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A Real-time Cutting Model Based on Finite Element and Order Reduction

Computer systems science & engineering 2022年第10期43卷 1-15页

作者： Xiaorui Zhang Wenzheng Zhang Wei Sun Hailun Wu Aiguo Song Sunil Kumar Jha Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Bioelectronics Jiangsu Key Lab of Remote Measurement and ControlSchool of Instrument Science and EngineeringSoutheast UniversityNanjing210096China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

Telemedicine plays an important role in Corona Virus Disease 2019(COVID-19).The virtual surgery simulation system,as a key component in telemedicine,requires to compute in ***,this paper proposes a realtime cutting model based on finite element and order reduction method,which improves the computational speed and ensure the real-time *** proposed model uses the finite element model to construct a deformation model of the virtual ***,a model order reduction method combining proper orthogonal decomposition and Galerkin projection is employed to reduce the amount of deformation *** addition,the cutting path is formed according to the collision intersection position of the surgical instrument and the lesion area of the virtual ***,the Bezier curve is adopted to draw the incision outline after the virtual lung has been ***,the simulation system is set up on the PHANTOM OMNI force haptic feedback device to realize the cutting simulation of the virtual *** results show that the proposed model can enhance the real-time performance of telemedicine,reduce the complexity of the cutting simulation and make the incision smoother and more natural.

关键词： Virtual surgery cutting model finite element model model order reduction Bezier curve

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All optical artificial synapses based on long-afterglow material for optical neural network

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Nano Research 2023年第7期16卷 10004-10010页

作者： Wenjie Lu Qizhen Chen Huaan Zeng Hui Wang Lujian Liu Tailiang Guo Huipeng Chen Rui Wang Institute of Optoelectronic Display National&Local United Engineering Lab of Flat Panel Display TechnologyFuzhou UniversityFuzhou 350002China Department of Neurosurgery Fujian Medical University Union HospitalFuzhou 350001China Fujian Science&Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou 350100China

Artificial neural network with broad application prospect has attracted particular attention due to the promise of solving the memory wall *** neural devices that mix light and electricity provide more degrees of freedom for the design of artificial neural network,but they still do not get rid of the shackles that the response signal needs circuit to *** exploration of all-optical neural devices(optical signal input and output)is expected to solve this ***,an all-optical synaptic device simply based on a long-afterglow material is *** optical properties of the all-optical synaptic device are similar to the responses in biological *** image displays and memory functions can be achieved by combining alloptical synaptic arrays with synaptic memory ***,the optical summation of all-optical synaptic array pixels can be completed by combining the focusing characteristics of convex lens,which realizes the photon transmission after preprocessing multiple input ***,the simple single-layer structure of all-optical synapses with polydimethylsiloxane(PDMS)as the carrier has high plasticity and is expected to achieve large-scale *** work enriches the diversity of artificial synapses and shows the huge development potential of photoelectric artificial neural networks.

关键词： synaptic plasticity all optical optical computation optical neural network

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Spatial Pattern of Cotton Yield Variability and Its Response to Climate Change in Cotton Belt of Pakistan

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Chinese Geographical science 2023年第2期33卷 351-362页

作者： YU Shan DU Wala ZHANG Xiang HONG Ying LIU Yang HONG Mei CHEN Siyu College of Geographic Science Inner Mongolia Normal UniversityHohhot 010022China Inner Mongolia Key Laboratory of Remote Sensing and Geographic Information Systems Hohhot 010022China Institute of Grassland Research Chinese Academy of Agricultural SciencesHohhot 010000China College of Science Inner Mongolia University of TechnologyHohhot 010051China

Cotton is a revenue source for cotton-producing countries;as the second-largest crop in Pakistan,it significantly contributes to its *** the past few decades,cotton productivity has become unstable in Pakistan,and climate change is one of the main factors that impact cotton *** to climate change,it becomes very important to understand the change trend and its impact on cotton yield at the regional ***,we investigate the relationship of standardized cotton yield variability with the variability of climate factors using a 15-yr moving *** piecewise regression was fitted to obtain the trend-shifting point of climate *** results show that precipitation has experienced an overall decreasing trend of–0.64 mm/yr during the study period,with opposing trends of–1.39 mm/yr and 1.52 mm/yr before and after the trend-shifting point,*** found that cotton yield variability increased at a rate of 0.17%/yr,and this trend was highly correlated with the variability of climate *** multiple regression analysis explains that climate variability is a dominant factor and controlled 81%of the cotton production in the study area from 1990 to 2019,while it controlled 73%of the production from 1990 to 2002 and 84%from 2002 to *** findings reveal that climate factors affact the distinct spatial pattern of changes in cotton yield variability at the tehsil level.

