Search results for: particle detection
4364 MAS Capped CdTe/ZnS Core/Shell Quantum Dot Based Sensor for Detection of Hg(II)
Authors: Dilip Saikia, Suparna Bhattacharjee, Nirab Adhikary
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In this piece of work, we have presented the synthesis and characterization of CdTe/ZnS core/shell (CS) quantum dots (QD). CS QDs are used as a fluorescence probe to design a simple cost-effective and ultrasensitive sensor for the detection of toxic Hg(II) in an aqueous medium. Mercaptosuccinic acid (MSA) has been used as a capping agent for the synthesis CdTe/ZnS CS QD. Photoluminescence quenching mechanism has been used in the detection experiment of Hg(II). The designed sensing technique shows a remarkably low detection limit of about 1 picomolar (pM). Here, the CS QDs are synthesized by a simple one-pot aqueous method. The synthesized CS QDs are characterized by using advanced diagnostics tools such as UV-vis, Photoluminescence, XRD, FTIR, TEM and Zeta potential analysis. The interaction between CS QDs and the Hg(II) ions results in the quenching of photoluminescence (PL) intensity of QDs, via the mechanism of excited state electron transfer. The proposed mechanism is explained using cyclic voltammetry and zeta potential analysis. The designed sensor is found to be highly selective towards Hg (II) ions. The analysis of the real samples such as drinking water and tap water has been carried out and the CS QDs show remarkably good results. Using this simple sensing method we have designed a prototype low-cost electronic device for the detection of Hg(II) in an aqueous medium. The findings of the experimental results of the designed sensor is crosschecked by using AAS analysis.Keywords: photoluminescence, quantum dots, quenching, sensor
Procedia PDF Downloads 2574363 Enhanced Traffic Light Detection Method Using Geometry Information
Authors: Changhwan Choi, Yongwan Park
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In this paper, we propose a method that allows faster and more accurate detection of traffic lights by a vision sensor during driving, DGPS is used to obtain physical location of a traffic light, extract from the image information of the vision sensor only the traffic light area at this location and ascertain if the sign is in operation and determine its form. This method can solve the problem in existing research where low visibility at night or reflection under bright light makes it difficult to recognize the form of traffic light, thus making driving unstable. We compared our success rate of traffic light recognition in day and night road environments. Compared to previous researches, it showed similar performance during the day but 50% improvement at night.Keywords: traffic light, intelligent vehicle, night, detection, DGPS
Procedia PDF Downloads 3184362 Quantum Dot Biosensing for Advancing Precision Cancer Detection
Authors: Sourav Sarkar, Manashjit Gogoi
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In the evolving landscape of cancer diagnostics, optical biosensing has emerged as a promising tool due to its sensitivity and specificity. This study explores the potential of CdS/ZnS core-shell quantum dots (QDs) capped with 3-Mercaptopropionic acid (3-MPA), which aids in the linking chemistry of QDs to various cancer antibodies. The QDs, with their unique optical and electronic properties, have been integrated into the biosensor design. Their high quantum yield and size-dependent emission spectra have been exploited to improve the sensor’s detection capabilities. The study presents the design of this QD-enhanced optical biosensor. The use of these QDs can also aid multiplexed detection, enabling simultaneous monitoring of different cancer biomarkers. This innovative approach holds significant potential for advancing cancer diagnostics, contributing to timely and accurate detection. Future work will focus on optimizing the biosensor design for clinical applications and exploring the potential of QDs in other biosensing applications. This study underscores the potential of integrating nanotechnology and biosensing for cancer research, paving the way for next-generation diagnostic tools. It is a step forward in our quest for achieving precision oncology.Keywords: quantum dots, biosensing, cancer, device
Procedia PDF Downloads 454361 Filtering Intrusion Detection Alarms Using Ant Clustering Approach
Authors: Ghodhbani Salah, Jemili Farah
