Cai, Yi Ren, Haopeng Wang, Jiexin School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China

Generating coherent and credible explanations remains a significant challenge in the field of AI. In recent years, researchers have delved into the utilization of entailment trees to depict explanations, which exhibits a reasoning process of how a hypothesis is deduced from the supporting facts. However, existing models often overlook the importance of generating intermediate conclusions with logical consistency from the given facts, leading to inaccurate conclusions and undermining the overall credibility of entailment trees. To address this limitation, we propose the logical pattern memory pre-trained model (LMPM). LMPM incorporates an external memory structure to learn and store the latent representations of logical patterns, which aids in generating logically consistent conclusions. Furthermore, to mitigate the influence of logically irrelevant domain knowledge in the Wikipedia-based data, we introduce an entity abstraction approach to construct the dataset for pre-training LMPM. The experimental results highlight the effectiveness of our approach in improving the quality of entailment tree generation. By leveraging logical entailment patterns, our model produces more coherent and reasonable conclusions that closely align with the underlying premises. Code and Data are released at https://***/YuanLi95/T5-LMPM. © 2024, CC BY-NC-ND.

关键词： Domain Knowledge

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