Search results for: TBM(tunnel boring machine)

Authors: Mahmoud Mohammad Salem Al-Suod, Ushkarenko O. Alexander, Dorogan I. Olga

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New algorithms using microprocessor systems have been proposed for protection the diesel-generator unit in autonomous power systems. The software structure is designed to enhance the control automata of the system, in which every protection module of diesel-generator encapsulates the finite state machine.

Keywords: diesel-generator unit, protection, state diagram, control system, algorithm, software components

Procedia PDF Downloads 398

Authors: Imane El Karaoui, Mohammed Maaroufi, Hamid Chaikhy

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Doubly Fed Induction Generator (DFIG) is one of the most reliable wind generator. Major problem in wind power generation is to generate Sinusoidal signal with very low THD on variable speed caused by inverter two levels used. This paper presents a multi-level inverter whose objective is to reduce the THD and the dimensions of the output filter. This work proposes a three-level NPC-type inverter, the results simulation are presented demonstrating the efficiency of the proposed inverter.

Keywords: DFIG, multilevel inverter, NPC inverter, THD, induction machine

Procedia PDF Downloads 234

Authors: N. Lakshmipathy, Ch. S. N. Murthy, M. Aruna

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The design of lighting systems for surface mines is not an easy task because of the unique environment and work procedures encountered in the mines. The primary objective of this paper is to identify the major problems encountered in mine lighting application and to provide guidance in the solution of these problems. In the surface mining reflectance of surrounding surfaces is one of the important factors, which improve the vision, in the night hours. But due to typical working nature in the mines it is very difficult to fulfill these requirements, and also the orientation of the light at work site is a challenging task. Due to this reason machine operator and other workers in a mine need to be able to orient themselves in a difficult visual environment. The haul roads always keep on changing to tune with the mining activity. Other critical area such as dumpyards, stackyards etc. also change their phase with time, and it is difficult to illuminate such areas. Mining is a hazardous occupation, with workers exposed to adverse conditions; apart from the need for hard physical labor, there is exposure to stress and environmental pollutants like dust, noise, heat, vibration, poor illumination, radiation, etc. Visibility is restricted when operating load haul dumper and Heavy Earth Moving Machinery (HEMM) vehicles resulting in a number of serious accidents. one of the leading causes of these accidents is the inability of the equipment operator to see clearly people, objects or hazards around the machine. Results indicate blind spots are caused primarily by posts, the back of the operator's cab, and by lights and light brackets. The careful designed and implemented, lighting systems provide mine workers improved visibility and contribute to improved safety, productivity and morale. Properly designed lighting systems can improve visibility and safety during working in the opencast mines.

Keywords: contrast, efficacy, illuminance, illumination, light, luminaire, luminance, reflectance, visibility

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Authors: Ling Liyun

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In the field of contemporary architecture, complex forms of architectural works continue to emerge in the world, along with some new terminology emerged: digital architecture, parametric design, algorithm generation, building information modeling, CNC construction and so on. Architects gradually mastered the new skills of mathematical logic in the form of exploration, virtual simulation, and the entire design and coordination in the construction process. Digital construction technology has a greater degree in controlling construction, and ensure its accuracy, creating a series of new construction techniques. As a result, the use of digital technology is an improvement and expansion of the practice of digital architecture design revolution. We worked by reading and analyzing information about the digital architecture development process, a large number of cases, as well as architectural design and construction as a whole process. Thus current developments were introduced and discussed in our paper, such as architectural discourse, design theory, digital design models and techniques, material selecting, as well as artificial intelligence space design. Our paper also pays attention to the representative three cases of digital design and construction experiment at great length in detail to expound high-informatization, high-reliability intelligence, and high-technique in constructing a humane space to cope with the rapid development of urbanization. We concluded that the opportunities and challenges of the shift existed in architectural paradigms, such as the cooperation methods, theories, models, technologies and techniques which were currently employed in digital design research and digital praxis. We also find out that the innovative use of space can gradually change the way people learn, talk, and control information. The past two decades, digital technology radically breaks the technology constraints of industrial technical products, digests the publicity on a particular architectural style (era doctrine). People should not adapt to the machine, but in turn, it’s better to make the machine work for users.

Keywords: artificial intelligence, collaboration, digital architecture, digital design theory, material selection, space construction

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Authors: Ebipatei Victoria Tunyan, T. A. Cao, Cheol Young Ock

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Detecting subjectively biased statements is a vital task. This is because this kind of bias, when present in the text or other forms of information dissemination media such as news, social media, scientific texts, and encyclopedias, can weaken trust in the information and stir conflicts amongst consumers. Subjective bias detection is also critical for many Natural Language Processing (NLP) tasks like sentiment analysis, opinion identification, and bias neutralization. Having a system that can adequately detect subjectivity in text will boost research in the above-mentioned areas significantly. It can also come in handy for platforms like Wikipedia, where the use of neutral language is of importance. The goal of this work is to identify the subjectively biased language in text on a sentence level. With machine learning, we can solve complex AI problems, making it a good fit for the problem of subjective bias detection. A key step in this approach is to train a classifier based on BERT (Bidirectional Encoder Representations from Transformers) as upstream model. BERT by itself can be used as a classifier; however, in this study, we use BERT as data preprocessor as well as an embedding generator for a Bi-LSTM (Bidirectional Long Short-Term Memory) network incorporated with attention mechanism. This approach produces a deeper and better classifier. We evaluate the effectiveness of our model using the Wiki Neutrality Corpus (WNC), which was compiled from Wikipedia edits that removed various biased instances from sentences as a benchmark dataset, with which we also compare our model to existing approaches. Experimental analysis indicates an improved performance, as our model achieved state-of-the-art accuracy in detecting subjective bias. This study focuses on the English language, but the model can be fine-tuned to accommodate other languages.

