Search results for: fault detection and classification

Authors: Zhao Tianxu, Cui Shumei

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This paper proposes a novel multiphase motor structure. The armature winding consists of several independent multiphase windings that have different rating rotate speed and power. Compared to conventional motor, the novel motor structure has more operation mode and fault tolerance mode, which makes it adapt to high-reliability requirement situation such as electric vehicle, aircraft and ship. Performance of novel motor structure varies with winding match. In order to find optimum control strategy, motor torque character, efficiency performance and fault tolerance ability under different operation mode are analyzed in this paper, and torque distribution strategy for efficiency optimization is proposed. Simulation analyze is taken and the result shows that proposed structure has the same efficiency on heavy load and higher efficiency on light load operation points, which expands high efficiency area of motor and cruise range of vehicle. The proposed structure can improve motor highest speed.

Keywords: multiphase motor, armature winding match, torque distribution strategy, efficiency

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Authors: Kamal KC, Zhendong Yin, Dasen Li, Zhilu Wu

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Intra-plant disease classification based on leaf images is a challenging computer vision task due to similarities in texture, color, and shape of leaves with a slight variation of leaf spot; and external environmental changes such as lighting and background noises. Deep convolutional neural network (DCNN) has proven to be an effective tool for binary classification. In this paper, two methods for binary classification of diseased plant leaves using DCNN are presented; model created from scratch and transfer learning. Our main contribution is a thorough evaluation of 4 networks created from scratch and transfer learning of 5 pre-trained models. Training and testing of these models were performed on a plant leaf images dataset belonging to 16 distinct classes, containing a total of 22,265 images from 8 different plants, consisting of a pair of healthy and diseased leaves. We introduce a deep CNN model, Optimized MobileNet. This model with depthwise separable CNN as a building block attained an average test accuracy of 99.77%. We also present a fine-tuning method by introducing the concept of a convolutional block, which is a collection of different deep neural layers. Fine-tuned models proved to be efficient in terms of accuracy and computational cost. Fine-tuned MobileNet achieved an average test accuracy of 99.89% on 8 pairs of [healthy, diseased] leaf ImageSet.

Keywords: deep convolution neural network, depthwise separable convolution, fine-grained classification, MobileNet, plant disease, transfer learning

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Authors: Mehmet Akköse

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Concern about seismic safety of concrete dams have been growing around the world, partly because the population at risk in locations downstream of major dams continues to expand and also because it is increasingly evident that the seismic design concepts in use at the time most existing dams were built were inadequate. Most of the investigations in the past have been conducted on large dams, typically above 100m high. A large number of concrete dams in our country and in other parts of the world are less than 50m high. Most of these dams were usually designed using pseudo-static methods, ignoring the dynamic characteristics of the structure as well as the characteristics of the ground motion. Therefore, it is important to carry out investigations on seismic behavior this category of dam in order to assess and evaluate the safety of existing dams and improve the knowledge for different high dams to be constructed in the future. In this study, size effects on structural performance of concrete gravity dams subjected to near and far-fault ground motions are investigated including dam-water-foundation interaction. For this purpose, a benchmark problem proposed by ICOLD (International Committee on Large Dams) is chosen as a numerical application. Structural performance of the dam having five different heights is evaluated according to damage criterions in USACE (U.S. Army Corps of Engineers). It is decided according to their structural performance if non-linear analysis of the dams requires or not. The linear elastic dynamic analyses of the dams to near and far-fault ground motions are performed using the step-by-step integration technique. The integration time step is 0.0025 sec. The Rayleigh damping constants are calculated assuming 5% damping ratio. The program NONSAP modified for fluid-structure systems with the Lagrangian fluid finite element is employed in the response calculations.

Keywords: concrete gravity dams, Lagrangian approach, near and far-fault ground motion, USACE damage criterions

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Authors: Ali Asheibi

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The rapid increase in the size of databases required to store power quality monitoring data has demanded new techniques for analysing and understanding the data. One suggested technique to assist in analysis is data mining. Preparing raw data to be ready for data mining exploration take up most of the effort and time spent in the whole data mining process. Clustering is an important technique in data mining and machine learning in which underlying and meaningful groups of data are discovered. Large amounts of harmonic data have been collected from an actual harmonic monitoring system in a distribution system in Australia for three years. This amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. In this paper, harmonic data preparation processes to better understanding of the data have been presented. Underlying classes in this data has then been identified using clustering technique based on the Minimum Message Length (MML) method. The underlying operational information contained within the clusters can be rapidly visualised by the engineers. The C5.0 algorithm was used for classification and interpretation of the generated clusters.

