The article discusses the issue of creating a dynamic system for managing and monitoring the quality of ore flow in mineral resource complexes using simulation modeling. The purpose of this study is to adapt the mathe...
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Storing files at the network edge has become a new paradigm of storage systems, which is promising to mitigate network congestion and reduce file retrieval latency. However, the traditional file storage scheme cannot ...
Storing files at the network edge has become a new paradigm of storage systems, which is promising to mitigate network congestion and reduce file retrieval latency. However, the traditional file storage scheme cannot effectively meet the requirements of rapid indexing and load balance when applied directly to the edge. Moreover, due to the dynamic nature of the edge environment where edge servers can join or leave at will, it is necessary for the storage scheme to adjust with minimal disruption. In this paper, we propose EdgeAnchor, a novel edge storage strategy that is composed of the two-layer hash mappings. The first layer, file-to-bucket mapping, adopts the pseudo-deletion algorithm to deal with the variations in file size, while the second layer utilizes the multiple bucket-to-server mapping to adapt to the heterogeneity in the servers’ storage capacities. Furthermore, EdgeAnchor constructs a list of deleted or added working sets for each bucket and creates a dictionary for the mappings between buckets and edge servers. In the manner, EdgeAnchor ensures a rapid file index and balances server load at the dynamic network edge. We also attach the mathematical analyses to EdgeAnchor, which theoretically proves its logarithmic complexity of hash operations and memory accesses. The experiments conducted on real-world datasets demonstrate that EdgeAnchor achieves the file index throughput twice as high as that of Consistent Hashing, under the constraints of load balance. Additionally, it ensures a low and stable data migration volume, when adding or removing edge servers consecutively.
A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical *** smart grid plays a vital role in the CPES model where informationtechnology(IT)can be related to the physical *** the...
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A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical *** smart grid plays a vital role in the CPES model where informationtechnology(IT)can be related to the physical *** the same time,the machine learning(ML)modelsfind useful for the smart grids integrated into the CPES for effective decision ***,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity *** this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in *** proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter *** OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing ***,the MBLSTM model is applied for the prediction of stability level of the smart grids in *** the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM *** ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several *** experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.
We demonstrate error-free 38 GBaud wavelength conversion with only 1 mW pump power by taking the advantage of AlGaAs-on- Insulator microresonators with high-quality factors, breaking the bandwidth-efficiency limit imp...
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This paper studies the distributed leader-follower output consensus problem for continuous-time uncertain multiagent linear systems with general input-output forms. Specifically, we extend the well-known output feedba...
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Strongly-coupled multicore fibers exhibit the improved tolerance to fiber non-linearity. Their potentials in optical submarine communications are investigated with considering the coupling length and spatial mode disp...
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This paper reports that Yb2O3/MoO3 stacks can be used effectively as a connecting electrode, enabling highly efficient tandem organic light-emitting diodes (OLEDs). The current efficiency of these tandem OLEDs more th...
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This paper reports that Yb2O3/MoO3 stacks can be used effectively as a connecting electrode, enabling highly efficient tandem organic light-emitting diodes (OLEDs). The current efficiency of these tandem OLEDs more than doubles, from approximately 90 to 200 cd/A, compared to the single-layer devices. Additionally, this connecting electrode introduces little increase in the tandem OLED driving voltage, maintaining a value close to the sum of two single-layer devices. However, the energy-level alignments measured by ultraviolet photoemission spectroscopy show significant energy barriers in the Yb2O3/MoO3 stack. Analysis using photoemission measurements revealed the presence of gap states in the oxide stack and a high differential work function, 2.28 vs 6.38 eV on each side of the Yb2O3/MoO3 stack. Variable-temperature current-voltage measurements on Yb2O3/MoO3 stacks revealed that the gap states behave like Anderson-Mott localized quantum states, which form efficient hopping conduction paths for electrons and holes. These findings demonstrate that the gap states in electrodes can be effectively used to transport charges in tandem semiconductor devices.
A unidirectional underwater acoustic transducer has the advantage of narrower beam width. In the low-frequency field, a single transducer is usually omnidirectional because its dimension is much smaller than the wavel...
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Dual-functional Radar and Communication (DFRC) has become one of the key technologies of advanced wire-less systems. This is because of the continuous development of mobile communication, the scarcity of spectrum and ...
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ISBN:
(数字)9798350350210
ISBN:
(纸本)9798350350227
Dual-functional Radar and Communication (DFRC) has become one of the key technologies of advanced wire-less systems. This is because of the continuous development of mobile communication, the scarcity of spectrum and the potential network applications. In this paper, we propose a symbol-level precoding optimization scheme for Multi-Input-Multi-Output (MIMO) radar and multi-user Multiple-Input-Single-Output (MU-MISO) communication systems. More specif-ically, the weight sum of communication rate and radar detection probability are maximized by jointly optimizing the precoding vector and bandwidth allocation. Meanwhile, it includes the condition of ensuring the receiving signal-to-interference-plus-noise ratio (SINR) at receivers, the interference to the radar and the transmission power budget at the base station (BS). Com-pared with the traditional beamforming based on Semidefinite Relaxation (SDR), the feasible area of the optimization problem is extended. Based on the concept of constructive interference, the harmful multi-user interference (MUI) is converted into a beneficial signal. So the communication rate is ensured and the system performance is improved. Numerical simulations show that our proposed method achieves a better result of the object function compared to benchmark methods, while obtaining a favorable bandwidth allocation.
The rapid expansion of devices and computers, alongside technological advancements, has led to a corresponding increase in malicious attacks. Malware attacks represent a significant global threat in cyberspace. Despit...
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ISBN:
(数字)9798350390025
ISBN:
(纸本)9798350390032
The rapid expansion of devices and computers, alongside technological advancements, has led to a corresponding increase in malicious attacks. Malware attacks represent a significant global threat in cyberspace. Despite the concerted efforts of security researchers and security companies to mitigate these attacks, they remain a persistent challenge. This has prompted a substantial focus on research endeavors to combat malicious software. Recent advancements in artificial intelligence have spurred various research efforts in malware detection. This paper focuses on detecting and classifying malware using transfer learning models, such as variants of VGG, ResNet, and MobileNet, on a grayscale image-based PE dataset with dimensions of
$64 \mathrm{x}64 \mathrm{x}3$
pixels. The proposed architecture comprises three modules. The first module is responsible for converting the PE binary file to a grayscale image. The second module aims to identify whether the input file is malicious or benign. Among the models tested, MobileNetV1 achieved the highest performance with an accuracy of 98.70
%
and an Fl-score of 96.46%. The third module, utilizing the ResNet101 model, is responsible for malware classification (five different types in the dataset). It achieved an accuracy of 98.78% and an Fl-score of 97.52% on the testing set.
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