Search results for: fault detection and isolation (FDI)

Authors: Abdulaziz Alsadhan, Naveed Khan

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In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP)

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Authors: Z. A. Al Marhoon, H. S. Al Ramis, C. Teodoriu

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Well-integrity is one of the major elements considered for drilling geothermal, oil, and gas wells. Well-integrity is minimizing the risk of unplanned fluid flow in the well bore throughout the well lifetime. Well integrity is maximized by applying technical concepts along with practical practices and strategic planning. These practices are usually governed by standardization and regulation entities. Practices during well construction can affect the integrity of the seal at the time of abandonment. On the other hand, achieving a perfect barrier system is impracticable due to the needed cost. This results in a needed balance between regulations requirements and practical applications. The guidelines are only effective when they are attainable in practical applications. Various governmental regulations and international standards have different guidelines on what constitutes high-quality isolation from unwanted flow. Each regulating or standardization body differ in requirements based on the abandonment objective. Some regulation account more for the environmental impact, water table contamination, and possible leaks. Other regulation might lean towards driving more economical benefits while achieving an acceptable isolation criteria. The research methodology used in this topic is derived from a literature review method combined with a compare and contrast analysis. The literature review on various zonal isolation regulations and standards has been conducted. A review includes guidelines from NORSOK (Norwegian governing entity), BSEE (USA offshore governing entity), API (American Petroleum Institute) combined with ISO (International Standardization Organization). The compare and contrast analysis is conducted by assessing the objective of each abandonment regulations and standardization. The current state of well barrier regulation is in balancing action. From one side of this balance, the environmental impact and complete zonal isolation is considered. The other side of the scale is practical application and associated cost. Some standards provide a fair amount of details concerning technical requirements and are often flexible with the needed associated cost. These guidelines cover environmental impact with laws that prevent major or disastrous environmental effects of improper sealing of wells. Usually these regulations are concerned with the near future of sealing rather than long-term. Consequently, applying these guidelines become more feasible from a cost point of view to the required plugging entities. On the other hand, other regulation have well integrity procedures and regulations that lean toward more restrictions environmentally with an increased associated cost requirements. The environmental impact is detailed and covered with its entirety, including medium to small environmental impact in barrier installing operations. Clear and precise attention to long-term leakage prevention is present in these regulations. The result of the compare and contrast analysis of the literature showed that there are various objectives that might tip the scale from one side of the balance (cost) to the other (sealing quality) especially in reference to zonal isolation. Furthermore, investing in initial well construction is a crucial part of ensuring safe final well abandonment. The safety and the cost saving at the end of the well life cycle is dependent upon a well-constructed isolation systems at the beginning of the life cycle. Long term studies on zonal isolation using various hydraulic or mechanical materials need to take place to further assess permanently abandoned wells to achieve the desired balance. Well drilling and isolation techniques will be more effective when they are operationally feasible and have reasonable associated cost to aid the local economy.

Keywords: plug and abandon, P&A regulation, P&A standards, international guidelines, gap analysis

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Authors: Christina Van Wingerden

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This paper focuses on a sense of belonging, isolation, and the use of a learning management system as a “third space” for connection and community. Given student use of learning management systems (LMS) for courses on campuses, moderate to high use of social media and hand-held devices, the author explores the possibilities of LMS as a third space. The COVID-19 pandemic has exacerbated student experiences of isolation, and research indicates that students who experience a sense of belonging have a greater likelihood for academic retention and success. The impacts on students of an LMS designed for student employee orientation and training were examined through a mixed methods approach, including a survey, individual interviews, and focus groups. The sample involved 250-450 undergraduate student employees at a US northwestern university. The goal of the study was to find out the efficiency and effectiveness of the orientation information for a wide range of student employees from multiple student affairs departments. And unexpected finding emerged within the study in 2015 and was noted again as a finding in the 2017 study. Students reported feeling like they individually connected to the department, and further to the university because of the LMS orientation. They stated they could see themselves as part of the university community and like they belonged. The orientation, through the LMS, was designed for and occurred online (asynchronous), prior to students traveling and beginning university life for the academic year. The students indicated connection and belonging resulting from some of the design features. With the onset of COVID-19 and prolonged sheltering in place in North America, as well as other parts of the world, students have been precluded from physically gathering to educate and learn. COVID-19 essentially paused face-to-face education in 2020. Media, governments, and higher education outlets have been reporting on widespread college student stress, isolation, loneliness, and sadness. In this context, the author conducted a current mixed methods study (online survey, online interviews) of students in advanced degree programs, like Ph.D. and Ed.D. specifically investigating isolation and sense of belonging. As a part of the study a prototype of a Canvas site was experienced by student interviewees for their reaction of this Canvas site prototype as a “third” space. Some preliminary findings of this study are presented. Doctoral students in the study affirmed the potential of LMS as a third space for community and social academic connection.

