Search results for: blue color detection

Authors: Manpreet Kaur

Abstract:

When legitimate credit card users are mistakenly labelled as fraudulent in numerous financial delated applications, there are numerous ethical problems. The innovative machine learning approach we have suggested in this research outperforms the current models and shows how to model a data set for credit card fraud detection while minimizing false positives. As a result, we advise using random forests as the best machine learning method for predicting and identifying credit card transaction fraud. The majority of victims of these fraudulent transactions were discovered to be credit card users over the age of 60, with a higher percentage of fraudulent transactions taking place between the specific hours.

Keywords: automated fraud detection, isolation forest method, local outlier factor, ML algorithm, credit card

Procedia PDF Downloads 99

Authors: Rafael A. B. De Medeiros, Zilmar M. P. Barros, Carlos B. O. De Carvalho, Eunice G. Fraga Neta, Maria I. S. Maciel, Patricia M. Azoubel

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The use of the sugar substitution technique (D3S) in mango was studied. It consisted of two stages and the use of ultrasound in one or both stages was evaluated in terms of water loss and solid gain. Higher water loss results were found subjecting the fruit samples to ultrasound in the first stage followed by immersion of the samples in Stevia-based solution with application of ultrasound in the second stage, while higher solids gain were obtained without application of ultrasound in second stage. Samples were evaluated in terms of total carotenoids content and total color difference. Samples submitted to ultrasound in both D3S stages presented higher carotenoid retention compared to samples sonicated only in the first stage. Color of man goes after the D3S process showed notable changes.

Keywords: Mangifera indica L., quality, Stevia rebaudiana, ultrasound

Procedia PDF Downloads 393

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 256

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 318

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 45

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 395

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 349

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 93

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 137

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 50

Authors: Ihsan Burak Cam, Ayhan Topuz

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Turkish delight has been produced by bleaching the plain delight mix (refined sugar, water and starch) via soapwort extract and powdered sugar. Soapwort extract which contains high amount of saponin, is an additive used in Turkish delight and tahini halvah production to improve consistency, chewiness and color due to its bioactive saponin content by acting as emulsifier. In this study, soapwort powder has been produced by determining optimum process conditions of soapwort extract by using response-surface method. This extract has been enriched with saponin by reverse osmosis (contains %63 saponin in dry bases). Büchi mini spray dryer B-290 was used to produce spray-dried soapwort powder (aw=0.254) from the enriched soapwort concentrate. Processing steps optimization and saponin content enrichment of soapwort extract has been tested on Turkish Delight production. Delight samples, produced by soapwort powder and commercial extract (control), were compared in chewiness, springiness, stickiness, adhesiveness, hardness, color and sensorial characteristics. According to the results, all textural properties except hardness of delights produced by powder were found to be statistically different than control samples. Chewiness, springiness, stickiness, adhesiveness and hardness values of samples (delights produced by the powder / control delights) were determined to be 361.9/1406.7, 0.095/0.251, -120.3/-51.7, 781.9/1869.3, 3427.3g/3118.4g, respectively. According to the quality analysis that has been ran with the end products it has been determined that; there is no statistically negative effect of the soapwort extract and the soapwort powder on the color and the appearance of Turkish Delight.