Keywords: subjective bias detection, machine learning, BERT–BiLSTM–Attention, text classification, natural language processing

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Authors: Kai Warsoenke, Maik Mackiewicz

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To achieve a high quality of assembled car body structures, tolerancing is used to ensure a geometric accuracy of the single car body parts. There are two main techniques to determine the required tolerances. The first is tolerance analysis which describes the influence of individually tolerated input values on a required target value. Second is tolerance synthesis to determine the location of individual tolerances to achieve a target value. Both techniques are based on classical statistical methods, which assume certain probability distributions. To ensure competitiveness in both saturated and dynamic markets, production processes in vehicle manufacturing must be flexible and efficient. The dimensional specifications selected for the individual body components and the resulting assemblies have a major influence of the quality of the process. For example, in the manufacturing of forming tools as operating equipment or in the higher level of car body assembly. As part of the metrological process monitoring, manufactured individual parts and assemblies are recorded and the measurement results are stored in databases. They serve as information for the temporary adjustment of the production processes and are interpreted by experts in order to derive suitable adjustments measures. In the production of forming tools, this means that time-consuming and costly changes of the tool surface have to be made, while in the body shop, uncertainties that are difficult to control result in cost-intensive rework. The stored measurement results are not used to intelligently design tolerances in future processes or to support temporary decisions based on real-world geometric data. They offer potential to extend the tolerancing methods through data analysis and machine learning models. The purpose of this paper is to examine real-world measurement data from individual car body components, as well as assemblies, in order to develop an approach for using the data in short-term actions and future projects. For this reason, the measurement data will be analyzed descriptively in the first step in order to characterize their behavior and to determine possible correlations. In the following, a database is created that is suitable for developing machine learning models. The objective is to create an intelligent way to determine the position and number of measurement points as well as the local tolerance range. For this a number of different model types are compared and evaluated. The models with the best result are used to optimize equally distributed measuring points on unknown car body part geometries and to assign tolerance ranges to them. The current results of this investigation are still in progress. However, there are areas of the car body parts which behave more sensitively compared to the overall part and indicate that intelligent tolerancing is useful here in order to design and control preceding and succeeding processes more efficiently.

Keywords: automotive production, machine learning, process optimization, smart tolerancing

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Authors: Bliss Singhal

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Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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Authors: Ian O'Loughlin

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Memory networks aim to integrate some of the recent successes in machine learning with a dynamic memory base that can be updated and deployed in artificial reasoning tasks. These models involve training networks to identify, update, and operate over stored elements in a large memory array in order, for example, to ably perform question and answer tasks parsing real-world and simulated discourses. This family of approaches still faces numerous challenges: the performance of these network models in simulated domains remains considerably better than in open, real-world domains, wide-context cues remain elusive in parsing words and sentences, and even moderately complex sentence structures remain problematic. This innovation, employing an array of stored and updatable ‘memory’ elements over which the system operates as it parses text input and develops responses to questions, is a compelling one for at least two reasons: first, it addresses one of the difficulties that standard machine learning techniques face, by providing a way to store a large bank of facts, offering a way forward for the kinds of long-term reasoning that, for example, recurrent neural networks trained on a corpus have difficulty performing. Second, the addition of a stored long-term memory component in artificial reasoning seems psychologically plausible; human reasoning appears replete with invocations of long-term memory, and the stored but dynamic elements in the arrays of memory networks are deeply reminiscent of the way that human memory is readily and often characterized. However, this apparent psychological plausibility is belied by a recent turn in the study of human memory in cognitive science. In recent years, the very notion that there is a stored element which enables remembering, however dynamic or reconstructive it may be, has come under deep suspicion. In the wake of constructive memory studies, amnesia and impairment studies, and studies of implicit memory—as well as following considerations from the cognitive neuroscience of memory and conceptual analyses from the philosophy of mind and cognitive science—researchers are now rejecting storage and retrieval, even in principle, and instead seeking and developing models of human memory wherein plasticity and dynamics are the rule rather than the exception. In these models, storage is entirely avoided by modeling memory using a recurrent neural network designed to fit a preconceived energy function that attains zero values only for desired memory patterns, so that these patterns are the sole stable equilibrium points in the attractor network. So although the array of long-term memory elements in memory networks seem psychologically appropriate for reasoning systems, they may actually be incurring difficulties that are theoretically analogous to those that older, storage-based models of human memory have demonstrated. The kind of emergent stability found in the attractor network models more closely fits our best understanding of human long-term memory than do the memory network arrays, despite appearances to the contrary.

Keywords: artificial reasoning, human memory, machine learning, neural networks

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Authors: Amirreza Fazely Hamedani, Muzzamil Aziz, Philipp Wieder, Ramin Yahyapour

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Software-defined networking in recent years came into the sight of so many network designers as a successor to the traditional networking. Unlike traditional networks where control and data planes engage together within a single device in the network infrastructure such as switches and routers, the two planes are kept separated in software-defined networks (SDNs). All critical decisions about packet routing are made on the network controller, and the data level devices forward the packets based on these decisions. This type of network is vulnerable to DDoS attacks, degrading the overall functioning and performance of the network by continuously injecting the fake flows into it. This increases substantial burden on the controller side, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to the legitimate users. Thus, the protection of this novel network architecture against denial of service attacks is essential. In the world of cybersecurity, attacks and new threats emerge every day. It is essential to have tools capable of managing and analyzing all this new information to detect possible attacks in real-time. These tools should provide a comprehensive solution to automatically detect, predict and prevent abnormalities in the network. Big data encompasses a wide range of studies, but it mainly refers to the massive amounts of structured and unstructured data that organizations deal with on a regular basis. On the other hand, it regards not only the volume of the data; but also that how data-driven information can be used to enhance decision-making processes, security, and the overall efficiency of a business. This paper presents an intelligent big data framework as a solution to handle illegitimate traffic burden on the SDN network created by the numerous DDoS attacks. The framework entails an efficient defence and monitoring mechanism against DDoS attacks by employing the state of the art machine learning techniques.

