322 research outputs found
Dimensionality Reduction and Dynamical Mode Recognition of Circular Arrays of Flame Oscillators Using Deep Neural Network
Oscillatory combustion in aero engines and modern gas turbines often has
significant adverse effects on their operation, and accurately recognizing
various oscillation modes is the prerequisite for understanding and controlling
combustion instability. However, the high-dimensional spatial-temporal data of
a complex combustion system typically poses considerable challenges to the
dynamical mode recognition. Based on a two-layer bidirectional long short-term
memory variational autoencoder (Bi-LSTM-VAE) dimensionality reduction model and
a two-dimensional Wasserstein distance-based classifier (WDC), this study
proposes a promising method (Bi-LSTM-VAE-WDC) for recognizing dynamical modes
in oscillatory combustion systems. Specifically, the Bi-LSTM-VAE dimension
reduction model was introduced to reduce the high-dimensional spatial-temporal
data of the combustion system to a low-dimensional phase space; Gaussian kernel
density estimates (GKDE) were computed based on the distribution of phase
points in a grid; two-dimensional WD values were calculated from the GKDE maps
to recognize the oscillation modes. The time-series data used in this study
were obtained from numerical simulations of circular arrays of laminar flame
oscillators. The results show that the novel Bi-LSTM-VAE method can produce a
non-overlapping distribution of phase points, indicating an effective
unsupervised mode recognition and classification. Furthermore, the present
method exhibits a more prominent performance than VAE and PCA (principal
component analysis) for distinguishing dynamical modes in complex flame
systems, implying its potential in studying turbulent combustion.Comment: research paper (18 pages, 1 table 10 figures) with supplementary
material (8 pages, 1 table, 5 figures
Sequential Pattern Mining with Multidimensional Interval Items
In real sequence pattern mining scenarios, the interval information between two item sets is very important. However, although existing algorithms can effectively mine frequent subsequence sets, the interval information is ignored. This paper aims to mine sequential patterns with multidimensional interval items in sequence databases. In order to address this problem, this paper defines and specifies the interval event problem in the sequential pattern mining task. Then, the interval event items framework is proposed to handle the multidimensional interval event items. Moreover, the MII-Prefixspan algorithm is introduced for the sequential pattern with multidimensional interval event items mining tasks. This algorithm adds the processing of interval event items in the mining process. We can get richer and more in line with actual needs information from mined sequence patterns through these methods. This scheme is applied to the actual website behaviour analysis task to obtain more valuable information for web optimization and provide more valuable sequence pattern information for practical problems. This work also opens a new pathway toward more efficient sequential pattern mining tasks
SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples
In real industrial processes, fault diagnosis methods are required to learn
from limited fault samples since the procedures are mainly under normal
conditions and the faults rarely occur. Although attention mechanisms have
become popular in the field of fault diagnosis, the existing attention-based
methods are still unsatisfying for the above practical applications. First,
pure attention-based architectures like transformers need a large number of
fault samples to offset the lack of inductive biases thus performing poorly
under limited fault samples. Moreover, the poor fault classification dilemma
further leads to the failure of the existing attention-based methods to
identify the root causes. To address the aforementioned issues, we innovatively
propose a supervised contrastive convolutional attention mechanism (SCCAM) with
ante-hoc interpretability, which solves the root cause analysis problem under
limited fault samples for the first time. The proposed SCCAM method is tested
on a continuous stirred tank heater and the Tennessee Eastman industrial
process benchmark. Three common fault diagnosis scenarios are covered,
including a balanced scenario for additional verification and two scenarios
with limited fault samples (i.e., imbalanced scenario and long-tail scenario).