Keywords: data mining, harmonic data, clustering, classification

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Authors: Abdulkader Helwan

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Iris cancer, so called intraocular melanoma is a cancer that starts in the iris; the colored part of the eye that surrounds the pupil. There is a need for an accurate and cost-effective iris cancer detection system since the available techniques used currently are still not efficient. The combination of the image processing and artificial neural networks has a great efficiency for the diagnosis and detection of the iris cancer. Image processing techniques improve the diagnosis of the cancer by enhancing the quality of the images, so the physicians diagnose properly. However, neural networks can help in making decision; whether the eye is cancerous or not. This paper aims to develop an intelligent system that stimulates a human visual detection of the intraocular melanoma, so called iris cancer. The suggested system combines both image processing techniques and neural networks. The images are first converted to grayscale, filtered, and then segmented using prewitt edge detection algorithm to detect the iris, sclera circles and the cancer. The principal component analysis is used to reduce the image size and for extracting features. Those features are considered then as inputs for a neural network which is capable of deciding if the eye is cancerous or not, throughout its experience adopted by many training iterations of different normal and abnormal eye images during the training phase. Normal images are obtained from a public database available on the internet, “Mile Research”, while the abnormal ones are obtained from another database which is the “eyecancer”. The experimental results for the proposed system show high accuracy 100% for detecting cancer and making the right decision.

Keywords: iris cancer, intraocular melanoma, cancerous, prewitt edge detection algorithm, sclera

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Authors: Marta D'Onofrio

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The debate on the digitalization of the economy has raised questions about how both labor and the regulation of work processes are changing due to the introduction of digital technologies in the productive system. Within the literature, the term ‘digital labor’ is commonly used to identify the impact of digitalization on labor. Despite the wide use of this term, it is still not available an unambiguous definition of it, and this could create confusion in the use of terminology and in the attempts of classification. As a consequence, the purpose of this paper is to provide for a definition and to propose a classification of ‘digital labor’, resorting to the theoretical approach of organizational studies.

Keywords: digital labor, digitalization, data-driven algorithms, big data, organizational studies

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Authors: Wagneur Edouard

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Tropical algebra is the algebra constructed over an idempotent semifield S. We show here that every m-dimensional tropical module M over S with strongly independent basis can be embedded into Sm, and provide an algebraic invariant -the Γ-matrix of M- which characterises the isomorphy class of M. The strong independence condition also yields a significant improvement to the Whitney embedding for tropical torsion modules published earlier We also show that the strong independence of the basis of M is equivalent to the unique representation of elements of M. Numerous examples illustrate our results.

Keywords: classification, idempotent semi-modules, strong independence, tropical algebra

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Authors: Ahmed Saeed, Sadok Sassi, Mohammad Roshun

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Gears are important components with a vital role in many rotating machines. One of the common gear failure causes is tooth fatigue crack; however, its early detection is still a challenging task. The objective of this study is to develop a numerical model that simulates the effect of teeth cracks on the resulting gears vibrations and permits consequently to perform an early fault detection. In contrast to other published papers, this work incorporates the possibility of multiple simultaneous cracks with different depths. As cracks alter significantly the stiffness of the tooth, finite element software is used to determine the stiffness variation with respect to the angular position, for different combinations of crack orientation and depth. A simplified six degrees of freedom nonlinear lumped parameter model of a one-stage spur gear system is proposed to study the vibration with and without cracks. The model developed for calculating the stiffness with the crack permitted to update the physical parameters of the second-degree-of-freedom equations of motions describing the vibration of the gearbox. The vibration simulation results of the gearbox were by obtained using Simulink/Matlab. The effect of one crack with different levels was studied thoroughly. The change in the mesh stiffness and the vibration response were found to be consistent with previously published works. In addition, various statistical time domain parameters were considered. They showed different degrees of sensitivity toward the crack depth. Multiple cracks were also introduced at different locations and the vibration response along with the statistical parameters were obtained again for a general case of degradation (increase in crack depth, crack number and crack locations). It was found that although some parameters increase in value as the deterioration level increases, they show almost no change or even decrease when the number of cracks increases. Therefore, the use of any statistical parameters could be misleading if not considered in an appropriate way.

