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Extracting a justification for OWL ontologies by critical axioms

Frontiers of Computer science 2020年第4期14卷 55-64页

作者： Yuxin YE Xianji CUI Dantong OUYANG College of Computer Science and Technology Jilin UniversityChangchun130012China Key Lab of Symbolic Computation and Knowledge Engineering(Jilin University) Ministry of EducationChangchun130012China College of Information and Communication Engineering Dalian Minzu UniversityDalian116600China

Extracting justifications for web ontology language(OWL)ontologies is an important mission in ontology *** this paper,we focus on black-box techniques which are based on ontology *** creating a recursive expansion procedure,all elements which are called critical axioms in the justification are explored one by *** this detection procedure,an axiom selection function is used to avoid testing irrelevant *** addition,an incremental reasoning procedure has been proposed in order to substitute series of standard reasoning tests *** is implemented by employing a pseudo model to detect“obvious”satisfiability *** experimental results show that our proposed strategy for extracting justifications for OWL ontologies by adopting incremental expansion is superior to traditional Black-box methods in terms of efficiency and performance.

关键词： description logics automated reasoning ontology engineering justification

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A 3D Wideband Non-Stationary GBSM for RIS-Assisted Communications in Outdoor Scenarios 4

A 3D Wideband Non-Stationary GBSM for RIS-Assisted Communica...

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4th IEEE International Conference on Electronics and Communication engineering, ICECE 2021

作者： Yang, Yinghui Zhang, Wensheng Sun, Jian Wang, Cheng-Xiang Shandong Provincial Key Lab of Wireless Communication Technologies School of Information Science and Engineering Shandong University Qingdao China Shandong Provincial Key Lab of Wireless Communication Technologies Shandong University Qingdao China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

ISBN: (纸本)9781728194226

In this paper, we propose a novel three-dimensional (3D) wideband non-stationary geometry-based stochastic model (GBSM) for reconfigurable intelligent surface (RIS)-assisted communications in outdoor scenarios. To mimic a more realistic outdoor environment, we introduce a twin-cluster model and consider the evolutions of clusters in time and antenna domain. The proposed model is a combination of transmitter (Tx)-RISreceiver (Rx) reflection components and Tx-Rx components. Based on the proposed model, some key statistical properties like power delay profile (PDP), achievable rate, temporal autocorrelation functions (ACFs) are derived and analyzed. The simulation results demonstrate the correctness of the proposed model and the non-stationary property. Although imperfections in phase knowledge acquirement are existed in real world, the use of the RIS still makes appreciable improvements in the communication system performance according to the simulations. ©2021 IEEE

关键词： Antennas

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EMAHA-DB1: A New Upper Limb sEMG Dataset for Classification of Activities of Daily Living

arXiv

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arXiv 2023年

作者： Karnam, Naveen Kumar Turlapaty, Anish Chand Dubey, Shiv Ram Gokaraju, Balakrishna The Biosignal Analysis Lab The Indian Institute of Information Technology A.P. Sri City India The Computer Vision and Biometrics Laboratory Indian Institute of Information Technology U.P. Allahabad Prayagraj211015 India Department of Computational Data Science An Engineering North Carolina A and T State University GreensboroNC United States

In this paper, we present electromyography analysis of human activity - database 1 (EMAHA-DB1), a novel dataset of multi-channel surface electromyography (sEMG) signals to evaluate the activities of daily living (ADL). The dataset is acquired from 25 able-bodied subjects while performing 22 activities categorised according to functional arm activity behavioral system (FAABOS) (3 - full hand gestures, 6 - open/close office draw, 8 - grasping and holding of small office objects, 2 - flexion and extension of finger movements, 2 - writing and 1 - rest). The sEMG data is measured by a set of five Noraxon Ultium wireless sEMG sensors with Ag/Agcl electrodes placed on a human hand. The dataset is analyzed for hand activity recognition classification performance. The classification is performed using four state-of-the-art machine learning classifiers, including Random Forest (RF), Fine K-Nearest Neighbour (KNN), Ensemble KNN (sKNN) and Support Vector Machine (SVM) with seven combinations of time domain and frequency domain feature sets. The state-of-the-art classification accuracy on five FAABOS categories is 83.21% by using the SVM classifier with the third order polynomial kernel using energy feature and auto regressive feature set ensemble. The classification accuracy on 22 class hand activities is 75.39% by the same SVM classifier with the log moments in frequency domain (LMF) feature, modified LMF, time domain statistical (TDS) feature, spectral band powers (SBP), channel cross correlation and local binary patterns (LBP) set ensemble. The analysis depicts the technical challenges addressed by the dataset. The developed dataset can be used as a benchmark for various classification methods as well as for sEMG signal analysis corresponding to ADL and for the development of prosthetics and other wearable robotics. Copyright © 2023, The Authors. All rights reserved.