Keywords: COVID-19, isolation, learning management system, sense of belonging

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Authors: N. Chaitawittanun, M. Munlin

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Due to rapid advancement of powerful image processing software, digital images are easy to manipulate and modify by ordinary people. Lots of digital images are edited for a specific purpose and more difficult to distinguish form their original ones. We propose a clustering method to detect a copy-move image forgery of JPEG, BMP, TIFF, and PNG. The process starts with reducing the color of the photos. Then, we use the clustering technique to divide information of measuring data by Hausdorff Distance. The result shows that the purposed methods is capable of inspecting the image file and correctly identify the forgery.

Keywords: image detection, forgery image, copy-paste, attack detection

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Authors: Lavrentiadou S. N., Angelou V., Chatzimisios K., Papazoglou L.

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The aim of this study was the isolation and culture of keratinocytes and fibroblasts from feline skin to ultimately create an artificial engineered skin (including dermis and epidermis) useful for the effective treatment of large cutaneous deficits in cats. Epidermal keratinocytes and dermal fibroblasts were freshly isolated from skin biopsies using an 8 mm biopsy punch obtained from 8 healthy cats that had undergone ovariohysterectomy. The owner’s consent was obtained. All cats had a complete blood count and a serum biochemical analysis and were screened for feline leukemia virus (FeLV) and feline immunodeficiency virus (FIV) preoperatively. The samples were cut into small pieces and incubated with collagenase (2 mg/ml) for 5-6 hours. Following digestion, cutaneous cells were filtered through a 100 μm cell strainer, washed with DMEM, and grown in DMEM supplemented with 10% FBS. The undigested epidermis was washed with DMEM and incubated with 0.05% Trypsin/0.02% EDTA (TE) solution. Keratinocytes recovered in the TE solution were filtered through a 100 μm and a 40 μm cell strainer and, following washing, were grown on a collagen type I matrix in DMEM: F12 (3:1) medium supplemented with 10% FΒS, 1 μm hydrocortisone, 1 μm isoproterenol and 0.1 μm insulin. Both fibroblasts and keratinocytes were grown in a humidified atmosphere with 5% CO2 at 37oC. The medium was changed twice a week and cells were cultured up to passage 4. Cells were grown to 70-85% confluency, at which point they were trypsinized and subcultured in a 1:4 dilution. The majority of the cells in each passage were transferred to a freezing medium and stored at -80oC. Fibroblasts were frozen in DMEM supplemented with 30% FBS and 10% DMSO, whereas keratinocytes were frozen in a complete keratinocyte growth medium supplemented with 10% DMSO. Both cell types were thawed and successfully grown as described above. Therefore, we can create a bank of fibroblasts and keratinocytes, from which we can recover cells for further culture and use for the generation of skin equivalent in vitro. In conclusion, cutaneous cell isolation and cell culture and expansion were successfully developed. To the authors’ best knowledge, this is the first study reporting isolation and culture of keratinocytes and fibroblasts from feline skin. However, these are preliminary results and thus, the development of autologous-engineered feline skin is still in process.

Keywords: cat, fibroblasts, keratinocytes, skin equivalent, wound

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Authors: Nasibeh Azizi Khereshki

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Olive leaf (OL) extract as a green reductant agent was utilized for the biogenic synthesis of silver nanoparticles (Ag NPs) for the first time in this study, and then its performance was evaluated for colorimetric detection of Fe3+ in different media. Some analytical methods were used to characterize the nanosensor. The effective sensing parameters were optimized by central composite design (CCD) combined with response surface methodology (RSM) application. Then, the prepared material's applicability in antibacterial and optical chemical sensing for naked-eye detection of Fe3+ ions in aqueous solutions were evaluated. Furthermore, OL-Ag NPs-loaded paper strips were successfully applied to the colorimetric visualization of Fe3+. The colorimetric probe based on OL-AgNPs illustrated excellent selectivity and sensitivity towards Fe3+ ions, with LOD and LOQ of 0.81 μM and 2.7 μM, respectively. In addition, the developed method was applied to detect Fe3+ ions in real water samples and validated with a 95% confidence level against a reference spectroscopic method.

Keywords: Ag NPs, colorimetric detection, Fe(III) ions, green synthesis, olive leaves

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Authors: Sehreen Moorat, Mussarat Lakho

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A method of artificial intelligence using digital mammograms data has been proposed in this paper for detection of breast cancer. Many researchers have developed techniques for the early detection of breast cancer; the early diagnosis helps to save many lives. The detection of breast cancer through mammography is effective method which detects the cancer before it is felt and increases the survival rate. In this paper, we have purposed image processing technique for enhancing the image to detect the graphical table data and markings. Texture features based on Gray-Level Co-Occurrence Matrix and intensity based features are extracted from the selected region. For classification purpose, neural network based supervised classifier system has been used which can discriminate between benign and malignant. Hence, 68 digital mammograms have been used to train the classifier. The obtained result proved that automated detection of breast cancer is beneficial for early diagnosis and increases the survival rates of breast cancer patients. The proposed system will help radiologist in the better interpretation of breast cancer.