Keywords: Spur gear, cracked tooth, numerical simulation, time-domain parameters

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: M. Khouil, N. Saber, M. Mestari

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This study aimed to propose, a different architecture of a Path Planning using the NECMOP. where several nonlinear objective functions must be optimized in a conflicting situation. The ability to detect and avoid collision is very important for mobile intelligent machines. However, many artificial vision systems are not yet able to quickly and cheaply extract the wealth information. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons linear and threshold logic, which simplified the actual implementation of all the networks proposed. This article represents a comprehensive algorithm that determine through the AMAXNET network a measure (a mini-maximum point) in a fixed time, which allows us to detect the presence of a potential collision.

Keywords: path planning, collision detection, convex polyhedron, neural network

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Authors: Zixian Zhang, Shanliang Yao, Zile Huang, Zhaodong Wu, Xiaohui Zhu, Yong Yue, Jieming Ma

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Unmanned Surface Vehicles (USVs) are valuable due to their ability to perform dangerous and time-consuming tasks on the water. Object detection tasks are significant in these applications. However, inherent challenges, such as the complex distribution of obstacles, reflections from shore structures, water surface fog, etc., hinder the performance of object detection of USVs. To address these problems, this paper provides a fusion method for USVs to effectively detect objects in the inland surface environment, utilizing vision sensors and 3+1D Millimeter-wave radar. MMW radar is complementary to vision sensors, providing robust environmental information. The radar 3D point cloud is transferred to 2D radar pseudo image to unify radar and vision information format by utilizing the point transformer. We propose a multi-source object detection network (RV-YOLOX )based on radar-vision fusion for inland waterways environment. The performance is evaluated on our self-recording waterways dataset. Compared with the YOLOX network, our fusion network significantly improves detection accuracy, especially for objects with bad light conditions.

Keywords: inland waterways, YOLO, sensor fusion, self-attention

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Authors: Amirali Mahouti, Hooshang Katebi

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Enlargement of Tabriz metropolis to the east and north-east caused urban construction to be built on Marl layers and because of increase in excavations depth, further information of this layer is inescapable. Looking at geotechnical investigation shows there is not enough information about Tabriz Marl and this soil has been classified only by color. Tabriz Marl is lacustrine carbonate sediment outcrops, surrounds eastern, northern and southern region of city in the East Azerbaijan Province of Iran and is known as bed rock of city under alluvium sediments. This investigation aims to characterize geotechnical parameters of this soil to identify and set it in classification system of carbonated soils. For this purpose, specimens obtained from 80 locations over the city and subjected to physical and mechanical tests, such as Atterberg limits, density, moisture content, unconfined compression, direct shear and consolidation. CaCO3 content, organic content, PH, XRD, XRF, TGA and geophysical downhole tests also have been done on some of them.

Keywords: carbonated soils, classification of soils, mineralogy, physical and mechanical tests for Marls, Tabriz Marl

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

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Authors: Jean-Pierre Dubois, Jihad S. Daba, H. Karam, J. Abdallah

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Tsunami early detection and warning systems have proved to be of ultimate importance, especially after the destructive tsunami that hit Japan in March 2012. Such systems are crucial to inform the authorities of any risk of a tsunami and of the degree of its danger in order to make the right decision and notify the public of the actions they need to take to save their lives. The purpose of this research is to enhance existing tsunami detection and warning systems. We first propose an automated and miniaturized model of an early tsunami detection and warning system. The model for the operation of a tsunami warning system is simulated using the data acquisition toolbox of Matlab and measurements acquired from specified internet pages due to the lack of the required real-life sensors, both seismic and hydrologic, and building a graphical user interface for the system. In the second phase of this work, we implement various satellite image filtering schemes to enhance the acquired synthetic aperture radar images of the tsunami affected region that are masked by speckle noise. This enables us to conduct a post-tsunami damage extent study and calculate the percentage damage. We conclude by proposing improvements to the existing telecommunication infrastructure of existing warning tsunami systems using a migration to IP-based networks and fiber optics links.

Keywords: detection, GIS, GSN, GTS, GPS, speckle noise, synthetic aperture radar, tsunami, wiener filter

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Authors: Miladi Yasmine, Bouhlel Salah, Garnit Hechmi

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Hydrothermal vein-type deposits of the Hammam Jedidi F-Ba(Pb-Zn-Cu) are hosted in Lower Jurassic, Cretaceous and Tertiary series, and located near a very important structural lineament (NE-SW) corresponding to the Hammam Jedidi Fault in the Tunisian Dorsale. The circulation of the ore forming fluid is triggered by a regional tectonic compressive phase which occurred during the miocène time. Mineralization occurs as stratabound and vein-type orebodies adjacent to the Triassic salt diapirs and within fault in Jurassic limestone. Fluid inclusions data show that two distinct fluids were involved in the mineralisation deposition: a warmer saline fluid (180°C, 20 wt % NaCl equivalent) and cooler less saline fluid (126°C, 5wt%NaCl equivalent). The contrasting salinities and halogen ratios suggest that this two fluid derived from one of the brine originated after the dissolution of halite as suggested by its high salinity. The other end member, as indicated by the low Cl/Br ratios, acquired its low salinity by dilution of Br enriched evaporated seawater. These results are compatible with Mississippi-Valley- type mineralization.