关键词： Local binary pattern

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EventTrojan: Manipulating Non-Intrusive Speech Quality Assessment via Imperceptible Events

arXiv

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arXiv 2023年

作者： Ren, Ying Shen, Kailai Ye, Zhe Yan, Diqun Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Shandong China Juphoon System Software Co. Ltd Ningbo China

Non-Intrusive speech quality assessment (NISQA) has gained significant attention for predicting speech’s mean opinion score (MOS) without requiring the reference speech. Researchers have gradually started to apply NISQA to various practical scenarios. However, little attention has been paid to the security of NISQA models. Backdoor attacks represent the most serious threat to deep neural networks (DNNs) due to the fact that backdoors possess a very high attack success rate once embedded. However, existing backdoor attacks assume that the attacker actively feeds samples containing triggers into the model during the inference phase. This is not adapted to the specific scenario of NISQA. And current backdoor attacks on regression tasks lack an objective metric to measure the attack performance. To address these issues, we propose a novel backdoor triggering approach (EventTrojan) that utilizes an event during the usage of the NISQA model as a trigger. Moreover, we innovatively provide an objective metric for backdoor attacks on regression tasks. Extensive experiments on four benchmark datasets demonstrate the effectiveness of the EventTrojan attack. Besides, it also has good resistance to several defense methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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MVI-Wise GAN: Synthetic MRI to Improve Microvascular Invasion Prediction in Hepatocellular Carcinoma

MVI-Wise GAN: Synthetic MRI to Improve Microvascular Invasio...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Jing Liu Yulin Yang Yang Ai Titinunt Kitrungrotsakul Fang Wang Lanfen Lin Ruofeng Tong Yen-Wei Chen Jingsong Li Zhejiang Lab Research Center for Healthcare Data Science Hangzhou China College of Information Science and Engineering Ritsumeikan University Shiga Japan Department of Radiology Sir Run Run Shaw Hospital Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

MRI is crucial for the diagnosis of HCC patients, especially when combined with CT images for MVI prediction, richer complementary information can be learned. Many studies have shown that whether hepatocellular carcinoma is accompanied by vascular invasion can be evidenced by imaging examinations such as CT or MR, so they can be used as a multimodal joint prediction to improve the prediction accuracy of MVI. However, it is high-risk, time-consuming and expensive in current clinical diagnosis due to the use of gadolinium-based contrast agent (CA) injection. If MRI could be synthesized without CA injection, there is no doubt that it would greatly optimize the diagnosis. Based on this, this paper proposes a high-quality image synthesis network, MVI-Wise GAN, that can be used to improve the prediction of microvascular invasion in HCC. It starts from the underlying imaging perspective, introduces K-space and feature-level constraints, and combines three related networks (an attention-aware generator, a convolutional neural network-based discriminator and a region-based convolutional neural network detector) Together, precise tumor region detection by synthetic tumor-specific MRI. Accurate MRI synthesis is achieved through backpropagation, the feature representation and context learning of HCC MVI are enhanced, and the performance of loss convergence is improved through residual learning. The model was tested on a dataset of 256 subjects from Run Run Shaw Hospital of Zhejiang University. Experimental results and quantitative evaluation show that MVI-Wise GAN achieves high-quality MRI synthesis with a tumor detection accuracy of 92.3%, which is helpful for the clinical diagnosis of liver tumor MVI.