Keywords: apache spark, apache kafka, big data, DDoS attack, machine learning, SDN network

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of Artificial Intelligence (AI), specifically Deep Learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our pioneering approach introduces a hybrid model, amalgamating the strengths of two renowned Convolutional Neural Networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging

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Authors: Marc Toutain, Pascale Leconte, Antoine Gauthier

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Introduction: Eating Disorders (ED), such as anorexia, bulimia, and binge eating, are psychiatric illnesses that mostly affect young people. The main symptoms concern eating (restriction, excessive food intake) and weight control behaviors (laxatives, vomiting). Psychological comorbidities (depression, executive function disorders, etc.) and problematic behaviors toward physical activity (PA) are commonly associated with ED. Acquaintances on ED risk factors are still lacking, and more community sample studies are needed to improve prevention and early detection. To our knowledge, studies are needed to specifically investigate the link between ED risk level, PA, and psychological risk factors in a community sample of adolescents. The aim of this study is to assess the relation between ED risk level, exercise (type, frequency, and motivations for engaging in exercise), and psychological factors based on the Jacobi risk factors model. We suppose that a high risk of ED will be associated with the practice of high caloric cost PA, motivations oriented to weight and shape control, and psychological disturbances. Method: An online survey destined for students has been sent to several middle schools and colleges in northwest France. This survey combined several questionnaires, the Eating Attitude Test-26 assessing ED risk; the Exercise Motivation Inventory–2 assessing motivations toward PA; the Hospital Anxiety and Depression Scale assessing anxiety and depression, the Contour Drawing Rating Scale; and the Body Esteem Scale assessing body dissatisfaction, Rosenberg Self-esteem Scale assessing self-esteem, the Exercise Dependence Scale-Revised assessing PA dependence, the Multidimensional Assessment of Interoceptive Awareness assessing interoceptive awareness and the Frost Multidimensional Perfectionism Scale assessing perfectionism. Machine learning analysis will be performed in order to constitute groups with a tree-based model clustering method, extract risk profile(s) with a bootstrap method comparison, and predict ED risk with a prediction method based on a decision tree-based model. Expected results: 1044 complete records have already been collected, and the survey will be closed at the end of May 2022. Records will be analyzed with a clustering method and a bootstrap method in order to reveal risk profile(s). Furthermore, a predictive tree decision method will be done to extract an accurate predictive model of ED risk. This analysis will confirm typical main risk factors and will give more data on presumed strong risk factors such as exercise motivations and interoceptive deficit. Furthermore, it will enlighten particular risk profiles with a strong level of proof and greatly contribute to improving the early detection of ED and contribute to a better understanding of ED risk factors.

Keywords: eating disorders, risk factors, physical activity, machine learning

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Authors: O. J. Akinsola, Yinan Zheng, Rose Anorlu, F. T. Ogunsola, Lifang Hou, Robert Leo-Murphy

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Cervical Cancer (CC) is the 2nd most common cancer among women living in low and middle-income countries, with no associated symptoms during formative periods. With the advancement and innovative medical research, there are numerous preventive measures being utilized, but the incidence of cervical cancer cannot be truncated with the application of only screening tests. The mortality associated with this invasive cervical cancer can be nipped in the bud through the important role of early-stage detection. This study research selected an array of different top features selection techniques which was aimed at developing a model that could validly diagnose the risk factors of cervical cancer. A retrospective clinic-based cohort study was conducted on 178 HIV-associated cervical cancer patients in Lagos University teaching Hospital, Nigeria (U54 data repository) in April 2022. The outcome measure was the automated prediction of the HIV-associated cervical cancer cases, while the predictor variables include: demographic information, reproductive history, birth control, sexual history, cervical cancer screening history for invasive cervical cancer. The proposed technique was assessed with R and Python programming software to produce the model by utilizing the classification algorithms for the detection and diagnosis of cervical cancer disease. Four machine learning classification algorithms used are: the machine learning model was split into training and testing dataset into ratio 80:20. The numerical features were also standardized while hyperparameter tuning was carried out on the machine learning to train and test the data. Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN). Some fitting features were selected for the detection and diagnosis of cervical cancer diseases from selected characteristics in the dataset using the contribution of various selection methods for the classification cervical cancer into healthy or diseased status. The mean age of patients was 49.7±12.1 years, mean age at pregnancy was 23.3±5.5 years, mean age at first sexual experience was 19.4±3.2 years, while the mean BMI was 27.1±5.6 kg/m2. A larger percentage of the patients are Married (62.9%), while most of them have at least two sexual partners (72.5%). Age of patients (OR=1.065, p<0.001**), marital status (OR=0.375, p=0.011**), number of pregnancy live-births (OR=1.317, p=0.007**), and use of birth control pills (OR=0.291, p=0.015**) were found to be significantly associated with HIV-associated cervical cancer. On top ten 10 features (variables) considered in the analysis, RF claims the overall model performance, which include: accuracy of (72.0%), the precision of (84.6%), a recall of (84.6%) and F1-score of (74.0%) while LR has: an accuracy of (74.0%), precision of (70.0%), recall of (70.0%) and F1-score of (70.0%). The RF model identified 10 features predictive of developing cervical cancer. The age of patients was considered as the most important risk factor, followed by the number of pregnancy livebirths, marital status, and use of birth control pills, The study shows that data mining techniques could be used to identify women living with HIV at high risk of developing cervical cancer in Nigeria and other sub-Saharan African countries.