Keywords: medical imaging, cancer, processing, neural network

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Authors: Sanjal Gavande, Thomas Mazzuchi, Shahram Sarkani

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In a complex aerospace operational environment, identifying failures in a procedure involving multiple human-machine interactions are difficult. These failures could lead to accidents causing loss of hardware or human life. The likelihood of failure further increases if operational procedures are tested for a novel system with multiple human-machine interfaces and with no prior performance data. The existing approach in the literature of reviewing complex operational tasks in a flowchart or tabular form doesn’t provide any insight into potential system failures due to human decision-making ability. To address these challenges, this research explores an agent-based simulation approach for reliability assessment and fault detection in complex human-machine systems while utilizing a human decision-making model. The simulation will predict the emergent behavior of the system due to the interaction between humans and their decision-making capability with the varying states of the machine and vice-versa. Overall system reliability will be evaluated based on a defined set of success-criteria conditions and the number of recorded failures over an assigned limit of Monte Carlo runs. The study also aims at identifying high-likelihood failure locations for the system. The research concludes that system reliability and failures can be effectively calculated when individual human and machine agent states are clearly defined. This research is limited to the operations phase of a system lifecycle process in an aerospace environment only. Further exploration of the proposed agent-based and human decision-making model will be required to allow for a greater understanding of this topic for application outside of the operations domain.

Keywords: agent-based model, complex human-machine system, human decision-making model, system reliability assessment

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Authors: Noureddine Mohtaram, Jeremy Patrix, Jerome Verny

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As an enormous number of online services have been developed into web applications, security problems based on web applications are becoming more serious now. Most intrusion detection systems rely on each request to find the cyber-attack rather than on user behavior, and these systems can only protect web applications against known vulnerabilities rather than certain zero-day attacks. In order to detect new attacks, we analyze the HTTP protocols of web servers to divide them into two categories: normal attacks and malicious attacks. On the other hand, the quality of the results obtained by deep learning (DL) in various areas of big data has given an important motivation to apply it to cybersecurity. Deep learning for attack detection in cybersecurity has the potential to be a robust tool from small transformations to new attacks due to its capability to extract more high-level features. This research aims to take a new approach, deep learning to cybersecurity, to classify these two categories to eliminate attacks and protect web servers of the defense sector which encounters different web traffic compared to other sectors (such as e-commerce, web app, etc.). The result shows that by using a machine learning method, a higher accuracy rate, and a lower false alarm detection rate can be achieved.

Keywords: anomaly detection, HTTP protocol, logs, cyber attack, deep learning

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Authors: Sonali Banrjee, Swarup Kumar Mitra, Aritra Acharyya

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A piano note recognition method has been proposed by the authors in this paper. The authors have used a comprehensive method for onset detection of each note present in a piano piece followed by segmented short-time Fourier transform (STFT) for the identification of piano notes. The performance evaluation of the proposed method has been carried out in different harsh noisy environments by adding different levels of additive white Gaussian noise (AWGN) having different signal-to-noise ratio (SNR) in the original signal and evaluating the note detection error rate (NDER) of different piano pieces consisting of different number of notes at different SNR levels. The NDER is found to be remained within 15% for all piano pieces under consideration when the SNR is kept above 8 dB.

Keywords: AWGN, onset detection, piano note, STFT

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Authors: S. Jagadeesh Kumar

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Face detection and recognition is an authoritative technology for image database management, video surveillance, and human computer interface (HCI). Face recognition is a rapidly nascent method, which has been extensively discarded in forensics such as felonious identification, tenable entree, and custodial security. This paper recommends an erudite technique using curvature analysis (CA) that has less false positives incidence, operative in different light environments and confiscates the artifacts that are introduced during image acquisition by ring correction in polar coordinate (RCP) method. This technique affronts mean and median filtering technique to remove the artifacts but it works in polar coordinate during image acquisition. Investigational fallouts for face detection and recognition confirms decent recitation even in diagonal orientation and stance variation.

Keywords: curvature analysis, ring correction in polar coordinate method, face detection, face recognition, human computer interaction

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Authors: K. Tahir, C. Belfedal, T. Allaoui, C. Gerard, M. Doumi

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In this paper, we presented an optimized RST controller using Particle Swarm Optimization (PSO) meta-heuristic technique of the active and reactive power regulation of a grid connected wind turbine based on a wound field synchronous generator. This regulation is achieved below the synchronous speed, by means of a maximum power point tracking algorithm. The control of our system is tested under typical wind variations and parameters variation, fault grid condition by simulation. Some results are presented and discussed to prove simplicity and efficiency of the WRSG control for WECS. On the other hand, according to simulation results, variable speed driven WRSG is not significantly impacted in fault conditions.

