Deep convolutional neural network-based transfer learning method for health condition identification of cable in cable-stayed bridge

The cables are extremely important and vulnerable components in the cable-stayed bridges. Because cable tension is one of the most crucial structural health indicators, therefore, assessing the cable condition based on the cable tension is a major interest in the structural health monitoring (SHM) of the cable-stayed bridges. This paper aims to develop a deep convolutional neural network (DCNN)-based transfer learning method that is integrated with a continuous wavelet transform (CWT) for the health condition identification of the cables in a cable-stayed bridge using the one-dimensional time series cable tension data. For this purpose, the CWT is adopted to convert the cable tension to the images of a time-frequency representation. The last three new layers emerged in the pre-trained DCNN model, which is called AlexNet, as a new learning framework to use for the identification of the cable condition. The performance of the proposed DCNN model is examined using several statistical measures that include accuracy, sensitivity, specificity, precision, recall, and the F-measure. The results show that the proposed DCNN model gives superior accuracy (100%) for the identification of the undamaged cables and the damaged cables based on the cable tension data

An Ultra Fast Semantic Segmentation Model for AMR’s Path Planning

Computer vision plays a significant role in mobile robot navigation due to the abundance of information extracted from digital images. On the basis of the captured images, mobile robots determine their location and proceed to the desired destination. Obstacle avoidance still requires a complex sensor system with a high computational efficiency requirement due to the complexity of the environment. This research provides a real-time solution to the issue of extracting corridor scenes from a single image. Using an ultra-fast semantic segmentation model to reduce the number of training parameters and the cost of computation. In addition, the mean Intersection over Union (mIoU) is 89%, and the high accuracy is 95%. To demonstrate the viability of the prosed method, the simulation results are contrasted to those of contemporary techniques. Finally, the authors employ the segmented image to construct the frontal view of the mobile robot in order to determine the available free areas for mobile robot path planning tasks

A Secured, Multilevel Face Recognition based on Head Pose Estimation, MTCNN and FaceNet

Artificial Intelligence and IoT have always attracted a lot of attention from scholars and researchers because of their high applicability, which make them a typical technology of the Fourth Industrial Revolution. The hallmark of AI is its self-learning ability, which enables computers to predict and analyze complex data such as bio data (fingerprints, irises, and faces), voice recognition, text processing. Among those application, the face recognition is under intense research due to the demand in users’ identification. This paper proposes a new, secured, two-step solution for an identification system that uses MTCNN and FaceNet networks enhanced with head pose estimation of the users. The model's accuracy ranges from 92% to 95%, which make it competitive with recent research to demonstrate the system's usability

Scrambling for higher metrics in the Journal Impact Factor bubble period: a real-world problem in science management and its implications

Universities and funders in many countries have been using Journal Impact Factor (JIF) as an indicator for research and grant assessment despite its controversial nature as a statistical representation of scientific quality. This study investigates how the changes of JIF over the years can affect its role in research evaluation and science management by using JIF data from annual Journal Citation Reports (JCR) to illustrate the changes. The descriptive statistics find out an increase in the median JIF for the top 50 journals in the JCR, from 29.300 in 2017 to 33.162 in 2019. Moreover, on average, elite journal families have up to 27 journals in the top 50. In the group of journals with a JIF of lower than 1, the proportion has shrunk by 14.53% in the 2015–2019 period. The findings suggest a potential ‘JIF bubble period’ that science policymaker, university, public fund managers, and other stakeholders should pay more attention to JIF as a criterion for quality assessment to ensure more efficient science management

Evaluation of Asaoka and Hyperbolic Methods for Settlement Prediction of Vacuum Preloading Combined with Prefabricated Vertical Drains in Soft Ground Treatment

