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检索条件"任意字段=Scientific Conference on Mechatronics Engineering and Computer Science"

共 38169 条记录，以下是801-810 订阅

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Intelligent Production Industrial Internet Platform Based on Multi-Scale Collaborative Analysis

Intelligent Production Industrial Internet Platform Based on...

引用

2024 International conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Zhang, Qian Liu, Yijun Huang, Xiaowei Chongqing University of Science and Technology Engineering Research Center Waste Oil Recovery Technology and Equipment Ministry of Education Chongqing Technology and Business University School of Computer Science and Engineering School of Artificial Intelligence Chongqing China Chongqing University of Science and Technology School of Computer Science and Engineering School of Artificial Intelligence Chongqing China

ISBN: (纸本)9798331527624

This platform leverages digital twin technology, focusing on intelligent production as the research subject, aiming to achieve intelligent comprehensive evaluation and supervision of the pork production process. Through digital twin modeling, a three-level performance evaluation model is constructed, encompassing model mapping, behavior mapping, and state mapping, to comprehensively reflect pork production capacity. To address imbalances in the production process, the system introduces an intelligent supervision module, utilizing elastic time windows and dynamic flow matching methods to enhance the robustness of the monitoring system. The entire system includes a big data platform based on 5G transmission technology, an intelligent pig farming model, and a digital twin-based intelligent pork production model, forming a multi-scale collaborative intelligent analysis platform that provides scientific support for improving pork production efficiency. © 2024 IEEE.

关键词： Smart manufacturing

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Analysis of the Zero-Day Detection of Metamorphic Malware 9

Analysis of the Zero-Day Detection of Metamorphic Malware

引用

9th International conference on computer science and engineering, UBMK 2024

作者： Gulmez, Sibel Kakisim, Arzu Gorgulu Sogukpinar, Ibrahim Gebze Technical University Computer Engineering Department Kocaeli Turkey Istanbul Medeniyet University Computer Engineering Department Istanbul Turkey

ISBN: (纸本)9798350365887

Metamorphic malware is a kind of malware that modifies its source code with each new infection. The source code modification is carried out by a morphing engine that applies particular metamorphism techniques, which allows each metamorphic variant of a malware to have a unique signature, disabling signature-based detection models. Therefore, behavioral analysis becomes essential for malware detection. A behavioral analysis-based detection model trained on data from a specific morphing engine can detect new metamorphic variants produced by that same engine. However, these models often struggle with samples generated by different morphing engines, i.e. zero-day metamorphic malware. Due to the concept drift problem, which is also challenging for many classification and detection problems, many existing malware detection methods tend to underperform when tested on a dataset different from the malware dataset they were trained on. In this study, we aim to explore this issue and examine potential solutions for zero-day metamorphic malware detection using different approaches based on static analysis along with machine and deep learning techniques. We also utilize ransomware samples in order to show their contribution to metamorphic malware detection. Experiments we conducted demonstrate that zero-day metamorphic malware can be effectively detected using a straightforward and efficient model that leverages opcode sequences. © 2024 IEEE.

关键词： Deep learning

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Design and Development of Real-Time Automatic Tracking and Decision System for RoboMaster Competition Robot Using Convolutional Neural Networks 8

Design and Development of Real-Time Automatic Tracking and D...

引用

8th IEEE International conference on Advanced Robotics and mechatronics, ICARM 2023

作者： Gu, Hao Zhao, Zihe Di, Hao Gu, Junhao Liu, Biao Li, Yuhang School of Automation Shenyang Aerospace University Shenyang China School of International Engineering Shenyang Aerospace University. Shenyang China School of Artificial Intelligence Shenyang Aerospace University Shenyang China School of Computer Science Shenyang Aerospace University Shenyang China

ISBN: (纸本)9798350300178

This paper presents the design and development of an automatic tracking and decision system for the Robo-Master competition robot using convolutional neural network algorithms. The system can track the target automatically and make decisions based on the target's position, improving the robot's performance in the competition. The system is robust and can handle complex lighting conditions. The paper also discusses improvements made to object detection and motion prediction, which increase the system's accuracy and efficiency. The proposed system runs in real-time and achieves a high hit rate, making it a valuable tool for RoboMaster participants. © 2023 IEEE.

关键词： Convolution

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Air parameters monitoring in urban area based on LoRaWAN: Data collection for environmental assessment 32

Air parameters monitoring in urban area based on LoRaWAN: Da...

