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检索条件"任意字段=3rd IEEE Intelligent Data Acquisition and Advanced Computing Systems"

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The use of simulation for teaching therapy management: An observational descriptive study on 2nd and 3rd year students of the nursing degree course of Reggio Emilia 10th

The use of simulation for teaching therapy management: An ob...

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10th International Conference in Methodologies and intelligent systems for Technology Enhanced Learning, MIS4TEL 2020

作者： Daniela, Mecugni Casadei, Turroni Elena Lucia, Doro Lorenza, Franceschini Simona, Lusetti Cinzia, Gradellini Giovanna, Amaducci Nursing Degree Course University of Modena and Reggio Emilia Reggio Emilia Italy Department of Surgery Medicine Dentistry and Morphological Sciences University of Modena and Reggio Emilia Reggio Emilia Italy

ISBN: (纸本)9783030522865

The patient safety is considered a strategic and ethical objective of health, also one of the factors that determine the care quality. To ensure the patient safety, the prevention and the reduction of medication errors is a crucial aspect. Therapy errors are identified as the most frequent committed in the healthcare context. Nurses plays an extremely important role in the patient safety, as the professional responsible for the correct application of the diagnostic/therapeutic prescriptions. Nursing students must have the opportunity to experiment the management of the therapy to acquire skills that can prevent the related risks. This study describe the perception of nursing students, regarding the acquisition of skills, in the therapy management, through a didactic module in where they can try the simulation using a therapy software. The study involved 503 students attending the second and the third year of the Nursing Course, considering two different cohorts. The data collection tool was used is a questionnaire with 10 closed-ended questions, using a Likert scale, and one open question. The students has perceived as an effective training, considering the achievement of the didactic objectives. The simulation it allowed the students to consolidate skills and/or to acquire new and more complex ones, through the experience and the learning from errors. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Students

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Deep Transfer Learning for Physical Layer Security in Wireless Communication systems 3

Deep Transfer Learning for Physical Layer Security in Wirele...

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3rd EEE International Conference on Trust, Privacy and Security in intelligent systems and Applications (TPS-ISA)

作者： Rawat, Danda B. Howard Univ Dept Elect Engn & Comp Sci DoD Ctr Excellence Artificial Intelligence & Mach Washington DC 20059 USA

ISBN: (纸本)9781665416238

Machine learning has been used in wireless networking for wireless RF allocation, power allocations, predicting traffic load and inferencing/estimating channels. However, machine learning in wireless networking faces several challenges because of the big or small data, unlabeled data, system complexity, long training process, resource constrained (energy, storage, computing) wireless devices, and constantly changing user mobility, dynamic network topology, susceptibility of wireless jammers and eavesdroppers, and operating wireless environment. In order to address these challenges, transfer learning is regarded as one of the viable solutions as it leverages the knowledge from similar past tasks for learning of new problems. In this paper, we investigate deep transfer learning for physical layer security in wireless networks with large-scale multiple-input multiple output (MIMO) system where previously trained model is transferred to a new wireless environment to predict secure waveform vectors and power levels for users who coexist with eavesdroppers (who overhear communications passively for resulting in lower secrecy rate for legitimate users) and jammers (who actively jam the channels or deteriorate the received signal at the legitimate receivers). In this case, a legitimate transmitter adapts it's transmit waveform and power using transfer learning to avoid jamming and eavesdropping impacts. This is analogous to training war-fighters in a training camp and deploying them to a completely new real battlefield where they are expected to (learn instantly and) defeat the enemy based on their past training. We evaluate the performance of the proposed approach using numerical results obtained from extensive simulations.

关键词： Deep transfer learning machine learning physical layer security wireless communications in Battlefield large scale MIMO secrecy rate

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Cutting Force Prediction in Drilling Operations Using Temporal Convolutional Networks (TCN) and Spindle Speed data 3

Cutting Force Prediction in Drilling Operations Using Tempor...

引用

3rd ieee International Conference on Automation and Computation, AUTOCOM 2025

作者： Ganesh V, Dilli Ikram, Mohsin Husain, Saif O. Ballabh, Jayanti Mohanraj, T. Aanandha Saravanan, K. Department of Mechanical Engineering Chennai India School of Agriculture Uttaranchal University Dehradun India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798331542375

The prediction of forces in drilling is vital for enhancement of machining process, reduction of tool wear rates, and quality improvement of the final product. The present work introduces a new method for force estimation based on Temporal Convolution Networks (TCNs) and trained with spindle speed data. TCNs take advantage of the ability to process sequential data that perform well in capturing short-term and long-term dependencies, and hence are used in time-series analysis to analyze machining environments that are ever changing. The process is real-time acquisition of spindle speed and cutting force signals, filtering of signals to remove noise, and feature extraction for training of the models. To make correct predictions while making efficient computations, a TCN architecture that has been specifically customized with causal and dilated convolutions is used. The proposed model has MAE = 2.15 N and 98.7% inclusive squared coefficient of determination, which is significantly better than traditional machine learning models like ANN, SVM, and LSTM and faster on inference time. Indeed, all of the present model's validation has been done across different spindle speeds and for different types of materials. Also, the TCN model implementation into real-time monitoring allows optimizing the modelling parameters in proximity to changing conditions, which is a feature of industry 4.0. It also strongly supports the practice of sustainable manufacturing, which in turn improves the efficiency of machining. High value is credited to the potential of TCNs as a transformative tool in intelligent machining systems. © 2025 ieee.

