Search results for: automated teller machine

Authors: Osvaldo N. Oliveira Jr.

Abstract:

The control of molecular architecture inherent in some experimental methods to produce nanostructured films has had great impact on devices of various types, including sensors and biosensors. The self-assembly monolayers (SAMs) and the electrostatic layer-by-layer (LbL) techniques, for example, are now routinely used to produce tailored architectures for biosensing where biomolecules are immobilized with long-lasting preserved activity. Enzymes, antigens, antibodies, peptides and many other molecules serve as the molecular recognition elements for detecting an equally wide variety of analytes. The principles of detection are also varied, including electrochemical methods, fluorescence spectroscopy and impedance spectroscopy. In this presentation an overview will be provided of biosensors made with nanostructured films to detect antibodies associated with tropical diseases and HIV, in addition to detection of analytes of medical interest such as cholesterol and triglycerides. Because large amounts of data are generated in the biosensing experiments, use has been made of computational and statistical methods to optimize performance. Multidimensional projection techniques such as Sammon´s mapping have been shown more efficient than traditional multivariate statistical analysis in identifying small concentrations of anti-HIV antibodies and for distinguishing between blood serum samples of animals infected with two tropical diseases, namely Chagas´ disease and Leishmaniasis. Optimization of biosensing may include a combination of another information visualization method, the Parallel Coordinate technique, with artificial intelligence methods in order to identify the most suitable frequencies for reaching higher sensitivity using impedance spectroscopy. Also discussed will be the possible convergence of technologies, through which machine learning and other computational methods may be used to treat data from biosensors within an expert system for clinical diagnosis.

Keywords: clinical diagnosis, information visualization, nanostructured films, layer-by-layer technique

Procedia PDF Downloads 326

Authors: Hien Thi Dieu Truong, Mahmoud Al-Sarayreh, Pullanagari Reddy, Marlon M. Reis, Richard Archer

Abstract:

New Zealand mānuka honey is a honeybee product derived mainly from Leptospermum scoparium nectar. The potent antibacterial activity of mānuka honey derives principally from methylglyoxal (MGO), in addition to the hydrogen peroxide and other lesser activities present in all honey. MGO is formed from dihydroxyacetone (DHA) unique to L. scoparium nectar. Mānuka honey also has an idiosyncratic phenolic profile that is useful as a chemical maker. Authentic mānuka honey is highly valuable, but almost all honey is formed from natural mixtures of nectars harvested by a hive over a time period. Once diluted by other nectars, mānuka honey irrevocably loses value. We aimed to apply hyperspectral imaging to honey frames before bulk extraction to minimise the dilution of genuine mānuka by other honey and ensure authenticity at the source. This technology is non-destructive and suitable for an industrial setting. Chemometrics using linear Partial Least Squares (PLS) and Support Vector Machine (SVM) showed limited efficacy in interpreting chemical footprints due to large non-linear relationships between predictor and predictand in a large sample set, likely due to honey quality variability across geographic regions. Therefore, an advanced modelling approach, one-dimensional convolutional neural networks (1D-CNN), was investigated for analysing hyperspectral data for extraction of biochemical information from honey. The 1D-CNN model showed superior prediction of honey quality (R² = 0.73, RMSE = 2.346, RPD= 2.56) to PLS (R² = 0.66, RMSE = 2.607, RPD= 1.91) and SVM (R² = 0.67, RMSE = 2.559, RPD=1.98). Classification of mono-floral manuka honey from multi-floral and non-manuka honey exceeded 90% accuracy for all models tried. Overall, this study reveals the potential of HSI and deep learning modelling for automating the evaluation of honey quality in frames.

Keywords: mānuka honey, quality, purity, potency, deep learning, 1D-CNN, chemometrics

Procedia PDF Downloads 129

Authors: Abderrahim Elmoataz

Abstract:

More and more contemporary applications involve data in the form of functions defined on irregular and topologically complicated domains (images, meshs, points clouds, networks, etc). Such data are not organized as familiar digital signals and images sampled on regular lattices. However, they can be conveniently represented as graphs where each vertex represents measured data and each edge represents a relationship (connectivity or certain affinities or interaction) between two vertices. Processing and analyzing these types of data is a major challenge for both image and machine learning communities. Hence, it is very important to transfer to graphs and networks many of the mathematical tools which were initially developed on usual Euclidean spaces and proven to be efficient for many inverse problems and applications dealing with usual image and signal domains. Historically, the main tools for the study of graphs or networks come from combinatorial and graph theory. In recent years there has been an increasing interest in the investigation of one of the major mathematical tools for signal and image analysis, which are Partial Differential Equations (PDEs) variational methods on graphs. The normalized p-laplacian operator has been recently introduced to model a stochastic game called tug-of-war-game with noise. Part interest of this class of operators arises from the fact that it includes, as particular case, the infinity Laplacian, the mean curvature operator and the traditionnal Laplacian operators which was extensiveley used to models and to solve problems in image processing. The purpose of this paper is to introduce and to study a new class of normalized p-Laplacian on graphs. The introduction is based on the extension of p-harmonious function introduced in as discrete approximation for both infinity Laplacian and p-Laplacian equations. Finally, we propose to use these operators as a framework for solving many inverse problems in image processing.

Keywords: normalized p-laplacian, image processing, stochastic game, inverse problems

Procedia PDF Downloads 501

Authors: Wallyson Thomas, Zsombor Fulop, Attila Szilagyi

Abstract:

Impact dampers are mainly used in the metal-mechanical industry in operations that generate too much vibration in the machining system. Internal turning processes become unstable during the machining of deep holes, in which the tool holder is used with long overhangs (high length-to-diameter ratios). The devices coupled with active dampers, are expensive and require the use of advanced electronics. On the other hand, passive impact dampers (PID – Particle Impact Dampers) are cheaper alternatives that are easier to adapt to the machine’s fixation system, once that, in this last case, a cavity filled with particles is simply added to the structure of the tool holder. The cavity dimensions and the diameter of the spheres are pre-determined. Thus, when passive dampers are employed during the machining process, the vibration is transferred from the tip of the tool to the structure of the boring bar, where it is absorbed by the fixation system. This work proposes to compare the behaviors of a conventional solid boring bar and a boring bar with a passive impact damper in turning while using the highest possible L/D (length-to-diameter ratio) of the tool and an Easy Fix fixation system (also called: Split Bushing Holding System). It is also intended to optimize the impact absorption parameters, as the filling percentage of the cavity and the diameter of the spheres. The test specimens were made of hardened material and machined in a Computer Numerical Control (CNC) lathe. The laboratory tests showed that when the cavity of the boring bar is totally filled with minimally spaced spheres of the largest diameter, the gain in absorption allowed of obtaining, with an L/D equal to 6, the same surface roughness obtained when using the solid boring bar with an L/D equal to 3.4. The use of the passive particle impact damper resulted in, therefore, increased static stiffness and reduced deflexion of the tool.

