Integrated Computer-Aided Engineering最新文献

{"title":"A spatio-temporal fusion deep learning network with application to lightning nowcasting","authors":"Changhai Zhou, Ling Fan, Ferrante Neri","doi":"10.3233/ica-240734","DOIUrl":"https://doi.org/10.3233/ica-240734","url":null,"abstract":"Lightning is a rapidly evolving phenomenon, exhibiting both mesoscale and microscale characteristics. Its prediction significantly relies on timely and accurate data observation. With the implementation of new generation weather radar systems and lightning detection networks, radar reflectivity image products, and lightning observation data are becoming increasingly abundant. Research focus has shifted towards lightning nowcasting (prediction of imminent events), utilizing deep learning (DL) methods to extract lightning features from very large data sets. In this paper, we propose a novel spatio-temporal fusion deep learning lightning nowcasting network (STF-LightNet) for lightning nowcasting. The network is based on a 3-dimensional U-Net architecture with encoder-decoder blocks and adopts a structure of multiple branches as well as the main path for the encoder block. To address the challenges of feature extraction and fusion of multi-source data, multiple branches are used to extract different data features independently, and the main path fuses these features. Additionally, a spatial attention (SA) module is added to each branch and the main path to automatically identify lightning areas and enhance their features. The main path fusion is conducted in two steps: the first step fuses features from the branches, and the second fuses features from the previous and current levels of the main path using two different methodsthe weighted summation fusion method and the attention gate fusion method. To overcome the sparsity of lightning observations, we employ an inverse frequency weighted cross-entropy loss function. Finally, STF-LightNet is trained using observations from the previous half hour to predict lightning in the next hour. The outcomes illustrate that the fusion of both the multi-branch and main path structures enhances the network’s ability to effectively integrate features from diverse data sources. Attention mechanisms and fusion modules allow the network to capture more detailed features in the images.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140199821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An advanced multimodal driver-assistance prototype for emergency-vehicle detection","authors":"Leonardo Gabrielli, Lucia Migliorelli, Michela Cantarini, Adriano Mancini, Stefano Squartini","doi":"10.3233/ica-240733","DOIUrl":"https://doi.org/10.3233/ica-240733","url":null,"abstract":"In the automotive industry, intelligent monitoring systems for advanced human-vehicle interaction aimed at enhancing the safety of drivers and passengers represent a rapidly growing area of research. Safe driving behavior relies on the driver’s awareness of the road context, enabling them to make appropriate decisions and act consistently in anomalous circumstances. A potentially dangerous situation can arise when an emergency vehicle rapidly approaches with sirens blaring. In such cases, it is crucial for the driver to perform the correct maneuvers to prioritize the emergency vehicle. For this purpose, an Advanced Driver Assistance System (ADAS) can provide timely alerts to the driver about an approaching emergency vehicle. In this work, we present a driver-assistance prototype that leverages multimodal information from an integrated audio and video monitoring system. In the initial stage, sound analysis technologies based on computational audio processing are employed to recognize the proximity of an emergency vehicle based on the sound of its siren. When such an event occurs, an in-vehicle monitoring system is activated, analyzing the driver’s facial patterns using deep-learning-based algorithms to assess their awareness. This work illustrates the design of such a prototype, presenting the hardware technologies, the software architecture, and the deep-learning algorithms for audio and video data analysis that make the driver-assistance prototype operational in a commercial car. At this initial experimental stage, the algorithms for analyzing the audio and video data have yielded promising results. The area under the precision-recall curve for siren identification stands at 0.92, while the accuracy in evaluating driver gaze orientation reaches 0.97. In conclusion, engaging in research within this field has the potential to significantly improve road safety by increasing driver awareness and facilitating timely and well-informed reactions to crucial situations. This could substantially reduce risks and ultimately protect lives on the road.