Keywords: associated cervical cancer, data mining, random forest, logistic regression

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Authors: Nir Nissim, Erez Shalom, Tomer Lancewiki, Yuval Elovici, Yuval Shahar

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Background: A Medical Device (MD) is an instrument, machine, implant, or similar device that includes a component intended for the purpose of the diagnosis, cure, treatment, or prevention of disease in humans or animals. Medical devices play increasingly important roles in health services eco-systems, including: (1) Patient Diagnostics and Monitoring; Medical Treatment and Surgery; and Patient Life Support Devices and Stabilizers. MDs are part of the medical device eco-system and are connected to the network, sending vital information to the internal medical information systems of medical centers that manage this data. Wireless components (e.g. Wi-Fi) are often embedded within medical devices, enabling doctors and technicians to control and configure them remotely. All these functionalities, roles, and uses of MDs make them attractive targets of cyber-attacks launched for many malicious goals; this trend is likely to significantly increase over the next several years, with increased awareness regarding MD vulnerabilities, the enhancement of potential attackers’ skills, and expanded use of medical devices. Significance: We propose to develop and implement Cyber-Med, a unique collaborative project of Ben-Gurion University of the Negev and the Clalit Health Services Health Maintenance Organization. Cyber-Med focuses on the development of a comprehensive detection framework that relies on a critical attack repository that we aim to create. Cyber-Med will allow researchers and companies to better understand the vulnerabilities and attacks associated with medical devices as well as providing a comprehensive platform for developing detection solutions. Methodology: The Cyber-Med detection framework will consist of two independent, but complementary detection approaches: one for known attacks, and the other for unknown attacks. These modules incorporate novel ideas and algorithms inspired by our team's domains of expertise, including cyber security, biomedical informatics, and advanced machine learning, and temporal data mining techniques. The establishment and maintenance of Cyber-Med’s up-to-date attack repository will strengthen the capabilities of Cyber-Med’s detection framework. Major Findings: Based on our initial survey, we have already found more than 15 types of vulnerabilities and possible attacks aimed at MDs and their eco-system. Many of these attacks target individual patients who use devices such pacemakers and insulin pumps. In addition, such attacks are also aimed at MDs that are widely used by medical centers such as MRIs, CTs, and dialysis engines; the information systems that store patient information; protocols such as DICOM; standards such as HL7; and medical information systems such as PACS. However, current detection tools, techniques, and solutions generally fail to detect both the known and unknown attacks launched against MDs. Very little research has been conducted in order to protect these devices from cyber-attacks, since most of the development and engineering efforts are aimed at the devices’ core medical functionality, the contribution to patients’ healthcare, and the business aspects associated with the medical device.

Keywords: medical device, cyber security, attack, detection, machine learning

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Authors: Kishor Chandra Kandpal, Amit Kumar

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The Indian Himalaya region harbours approximately 1748 plants of medicinal importance, and as per International Union for Conservation of Nature (IUCN), the 112 plant species among these are threatened and endangered. To ease the pressure on these plants, the government of India is encouraging its in-situ cultivation. The Saussurea costus, Valeriana jatamansi, and Picrorhiza kurroa have also been prioritized for large scale cultivation owing to their market demand, conservation value and medicinal properties. These species are found from 1000 m to 4000 m elevation ranges in the Indian Himalaya. Identification of these plants in the field requires taxonomic skills, which is one of the major bottleneck in the conservation and management of these plants. In recent years, Hyperspectral remote sensing techniques have been precisely used for the discrimination of plant species with the help of their unique spectral signatures. In this background, a spectral library of the above 03 medicinal plants was prepared by collecting the spectral data using a handheld spectroradiometer (325 to 1075 nm) from farmer’s fields of Himachal Pradesh and Uttarakhand states of Indian Himalaya. The Random forest (RF) model was implied on the spectral data for the classification of the medicinal plants. The 80:20 standard split ratio was followed for training and validation of the RF model, which resulted in training accuracy of 84.39 % (kappa coefficient = 0.72) and testing accuracy of 85.29 % (kappa coefficient = 0.77). This RF classifier has identified green (555 to 598 nm), red (605 nm), and near-infrared (725 to 840 nm) wavelength regions suitable for the discrimination of these species. The findings of this study have provided a technique for rapid and onsite identification of the above medicinal plants in the field. This will also be a key input for the classification of hyperspectral remote sensing images for mapping of these species in farmer’s field on a regional scale. This is a pioneer study in the Indian Himalaya region for medicinal plants in which the applicability of hyperspectral remote sensing has been explored.

Keywords: himalaya, hyperspectral remote sensing, machine learning; medicinal plants, random forests

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Authors: Rahul Paul, Peter Mctaggart, Luke Skinner

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Maintaining public safety and network reliability are the core objectives of all electricity distributors globally. For many electricity distributors, managing vegetation clearances from their above ground assets (poles and conductors) is the most important and costly risk mitigation control employed to meet these objectives. Light Detection And Ranging (LiDAR) is widely used by utilities as a cost-effective method to inspect their spatially-distributed assets at scale, often captured using high powered LiDAR scanners attached to fixed wing or rotary aircraft. The resulting 3D point cloud model is used by these utilities to perform engineering grade measurements that guide the prioritisation of vegetation cutting programs. Advances in computer vision and machine-learning approaches are increasingly applied to increase automation and reduce inspection costs and time; however, real-world LiDAR capture variables (e.g., aircraft speed and height) create complexity, noise, and missing data, reducing the effectiveness of these approaches. This paper proposes a method for identifying each conductor from LiDAR data via clustering methods that can precisely reconstruct conductors in complex real-world configurations in the presence of high levels of noise. It proposes 3D catenary models for individual clusters fitted to the captured LiDAR data points using a least square method. An iterative learning process is used to identify potential conductor models between pole pairs. The proposed method identifies the optimum parameters of the catenary function and then fits the LiDAR points to reconstruct the conductors.