Keywords: active damper, fixation system, hardened material, passive damper

Procedia PDF Downloads 206

Authors: Alfredo Fortunato, Carmelofrancesco Origlia, Sara Laurita, Rossella Nicoletti

Abstract:

In the post-pandemic recovery phase of tourism, the role of a Destination Management Organization (DMO) as a coordinated management system of all the elements that make up a destination (attractions, access, marketing, human resources, brand, pricing, etc.) is also becoming relevant for local territories. The objective of a DMO is to maximize the visitor's perception of value and quality while ensuring the competitiveness and sustainability of the destination, as well as the long-term preservation of its natural and cultural assets, and to catalyze benefits for the local economy and residents. In carrying out the multiple functions to which it is called, the DMO can leverage a collective intelligence that comes from the ability to pool information, explicit and tacit knowledge, and relationships of the various stakeholders: policymakers, public managers and officials, entrepreneurs in the tourism supply chain, researchers, data journalists, schools, associations and committees, citizens, etc. The DMO potentially has at its disposal large volumes of data and many of them at low cost, that need to be properly processed to produce value. Based on these assumptions, the paper presents a conceptual framework for building an information system to support the DMO in the intelligent management of a tourist destination tested in an area of southern Italy. The approach adopted is data-informed and consists of four phases: (1) formulation of the knowledge problem (analysis of policy documents and industry reports; focus groups and co-design with stakeholders; definition of information needs and key questions); (2) research and metadatation of relevant sources (reconnaissance of official sources, administrative archives and internal DMO sources); (3) gap analysis and identification of unconventional information sources (evaluation of traditional sources with respect to the level of consistency with information needs, the freshness of information and granularity of data; enrichment of the information base by identifying and studying web sources such as Wikipedia, Google Trends, Booking.com, Tripadvisor, websites of accommodation facilities and online newspapers); (4) definition of the set of indicators and construction of the information base (specific definition of indicators and procedures for data acquisition, transformation, and analysis). The framework derived consists of 6 thematic areas (accommodation supply, cultural heritage, flows, value, sustainability, and enabling factors), each of which is divided into three domains that gather a specific information need to be represented by a scheme of questions to be answered through the analysis of available indicators. The framework is characterized by a high degree of flexibility in the European context, given that it can be customized for each destination by adapting the part related to internal sources. Application to the case study led to the creation of a decision support system that allows: •integration of data from heterogeneous sources, including through the execution of automated web crawling procedures for data ingestion of social and web information; •reading and interpretation of data and metadata through guided navigation paths in the key of digital story-telling; •implementation of complex analysis capabilities through the use of data mining algorithms such as for the prediction of tourist flows.

Keywords: collective intelligence, data framework, destination management, smart tourism

Procedia PDF Downloads 113

Authors: Paul Lemarignier, Olivier Fiquet, Vincent Pateloup

Abstract:

In line with the strong desire of nuclear energy players to have ever more effective products in terms of safety, research programs on E-ATF (Enhanced-Accident Tolerant Fuels) that are more resilient, particularly to the loss of coolant, have been launched in all countries with nuclear power plants. Among the multitude of solutions being developed internationally, carcinoembryonic antigen (CEA) and its partners are investigating a promising solution, which is the realization of CERMET (CERamic-METal) type fuel pellets made of a matrix of fissile material, uranium dioxide UO2, which has a low thermal conductivity, and a metallic phase with a high thermal conductivity to improve heat evacuation. Work has focused on the development by powder metallurgy of micro-structured CERMETs, characterized by networks of metallic phase embedded in the UO₂ matrix. Other types of macro-structured CERMETs, based on concepts proposed by thermal simulation studies, have been developed with a metallic phase with a specific geometry to optimize heat evacuation. This solution could not be developed using traditional processes, so additive manufacturing, which revolutionizes traditional design principles, is used to produce these innovative prototype concepts. At CEA Cadarache, work is first carried out on a non-radioactive surrogate material, alumina, in order to acquire skills and to develop the equipment, in particular the robocasting machine, an additive manufacturing technique selected for its simplicity and the possibility of optimizing the paste formulations. A manufacturing chain was set up, with the pastes production, the 3D printing of pellets, and the associated thermal post-treatment. The work leading to the first elaborations of macro-structured alumina/molybdenum CERMETs will be presented. This work was carried out with the support of Framatome and EdF.

Keywords: additive manufacturing, alumina, CERMET, molybdenum, nuclear safety

Procedia PDF Downloads 63

Authors: Peifang Hsieh

Abstract:

The prevention of child abuse has aroused serious concerns in Taiwan because of the disparity between the increasing amount of reported child abuse cases that doubled over the past decade and the scarcity of social workers. New Taipei city, with the most population in Taiwan and over 70% of its 4 million citizens are migrant families in which the needs of children can be easily neglected due to insufficient support from relatives and communities, sees urgency for a social support system, by preemptively identifying and outreaching high-risk families of child abuse, so as to offer timely assistance and preventive measure to safeguard the welfare of the children. Big data analysis is the inspiration. As it was clear that high-risk families of child abuse have certain characteristics in common, New Taipei city decides to consolidate detailed background information data from departments of social affairs, education, labor, and health (for example considering status of parents’ employment, health, and if they are imprisoned, fugitives or under substance abuse), to cross-reference for accurate and prompt identification of the high-risk families in need. 'The Service Center for High-Risk Families' (SCHF) was established to integrate data cross-departmentally. By utilizing the machine learning 'random forest method' to build a risk prediction model which can early detect families that may very likely to have child abuse occurrence, the SCHF marks high-risk families red, yellow, or green to indicate the urgency for intervention, so as to those families concerned can be provided timely services. The accuracy and recall rates of the above model were 80% and 65%. This prediction model can not only improve the child abuse prevention process by helping social workers differentiate the risk level of newly reported cases, which may further reduce their major workload significantly but also can be referenced for future policy-making.