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With the growth of cyber attacks, information safety has become an important issue all over the world. Many firms rely on security technologies such as intrusion detection systems (IDSs) to manage information technology security risks. IDSs are considered to be the last line of defense to secure a network and play a very important role in detecting large number of attacks. However the main problem with today’s most popular commercial IDSs is generating high volume of alerts and huge number of false positives. This drawback has become the main motivation for many research papers in IDS area. Hence, in this paper we present a data mining technique to assist network administrators to analyze and reduce false positive alarms that are produced by an IDS and increase detection accuracy. Our data mining technique is unsupervised clustering method based on hybrid ANT algorithm. This algorithm discovers clusters of intruders’ behavior without prior knowledge of a possible number of classes, then we apply K-means algorithm to improve the convergence of the ANT clustering. Experimental results on real dataset show that our proposed approach is efficient with high detection rate and low false alarm rate.Keywords: intrusion detection system, alarm filtering, ANT class, ant clustering, intruders’ behaviors, false alarms
Procedia PDF Downloads 3954360 Optimal Tuning of Linear Quadratic Regulator Controller Using a Particle Swarm Optimization for Two-Rotor Aerodynamical System
Authors: Ayad Al-Mahturi, Herman Wahid
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This paper presents an optimal state feedback controller based on Linear Quadratic Regulator (LQR) for a two-rotor aero-dynamical system (TRAS). TRAS is a highly nonlinear multi-input multi-output (MIMO) system with two degrees of freedom and cross coupling. There are two parameters that define the behavior of LQR controller: state weighting matrix and control weighting matrix. The two parameters influence the performance of LQR. Particle Swarm Optimization (PSO) is proposed to optimally tune weighting matrices of LQR. The major concern of using LQR controller is to stabilize the TRAS by making the beam move quickly and accurately for tracking a trajectory or to reach a desired altitude. The simulation results were carried out in MATLAB/Simulink. The system is decoupled into two single-input single-output (SISO) systems. Comparing the performance of the optimized proportional, integral and derivative (PID) controller provided by INTECO, results depict that LQR controller gives a better performance in terms of both transient and steady state responses when PSO is performed.Keywords: LQR controller, optimal control, particle swarm optimization (PSO), two rotor aero-dynamical system (TRAS)
Procedia PDF Downloads 3174359 Anomaly Detection with ANN and SVM for Telemedicine Networks
Authors: Edward Guillén, Jeisson Sánchez, Carlos Omar Ramos
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In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.Keywords: anomaly detection, back-propagation neural networks, network intrusion detection systems, support vector machines
Procedia PDF Downloads 3494358 Enhancement Method of Network Traffic Anomaly Detection Model Based on Adversarial Training With Category Tags
Authors: Zhang Shuqi, Liu Dan
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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation
Procedia PDF Downloads 934357 Structural, Optical and Electrical Thin-Film Characterization Using Graphite-Bioepoxy Composite Materials
Authors: Anika Zafiah M. Rus, Nur Munirah Abdullah, M. F. L. Abdullah
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The fabrication and characterization of composite films of graphite- bioepoxy is described. Free-standing thin films of ~0.1 mm thick are prepared using a simple solution mixing with mass proportion of 7/3 (bioepoxy/graphite) and drop casting at room temperature. Fourier transform infra-red spectroscopy (FTIR) and Ultraviolet-visible (UV-vis) spectrophotometer are performed to evaluate the changes in chemical structure and adsorption spectra arising with the increasing of graphite weight loading (wt.%) into the biopolymer matrix. The morphologic study shows a homogeneously dispersed and strong particle bonding between the graphite and the bioepoxy, with conductivity of the film 103 S/m, confirming the efficiency of the processes.Keywords: absorbance peak, biopolymer, graphite- bioepoxy composites, particle bonding
Procedia PDF Downloads 5014356 A Machine Learning Approach for Detecting and Locating Hardware Trojans
Authors: Kaiwen Zheng, Wanting Zhou, Nan Tang, Lei Li, Yuanhang He
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The integrated circuit industry has become a cornerstone of the information society, finding widespread application in areas such as industry, communication, medicine, and aerospace. However, with the increasing complexity of integrated circuits, Hardware Trojans (HTs) implanted by attackers have become a significant threat to their security. In this paper, we proposed a hardware trojan detection method for large-scale circuits. As HTs introduce physical characteristic changes such as structure, area, and power consumption as additional redundant circuits, we proposed a machine-learning-based hardware trojan detection method based on the physical characteristics of gate-level netlists. This method transforms the hardware trojan detection problem into a machine-learning binary classification problem based on physical characteristics, greatly improving detection speed. To