Keywords: point cloud, LİDAR data, machine learning, computer vision, catenary curve, vegetation management, utility industry

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Authors: Kumari Sweta, Ajanta Goswami, Abhilasha Dixit

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Landslide is a geomorphic process that plays an essential role in the evolution of the hill-slope and long-term landscape evolution. But its abrupt nature and the associated catastrophic forces of the process can have undesirable socio-economic impacts, like substantial economic losses, fatalities, ecosystem, geomorphologic and infrastructure disturbances. The estimated fatality rate is approximately 1person /100 sq. Km and the average economic loss is more than 550 crores/year in the Himalayan belt due to landslides. This study presents a comparative performance of a statistical bivariate method and a machine learning technique for landslide susceptibility mapping in and around Dharamshala, Himachal Pradesh. The final produced landslide susceptibility maps (LSMs) with better accuracy could be used for land-use planning to prevent future losses. Dharamshala, a part of North-western Himalaya, is one of the fastest-growing tourism hubs with a total population of 30,764 according to the 2011 census and is amongst one of the hundred Indian cities to be developed as a smart city under PM’s Smart Cities Mission. A total of 209 landslide locations were identified in using high-resolution linear imaging self-scanning (LISS IV) data. The thematic maps of parameters influencing landslide occurrence were generated using remote sensing and other ancillary data in the GIS environment. The landslide causative parameters used in the study are slope angle, slope aspect, elevation, curvature, topographic wetness index, relative relief, distance from lineaments, land use land cover, and geology. LSMs were prepared using information value (Info Val), and Fuzzy Expert System (FES) models. Info Val is a statistical bivariate method, in which information values were calculated as the ratio of the landslide pixels per factor class (Si/Ni) to the total landslide pixel per parameter (S/N). Using this information values all parameters were reclassified and then summed in GIS to obtain the landslide susceptibility index (LSI) map. The FES method is a machine learning technique based on ‘mean and neighbour’ strategy for the construction of fuzzifier (input) and defuzzifier (output) membership function (MF) structure, and the FR method is used for formulating if-then rules. Two types of membership structures were utilized for membership function Bell-Gaussian (BG) and Trapezoidal-Triangular (TT). LSI for BG and TT were obtained applying membership function and if-then rules in MATLAB. The final LSMs were spatially and statistically validated. The validation results showed that in terms of accuracy, Info Val (83.4%) is better than BG (83.0%) and TT (82.6%), whereas, in terms of spatial distribution, BG is best. Hence, considering both statistical and spatial accuracy, BG is the most accurate one.

Keywords: bivariate statistical techniques, BG and TT membership structure, fuzzy expert system, information value method, machine learning technique

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: Waqas Ashraf, Yasir Nawab, Haritham Khan, Habib Awais, Shahbaz Ahmad

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Seersucker is a popular lightweight fabric widely used in men’s and women’s suiting, casual wear, children’s clothing, house robes, bed spreads and for spring and summer wear. The puckered effect generates air spaces between body and the fabric, keeping the wearer cool in hot conditions. The aim of this work was to develop knitted seersucker fabric on single cylinder weft knitting machine using plain jersey structure. Core spun cotton yarn and cotton spun yarn of same linear density were used. Core spun cotton yarn, contains cotton fiber in the sheath and elastase filament in the core. The both yarn were fed at regular interval to feeders on the machine. The loop length and yarn tension were kept constant at each feeder. The samples were then scoured and bleached. After wet processing, the fabric samples were washed and tumble dried. Parameters like loop length, stitch density and areal density were measured after conditioning these samples for 24 hours in Standard atmospheric condition. Produced sample has a regular puckering stripe along the width of the fabric with same height. The stitch density of both the flat and puckered area of relaxed fabric was found to be different .Air permeability and moisture management tests were performed. The results indicated that the knitted seersucker fabric has better wicking and moisture management properties as the flat area contact, whereas puckered area held away from the skin. Seersucker effect in knitted fabric was achieved by the difference of contraction of both sets of courses produced from different types of yarns. The seer sucker fabric produce by knitting technique is less expensive as compared to woven seer sucker fabric as there is no need of yarn preparation. The knitted seersucker fabric is more practicable for summer dresses, skirts, blouses, shirts, trousers and shorts.