Keywords: child abuse, high-risk families, big data analysis, risk prediction model

Procedia PDF Downloads 121

Authors: Shivahari Revathi Venkateswaran

Abstract:

Segmenting customers plays a significant role in churn prediction. It helps the marketing team with proactive and reactive customer retention. For the reactive retention, the retention team reaches out to customers who already showed intent to disconnect by giving some special offers. When coming to proactive retention, the marketing team uses churn prediction model, which ranks each customer from rank 1 to 100, where 1 being more risk to churn/disconnect (high ranks have high propensity to churn). The churn prediction model is built by using XGBoost model. However, with the churn rank, the marketing team can only reach out to the customers based on their individual ranks. To profile different groups of customers and to frame different marketing strategies for targeted groups of customers are not possible with the churn ranks. For this, the customers must be grouped in different segments based on their profiles, like demographics and other non-controllable attributes. This helps the marketing team to frame different offer groups for the targeted audience and prevent them from disconnecting (proactive retention). For segmentation, machine learning approaches like k-mean clustering will not form unique customer segments that have customers with same attributes. This paper finds an alternate approach to find all the combination of unique segments that can be formed from the user attributes and then finds the segments who have uplift (churn rate higher than the baseline churn rate). For this, search algorithms like fast search and recursive search are used. Further, for each segment, all customers can be targeted using individual churn ranks from the churn prediction model. Finally, a UI (User Interface) is developed for the marketing team to interactively search for the meaningful segments that are formed and target the right set of audience for future marketing campaigns and prevent them from disconnecting.

Keywords: churn prediction modeling, XGBoost model, uplift segments, proactive marketing, search algorithms, retention, k-mean clustering

Procedia PDF Downloads 66

Authors: Waleed Al-Nuaimy, Luke Anastassiou, Manjinder Kainth

Abstract:

As the process of teaching has evolved with the advent of new technologies over the ages, so has the process of learning. Educators have perpetually found themselves on the lookout for new technology-enhanced methods of teaching in order to increase learning efficiency and decrease ever expanding workloads. Shortly after the invention of the internet, web-based learning started to pick up in the late 1990s and educators quickly found that the process of providing learning material and marking assignments could change thanks to the connectivity offered by the internet. With the creation of early web-based virtual learning environments (VLEs) such as SPIDER and Blackboard, it soon became apparent that VLEs resulted in higher reported computer self-efficacy among students, but at the cost of students being less satisfied with the learning process . It may be argued that the impersonal nature of VLEs, and their limited functionality may have been the leading factors contributing to this reported dissatisfaction. To this day, often faced with the prospects of assigning colossal engineering cohorts their homework and assessments, educators may frequently choose optimally curated assessment formats, such as multiple-choice quizzes and numerical answer input boxes, so that automated grading software embedded in the VLEs can save time and mark student submissions instantaneously. A crucial skill that is meant to be learnt during most science and engineering undergraduate degrees is gaining the confidence in using, solving and deriving mathematical equations. Equations underpin a significant portion of the topics taught in many STEM subjects, and it is in homework assignments and assessments that this understanding is tested. It is not hard to see that this can become challenging if the majority of assignment formats students are engaging with are multiple-choice questions, and educators end up with a reduced perspective of their students’ ability to manipulate equations. Artificial intelligence (AI) has in recent times been shown to be an important consideration for many technologies. In our paper, we explore the use of new AI based software designed to work in conjunction with current VLEs. Using our experience with the software, we discuss its potential to solve a selection of problems ranging from impersonality to the reduction of educator workloads by speeding up the marking process. We examine the software’s potential to increase learning efficiency through its features which claim to allow more customized and higher-quality feedback. We investigate the usability of features allowing students to input equation derivations in a range of different forms, and discuss relevant observations associated with these input methods. Furthermore, we make ethical considerations and discuss potential drawbacks to the software, including the extent to which optical character recognition (OCR) could play a part in the perpetuation of errors and create disagreements between student intent and their submitted assignment answers. It is the intention of the authors that this study will be useful as an example of the implementation of AI in a practical assessment scenario insofar as serving as a springboard for further considerations and studies that utilise AI in the setting and marking of science and engineering assignments.

Keywords: engineering education, assessment, artificial intelligence, optical character recognition (OCR)

Procedia PDF Downloads 114

Authors: Aschalew Cherie Workneh, K. S. Hari Prasad, Chandra Shekhar Prasad Ojha

Abstract:

Due to the increase in water scarcity, the crop water stress index (CWSI) is receiving significant attention these days, especially in arid and semiarid regions, for quantifying water stress and effective irrigation scheduling. Nowadays, machine learning techniques such as neural networks are being widely used to determine CWSI. In the present study, the performance of two artificial neural networks, namely, Self-Organizing Maps (SOM) and Feed Forward-Back Propagation Artificial Neural Networks (FF-BP-ANN), are compared while determining the CWSI of rice crop. Irrigation field experiments with varying degrees of irrigation were conducted at the irrigation field laboratory of the Indian Institute of Technology, Roorkee, during the growing season of the rice crop. The CWSI of rice was computed empirically by measuring key meteorological variables (relative humidity, air temperature, wind speed, and canopy temperature) and crop parameters (crop height and root depth). The empirically computed CWSI was compared with SOM and FF-BP-ANN predicted CWSI. The upper and lower CWSI baselines are computed using multiple regression analysis. The regression analysis showed that the lower CWSI baseline for rice is a function of crop height (h), air vapor pressure deficit (AVPD), and wind speed (u), whereas the upper CWSI baseline is a function of crop height (h) and wind speed (u). The performance of SOM and FF-BP-ANN were compared by computing Nash-Sutcliffe efficiency (NSE), index of agreement (d), root mean squared error (RMSE), and coefficient of correlation (R²). It is found that FF-BP-ANN performs better than SOM while predicting the CWSI of rice crops.

Keywords: artificial neural networks; crop water stress index; canopy temperature, prediction capability

Procedia PDF Downloads 101

Authors: Gustave Semugaza, Anne Zora Gierth, Tommy Mielke, Marianela Escobar Castillo, Nat Doru C. Lupascu

Abstract:

