1,720,956 research outputs found
Multi-Sensor Fusion for Classifying Challenging Weather Conditions in Autonomous Driving
No full textThis thesis explores multimodal deep learning approaches for weathertype classification in autonomous systems by fusing LiDAR,radar, and RGB sensor data. A range of fusion strategies were implemented and evaluated across two distinct frameworks: one based on the 3D point cloud encoder PointPillars, and the other on the lightweight image-based EfficientNet-B0 architecture. Both early and mid-level fusion combinations were tested to assess the complementary value of spatial and semantic features. Experiments were conducted on the Michigan State University Four Seasons (MSU-4S) dataset, which provides synchronized data from all three modalities. Among several fusion configurations, the most effective results were achieved through early fusion of LiDAR and radar features, followed by gated mid-level fusion with RGB. The PointPillars-based model attained the highest performance with 87.77% test accuracy and a macro-averaged F1 score of 0.870, while the EfficientNet-B0-based model achieved comparable accuracy(86.77%) and F1 score (0.8452) with significantly lower computational cost and faster inference.These results highlight the effectiveness of multimodal gated fusion and underscore the trade-offs between spatial precision and computational efficiency, offering valuable insights for real-time weather classification in autonomous perception systems
Multi-Sensor Fusion for Classifying Challenging Weather Conditions in Autonomous Driving
No full textThis thesis explores multimodal deep learning approaches for weathertype classification in autonomous systems by fusing LiDAR,radar, and RGB sensor data. A range of fusion strategies were implemented and evaluated across two distinct frameworks: one based on the 3D point cloud encoder PointPillars, and the other on the lightweight image-based EfficientNet-B0 architecture. Both early and mid-level fusion combinations were tested to assess the complementary value of spatial and semantic features. Experiments were conducted on the Michigan State University Four Seasons (MSU-4S) dataset, which provides synchronized data from all three modalities. Among several fusion configurations, the most effective results were achieved through early fusion of LiDAR and radar features, followed by gated mid-level fusion with RGB. The PointPillars-based model attained the highest performance with 87.77% test accuracy and a macro-averaged F1 score of 0.870, while the EfficientNet-B0-based model achieved comparable accuracy(86.77%) and F1 score (0.8452) with significantly lower computational cost and faster inference.These results highlight the effectiveness of multimodal gated fusion and underscore the trade-offs between spatial precision and computational efficiency, offering valuable insights for real-time weather classification in autonomous perception systems
Forecasting Visitors in Smart Building Environments : Modeling and estimation of the number of guests using SARIMAX
No full textTime series modeling is a commonly used approach in exchange for studying and analyzing the data to support decision-making in companies based on historical data and thereby help them to save costs. This work introduces a forecasting framework that utilizes a seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) model to forecast the number of people expected to enter a building within a short period. We applied the model to forecast the abovementioned value at California University Irvine's main door using an open-source dataset that comprised data spanning four months. The experimental results demonstrate that the SARIMAX model exhibits encouraging performance in classification andevaluation, as evidenced by the promising results. The RMSE values for one,two, three, and four prediction weeks are 24.6, 40.4, 36, and 38.7, respectively, accompanied by corresponding percentage errors of 2%, 4.8%,4.76%, and 1.01%. These metrics highlight the model's ability to predict outcomes accurately and indicate its effectiveness in forecasting over various time horizons. Furthermore, the proposed model addresses the issue of inadequate future planning and analyzes foot traffic to provide a reliable forecasting technique, which is essential for modern building facilities management
Forecasting Visitors in Smart Building Environments : Modeling and estimation of the number of guests using SARIMAX
No full textTime series modeling is a commonly used approach in exchange for studying and analyzing the data to support decision-making in companies based on historical data and thereby help them to save costs. This work introduces a forecasting framework that utilizes a seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) model to forecast the number of people expected to enter a building within a short period. We applied the model to forecast the abovementioned value at California University Irvine's main door using an open-source dataset that comprised data spanning four months. The experimental results demonstrate that the SARIMAX model exhibits encouraging performance in classification andevaluation, as evidenced by the promising results. The RMSE values for one,two, three, and four prediction weeks are 24.6, 40.4, 36, and 38.7, respectively, accompanied by corresponding percentage errors of 2%, 4.8%,4.76%, and 1.01%. These metrics highlight the model's ability to predict outcomes accurately and indicate its effectiveness in forecasting over various time horizons. Furthermore, the proposed model addresses the issue of inadequate future planning and analyzes foot traffic to provide a reliable forecasting technique, which is essential for modern building facilities management
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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