关键词： cotton crop yield variability climate impact on cotton yield regression analysis 15-yr moving window Pakistan

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Pretrained Model with CNN for Lung Cancer Identification and Classification 7

Pretrained Model with CNN for Lung Cancer Identification and...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Kanwal, Shaista Ali, Sajid Alahmadi, Tahani Jaser Assam, Muhammad Ansari, Ghulam Jillani Bhatti, Uzair Aslam University of Education Department of Information Sciences Lahore Pakistan College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Department of Information Systems P.O. Box 84428 Riyadh Saudi Arabia University of Science and Technology Department of Software Engineering KPK Bannu Pakistan School of Information and Communication Engineering Hainan University China

ISBN: (纸本)9798350350890

Lung cancer impacts both genders, and its early detection is key to lowering death rates. Current deep learning techniques for automated identification and classification of lung carcinoma face issues with interpretability, data variability handling, and broader applicability. In our research, we propose a novel method that merges a convolutional neural network (CNN) with a streamlined RESNET18 framework. This approach is designed for lung cancer detection and categorization from CT scans, involving image preprocessing like resizing and rescaling. It effectively differentiates between healthy and cancerous tissues, further classifying them as either Normal, Benign, or Malignant, including variants such as adenocarcinoma and squamous cell carcinoma. Our CNN+RESNET18 model boasts an impressive 99% accuracy, supporting radiologists in the intricate task of image analysis, reducing risks of incorrect or delayed diagnosis, and fostering prompt treatment for improved patient outcomes. © 2024 IEEE.

关键词： Lung cancer

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Design and Control of a Novel 5-DoF Tilt-Rotor Quadcopter

Design and Control of a Novel 5-DoF Tilt-Rotor Quadcopter

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2024 China Automation Congress, CAC 2024

作者： Lin, Zhenwei Liu, Wei Xu, Dabo Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou China Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350368604

This paper presents a novel 5-DoF (degrees of freedom) tilt-rotor quadcopter. Compared to traditional quadcopters, the vehicle offers more DoF by adding two servo-driven mechanisms. The extra DoF allow it to move at a constant pitch angle, thus adapting it to more scenarios, such as slope landings. In addition, the proposed tilt-rotor quadcopter can generate greater torque around the z-axis of the body coordinate system. This gives it an advantage in some scenarios, such as carrying a manipulator to turn valves in the air. In this paper, we first design and model the proposed tilt-rotor quadcopter. Then, we design the controller and control allocation algorithm for the proposed tilt-rotor quadcopter. The control allocation algorithm has clear analytical solutions, which benefits its implementation and deployment. Finally, the structure and control strategy of the tilt-rotor quadcopter are validated by an experiment. © 2024 IEEE.

关键词： Aircraft control

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Approximate Ontology Reasoning for Domain-Specific Knowledge Graph based on Deep Learning 7

Approximate Ontology Reasoning for Domain-Specific Knowledge...

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7th International Conference on Big Data and information Analytics, BigDIA 2021

作者： Zhu, Xixi Liu, Bin Ding, Zhaoyun Zhu, Cheng Yao, Li National University of Defense Technology Science and Technology on Information Systems and Engineering Laboratory Changsha China

ISBN: (纸本)9781665424660

Ontology reasoning has great potential in application of domain-specific knowledge graph. However, the traditional ontology reasoner is difficult to perform rapidly reasoning on domain-specific knowledge graph as its complex logical expression and large-scale assertion set. On the other hand, the knowledge graph reasoning method based on presentation learning has better scalability, but it is hard to incorporate expert knowledge in the schema-level information effectively, so it can't implement complex reasoning. The above restricts the application of reasoning in domain-specific knowledge graph. In order to solve the problem, researchers try to combine ontology reasoning with deep learning to achieve approximate ontology reasoning. However, the state-of-The-Art method has shortcomings of model design and evaluation which make it lose generalization ability. This paper proposes a novel method to transform ontology reasoning into a graph-To-graph relational mapping learning model, and realizes approximate ontology reasoning based on deep learning. Besides, this paper also defines new knowledge graph reasoning evaluation criterion, and synthesizes new test data. On this basis, relevant comparative experiments are carried out. The results show that the proposed method achieves good performance on LUBM synthetic dataset, even though the data mode that not appear in the training can also be inferred effectively, which can make the model applicable to a wider range of application scenarios. © 2021 IEEE.

关键词： Knowledge graph

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Interface Modification of TiO_(2)Electron Transport Layer with PbCl_(2)for Perovskiote Solar Cells with Carbon Electrode

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Tsinghua science and technology 2022年第4期27卷 741-750页

作者： Abolfazl Amraeinia Yuhua Zuo Jun Zheng Zhi Liu Guangze Zhang Liping Luo Buwen Cheng Xiaoping Zou Chunbo Li Key Laboratory on Integrated Optoelectronics Institute of SemiconductorChinese Academy of SciencesBeijing 100083China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Key Laboratory for SensorMOE Key Laboratory for Modern Measurement and Control TechnologyBeijing Information Science and Technology UniversityBeijing 100101China School of Science Minzu University of ChinaBeijing 100081China

Perovskite Solar Cells(PSCs)have attracted considerable attention because of their unique features and high ***,the stability of perovskite solar cells remains to be *** this study,we modified the TiO_(2)Electron Transport Layer(ETL)interface with PbCl_(2).The efficiency of the perovskite solar cells with carbon electrodes increased from 11.28%to 13.34%,and their stability obviously *** addition of PbCl_(2)had no effect on the morphology,crystal structure,and absorption property of the perovskite absorber ***,it affected the band energy level alignment of the solar cells and accelerated the electron extraction and transfer at the interface between the perovskite layer and the ETL,thus enhancing the overall photovoltaic *** interfacial modification of ETL with PbCl_(2)is a promising way for the potential commercialization of low-cost carbon electrode-based perovskite solar cells.