Keywords: saponin, delight, soapwort powder, spray drying

Procedia PDF Downloads 246

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 119

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 293

Authors: P. Barreto, A. Guevara, V. Ibujes

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In this study, the removal of arsenic from synthetic solutions and natural water from Papallacta Lagoon was evaluated, by using the rhizofiltration method with terrestrial and aquatic plant species. Ecuador is a country of high volcanic activity, that is why most of water sources come from volcanic glaciers. Therefore, it is necessary to find new, affordable and effective methods for treating water. The water from Papallacta Lagoon shows levels from 327 µg/L to 803 µg/L of arsenic. The evaluation for the removal of arsenic began with the selection of 16 different species of terrestrial and aquatic plants. These plants were immersed to solutions of 4500 µg/L arsenic concentration, for 48 hours. Subsequently, 3 terrestrial species and 2 aquatic species were selected based on the highest amount of absorbed arsenic they showed, analyzed by plasma optical emission spectrometry (ICP-OES), and their best capacity for adaptation into the arsenic solution. The chosen terrestrial species were cultivated from their seed with hydroponics methods, using coconut fiber and polyurethane foam as substrates. Afterwards, the species that best adapted to hydroponic environment were selected. Additionally, a control of the development for the selected aquatic species was carried out using a basic nutrient solution to provide the nutrients that the plants required. Following this procedure, 30 plants from the 3 types of species selected were exposed to a synthetic solution with levels of arsenic concentration of 154, 375 and 874 µg/L, for 15 days. Finally, the plant that showed the highest level of arsenic absorption was placed in 3 L of natural water, with arsenic levels of 803 µg/L. The plant laid in the water until it reached the desired level of arsenic of 10 µg/L. This experiment was carried out in a total of 30 days, in which the capacity of arsenic absorption of the plant was measured. As a result, the five species initially selected to be used in the last part of the evaluation were: sunflower (Helianthus annuus), clover (Trifolium), blue grass (Poa pratensis), water hyacinth (Eichhornia crassipes) and miniature aquatic fern (Azolla). The best result of arsenic removal was showed by the water hyacinth with a 53,7% of absorption, followed by the blue grass with 31,3% of absorption. On the other hand, the blue grass was the plant that best responded to the hydroponic cultivation, by obtaining a germination percentage of 97% and achieving its full growth in two months. Thus, it was the only terrestrial species selected. In summary, the final selected species were blue grass, water hyacinth and miniature aquatic fern. These three species were evaluated by immersing them in synthetic solutions with three different arsenic concentrations (154, 375 and 874 µg/L). Out of the three plants, the water hyacinth was the one that showed the highest percentages of arsenic removal with 98, 58 and 64%, for each one of the arsenic solutions. Finally, 12 plants of water hyacinth were chosen to reach an arsenic level up to 10 µg/L in natural water. This significant arsenic concentration reduction was obtained in 5 days. In conclusion, it was found that water hyacinth is the best plant to reduce arsenic levels in natural water.

Keywords: arsenic, natural water, plant species, rhizofiltration, synthetic solutions

Procedia PDF Downloads 117

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 228

Authors: Thanyaporn Senarai, Rapeepun Vanichviriyakit, Shinji Miyata, Chihiro Sato, Prapee Sretarugsa, Wattana Weerachatyanukul, Ken Kitajima

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In this study, we characterized a region-specific 250 kDa protein that was secreted of MSD fluid, which is believed to play dual functions in forming a spermatophoric wall for sperm physical protection, and in sperm membrane modification as part of sperm maturation process. The partial amino acid sequence and N-terminal sequencing revealed that the MSD-specific 250 kDa protein showed a high similarity with a plasma-rich protein, α-2 macroglobulin (α2M), so termed pp-α2M. This protein was a large glycoprotein contained predominantly mannose and GlcNAc. The expression of pp-α2M mRNA was detected in spermatic duct (SD), androgenic gland (AG) and hematopoietic tissue, while the protein expression was rather specific to the apical cytoplasm of MSD epithelium. The secretory pp-α2M in MSD fluid was acquired onto the MSD sperm membrane and was also found within the matrix of the acrosome. Distally, pp-α2M was removed from spermathecal sperm membrane, while its level kept constant in the sperm AC. Together the results indicate that pp-α2M is a 250 kDa region-specific secretory protein which plays roles in sperm physical protection and also acts as maturation factor in the P. pelagicus sperm.