The generation of Construction and Demolition Waste (CDW) has globally increased enormously due to the enhanced need in construction, renovation, and demolition of construction structures. Several studies investigated the use of CDW materials in the production of new concrete and indicated the lower mechanical properties of the resulting concrete. Many other researchers considered the possibility of using the Hydrated Cement Powder (HCP) to replace a part of Ordinary Portland Cement (OPC), but only very few investigated the use of Recycled Concrete Powder (RCP) from CDW. The partial replacement of OPC for making new concrete intends to decrease the CO₂ emissions associated with OPC production. However, the RCP and HCP need treatment to produce the new concrete of required mechanical properties. The thermal treatment method has proven to improve HCP properties before their use. Previous research has stated that for using HCP in concrete, the optimum results are achievable by heating HCP between 400°C and 800°C. The optimum heating temperature depends on the type of cement used to make the Hydrated Cement Specimens (HCS), the crushing and heating method of HCP, and the curing method of the Rehydrated Cement Specimens (RCS). This research assessed the quality of recycled materials by using different techniques such as X-ray Diffraction (XRD), Differential Scanning Calorimetry (DSC) and thermogravimetry (TG), Scanning electron Microscopy (SEM), and X-ray Fluorescence (XRF). These recycled materials were thermally pretreated at different temperatures from 200°C to 1000°C. Additionally, the research investigated to what extent the thermally treated recycled cement could partially replace the OPC and if the new concrete produced would achieve the required mechanical properties. The mechanical properties were evaluated on the RCS, obtained by mixing the Dehydrated Cement Powder and Recycled Powder (DCP and DRP) with water (w/c = 0.6 and w/c = 0.45). The research used the compressive testing machine for compressive strength testing, and the three-point bending test was used to assess the flexural strength.

Keywords: hydrated cement powder, dehydrated cement powder, recycled concrete powder, thermal treatment, reactivation, mechanical performance

Procedia PDF Downloads 140

Authors: WonSun Chen, James Boylan, Erwin Muharemovic, Denny Meyer

Abstract:

In Australia, drivers aged between 18 and 24 remained at high risk of road fatality over the last decade. Despite the successful implementation of the Graduated Licensing System (GLS) that supports young drivers in their early phases of driving, the road fatality statistics for these drivers remains high. In response to these statistics, studies conducted in Australia prior to the start of the COVID-19 pandemic have demonstrated the benefits of using telematics devices for improving driving behaviour, However, the impact of COVID-19 lockdown on young drivers’ driving behaviour has emerged as a global concern. Therefore, this naturalistic study aimed to evaluate and compare the driving behaviour(such as acceleration, braking, speeding, etc.) of young drivers with the adoption of in-vehicle telematics devices. Forty-two drivers aged between 18 and 30 and residing in the Australian state of Victoria participated in this study during the period of May to October 2022. All participants drove with the telematics devices during the first 30-day. At the start of the second 30-day, twenty-one participants were randomised to an intervention group where they were provided with an additional telematics ray device that provided visual feedback to the drivers, especially when they committed to aggressive driving behaviour. The remaining twenty-one participants remined their driving journeys without the extra telematics ray device (control group). Such trustworthy data enabled the assessment of changes in the driving behaviour of these young drivers using a machine learning approach in Python. Results are expected to show participants from the intervention group will show improvements in their driving behaviour compared to those from the control group.Furthermore, the telematics data enable the assessment and quantification of such improvements in driving behaviour. The findings from this study are anticipated to shed some light in guiding the development of customised campaigns and interventions to further address the high road fatality among young drivers in Australia.

Keywords: driving behaviour, naturalistic study, telematics data, young drivers

Procedia PDF Downloads 113

Authors: Abhishek Pandey

Abstract:

Ibuprofen and Acetaminophen is widely used as prescription & non-prescription medicine. Ibuprofen mainly used in the treatment of mild to moderate pain related to headache, migraine, postoperative condition and in the management of spondylitis, osteoarthritis and rheumatoid arthritis. Acetaminophen is used as an analgesic and antipyretic drug. Ibuprofen having high tendency of sticking to punches of tablet punching machine while Acetaminophen is not ordinarily compressible to tablet formulation because Acetaminophen crystals are very hard and brittle in nature and fracture very easily when compressed producing capping and laminating tablet defects therefore wet granulation method is used to make them compressible. The aim of study was to prepare Ibuprofen and Acetaminophen tablets by direct compression and top spray granulation technique. In this Investigation tablets were prepared by using directly compressible grade excipients. Dibasic calcium phosphate, lactose anhydrous (DCL21), microcrystalline cellulose (Avicel PH 101). In order to obtain best or optimized formulation, nine different formulations were generated among them batch F7, F8, F9 shows good results and within the acceptable limit. Formulation (F7) selected as optimize product on the basis of dissolution study. Furtherly, directly compressible granules of both drugs were prepared by using top spray granulation technique in fluidized bed processor equipment and compressed .In order to obtain best product process optimization was carried out by performing four trials in which various parameters like inlet air temperature, spray rate, peristaltic pump rpm, % LOD, properties of granules, blending time and hardness were optimized. Batch T3 coined as optimized batch on the basis physical & chemical evaluation. Finally formulations prepared by both techniques were compared.

Keywords: direct compression, top spray granulation, process optimization, blending time

Procedia PDF Downloads 352

Authors: Mohammed Abu Shquier

Abstract:

This paper introduces a word segmentation method using the novel BP-LSTM-CRF architecture for processing semantic output training. The objective of web morphological analysis tools is to link a formal morpho-syntactic description to a lemma, along with morpho-syntactic information, a vocalized form, a vocalized analysis with morpho-syntactic information, and a list of paradigms. A key objective is to continuously enhance the proposed system through an inductive learning approach that considers semantic influences. The system is currently under construction and development based on data-driven learning. To evaluate the tool, an experiment on homograph analysis was conducted. The tool also encompasses the assumption of deep binary segmentation hypotheses, the arbitrary choice of trigram or n-gram continuation probabilities, language limitations, and morphology for both Modern Standard Arabic (MSA) and Dialectal Arabic (DA), which provide justification for updating this system. Most Arabic word analysis systems are based on the phonotactic morpho-syntactic analysis of a word transmitted using lexical rules, which are mainly used in MENA language technology tools, without taking into account contextual or semantic morphological implications. Therefore, it is necessary to have an automatic analysis tool taking into account the word sense and not only the morpho-syntactic category. Moreover, they are also based on statistical/stochastic models. These stochastic models, such as HMMs, have shown their effectiveness in different NLP applications: part-of-speech tagging, machine translation, speech recognition, etc. As an extension, we focus on language modeling using Recurrent Neural Network (RNN); given that morphological analysis coverage was very low in dialectal Arabic, it is significantly important to investigate deeply how the dialect data influence the accuracy of these approaches by developing dialectal morphological processing tools to show that dialectal variability can support to improve analysis.

