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Full-Text Articles in Engineering
A Hybrid Unsupervised Clustering-Based Anomaly Detection Method, Guo Pu, Lijuan Wang, Jun Shen, Fang Dong
A Hybrid Unsupervised Clustering-Based Anomaly Detection Method, Guo Pu, Lijuan Wang, Jun Shen, Fang Dong
Faculty of Engineering and Information Sciences - Papers: Part B
In recent years, machine learning-based cyber intrusion detection methods have gained increasing popularity. The number and complexity of new attacks continue to rise; therefore, effective and intelligent solutions are necessary. Unsupervised machine learning techniques are particularly appealing to intrusion detection systems since they can detect known and unknown types of attacks as well as zero-day attacks. In the current paper, we present an unsupervised anomaly detection method, which combines Sub-Space Clustering (SSC) and One Class Support Vector Machine (OCSVM) to detect attacks without any prior knowledge. The proposed approach is evaluated using the well-known NSL-KDD dataset. The experimental results demonstrate …
Deep Gabor Neural Network For Automatic Detection Of Mine-Like Objects In Sonar Imagery, Hoang Thanh Le, Son Lam Phung, Philip B. Chapple, Abdesselam Bouzerdoum, Christian H. Ritz, Le Chung Tran
Deep Gabor Neural Network For Automatic Detection Of Mine-Like Objects In Sonar Imagery, Hoang Thanh Le, Son Lam Phung, Philip B. Chapple, Abdesselam Bouzerdoum, Christian H. Ritz, Le Chung Tran
Faculty of Engineering and Information Sciences - Papers: Part B
With the advances in sonar imaging technology, sonar imagery has increasingly been used for oceanographic studies in civilian and military applications. High-resolution imaging sonars can be mounted on various survey platforms, typically autonomous underwater vehicles, which provide enhanced speed and improved data quality with long-range support. This paper addresses the automatic detection of mine-like objects using sonar images. The proposed Gabor-based detector is designed as a feature pyramid network with a small number of trainable weights. Our approach combines both semantically weak and strong features to handle mine-like objects at multiple scales effectively. For feature extraction, we introduce a parameterized …
Ensemble Machine Learning Approaches For Webshell Detection In Internet Of Things Environments, Binbin Yong, Wei Wei, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Marcin Wozniak, Dawid Polap, Robertas Damasevicius
Ensemble Machine Learning Approaches For Webshell Detection In Internet Of Things Environments, Binbin Yong, Wei Wei, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Marcin Wozniak, Dawid Polap, Robertas Damasevicius
Faculty of Engineering and Information Sciences - Papers: Part B
The Internet of things (IoT), made up of a massive number of sensor devices interconnected, can be used for data exchange, intelligent identification, and management of interconnected “things.” IoT devices are proliferating and playing a crucial role in improving the living quality and living standard of the people. However, the real IoT is more vulnerable to attack by countless cyberattacks from the Internet, which may cause privacy data leakage, data tampering and also cause significant harm to society and individuals. Network security is essential in the IoT system, and Web injection is one of the most severe security problems, especially …
Air Void Detection Using Variational Mode Decomposition With Low Rank, Fok Hing Chi Tivive, Abdesselam Bouzerdoum, Shivakumar Karekal
Air Void Detection Using Variational Mode Decomposition With Low Rank, Fok Hing Chi Tivive, Abdesselam Bouzerdoum, Shivakumar Karekal
Faculty of Engineering and Information Sciences - Papers: Part B
This paper presents an air-void detection technique for air-coupled radar, which emits electromagnetic waves to interrogate an air-void inside a medium or between two media. The reflections from the air-medium interfaces are usually corrupted by air-coupling, antenna ringing, and internal reflections, rendering air-void detection very difficult or, in certain cases, impossible. The proposed method exploits the low-rank structure of the background clutter to suppress these nuisance signals. A variational mode decomposition model is developed to extract the backscattering at different air-medium interfaces as signal modes. Real experiments are conducted using a stepped frequency radar. The experimental results show that the …