address the problem of imbalanced data, where the number of pure circuit samples is far less than that of HTs circuit samples, we used the SMOTETomek algorithm to expand the dataset and further improve the performance of the classifier. We used three machine learning algorithms, K-Nearest Neighbors, Random Forest, and Support Vector Machine, to train and validate benchmark circuits on Trust-Hub, and all achieved good results. In our case studies based on AES encryption circuits provided by trust-hub, the test results showed the effectiveness of the proposed method. To further validate the method’s effectiveness for detecting variant HTs, we designed variant HTs using open-source HTs. The proposed method can guarantee robust detection accuracy in the millisecond level detection time for IC, and FPGA design flows and has good detection performance for library variant HTs.Keywords: hardware trojans, physical properties, machine learning, hardware security
Procedia PDF Downloads 1374355 Analytical Modeling of Drain Current for DNA Biomolecule Detection in Double-Gate Tunnel Field-Effect Transistor Biosensor
Authors: Ashwani Kumar
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Abstract- This study presents an analytical modeling approach for analyzing the drain current behavior in Tunnel Field-Effect Transistor (TFET) biosensors used for the detection of DNA biomolecules. The proposed model focuses on elucidating the relationship between the drain current and the presence of DNA biomolecules, taking into account the impact of various device parameters and biomolecule characteristics. Through comprehensive analysis, the model offers insights into the underlying mechanisms governing the sensing performance of TFET biosensors, aiding in the optimization of device design and operation. A non-local tunneling model is incorporated with other essential models to accurately trace the simulation and modeled data. An experimental validation of the model is provided, demonstrating its efficacy in accurately predicting the drain current response to DNA biomolecule detection. The sensitivity attained from the analytical model is compared and contrasted with the ongoing research work in this area.Keywords: biosensor, double-gate TFET, DNA detection, drain current modeling, sensitivity
Procedia PDF Downloads 504354 Transport of Inertial Finite-Size Floating Plastic Pollution by Ocean Surface Waves
Authors: Ross Calvert, Colin Whittaker, Alison Raby, Alistair G. L. Borthwick, Ton S. van den Bremer
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Large concentrations of plastic have polluted the seas in the last half century, with harmful effects on marine wildlife and potentially to human health. Plastic pollution will have lasting effects because it is expected to take hundreds or thousands of years for plastic to decay in the ocean. The question arises how waves transport plastic in the ocean. The predominant motion induced by waves creates ellipsoid orbits. However, these orbits do not close, resulting in a drift. This is defined as Stokes drift. If a particle is infinitesimally small and the same density as water, it will behave exactly as the water does, i.e., as a purely Lagrangian tracer. However, as the particle grows in size or changes density, it will behave differently. The particle will then have its own inertia, the fluid will exert drag on the particle, because there is relative velocity, and it will rise or sink depending on the density and whether it is on the free surface. Previously, plastic pollution has all been considered to be purely Lagrangian. However, the steepness of waves in the ocean is small, normally about α = k₀a = 0.1 (where k₀ is the wavenumber and a is the wave amplitude), this means that the mean drift flows are of the order of ten times smaller than the oscillatory velocities (Stokes drift is proportional to steepness squared, whilst the oscillatory velocities are proportional to the steepness). Thus, the particle motion must have the forces of the full motion, oscillatory and mean flow, as well as a dynamic buoyancy term to account for the free surface, to determine whether inertia is important. To track the motion of a floating inertial particle under wave action requires the fluid velocities, which form the forcing, and the full equations of motion of a particle to be solved. Starting with the equation of motion of a sphere in unsteady flow with viscous drag. Terms can added then be added to the equation of motion to better model floating plastic: a dynamic buoyancy to model a particle floating on the free surface, quadratic drag for larger particles and a slope sliding term. Using perturbation methods to order the equation of motion into sequentially solvable parts allows a parametric equation for the transport of inertial finite-sized floating particles to be derived. This parametric equation can then be validated using numerical simulations of the equation of motion and flume experiments. This paper presents a parametric equation for the transport of inertial floating finite-size particles by ocean waves. The equation shows an increase in Stokes drift for larger, less dense particles. The equation has been validated using numerical solutions of the equation of motion and laboratory flume experiments. The difference in the particle transport equation and a purely Lagrangian tracer is illustrated using worlds maps of the induced transport. This parametric transport equation would allow ocean-scale numerical models to include inertial effects of floating plastic when predicting or tracing the transport of pollutants.Keywords: perturbation methods, plastic pollution transport, Stokes drift, wave flume experiments, wave-induced mean flow
