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14th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2022

作者： Popa, Alexandru Gabriel Ichim, Loretta Popescu, Dan University Politehnica of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania

ISBN: (数字)9781665495356

ISBN: (纸本)9781665495356

Detection of persons from restricted areas with the help of UAVs is one of the current trends in ensuring the security of regions of interest. For the processing of images acquired by UAVs of special interest are machine learning techniques with an emphasis on neural networks. For this task, two neural networks are proposed and tested in this paper: YOLO v5m and Faster R-CNN. They were learned and tested on two data sets: HERIDAL and a proprietary video stream data set. Both networks gave good results, and the best results were obtained on their own data set (because the images in HERIDAL were taken at a higher height). © 2022 IEEE.

关键词： Learning systems

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A Framework for Real-Time Monitoring of Power Consumption of Distributed Calculation on Computational Cluster 15

A Framework for Real-Time Monitoring of Power Consumption of...

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15th International Conference on Cloud Computing and Services science, CLOSER 2025

作者： Krechowicz, Adam Faculty of Electrical Engineering Automatic Control and Computer Science Kielce University of Technology al. 1000-lecia PP 7 Kielce Poland

ISBN: (纸本)9789897587474

This paper proposes a framework for real-time monitoring of the power consumption of distributed calculation on the nodes of the cluster. The framework allows to visualize and analyze the provider results based on the context information about the performed calculation. The first part of the framework is devoted to monitoring the power consumption during the execution of machine learning algorithms and the performance of NoSQL storage. The second part is dedicated to the testing of distributed data storage, called Scalable Distributed Two-Layered Data Structure (SD2DS). The results show that the framework can be used in the development of a management system that could schedule computations to take full advantage of renewable energy. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Cluster computing

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DNN-based Skeleton Independent Motion Retargeting 27

DNN-based Skeleton Independent Motion Retargeting

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Akber, Syed Muhammad Abrar Kazmi, Sadia Nishat Szczesna, Agnieszka Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

In the modern digital world, the use of animated applications has increased significantly and such applications have quietly become an integral part of life. Capturing human motions is a fundamental aspect of these applications which is performed by motion-capturing (MoCap) systems. Due to the heterogeneity of MoCap systems and marker configurations, the captured skeletons may vary in their structure, number of joints, and bone lengths. The need for motion retargeting arises to transfer motion captured from one articulated actor to another. Although motion retargeting is a long-standing issue, current methods cannot automatically execute retargeting between skeletons. This paper presents the retraining of a popular motion retargeting network with a new large-scale motion dataset. The retraining aims to demonstrate the capabilities of the motion retargeting network to handle the problem of motion retargeting between skeletons that may have different structures. The paper first, presents the motion retargeting network that employs an encoder-decoder architecture. Afterward, we re-train the motion retargeting network with a new MoCap dataset. To validate the effectiveness of the network, we visually compare the generated results with the existing retargeting model. The results show that the motion retargeting network effectively retargets the different motions. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Decoder Deep neural network Encoder Motion retargeting

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Dual Networks Based System for Detecting and Classifying Harmful Insects in Orchards

Dual Networks Based System for Detecting and Classifying Har...

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2022 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Popescu, Dan Serghei, Trandafir-Liviu Ichim, Loretta Faculty of Automatic Control and Computer Science University POLITEHNICA of Bucharest Bucharest Romania

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Halyomorpha Halys is a type of a dangerous insect for agricultural crops, in general, and especially for orchards. The detection and evaluation of their abundance are of great importance for starting the elimination procedure in ecological conditions. There are few studies conducted on insect detection using deep neural networks, but none are dedicated to the Pentatomidae family. The paper proposes an intelligent system for the detection of two similar species, Halyomorpha Halys and N ezara Viridula, the first being the dangerous one. As a novelty, the paper proposes an integrated intelligent system consisting of two interconnected neural networks trained on a custom data set using transfer learning. The first is configurated for insect detection (YOLOv.4-tiny) and the second is dedicated to insect classification (EfficientNet-B3). This approach has the goal of decreasing the values of false negative and false positive errors, thus increasing the detection performance. © 2022 IEEE.

关键词： Orchards

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Coupling deep and handcrafted features to assess smile genuineness

arXiv

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arXiv 2025年

作者： Pawlus, Benedykt Smolka, Bogdan Kawulok, Jolanta Kawulok, Michal Faculty of Automatic Control Electronics and Computer Science Gliwice Poland

Assessing smile genuineness from video sequences is a vital topic concerned with recognizing facial expression and linking them with the underlying emotional states. There have been a number of techniques proposed underpinned with handcrafted features, as well as those that rely on deep learning to elaborate the useful features. As both of these approaches have certain benefits and limitations, in this work we propose to combine the features learned by a long short-term memory network with the features handcrafted to capture the dynamics of facial action units. The results of our experiments indicate that the proposed solution is more effective than the baseline techniques and it allows for assessing the smile genuineness from video sequences in real-time. © 2025, CC BY.

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Human Detection in Restricted Areas Using Deep Convolutional Neural Networks 30

Human Detection in Restricted Areas Using Deep Convolutional...

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30th Telecommunications Forum, TELFOR 2022

作者： Serghei, Trandafir-Liviu Ichim, Loretta Popescu, Dan University POLITEHNICA of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

With the help of state-of-the-art DCNNs precise detection of persons is possible in images and videos acquired from UAVs at low and medium altitudes. The current paper proposes for comparison of two DCNNs: Scaled-YOLOv4 and YOLOv7 trained on a custom dataset through transfer learning with data acquired from UAV. The aim is to take advantage of the capabilities of YOLOv7 to train a lightweight model with good accuracy that can be loaded on embedded systems present onboard UAVs capable of real-time person detection. Through transfer learning, the model achieves detection scores above 90% at altitudes of 30m using YOLOv7 architecture. Experiments were conducted to prove its ability to successfully multiple human detection frame-by-frame with over 70% confidence scores. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Remaining Useful Life Prediction of Aircraft Engines based on FA-SDAE-BiGRU 6

Remaining Useful Life Prediction of Aircraft Engines based o...