Keywords: wind energy, particle swarm optimization, wound rotor synchronous generator, power control, RST controller, maximum power point tracking

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Authors: Nomfundo Ngombane, Topside E. Mathonsi

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Remote sensing and satellite imaging have been widely used to detect wildfires; nevertheless, the technologies present some limitations in terms of early wildfire detection as the technologies are greatly influenced by weather conditions and can miss small fires. The fires need to have spread a few kilometers for the technologies to provide accurate detection. The South African Advanced Fire Information System uses MODIS (Moderate Resolution Imaging Spectroradiometer) as satellite imaging. MODIS has limitations as it can exclude small fires and can fall short in validating fire vulnerability. Thus in the future, a Machine Learning algorithm will be designed and implemented for the early detection of wildfires. A simulator will be used to evaluate the effectiveness of the proposed solution, and the results of the simulation will be presented.

Keywords: moderate resolution imaging spectroradiometer, advanced fire information system, machine learning algorithm, detection of wildfires

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Authors: In-Geun Lim, Sung-Woong Ra

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As high resolution satellite images can be used, lots of studies are carried out for exploiting these images in various fields. This paper proposes the method based on mathematical morphology for extracting the ‘horse's hoof shaped object’. This proposed method can make an automatic object detection system to track the meaningful object in a large satellite image rapidly. Mathematical morphology process can apply in binary image, so this method is very simple. Therefore this method can easily extract the ‘horse's hoof shaped object’ from any images which have indistinct edges of the tracking object and have different image qualities depending on filming location, filming time, and filming environment. Using the proposed method by which ‘horse's hoof shaped object’ can be rapidly extracted, the performance of the automatic object detection system can be improved dramatically.

Keywords: facility detection, satellite image, object, mathematical morphology

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Authors: Abdulrahman S. Alturki, John S. Loomis

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This paper discusses a corner detection algorithm for camera calibration. Calibration is a necessary step in many computer vision and image processing applications. Robust corner detection for an image of a checkerboard is required to determine intrinsic and extrinsic parameters. In this paper, an algorithm for fully automatic and robust X-corner detection is presented. Checkerboard corner points are automatically found in each image without user interaction or any prior information regarding the number of rows or columns. The approach represents each X-corner with a quadratic fitting function. Using the fact that the X-corners are saddle points, the coefficients in the fitting function are used to identify each corner location. The automation of this process greatly simplifies calibration. Our method is robust against noise and different camera orientations. Experimental analysis shows the accuracy of our method using actual images acquired at different camera locations and orientations.

Keywords: camera calibration, corner detector, edge detector, saddle points

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Authors: Kashika P. H.

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The development of computer vision systems has been greatly aided by the efficient and precise detection of images and videos. Although the ability to recognize and comprehend images is a strength of the human brain, employing technology to tackle this issue is exceedingly challenging. In the past few years, the use of Deep Learning algorithms to treat object detection has dramatically expanded. One of the key issues in the realm of image recognition is the recognition and detection of certain notable people from randomly acquired photographs. Face recognition uses a way to identify, assess, and compare faces for a variety of purposes, including user identification, user counting, and classification. With the aid of an accessible deep learning-based API, this article intends to recognize various faces of people and their facial descriptors more accurately. The purpose of this study is to locate suitable individuals and deliver accurate information about them by using the Amazon Rekognition system to identify a specific human from a vast image dataset. We have chosen the Amazon Rekognition system, which allows for more accurate face analysis, face comparison, and face search, to tackle this difficulty.

Keywords: Amazon rekognition, API, deep learning, computer vision, face detection, text detection

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Authors: Ramzi Rihane, Yassine Benayed

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Epilepsy is a chronic neurological disorder characterized by recurrent, unprovoked seizures resulting from abnormal electrical activity in the brain. Timely and accurate detection of these seizures is essential for improving patient care. In this study, we leverage the UK Bonn University open-source EEG dataset and employ advanced deep-learning techniques to automate the detection of epileptic seizures. By extracting key features from both time and frequency domains, as well as Spectrogram features, we enhance the performance of various deep learning models. Our investigation includes architectures such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), 1D Convolutional Neural Networks (1D-CNN), and hybrid CNN-LSTM and CNN-BiLSTM models. The models achieved impressive accuracies: LSTM (98.52%), Bi-LSTM (98.61%), CNN-LSTM (98.91%), CNN-BiLSTM (98.83%), and CNN (98.73%). Additionally, we utilized a data augmentation technique called SMOTE, which yielded the following results: CNN (97.36%), LSTM (97.01%), Bi-LSTM (97.23%), CNN-LSTM (97.45%), and CNN-BiLSTM (97.34%). These findings demonstrate the effectiveness of deep learning in capturing complex patterns in EEG signals, providing a reliable and scalable solution for real-time seizure detection in clinical environments.

