Abstract Vision‐based autonomous inspection of concrete surface defects is crucial for efficient maintenance and rehabilitation infrastructures has become a research hot spot. However, most existing vision‐based methods mainly focus on detecting one kind defect in nearly uniform testing background where are relatively large easily recognizable. But the real‐world scenarios, multiple types often occur simultaneously. And them occupy only small fractions images swamped cluttered background,...

With the rapid development of autonomous driving, collision avoidance has attracted attention from both academia and industry. Many strategies have emerged in recent years, but dynamic complex nature driving environment poses a challenge to develop robust algorithms. Therefore, this paper, we propose decentralized framework named RACE: Reinforced Cooperative Autonomous Vehicle Collision AvoidancE. Leveraging hierarchical architecture an algorithm Co-DDPG efficiently train vehicles. Through...

Automatic inspection of surface defects is crucial in industries for real-time applications. Nowadays, computer vision-based approaches have been successfully employed. However, most the existing works need a large number training samples to achieve satisfactory classification results, while collecting massive datasets labor-intensive and financially costly. Moreover, them obtain high accuracy at expense latency, are thus not suitable In this work, novel Concurrent Convolutional Neural...

Large private and government networks are often subjected to attacks like data extrusion service disruption. Existing anomaly detection systems use offline supervised learning employ experts for labeling. Hence they cannot detect anomalies in real-time. Even though unsupervised algorithms increasingly used nowadays, readily adapt newer threats. Moreover, many such also suffer from high cost of storage require extensive computational resources. In this paper, we propose ADA: Adaptive Deep Log...

Internet of Things (IoTs) have been widely applied in Collaborative Intelligent Transportation Systems (C-ITS) for the prevention road accidents. As one primary causes accidents C-ITS, efficient detection and early alarm hazards are paramount importance. Given importance, extensive research has explored this topic obtained favorable results. However, most existing solutions only explore single-modality data, struggle with high computation communication overhead, or suffer from curse...

Road damages have caused numerous fatalities, thus the study of road damage detection, especially hazardous detection and warning is critical for traffic safety. Existing systems mainly process data at cloud, which suffers from a high latency by long-distance. Meanwhile, supervised machine learning algorithms are usually used in these requiring large precisely labeled sets to achieve good performance. In this article, we propose EcRD: an edge-cloud-based framework, that leverages...

Data analytics is regarded as an important function of 5G networks. The Network Analytics Function (NWDAF) standardized in 3GPP to enhance network performance by analyzing data from functions and user equipment. Abnormal behavior detection, which part the NWDAF framework, has potential be a powerful tool improve security. Despite this, only limited research been conducted area so far. This paper explains abnormal detection specified 3GPP. Furthermore, we extensively review related work...

Network intrusion detection is a relatively mature research topic, but one that remains challenging particular as technologies and threat landscape evolve. Here, semi-supervised tri-Adaboost (STA) algorithm proposed. In the algorithm, three different Adaboost algorithms are used weak classifiers (both for continuous categorical data), constituting decision stumps in tri-training method. addition, chi-square method to reduce dimension of feature improve computational efficiency. We then...

Abstract Automatic defect classification is vital to ensure product quality, especially for steel production. In the real world, amount of collected samples with labels limited due high labor costs, and gathered dataset usually imbalanced, making accurate very challenging. this paper, a novel deep learning model imbalanced multi-label surface classification, named ImDeep, proposed. It can be deployed easily in production lines identify different types on steel’s surface. ImDeep incorporates...

Nowadays, autonomous electric vehicles (AEVs) are increasingly popular due to low resource consumption, pollutant emission, and high efficiency. In practice, Vehicle-to-Grid (V2G) networks supply energy power EVs ensure the usage of EVs. However, there still certain security privacy concerns in V2G connections, such as identity impersonation message manipulation. Additionally, widespread brings significant pressure on grid, leading undesirable effects like voltage deviations if EVs' charging...

Recently, with various developing sensors, mobile devices have become interesting in the research community for indoor localization. In this paper, we propose Twi-Adaboost, a collaborative localization algorithm fusion of internal such as accelerometer, gyroscope, and magnetometer from multiple devices. Specifically, data sets are collected first by one person wearing two simultaneously: smartphone smartwatch, each collecting multivariate represented their parameters real environment. Then,...

The proliferation of the Internet Things (IoT) has heightened vulnerability to cyber threats, making it imperative develop Anomaly Detection Systems (ADSs) capable adapting emerging or novel attacks. Prior research predominantly concentrated on offline unsupervised learning techniques protect ADSs, which are impractical for real-world applications. Furthermore, these studies often rely heavily assumption known legitimate behaviors and fall short meeting interpretability requirements in...

Semi-supervised learning holds a pivotal position in anomaly detection applications, yet identifying patterns with limited number of labeled samples poses significant challenge. Furthermore, the absence interpretability major obstacles to practical adoption semi-supervised frameworks. The majority existing interpretation techniques are tailored for supervised/unsupervised frameworks or non-security domains, falling short providing dependable interpretations. In this research paper, we...

The 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> generation of mobile networks introduces a new Network Function (NF) that was not present in previous generations, namely the Data Analytics (NWDAF). Its primary objective is to provide advanced analytics services various entities within network and also towards external application 5G ecosystem. One key use cases NWDAF mobility trajectory prediction, which aims accurately support...

With the rapid development of autonomous driving, collision avoidance has attracted attention from both academia and industry. Many strategies have emerged in recent years, but dynamic complex nature driving environment poses a challenge to develop robust algorithms. Therefore, this paper, we propose decentralized framework named RACE: Reinforced Cooperative Autonomous Vehicle Collision AvoidancE. Leveraging hierarchical architecture an algorithm Co-DDPG efficiently train vehicles. Through...