The electricity market is a rather complex market and the prices depend on several different factors. The price dynamics are bound to get even more volatile, with a stronger integration between European electricity ma...
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Turing instability is a fundamental mechanism of nonequilibrium self-organization. However, despite the universality of its essential mechanism, Turing instability has thus far been investigated mostly in classical sy...
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This paper presents a method which detects faults in the measurement required for navigation of an unmanned surface vehicle. The measurement includes acceleration and angular rate by an inertial measurement unit (IMU)...
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This paper presents a method which detects faults in the measurement required for navigation of an unmanned surface vehicle. The measurement includes acceleration and angular rate by an inertial measurement unit (IMU), and the location by global navigation satellite system (GNSS). The method uses Kalman filter to estimate the state variables which represent the amount of faulty measurements. The method is tested by the experiment in which a boat equipped by commercial IMU and GNSS navigates in a sea trial. The result verifies that faults in acceleration and angular rate measurements are detected robustly and the GNSS position fault is detected when there is considerable location error.
This paper proposes an algorithm to upper-bound maximal quantile statistics of a state function over the course of a Stochastic Differential Equation (SDE) system execution. This chance-peak problem is posed as a nonc...
This paper proposes an algorithm to upper-bound maximal quantile statistics of a state function over the course of a Stochastic Differential Equation (SDE) system execution. This chance-peak problem is posed as a nonconvex program aiming to maximize the Value-at-Risk (VaR) of a state function along SDE state distributions. The VaR problem is upper-bounded by an infinite-dimensional Second-Order Cone Program in occupation measures through the use of one-sided Cantelli or Vysochanskii-Petunin inequalities. These upper bounds on the true quantile statistics may be approximated from above by a sequence of Semidefinite Programs in increasing size using the moment-Sum-of-Squares hierarchy when all data is polynomial. Effectiveness of this approach is demonstrated on example stochastic polynomial dynamical systems.
The counterfeit goods trade is a global issue. Traditionally, customized equipment, elaborate labeling, and expert inspection are the primary ways to identify fakes. In recent years, deep learning technology has been ...
The counterfeit goods trade is a global issue. Traditionally, customized equipment, elaborate labeling, and expert inspection are the primary ways to identify fakes. In recent years, deep learning technology has been used for counterfeit identification based on image detection and classification. There are two critical challenges. First, in real-world situations, the counterfeiting patterns are diverse, while the genuine ones are often identical. This is because counterfeit goods may be partially assembled from some parts of genuine products. Second, authenticating a counterfeit as genuine can cause a lot of trouble. To address these issues, we propose a novel end-to-end algorithm that combines a new Multi-Head Convolution Neural Network (MH-CNN) with a new Loss function named Attractor Loss (AtL) and applies it to image classification of real and fake goods. Technically, our method takes multiple images of different positions of an object as input. Then it outputs 1) the classification probability of belonging to a real class, and 2) a feature vector that is used for nearest neighbor retrieval. On engineering implementation, we integrate an MH-CNN structure, which contains a shared CNN backbone for extracting deep features and multiple heads for computing the deep representation of different positions. To improve the recognition performance, we propose a joint training method to train the MH-CNN model and propose a new AtL to filter vector representation of images from real class and pull them closer for enhancing the recognition rate without loss of accuracy. Extensive experimental results demonstrate the superior performance of our proposed method.
In this work, a re-design of the Moodledata module functionalities is presented to share learning objects between e-learning content platforms, e.g., Moodle and G-Lorep, in a linkable object format. The e-learning cou...
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it is shown that long-term operation of electrohydraulic complexes is accompanied by a change in the parameters of the hydraulic system: the resistance and reactance of the pump and the pipeline network. It is noted t...
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Facility gardening with complex environmental controlsystems has potential in improving productivity and producing high value-added agricultural products; however, its introduction is expensive. Thus, this study prop...
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Facility gardening with complex environmental controlsystems has potential in improving productivity and producing high value-added agricultural products; however, its introduction is expensive. Thus, this study proposes a greenhouse temperature and humidity control system using relatively inexpensive heaters, humidifiers, and ventilation fans. Humidity control is expected to promote photosynthesis and prevent plant disease. The concept of model predictive control (MPC) was applied to a small greenhouse to minimise the sum of squared errors. Compared with the two conventional methods, the simulation results showed that MPC reduced the relative RMS error of the temperature and humidity deficit to 23.5% and 13.1%, respectively.
This paper introduces exact time (ExT) distributed secondary control for an autonomous microgrid (MG) that suffers from diverse time delays. It focuses on adjusting the output states of various distributed generators ...
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ISBN:
(数字)9798350372717
ISBN:
(纸本)9798350372724
This paper introduces exact time (ExT) distributed secondary control for an autonomous microgrid (MG) that suffers from diverse time delays. It focuses on adjusting the output states of various distributed generators (DGs) to achieve their defined targets at the desired arbitrary ExT. The developed ExT controller not only restores the MG's frequency but also optimizes the distribution of load power across all DGs. Utilizing Artstein's transformation strategy, the designed control effectively alleviates the adverse impacts of time delays, ensuring ExT convergence. The validity of this ExT convergence is confirmed through a detailed Lyapunov stability analysis. Additionally, comprehensive simulation results showcase the superiority of the proposed control approach over the state-of-the-art techniques.
Propagation path loss prediction is critical in wireless communication, playing a significant role in wireless network planning, signal transmission optimization, and wireless coverage evaluation. However, traditional...
Propagation path loss prediction is critical in wireless communication, playing a significant role in wireless network planning, signal transmission optimization, and wireless coverage evaluation. However, traditional path loss prediction methods often rely on complex physical models and environmental parameters, leading to time-consuming and computationally intensive calculations. Several machine learning methods have been proposed to predict path loss, but their accuracy still needs improvement. To address these issues, this paper proposes a novel machine learning method called RadioResUNet, which is an improved version of ResUNet. RadioResUnet incorporates progressive multilevel encoder, attention mechanism, depthwise separable convolution, and fine-grained downsampling into ResUnet to adapt to the path loss prediction problem. Compared to traditional path loss prediction methods, our method directly predicts path loss from the input (city map) by learning map features and spatial relationships, reducing the reliance on complex physical models and environmental parameters, simplifying the computation process, and enhancing path loss prediction accuracy. Through experiments on the RadioMapSeer dataset, we demonstrate that our proposed method outperforms the existing methods in terms of prediction accuracy.
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