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2023 International Conference on computer, Artificial Intelligence, and Control engineering, CAICE 2023

作者： Wang, Jingyu Fu, Chengjie Wang, Tiejun Wang, Yuntao Chen, Liangzhong School of Aeronautics and Astronautics Sichuan University Chengdu China Tianfu Engineering-Oriented Numerical Simulation & Software Innovation Center Sichuan University Chengdu China School of Computer Science Chengdu University of Information Technology Chengdu China College of Computer Science Sichuan University Chengdu China

ISBN: (纸本)9781510665040

In large-scale engineering experiments, such as hydrodynamics and aerodynamics experiments, computer aided software (CAE) is always used to manage a large number of parameters and experimental data to simulate the physical field through finite element analysis. To improve work efficiency, one of the most practical methods at present is to record user operations in the CAE software to script files with domain specific language (DSL), such as Python, and replay those script files to reproduce user operations. In this paper, a common script module ScriptEngine supporting the recording and playback function is introduced, and it can be embedded into any CAE software to automatically complete grid division, flow field calculation, pre-processing and post-processing by running the scripts. Furthermore, a demo program embedding ScriptEngine into an opensource FastCAE was developed and the results show that running the scripts recorded by ScriptEngine could obtain the same results as the user operations and ScriptEngine can be used as a common script module in any CAE software and other desktop software which needs the recording and playback function. © 2023 SPIE.

关键词： Python

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Group Signature with Time-Bound Keys for Secure E-health Record Sharing*

Group Signature with Time-Bound Keys for Secure E-health Rec...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Junze Lu Chunqiang Hu Conghao Ruan Bin Cai Tao Xiang School of Big Data & Software Engineering Chongqing University China College of Computer Science Chongqing University China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With the advent of various mobile IoT devices, a large amount of e-health record (EHR) data has been generated. This data has great potential to improve medical research. However, there are many challenges regarding the sharing of medical data. Firstly, users are more inclined to interact anonymously. Secondly, verifying the validity of certificates in the case of anonymous interactions is challenging. In addition, it is necessary to uncover the identities of the actual interacting parties in the event of malicious behavior. Therefore, we address the above challenges and propose group signatures with time constraints to support anonymous and traceable EHR data sharing. First, we propose a group signature scheme that supports traceability. Second, to address the issue of validating anonymous certificates, we propose group signatures with time constraints that enable dynamic updates to validity. Through this, we can dynamically revoke group members. Lastly, security proofs and efficiency analyses demonstrate that our scheme is both secure and efficient.

关键词： Buildings Solids Security Time factors Internet of Things Bioinformatics

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A Logical Evaluation of Predicting Immune System Strength by using Enhanced Deep Learning Strategy

A Logical Evaluation of Predicting Immune System Strength by...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Malathi, K. Ramkumar, M. Ruby, E. D. Kanmani Santhi, G.B. Korde, Vivek Aljawawdeh, Hamzeh J. Saveetha University Department of Cse Chennai India Veltech Rangarajan Dr Sagunthala R & D Institute of Science & Technology Department of Ece Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of information technology Department of software engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The immune system plays a crucial role in defending the body against diseases, making it vital to develop models that can accurately predict immune system strength. This study presents an Enhanced Deep Learning Strategy (EDLS) designed to predict immune system strength based on immune-related biomarkers such as White Blood Cell (WBC) count and Immunoglobulins. A dataset consisting of 23,654 records with 12 features was collected from clinical sources. The data underwent comprehensive preprocessing, including noise reduction using a Median Filter, feature selection with Random Forest, and data fusion using Kalman Filters. The EDLS model integrates AutoEncoder for dimensionality reduction and Cascaded Long Short-Term Memory (LSTM) for capturing temporal dependencies. The proposed model outperforms nine traditional and advanced machine learning models, including Logistic Regression, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), with an accuracy of 94.72%. Evaluation metrics such as Precision (92.01%), Recall (93.77%), and F1-Score (92.88%) highlight the model's effectiveness in predicting immune system strength. Feature importance analysis reveals that WBC count and Immunoglobulins are the most influential predictors. This study demonstrates the potential of EDLS for enhancing immune strength prediction, which can have significant implications for personalized healthcare and disease prevention. © 2024 IEEE.