Keywords: electroencephalogram, epileptic seizure, deep learning, LSTM, CNN, BI-LSTM, seizure detection

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Authors: Richard O’Riordan, Saritha Unnikrishnan

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Autonomous Vehicles (AVs) are incorporating an increasing number of ADAS features, including automated lane-keeping systems. In recent years, many research papers into lane detection algorithms have been published, varying from computer vision techniques to deep learning methods. The transition from lower levels of autonomy defined in the SAE framework and the progression to higher autonomy levels requires increasingly complex models and algorithms that must be highly reliable in their operation and functionality capacities. Furthermore, these algorithms have no room for error when operating at high levels of autonomy. Although the current research details existing computer vision and deep learning algorithms and their methodologies and individual results, the research also details challenges faced by the algorithms and the resources needed to operate, along with shortcomings experienced during their detection of lanes in certain weather and lighting conditions. This paper will explore these shortcomings and attempt to implement a lane detection algorithm that could be used to achieve improvements in AV lane detection systems. This paper uses a pre-trained LaneNet model to detect lane or non-lane pixels using binary segmentation as the base detection method using an existing dataset BDD100k followed by a custom dataset generated locally. The selected roads will be modern well-laid roads with up-to-date infrastructure and lane markings, while the second road network will be an older road with infrastructure and lane markings reflecting the road network's age. The performance of the proposed method will be evaluated on the custom dataset to compare its performance to the BDD100k dataset. In summary, this paper will use Transfer Learning to provide a fast and robust lane detection algorithm that can handle various road conditions and provide accurate lane detection.

Keywords: ADAS, autonomous vehicles, deep learning, LaneNet, lane detection

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Authors: Xinghang Lv, Tao Peng, Jia Chen, Junping Liu, Xinrong Hu, Ruhan He, Minghua Jiang, Wenli Cao

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As one of the most dangerous vulnerabilities, effective detection of buffer overflow vulnerabilities is extremely necessary. Traditional detection methods are not accurate enough and consume more resources to meet complex and enormous code environment at present. In order to resolve the above problems, we propose the method for Buffer overflow detection based on Abstract syntax tree, Control flow graph, and Data dependency graph (BodeACD) in C/C++ programs with source code. Firstly, BodeACD constructs the function samples of buffer overflow that are available on Github, then represents them as code representation sequences, which fuse control flow, data dependency, and syntax structure of source code to reduce information loss during code representation. Finally, BodeACD learns vulnerability patterns for vulnerability detection through deep learning. The results of the experiments show that BodeACD has increased the precision and recall by 6.3% and 8.5% respectively compared with the latest methods, which can effectively improve vulnerability detection and reduce False-positive rate and False-negative rate.

Keywords: vulnerability detection, abstract syntax tree, control flow graph, data dependency graph, code representation, deep learning

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Authors: Atinkut Atinafu Yilma, Eyob Messele Sefene

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Anomaly detection is one of the essential mechanisms to control and reduce production loss, especially in today's smart manufacturing. Quick anomaly detection aids in reducing the cost of production by minimizing the possibility of producing defective products. However, developing an anomaly detection model that can rapidly detect a production change is challenging. This paper proposes Gated Recurrent Unit (GRU) combined with Random Forest (RF) to detect anomalies in the production process in real-time quickly. The GRU is used as a feature detector, and RF as a classifier using the input features from GRU. The model was tested using various synthesis and real-world datasets against benchmark methods. The results show that the proposed GRU-RF outperforms the benchmark methods with the shortest time taken to detect anomalies in the production process. Based on the investigation from the study, this proposed model can eliminate or reduce unnecessary production costs and bring a competitive advantage to manufacturing industries.

Keywords: anomaly detection, multivariate time series data, smart manufacturing, gated recurrent unit network, random forest

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Authors: Maryam Heidari, James H. Jones Jr.

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Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event or product. However, this use raises an important question: what percentage of information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a bot, instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. In this paper, we introduce a model for social media bot detection which uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features. Our use of a Natural Language Processing approach to derive topic-independent features for our new bot detection model distinguishes this work from previous bot detection models. We achieve 94\% accuracy classifying the contents of data as generated by a bot or a human, where the most accurate prior work achieved accuracy of 92\%.

Keywords: bot detection, natural language processing, neural network, social media

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Authors: H. Bakhshi, E. Khayyamian

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Cognitive radios have been recognized as one of the most promising technologies dealing with the scarcity of the radio spectrum. In cognitive radio systems, secondary users are allowed to utilize the frequency bands of primary users when the bands are idle. Hence, how to accurately detect the idle frequency bands has attracted many researchers’ interest. Detection performance is sensitive toward noise power and gain fluctuation. Since signal to noise ratio (SNR) between primary user and secondary users are not the same and change over the time, SNR and noise power estimation is essential. In this paper, we present a cooperative spectrum sensing algorithm using SNR estimation to improve detection performance in the real situation.

