Wide Bandgap devices (WBG) have led to an era of high-speed and high-voltage operations that were not previously achievable with silicon devices. However, packaging these devices in the power module has been a challen...
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Outdoor radio coverage map estimation is an important tool for network planning and resource management in modern Internet of Things (IoT) and cellular systems. A radio map spatially describes radio signal strength di...
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False Data Injection Attacks (FDIA) pose a significant threat to the stability of smart grids. Traditional Bad Data Detection (BDD) algorithms, deployed to remove low-quality data, can easily be bypassed by these atta...
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False Data Injection Attacks (FDIA) pose a significant threat to the stability of smart grids. Traditional Bad Data Detection (BDD) algorithms, deployed to remove low-quality data, can easily be bypassed by these attacks which require minimal knowledge about the parameters of the power bus systems. This makes it essential to develop defence approaches that are generic and scalable to all types of power systems. Deep learning algorithms provide state-of-the-art detection for FDIA while requiring no knowledge about system parameters. However, there are very few works in the literature that evaluate these models for FDIA detection at the level of an individual node in the power system. In this paper, we compare several recent deep learning-based model that proven their high performance and accuracy in detecting the exact location of the attack node, which are convolutional neural networks (CNN), Long Short-Term Memory (LSTM), attention-based bidirectional LSTM, and hybrid models. We, then, compare their performance with baseline multi-layer perceptron (MLP)., All the models are evaluated on IEEE-14 and IEEE-118 bus systems in terms of row accuracy (RACC), computational time, and memory space required for training the deep learning model. Each model was further investigated through a manual grid search to determine the optimal architecture of the deep learning model, including the number of layers and neurons in each layer. Based on the results, CNN model exhibited consistently high performance in very short training time. LSTM achieved the second highest accuracy;however, it had required an averagely higher training time. The attention-based LSTM model achieved a high accuracy of 94.53 during hyperparameter tuning, while the CNN model achieved a moderately lower accuracy with only one-fourth of the training time. Finally, the performance of each model was quantified on different variants of the dataset—which varied in their l2-norm. Based on the results, LSTM, CNN obta
This paper proposes a coplanar L-strip feeding technique to excite the dominant transverse electric (TE) mode in a rectangular microstrip patch antenna. To excite the dominant TE10 mode, the patch and ground layers ar...
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作者：
Liawatimena, SuryadiputraGunawan, DevinaBina Nusantara University
Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Computer Science Deparment BINUS Graduate Program Master of Computer Science Jakarta11480 Indonesia Bina Nusantara University
Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Jakarta11480 Indonesia
Modern retail businesses face a significant challenge with the inefficiency of manually changing price labels on shelves. This manual process not only consumes valuable time and resources but also increases the likeli...
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Understanding the thermodynamic properties of electrolyte solutions is of vital importance for a myriad of physiological and technological *** mean activity coefficientγ±is associated with the deviation of an el...
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Understanding the thermodynamic properties of electrolyte solutions is of vital importance for a myriad of physiological and technological *** mean activity coefficientγ±is associated with the deviation of an electrolyte solution from its ideal behavior and may be obtained by combining the Debye-Hückel(DH)and Born(B)***,the DH and B equations depend on the concentration and temperature-dependent static permittivity of the solutionεr(c,T)and the size of the solvated ions ri,whose experimental data is often not ***,we use a combination of molecular dynamics and density functional theory to predictεr(c,T)and ri,which enables us to apply the DH and B equations to any technologically relevant aqueous and nonaqueous electrolyte at any concentration and temperature of interest.
Induction motors(IMs)typically fail due to the rate of stator *** of the similarity of the thermal images produced by various instances of short-circuit and the minor interclass distinctions between categories,non-des...
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Induction motors(IMs)typically fail due to the rate of stator *** of the similarity of the thermal images produced by various instances of short-circuit and the minor interclass distinctions between categories,non-destructive fault detection is universally perceived as a difficult *** paper adopts the deep learning model combined with feature fusion methods based on the image’s low-level features with higher resolution and more position and details and high-level features with more semantic information to develop a high-accuracy classification-detection approach for the fault diagnosis of *** on the publicly available thermal images(IRT)dataset related to condition monitoring of electrical equipment-IMs,the proposed approach outperforms the highest training accuracy,validation accuracy,and testing accuracy,i.e.,99%,100%,and 94%,respectively,compared with 8 benchmark approaches based on deep learning models and 3 existing approaches in the literature for 11-class IMs *** the training loss,validation loss,and testing loss of the eleven deployed deep learning models meet industry standards.
With the rapid transformation from fossil fuel-based energy systems to renewable-based energy systems the application of a high gain boost DC-DC converter is becoming the main attraction due to its wide range of numer...
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The purpose of this article is to propose Stability-based Energy-Efficient Link-State Hybrid Routing(S-ELHR),a low latency routing proto-col that aims to provide a stable mechanism for routing in unmanned aerial vehic...
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The purpose of this article is to propose Stability-based Energy-Efficient Link-State Hybrid Routing(S-ELHR),a low latency routing proto-col that aims to provide a stable mechanism for routing in unmanned aerial vehicles(UAV).The S-ELHR protocol selects a number of network nodes to create a Connected Dominating Set(CDS)using a parameter known as the Stability Metric(SM).The SM considers the node’s energy usage,connectivity time,and node’s *** the highest SM nodes are chosen to form *** node declares a Willingness to indicate that it is prepared to serve as a relay for its neighbors,by employing its own energy state.S-ELHR is a hybrid protocol that stores only partial topological information and routing tables on CDS *** of relying on the routing information at each intermediary node,it uses source routing,in which a route is generated on-demand,and data packets contain the addresses of the nodes the packet will transit.A route recovery technique is additionally utilized,which first locates a new route to the destination before forwarding packets along *** simulation for various network sizes and mobility speeds,the efficiency of S-ELHR is *** findings demonstrate that S-ELHR performs better than Optimized Link State Routing(OLSR)and Energy Enhanced OLSR(EE-OLSR)in terms of packet delivery ratio,end-to-end delay,and energy consumption.
Magnetic nanoparticles have been used in various biomedical applications. Among these, magnetic particle imaging and hyperthermia are considered clinically useful diagnostic and therapeutic methods, respectively. An a...
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