关键词： Cutting

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Research on Vehicle Wireless Positioning Technology Based on Embedded System

Research on Vehicle Wireless Positioning Technology Based on...

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2023 Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2023

作者： Zhai, Suibing Hou, Meilin Zhang, Kaisheng Liu, Jin Intellectual Property Information Service Center Shaanxi Fashion Engineering University Xianyang China University of Wollongong Wollongong Australia

ISBN: (纸本)9781665457033

Vehicle positioning is one of the three key technologies of intelligent transportation system. Under the background of new technology era, how to combine network information and intelligent processing to achieve fast and accurate positioning technology has become a hot issue in China's research. In this paper, the vehicle wireless positioning technology based on embedded system is studied. Combining with the hardware design of the system, the positioning algorithm of wireless sensor network is studied. Considering the characteristics of high real-time requirement of vehicle positioning, an improved positioning algorithm with small computation, fast computing speed and good positioning accuracy is proposed-Weighted Centroid Localization Algorithm (Weighted Centroid Localization Based on the Difference of Differential RSSI). The experimental results show that the improved algorithm in this paper is more accurate and can display the position information of the terminal node on the user interface. The research of this paper is of great practical significance for the vehicle positioning in the area which cannot be covered by satellite positioning system. © 2023 ieee.

关键词： Embedded systems

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Quality Assurance and Control of Steel Blade Production Using Full Non-Contact Frequency Response Analysis and 3D Laser Doppler Scanning Vibrometry System

Quality Assurance and Control of Steel Blade Production Usin...

引用

ieee International Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Lukasz Scislo Faculty of Electrical and Computer Engineering Cracow University of Technology Cracow

ISBN: (纸本)9781665426060

Quality management is one of the crucial aspects of the Industry 4.0 concept of industrial production. The key matter is to limit the time for the quality system processes in the total production time. This paper presents the use of non-destructive, non-contact experimental modal analysis using the single or multipoint approach for measurement over the surface of the product. The example of such quality assurance measurements, with the use of a 3D Laser Doppler Vibrometry System, is a proposal of advanced instrumentation and data acquisition systems which is the perfect fit for Industry 4.0 factory quality management systems.

关键词： Three-dimensional displays Quality assurance data acquisition Measurement by laser beam Process control Laser excitation Frequency response

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Feasibility study of an intelligent monitoring platform for the power transformers in Saudi Arabia and the Middle East region

Feasibility study of an intelligent monitoring platform for ...

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Power systems and Electrical Technology (PSET), ieee International Conference on

作者： Hamad Turki Alsubaie Bharat B. Bhalavane Department of Digitalization & Technical Development Saudi Electricity Company Riyadh Saudi Arabia Department of Transmission Project East Saudi Electricity Company Dammam Saudi Arabia

ISBN: (数字)9798350353204

ISBN: (纸本)9798350353211

Power transformers are essential and costly components of electrical generation, transmission, and distribution networks. Their failure can result in significant disruptions and pose risks such as fires and chemical leaks, threatening human safety and the environment. The shift towards digitized grid automated systems has heightened the focus on Transformer Live Performance Management (TLFM) among industry and academia. Consequently, various continuous and offsite performance tracking methods have been developed for electrical transformers. This paper presents an advanced review of these status-tracking techniques for oil transformers, including monitoring methodologies, outcome evaluations, and the evolution of long-term strategies. Additionally, it provides a framework for developing an online platform to enhance transformer lifecycle information, thereby improving grid reliability and sustainability. The study investigates transformer failures in Saudi Arabia, analyzing over 85% of transformer assets from Saudi Arabia. data from 4 years reveal that non-catastrophic failures predominantly occur in transformers very old transformers, while catastrophic failures are more frequent in those aged 11-20 years and over 40 years. The paper comprehensively compares various tracking techniques, discussing their advantages, limitations, current technological constraints, and potential future applications.

关键词： Technological innovation Voltage transformers Oil insulation Maintenance Power system reliability Safety Power transformers Circuit faults Sustainable development Monitoring

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intelligent Evaluation Model of Precision Mobile Network Software Evaluation Accuracy 3

Intelligent Evaluation Model of Precision Mobile Network Sof...

