Stochastic Interval Analysis of Structures in the Presence of Uncertain Fields

An efficient framework for non-deterministic problems in view of Galerkin spectral methods is presented in this thesis. The present work adopts different models of uncertainty involving spatial variation on random domain, interval modelling and mixed probabilistic/non-probabilistic models. The aim is to establish an appropriate processing of modelling uncertainty quantities in the early stage of structural design so that the empirical information is effectively reflected in procedures of analysis to attain reliable results. Notably, while the mixed uncertainty such as stochastic field combined with interval variables or mixed probabilistic/non-probabilistic models make the physical models to be presented in a more realistic manner, it may lead to the introduction of intricate formalism in terms of mathematical presentations. This concern is efficiently addressed in this thesis with the integration of computational stochastic mechanics in view of Galerkin spectral methods into analysis, known as the spectral stochastic finite element method (SSFEM). The present work introduces the combination of SSFEM and interval analysis, namely interval spectral stochastic finite element method (ISSFEM), to accommodate the hybrid uncertainty wherein stochastic fields and interval parameters are simultaneously considered. Then, multi-imprecise random fields are incorporated into SSFEM to further enrich the mixed probabilistic/non-probabilistic models. The distinguished advantage of the developed framework is the presentation of a high degree of generality in which uncertainty with characteristics falling between the traditional probabilistic models and non-probabilistic models. On the side of improving the solution technique especially for stochastic cracked problems, the aggregation of the scaled boundary finite element method (SBFEM) and SSFEM inheriting both of their advantages produce a high-performance approach. The results attained from methods devised in this thesis are verified by those achieved by other approaches and referenced studies. The good agreements demonstrate the accuracy of those methods while the less time consuming and possibility of treating multiple interval variables highlight their outstanding features compared to other techniques. The developed non-deterministic analysis framework consolidates the base which is essential to engineering practitioners for cost-effective new design and timely rehabilitation of existing infrastructure

Impacts of pollution discharges from Dinh Vu industrial zone on water quality in the Hai Phong coastal area

The hydrodynamic and water quality models (the Delft3D model) were established based on the measured data and the estimated pollution discharges from Dinh Vu industrial zones to Nam Trieu estuary. With seven separate simulation scenarios, the results show that in case of increased wastewater with the control of pollution discharge (water and concentration), the impact of pollution is only limited to a small area around the discharge point. Their influences on water quality in other areas in Nam Trieu estuary are quite small. Meanwhile, in case of environmental risk, a strongly increasing pollution load would cause the significantly increasing pollutant concentration in this area, they have almost exceeded the value in the National Technical Regulation on surface water quality (QCVN 10-MT:2015/BTNMT), such as NH4, COD, and BOD. Dissolved oxygen in the water would also decrease significantly. The spatial influence extends from the discharge point to Nam Trieu estuary, inside Cam, Bach Dang rivers, and Cat Hai coastal area

A Novel Wideband Bandpass Filter using H-shaped DGS

This paper presents a novel compact wide-band bandpass filter (BPF) having good selectivity. It is designed using a dual-plane structure which consists of a parallel-coupled microstrip line on the upper surface and three H-shape defected ground structures (DGS) on the ground plane. By adding three H-shape DGS units on the ground plane, then properly adjusting their dimensions and position, the bandwidth and selectivity of the designed filter can be significantly improved. A compact prototype of wide-band microstrip bandpass filter has been designed, fabricated and measured for the wireless systems applications. The filter exhibits a center frequency at 4.8 GHz, passband from 2.8 GHz to 6.8 GHz with best insertion loss and return loss of 0.8 dB and 40 dB, respectively. The measured results agrees well with the theoretical expectations validating the proposed design