关键词： Perovskite Solar Cell(PSC) Electron Transport Layer(ETL) electron and hole recombination modification stability

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Fairness Testing of Machine Learning Models using Combinatorial Testing in Latent Space 18

Fairness Testing of Machine Learning Models using Combinator...

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18th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Dahal, Arjun Shree, Sunny Lei, Yu Kacker, Raghu N. Richard Kuhn, D. The University of Texas at Arlington Department of Computer Science and Engineering Arlington United States National Institute of Standards and Technology Information Technology Laboratory Gaithersburg United States

ISBN: (纸本)9798331534677

Decision-making by Machine Learning (ML) models can exhibit biased behavior, resulting in unfair outcomes. Testing ML models for such biases is essential to ensure unbiased decision-making. In this paper, we propose a combinatorial testing-based approach in the latent space of a generative model to generate instances that assess the fairness of black-box ML models. Our approach involves a two-step process: generating t-way test cases in the latent space of a Variational AutoEncoder and performing fairness testing using the instances reconstructed from these test cases. We experimentally evaluated our approach against an approach that generates t-way instances in the input space for fairness testing. The results indicate that the latent-space approach produces more natural test cases while detecting the first fairness violation faster and achieving a higher ratio of discriminatory instances to the total number of generated instances. © 2025 IEEE.

关键词： Variational techniques

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Terrain Detection for Lower Limb Prostheses Using Depth Camera Assistance 17

Terrain Detection for Lower Limb Prostheses Using Depth Came...

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17th International Convention on Rehabilitation engineering and Assistive technology, i-CREATe 2024

作者： Liao, WenXiang Li, Linrong Yu, Hongliu Institute of Rehabilitation Engineering and Technology University of Shanghai for Science and Technology Shanghai200093 China Shanghai Engineering Research Center of Assistive Devices Shanghai200093 China Key Laboratory of Neural-Functional Information and Rehabilitation Engineering The Ministry of Civil Afairs Shanghai200093 China

ISBN: (纸本)9798350355154

This study proposes a novel method utilizing depth cameras to assist lower limb prosthetic systems in terrain recognition and gait prediction. The algorithm, based on geometric methods, accurately classifies terrain types and calculates terrain parameters such as step height, width, and slope. The experimental validation demonstrates that this algorithm possesses strong classification capability and maintains terrain parameter calculation errors at a low level, thereby achieving accurate terrain reconstruction. Additionally, the present study introduces a camera-based method for stride length calculation, which under certain conditions can compute precise stride lengths, thus contributing to enhancing the stability of prosthetic limb control. This study marks a promising advancement in the use of visual sensors for prosthetic devices, paving the way for enhanced user comfort and stability during walking activities. © 2024 IEEE.

关键词： Artificial limbs

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Optimal Sensing Time Design in ISAC-Enabled Vehicular Networks

Optimal Sensing Time Design in ISAC-Enabled Vehicular Networ...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Bai, Zhiyue Hou, Fen Shan, Hangguan University of Macau Faculty of Science and Technology State Key Laboratory of Iotsc and Department of Ece China Zhejiang University Department of Information Science and Electronic Engineering Hangzhou China

ISBN: (纸本)9798350345384

Integrated sensing and communication (ISAC) technology is critical to the realization of Vehicle-to-Everything (V2X) intelligent interactivity. However, there is limited research conducted regarding the optimal time design within a specific frame structure. To this end, we propose a time optimization scheme that maximizes the utility of Age of information (AoI) to improve the timeliness and effectiveness of the system, under the premise of ensuring the completion of bit information intra-sharing of every vehicle pair with mutual information as metric and meeting the quality requirement for sensing. Due to the non-convex nature of the formulated problem, we employ some transformations to convert it into an equivalent convex problem, enabling us to obtain the optimal solution. Simulation results demonstrate the superiority of our proposed method and optimal design compared with an existing method. In specific, our proposed method can significantly improve the AoI-based utility and provide a faster update of the sensing information by highly reducing AoI. With the sensing SINR requirement of 200, our proposed method can improve the AoI-based utility by 312% while reducing AoI by 48%, respectively, compared to the counterpart. © 2023 IEEE.

关键词： Vehicle to Everything

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