Keywords: NLP, DL, ML, analyser, MSA, RNN, CNN

Procedia PDF Downloads 31

Authors: Arjun Kumar Rath, Titus Dhanasingh

Abstract:

Embedded product development requires software to test hardware functionality during development and finding issues during manufacturing in larger quantities. As the components are getting integrated, the devices are tested for their full functionality using advanced software tools. Benchmarking tools are used to measure and compare the performance of product features. At present, these tests are based on a variety of methods involving varying hardware and software platforms. Typically, these tests are custom built for every product and remain unusable for other variants. A majority of the tests goes undocumented, not updated, unusable when the product is released. To bridge this gap, a solution accelerator in the form of a framework can address these issues for running all these tests from one place, using an off-the-shelf tests library in a continuous integration environment. There are many open-source test frameworks or tools (fuego. LAVA, AutoTest, KernelCI, etc.) designed for testing embedded system devices, with each one having several unique good features, but one single tool and framework may not satisfy all of the testing needs for embedded systems, thus an extensible framework with the multitude of tools. Embedded product testing includes board bring-up testing, test during manufacturing, firmware testing, application testing, and assembly testing. Traditional test methods include developing test libraries and support components for every new hardware platform that belongs to the same domain with identical hardware architecture. This approach will have drawbacks like non-reusability where platform-specific libraries cannot be reused, need to maintain source infrastructure for individual hardware platforms, and most importantly, time is taken to re-develop test cases for new hardware platforms. These limitations create challenges like environment set up for testing, scalability, and maintenance. A desirable strategy is certainly one that is focused on maximizing reusability, continuous integration, and leveraging artifacts across the complete development cycle during phases of testing and across family of products. To get over the stated challenges with the conventional method and offers benefits of embedded testing, an embedded test framework (ETF), a solution accelerator, is designed, which can be deployed in embedded system-related products with minimal customizations and maintenance to accelerate the hardware testing. Embedded test framework supports testing different hardwares including microprocessor and microcontroller. It offers benefits such as (1) Time-to-Market: Accelerates board brings up time with prepacked test suites supporting all necessary peripherals which can speed up the design and development stage(board bring up, manufacturing and device driver) (2) Reusability-framework components isolated from the platform-specific HW initialization and configuration makes the adaptability of test cases across various platform quick and simple (3) Effective build and test infrastructure with multiple test interface options and preintegrated with FUEGO framework (4) Continuos integration - pre-integrated with Jenkins which enabled continuous testing and automated software update feature. Applying the embedded test framework accelerator throughout the design and development phase enables to development of the well-tested systems before functional verification and improves time to market to a large extent.

Keywords: board diagnostics software, embedded system, hardware testing, test frameworks

Procedia PDF Downloads 133

Authors: Jhe-Hao Huang, Kun-Da Wu, Wei-Cheng Shih, Jui-Pin Hung

Abstract:

This study presented the investigation on the dynamic characteristics of a spindle tool system by experimental and finite element modeling approaches. As well known facts, the machining stability is greatly determined by the dynamic characteristics of the spindle tool system. Therefore, understanding the factors affecting dynamic behavior of a spindle tooling system is a prerequisite in dominating the final machining performance of machine tool system. To this purpose, a physical spindle unit was employed to assess the dynamic characteristics by vibration tests. Then, a three-dimensional finite element model of a high-speed spindle system integrated with tool holder was created to simulate the dynamic behaviors. For modeling the angular contact bearings, a series of spring elements were introduced between the inner and outer rings. The spring constant can be represented by the contact stiffness of the rolling bearing based on Hertz theory. The interface characteristic between spindle nose and tool holder taper can be quantified from the comparison of the measurements and predictions. According to the results obtained from experiments and finite element predictions, the vibration behavior of the spindle is dominated by the bending deformation of the spindle shaft in different modes, which is further determined by the stiffness of the bearings in spindle housing. Also, the spindle unit with tool holder shows a different dynamic behavior from that of spindle without tool holder. This indicates the interface property between tool holder and spindle nose plays an dominance on the dynamic characteristics the spindle tool system. Overall, the dynamic behaviors the spindle with and without tool holder can be successfully investigated through the finite element model proposed in this study. The prediction accuracy is determined by the modeling of the rolling interface of ball bearings in spindles and the interface characteristics between tool holder and spindle nose. Besides, identifications of the interface characteristics of a ball bearing and spindle tool holder are important for the refinement of the spindle tooling system to achieve the optimum machining performance.

Keywords: contact stiffness, dynamic characteristics, spindle, tool holder interface

Procedia PDF Downloads 289

Authors: Sayyed Benyamin Alavi, Giovanni Cerri, Leila Chennaoui, Ambra Giovannelli, Stefano Mazzoni

Abstract:

Primary energy consumption and emissions of pollutants (including CO2) sustainability call to search methodologies to lower power absorption for unit of a given product. Cool production plants based on vapour compression are widely used for many applications: air conditioning, food conservation, domestic refrigerators and freezers, special industrial processes, etc. In the field of cool production, the amount of Yearly Consumed Primary Energy is enormous, thus, saving some percentage of it, leads to big worldwide impact in the energy consumption and related energy sustainability. Among various techniques to reduce power required by a Vapour Compression Cool Production Plant (VCCPP), the technique based on Power Regeneration by means of Internal Direct Cycle (IDC) will be considered in this paper. Power produced by IDC reduces power need for unit of produced Cool Power by the VCCPP. The paper contains basic concepts that lead to develop IDCs and the proposed options to use the IDC Power. Among various selections for using turbo machines, Best Economically Available Technologies (BEATs) have been explored. Based on vehicle engine turbochargers, they have been taken into consideration for this application. According to BEAT Database and similarity rules, the best turbo machine selection leads to the minimum nominal power required by VCCPP Main Compressor. Results obtained installing the prototype in “ad hoc” designed test bench will be discussed and compared with the expected performance. Forecasts for the upgrading VCCPP, various applications will be given and discussed. 4-6% saving is expected for air conditioning cooling plants and 15-22% is expected for cryogenic plants.

Keywords: Refrigeration Plant, Vapour Pressure Amplifier, Compressor, Expander, Turbine, Turbomachinery Selection, Power Saving

Procedia PDF Downloads 422

Authors: Halkiopoulos Constantinos, Gkintoni Evgenia, Koutsopoulou Ioanna, Antonopoulou Hera

Abstract:

E-sports and e-gaming have emerged in recent years since the increase in internet use have become universal and e-gamers are the new reality in our homes. The excessive involvement of young adults with e-sports has already been revealed and the adverse consequences have been reported in researches in the past few years, but the issue has not been fully studied yet. The present research is conducted in Greece and studies the psychological profile of video game players and provides information on personality traits, habits and emotional status that affect online gamers’ behaviors in order to help professionals and policy makers address the problem. Three standardized self-report questionnaires were administered to participants who were young male and female adults aged from 19-26 years old. The Profile of Mood States (POMS) scale was used to evaluate people’s perceptions of their everyday life mood; the personality features that can trace back to people’s habits and anticipated reactions were measured by Eysenck Personality Questionnaire (EPQ), and the Trait Emotional Intelligence Questionnaire (TEIQue) was used to measure which cognitive (gamers’ beliefs) and emotional parameters (gamers’ emotional abilities) mainly affected/ predicted gamers’ behaviors and leisure time activities?/ gaming behaviors. Data mining techniques were used to analyze the data, which resulted in machine learning algorithms that were included in the software package R. The research findings attempt to designate the effect of personality traits, emotional status and emotional intelligence influence and correlation with e-sports, gamers’ behaviors and help policy makers and stakeholders take action, shape social policy and prevent the adverse consequences on young adults. The need for further research, prevention and treatment strategies is also addressed.