引用

32nd International scientific conference Electronics, ET 2023

作者： Lishev, Stefan N. Spasov, Grisha V. Petrova, Galidiya I. Pavlova, Petya E. Technical University of Sofia Faculty of Electronics and Automation Department of Computer Systems 25 Tsanko Djustabanov Str Plovdiv4000 Bulgaria Technical University of Sofia Faculty of Electronics and Automation Department of Elecronics 25 Tsanko Djustabanov Str Plovdiv4000 Bulgaria

ISBN: (纸本)9798350302004

Advances in Internet of Things (IoT) technologies have given a strong progress in environmental monitoring, particularly in air quality assessment. Various technologies are being considered to realize IoT devices, of which LoRaWAN is one of the most promising. This paper presents a prototype of LoRaWAN End device for real-time environmental assessment that stores the measured data in the cloud. The user interface consists of a mobile application that connects to the cloud server to retrieve data and set connection settings. © 2023 IEEE.

关键词： User interfaces

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Fault Diagnosis of Electric Drive Systems Based on Dynamic Independent Component Analysis and A Hidden Markov Model 8

Fault Diagnosis of Electric Drive Systems Based on Dynamic I...

引用

8th IEEE International conference on Advanced Robotics and mechatronics, ICARM 2023

作者： Wang, Jingkun Wang, Hongmei Cheng, Chao Chen, Hongtian School of Computer Science and Engineering Changchun University of Technology Changchun China Shanghai Jiao Tong University Department of Automation Shanghai China

ISBN: (纸本)9798350300178

Due to the rapid development of high-speed trains, the fault diagnosis of electrical drive systems is a hot issue. A fault diagnosis method for electrical drive systems based on dynamic inner independent component analysis and the hidden Markov model is proposed. First, the offline data of the electric drive systems under different operating states are processed by using the dynamic inner independent component analysis algorithm. The dynamic features in the offline data are extracted by establishing equations that maximize the non-Gaussianity of the predicted latent variables. Second, these dynamic features are used to train a Hidden Markov Model to represent various states of the electric drive systems. Finally, the online data are injected into the Hidden Markov Model for fault diagnosis, and the experimental results on the electric drive systems simulation platform show the effectiveness of the method. © 2023 IEEE.

关键词： Failure analysis

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A Comparative Analysis of the Junction Formation Mechanisms of the Boron on Silicon Junction 33

A Comparative Analysis of the Junction Formation Mechanisms ...

引用

33rd International scientific conference Electronics, ET 2024

作者： Fang, Piet Nihtianov, Stoyan TU Delft Faculty of Electrical Engineering Mathematics and Computer Science Department of Microelectronics Mekelweg 4 Delft Netherlands

ISBN: (纸本)9798350376449

Photodiodes based on the Boron on Silicon junction (B-Si) show excellent responsivity to DUV and VUV photons, radiation hardness, and impressive electrical characteristics. However, the proposed models describing the junction formation mechanism do not sufficiently predict the junction's properties. We analyze two previously proposed models: the ultra-shallow p-n junction model and the charge transfer heterojunction model. We additionally apply the Schottky-Mott theory, a semiconductor-metal heterojunction model. Both the commonalities and incompatibilities between these models are discussed. © 2024 IEEE.

关键词： Heterojunctions

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A Data-Driven Machine Learning Approach for Human Gender Classification Using Explainable AI 4

A Data-Driven Machine Learning Approach for Human Gender Cla...

引用

4th International conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Dipto, Shakib Mahmud Habib, Adria Binte Mim, Nadia Tasnim Eunus, Salman Ibne Orchi, Nabiha Tasnim University of Liberal Arts Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Brac University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350389128

Since gender recognition contains a wealth of information about the differences between male and female characteristics, it is crucial and essential for many applications in commercial domains, such as human-computer interaction applications and computer-aided physiological or psychological analysis. There are lots of scientific studies that contain different approaches to classify gender. Apart from those, we have included Explainable Artificial Intelligence to interpret the usability of the model. In this paper, we study a gender classification dataset to predict gender from tabular data of specific human body features such as hair length, forehead length, height, nose width, and length. We have used several Machine Learning (ML) algorithms such as - Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), and Stochastic Gradient Descent (SGD) to predict gender. After that, we performed a comparison study and implemented Lime on the best performing ML model to interpret the prediction of the model. The Gaussian Naive Bayes architecture with the dataset shows a reasonable accuracy of 98%, proving the usability of the model. © 2024 IEEE.