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6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Bai, Xiaojun Sun, Yunpeng Meng, Gong Xi'an Technological University School of Computer Science and Engineering Xi'an China Beijing Aerospace Automatic Control Institution Beijing China

ISBN: (纸本)9798350305388

Aiming at the problems of insufficient feature extraction and inaccurate prediction in existing aircraft engines remaining useful life prediction algorithms, a fusion model based on feature attention mechanism, stacked noise reduction autoencoder and bidirectional gated cycle units (BiGRU) was proposed. Firstly, weight is assigned to each sensor data of aircraft engine by the feature attention mechanism, and new weight data is obtained. Next, the stack noise reduction autoencoder is used to extract the weighted data. Then, bidirectional gated recurrent units are employed to predict the time series data and obtain the remaining useful life of the aircraft engine. To accurately select the hyperparameters of BiGRU, the Bayesian optimization algorithm is utilized for hyperparameter optimization. Finally, ablation experiments were conducted on the CMPASS dataset to validate the effectiveness of the proposed method, resulting in a root mean square error and score of 13.51 and 252.61, respectively. © 2023 IEEE.

关键词： Aircraft engines

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Target Localization of Short Vertical Line Array Based on Least Squares Method 6

Target Localization of Short Vertical Line Array Based on Le...

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6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Qu, Shaochun Cai, Zhipeng Ma, Jing Xi'an Technological University School of Computer Science and Engineering Xi'an China North Automatic Control Technology Institute Taiyuan China

ISBN: (纸本)9798350305388

In the shallow sea environment, array of small array elements may not be able to accurately locate targets due to undersampling. To address this issue, the paper proposed a method called sound field extension based on the reception of array of small array elements, which generates a virtual measurement field of a vertical array with lager number. The method utilizes the extended acoustic field for VTRM. This approach significantly improves the focusing and positioning performance of the TRM. The key implementation lies in estimating the normal wave mode coefficients from the acoustic field acquired by the short vertical line array. The paper uses the least squares method to estimate the mode coefficients. Experimental results demonstrate that compared to conventional time processing methods for an array of small array elements, the VTRM method using the a vertical array with a large number of elements can effectively enhance targeting and focusing performance of the short array. For array of small array elements with 21 elements, the SNR (Signal Interference to Noise Ratio) effectively improves by 4.32 dB. © 2023 IEEE.

关键词： Acoustic fields

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Reliability Prediction Based on SVR and Improved SCSO 6

Reliability Prediction Based on SVR and Improved SCSO

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6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Bai, Xiaojun Liu, Hongyue Meng, Gong Xi'an Technological University School of Computer Science and Engineering Xi'an China Beijing Aerospace Automatic Control Institution Beijing China

ISBN: (纸本)9798350305388

Reliability prediction can provide basis for the identification of potential improvement area, cost control, and mission reliability assessment, etc. However, for complex equipment, there are many reliability influencing factors and incomplete knowledge of failure causes, which lead to a significant disparity between the predicted outcomes and the real values for traditional reliability prediction methods. To address the above issues, this research paper introduces an approach that utilizes the Support Vector Regression (SVR) model and Sand Cat Swarm Optimization (SCSO). To begin with, the sliding window technique is employed on the historical reliability data to generate time series samples, with the 5 adjacent data as sample data, and the sixth as label of the sample, and train SVR model on these samples;Second, the SVR model parameters are optimized using the ISCSO algorithm to obtain the optimal combination of parameters. In the testing stage, firstly, historical reliability data was used to predicted future data by the model, and the predicted data are then added to the sequence to form new samples, thus old data are discarded and new data are predicted continuously to realize continuous reliability prediction. Finally, the algorithm proposed in this paper is validated on a diesel engine reliability dataset. The algorithm proposed in this paper demonstrates its superiority through the Normalized Root Mean Squared Error (NRMSE) evaluation. The NRMSE of SVR-ISCSO is 8.86E-05, showcasing a remarkable 99.24% year-on-year decrease compared to the standard SVR. Additionally, it exhibits a 5.86% year-on-year decrease compared to SVR-SCSO, further validating the effectiveness of the proposed algorithm. © 2023 IEEE.

关键词： Mean square error

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Enhancing neural network with LIDAR data for navigation 24

Enhancing neural network with LIDAR data for navigation

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24th International Conference on control Systems and computer science, CSCS 2023

作者： Baicu, Diana Craciunescu, Mihai Duvinage, Thomas Mocanu, Stefan University Politehnica of Buchaerst Faculty of Automatic Control and Computer Science Bucharest Romania University of Belfort Montbéliard Belfort France

ISBN: (纸本)9798350313390

In this paper, a method of fusing distance information and camera data is presented, the objective being that of combining LIDAR measurements with the output of a segmentation neural network, applied on the merged image from three frontal cameras, for navigating in an indoor environment. Usually, these types of environments are not structured, being diverse in their composition: for example, some buildings have more glass walls and doors, others might have large corridors and concrete structures. The proposed fusing method is used for reducing the errors generated by both techniques individually, which is then applied to control the trajectory of an electric wheelchair, as well as to gather a dataset of corrected and annotated images for reducing the dependency on the LIDAR data in the future. © 2023 IEEE.

关键词： Cameras

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