Large Expert-Curated Database For Benchmarking Document Similarity Detection In Biomedical Literature Search, Peter Brown, Relish Consortium, Yaoqi Zhou
Large Expert-Curated Database For Benchmarking Document Similarity Detection In Biomedical Literature Search, Peter Brown, Relish Consortium, Yaoqi Zhou
Faculty of Engineering and Information Sciences - Papers: Part B
Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were …
A Numerical Approach To Design The Kretschmann Configuration Based Refractive Index Graphene-Mos2 Hybrid Layers With Tio2-Sio2 Nano For Formalin Detection, Md. Biplob Hossain, Tamanna Tasnim, Lway Abdulrazak, Md Masud Rana, Md Rabiul Islam
A Numerical Approach To Design The Kretschmann Configuration Based Refractive Index Graphene-Mos2 Hybrid Layers With Tio2-Sio2 Nano For Formalin Detection, Md. Biplob Hossain, Tamanna Tasnim, Lway Abdulrazak, Md Masud Rana, Md Rabiul Islam
Faculty of Engineering and Information Sciences - Papers: Part B
In this paper, a Kretschmann configuration based surface plasmon resonance (SPR) sensor is numerically designed using graphene-MoS2 hybrid structure TiO2-SiO2 nano particles for formalin detection. In this design, the observations of SPR angle versus minimum reflectance and SPR frequency (FSPR) versus maximum transmittance (Tmax) are considered. The chitosan is used as probe legend to perform reaction with the formalin (40% formaldehyde) which acts as target legend. In this paper, both graphene and MoS2 are used as biomolecular acknowledgment element (BAE) and TiO2 as well as SiO2 bilayers is used to improve the sensitivity of the sensor. The numerical results show …
Integrated Condition Monitoring And Prognosis Method For Incipient Defect Detection And Remaining Life Prediction Of Low Speed Slew Bearings, Wahyu Caesarendra, Tegoeh Tjahjowidodo, Buyung Kosasih, Anh Kiet Tieu
Integrated Condition Monitoring And Prognosis Method For Incipient Defect Detection And Remaining Life Prediction Of Low Speed Slew Bearings, Wahyu Caesarendra, Tegoeh Tjahjowidodo, Buyung Kosasih, Anh Kiet Tieu
Faculty of Engineering and Information Sciences - Papers: Part B
This paper presents an application of multivariate state estimation technique (MSET), sequential probability ratio test (SPRT) and kernel regression for low speed slew bearing condition monitoring and prognosis. The method is applied in two steps. Step (1) is the detection of the incipient slew bearing defect. In this step, combined MSET and SPRT is used with circular-domain kurtosis, time-domain kurtosis, wavelet decomposition (WD) kurtosis, empirical mode decomposition (EMD) kurtosis and the largest Lyapunov exponent (LLE) feature. Step (2) is the prediction of the selected features' trends and the estimation of the remaining useful life (RUL) of the slew bearing. In …
Gpu Based Simulations Of Collision Detection Of Irregular Vessel Walls, Binbin Yong, Jun Shen, Hongyu Sun, Zijian Xu, Jingfeng Liu, Qingguo Zhou
Gpu Based Simulations Of Collision Detection Of Irregular Vessel Walls, Binbin Yong, Jun Shen, Hongyu Sun, Zijian Xu, Jingfeng Liu, Qingguo Zhou
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Gvm Based Intuitive Simulation Web Application For Collision Detection, Binbin Yong, Jun Shen, Zebang Shen, Huaming Chen, Xin Wang, Qingguo Zhou
Gvm Based Intuitive Simulation Web Application For Collision Detection, Binbin Yong, Jun Shen, Zebang Shen, Huaming Chen, Xin Wang, Qingguo Zhou
Faculty of Engineering and Information Sciences - Papers: Part B
Computer simulation, which has been proved to be an effective approach to problem solving, is nowadays widely used in modern science. However, it requires a lot of computing resources, which are difficult for general users to acquire. In this paper, we design a Web based system to implement on-line simulation system for ordinary users. As a useful example, the simulation of one type of collision detection model is presented in this paper. Moreover, the software application of simulation is offered as a service on Web. Meanwhile, the incorporation of general vector machine (GVM, a type of neural network) to intelligently …
Detecting Visual Spoofing Using Classical Cryptanalysis Methods In Plagiarism Detection Systems, Yang-Wai Chow, Willy Susilo, Ilung Pranata, Ari Moesriami Barmawi
Detecting Visual Spoofing Using Classical Cryptanalysis Methods In Plagiarism Detection Systems, Yang-Wai Chow, Willy Susilo, Ilung Pranata, Ari Moesriami Barmawi
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.