In this paper, we consider the analysis and control of continuous-time nonlinear systems to ensure universal shifted stability and performance, i.e., stability and performance w.r.t. each forced equilibrium point of t...
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Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. Firs...
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This paper presents a composable machine learning method for generalizing the quality-of-transmission (QoT) metric estimation in optical networks. The composable machine learning approach characterizes this metric for...
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Microgrids (MGs) have become more unpredictable due to integration of renewable generation sources. Several methods are used to solve load flow problems in MGs. Load flow analysis is a complex problem for islanded MGs...
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ISBN:
(数字)9798350372472
ISBN:
(纸本)9798350372489
Microgrids (MGs) have become more unpredictable due to integration of renewable generation sources. Several methods are used to solve load flow problems in MGs. Load flow analysis is a complex problem for islanded MGs due to the absence of slack bus and dependency of active power generation of droop-controlled DGs on frequency. In this paper, a Modified Newton Raphson (MNR) approach is used to perform load flow analysis of isolated MGs considering various types of electric vehicle (EV) loads. The presented method has been validated on 6-bus and 38-bus test systems of the islanded microgrid to give an accurate and simple approach for computing the load flow solutions.
Short-circuit problems in unbalanced radial distribution networks have become more common as a result of distributed generation connectivity and smart grid advancements. An accurate and effective strategy for assessin...
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ISBN:
(数字)9798350372472
ISBN:
(纸本)9798350372489
Short-circuit problems in unbalanced radial distribution networks have become more common as a result of distributed generation connectivity and smart grid advancements. An accurate and effective strategy for assessing these faults due to short circuits is presented in this study. In contrast to traditional methods, this approach uses relationship matrices that are created expressly to take use of distribution network's radial characteristics. Through the examination of variations in bus voltages, bus current injections, and branch currents during fault conditions, the suggested method formulates the analysis of various fault types. The accuracy of developed method has been validated on IEEE-123 bus modified test network by comparing the obtained results with PSCAD/EMTDC simulation results.
In this paper, a novel bilateral teleoperation control is designed specifically for manipulation in contact environment. Firstly, serval interaction conditions (e.g., free motion and rigid interaction) are defined and...
We consider the problem of dynamically decoding human intention via electroencephalography (EEG). We present two hierarchical frameworks to approach this problem. One framework processes the activities recorded in the...
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Swarm of UAVs (S-UAVs) refers to an assembly of unmanned aerial vehicles (UAVs) working together to accomplish prearranged missions. In emergency scenarios, such as a fire, any UAV is susceptible to damage. Among the ...
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ISBN:
(数字)9798350377644
ISBN:
(纸本)9798350377651
Swarm of UAVs (S-UAVs) refers to an assembly of unmanned aerial vehicles (UAVs) working together to accomplish prearranged missions. In emergency scenarios, such as a fire, any UAV is susceptible to damage. Among the routing algorithms that S-UAVs utilize the most frequently is clustering, where UAVs are divided into clusters. Each cluster consists of a cluster head (CH) and cluster members (CM). The selection of the CH is a topic of continuous research because of its crucial significance in inter-cluster communication. We propose a clustered weighted method with dynamic weight modification and redundancy to ensure end-to-end communication despite non-functional CH. Interspace, speed, and performance indicators are combined into a weighted metric that is used to select the CH, redundant CHs, and CMs. The suggested technique optimizes UAV role selection by dynamically and autonomously adjusting the weights. Based on the results of the executed simulation, this is a promising strategy that minimizes data loss in an emergency situation and minimizes delay.
In today's rapidly evolving technological landscape, the need for efficient and reliable remote monitoring of under-water assets has never been more critical. A subsea cellular network solution ensures seamless co...
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ISBN:
(数字)9798331540081
ISBN:
(纸本)9798331540098
In today's rapidly evolving technological landscape, the need for efficient and reliable remote monitoring of under-water assets has never been more critical. A subsea cellular network solution ensures seamless connectivity and real-time data access to underwater assets such as marine robots and subsea IoT sensors, empowering organizations to optimize their operations and enhance asset management. With this need in mind, we present an underwater cellular network system that facilitates seamless communication with an arbitrary number of underwater assets and at the same time provides zero-overhead localization of the same. The presented system was tested out at sea in January 2024, and this paper describes in detail both the theoretical design of the system and the practical experience gained during these trials.
Several recent empirical studies demonstrate that important machine learning tasks, e.g., training deep neural networks, exhibit low-rank structure, where the loss function varies significantly in only a few direction...
Several recent empirical studies demonstrate that important machine learning tasks, e.g., training deep neural networks, exhibit low-rank structure, where the loss function varies significantly in only a few directions of the input space. In this paper, we leverage such low-rank structure to reduce the high computational cost of canonical gradient-based methods such as gradient descent (GD). Our proposed Low-Rank Gradient Descent (LRGD) algorithm finds an $\epsilon$ -minimizer of a p-dimensional function by first identifying $r\leq p$ significant directions, and then estimating the true p-dimensional gradient at every iteration by computing directional derivatives only along those $r$ directions. We establish that the “directional oracle complexity” of LRGD for strongly convex objective functions is $\mathrm{O}(r\log(1/\epsilon)+rp)$ . Therefore, when $r\ll p$ , LRGD provides significant improvement over the known complexity of $\mathcal{O}(p\log(1/\epsilon))$ of GD in the strongly convex setting. Furthermore, using real and synthetic data, we empirically find that LRGD provides significant gains over GD when the data has low-rank structure, and in the absence of such structure, LRGD does not degrade performance compared to GD.
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