Keywords: alpha-2 macroglobulin, blue swimming crab, sperm maturation, spermatic duct

Procedia PDF Downloads 320

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 254

Authors: Leah Ning

Abstract:

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 84

Authors: Juan Sebastian Huertas Piedrahita, Jaime Arturo Lopez Duque, Eduardo Luis Perez Londoño, Julián S. Rodríguez

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The process of generating computer vision is called artificial vision. The artificial vision is a branch of artificial intelligence that allows the obtaining, processing, and analysis of any type of information especially the ones obtained through digital images. Actually the artificial vision is used in manufacturing areas for quality control and production, as these processes can be realized through counting algorithms, positioning, and recognition of objects that can be measured by a single camera (or more). On the other hand, the companies use assembly lines formed by conveyor systems with actuators on them for moving pieces from one location to another in their production. These devices must be previously programmed for their good performance and must have a programmed logic routine. Nowadays the production is the main target of every industry, quality, and the fast elaboration of the different stages and processes in the chain of production of any product or service being offered. The principal base of this project is to program a computer that recognizes geometric figures (circle, square, and triangle) through a camera, each one with a different color and link it with a group of conveyor systems to organize the mentioned figures in cubicles, which differ from one another also by having different colors. This project bases on artificial vision, therefore the methodology needed to develop this project must be strict, this one is detailed below: 1. Methodology: 1.1 The software used in this project is QT Creator which is linked with Open CV libraries. Together, these tools perform to realize the respective program to identify colors and forms directly from the camera to the computer. 1.2 Imagery acquisition: To start using the libraries of Open CV is necessary to acquire images, which can be captured by a computer’s web camera or a different specialized camera. 1.3 The recognition of RGB colors is realized by code, crossing the matrices of the captured images and comparing pixels, identifying the primary colors which are red, green, and blue. 1.4 To detect forms it is necessary to realize the segmentation of the images, so the first step is converting the image from RGB to grayscale, to work with the dark tones of the image, then the image is binarized which means having the figure of the image in a white tone with a black background. Finally, we find the contours of the figure in the image to detect the quantity of edges to identify which figure it is. 1.5 After the color and figure have been identified, the program links with the conveyor systems, which through the actuators will classify the figures in their respective cubicles. Conclusions: The Open CV library is a useful tool for projects in which an interface between a computer and the environment is required since the camera obtains external characteristics and realizes any process. With the program for this project any type of assembly line can be optimized because images from the environment can be obtained and the process would be more accurate.

Keywords: artificial intelligence, artificial vision, binarized, grayscale, images, RGB

Procedia PDF Downloads 373

Authors: Zhen Peng, Baifeng Wu

Abstract:

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 281

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 181

Authors: ChunYi Peng, Wei Hsuan CHeng, Shyh Kuang Ueng

Abstract:

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 43

Authors: F. Sallem, B. Dahhou, A. Kamoun

Abstract:

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 391

Authors: Aleksandar Zhelyazkov, Daniele Zonta, Helmut Wenzel, Peter Furtner

Abstract:

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 220

Authors: Zainab Dahrouch, Beatrix Petrovičová, Claudia Triolo, Fabiola Pantò, Angela Malara, Salvatore Patanè, Maria Allegrini, Saveria Santangelo

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Synthetic dyes dispersed in water cause environmental damage and have harmful effects on human health. Methylene blue (MB) is broadly used as a dye in the textile, pharmaceutical, printing, cosmetics, leather, and food industries. The complete removal of MB is difficult due to the presence of aromatic rings in its structure. The present study is focused on electrospun nanofibers (NFs) with engineered architecture and surface to be used as catalysts for the photodegradation of MB. Ti and/or Zn oxide NFs are produced by electrospinning precursor solutions with different Ti: Zn molar ratios (from 0:1 to 1:0). Subsequent calcination and cooling steps are operated at fast rates to generate porous NFs with capture centers to reduce the recombination rate of the photogenerated charges. The comparative evaluation of the NFs as photocatalysts for the removal of MB from an aqueous solution with a dye concentration of 15 µM under UV irradiation shows that the binary (wurtzite ZnO and anatase TiO₂) oxides exhibit higher catalytic activity compared to ternary (ZnTiO₃ and Zn₂TiO₄) oxides. The higher band gap and lower crystallinity of the ternary oxides are responsible for their lower photocatalytic activity. It has been found that the optimal load for the wurtzite ZnO is 0.66 mg mL⁻¹, obtaining a degradation rate of 7.94.10⁻² min⁻¹. The optimal load for anatase TiO₂ is lower (0.33 mg mL⁻¹) and the corresponding rate constant (1.12×10⁻¹ min⁻¹) is higher. This finding (higher activity with lower load) is of crucial importance for the scaling up of the process on an industrial scale. Indeed, the anatase NFs outperform even the commonly used P25-TiO₂ benchmark. Besides, they can be reused twice without any regeneration treatment, with 5.2% and 18.7% activity decrease after second and third use, respectively. Thanks to the scalability of the electrospinning technique, this laboratory-scale study provides a perspective towards the sustainable large-scale manufacture of photocatalysts for the treatment of industry effluents.