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Vision-Guided Attention-Enhanced Network for Predicting Microvascular Invasion in Hepatocellular Carcinoma

Vision-Guided Attention-Enhanced Network for Predicting Micr...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Jing Liu Jing Ma Yang Ai Jiayue Zhao Fang Wang Lanfen Lin Ruofeng Tong Yen-Wei Chen Jingsong Li Zhejiang Lab Research Center for Healthcare Data Science Hangzhou China College of Information Science and Engineering Ritsumeikan University Shiga Japan Department of Radiology Sir Run Run Shaw Hospital Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

As the most common malignant tumor worldwide, hepatocellular carcinoma (HCC) has a high rate of death and recurrence, and microvascular invasion (MVI) is considered to be an independent risk factor affecting its early recurrence and poor survival rate. Accurate preoperative prediction of MVI is of great significance for the formulation of individualized treatment plans and long-term prognosis assessment for HCC patients. However, as the mechanism of MVI is still unclear, existing studies use deep learning methods to directly train CT or MR images, with limited predictive performance and lack of explanation. We map the pathological "7-point" baseline sampling method used to confirm the diagnosis of MVI onto MR images, propose a vision-guided attention-enhanced network to improve the prediction performance of MVI, and validate the prediction on the corresponding pathological images reliability of the results. Specifically, we design a learnable online class activation map (CAM) to guide the network to focus on high-incidence regions of MVI guided by an extended tumor mask. Further, an attention-enhanced module is proposed to force the network to learn image regions that can explain the MVI results. The generated attention maps capture long-distance dependencies and can be used as spatial priors for MVI to promote the learning of vision-guided module. The experimental results on the constructed multi-center dataset show that the proposed algorithm achieves the state-of-the-art compared to other models.

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Hexagonally ordered microbowl arrays decorated with ultrathin CuInS_(2) nanosheets for enhanced photoelectrochemical performance

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Journal of Energy Chemistry 2020年第12期29卷 134-142页

作者： Ming Li Le Chen Yanjie Su Huan Yin Kexiang Hu State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyKey Laboratory of Optoelectronic Information Processing of Guangxi Colleges and Uniy/ersitiesGuilin 541004GuangxiChina Key Laboratory for Thin Film and Microfabrication of the Ministry of Education Department of Micro/Nano ElectronicsSchool of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China National and Local Joint Engineering Laboratory of Internet Technology on Mine School of Information and Control EngineeringChina University of Mining and TechnologyXuzhou 221008JiangsuChina School of Physics and Telecommunication Engineering Guangxi Colleges and Universities Key Lab of Complex System Optimization and Big Data ProcessingYulin Normal UniversityYulin 537400GuangxiChina

This paper demonstrates the design and fabrication of three-dimensional(3 D) hexagonally ordered microbowl arrays(MBAs) decorated with Cu In S2 nanosheets for enhanced photoelectrochemical(PEC) performance. The 3 D MBAs are fabricated by a micro-fabrication technique. The ultrathin CuInS2 nanosheets are grown on the 3 D electrodes by solvothermal transformation of Cu film. The photocurrent density of 3 D photocathode(CuInS2@MBAs) is about two times higher than that of the planar counterpart(CuInS2@Planar). The improved PEC performance can be ascribed to the elevated light trapping ability and the increased surface area for loading photocatalysts. In addition, CdS quantum dots as cocatalysts are modified onto the Cu In S2 nanosheets to further enhance the PEC activity because the formed p-n heterojunction can accelerate the separation of photogenerated carriers. As a result, the 3 D photocathode of CuInS2/CdS@MBAs shows an optimal incident photon to current efficiency of 10% at the wavelength of400 nm. It is believed that this work can be generalized to design other hierarchical 3 D photoelectrodes for improved solar water splitting.

关键词： Microbowl array CuInS_(2) Nanosheet Photocathode Photoelectrochemical performance

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Generalization of CNNs on Relational Reasoning with Bar Charts

arXiv

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arXiv 2025年

作者： Cui, Zhenxing Chen, Lu Wang, Yunhai Haehn, Daniel Wang, Yong Pfister, Hanspeter School of Computer Science and Technology Shandong University China State Key Lab of CAD&CG Zhejiang University China School of Information Renmin University of China China College of Science and Mathematics University of Massachusetts Boston United States College of Computing and Data Science Nanyang Technological University Singapore School of Engineering and Applied Sciences Harvard University United States