This study evaluated the use of the Asaoka and hyperbolic methods to estimate the ultimate settlement of soft ground treated by vacuum preloading combined with prefabricated vertical drains. For this aim, a large-scale physical laboratory model was constructed. The model was a reinforced-tempered glass box containing a soil mass with dimensions of 2.0 × 1.0 × 1.2 m (length × width × depth). Physical models of this scale for the same purpose are rare in the literature. The soil was taken from a typical coastal region in Dinh Vu Hai Phong, Vietnam. The surface settlement near and between the two drains was measured right after the vacuum preloading started. Important properties of the soil were tested to evaluate the effectiveness of the treatment method. The measured settlement was used in the Asaoka and hyperbolic methods to predict the potential ultimate settlement. The results showed the superiority of the vacuum consolidation approach in improving fundamental engineering properties of soft soil. Furthermore, the ultimate settlement predicted by both methods showed a good agreement with the measured value, proving that the Asaoka and hyperbolic methods are suitable for the estimation of the ultimate settlement of soft soil treated with vacuum consolidation

Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks

Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn knowledge from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning knowledge among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% as compared to the state-of-the-art deep learning based approaches.Comment: 12 page

Damage detection for a cable-stayed Bridge under the effect of moving loads using Transmissibility and Artificial Neural Network

Artificial Neural Network (ANN) has been widely used for Structural Health Monitoring (SHM) in the last decades. To detect damage in the structure, ANN often uses input data consisting of natural frequencies or mode shapes. However, this data is not sensitive enough to accurately identify minor structural defects. Therefore, in this study, we propose to use transmissibility to generate input data for the input layer of ANN. Transmissibility uses output signals exclusively to preserve structural dynamic properties and is sensitive to damage characteristics. To evaluate the efficiency of the proposed approach, a cable-stayed bridge with a wide variety of damage scenarios is employed. The results show that the combination of transmissibility and ANN not only accurately detect damages but also outperforms natural frequencies-based ANN in terms of accuracy and computational cost

Evaluation Of Allelopathic Potential Of Cissus sicyoides Against the Growth Of Echinochloa Crus-Galli And Some Tested Plants

Many plant species in nature exert significant allelopathic potential as part of the defense mechanism system, many among their secondary metabolites (allelochemicals), including mineral constituents, which are responsible for the suppression of weeds and enhancing crop yield when directly incorporated into paddy fields. Cissus sicyoides is considered a high-potential allelopathic plant because of its invasion in nature and detected allelochemicals from the plant parts in some studies. The objective of this research was to exploit the allelopathic properties of C.sicyoides against paddy weeds and some indicator plants under laboratory bioassays and greenhouse conditions. The results demonstrated that C. sicyoides had significant inhibition on E. crus-galli, tested plants, and other paddy weeds. In the laboratory conditions, the extracts from C.sicyoides leaves inhibited the growth of Echinochloa crus-galli by 54.3%. The powders from C.sicyoides leaves inhibited the emergence of paddy weeds by approximately 100.0%. In the greenhouse conditions, the powders from C.sicyoides leaves by adding after 3 and 13 days inhibited the growth of E. crus-galli and the emergence of paddy weeds by 64.4%. Remarkably, negligible harmful effects on rice growth were observed. The findings of the study may provide useful information for the exploitation of this plant species to effectively control weeds in the rice fields for sustainable agriculture production

Damage detection for a cable-stayed Bridge under the effect of moving loads using Transmissibility and Artificial Neural Network

Artificial Neural Network (ANN) has been widely used for Structural Health Monitoring (SHM) in the last decades. To detect damage in the structure, ANN often uses input data consisting of natural frequencies or mode shapes. However, this data is not sensitive enough to accurately identify minor structural defects. Therefore, in this study, we propose to use transmissibility to generate input data for the input layer of ANN. Transmissibility uses output signals exclusively to preserve structural dynamic properties and is sensitive to damage characteristics. To evaluate the efficiency of the proposed approach, a cable-stayed bridge with a wide variety of damage scenarios is employed. The results show that the combination of transmissibility and ANN not only accurately detect damages but also outperforms natural frequencies-based ANN in terms of accuracy and computational cost