Web-based industrial controlsystems are developing rapidly as an important application of information physical systems (CPSs). However, malicious cyber-attacks against industrial controlsystems in recent years have ...
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A multiple stream process (MSP) is a process at a point in time that generates several streams of output with a quality variable of interest and specifications that are identical in all streams. In this article, a new...
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A multiple stream process (MSP) is a process at a point in time that generates several streams of output with a quality variable of interest and specifications that are identical in all streams. In this article, a new control charting framework based on artificial neural networks (NNs), whose performance is prone to be measured in terms of ARL0$$ AR{L}_0 $$ and ARL1$$ AR{L}_1 $$, is proposed to improve the monitoring of a MSP and the detection of changes in individual streams. To the best of our knowledge, this is the first time that a NN has been applied to the monitoring of a MSP. The performance of the proposed control charting is evaluated through a wide Monte Carlo simulation and is compared with the traditional Mortell and Runger's MSP control charts based on the range statistic. The proposed method's potential is demonstrated by means of a real-case study in the monitoring of heating, ventilation and air conditioning (HVAC) systems installed on board modern trains. The NN4MSP package that implements the proposed monitoring scheme through the software environment Python, and the HVAC data set are made openly available online at and on PyPI, together with a tutorial that shows how to practically implement the proposed methodology to the real-case study.
Adaptive optics systems are usually prototyped in a convenient but slow language like MATLAB or Python, and then re-written from scratch using high-performance C/C++ to perform real-time control. This duplication of e...
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ISBN:
(纸本)9781510675186;9781510675179
Adaptive optics systems are usually prototyped in a convenient but slow language like MATLAB or Python, and then re-written from scratch using high-performance C/C++ to perform real-time control. This duplication of effort adds costs and slows the experimentation process. We present instead a technical demonstration of performing real time, sub-millisecond latency control with an adaptive optics system using the high-level Julia programming language. This open-source software demonstrates support for multiple cameras, pixel streaming, and network-transparency distributed computing. Furthermore, it is easy to interface it with other programming languages as desired.
To meet the communication needs of new power sources connected to the distribution network, we propose an access selection strategy based on traffic prediction, which can predict the situation of business traffic. Fir...
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This paper presents an new approach to enhance the detection of smoking behavior using object detection neural networks, focusing on the challenge of small object detection, namely cigarettes in video frames. We intro...
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In this paper, robust control problems are investigated for nonlinear continuous-time systems. A momentum-based gradient descent (GD) approach is developed to enhance the convergence performance of parameters in adapt...
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ISBN:
(纸本)9798350366907;9789887581581
In this paper, robust control problems are investigated for nonlinear continuous-time systems. A momentum-based gradient descent (GD) approach is developed to enhance the convergence performance of parameters in adaptive dynamic programming (ADP). By introducing the idea of momentum, the oscillation in the process of GD is alleviated and the selection of the learning rate becomes more flexible. Under the framework of ADP, the robust control problem is transformed into the optimal control problem by modifying the cost function. To avoid limitations of the initial admissible condition, an additional term is employed in the computation of the current gradient. Based on the online policy iteration algorithm, the momentum-based GD approach is constructed as an improved learning algorithm to optimize the critic network weights. Finally, a simulation is conducted to verify the effectiveness of the established learning strategy.
It is becoming increasingly important that urban traffic control (UTC) addresses and promotes multimodality. For the implementation of multimodal UTC in cities, a holistic, multimodal assessment of UTC measures is nee...
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ISBN:
(纸本)9781665455305
It is becoming increasingly important that urban traffic control (UTC) addresses and promotes multimodality. For the implementation of multimodal UTC in cities, a holistic, multimodal assessment of UTC measures is needed that asks which traffic participants should be prioritized. This paper proposes a Multimodal performance Index (MPI), which considers several factors such as delays and number of stops for each transport mode and weights them to each other. Thereby, the MPI should not only serve as mode-combining evaluation criterion, but also as decision criterion for cities to implement certain UTC measures. Different traffic control measures such as bus prioritization, coordination for bicyclists, or coordination for motorized traffic are assessed according to varying weights per mode in a microscopic traffic simulation. The evaluation of the multimodal urban traffic control measures is done both intersection-specific and network-wide. A case study conducted in the city of Ingolstadt, Bavaria, reveals that a weight setting according to the occupancy level of every vehicle, as mainly proposed in the literature so far, is not the best solution. Instead, our approach weights each mode based on a combination of occupancy and sustainability, which enables city authorities to promote sustainable modes, particularly cycling.
The present study addresses the technical opportunity of supplying reactive power from a coordinated group of inverter-based renewable resources (IBRs) integrated into an active distribution network (ADN), using a cen...
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ISBN:
(纸本)9798350324723
The present study addresses the technical opportunity of supplying reactive power from a coordinated group of inverter-based renewable resources (IBRs) integrated into an active distribution network (ADN), using a centralized control algorithm with unidirectional communication. The key feature of the proposed control is its simplicity, in terms of the control and the required communication infrastructure. A linear formulation allows for demonstrating stability in the sense of Lyapunov. An IoT platform was designed to evaluate the performance of the proposed control under a realistic situation, considering variations in renewable power generation and communication delays.
In today's constantly changing cybersecurity world, the demand for sophisticated intrusion prevention systems (IPS) capable of detecting zero-day threats is greater than ever. Traditional intrusion prevention syst...
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Due to the scarcity of fault samples and the weakness of processing higher-order interactive information, the most existing intelligence methods fail to achieve the optimal effect in fault diagnosis. To address these ...
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ISBN:
(纸本)9798350321050
Due to the scarcity of fault samples and the weakness of processing higher-order interactive information, the most existing intelligence methods fail to achieve the optimal effect in fault diagnosis. To address these problems, a time-frequency hypergraph neural network-based fault diagnosis method is proposed. In the proposed network, the limited data is initially segmented using the sliding window mechanism to obtain a set of time-domain signal instances. Additionally, the Fast Fourier Transform (FFT) is applied to each signal instance to extract corresponding frequency-domain signals, so as to capture more fault-sensitive features. Subsequently, a two-layer convolutional neural network is used to extract fault-attention features from both the time and frequency domain signals. Also, in order to reduce computational complexity, the time-frequency domain features are adaptively stacked based on a self-attention mechanism. Furthermore, a feature similarity graph is constructed for the time-frequency domain features using a k-nearest neighbor algorithm. This graph is then input into the hypergraph neural network (HGNN) to obtain the final diagnosis results. One comparative experiment shows that the proposed method not only mitigates the performance degradation caused by limited samples and noisy environments, but also effectively leverages the higher-order interaction information among nodes in the hypergraph.
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