Rivers and canals flowing through cities are often used illegally for dumping trash. This contaminates fresh water channels as well as causes blockage in sewerage resulting urban flooding. When this contaminated water...
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Spine MRI images generally have the characteristics of low contrast and much noise. Because the variable shape of the spine edge, the traditional spine image segmentation method requires a lot of preprocessing and can...
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Aiming at the accurate and effective coaxiality measurement for twist drill with irregular surface, an optical measurement mechanism is proposed in this paper. First, A high-precision rotation instrument based on four...
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The demand for a variety of situational data from the traffic environment and its participants has intensified with the development of applications in Intelligent Transport Systems (ITS). Among these data, the road su...
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ISBN:
(数字)9781728182865
ISBN:
(纸本)9781728182872
The demand for a variety of situational data from the traffic environment and its participants has intensified with the development of applications in Intelligent Transport Systems (ITS). Among these data, the road surface type classification is one of the most important and can be used in the entire ITS domain. For its widespread application, it is necessary to employ a robust technology for the generation of raw data and to develop of a reliable and stable model to process these data in order to produce the classification. The developed model must operate correctly in different vehicles, under different driving styles and in different environments in which a vehicle can travel. In this work we employ inertial sensors, represented by accelerometers and gyroscopes, which are a safe, non-polluting, and low-cost alternative, ideal for large-scale use. We collect nine datasets with contextual variations, including three different vehicles, with three different drivers, in three different environments, in which there are three different road surface types, in addition to variations in the conservation state and presence of anomalies and obstacles such as potholes and speed bumps. After data collection, these data were used in experiments to evaluate various aspects, such as the influence of the vehicle data collection point, the analysis domain, the model input features, and the data window. Afterwards we evaluated the learning and generalization capacity of the models for unknown contexts. In a third step, the data were used in three Deep Neural Network (DNN) models: LSTM-based, GRU-based, and CNN-based. Through a multi-aspect and multi-contextual analysis, we considered the CNN-based model as the best one, which obtained an average accuracy between the data collection placements of 94.27% for learning and 92.70% for validation, classifying the road surface between asphalt, cobblestone or dirt road segments.
Three-dimensional geovisualizations are currently pushed both by technological development and by the demands of experts in various applied *** the presented empirical study,we compared the features of real 3D(stereos...
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Three-dimensional geovisualizations are currently pushed both by technological development and by the demands of experts in various applied *** the presented empirical study,we compared the features of real 3D(stereoscopic)versus pseudo 3D(monoscopic)geovisualizations in static and interactive digital elevation *** tested 39 high-school students in their ability to identify the correct terrain profile from digital elevation ***’performance was recorded and further analysed with respect to their spatial abilities,which were measured by a psychological mental rotation test and think aloud *** results of the study indicated that the influence of the type of 3D visualization(monoscopic/stereoscopic)on the performance of the users is not clear,the level of navigational interactivity has significant influence on the usability of a particular 3D visualization,and finally no influences of the spatial abilities on the performance of the user within the 3D environment were identified.
The easy access and availability of Virtual Reality (VR) technologies opens up a plethora of application fields as it is becoming increasingly easier to use from a technical point of view and more affordable from a co...
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3D reconstruction has been developing all these two decades, from moderate to medium size and to large scale. It’s well known that bundle adjustment plays an important role in 3D reconstruction, mainly in Structure f...
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In the months following our SHREC 2018 - 2D Scene Image-Based 3D Scene Retrieval (SceneIBR2018) track, we have extended the number of the scene categories from the initial 10 classes in the SceneIBR2018 benchmark to 3...
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Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features...
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ISBN:
(数字)9781728180090
ISBN:
(纸本)9781728180106
Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series. We propose a visual detection scheme for discovering concept drifts from multiple sourced time-series based on prediction models. We design a drift level index to depict the dynamics, and a consistency judgment model to justify whether the concept drifts from various sources are consistent. Our integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drifts from multi-source time-series data. We conduct three case studies and expert interviews to verify the effectiveness of our approach.
Sketch-based 3D scene retrieval is to retrieve 3D scene models given a user's hand-drawn 2D scene sketch. It is a brand new but also very challenging research topic in the field of 3D object retrieval due to the s...
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