Keywords: Jebel Oust, fluid inclusions, North Eastern Tunisia, mineralization

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Authors: Ronak Khosravi, Mahmood Abbasi Layegh, Siamak Haghipour, Avin Esmaili

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In this paper, a study on classification of musical woodwind instruments using a small set of features selected from a broad range of extracted ones by the sequential forward selection method was carried out. Firstly, we extract 42 features for each record in the music database of 402 sound files belonging to five different groups of Flutes (end blown and internal duct), Single –reed, Double –reed (exposed and capped), Triple reed and Quadruple reed. Then, the sequential forward selection method is adopted to choose the best feature set in order to achieve very high classification accuracy. Two different classification techniques of support vector machines and relevance vector machines have been tested out and an accuracy of up to 96% can be achieved by using 21 time, frequency and coefficient features and relevance vector machine with the Gaussian kernel function.

Keywords: coefficient features, relevance vector machines, spectral features, support vector machines, temporal features

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Authors: Manpreet Kaur

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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

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Authors: Carlos Huertas, Reyes Juarez-Ramirez

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Public health is one of the most critical issues today; therefore, there is great interest to improve technologies in the area of diseases detection. With machine learning and feature selection, it has been possible to aid the diagnosis of several diseases such as cancer. In this work, we present an extension to the Heat Map Based Feature Selection algorithm, this modification allows automatic threshold parameter selection that helps to improve the generalization performance of high dimensional data such as mass spectrometry. We have performed a comparison analysis using multiple cancer datasets and compare against the well known Recursive Feature Elimination algorithm and our original proposal, the results show improved classification performance that is very competitive against current techniques.

Keywords: biomarker discovery, cancer, feature selection, mass spectrometry

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Authors: Mohammed Mustapha Alhaji, Sadiku Salawu

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A clay soil which classified under A-7-6 soil according to AASHTO soil classification system and CH according to the unified soil classification system was stabilized using A-3 soil (AASHTO soil classification system). The clay soil was replaced with 0%, 10%, 20% to 100% A-3 soil, compacted at both the BSL and BSH compaction energy level and using unconfined compressive strength as evaluation criteria. The MDD of the compactions at both the BSL and BSH compaction energy levels showed increase in MDD from 0% A-3 soil replacement to 40% A-3 soil replacement after which the values reduced to 100% A-3 soil replacement. The trend of the OMC with varied A-3 soil replacement is similar to that of MDD but in a reversed order. The OMC reduced from 0% A-3 soil replacement to 40% A-3 soil replacement after which the values increased to 100% A-3 soil replacement. This trend was attributed to the observed reduction in the void ratio from 0% A-3 soil replacement to 40% A-3 soil replacement after which the void ratio increased to 100% A-3 soil replacement. The maximum UCS for clay at varied A-3 soil replacement increased from 272 and 770kN/m2 for BSL and BSH compaction energy level at 0% A-3 soil replacement to 295 and 795kN/m2 for BSL and BSH compaction energy level respectively at 10% A-3 soil replacement after which the values reduced to 22 and 60kN/m2 for BSL and BSH compaction energy level respectively at 70% A-3 soil replacement. Beyond 70% A-3 soil replacement, the mixture cannot be moulded for UCS test.

Keywords: A-3 soil, clay minerals, pozzolanic action, stabilization

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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

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Authors: Amr Mansour Mohsen, Hesham Ahmed Hassan, Amira M. Idrees

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Document Analysis is an important research field that aims to gather the information by analyzing the data in documents. As one of the important targets for many fields is to understand what people actually want, sentimental analysis field has been one of the vital fields that are tightly related to the document analysis. This research focuses on analyzing text documents to classify each document according to its opinion. The aim of this research is to detect the emotions from text documents based on enriching the lexicon with adapting their content based on semantic patterns extraction. The proposed approach has been presented, and different experiments are applied by different perspectives to reveal the positive impact of the proposed approach on the classification results.