Cloning and expression of pigC gene in Escherichia coli

Prodigiosin (Pg), which is particularly of interest because of anticancer and antimicrobial activities, can be produced through the PigC-catalyzed condensation reaction of 4-methoxy-2, 2’-bipyrrole-5-carboxyaldehyde (MBC) and 2-methyl-3-amylpyrrole (MAP). Therefore, the PigC protein plays an important role in prodigiosin biosynthetic pathway. However, studies related to PigC protein have not been carried out in Vietnam yet. In this work, the pigC gene was cloned and expressed in Escherichia coli DH10B and BL21 (DE3), respectively. Using PCR and universal primers, we amplified a fragment of 3 kb covering entire coding region of the pigC gene from Serratia sp. strain M5. The pigC gene was inserted into pJET1.2 vector, and then transformed into E. coli DH10B. The sequence of a recombinant vector pJET1.2/pigC was evaluated by using whole colony PCR amplification. Sequence alignment results revealed that the obtained pigC gene possesses 71.5% and 75.4% of nucleotide identity in comparison with two strains, Serratia 39006 and Serratia sp. AS9 published in GenBank with their respective accession numbers of AJ833001 and CP002773. The recombinant vector pJET1.2/pigC was used to reamplify pigC, and the acquired amplicon was inserted into pET22b vector at the site of HindIII and XhoI. The clone E. coli BL21 (DE3) containing recombinant vector pET22b/pigC was expressed in the auto-induced medium. The presence of PigC protein in the lysate was identified as a 100 kDa band through Western Blot analysis using anti his-tag antibody. Afterward, the PigC protein was purified by Ni-NTA column, and its expression level was quantified through SDS-PAGE analysis. The results of our study provide a potential material for producing prodigiosin from recombinant protein in Vietnam

Multi-layer moving plate method for dynamic analysis of pavement structures subjected to moving loads

This paper presents a new approach, namely multi-layer moving plate method (MMPM), for the dynamic analysis of pavement structures subjected to moving load. The pavement resting on multi-layer foundation is modeled as a two-layer plate connected by a spring-damper system resting on a viscoelastic foundation. This model gives a accurately pavement structure model so that the dynamic responses of the surface slab and the base can be obtained. The governing equations as well as the plate element mass, damping and stiffness matrices are formulated in a convected coordinate with the idea of attaching its origin to the applied point of the moving load. The proposed method simply treats the moving load as ‘stationary’ at the node of the plate to avoid the updating of the location of moving load due to change of contact points on the plate. Numerical examples related to the dynamic analysis of the pavement structure subjected to a moving load are conducted to investigate the effects of various parameters such as concrete slab thickness, base thickness, foundation stiffness and the load’s velocity on dynamic responses of the pavement structure

MirrorNet: Bio-Inspired Camouflaged Object Segmentation

Camouflaged objects are generally difficult to be detected in their natural environment even for human beings. In this paper, we propose a novel bio-inspired network, named the MirrorNet, that leverages both instance segmentation and mirror stream for the camouflaged object segmentation. Differently from existing networks for segmentation, our proposed network possesses two segmentation streams: the main stream and the mirror stream corresponding with the original image and its flipped image, respectively. The output from the mirror stream is then fused into the main stream's result for the final camouflage map to boost up the segmentation accuracy. Extensive experiments conducted on the public CAMO dataset demonstrate the effectiveness of our proposed network. Our proposed method achieves 89% in accuracy, outperforming the state-of-the-arts. Project Page: https://sites.google.com/view/ltnghia/research/camoComment: Under Revie

Impact of sand mining on sediment transport and morphological change of Hai Phong coastal area

The paper presents the results of simulating the impact of sand mining on sediment transport and morphological change in Hai Phong coastal area based on Delft3D model. Scenario groups are established: Present scenarios without sand mining and simulated scenarios of the present sand mining (assuming 30% sand compared to plan). The scenario groups are calculated in the dry and rainy seasons for main wind directions (NE, SE, SW) in the case of moderate wind and strong wind. The results show that sand mining reduces sediment flow alongshore (2–81%) and seawards (5–83%). Besides, the increase in depth causes morphological change in this area: Rising the deposition rate (5–10 mm/month) at the sand mining locations; decreasing accretion rate and increasing the erosion rate in the neighboring areas of sand mining location

Fed-LSAE: Thwarting Poisoning Attacks against Federated Cyber Threat Detection System via Autoencoder-based Latent Space Inspection

The significant rise of security concerns in conventional centralized learning has promoted federated learning (FL) adoption in building intelligent applications without privacy breaches. In cybersecurity, the sensitive data along with the contextual information and high-quality labeling in each enterprise organization play an essential role in constructing high-performance machine learning (ML) models for detecting cyber threats. Nonetheless, the risks coming from poisoning internal adversaries against FL systems have raised discussions about designing robust anti-poisoning frameworks. Whereas defensive mechanisms in the past were based on outlier detection, recent approaches tend to be more concerned with latent space representation. In this paper, we investigate a novel robust aggregation method for FL, namely Fed-LSAE, which takes advantage of latent space representation via the penultimate layer and Autoencoder to exclude malicious clients from the training process. The experimental results on the CIC-ToN-IoT and N-BaIoT datasets confirm the feasibility of our defensive mechanism against cutting-edge poisoning attacks for developing a robust FL-based threat detector in the context of IoT. More specifically, the FL evaluation witnesses an upward trend of approximately 98% across all metrics when integrating with our Fed-LSAE defense