Keywords: anatase, capture centers, methylene blue dye, nanofibers, photodegradation, zinc oxide

Procedia PDF Downloads 149

Authors: Peng Chun-Yi, Chen Wei-Hsuan, Ueng Shyh-Kuang

Abstract:

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 50

Authors: Pyar S. Jassal, Sonal Gupta, Neema Chand, Rajni Johar

Abstract:

In present study, Low molecular weight chitosan naoparticles (LMWCNP) were synthesized by using low molecular weight chitosan (LMWC) and sodium tripolyphosphate for the adsorption of anthraquinone dyes from waste water. The ionic-gel technique was used for this purpose. Size of nanoparticles was determined by “Scherrer equation”. The absorbance was carried out with UV-visible spectrophotometer for Acid Green 25 (AG25) and Reactive Blue 4 (RB4) dyes solutions at λmax 644 and λmax 598 nm respectively. The removal of dyes was dependent on the pH and the optimum adsorption was between pH 2 to 9. The extraction of dyes was linearly dependent on temperature. The equilibrium parameters, RL was calculated by using the Langmuir isotherm and shows that adsorption of dyes is favorable on the LMWCNP. The XRD images of LMWC show a crystalline nature whereas LMWCNP is amorphous one. The thermo gravimetric analysis (TGA) shows that LMWCNP thermally more stable than LMWC. As the contact time increases, percentage removal of Acid Green 25 and Reactive Blue 4 dyes also increases. TEM images reveal the size of the LMWCNP were in the range of 45-50 nm. The capacity of AG25 dye on LMWC was 5.23 mg/g, it compared with LMWCNP capacity which was 6.83 mg/g respectively. The capacity of RB4 dye on LMWC was 2.30 mg/g and 2.34 mg/g was on LMWCNP.

Keywords: low molecular weight chitosan nanoparticles, anthraquinone dye, removal efficiency, adsorption isotherm

Procedia PDF Downloads 126

Authors: Houda Najeh, Stéphane Ploix, Mahendra Pratap Singh, Karim Chabir, Mohamed Naceur Abdelkrim

Abstract:

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 236

Authors: Oladapo Tolulope Ibitoye

Abstract:

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 58

Authors: A. Biran, P. Sobhe Bidari, K. Raahemifar

Abstract:

Diabetic Retinopathy (DR) is an eye disease that leads to blindness. The earliest signs of DR are the appearance of red and yellow lesions on the retina called hemorrhages and exudates. Early diagnosis of DR prevents from blindness; hence, many automated algorithms have been proposed to extract hemorrhages and exudates. In this paper, an automated algorithm is presented to extract hemorrhages and exudates separately from retinal fundus images using different image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. Since Optic Disc is the same color as the exudates, it is first localized and detected. The presented method has been tested on fundus images from Structured Analysis of the Retina (STARE) and Digital Retinal Images for Vessel Extraction (DRIVE) databases by using MATLAB codes. The results show that this method is perfectly capable of detecting hard exudates and the highly probable soft exudates. It is also capable of detecting the hemorrhages and distinguishing them from blood vessels.

Keywords: diabetic retinopathy, fundus, CHT, exudates, hemorrhages

Procedia PDF Downloads 262