关键词： Median filters

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Architectural Co-LOD Generation

arXiv

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arXiv 2024年

作者： Zhang, Runze Pan, Shanshan Lv, Chenlei Gong, Minglun Huang, Hui Shenzhen University China University of Guelph Canada College of Computer Science & Software Engineering Shenzhen University China

Managing the level-of-detail (LOD) in architectural models is crucial yet challenging, particularly for effective representation and visualization of buildings. Traditional approaches often fail to deliver controllable detail alongside semantic consistency, especially when dealing with noisy and inconsistent inputs. We address these limitations with Co-LOD, a new approach specifically designed for effective LOD management in architectural modeling. Co-LOD employs shape co-analysis to standardize geometric structures across multiple buildings, facilitating the progressive and consistent generation of LODs. This method allows for precise detailing in both individual models and model collections, ensuring semantic integrity. Extensive experiments demonstrate that Co-LOD effectively applies accurate LOD across a variety of architectural inputs, consistently delivering superior detail and quality in LOD representations. Copyright © 2024, The Authors. All rights reserved.

关键词： 3D reconstruction

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PP-TIL: Personalized Planning for Autonomous Driving with Instance-based Transfer Imitation Learning

PP-TIL: Personalized Planning for Autonomous Driving with In...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Fangze Lin Ying He Fei Yu College of Computer Science and Software Engineering Shenzhen University P.R. China Carleton University Canada

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Personalized motion planning holds significant importance within urban automated driving, catering to the unique requirements of individual users. Nevertheless, prior endeavors have frequently encountered difficulties in simultaneously addressing two crucial aspects: personalized planning within intricate urban settings and enhancing planning performance through data utilization. The challenge arises from the expensive and limited nature of user data, coupled with the scene state space tending towards infinity. These factors contribute to overfitting and poor generalization problems during model training. Henceforth, we propose an instance-based transfer imitation learning approach. This method facilitates knowledge transfer from extensive expert domain data to the user domain, presenting a resolution to these issues. We initially train a pre-trained model using large-scale expert data. Subsequently, during the fine-tuning phase, we feed the batch data, which comprises expert and user data. Employing the inverse reinforcement learning technique, we extract the style feature distribution from user demonstrations, constructing the regularization term for the approximation of user style. In our experiments, we conducted extensive evaluations of the proposed method. Compared to the baseline methods, our approach mitigates the overfitting issue caused by sparse user data. Furthermore, we discovered that integrating the driving model with a differentiable nonlinear optimizer as a safety protection layer for end-to-end personalized fine-tuning results in superior planning performance. The code will be available at https://***/LinFunster/PP-TIL.

关键词： Training Imitation learning Measurement uncertainty Reinforcement learning Feature extraction Planning Trajectory Protection Knowledge transfer Overfitting

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A Deep Learning Method to Translate Phonocardiogram (PCG) Signals to Electrocardiogram (ECG) Waveforms 13

A Deep Learning Method to Translate Phonocardiogram (PCG) Si...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Chen, Wei Li, Zhi Zhang, Guanglie College of Computer Science and Software Engineering Shenzhen University Shenzhen China City University of Hong Kong Department of Mechanical Engineering Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen China

ISBN: (纸本)9798350315196

In clinical practice, an electrocardiogram (ECG), which analyzes the electrical activity of the human heart, is a common cardiovascular diagnostic test. The ECG data can be stored and diagnosed effectively. However, precise positioning of the ECG electrodes is necessary for a reliable ECG tracing. This has an impact on how ECG monitors are used to check on patients at home. On the other hand, the Phonocardiogram (PCG) is a standard method for quantifying the properties of heart sounds in order to monitor and assess cardiac activity. With the advancement of wearable sensors, it is now simpler to record the heart's sound. We proposed a deep learning approach to translating PCG signals to ECG waveforms. This technique can be utilized to monitor cardiovascular disease in consumer wearable device development. The proposed method is evaluated on a benchmark dataset of normal and anormal cardiac data. Experimental results show that our proposed approach is reasonable and effective. The generated ECG outputs can achieve a high accuracy, and the root mean squared error (RMSE) is 0.053. © 2023 IEEE.

关键词： Electrocardiograms

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Accelerating the Task Activation and Data Communication for Dataflow Computing 22

Accelerating the Task Activation and Data Communication for ...

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Workshop Proceedings of the 51st International Conference on Parallel Processing

作者： Du Zheng Zhao Wenjie Wen Zhiwei Luo Qiuming Department of Computer Science and Technology Tsinghua University China College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781450394451

The hybrid dataflow/von-Neumann [1] architectures may differ in implementations but all follow similar principles: they harness the parallelism and data synchronization inherent to the dataflow model, yet maintain the programmability of the von-Neumann model. In this paper, we raise a new kind of hybrid dataflow/von-Neumann architectures, which contains TAU (Task Activated Unit) and SPM [9] (scratchpad memory) components, by which we can enhance parallel efficiency. We also implement the prototype design, integrated with peripheral devices and verify the whole system on FPGA. Finally, we deploy operating system on the hardware system and profile the performance. The experimental results show that the performance is improved by 3.07%∼10.32% under the random data flow graph, the performance of inter-core communication is improved by 4% and the hardware acceleration effect is achieved.