Keywords: cognitive radio, cooperative spectrum sensing, energy detection, SNR estimation, spectrum sensing, rayleigh fading channel

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Authors: Nacera Yassa, Abdelmalek Saidoune, Ghania Ouadfel, Hamza Houassine

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The rapid advancement in electrical technologies has underscored the increasing importance of multiphase machines across various industrial sectors. These machines offer significant advantages in terms of efficiency, compactness, and reliability compared to their single-phase counterparts. However, early detection and diagnosis of electrical faults remain critical challenges to ensure the durability and safety of these complex systems. This paper presents an advanced analytical model for multiphase machines, with a particular focus on dual stator asynchronous machines. The primary objective is to develop a robust diagnostic tool capable of effectively detecting and locating electrical faults in these machines, including short circuits, winding faults, and voltage imbalances. The proposed methodology relies on an analytical approach combining electrical machine theory, modeling of magnetic and electrical circuits, and advanced signal analysis techniques. By employing detailed analytical equations, the developed model accurately simulates the behavior of multiphase machines in the presence of electrical faults. The effectiveness of the proposed model is demonstrated through a series of case studies and numerical simulations. In particular, special attention is given to analyzing the dynamic behavior of machines under different types of faults, as well as optimizing diagnostic and recovery strategies. The obtained results pave the way for new advancements in the field of multiphase machine diagnostics, with potential applications in various sectors such as automotive, aerospace, and renewable energies. By providing precise and reliable tools for early fault detection, this research contributes to improving the reliability and durability of complex electrical systems while reducing maintenance and operation costs.

Keywords: faults, diagnosis, modelling, multiphase machine

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Authors: Aymen Arfaoui, Abdelkader Soumaya, Noureddine Ben Ayed

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The study region is located in the Tunisian Dorsal Backland (Central Mediterranean), which is the easternmost part of the Saharan Atlas mountain range, trending southwest-northeast. Based on our fieldwork, seismic tomography images, seismicity, and previous studies, we propose an interpretation of the relationship between the surface deformation and fault kinematics in the study area and the internal dynamic processes acting in the Central Mediterranean from the Cenozoic to the present. The subduction and dynamics of internal forces beneath the complicated Maghrebides mobile belt have an impact on the Tertiary and Quaternary tectonic regimes in the Pelagian and Atlassic foreland that is part of our study region. The left lateral reactivation of the major "Tunisian N-S Axis fault" and the development of a compressional relay between the Hammamet Korbous and Messella-Ressas faults are possibly a result of tectonic stresses due to the slab roll-back following the Africa/Eurasia convergence. After the slab segmentation and its eastward migration (5–4 Ma) and the formation of the Strait of Sicily "rift zone" further east, a transtensional tectonic regime has been installed in this area. According to seismic tomography images, the STEP fault of the "North-South Axis" at Hammamet-Korbous coincides with the western edge of the "Slab windows" of the Sicilian Channel and the eastern boundary of the positive anomalies attributed to the residual Slab of Tunisia. On the other hand, significant E-W Plio-Quaternary tectonic activity may be observed along the eastern portion of this STEP fault system in the Grombalia zone as a result of recent vertical lithospheric motion in response to the lateral slab migration eastward to Sicily Channel. According to SKS fast splitting directions, the upper mantle flow pattern beneath Tunisian Dorsal is parallel to the NE-SW to E-W orientation of the Shmin identified in the study area, similar to the Plio-Quaternary extensional orientation in the Central Mediterranean. Additionally, the removal of the lithosphere and the subsequent uplift of the sub-lithospheric mantle beneath the topographic highs of the Dorsal and its surroundings may be the cause of the dominant extensional to transtensional Quaternary regime. The occurrence of strike-slip and extensional seismic events in the Pelagian block reveals that the regional transtensional tectonic regime persists today. Finally, we believe that the geodynamic history of the study area since the Cenozoic is primarily influenced by the preexisting weak zones, the African slab detachment, and the upper mantle flow pattern in the central Mediterranean.

Keywords: Tunisia, lithospheric discontinuity (STEP fault), geodynamic evolution, Tunisian dorsal backland, strike-slip fault, seismic tomography, seismicity, central Mediterranean

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Authors: Sheldon Liu, Gordon Wang, Lei Liu, Xuefeng Liu

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Anomaly detection, also referred to as one-class classification, plays a crucial role in identifying product images that deviate from the expected distribution. This study introduces Data-centric Anomaly Detection with Diffusion Models (DCADDM), presenting a systematic strategy for data collection and further diversifying the data with image generation via diffusion models. The algorithm addresses data collection challenges in real-world scenarios and points toward data augmentation with the integration of generative AI capabilities. The paper explores the generation of normal images using diffusion models. The experiments demonstrate that with 30% of the original normal image size, modeling in an unsupervised setting with state-of-the-art approaches can achieve equivalent performances. With the addition of generated images via diffusion models (10% equivalence of the original dataset size), the proposed algorithm achieves better or equivalent anomaly localization performance.