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140199906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural architecture search for radio map reconstruction with partially labeled data","authors":"Aleksandra Malkova, Massih-Reza Amini, Benoît Denis, Christophe Villien","doi":"10.3233/ica-240732","DOIUrl":"https://doi.org/10.3233/ica-240732","url":null,"abstract":"In this paper, we tackle the challenging task of reconstructing Received Signal Strength (RSS) maps by harnessing location-dependent radio measurements and augmenting them with supplementary data related to the local environment. This side information includes city plans, terrain elevations, and the locations of gateways. The quantity of available supplementary data varies, necessitating the utilization of Neural Architecture Search (NAS) to tailor the neural network architecture to the specific characteristics of each setting. Our approach takes advantage of NAS’s adaptability, allowing it to automatically explore and pinpoint the optimal neural network architecture for each unique scenario. This adaptability ensures that the model is finely tuned to extract the most relevant features from the input data, thereby maximizing its ability to accurately reconstruct RSS maps. We demonstrate the effectiveness of our approach using three distinct datasets, each corresponding to a major city. Notably, we observe significant enhancements in areas near the gateways, where fluctuations in the mean received signal power are typically more pronounced. This underscores the importance of NAS-driven architectures in capturing subtle spatial variations. We also illustrate how NAS efficiently identifies the architecture of a Neural Network using both labeled and unlabeled data for Radio Map reconstruction. Our findings emphasize the potential of NAS as a potent tool for improving the precision and applicability of RSS map reconstruction techniques in urban environments.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140199819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing peak prediction in residential load forecasting with soft dynamic time wrapping loss functions","authors":"Yuyao Chen, Christian Obrecht, Frédéric Kuznik","doi":"10.3233/ica-230731","DOIUrl":"https://doi.org/10.3233/ica-230731","url":null,"abstract":"Short-term residential load forecasting plays a crucial role in smart grids, ensuring an optimal match between energy demands and generation. With the inherent volatility of residential load patterns, deep learning has gained attention due to its ability to capture complex nonlinear relationships within hidden layers. However, most existing studies have relied on default loss functions such as mean squared error (MSE) or mean absolute error (MAE) for neural networks. These loss functions, while effective in overall prediction accuracy, lack specialized focus on accurately predicting load peaks. This article presents a comparative analysis of soft-DTW loss function, a smoothed formulation of Dynamic Time Wrapping (DTW), compared to other commonly used loss functions, in order to assess its effectiveness in improving peak prediction accuracy. To evaluate peak performance, we introduce a novel evaluation methodology using confusion matrix and propose new errors for peak position and peak load, tailored specifically for assessing peak performance in short-term load forecasting. Our results demonstrate the superiority of soft-DTW in capturing and predicting load peaks, surpassing other commonly used loss functions. Furthermore, the combination of soft-DTW with other loss functions, such as soft-DTW + MSE, soft-DTW + MAE, and soft-DTW + TDI (Time Distortion Index), also enhances peak prediction. However, the differences between these combined soft-DTW loss functions are not substantial. These findings highlight the significance of utilizing specialized loss functions, like soft-DTW, to improve peak prediction accuracy in short-term load forecasting.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139768751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intuitionistic fuzzy divergence for evaluating the mechanical stress state of steel plates subject to bi-axial loads","authors":"Mario Versaci, Giovanni Angiulli, Fabio La Foresta, Filippo Laganà, Annunziata Palumbo","doi":"10.3233/ica-230730","DOIUrl":"https://doi.org/10.3233/ica-230730","url":null,"abstract":"The uncertainty that characterizes the external mechanical loads to which any connection plate in steel structures is subjected determines the non-uniqueness of the isochoric deformation distributions. Since the eddy currents induced on the plates produce magnetic field maps with a high fuzziness content, similar to those of the isochoric deformations, their use can be exploited to evaluate the extent of the external load that determines a specific induced current map. Starting from an approach known in the literature, according to which the map-external load association is operated through fuzzy similarity computations, in this paper, we generalize this method by reformulating it in terms of intuitionistic fuzzy logic by proposing a classification based on divergence computations. Our approach, acting adaptively on the fuzzification of the maps, results in a better classification percentage, besides significantly reducing the presence of doubtful cases due to the uncertainty of each applied load. Furthermore, a FEM software tool was developed, which turned out to be, to a certain extent, a substitute for the experimental procedure, notoriously more expensive. Even if the procedure was applied on plates subjected to bi-axial loads, it could be used for other types of loads since the classification operator processes the eddy current maps exclusively, regardless of their cause.