关键词： Contrastive Learning

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Crow algorithm based on variation and parameter recurrence 5

Crow algorithm based on variation and parameter recurrence

引用

5th International conference on mechatronics and computer Technology engineering, MCTE 2022

作者： Chen, Yifu Li, Jun School of Information Engineering Jiangxi University of Science and Technology Jiangxi Ganzhou341000 China Institute of Artificial Intelligence Jiangxi University of Applied Science Jiangxi Nanchang330100 China

ISBN: (纸本)9781510661073

In order to improve the global search ability and easy to fall into local extreima, we propose a modified crow search algorithm (ISA) based on differential variation perturbation factor and dynamic recurrence rules of model parameters. Introduce the differential variation perturbation factor to accelerate the information exchange and improve the convergence rate, and then introduce the model parameter dynamic recurrence rule to improve the perception probability and flight length to balance the global search and local *** experimental results of the ablation experiments on 2 engineering optimization design problems show that the optimization performance of the improved crow search algorithm improves significantly, which proves the effectiveness and correctness of the improved method. © 2022 SPIE.

关键词： Parameter estimation

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Wildfire Spread Prediction Using Attention Mechanisms in U-NET

Wildfire Spread Prediction Using Attention Mechanisms in U-N...

引用

2023 International conference on Electrical, computer, Communications and mechatronics engineering, ICECCME 2023

作者： Shah, Kamen Pantoja, Maria California Polytechnic State University Dept. Computer Science and Software Engineering San Luis Obispo United States

ISBN: (纸本)9798350322972

Global warming has increased large-scale natural disasters at an alarmingly greater frequency. These natural disasters affect humans and the ecosystems that all species rely upon for food and shelter. A significant byproduct of global warming is the emergence of mega-fires, which are wildfires that burn more than 100,000 acres. Mega fires have exhibited the capacity to destroy entire ecosystems and urban areas. Researchers forecast a significant increase in wildfires during the next decade. This is a fundamentally growing problem that mandates modern solutions. Currently, simulation models are employed to predict wildfire propagation and aid firefighters in suppressing them. However, these methods are computationally expensive and often inaccurate. A recent advance in this area is the usage of neural networks (NN), which are systematically more accurate and computationally efficient. This paper focuses on predicting wildfire spread using NN with an attention mechanism to improve spatial recognition and overall performance in neural networks for wildfire spread prediction. We train different models on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. Results show that when augmenting segmentation models with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, we use ensemble modeling to reduce bias and variation, leading to more consistent and accurate predictions. The architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1% when inferencing on wildfire propagation at 30-minute intervals. © 2023 IEEE.

关键词： Forecasting

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A Novel Approach of Particle Swarm and ANT Colony Optimization for Task Scheduling in Cloud 14

A Novel Approach of Particle Swarm and ANT Colony Optimizati...

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14th International conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Kaliappan, Seeniappan Paranthaman, V. Kamal, M.D. Raj Sudhakar, A.V.V. Muthukannan, M. Kcg College of Technology Department of Mechatronics Engineering Karapakkam Tamil Nadu Chennai600097 India Adhi College of Engineering and Technology Department of Mechanical Engineering Tamilnadu Chennai India Velammal Institute of Technology Department of Mechanical Engineering Tamil Nadu Chennai601204 India Sr University Department of Electrical and Electronics Engineering Telangana Warangal1506371 India Kcg College of Technology Department of Civil Engineering Karapakkam Chennai600097 India

ISBN: (纸本)9798350344837

'The cloud' is shorthand for a cloud-based computer infrastructure that helps make other technologies possible. The workload is split among several cloud-based, virtual computers according to some factors. However, cloud computing (CC) provides options for dealing with issues like job scheduling, such as improved task scheduling. To begin, we present a Dynamic Weighted Round-Robin strategy for more efficient cloud-based task allocation. Because of its efficiency in optimizing schedules in light of finite resources, jobs' relative importance, and available time, the SPMOA approach comes highly recommended. Second, a heuristic method called Hybrid Particle Swarm Parallel Ant Colony Optimization is introduced to solve the problem of task execution latency in SPMOA-based task scheduling. In conclusion, we enhance cloud-based task scheduling by creating a fuzzy logic framework for MLOA. Both the inertial mass of the PSO and the pheromone trails left by the P ACO are modified using fuzzy methods. Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization is the optimal method for scheduling tasks in the cloud. The strategy reduces downtime due to waiting and procedures while simultaneously increasing output and minimizing inefficiency. © 2024 IEEE.

关键词： Ant colony optimization

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