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3rd ieee International Conference on Information systems and Computer Aided Education, ICISCAE 2020

作者： Wan, Biao Xu, Chunmei Wuhan Polytechnic Wuhan China

ISBN: (纸本)9781728183039

The existing intelligent evaluation models of the evaluation accuracy of mobile network software all have some disadvantages, such as poor evaluation accuracy and large evaluation error. Therefore, the intelligent evaluation model of fine mobile network software evaluation accuracy is put forward to study on the intelligent evaluation model of the evaluation accuracy of mobile network software. The data acquisition card is used to collect the evaluation data of fine mobile network software, and it is pre-processed by data checking and data cleaning. Based on the pre-processed data, Bootstrap sampling method is used to obtain the evaluation data. Based on the accurate evaluation data of fine mobile network software, this paper uses integrated learning algorithm to intelligently evaluate the accuracy of fine mobile network software evaluation. The intelligent evaluation of the accuracy of fine mobile network software evaluation is realized. The experimental results show that compared with the existing intelligent evaluation model for the accuracy of fine mobile network software, the intelligent evaluation model for the accuracy of fine mobile network software improves the evaluation accuracy and reduces the evaluation error. It fully shows that the intelligent evaluation model of fine mobile network software evaluation accuracy has better evaluation performance. © 2020 ieee.

关键词： data acquisition

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Design of Multi-Layer Information Security Protection Scheme for Vehicle-to-Grid System

Design of Multi-Layer Information Security Protection Scheme...

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Computer Science, Electronic Information Engineering and intelligent Control Technology (CEI), ieee International Conference on

作者： Shan He Fafu Lin Yuming Zhao Yong Xiao Wenjun Tang Fan Zhang Xiaoming Lin Shenzhen Power Supply Bureau Co. Ltd Shenzhen Guangdong Province China Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid Electric Power Research Institute CSG Guangzhou Guangdong Province China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

Vehicle-to-grid(V2G) is an important tool for enhancing the flexibility of new power systems and ensuring real-time power balance. The V2G system has the characteristics of multiple levels of entities and strong openness, making it easy to become the main target and springboard for attacking the power grid. In this context, the article analyzes the communication architecture and potential information security risks of the V2G system. Based on the domestic password algorithm system, a multi-layer and full link V2G system information security protection architecture is constructed, which ensures the safe and reliable transmission of V2G system information through four key technologies: identity authentication, encryption protection, situational awareness, and data sharing. Through testing, it has been shown that in order to ensure the success rate of data encryption and decryption in the V2G system, the system must have a concurrency of less than 1000 times. The encryption and decryption time, throughput, and anomaly rate indicators of the system are excellent. This data scheme can meet the interactive business needs of the V2G and provide important guidance for the information security construction of V2G networks.

关键词： Vehicle-to-grid Information security Authentication Computer architecture Throughput Real-time systems Encryption

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Melanoma Detection Using Two Neural Networks. A Comparison Study

Melanoma Detection Using Two Neural Networks. A Comparison S...

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ieee International Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Hakan Meva Dan Popescu Loretta Ichim University POLITEHNICA of Bucharest Bucharest Romania

ISBN: (纸本)9781665426060

If not detected and treated in time, melanoma is one of the most dangerous skin cancers. This article analyzes two models of neural networks, adapted for the classification of skin lesions in melanoma and nonmelanoma: InceptionV3 and MobileNetV2. Transfer learning methods and the HAM10000 database were used for learning. Finally, from the analysis of statistical and time performance, recommendations were made for computer or mobile applications.

关键词： Analytical models Computational modeling Neural networks Transfer learning Melanoma data models Skin

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Sequential Emotion-Aware Recommendation: A Preliminary Study on data acquisition and Modeling 3

Sequential Emotion-Aware Recommendation: A Preliminary Study...

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2021 3rd International Conference on Computer Modeling, Simulation and Algorithm, CMSA 2021

作者： Chen, Weihao Zheng, Lin Jiang, Dazhi Department of Computer Science College of Engineering Shantou University China Key Laboratory of Intelligent Manufacturing Technology Shantou University Ministry of Education China

The rapid development of recommender systems makes researchers pay more and more attention to the utilization of users' subjective factors. Among various subjective factors, emotion plays an important role that can truly reflect the temporal changes of users' preferences. However, the acquisition of user emotion is difficult, and is obtained passively in many methods. Moreover, there are few datasets suitable for continuous emotion modeling in existing recommendation research. To address these shortcomings, this work collects and organizes a dataset of users watching movies containing their temporal emotional data, which is appropriate for recommendation tasks based on sequential emotions. In addition, we propose a sequential emotion-aware recommendation method suitable for continuous emotion modeling, which outperforms other advanced sequential modeling approaches on our dataset. The contribution of this work is to conduct preliminary study and exploration on the modeling of users' sequential emotion, so that this research direction can be further developed in future. The Sequential EMotion-Aware Recommendation (SEMAR) dataset we collected is available at: https://***/LinZheng666/Sequential-Emotion-dataset-for-Recommendation. © Published under licence by IOP Publishing Ltd.

关键词： data acquisition

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