关键词： scratchpad memory RISC-V Dataflow/von-Neumann architecture parallel computation

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Transforming Diabetes Care: A Review of IoT-Based Mobile Health Systems 10th

Transforming Diabetes Care: A Review of IoT-Based Mobile He...

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10th EAI International Conference on IoT Technologies for Health-Care, HealthyIoT 2023, and 4th EAI International Conference on Wearables in Healthcare, HealthWear 2023

作者： Ashfaq, Fiza Ahad, Abdul Hussain, Mudassar Madeira, Filipe Department of Computer Science University of Management and Technology Sialkot51040 Pakistan School of Software Northwestern Polytechnical University Shaanxi Xian China Istanbul34467 Turkey /Pole of Digital Literacy and Social Inclusion Polytechnic Institute of Santarem Santarem2001-904 Portugal Department of Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman

ISBN: (纸本)9783031719103

Diabetes, a chronic ailment requiring ongoing care and monitoring, has undergone a healthcare shift thanks to the Internet of Things (IoT). Real-time monitoring, individualized treatment plans, and improved patient-provider communication are all features of IoT-based mobile health solutions. This study explores the components, functionalities, benefits, and disadvantages of IoT-based mobile health systems for the management of diabetes. It investigates wearable sensors, smartphone apps, and linked devices like glucose meter and monitors, placing a focus on their precision, dependability, and usability. The study explores data analytics techniques that allow for customized advice and actions. Patients can effectively control their diabetes with the use of individualized treatments and real-time monitoring. The research also looks into the data analytical methods used in IoT-based mobile health systems for the treatment of diabetes. IoT-based mobile health system issues and implications are also covered. These include issues with data security and privacy, platform and device compatibility, standardization of data formats, and ensuring that all people with diabetes have equal access to technology. Healthcare workers, academics, and policymakers may learn more about the potential of IoT-based mobile health systems to revolutionize diabetes treatment through this thorough assessment. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Electronic health record

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Speed Control of DC Motor based on H∞ Mixed-sensitivity Algorithm 3

Speed Control of DC Motor based on H∞ Mixed-sensitivity Alg...

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3rd International Conference on Electrical engineering and Mechatronics Technology, ICEEMT 2023

作者： Lu, Zhen-Wei Wang, Xin-Zui Cao, Fu-Cheng Yang, Yan-Li Jihua Medical Institute Jihua Laboratory Foshan528251 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Chinese Academy of Sciences Suzhou Institute of Biomedical Engineering and Technology Suzhou215163 China

ISBN: (纸本)9798350303698

In order to restrain the uncertainty of system parameters and the influence of external disturbances on the speed of DC motor, this paper adopts the H+ mixed-sensitivity algorithm to design a speed controller based on the model of the controlled motor. First, the Pseudo Random Binary Sequence (PRBS) is input to the motor and the corresponding speed output sequence is measured. The Least Squares Method (LSM) is used to conduct system identification;Then, design a weighting function based on the established system performance indicators, and use MATLAB to synthesize the H+ controller;Finally, this article adopts the direct form conversion method to implement the controller, and successfully controls the speed of the DC motor through the STM32F407ZGT6 microcontroller. The experimental results show that this algorithm can achieve good speed tracking performance and anti-disturbances ability for DC motors. © 2023 IEEE.

关键词： DC motors

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A Novel Data Mining Framework for Discovering Association Rules in Multivariate Time Series

A Novel Data Mining Framework for Discovering Association Ru...

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Advanced Algorithms and Control engineering (ICAACE), International Conference on

作者： Shengwei Wang Jianhua Lyu Baili Zhang School of Software Southeast University Nanjing China College of Computer Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

As a significant field within data mining, association rule mining plays a pivotal role in unveiling relationships between items within a dataset. Existing approaches in multivariate time series (MTS) association rule mining primarily emphasize the generation of association rules by combining features from different variables, but neglect to adequately consider the temporal relationship between these features and ignore the trend information of variables, resulting in an incomplete capture of the essential characteristics inherent in MTS data. In this paper, we introduce a novel framework for association rule mining in multivariate time series. First, we discretize the multivariate time series into the symbolic sequence using trend-based symbolic aggregate approximation(TSAX). Then, a rule mining method is introduced to discover association rules that consider the temporal relationship among symbols of different variables. The results of experiments demonstrate the discovery of significant rules within the “Weather&Traffic” dataset which gives the traffic conditions in France depending on the weather. Further, we also provide the outcomes of the sensitivity analysis conducted on our method, illustrating that significant rules can be mined by adjusting the parameters of TSAX to different settings. The experimental results highlight the utility of our proposed framework in discovering temporal association rules within multivariate time series datasets.

关键词： Control engineering Sensitivity analysis Aggregates Time series analysis Symbols Market research Approximation algorithms

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