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139507468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gap imputation in related multivariate time series through recurrent neural network-based denoising autoencoder1","authors":"Serafín Alonso, Antonio Morán, Daniel Pérez, Miguel A. Prada, Juan J. Fuertes, Manuel Domínguez","doi":"10.3233/ica-230728","DOIUrl":"https://doi.org/10.3233/ica-230728","url":null,"abstract":"<h4><span>Abstract</span></h4><p>Technological advances in industry have made it possible to install many connected sensors, generating a great amount of observations at high rate. The advent of Industry 4.0 requires analysis capabilities of heterogeneous data in form of related multivariate time series. However, missing data can degrade processing and lead to bias and misunderstandings or even wrong decision-making. In this paper, a recurrent neural network-based denoising autoencoder is proposed for gap imputation in related multivariate time series, i.e., series that exhibit spatio-temporal correlations. The denoising autoencoder (DAE) is able to reproduce input missing data by learning to remove intentionally added gaps, while the recurrent neural network (RNN) captures temporal patterns and relationships among variables. For that reason, different unidirectional (simple RNN, GRU, LSTM) and bidirectional (BiSRNN, BiGRU, BiLSTM) architectures are compared with each other and to state-of-the-art methods using three different datasets in the experiments. The implementation with BiGRU layers outperforms the others, effectively filling gaps with a low reconstruction error. The use of this approach is appropriate for complex scenarios where several variables contain long gaps. However, extreme scenarios with very short gaps in one variable or no available data should be avoided.</p>","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139067089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Highly compressed image representation for classification and content retrieval","authors":"Stanisław Łażewski, Bogusław Cyganek","doi":"10.3233/ica-230729","DOIUrl":"https://doi.org/10.3233/ica-230729","url":null,"abstract":"<h4><span>Abstract</span></h4><p>In this paper, we propose a new method of representing images using highly compressed features for classification and image content retrieval – called <i>PCA-ResFeats</i>. They are obtained by fusing high- and low-level features from the outputs of ResNet-50 residual blocks and applying to them principal component analysis, which leads to a significant reduction in dimensionality. Further on, by applying a floating-point compression, we are able to reduce the memory required to store a single image by up to 1,200 times compared to jpg images and 220 times compared to features obtained by simple output fusion of ResNet-50. As a result, the representation of a single image from the dataset can be as low as 35 bytes on average. In comparison with the classification results on features from fusion of the last ResNet-50 residual block, we achieve a comparable accuracy (no worse than five percentage points), while preserving two orders of magnitude data compression. We also tested our method in the content-based image retrieval task, achieving better results than other known methods using sparse features. Moreover, our method enables the creation of concise summaries of image content, which can find numerous applications in databases.</p>","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139067095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vehicle side-slip angle estimation under snowy conditions using machine learning","authors":"Georg Novotny, Yuzhou Liu, Walter Morales-Alvarez, Wilfried Wöber, Cristina Olaverri-Monreal","doi":"10.3233/ica-230727","DOIUrl":"https://doi.org/10.3233/ica-230727","url":null,"abstract":"Adverse weather conditions, such as snow-covered roads, represent a challenge for autonomous vehicle research. This is particularly challenging as it might cause misalignment between the longitudinal axis of the vehicle and the actual direction of travel. In this paper, we extend previous work in the field of autonomous vehicles on snow-covered roads and present a novel approach for side-slip angle estimation that combines perception with a hybrid artificial neural network pushing the prediction horizon beyond existing approaches. We exploited the feature extraction capabilities of convolutional neural networks and the dynamic time series relationship learning capabilities of gated recurrent units and combined them with a motion model to estimate the side-slip angle. Subsequently, we evaluated the model using the 3DCoAutoSim simulation platform, where we designed a suitable simulation environment with snowfall, friction, and car tracks in snow. The results revealed that our approach outperforms the baseline model for prediction horizons ⩾ 2 seconds. This extended prediction horizon has practical implications, by providing drivers and autonomous systems with more time to make informed decisions, thereby enhancing road safety.