WirePlanner: Fast, Secure and Cost-Efficient Route Configuration for SD-WAN

As enterprises increasingly migrate their applications to the cloud, the demand for secure and cost-effective Wide Area Networking (WAN) solutions for data transmission between branches and data centers grows. Among these solutions, Software-Defined Wide Area Networking (SD-WAN) has emerged as a promising approach. However, existing SD-WAN implementations largely rely on IPSec tunnels for data encryption between edge routers, resulting in drawbacks such as extended setup times and limited throughput. Additionally, the SD-WAN control plane rarely takes both latency and monetary cost into consideration when determining routes between nodes, resulting in unsatisfactory Quality of Service (QoS). We propose WirePlanner, an SD-WAN solution that employs a novel algorithm for path discovery, optimizing both latency and cost, and configures WireGuard tunnels for secure and efficient data transmission. WirePlanner considers two payment methods: Pay-As-You-Go, where users pay for a fixed amount of bandwidth over a certain duration, and Pay-For-Data-Transfer, where users pay for the volume of transmitted data. Given an underlay topology of edge routers and a user-defined budget constraint, WirePlanner identifies a path between nodes that minimizes latency and remains within the budget, while utilizing WireGuard for secure data transmission

Open the Black Box – Visualising CNN to Understand Its Decisions on Road Network Performance Level

Visualisation helps explain the operating mechanisms of deep learning models, but its applications are rarely seen in traffic analysis. This paper employs a convolu-tional neural network (CNN) to evaluate road network performance level (NPL) and visualises the model to en-lighten how it works. A dataset of an urban road network covering a whole year is used to produce performance maps to train a CNN. In this process, a pretrained network is introduced to overcome the common issue of inadequa-cy of data in transportation research. Gradient weighted class activation mapping (Grad-CAM) is applied to vi-sualise the CNN, and four visualisation experiments are conducted. The results illustrate that the CNN focuses on different areas when it identifies the road network as dif-ferent NPLs, implying which region contributes the most to the deteriorating performance. There are particular visual patterns when the road network transits from one NPL to another, which may help performance prediction. Misclassified samples are analysed to determine how the CNN fails to make the right decisions, exposing the model’s deficiencies. The results indicate visualisation’s potential to contribute to comprehensive management strategies and effective model improvement

Interpretable Risk Assessment Methods for Medical Image Processing via Dynamic Dilated Convolution and a Knowledge Base on Location Relations

Existing approaches to image risk assessment start with the uncertainty of the model, yet ignore the uncertainty that exists in the data itself. In addition, the decisions made by the models still lack interpretability, even with the ability to assess the credibility of the decisions. This paper proposes a risk assessment model that unites a model, a sample and an external knowledge base, which includes: 1. The uncertainty of the data is constructed by masking the different decision-related parts of the image data with a random mask of probabilities. 2. A dynamically distributed dilated convolution method based on random directional field perturbations is proposed to construct the uncertainty of the model. The method evaluates the impact of different components on the decisions within the local region by locally perturbing the attention region of the dilated convolution. 3. A triadic external knowledge base with relative interpretability is presented to reason and validate the model's decisions. The experiments are implemented on the dataset of CT images of the stomach, which shows that our proposed method outperforms current state-of-the-art methods

Undamaged measurement of the sub-micron diaphragm and gap by tri-beam interference

A simple, high-accuracy and non-destructive method for the measurement of diaphragm thickness and microgap width based on modulated tri-beam interference is demonstrated. With this method, a theoretical estimation error less than 0.5% for a diaphragm thickness of ~1 μm is achievable. Several fiber-tip air bubbles with different diaphragm thicknesses (6.25, 5.0, 2.5 and 1.25 μm) were fabricated to verify our proposed measurement method. Furthermore, an improved technique was introduced by immersing the measured object into a liquid environment to simplify a four-beam interference into tri-beam one. By applying this improved technique, the diaphragm thickness of a fabricated in-fiber rectangular air bubble is measured to be about 1.47 μm, and the averaged microgap width of a standard silica capillary is measured to be about 10.07 μm, giving a corresponding measurement error only 1.27% compared with actual scanning electron microscope (SEM) results

The association between future self-continuity and problematic mobile video gaming among Chinese college students: the serial mediation of consideration of future consequences and state self-control capacity

Abstract Background To explore the relationship between future self-continuity and problematic mobile video gaming among Chinese college students and to examine the serial mediation of consideration of future consequences and state self-control capacity on the association between future self-continuity and problematic mobile video gaming, based on Identity-Based Motivation Theory. Methods The Problematic Mobile Video Gaming Scale, Future Self-continuity Scale, Consideration of Future Consequences Scale, and Short Version of State Self-control Capacity Scale were administered to a sample comprising 800 college students (338 males accounting for 42.3%). Multivariate analysis and latent variables analysis were utilized to explore the separate mediating role consideration of future consequences and state self-control capacity played in the association between future self-continuity and problematic mobile video gaming, and their serial mediation also was investigated. The Bootstrap method was employed to test the significance of these mediation effects. Results The negative association between future self-continuity and problematic mobile video gaming was moderately found. Students with increased consideration of future consequences from higher levels of future self-continuity have decreased their problematic mobile video gaming. Future self-continuity significantly positively predicted state self-control capacity, which in turn significantly negatively predicted problematic mobile video gaming. The serial mediation was also found. Conclusion The findings revealed why differences in identification with the current and future selves become influencing factors in problematic mobile video gaming. This study observed the mediating role that consideration of future consequences and state self-control capacity play in the association between future self-continuity and problematic mobile video gaming