Keywords: document analysis, sentimental analysis, emotion detection, WEKA tool, NRC lexicon

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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

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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

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Authors: S. Bouchami, M.N. Lakhoua

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The automotive market is a dynamic market that continues to grow. That’s why several companies belonging to this sector adopt a quality improvement approach. Wanting to be competitive and successful in the environment in which they operate, these companies are dedicated to establishing a system of quality management to ensure the achievement of the objective quality, improving the products and process as well as the satisfaction of the customers. In this paper, the management of the quality and the improvement of a production line in an industrial company is presented. In fact, the project is divided into two essential parts: the creation of the technical line documentation and the quality assurance documentation and the resolution of defects at the line, as well as those claimed by the customer. The creation of the documents has required a deep understanding of the manufacturing process. The analysis and problem solving were done through the implementation of PDCA (Plan Do Check Act) and FTA (Fault Tree Analysis). As perspective, in order to better optimize production and improve the efficiency of the production line, a study on the problems associated with the supply of raw materials should be made to solve the problems of stock-outs which cause delays penalizing for the industrial company.

Keywords: quality management, documentary system, Plan Do Check Act (PDCA), fault tree analysis (FTA) method

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Authors: Chimey Lhamo, Ngawang Tsering

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Traditional Tibetan medicine, known as Sowa Rigpa (Science of healing), originated more than 2500 years ago with an insightful background, and it has been growing significant attention in many Asian countries like China, India, Bhutan, and Nepal. Particularly, the Indian government has targeted Traditional Tibetan medicine as its major Indian medical system, including Ayurveda. Although Traditional Tibetan medicine has been growing interest and has a long history, it is not easily recognized worldwide because it exists only in the Tibetan language and it is neither accessible nor understood by patent examiners at the international patent office, data about Traditional Tibetan medicine is not yet broadly exist in the Internet. There has also been the exploitation of traditional Tibetan medicine increasing. The Traditional Knowledge Digital Library is a database aiming to prevent the patenting and misappropriation of India’s traditional medicine knowledge by using India’s Traditional knowledge Digital Library on Sowa Rigpa in order to prevent its exploitation at international patent with the help of information technology tools and an innovative classification systems-traditional knowledge resource classification (TKRC). As of date, more than 3000 Sowa Rigpa formulations have been transcribed into a Traditional Knowledge Digital Library database. In this paper, we are presenting India's Traditional Knowledge Digital Library for Traditional Tibetan medicine, and this database system helps to preserve and prevent the exploitation of Sowa Rigpa. Gradually it will be approved and accepted globally.

Keywords: traditional Tibetan medicine, India's traditional knowledge digital library, traditional knowledge resources classification, international patent classification

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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

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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

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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

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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

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Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Reza Ehsani, Hawa Ze Jaffar, Ishak Aris

Abstract:

Ripeness estimation of oil palm fresh fruit is important processes that affect the profitableness and salability of oil palm fruits. The adulthood or ripeness of the oil palm fruits influences the quality of oil palm. Conventional procedure includes physical grading of Fresh Fruit Bunches (FFB) maturity by calculating the number of loose fruits per bunch. This physical classification of oil palm FFB is costly, time consuming and the results may have human error. Hence, many researchers try to develop the methods for ascertaining the maturity of oil palm fruits and thereby, deviously the oil content of distinct palm fruits without the need for exhausting oil extraction and analysis. This research investigates the potential of infrared images (Thermal Images) as a predictor to classify the oil palm FFB ripeness. A total of 270 oil palm fresh fruit bunches from most common cultivar of oil palm bunches Nigresens according to three maturity categories: under ripe, ripe and over ripe were collected. Each sample was scanned by the thermal imaging cameras FLIR E60 and FLIR T440. The average temperature of each bunches were calculated by using image processing in FLIR Tools and FLIR ThermaCAM researcher pro 2.10 environment software. The results show that temperature content decreased from immature to over mature oil palm FFBs. An overall analysis-of-variance (ANOVA) test was proved that this predictor gave significant difference between underripe, ripe and overripe maturity categories. This shows that the temperature as predictors can be good indicators to classify oil palm FFB. Classification analysis was performed by using the temperature of the FFB as predictors through Linear Discriminant Analysis (LDA), Mahalanobis Discriminant Analysis (MDA), Artificial Neural Network (ANN) and K- Nearest Neighbor (KNN) methods. The highest overall classification accuracy was 88.2% by using Artificial Neural Network. This research proves that thermal imaging and neural network method can be used as predictors of oil palm maturity classification.

Keywords: artificial neural network, maturity classification, oil palm FFB, thermal imaging

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