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139410512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing smart home appliance recognition with wavelet and scalogram analysis using data augmentation","authors":"José L. Salazar-González, José María Luna-Romera, Manuel Carranza-García, Juan A. Álvarez-García, Luis M. Soria-Morillo","doi":"10.3233/ica-230726","DOIUrl":"https://doi.org/10.3233/ica-230726","url":null,"abstract":"<h4><span>Abstract</span></h4><p>The development of smart homes, equipped with devices connected to the Internet of Things (IoT), has opened up new possibilities to monitor and control energy consumption. In this context, non-intrusive load monitoring (NILM) techniques have emerged as a promising solution for the disaggregation of total energy consumption into the consumption of individual appliances. The classification of electrical appliances in a smart home remains a challenging task for machine learning algorithms. In the present study, we propose comparing and evaluating the performance of two different algorithms, namely Multi-Label K-Nearest Neighbors (MLkNN) and Convolutional Neural Networks (CNN), for NILM in two different scenarios: without and with data augmentation (DAUG). Our results show how the classification results can be better interpreted by generating a scalogram image from the power consumption signal data and processing it with CNNs. The results indicate that the CNN model with the proposed data augmentation performed significantly higher, obtaining a mean F1-score of 0.484 (an improvement of <span><mml:math alttext=\"+\" display=\"inline\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\"><mml:mo>+</mml:mo></mml:math></span>0.234), better than the other methods. Additionally, after performing the Friedman statistical test, it indicates that it is significantly different from the other methods compared. Our proposed system can potentially reduce energy waste and promote more sustainable energy use in homes and buildings by providing personalized feedback and energy savings tips.</p>","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139410513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep deterministic policy gradient with constraints for gait optimisation of biped robots","authors":"Xingyang Liu, Haina Rong, Ferrante Neri, Peng Yue, Gexiang Zhang","doi":"10.3233/ica-230724","DOIUrl":"https://doi.org/10.3233/ica-230724","url":null,"abstract":"In this paper, we propose a novel Reinforcement Learning (RL) algorithm for robotic motion control, that is, a constrained Deep Deterministic Policy Gradient (DDPG) deviation learning strategy to assist biped robots in walking safely and accurately. The previous research on this topic highlighted the limitations in the controller’s ability to accurately track foot placement on discrete terrains and the lack of consideration for safety concerns. In this study, we address these challenges by focusing on ensuring the overall system’s safety. To begin with, we tackle the inverse kinematics problem by introducing constraints to the damping least squares method. This enhancement not only addresses singularity issues but also guarantees safe ranges for joint angles, thus ensuring the stability and reliability of the system. Based on this, we propose the adoption of the constrained DDPG method to correct controller deviations. In constrained DDPG, we incorporate a constraint layer into the Actor network, incorporating joint deviations as state inputs. By conducting offline training within the range of safe angles, it serves as a deviation corrector. Lastly, we validate the effectiveness of our proposed approach by conducting dynamic simulations using the CRANE biped robot. Through comprehensive assessments, including singularity analysis, constraint effectiveness evaluation, and walking experiments on discrete terrains, we demonstrate the superiority and practicality of our approach in enhancing walking performance while ensuring safety. Overall, our research contributes to the advancement of biped robot locomotion by addressing gait optimisation from multiple perspectives, including singularity handling, safety constraints, and deviation learning.","PeriodicalId":50358,"journal":{"name":"Integrated Computer-Aided Engineering","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138818438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}