Procedia PDF Downloads 1104353 Labview-Based System for Fiber Links Events Detection
Authors: Bo Liu, Qingshan Kong, Weiqing Huang
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With the rapid development of modern communication, diagnosing the fiber-optic quality and faults in real-time is widely focused. In this paper, a Labview-based system is proposed for fiber-optic faults detection. The wavelet threshold denoising method combined with Empirical Mode Decomposition (EMD) is applied to denoise the optical time domain reflectometer (OTDR) signal. Then the method based on Gabor representation is used to detect events. Experimental measurements show that signal to noise ratio (SNR) of the OTDR signal is improved by 1.34dB on average, compared with using the wavelet threshold denosing method. The proposed system has a high score in event detection capability and accuracy. The maximum detectable fiber length of the proposed Labview-based system can be 65km.Keywords: empirical mode decomposition, events detection, Gabor transform, optical time domain reflectometer, wavelet threshold denoising
Procedia PDF Downloads 1194352 Indicator-Immobilized, Cellulose Based Optical Sensing Membrane for the Detection of Heavy Metal Ions
Authors: Nisha Dhariwal, Anupama Sharma
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The synthesis of cellulose nanofibrils quaternized with 3‐chloro‐2‐hydroxypropyltrimethylammonium chloride (CHPTAC) in NaOH/urea aqueous solution has been reported. Xylenol Orange (XO) has been used as an indicator for selective detection of Sn (II) ions, by its immobilization on quaternized cellulose membrane. The effects of pH, reagent concentration and reaction time on the immobilization of XO have also been studied. The linear response, limit of detection, and interference of other metal ions have also been studied and no significant interference has been observed. The optical chemical sensor displayed good durability and short response time with negligible leaching of the reagent.Keywords: cellulose, chemical sensor, heavy metal ions, indicator immobilization
Procedia PDF Downloads 2944351 Minimizing the Impact of Covariate Detection Limit in Logistic Regression
Authors: Shahadut Hossain, Jacek Wesolowski, Zahirul Hoque
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In many epidemiological and environmental studies covariate measurements are subject to the detection limit. In most applications, covariate measurements are usually truncated from below which is known as left-truncation. Because the measuring device, which we use to measure the covariate, fails to detect values falling below the certain threshold. In regression analyses, it causes inflated bias and inaccurate mean squared error (MSE) to the estimators. This paper suggests a response-based regression calibration method to correct the deleterious impact introduced by the covariate detection limit in the estimators of the parameters of simple logistic regression model. Compared to the maximum likelihood method, the proposed method is computationally simpler, and hence easier to implement. It is robust to the violation of distributional assumption about the covariate of interest. In producing correct inference, the performance of the proposed method compared to the other competing methods has been investigated through extensive simulations. A real-life application of the method is also shown using data from a population-based case-control study of non-Hodgkin lymphoma.Keywords: environmental exposure, detection limit, left truncation, bias, ad-hoc substitution
Procedia PDF Downloads 2284350 Hybrid Anomaly Detection Using Decision Tree and Support Vector Machine
Authors: Elham Serkani, Hossein Gharaee Garakani, Naser Mohammadzadeh, Elaheh Vaezpour
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Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset.Keywords: decision tree, feature selection, intrusion detection system, support vector machine
Procedia PDF Downloads 2544349 Developing an Accurate AI Algorithm for Histopathologic Cancer Detection
Authors: Leah Ning