Keywords: air permeability, knitted structure, moisture management, seersucker

Procedia PDF Downloads 308

Authors: Joseph Bertrand Iboum Kissaaka, Charles Fonyuy Ngum Tchioben, Paul Gustave Fowe Kwetche, Jeannette Ngo Elogan Ntem, Joseph Binyet Njebakal, Ribert Yvan Makosso-Tchapi, François Mvondo Owono, Marie Joseph Ntamak-Nida

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Located in the Gulf of Guinea, the Kribi-Campo sub-basin belongs to the Aptian salt basins along the West African Margin. In this paper, we investigated the tectono-stratigraphic architecture of the basin, focusing on the role of salt tectonics and strike-slip faults along the Kribi Fracture Zone with implications for reservoir prediction. Using 2D seismic data and well data interpreted through sequence stratigraphy with integrated seismic attributes analysis with Python Programming and unsupervised Machine Learning, at least six second-order sequences, indicating three main stages of tectono-stratigraphic evolution, were determined: pre-salt syn-rift, post-salt rift climax and post-rift stages. The pre-salt syn-rift stage with KTS1 tectonosequence (Barremian-Aptian) reveals a transform rifting along NE-SW transfer faults associated with N-S to NNE-SSW syn-rift longitudinal faults bounding a NW-SE half-graben filled with alluvial to lacustrine-fan delta deposits. The post-salt rift-climax stage (Lower to Upper Cretaceous) includes two second-order tectonosequences (KTS2 and KTS3) associated with the salt tectonics and Campo High uplift. During the rift-climax stage, the growth of salt diapirs developed syncline withdrawal basins filled by early forced regression, mid transgressive and late normal regressive systems tracts. The early rift climax underlines some fine-grained hangingwall fans or delta deposits and coarse-grained fans from the footwall of fault scarps. The post-rift stage (Paleogene to Neogene) contains at least three main tectonosequences KTS4, KTS5 and KTS6-7. The first one developed some turbiditic lobe complexes considered as mass transport complexes and feeder channel-lobe complexes cutting the unstable shelf edge of the Campo High. The last two developed submarine Channel Complexes associated with lobes towards the southern part and braided delta to tidal channels towards the northern part of the Kribi-Campo sub-basin. The reservoir distribution in the Kribi-Campo sub-basin reveals some channels, fan lobes reservoirs and stacked channels reaching up to the polygonal fault systems.

Keywords: tectono-stratigraphic architecture, Kribi-Campo sub-basin, machine learning, pre-salt sequences, post-salt sequences

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Authors: Dona Abdulwassi, Dhaee Dahlawi, Yara Zainy, Leen Joharji

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The relationship between Netflix and artificial intelligence is discussed in this paper. Netflix uses the most effective and efficient approaches to apply artificial intelligence, machine learning, and data science. Netflix employs the personalization tool for their users, recommending or suggesting shows based on what those users have already watched. The researchers conducted an experiment to learn more about how Netflix is used and how AI affects the user experience. The main conclusions of this study are that Netflix has a wide range of AI features, most users are happy with their Netflix subscriptions, and the majority prefer Netflix to alternative apps.

Keywords: easy accessibility, recommends, accuracy, privacy

Procedia PDF Downloads 51

Authors: Marcin Baranski

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This article presents a vibration diagnostic method designed for permanent magnets (PM) traction motors. Those machines are commonly used in traction drives of electrical vehicles. Specific structural properties of machines excited by permanent magnets are used in this method - electromotive force (EMF) generated due to vibrations. This work presents: field-circuit model, results of static tests, results of calculations and simulations.

Keywords: electrical vehicle, permanent magnet, traction drive, vibrations, electrical machine, eccentricity

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Authors: Okba Belahssen, Said Benramache

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This paper presents wear behavior of the plasma-nitrided 20MnCr5 steel. Untreated and plasma nitrided samples were tested. The morphology was observed by scanning electron microscopy (SEM). The plasma nitriding behaviors of 20MnCr5 steel have been assessed by evaluating tribological properties and surface hardness by using a pin-on-disk wear machine and microhardness tester. Experimental results showed that the nitrides ε-Fe2−3N and γ′-Fe4N present in the white layer improve the wear resistance.

Keywords: plasma-nitriding, alloy 20mncr5, steel, friction, wear

Procedia PDF Downloads 537

Authors: Ali Kazemi

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The digital evolution of financial services, while offering unprecedented convenience and accessibility, has also escalated the vulnerabilities to fraudulent activities. In this study, we introduce a distinct approach to real-time anomaly detection in financial transactions, aiming to fortify the defenses of banking and financial institutions against such threats. Utilizing unsupervised machine learning algorithms, specifically autoencoders and isolation forests, our research focuses on identifying irregular patterns indicative of fraud within transactional data, thus enabling immediate action to prevent financial loss. The data we used in this study included the monetary value of each transaction. This is a crucial feature as fraudulent transactions may have distributions of different amounts than legitimate ones, such as timestamps indicating when transactions occurred. Analyzing transactions' temporal patterns can reveal anomalies (e.g., unusual activity in the middle of the night). Also, the sector or category of the merchant where the transaction occurred, such as retail, groceries, online services, etc. Specific categories may be more prone to fraud. Moreover, the type of payment used (e.g., credit, debit, online payment systems). Different payment methods have varying risk levels associated with fraud. This dataset, anonymized to ensure privacy, reflects a wide array of transactions typical of a global banking institution, ranging from small-scale retail purchases to large wire transfers, embodying the diverse nature of potentially fraudulent activities. By engineering features that capture the essence of transactions, including normalized amounts and encoded categorical variables, we tailor our data to enhance model sensitivity to anomalies. The autoencoder model leverages its reconstruction error mechanism to flag transactions that deviate significantly from the learned normal pattern, while the isolation forest identifies anomalies based on their susceptibility to isolation from the dataset's majority. Our experimental results, validated through techniques such as k-fold cross-validation, are evaluated using precision, recall, and the F1 score alongside the area under the receiver operating characteristic (ROC) curve. Our models achieved an F1 score of 0.85 and a ROC AUC of 0.93, indicating high accuracy in detecting fraudulent transactions without excessive false positives. This study contributes to the academic discourse on financial fraud detection and provides a practical framework for banking institutions seeking to implement real-time anomaly detection systems. By demonstrating the effectiveness of unsupervised learning techniques in a real-world context, our research offers a pathway to significantly reduce the incidence of financial fraud, thereby enhancing the security and trustworthiness of digital financial services.