This paper presents a systematic study of the generalization of convolutional neural networks (CNNs) and humans on relational reasoning tasks with bar charts. We first revisit previous experiments on graphical perception and update the benchmark performance of CNNs. We then test the generalization performance of CNNs on a classic relational reasoning task: estimating bar length ratios in a bar chart, by progressively perturbing the standard visualizations. We further conduct a user study to compare the performance of CNNs and humans. Our results show that CNNs outperform humans only when the training and test data have the same visual encodings. Otherwise, they may perform worse. We also find that CNNs are sensitive to perturbations in various visual encodings, regardless of their relevance to the target bars. Yet, humans are mainly influenced by bar lengths. Our study suggests that robust relational reasoning with visualizations is challenging for CNNs. Improving CNNs’ generalization performance may require training them to better recognize task-related visual properties. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Design of an Integrated A/D Converter for Dissolved Oxygen Sensor Interface in Aquaculture

Design of an Integrated A/D Converter for Dissolved Oxygen S...

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2022 International Conference on Mechanisms and Robotics, ICMAR 2022

作者： Zuo, Qu Tian, YunChen Ma, GuoQiang Li, ZhenZhong Sun, ZhenLong School of Computer and Information Engineering Tianjin Agricultural University Tianjin300384 China Tianjin Key Lab of Aqua-ecology and aquaculture Tianjin300384 China Shandong Dongrun Instrument Science and Technology Co. Ltd Shandong264003 China Nantong Longyang Aquaculture Co. Ltd Jiangsu226600 China

ISBN: (纸本)9781510657328

China is a big country in aquaculture, but not a strong country, and the need for transformation and upgrading is urgent. The rapid development of intensive and large-scale aquaculture has put forward new requirements for online monitoring of water quality and automatic control of equipment. The current water quality monitoring IoT system has multiple brands of dissolved oxygen sensor equipment is not compatible, the application cost of replacing the dissolved oxygen sensor is high. In this research, an integrated A/D converter device for dissolved oxygen sensor interface is designed, without changing the premise of water quality monitoring IOT system architecture, to achieve different brands of dissolved oxygen sensor "plug and play". This research analyzes the mainstream brand dissolved oxygen sensors and designs the system architecture of the integrated A/D converter device. The integrated A/D converter device takes STM32F103C8T6 as the control core, is composed of power module, processing chip module, data storage module, signal acquisition module and data transmission module, and introduces the function and structure of each module in detail. Then a prototype was built, and the testing of the prototype was completed by building a test platform. The test result shows that the prototype can operate normally. Finally, areas for improvement and next works for the article are given. © 2022 SPIE.

关键词： Data acquisition

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Nonlinear Control Technique for Proton Exchange Membrane Fuel Cells: An In-Depth Assessment under Diverse Operational Conditions

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IFAC-PapersOnLine 2024年第13期58卷 769-773页

作者： A.O. Elgharib M. Benzaouia S. Benzaouia A. Rabhi M.F. Moussa Basic and Applied Science Department Arab Academy for Science Technology and Maritime Transport (AASTMT) Giza Egypt National School of Applied Sciences Mohamed First University Renewable Energy Embedded System and Data Processing Laboratory Morocco Oujda Capgemini Engineering Direction Research & Innovation F -31700 Blagnac France University of Picardie Jules Verne 3 rue Saint Leu Laboratory of Modelization Information and Systems 80039 Amiens Cedex 1 France

In the pursuit of enhancing and optimizing the efficiency of Proton Exchange Membrane Fuel Cell (PEMFC) systems, the utilization of linear controllers for DC/DC converters is a prevalent practice. However, the use of these classical controllers introduces the issue of slow response time, under/overshooting, and highly oscillatory dynamics during system responses. In light of these drawbacks, this paper presents the design and assessment of a non-linear approach based on a Sliding Mode Control with Exponential Reaching Law (SMC-ERL) as a viable solution. The stability of the closed-loop control is ensured through the Lyapunov approach. The efficacy of the proposed control methodology has been assessed via comparative analysis within the Matlab/Simulink environment, encompassing two distinct scenarios, response to dynamic variations in current references and load fluctuations. The obtained results prove that the designed SMC-ERL achieves the fastest convergence with a reduction of steady-state error compared to the PI controller allowing for robust and precise control.

关键词： Proton Exchange Membrane Fuel Cell (PEMFC) DC/DC converter Control PID SMC Lyapunov analysis

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