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This paper discusses the development of a machine learning algorithm that accurately detects metastatic breast cancer (cancer has spread elsewhere from its origin part) in selected images that come from pathology scans of lymph node sections. Being able to develop an accurate artificial intelligence (AI) algorithm would help significantly in breast cancer diagnosis since manual examination of lymph node scans is both tedious and oftentimes highly subjective. The usage of AI in the diagnosis process provides a much more straightforward, reliable, and efficient method for medical professionals and would enable faster diagnosis and, therefore, more immediate treatment. The overall approach used was to train a convolution neural network (CNN) based on a set of pathology scan data and use the trained model to binarily classify if a new scan were benign or malignant, outputting a 0 or a 1, respectively. The final model’s prediction accuracy is very high, with 100% for the train set and over 70% for the test set. Being able to have such high accuracy using an AI model is monumental in regard to medical pathology and cancer detection. Having AI as a new tool capable of quick detection will significantly help medical professionals and patients suffering from cancer.Keywords: breast cancer detection, AI, machine learning, algorithm
Procedia PDF Downloads 844348 Collision Detection Algorithm Based on Data Parallelism
Authors: Zhen Peng, Baifeng Wu
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Modern computing technology enters the era of parallel computing with the trend of sustainable and scalable parallelism. Single Instruction Multiple Data (SIMD) is an important way to go along with the trend. It is able to gather more and more computing ability by increasing the number of processor cores without the need of modifying the program. Meanwhile, in the field of scientific computing and engineering design, many computation intensive applications are facing the challenge of increasingly large amount of data. Data parallel computing will be an important way to further improve the performance of these applications. In this paper, we take the accurate collision detection in building information modeling as an example. We demonstrate a model for constructing a data parallel algorithm. According to the model, a complex object is decomposed into the sets of simple objects; collision detection among complex objects is converted into those among simple objects. The resulting algorithm is a typical SIMD algorithm, and its advantages in parallelism and scalability is unparalleled in respect to the traditional algorithms.Keywords: data parallelism, collision detection, single instruction multiple data, building information modeling, continuous scalability
Procedia PDF Downloads 2824347 Root Mean Square-Based Method for Fault Diagnosis and Fault Detection and Isolation of Current Fault Sensor in an Induction Machine
Authors: Ahmad Akrad, Rabia Sehab, Fadi Alyoussef
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Nowadays, induction machines are widely used in industry thankful to their advantages comparing to other technologies. Indeed, there is a big demand because of their reliability, robustness and cost. The objective of this paper is to deal with diagnosis, detection and isolation of faults in a three-phase induction machine. Among the faults, Inter-turn short-circuit fault (ITSC), current sensors fault and single-phase open circuit fault are selected to deal with. However, a fault detection method is suggested using residual errors generated by the root mean square (RMS) of phase currents. The application of this method is based on an asymmetric nonlinear model of Induction Machine considering the winding fault of the three axes frame state space. In addition, current sensor redundancy and sensor fault detection and isolation (FDI) are adopted to ensure safety operation of induction machine drive. Finally, a validation is carried out by simulation in healthy and faulty operation modes to show the benefit of the proposed method to detect and to locate with, a high reliability, the three types of faults.Keywords: induction machine, asymmetric nonlinear model, fault diagnosis, inter-turn short-circuit fault, root mean square, current sensor fault, fault detection and isolation
Procedia PDF Downloads 1814346 Self-Organizing Maps for Credit Card Fraud Detection
Authors: ChunYi Peng, Wei Hsuan CHeng, Shyh Kuang Ueng
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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies
Procedia PDF Downloads 434345 Bi-Component Particle Segregation Studies in a Spiral Concentrator Using Experimental and CFD Techniques
Authors: Prudhvinath Reddy Ankireddy, Narasimha Mangadoddy