Keywords: e-sports, e-gamers, personality traits, POMS, emotional intelligence, data mining, R

Procedia PDF Downloads 222

Authors: J. Ritonja, B. Grcar

Abstract:

For the synchronous generator simulation and analysis and for the power system stabilizer design and synthesis a mathematical model of synchronous generator is needed. The model has to accurately describe dynamics of oscillations, while at the same time has to be transparent enough for an analysis and sufficiently simplified for design of control system. To study the oscillations of the synchronous generator against to the rest of the power system, the model of the synchronous machine connected to an infinite bus through a transmission line having resistance and inductance is needed. In this paper, the linearized reduced order dynamic model of the synchronous generator connected to the infinite bus is presented and analysed in details. This model accurately describes dynamics of the synchronous generator only in a small vicinity of an equilibrium state. With the digression from the selected equilibrium point the accuracy of this model is decreasing considerably. In this paper, the equations’ descriptions and the parameters’ determinations for the linearized reduced order mathematical model of the synchronous generator are explained and summarized and represent the useful origin for works in the areas of synchronous generators’ dynamic behaviour analysis and synchronous generator’s control systems design and synthesis. The main contribution of this paper represents the detailed analysis of the accuracy of the linearized reduced order dynamic model in the entire synchronous generator’s operating range. Borders of the areas where the linearized reduced order mathematical model represents accurate description of the synchronous generator’s dynamics are determined with the systemic numerical analysis. The thorough eigenvalue analysis of the linearized models in the entire operating range is performed. In the paper, the parameters of the linearized reduced order dynamic model of the laboratory salient poles synchronous generator were determined and used for the analysis. The theoretical conclusions were confirmed with the agreement of experimental and simulation results.

Keywords: eigenvalue analysis, mathematical model, power system stability, synchronous generator

Procedia PDF Downloads 236

Authors: Ergin Kosa, Ali Göksenli

Abstract:

Erosion and abrasion are wear mechanisms reducing the lifetime of machine elements like valves, pump and pipe systems. Both wear mechanisms are acting at the same time, causing a “Synergy” effect, which leads to a rapid damage of the surface. Different parameters are effective on erosive abrasive wear rate. In this study effect of particle impact angle on wear rate and wear mechanism of ductile and brittle materials was investigated. A new slurry pot was designed for experimental investigation. As abrasive particle, silica sand was used. Particle size was ranking between 200-500 µm. All tests were carried out in a sand-water mixture of 20% concentration for four hours. Impact velocities of the particles were 4,76 m/s. As ductile material steel St 37 with Brinell Hardness Number (BHN) of 245 and quenched St 37 with 510 BHN was used as brittle material. After wear tests, morphology of the eroded surfaces were investigated for better understanding of the wear mechanisms acting at different impact angles by using optical microscopy and Scanning Electron Microscope. The results indicated that wear rate of ductile material was higher than brittle material. Maximum wear was observed by ductile material at a particle impact angle of 300. On the contrary wear rate increased by brittle materials by an increase in impact angle and reached maximum value at 450. High amount of craters were detected after observation on ductile material surface Also plastic deformation zones were detected, which are typical failure modes for ductile materials. Craters formed by particles were deeper according to brittle material worn surface. Amount of craters decreased on brittle material surface. Microcracks around craters were detected which are typical failure modes of brittle materials. Deformation wear was the dominant wear mechanism on brittle material. At the end it is concluded that wear rate could not be directly related to impact angle of the hard particle due to the different responses of ductile and brittle materials.

Keywords: erosive wear, particle impact angle, silica sand, wear rate, ductile-brittle material

Procedia PDF Downloads 379

Authors: Mathew Wakefield, Matthew Mitchell, Lisa Wise, Christopher McCarthy

Abstract:

The properties of memory representations in artificial neural networks have cognitive implications. Distributed representations that encode instances as a pattern of activity across layers of nodes afford memory compression and enforce the selection of a single point in instance space. These encoding schemes also appear to distort the representational space, as well as trading off the ability to validate that input information is within the bounds of past experience. In contrast, a localist representation which encodes some meaningful information into individual nodes in a network layer affords less memory compression while retaining the integrity of the representational space. This allows the validity of an input to be determined. The validity (or familiarity) of input along with the capacity of localist representation for multiple instance selections affords a memory sampling approach that dynamically balances the bias-variance trade-off. When the input is familiar, bias may be high by referring only to the most similar instances in memory. When the input is less familiar, variance can be increased by referring to more instances that capture a broader range of features. Using this approach in a localist instance memory network, an experiment demonstrates a relationship between representational conflict, generalization performance, and memorization demand. Relatively small sampling ranges produce the best performance on a classic machine learning dataset of visual objects. Combining memory validity with conflict detection produces a reliable confidence judgement that can separate responses with high and low error rates. Confidence can also be used to signal the need for supervisory input. Using this judgement, the need for supervised learning as well as memory encoding can be substantially reduced with only a trivial detriment to classification performance.

Keywords: artificial neural networks, representation, memory, conflict monitoring, confidence

Procedia PDF Downloads 120

Authors: Gary Pattemore, Michael Edgar, Andrew Job, Marina Auad, Kathryn Job

Abstract:

As critical commodities become more scarce, significant time and resources have been used to better understand complicated ore bodies and extract their full potential. These challenging ore bodies provide several pain points for geologists and engineers to overcome, poor handling of these issues flows downs stream to the processing plant affecting throughput rates and recovery. Many open cut mines utilise blast hole drilling to extract additional information to feed back into the modelling process. This method requires samples to be collected during or after blast hole drilling. Samples are then sent for assay with turnaround times varying from 1 to 12 days. This method is time consuming, costly, requires human exposure on the bench and collects elemental data only. To address this challenge, research has been undertaken to utilise hyperspectral imaging across a broad spectrum to scan samples, collars or take down hole measurements for minerals and moisture content and grade abundances. Automation of this process using unmanned vehicles and on-board processing reduces human in pit exposure to ensure ongoing safety. On-board processing allows data to be integrated into modelling workflows with immediacy. The preliminary results demonstrate numerous direct and indirect benefits from this new technology, including rapid and accurate grade estimates, moisture content and mineralogy. These benefits allow for faster geo modelling updates, better informed mine scheduling and improved downstream blending and processing practices. The paper presents recommendations for implementation of the technology in open cut mining environments.