Keywords: diffusion models, anomaly detection, data-centric, generative AI

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Authors: Jui-Chien Hsieh

Abstract:

Background:12-lead electrocardiography(ECG) is one of frequently-used tools to detect atrial fibrillation (AF), which might degenerate into life-threaten stroke, in clinical Practice. Based on this study, the AF detection by the clinically-used 12-lead ECG device has only 0.73~0.77 positive predictive value (ppv). Objective: It is on great demand to develop a new algorithm to improve the precision of AF detection using 12-lead ECG. Due to the progress on artificial intelligence (AI), we develop an ECG deep model that has the ability to recognize AF patterns and reduce false-positive errors. Methods: In this study, (1) 570-sample 12-lead ECG reports whose computer interpretation by the ECG device was AF were collected as the training dataset. The ECG reports were interpreted by 2 senior cardiologists, and confirmed that the precision of AF detection by the ECG device is 0.73.; (2) 88 12-lead ECG reports whose computer interpretation generated by the ECG device was AF were used as test dataset. Cardiologist confirmed that 68 cases of 88 reports were AF, and others were not AF. The precision of AF detection by ECG device is about 0.77; (3) A parallel 4-layer 1 dimensional convolutional neural network (CNN) was developed to identify AF based on limb-lead ECGs and chest-lead ECGs. Results: The results indicated that this model has better performance on AF detection than traditional computer interpretation of the ECG device in 88 test samples with 0.94 ppv, 0.98 sensitivity, 0.80 specificity. Conclusions: As compared to the clinical ECG device, this AI ECG model promotes the precision of AF detection from 0.77 to 0.94, and can generate impacts on clinical applications.

Keywords: 12-lead ECG, atrial fibrillation, deep learning, convolutional neural network

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Authors: Lina Pan

Abstract:

In non-Gaussian clutter of a spherically invariant random vector, in the cases that a certain estimated covariance matrix could become singular, the adaptive target detection of high-range-resolution radar is addressed. Firstly, the restricted maximum likelihood (RML) estimates of unknown covariance matrix and scatterer amplitudes are derived for non-Gaussian clutter. And then the RML estimate of texture is obtained. Finally, a novel detector is devised. It is showed that, without secondary data, the proposed detector outperforms the existing Kelly binary integrator.

Keywords: non-Gaussian clutter, covariance matrix estimation, target detection, maximum likelihood

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Authors: Nir Nissim, Ran Yahalom, Tomer Lancewiki, Yuval Elovici, Boaz Lerner

Abstract:

Background: Attackers increasingly take advantage of innocent users who tend to use USB devices casually, assuming these devices benign when in fact they may carry an embedded malicious behavior or hidden malware. USB devices have many properties and capabilities that have become the subject of malicious operations. Many of the recent attacks targeting individuals, and especially organizations, utilize popular and widely used USB devices, such as mice, keyboards, flash drives, printers, and smartphones. However, current detection tools, techniques, and solutions generally fail to detect both the known and unknown attacks launched via USB devices. Significance: We propose USBWARE, a project that focuses on the vulnerabilities of USB devices and centers on the development of a comprehensive detection framework that relies upon a crucial attack repository. USBWARE will allow researchers and companies to better understand the vulnerabilities and attacks associated with USB devices as well as providing a comprehensive platform for developing detection solutions. Methodology: The framework of USBWARE is aimed at accurate detection of both known and unknown USB-based attacks by a process that efficiently enhances the framework's detection capabilities over time. The framework will integrate two main security approaches in order to enhance the detection of USB-based attacks associated with a variety of USB devices. The first approach is aimed at the detection of known attacks and their variants, whereas the second approach focuses on the detection of unknown attacks. USBWARE will consist of six independent but complimentary detection modules, each detecting attacks based on a different approach or discipline. These modules include novel ideas and algorithms inspired from or already developed within our team's domains of expertise, including cyber security, electrical and signal processing, machine learning, and computational biology. The establishment and maintenance of the USBWARE’s dynamic and up-to-date attack repository will strengthen the capabilities of the USBWARE detection framework. The attack repository’s infrastructure will enable researchers to record, document, create, and simulate existing and new USB-based attacks. This data will be used to maintain the detection framework’s updatability by incorporating knowledge regarding new attacks. Based on our experience in the cyber security domain, we aim to design the USBWARE framework so that it will have several characteristics that are crucial for this type of cyber-security detection solution. Specifically, the USBWARE framework should be: Novel, Multidisciplinary, Trusted, Lightweight, Extendable, Modular and Updatable and Adaptable. Major Findings: Based on our initial survey, we have already found more than 23 types of USB-based attacks, divided into six major categories. Our preliminary evaluation and proof of concepts showed that our detection modules can be used for efficient detection of several basic known USB attacks. Further research, development, and enhancements are required so that USBWARE will be capable to cover all of the major known USB attacks and to detect unknown attacks. Conclusion: USBWARE is a crucial detection framework that must be further enhanced and developed.