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Spiral concentrators are commonly used in various industries, including mineral and coal processing, to efficiently separate materials based on their density and size. In these concentrators, a mixture of solid particles and fluid (usually water) is introduced as feed at the top of a spiral channel. As the mixture flows down the spiral, centrifugal and gravitational forces act on the particles, causing them to stratify based on their density and size. Spiral flows exhibit complex fluid dynamics, and interactions involve multiple phases and components in the process. Understanding the behavior of these phases within the spiral concentrator is crucial for achieving efficient separation. An experimental bi-component particle interaction study is conducted in this work utilizing magnetite (heavier density) and silica (lighter density) with different proportions processed in the spiral concentrator. The observation separation reveals that denser particles accumulate towards the inner region of the spiral trough, while a significant concentration of lighter particles are found close to the outer edge. The 5th turn of the spiral trough is partitioned into five zones to achieve a comprehensive distribution analysis of bicomponent particle segregation. Samples are then gathered from these individual streams using an in-house sample collector, and subsequent analysis is conducted to assess component segregation. Along the trough, there was a decline in the concentration of coarser particles, accompanied by an increase in the concentration of lighter particles. The segregation pattern indicates that the heavier coarse component accumulates in the inner zone, whereas the lighter fine component collects in the outer zone. The middle zone primarily consists of heavier fine particles and lighter coarse particles. The zone-wise results reveal that there is a significant fraction of segregation occurs in inner and middle zones. Finer magnetite and silica particles predominantly accumulate in outer zones with the smallest fraction of segregation. Additionally, numerical simulations are also carried out using the computational fluid dynamics (CFD) model based on the volume of fluid (VOF) approach incorporating the RSM turbulence model. The discrete phase model (DPM) is employed for particle tracking, thereby understanding the particle segregation of magnetite and silica along the spiral trough.Keywords: spiral concentrator, bi-component particle segregation, computational fluid dynamics, discrete phase model
Procedia PDF Downloads 594344 On the Representation of Actuator Faults Diagnosis and Systems Invertibility
Authors: F. Sallem, B. Dahhou, A. Kamoun
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In this work, the main problem considered is the detection and the isolation of the actuator fault. A new formulation of the linear system is generated to obtain the conditions of the actuator fault diagnosis. The proposed method is based on the representation of the actuator as a subsystem connected with the process system in cascade manner. The designed formulation is generated to obtain the conditions of the actuator fault detection and isolation. Detectability conditions are expressed in terms of the invertibility notions. An example and a comparative analysis with the classic formulation illustrate the performances of such approach for simple actuator fault diagnosis by using the linear model of nuclear reactor.Keywords: actuator fault, Fault detection, left invertibility, nuclear reactor, observability, parameter intervals, system inversion
Procedia PDF Downloads 3924343 A Procedure for Post-Earthquake Damage Estimation Based on Detection of High-Frequency Transients
Authors: Aleksandar Zhelyazkov, Daniele Zonta, Helmut Wenzel, Peter Furtner
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In the current research structural health monitoring is considered for addressing the critical issue of post-earthquake damage detection. A non-standard approach for damage detection via acoustic emission is presented - acoustic emissions are monitored in the low frequency range (up to 120 Hz). Such emissions are termed high-frequency transients. Further a damage indicator defined as the Time-Ratio Damage Indicator is introduced. The indicator relies on time-instance measurements of damage initiation and deformation peaks. Based on the time-instance measurements a procedure for estimation of the maximum drift ratio is proposed. Monitoring data is used from a shaking-table test of a full-scale reinforced concrete bridge pier. Damage of the experimental column is successfully detected and the proposed damage indicator is calculated.Keywords: acoustic emission, damage detection, shaking table test, structural health monitoring
Procedia PDF Downloads 2204342 Self-Organizing Maps for Credit Card Fraud Detection and Visualization
Authors: Peng Chun-Yi, Chen Wei-Hsuan, Ueng Shyh-Kuang
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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies
Procedia PDF Downloads 514341 Automatic Thresholding for Data Gap Detection for a Set of Sensors in Instrumented Buildings
Authors: Houda Najeh, Stéphane Ploix, Mahendra Pratap Singh, Karim Chabir, Mohamed Naceur Abdelkrim