Keywords: anomaly detection, financial fraud, machine learning, autoencoders, isolation forest, transactional data analysis

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Authors: Izadi Banafsheh, Sedaghat Reza

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Maintenance, Repair, and Overhaul (MRO) of machinery are a key recent issue concerning the automotive industry. It has always been a debated question what order or priority should be adopted for the MRO of machinery. This study attempts to examine several criteria including process sensitivity, average time between machine failures, average duration of repair, availability of parts, availability of maintenance personnel and workload through a literature review and experts survey so as to determine the condition of the machine. According to the mentioned criteria, the machinery were ranked in four modes below: A) Need for inspection, B) Need for minor repair, C) Need for part replacement, and D) Need for major repair. The Fuzzy AHP was employed to determine the weighting of criteria. At the end, the obtained weights were ranked through the AHP for each criterion, three groups were specified: shaving machines, assembly and painting in four modes. The statistical population comprises the elite in the Iranian automotive industry at IKCO covering operation managers, CEOs and maintenance professionals who are highly specialized in MRO and perfectly knowledgeable in how the machinery function. The information required for this study were collected from both desk research and field review, which eventually led to construction of a questionnaire handed out to the sample respondents in order to collect information on the subject matter. The results of the AHP for weighting the criteria revealed that the availability of maintenance personnel was the top priority at coefficient of 0.206, while the process sensitivity took the last priority at coefficient of 0.066. Furthermore, the results of TOPSIS for prioritizing the IKCO machinery suggested that at the mode where there is need for inspection, the assembly machines took the top priority while paining machines took the third priority. As for the mode where there is need for minor repairs, the assembly machines took the top priority while the third priority belonged to the shaving machines. As for the mode where there is need for parts replacement, the assembly machines took the top priority while the third belonged to the paining machinery. Finally, as for the mode where there is need for major repair, the assembly machines took the top priority while the third belonged to the paining machinery.

Keywords: maintenance, repair, overhaul, MRO, prioritization of machinery, fuzzy logic, AHP, TOPSIS

Procedia PDF Downloads 271

Authors: Binder Hans

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Cancer is no longer seen as solely a genetic disease where genetic defects such as mutations and copy number variations affect gene regulation and eventually lead to aberrant cell functioning which can be monitored by transcriptome analysis. It has become obvious that epigenetic alterations represent a further important layer of (de-)regulation of gene activity. For example, aberrant DNA methylation is a hallmark of many cancer types, and methylation patterns were successfully used to subtype cancer heterogeneity. Hence, unraveling the interplay between different omics levels such as genome, transcriptome and epigenome is inevitable for a mechanistic understanding of molecular deregulation causing complex diseases such as cancer. This objective requires powerful downstream integrative bioinformatics methods as an essential prerequisite to discover the whole genome mutational, transcriptome and epigenome landscapes of cancer specimen and to discover cancer genesis, progression and heterogeneity. Basic challenges and tasks arise ‘beyond sequencing’ because of the big size of the data, their complexity, the need to search for hidden structures in the data, for knowledge mining to discover biological function and also systems biology conceptual models to deduce developmental interrelations between different cancer states. These tasks are tightly related to cancer biology as an (epi-)genetic disease giving rise to aberrant genomic regulation under micro-environmental control and clonal evolution which leads to heterogeneous cellular states. Machine learning algorithms such as self organizing maps (SOM) represent one interesting option to tackle these bioinformatics tasks. The SOMmethod enables recognizing complex patterns in large-scale data generated by highthroughput omics technologies. It portrays molecular phenotypes by generating individualized, easy to interpret images of the data landscape in combination with comprehensive analysis options. Our image-based, reductionist machine learning methods provide one interesting perspective how to deal with massive data in the discovery of complex diseases, gliomas, melanomas and colon cancer on molecular level. As an important new challenge, we address the combined portrayal of different omics data such as genome-wide genomic, transcriptomic and methylomic ones. The integrative-omics portrayal approach is based on the joint training of the data and it provides separate personalized data portraits for each patient and data type which can be analyzed by visual inspection as one option. The new method enables an integrative genome-wide view on the omics data types and the underlying regulatory modes. It is applied to high and low-grade gliomas and to melanomas where it disentangles transversal and longitudinal molecular heterogeneity in terms of distinct molecular subtypes and progression paths with prognostic impact.

Keywords: integrative bioinformatics, machine learning, molecular mechanisms of cancer, gliomas and melanomas

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Authors: Madushani Rodrigo, Banuka Athuraliya