Keywords: USB, device, cyber security, attack, detection

Procedia PDF Downloads 397

Authors: Ping-Hsiung Wang, Kuo-Chun Chang

Abstract:

There has been a common trend worldwide in the seismic design and evaluation of bridges towards the performance-based method where the lateral displacement or the displacement ductility of bridge column is regarded as an important indicator for performance assessment. However, the seismic response of a bridge to an earthquake is a combined result of cyclic displacements and accumulated energy dissipation, causing damage to the bridge, and hence the lateral displacement (ductility) alone is insufficient to tell its actual seismic performance. This study aims to propose a damage-based seismic design and evaluation method for reinforced concrete bridges on the basis of the newly developed capacity-based inelastic displacement spectra. The capacity-based inelastic displacement spectra that comprise an inelastic displacement ratio spectrum and a corresponding damage state spectrum was constructed by using a series of nonlinear time history analyses and a versatile, smooth hysteresis model. The smooth model could take into account the effects of various design parameters of RC bridge columns and correlates the column’s strength deterioration with the Park and Ang’s damage index. It was proved that the damage index not only can be used to accurately predict the onset of strength deterioration, but also can be a good indicator for assessing the actual visible damage condition of column regardless of its loading history (i.e., similar damage index corresponds to similar actual damage condition for the same designed columns subjected to very different cyclic loading protocols as well as earthquake loading), providing a better insight into the seismic performance of bridges. Besides, the computed spectra show that the inelastic displacement ratio for far-field ground motions approximately conforms to the equal displacement rule when structural period is larger than around 0.8 s, but that for near-fault ground motions departs from the rule in the whole considered spectral regions. Furthermore, the near-fault ground motions would lead to significantly greater inelastic displacement ratio and damage index than far-field ground motions and most of the practical design scenarios cannot survive the considered near-fault ground motion when the strength reduction factor of bridge is not less than 5.0. Finally, the spectrum formula is presented as a function of structural period, strength reduction factor, and various column design parameters for far-field and near-fault ground motions by means of the regression analysis of the computed spectra. And based on the developed spectrum formula, a design example of a bridge is presented to illustrate the proposed damage-based seismic design and evaluation method where the damage state of the bridge is used as the performance objective.

Keywords: damage index, far-field, near-fault, reinforced concrete bridge, seismic design and evaluation

Procedia PDF Downloads 125

Authors: Ahilya Singh, Brad Carte, Ramesh Subramani, William Aalbersberg

Abstract:

A total of 37 actinomycete strains were purified from 25 Solomon Islands marine sediments using four different types of isolation media. Among them, 54% of the strains had obligate requirement of seawater for growth. The ethyl acetate extract of 100 ml fermentation product of each strain was screened for antimicrobial activity against multidrug resistant human pathogens and cytotoxic activity against brine shrimps. A total of 67% of the ethyl acetate extracts showed antimicrobial and/or cytotoxic activities. A strain F-1915 was selected for isolation and evaluation of bioactive compound(s) based on its bioactive properties and chemical profile analysis using the LC-MS. The strain F-1915 was identified to have 96% sequence similarity to Streptomyces violaceusniger on the basis of 16S rDNA sequences using BLAST analysis. The 16S rDNA revealed that the strain F-1915 is a new member of MAR4 clade of actinomycetes. The MAR4 clade is an interesting clade of actinomycetes known for the production of pharmaceutically important hybrid isoprenoid compounds. The ethyl acetate extract of the fermentation product of this strain was purified by silica gel column chromatography and afforded the isolation of one bioactive pure compound. Based on the 1D and 2D NMR spectral data of compound 1 it was identified as a new mono-brominated phenazinone, Marinophenazimycin A, a structure which has already been studied by external collaborators at Scripps Institution of Oceanography but is yet to be published. Compound 1 displayed significant antimicrobial activity against drug resistant human pathogens. The minimum inhibitory concentration (MIC) of compound 1 was against Methicillin Resistant Staphylococcus aureus (MRSA) was about 1.9 μg/ml and MIC recorded against Amphotericin Resistant Candida albicans (ARCA) was about 0.24 μg/ml. The bioactivity of compound 1 against ARCA was found to be better than the standard antifungal agent amphotericin B. Compound 1 however did not show any cytotoxic activity against brine shrimps.

Keywords: actinomycetes, antimicrobial activity, brominated phenazine, MAR4 clade, marine natural products, multidrug resistent, 1D and 2D NMR

Procedia PDF Downloads 337