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Building systems are highly vulnerable to different kinds of faults and failures. In fact, various faults, failures and human behaviors could affect the building performance. This paper tackles the detection of unreliable sensors in buildings. Different literature surveys on diagnosis techniques for sensor grids in buildings have been published but all of them treat only bias and outliers. Occurences of data gaps have also not been given an adequate span of attention in the academia. The proposed methodology comprises the automatic thresholding for data gap detection for a set of heterogeneous sensors in instrumented buildings. Sensor measurements are considered to be regular time series. However, in reality, sensor values are not uniformly sampled. So, the issue to solve is from which delay each sensor become faulty? The use of time series is required for detection of abnormalities on the delays. The efficiency of the method is evaluated on measurements obtained from a real power plant: an office at Grenoble Institute of technology equipped by 30 sensors.Keywords: building system, time series, diagnosis, outliers, delay, data gap
Procedia PDF Downloads 2364340 A Dynamic Neural Network Model for Accurate Detection of Masked Faces
Authors: Oladapo Tolulope Ibitoye
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Neural networks have become prominent and widely engaged in algorithmic-based machine learning networks. They are perfect in solving day-to-day issues to a certain extent. Neural networks are computing systems with several interconnected nodes. One of the numerous areas of application of neural networks is object detection. This is a prominent area due to the coronavirus disease pandemic and the post-pandemic phases. Wearing a face mask in public slows the spread of the virus, according to experts’ submission. This calls for the development of a reliable and effective model for detecting face masks on people's faces during compliance checks. The existing neural network models for facemask detection are characterized by their black-box nature and large dataset requirement. The highlighted challenges have compromised the performance of the existing models. The proposed model utilized Faster R-CNN Model on Inception V3 backbone to reduce system complexity and dataset requirement. The model was trained and validated with very few datasets and evaluation results shows an overall accuracy of 96% regardless of skin tone.Keywords: convolutional neural network, face detection, face mask, masked faces
Procedia PDF Downloads 584339 Multi-Vehicle Detection Using Histogram of Oriented Gradients Features and Adaptive Sliding Window Technique
Authors: Saumya Srivastava, Rina Maiti
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In order to achieve a better performance of vehicle detection in a complex environment, we present an efficient approach for a multi-vehicle detection system using an adaptive sliding window technique. For a given frame, image segmentation is carried out to establish the region of interest. Gradient computation followed by thresholding, denoising, and morphological operations is performed to extract the binary search image. Near-region field and far-region field are defined to generate hypotheses using the adaptive sliding window technique on the resultant binary search image. For each vehicle candidate, features are extracted using a histogram of oriented gradients, and a pre-trained support vector machine is applied for hypothesis verification. Later, the Kalman filter is used for tracking the vanishing point. The experimental results show that the method is robust and effective on various roads and driving scenarios. The algorithm was tested on highways and urban roads in India.Keywords: gradient, vehicle detection, histograms of oriented gradients, support vector machine
Procedia PDF Downloads 1144338 Concentric Circle Detection based on Edge Pre-Classification and Extended RANSAC
Authors: Zhongjie Yu, Hancheng Yu
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In this paper, we propose an effective method to detect concentric circles with imperfect edges. First, the gradient of edge pixel is coded and a 2-D lookup table is built to speed up normal generation. Then we take an accumulator to estimate the rough center and collect plausible edges of concentric circles through gradient and distance. Later, we take the contour-based method, which takes the contour and edge intersection, to pre-classify the edges. Finally, we use the extended RANSAC method to find all the candidate circles. The center of concentric circles is determined by the two circles with the highest concentricity. Experimental results demonstrate that the proposed method has both good performance and accuracy for the detection of concentric circles.Keywords: concentric circle detection, gradient, contour, edge pre-classification, RANSAC
Procedia PDF Downloads 1244337 Optimization of Coefficients of Fractional Order Proportional-Integrator-Derivative Controller on Permanent Magnet Synchronous Motors Using Particle Swarm Optimization
Authors: Ali Motalebi Saraji, Reza Zarei Lamuki