Keywords: grade control, hyperspectral scanning, artificial intelligence, autonomous mining, machine learning

Procedia PDF Downloads 101

Authors: Basavaraj R. Endigeri, S. G. Sarganachari

Abstract:

Steel is widely used in machine parts, structural equipment and many other applications. In many steel structural elements, holes of different shapes and orientations are made with a view to satisfy the design requirements. The presence of holes in steel elements creates stress concentration, which eventually reduce the mechanical strength of the structure. Therefore, it is of great importance to investigate the state of stress around the holes for the safety and properties design of such elements. By literature survey, it is known that till date, there is no analytical solution to reduce the stress concentration by providing auxiliary holes at a definite location and radii in a steel plate. The numerical method can be used to determine the optimum location and radii of auxiliary holes. In the present work plate with an elliptical hole, for a steel material subjected to uniaxial load is analyzed and the effect of stress concentration is graphically represented .The introduction of auxiliary holes at a optimum location and radii with its effect on stress concentration is also represented graphically. The finite element analysis package ANSYS 11.0 is used to analyse the steel plate. The analysis is carried out using a plane 42 element. Further the ANSYS optimization model is used to determine the location and radii for optimum values of auxiliary hole to reduce stress concentration. All the results for different diameter to plate width ratio are presented graphically. The results of this study are in the form of the graphs for determining the locations and diameter of optimal auxiliary holes. The graph of stress concentration v/s central hole diameter to plate width ratio. The Finite Elements results of the study indicates that the stress concentration effect of central elliptical hole in an uniaxial loaded plate can be reduced by introducing auxiliary holes on either side of the central circular hole.

Keywords: finite element method, optimization, stress concentration factor, auxiliary holes

Procedia PDF Downloads 443

Authors: Ayman H. Amer Eissa, Abdul Rahman O. Alghannam

Abstract:

The agricultural crop residuals are considered one of the most important problems faced by the environmental life and farmers in the world. A study was carried out to evaluate the physical characteristics of chopped date palm stalks (fronds and leaflets). These properties are necessary to apply normal design procedures such as pneumatic conveying, fluidization, drying, and combustion. The mechanical treatment by cutting, crushing or chopping and briquetting processes are the primary step and the suitable solution for solving this problem and recycling these residuals to be transformed into useful products. So the aim of the present work to get a high quality for agriculture residues such as date palm stalks (fronds), date palm leaflets briquettes. The results obtained from measuring the mechanical properties (average shear and compressive strength) for date palm stalks at different moisture content (12.63, 33.21 and 60.54%) was (6.4, 4.7 and 3.21MPa) and (3.8, 3.18 and 2.86MPa) respectively. The modulus of elasticity and toughness were evaluated as a function of moisture content. As the moisture content of the stalk regions increased the modulus of elasticity and toughness decreased indicating a reduction in the brittleness of the stalk regions. Chopped date palm stalks (palm fronds), date palm leaflets having moisture content of 8, 10 and 12% and 8, 10 and 12.8% w.b. were dandified into briquettes without binder and with binder (urea-formaldehyde) using a screw press machine. Quality properties for briquettes were durability, compression ratio hardness, bulk density, compression ratio, resiliency, water resistance and gases emission. The optimum quality properties found for briquettes at 8 % moisture content and without binder. Where the highest compression stress and durability were 8.95, 10.39 MPa and 97.06 %, 93.64 % for date palm stalks (palm fronds), date palm leaflets briquettes, respectively. The CO and CO2 emissions for date palm stalks (fronds), date palm leaflets briquettes were less than these for loose residuals.

Keywords: residues, date palm stalks, chopper, briquetting, quality properties

Procedia PDF Downloads 531

Authors: Selim M. Khan, Aaron A. Goodarzi, Joshua M. Taron, Tryggve Rönnqvist

Abstract:

Indoor air quality, a prime determinant of health, is strongly influenced by the presence of hazardous radon gas within the built environment. As a health issue, dangerously high indoor radon arose within the 20th century to become the 2nd leading cause of lung cancer. While the 21st century building metrics and human behaviors have captured, contained, and concentrated radon to yet higher and more hazardous levels, the issue is rapidly worsening in Canada. It is established that Canadians in the Prairies are the 2nd highest radon-exposed population in the world, with 1 in 6 residences experiencing 0.2-6.5 millisieverts (mSv) radiation per week, whereas the Canadian Nuclear Safety Commission sets maximum 5-year occupational limits for atomic workplace exposure at only 20 mSv. This situation is also deteriorating over time within newer housing stocks containing higher levels of radon. Deep machine learning (LSTM) algorithms were applied to analyze multiple quantitative and qualitative features, determine the most important contributory factors, and predicted radon levels in the known past (1990-2020) and projected future (2021-2050). The findings showed gradual downwards patterns in Sweden, whereas it would continue to go from high to higher levels in Canada over time. The contributory factors found to be the basement porosity, roof insulation depthness, R-factor, and air dynamics of the indoor environment related to human window opening behaviour. Building codes must consider including these factors to ensure adequate indoor ventilation and healthy living that can prevent lung cancer in non-smokers.

Keywords: radon, building metrics, deep learning, LSTM prediction model, lung cancer, canada, sweden

Procedia PDF Downloads 104

Authors: Pankaj Dhiman, Simranjeet Kaur

Abstract:

This research paper presents a comprehensive sentiment analysis of social media responses to videos on Facebook, YouTube, Twitter, and Instagram during the 2024 Indian general elections. The study focuses on the sentiment patterns of voters towards the National Democratic Alliance (NDA) and The Indian National Developmental Inclusive Alliance (INDIA) on these platforms. The analysis aims to understand the impact of social media on voter sentiment and its correlation with the election outcome. The study employed a mixed-methods approach, combining both quantitative and qualitative methods. With a total of 200 posts analysed during general election-2024 final phase, the sentiment analysis was conducted using natural language processing (NLP) techniques, including sentiment dictionaries and machine learning algorithms. The results show that NDA received significantly more positive sentiment responses across all platforms, with a positive sentiment score of 47% compared to INDIA's score of 38.98 %. The analysis also revealed that Twitter and YouTube were the most influential platforms in shaping voter sentiment, with 60% of the total sentiment score coming from these two platforms. The study's findings suggest that social media sentiment analysis can be a valuable tool for understanding voter sentiment and predicting election outcomes. The results also highlight the importance of social media in shaping public opinion and the need for political parties to engage effectively with voters on these platforms. The study's implications are significant, as they indicate that social media can be a key factor in determining the outcome of elections. The findings also underscore the need for political parties to develop effective social media strategies to engage with voters and shape public opinion.