The comprehensive results demonstrate that the proposed SCCAM method can
achieve better performance compared with the state-of-the-art methods on fault
classification and root cause analysis
An RNN Model for Generating Sentences with a Desired Word at a Desired Position
Generating sentences with a desired word is useful in many natural language processing tasks. State-of-the-art recurrent neural network (RNN)-based models mainly generate sentences in a left-to-right manner, which does not allow explicit and direct constraints on the words at arbitrary positions in a sentence. To address this issue, we propose a generative model of sentences named Coupled-RNN. We employ two RNN\u27s to generate sentences backwards and forwards respectively starting from a desired word, and inject position embeddings into the model to solve the problem of position information loss. We explore two coupling mechanisms to optimize the reconstruction loss globally. Experimental results demonstrate that Coupled-RNN can generate high quality sentences that contain a desired word at a desired position
DVI-SLAM: A Dual Visual Inertial SLAM Network
Recent deep learning based visual simultaneous localization and mapping
(SLAM) methods have made significant progress. However, how to make full use of
visual information as well as better integrate with inertial measurement unit
(IMU) in visual SLAM has potential research value. This paper proposes a novel
deep SLAM network with dual visual factors. The basic idea is to integrate both
photometric factor and re-projection factor into the end-to-end differentiable
structure through multi-factor data association module. We show that the
proposed network dynamically learns and adjusts the confidence maps of both
visual factors and it can be further extended to include the IMU factors as
well. Extensive experiments validate that our proposed method significantly
outperforms the state-of-the-art methods on several public datasets, including
TartanAir, EuRoC and ETH3D-SLAM. Specifically, when dynamically fusing the
three factors together, the absolute trajectory error for both monocular and
stereo configurations on EuRoC dataset has reduced by 45.3% and 36.2%
respectively.Comment: 7 pages, 3 figure
A Collaborative PHY-Aided Technique for End-to-End IoT Device Authentication
Nowadays, Internet of Things (IoT) devices are rapidly proliferating to support a vast number of end-to-end (E2E) services and applications, which require reliable device authentication for E2E data security. However, most low-cost IoT end devices with limited computing resources have difficulties in executing the increasingly complicated cryptographic security protocols, resulting in increased vulnerability of the virtual authentication credentials to malicious cryptanalysis. An attacker possessing compromised credentials could be deemed legitimate by the conventional cryptography-based authentication. Although inherently robust to upper-layer unauthorized cryptanalysis, the device-to-device physical-layer (PHY) authentication is practically difficult to be applied to the E2E IoT scenario and to be integrated with the existing, well-established cryptography primitives without any conflict. This paper proposes an enhanced E2E IoT device authentication that achieves seamless integration of PHY security into traditional asymmetric cryptography-based authentication schemes. Exploiting the collaboration of several intermediate nodes (e.g., edge gateway, access point, and full-function device), multiple radio-frequency features of an IoT device can be estimated, quantized, and used in the proposed PHY identity-based cryptography for key protection. A closed-form expression of the generated PHY entropy is derived for measuring the security enhancement. The evaluation results of our cross-layer authentication demonstrate an elevated resistance to various computation-based impersonation attacks. Furthermore, the proposed method does not impose any extra implementation overhead on resource-constrained IoT devices
Illumination Estimation based on Bilayer Sparse Coding
Abstract Computational color constancy is a very important topic in computer visio
Preparation and PEGylation of recombinant human interferon lambda3
The purpose of this study was to express recombinant human interferon lambda3 (rhIFN-λ3) in Escherichia coli, and prepare PEGylated recombinant human interferon lambda3 (PEG-rhIFN-λ3). The rhIFN-λ3 gene was inserted into pThioHisA vector after codon optimization and transformed into E. coli top10 strain, and then it was induced with isopropyl-β-D-thio-galactoside (IPTG). The recombinant protein was subjected to mPEG-ButyrALD modification after dialysis, renaturation and chromatographic purification. Subsequently, the modified product was preliminary isolated and purified for determining its activity. Results show that the recombinant protein was expressed in the form of inclusion bodies. After ion exchange, molecular sieve and other column chromatography purification, the purity of the purified rhIFN-λ3 was as high as 90% and the purity of the mono-PEGylated rhIFN-λ3 after cation-exchange chromatography was as high as 86%. The 50% effective concentration (EC50) of rhIFN-λ3 in WISH cells against vesicular stomatitis virus (VSV) was 8.43 ng/mL, while the EC50 of mono-PEGylated rhIFN-λ3 was 49.19 ng/mL, which reserved 17.14% of the in vitro activity and supported further studies of this new type of investigational interferon. Further study is needed to better understand the in vivo immunogenicity, antigenicity, stability and antiviral activity of PEG-rhIFN-λ3.Keywords: Recombinant human interferon lambda3, prokaryotic expression, purification, mPEG-ButyrALD, antiviral activity
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