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Speed control and behavior improvement of permanent magnet synchronous motors (PMSM) that have reliable performance, low loss, and high power density, especially in industrial drives, are of great importance for researchers. Because of its importance in this paper, coefficients optimization of proportional-integrator-derivative fractional order controller is presented using Particle Swarm Optimization (PSO) algorithm in order to improve the behavior of PMSM in its speed control loop. This improvement is simulated in MATLAB software for the proposed optimized proportional-integrator-derivative fractional order controller with a Genetic algorithm and compared with a full order controller with a classic optimization method. Simulation results show the performance improvement of the proposed controller with respect to two other controllers in terms of rising time, overshoot, and settling time.Keywords: speed control loop of permanent magnet synchronous motor, fractional and full order proportional-integrator-derivative controller, coefficients optimization, particle swarm optimization, improvement of behavior
Procedia PDF Downloads 1374336 Facile Synthesis and Structure Characterization of Europium (III) Tungstate Nanoparticles
Authors: Mehdi Rahimi-Nasrabadi, Seied Mahdi Pourmortazavi
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Taguchi robust design as a statistical method was applied for optimization of the process parameters in order to tunable, simple and fast synthesis of europium (III) tungstate nanoparticles. Europium (III) tungstate nanoparticles were synthesized by a chemical precipitation reaction involving direct addition of europium ion aqueous solution to the tungstate reagent solved in aqueous media. Effects of some synthesis procedure variables i.e., europium and tungstate concentrations, flow rate of cation reagent addition, and temperature of reaction reactor on the particle size of europium (III) tungstate nanoparticles were studied experimentally in order to tune particle size of europium (III) tungstate. Analysis of variance shows the importance of controlling tungstate concentration, cation feeding flow rate and temperature for preparation of europium (III) tungstate nanoparticles by the proposed chemical precipitation reaction. Finally, europium (III) tungstate nanoparticles were synthesized at the optimum conditions of the proposed method and the morphology and chemical composition of the prepared nano-material were characterized by means of X-Ray diffraction, scanning electron microscopy, transmission electron microscopy, FT-IR spectroscopy, and fluorescence.Keywords: europium (III) tungstate, nano-material, particle size control, procedure optimization
Procedia PDF Downloads 3864335 Electrochemical Bioassay for Haptoglobin Quantification: Application in Bovine Mastitis Diagnosis
Authors: Soledad Carinelli, Iñigo Fernández, José Luis González-Mora, Pedro A. Salazar-Carballo
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Mastitis is the most relevant inflammatory disease in cattle, affecting the animal health and causing important economic losses on dairy farms. This disease takes place in the mammary gland or udder when some opportunistic microorganisms, such as Staphylococcus aureus, Streptococcus agalactiae, Corynebacterium bovis, etc., invade the teat canal. According to the severity of the inflammation, mastitis can be classified as sub-clinical, clinical and chronic. Standard methods for mastitis detection include counts of somatic cells, cell culture, electrical conductivity of the milk, and California test (evaluation of “gel-like” matrix consistency after cell lysed with detergents). However, these assays present some limitations for accurate detection of subclinical mastitis. Currently, haptoglobin, an acute phase protein, has been proposed as novel and effective biomarker for mastitis detection. In this work, an electrochemical biosensor based on polydopamine-modified magnetic nanoparticles (MNPs@pDA) for haptoglobin detection is reported. Thus, MNPs@pDA has been synthesized by our group and functionalized with hemoglobin due to its high affinity to haptoglobin protein. The protein was labeled with specific antibodies modified with alkaline phosphatase enzyme for its electrochemical detection using an electroactive substrate (1-naphthyl phosphate) by differential pulse voltammetry. After the optimization of assay parameters, the haptoglobin determination was evaluated in milk. The strategy presented in this work shows a wide range of detection, achieving a limit of detection of 43 ng/mL. The accuracy of the strategy was determined by recovery assays, being of 84 and 94.5% for two Hp levels around the cut off value. Milk real samples were tested and the prediction capacity of the electrochemical biosensor was compared with a Haptoglobin commercial ELISA kit. The performance of the assay has demonstrated this strategy is an excellent and real alternative as screen method for sub-clinical bovine mastitis detection.Keywords: bovine mastitis, haptoglobin, electrochemistry, magnetic nanoparticles, polydopamine
Procedia PDF Downloads 161