Keywords: Indian Elections-2024, NDA, INDIA, sentiment analysis, social media, democracy

Procedia PDF Downloads 34

Authors: Dipankar Saha, J. P. Singh, C. B. Pandey

Abstract:

Indian Thar desert being the seventh largest in the world is the main hot sand desert occupies nearly 385,000km2 and about 9% of the area of the country harbours several species likely the flora of 682 species (63 introduced species) belonging to 352 genera and 87 families. The degree of endemism of plant species in the Thar desert is 6.4 percent, which is relatively higher than the degree of endemism in the Sahara desert which is very significant for the conservationist to envisage. The advent and development of computer technology for digitization and data base management coupled with the rapidly increasing importance of biodiversity conservation resulted in the invention of biodiversity informatics as discipline of basic sciences with multiple applications. Aichi Target 19 as an outcome of Convention of Biological Diversity (CBD) specifically mandates the development of an advanced and shared biodiversity knowledge base. Information on species distributions in space is the crux of effective management of biodiversity in the rapidly changing world. The efficiency of biodiversity management is being increased rapidly by various stakeholders like researchers, policymakers, and funding agencies with the knowledge and application of biodiversity informatics. Herbarium specimens being a vital repository for biodiversity conservation especially in climate change scenario the digitization process usually aims to improve access and to preserve delicate specimens and in doing so creating large sets of images as a part of the existing repository as arid plant information facility for long-term future usage. As the leaf characters are important for describing taxa and distinguishing between them and they can be measured from herbarium specimens as well. As a part of this activity, laminar characterization (leaves being the most important characters in assessing climate change impact) initially resulted in classification of more than thousands collections belonging to ten families like Acanthaceae, Aizoaceae, Amaranthaceae, Asclepiadaceae, Anacardeaceae, Apocynaceae, Asteraceae, Aristolochiaceae, Berseraceae and Bignoniaceae etc. Taxonomic diversity indices has also been worked out being one of the important domain of biodiversity informatics approaches. The digitization process also encompasses workflows which incorporate automated systems to enable us to expand and speed up the digitisation process. The digitisation workflows used to be on a modular system which has the potential to be scaled up. As they are being developed with a geo-referencing tool and additional quality control elements and finally placing specimen images and data into a fully searchable, web-accessible database. Our effort in this paper is to elucidate the role of BIs, present effort of database development of the existing botanical collection of institute repository. This effort is expected to be considered as a part of various global initiatives having an effective biodiversity information facility. This will enable access to plant biodiversity data that are fit-for-use by scientists and decision makers working on biodiversity conservation and sustainable development in the region and iso-climatic situation of the world.

Keywords: biodiversity informatics, climate change, digitization, herbarium, laminar characters, web accessible interface

Procedia PDF Downloads 218

Authors: David Lloyd, David Owen, Martin Jardine

Abstract:

Introduction: European weight regulations with respect to food products require a full understanding of regulation guidelines to assure regulatory compliance. It is suggested that the complexity of regulation leads to practices which result to over filling of food packages by food processors. Purpose: To establish current practices by food processors and the financial, sustainable and societal impacts on the food supply chain of ineffective food production practices. Methods: An analysis of food packing controls with 10 companies of varying food categories and quantitative based research of a further 15 food processes on the confidence in weight control analysis of finished food packs within their organisation. Results: A process floor analysis of manufacturing operations focussing on 10 products found over fill of packages ranging from 4.8% to 20.2%. Standard deviation figures for all products showed a potential for reducing average weight of the pack whilst still retain the legal status of the product. In 20% of cases, an automatic weight analysis machine was in situ however weight packs were still significantly overweight. Collateral impacts noted included the effect of overfill on raw material purchase and added food miles often on a global basis with one raw material alone creating 10,000 extra food miles due to the poor weight control of the processing unit. A case study of a meat and bakery product will be discussed with the impact of poor controls resulting from complex legislation. The case studies will highlight extra energy costs in production and the impact of the extra weight on fuel usage. If successful a risk assessment model used primarily on food safety but adapted to identify waste /sustainability risks will be discussed within the presentation.

Keywords: legislation, overfill, profile, waste

Procedia PDF Downloads 397

Authors: Getaneh Berie Tarekegn

Abstract:

A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

Procedia PDF Downloads 61

Authors: M. F. Wu, F. T. Cheng, C. S. Wu, M. C. Tan

Abstract:

It is a critical time to upgrade technology and increase value added with manufacturing skills developing and management strategies that will highly satisfy the customers need in the precision machinery global market. In recent years, the supply side, each precision machinery manufacturers in each country are facing the pressures of price reducing from the demand side voices that pushes the high-end precision machinery manufacturers adopts low-cost and high-quality strategy to retrieve the market. Because of the trend of the global market, the manufacturers must take price reducing strategies and upgrade technology of low-end machinery for differentiations to consolidate the market. By using six key success factors (KSFs), customer perceived value, customer satisfaction, customer service, product design, product effectiveness and machine structure quality are causal conditions to explore the impact of competitive advantage of the enterprise, such as overall profitability and product pricing power. This research uses key success paths (KSPs) approach and f/s QCA software to explore various combinations of causal relationships, so as to fully understand the performance level of KSFs and business objectives in order to achieve competitive advantage. In this study, the combination of a causal relationships, are called Key Success Paths (KSPs). The key success paths guide the enterprise to achieve the specific outcomes of business. The findings of this study indicate that there are thirteen KSPs to achieve the overall profitability, sixteen KSPs to achieve the product pricing power and seventeen KSPs to achieve both overall profitability and pricing power of the enterprise. The KSPs provide the directions of resources integration and allocation, improve utilization efficiency of limited resources to realize the continuous vision of the enterprise.

Keywords: precision machinery industry, key success factors (KSFs), key success paths (KSPs), overall profitability, product pricing power, competitive advantages

Procedia PDF Downloads 258