Technology transfer is central to the development of an iconic entrepreneurial university. Academic science has become increasingly entrepreneurial, not only through industry connections for research support or transf...
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The paper proposes a model of a spatially distributed monitoring system with a pre-fractal dynamic structure that describes the process of assessing the state of the system in real time under the influence of destabil...
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ISBN:
(数字)9798331532178
ISBN:
(纸本)9798331532185
The paper proposes a model of a spatially distributed monitoring system with a pre-fractal dynamic structure that describes the process of assessing the state of the system in real time under the influence of destabilizing factors. The basis of the model is a multilevel pre-fractal graph dynamically changing in time, which allows to take into account the non-determinism of the structure and weight characteristics of communication channels. The model implements the problem of multi-criteria optimization taking into account the prioritization of information channels to ensure the structural and functional stability of the system.
This paper addresses a robust Safe Landing Zone (SLZ) perception method for the autonomous landing of UAV. First, a detection method for identifying SLZs is presented, which makes use of the RANSAC plane fitting algor...
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ISBN:
(纸本)9788993215236
This paper addresses a robust Safe Landing Zone (SLZ) perception method for the autonomous landing of UAV. First, a detection method for identifying SLZs is presented, which makes use of the RANSAC plane fitting algorithm and several constraints. Meanwhile, the inherent uncertainties in the navigational and perception sensors of UAV make SLZ detection and tracking difficult. Therefore, in this paper, the state of SLZs and UAV is modeled as a Random Finite Set, and the tracking process is implemented using the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter. The performance of the proposed systems is demonstrated in the GAZEBO simulation environment's volcano map.
The paper is devoted to exploration of thermal processes during the growth of leucosapphire by the horizontal directional crystallization method. A mathematical and computer model of the technological process has been...
The paper is devoted to exploration of thermal processes during the growth of leucosapphire by the horizontal directional crystallization method. A mathematical and computer model of the technological process has been developed to predict the dynamics of temperature field in the carbon-graphite thermal node. To solve the problem of providing the required temperature field, the synthesis of a distributed high-precision regulator has been performed, the settings of which allow to provide a given temperature distribution at the crystallization front by controlling the heat flow at the sections of the lower heater. The analysis of transient graphs obtained by modeling of the closed-loop automatic control system confirmed the compliance of the developed system with the requirements of the technological process in terms of temperature field distribution in the leucosapphire.
In the process of rapid socio-economic development, people's living standards have been improved to varying degrees. At the same time, more and more people are turning their attention to physical health while sati...
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Accurate and spatially reliable temperature data are crucial for effective agricultural management and climate adaptation. This study presents an innovative methodology for temperature correction using topographic fac...
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ISBN:
(纸本)9798350378115
Accurate and spatially reliable temperature data are crucial for effective agricultural management and climate adaptation. This study presents an innovative methodology for temperature correction using topographic factors and adiabatic models. Initial temperature information, sourced from the automatic weather stations (AWS) of the Dirección General de Aeronáutica Civil (DGAC), often has limited spatial representation, covering approximately 30 km in radius, however, significant temperature variations within this area require refined data for precise agronomic applications. This research proposes a method to spatialize and correct temperature data to enhance its reliability for agronomic use. More accurate spatial temperature maps are achieved by utilizing Digital Elevation Models (DEM) and applying corrections restricted to locations within 100 meters of elevation difference from the AWS. These corrections include wet and dry adiabatic lapse rates, which are applied appropriately. The corrected temperature data are then used to create detailed spatial maps, essential for modeling agronomic variables in changing environments. These maps enable better decision-making for irrigation scheduling, crop stress monitoring, and other critical agricultural practices. The methodology was tested in four distinct sites within the central-southern macrozone of Chile, specifically in the regions of O'Higgins, Maule, Nuble, and Biobío. The results from these case studies provided valuable insights into the applicability and accuracy of this technique in real-world agricultural settings, demonstrating significant improvements in climate resilience and sustainability. By improving the spatial accuracy of temperature data, this methodology supports more effective resource management and enhances the sustainability of agricultural operations. The findings highlight the potential of advanced topographic and adiabatic corrections to transform temperature dataanalysis, providing a valua
In this study we describe a strategy for many objects whose number is unknown and varies during tracking. The multiple objects tracking approach retains many assumptions about the object's quantity and trajectorie...
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process monitoring technology has developed rapidly in response to the increasing demand for safer and more reliable systems in modern process operations. Online process monitoring plays an important role not only in ...
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To shorten the flight time and reduce the probability of being intercepted when the flight reaches the specified area, the overall design of a spring-foldable rotorcraft is presented. Virtual modelinganalysis and mot...
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Handling processdata characterized by strong nonlinearity, high dimensionality, cross-correlations, and auto-correlations presents a considerable hurdle for data-driven soft sensor modeling. While traditional slow fe...
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ISBN:
(数字)9798350387780
ISBN:
(纸本)9798350387797
Handling processdata characterized by strong nonlinearity, high dimensionality, cross-correlations, and auto-correlations presents a considerable hurdle for data-driven soft sensor modeling. While traditional slow feature analysis (SFA) adeptly captures slow and static features from linear data, it may falter in capturing the nonlinear, high-dimensional, and dynamic features inherent in time series data. Conversely, although Long Short-Term Memory (LSTM) networks are designed to address long-term dependencies within sequences, they encounter challenges, particularly with very lengthy data sequences, in effectively capturing these dependencies. Consequently, relying solely on SFA or LSTM may prove inadequate for addressing the complexities of time series data in industrial processes. To confront these challenges, this study proposes an innovative approach termed Slow LSTM (SLSTM), which amalgamates the hidden layers of LSTM and SFA to enhance feature extraction. These extracted features are subsequently fed into a fully connected layer for prediction. The efficacy of this proposed method is validated through comparisons with various methods, including SFA-FC, LSTM, and RNN.
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