Early Weak Fault Signal Enhancement and Recognition Method of Rudder Paddle Bearings Based on Parameter Adaptive Stochastic Resonance

Aiming at the problem that the characteristic frequency amplitude of early bearing fault signals is weak and difficult to extract under the strong noise, a vibration signal enhancement method based on maximum overlapping wavelet packet transform (MODWPT) and adaptive stochastic resonance (SR) is proposed. Based on multiple single-component signals decomposed by MODWPT, a signal enhancement and reconstruction model of the ordinary variable scale SR system is constructed. At the same time, the whale optimization algorithm (WOA) algorithm is adopted to optimize the parameters of the SR system adaptively with the improved signal noise ratio (ISNR) index as the fitness function. Finally, a feature-enhanced version of the original signal is obtained and transformed into a time-frequency image dataset using a two-dimensional wavelet transform, which can be input into the Resnet network for fault identification and classification. Based on a set of early fault data from IMS and marine bearing data, the significant effect of the proposed method on early fault signal feature enhancement is verified. By comparing the fault characteristic frequency and its frequency doubling before and after the enhancement of the envelope spectrum, the component noise component is suppressed, and the fault frequency amplitude is highlighted under the action of SR potential function. This method can effectively improve the fault recognition accuracy, which has a certain application prospect

A Bearing Performance Degradation Modeling Method Based on EMD-SVD and Fuzzy Neural Network

Bearing performance degradation assessment has great significance to condition-based maintenance (CBM). A novel degradation modeling method based on EMD-SVD and fuzzy neural network (FNN) was proposed to identify and evaluate the degradation process of bearings in the whole life cycle accurately. Firstly, the vibration signals of bearings in known states were decomposed by empirical mode decomposition (EMD) to obtain the intrinsic mode functions (IMFs) containing feature information. Then, the selected key IMFs which contain the main features were decomposed by singular value decomposition (SVD). And the decomposed results were used as the training samples of FNN. At last, the output results of the tested data were normalized to the health index (HI) through learning and training of FNN, and then the performance degradation degree could be described by the distance between the test sample and the normal one. According to the case study, this modeling method could evaluate the performance degradation of bearings effectively and identify the early fault features accurately. This method also provided an important maintenance strategy for the CBM of bearings

A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of Gearbox

Aiming at the problems of poor self-adaptive ability in traditional feature extraction methods and weak generalization ability in single classifier under big data, an internal parameter-optimized Deep Belief Network (DBN) method based on grasshopper optimization algorithm (GOA) is proposed. First, the minimum Root Mean Square Error (RMSE) in the network training is taken as the fitness function, in which GOA is used to search for the optimal parameter combination of DBN. After that the learning rate and the number of batch learning in DBN which have great influence on the training error would be properly selected. At the same time, the optimal structure distribution of DBN is given through comparison. Then, FFT and linear normalization are introduced to process the original vibration signal of the gearbox, preprocess the data from multiple sensors and construct the input samples for DBN. Finally, combining with deep learning featured by powerful self-adaptive feature extraction and nonlinear mapping capabilities, the obtained samples are input into DBN for training, and the fault diagnosis model for gearbox based on DBN would be established. After several tests with the remaining samples, the diagnosis rate of the model could reach over 99.5%, which is far better than the traditional fault diagnosis method based on feature extraction and pattern recognition. The experimental results show that this method could effectively improve the self-adaptive feature extraction ability of the model as well as its accuracy of fault diagnosis, which has better generalization performance

Pre-treatments for enhanced biochemical methane potential of bamboo waste

Various pre-treatments (acid, alkaline, enzyme and alkaline aided enzyme also termed combined) were evaluated on different fractions of bamboo waste from a chopstick production factory. Chemical oxygen demand (COD) solubilisation, monomeric/dimeric sugar yield, methane yield enhancement and methane production rate were assessed. The biochemical methane potential was determined in batch assays under mesophilic conditions (37 1 C) using the Automatic Methane Potential Test System (AMPTS-II). Pre-treatments led to enhanced COD solubilisation as compared to raw sample. Alkaline aided enzymatic pre-treatment led to the highest sugar yield, comparable to the theoretical yield. However, high sugar yield did not translate to high methane yield. The best pre-treatment in terms of methane yield was alkaline pre-treatment which resulted in a surplus of up to 88% methane yield. There was a positive correlation between dissolved COD and methane yield. Methane yield and methane production rate also increased with decreasing particle sizes. In all investigated scenarios, pre-treatment led to an improved methane production rate as compared to the raw samples. These results demonstrated that alkaline pre-treatment at ambient temperature was an efficient treatment option to improve methane yield of bamboo waste. (C) 2013 Elsevier B.V. All rights reserved