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In today's world of medical diagnosis and prediction, machine learning stands out as a strong tool, transforming old ways of caring for health. This study analyzes the use of machine learning in the specialized domain of sports medicine, with a focus on the timely and accurate detection of bone fractures in cricket athletes. Failure to identify bone fractures in real time can result in malunion or non-union conditions. To ensure proper treatment and enhance the bone healing process, accurately identifying fracture locations and types is necessary. When interpreting X-ray images, it relies on the expertise and experience of medical professionals in the identification process. Sometimes, radiographic images are of low quality, leading to potential issues. Therefore, it is necessary to have a proper approach to accurately localize and classify fractures in real time. The research has revealed that the optimal approach needs to address the stated problem and employ appropriate radiographic image processing techniques and object detection algorithms. These algorithms should effectively localize and accurately classify all types of fractures with high precision and in a timely manner. In order to overcome the challenges of misidentifying fractures, a distinct model for fracture localization and classification has been implemented. The research also incorporates radiographic image enhancement and preprocessing techniques to overcome the limitations posed by low-quality images. A classification ensemble model has been implemented using ResNet18 and VGG16. In parallel, a fracture segmentation model has been implemented using the enhanced U-Net architecture. Combining the results of these two implemented models, the FracXpert system can accurately localize exact fracture locations along with fracture types from the available 12 different types of fracture patterns, which include avulsion, comminuted, compressed, dislocation, greenstick, hairline, impacted, intraarticular, longitudinal, oblique, pathological, and spiral. This system will generate a confidence score level indicating the degree of confidence in the predicted result. Using ResNet18 and VGG16 architectures, the implemented fracture segmentation model, based on the U-Net architecture, achieved a high accuracy level of 99.94%, demonstrating its precision in identifying fracture locations. Simultaneously, the classification ensemble model achieved an accuracy of 81.0%, showcasing its ability to categorize various fracture patterns, which is instrumental in the fracture treatment process. In conclusion, FracXpert has become a promising ML application in sports medicine, demonstrating its potential to revolutionize fracture detection processes. By leveraging the power of ML algorithms, this study contributes to the advancement of diagnostic capabilities in cricket athlete healthcare, ensuring timely and accurate identification of bone fractures for the best treatment outcomes.

Keywords: multiclass classification, object detection, ResNet18, U-Net, VGG16

Procedia PDF Downloads 68

Authors: Shahnewaz Khan

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Machine learning (ML) techniques are often used in projects for creating data-driven applications. These tasks typically demand additional research and analysis. The proper technique and strategy must be chosen to ensure the success of data-driven projects. Otherwise, even exerting a lot of effort, the necessary development might not always be possible. In this post, an effort to examine the workflow of data-driven software development projects and its implementation process in order to describe how to manage a project successfully. Which will assist in minimizing the added workload.

Keywords: data, data-driven projects, data science, NLP, software project

Procedia PDF Downloads 66

Authors: Jigg Pelayo, Ricardo Villar

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The Philippines is long considered as a valuable producer of high value crops globally. The country’s employment and economy have been dependent on agriculture, thus increasing its demand for the efficient agricultural mechanism. Remote sensing and geographic information technology have proven to effectively provide applications for precision agriculture through image-processing technique considering the development of the aerial scanning technology in the country. Accurate information concerning the spatial correlation within the field is very important for precision farming of high value crops, especially. The availability of height information and high spatial resolution images obtained from aerial scanning together with the development of new image analysis methods are offering relevant influence to precision agriculture techniques and applications. In this study, an algorithm was developed and implemented to detect and count high value crops simultaneously through adaptive scaling of support vector machine (SVM) algorithm subjected to object-oriented approach combining watershed transformation and local maxima filter in enhancing tree counting and detection. The methodology is compared to cutting-edge template matching algorithm procedures to demonstrate its effectiveness on a demanding tree is counting recognition and delineation problem. Since common data and image processing techniques are utilized, thus can be easily implemented in production processes to cover large agricultural areas. The algorithm is tested on high value crops like Palm, Mango and Coconut located in Misamis Oriental, Philippines - showing a good performance in particular for young adult and adult trees, significantly 90% above. The s inventories or database updating, allowing for the reduction of field work and manual interpretation tasks.

Keywords: high value crop, LiDAR, OBIA, precision agriculture

Procedia PDF Downloads 385

Authors: Semachew M. Kassa, Africa M Geremew, Tezera F. Azmatch, Nandyala Darga Kumar

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Frequency ratio (FR) and analytical hierarchy process (AHP) methods are developed based on past landslide failure points to identify the landslide susceptibility mapping because landslides can seriously harm both the environment and society. However, it is still difficult to select the most efficient method and correctly identify the main driving factors for particular regions. In this study, we used fourteen landslide conditioning factors (LCFs) and five soft computing algorithms, including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Artificial Neural Network (ANN), and Naïve Bayes (NB), to predict the landslide susceptibility at 12.5 m spatial scale. The performance of the RF (F1-score: 0.88, AUC: 0.94), ANN (F1-score: 0.85, AUC: 0.92), and SVM (F1-score: 0.82, AUC: 0.86) methods was significantly better than the LR (F1-score: 0.75, AUC: 0.76) and NB (F1-score: 0.73, AUC: 0.75) method, according to the classification results based on inventory landslide points. The findings also showed that around 35% of the study region was made up of places with high and very high landslide risk (susceptibility greater than 0.5). The very high-risk locations were primarily found in the western and southeastern regions, and all five models showed good agreement and similar geographic distribution patterns in landslide susceptibility. The towns with the highest landslide risk include Amhara Saint Town's western part, the Northern part, and St. Gebreal Church villages, with mean susceptibility values greater than 0.5. However, rainfall, distance to road, and slope were typically among the top leading factors for most villages. The primary contributing factors to landslide vulnerability were slightly varied for the five models. Decision-makers and policy planners can use the information from our study to make informed decisions and establish policies. It also suggests that various places should take different safeguards to reduce or prevent serious damage from landslide events.

Keywords: artificial neural network, logistic regression, landslide susceptibility, naïve Bayes, random forest, support vector machine

Procedia PDF Downloads 55

Authors: Amal Machtalay

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In this survey, we aim to review the rapidly growing literature on numerical methods to solve different forms of mean field problems, namely mean field games (MFG), mean field controls (MFC), potential MFGs, and master equations, as well as their corresponding recent applications. Here, we distinguish two families of numerical methods: iterative methods based on mesh generation and those called mesh-free, normally related to neural networking and learning frameworks.

Keywords: mean-field games, numerical schemes, partial differential equations, complex systems, machine learning

Procedia PDF Downloads 94