Search results for: virtual machine

Authors: Alexandre Marin, Alexandra Bac, Laurent Astart

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New numerical methods for PDE resolution (such as Finite Volumes (FV) or Virtual Elements Method (VEM)) open new needs in terms of meshing of domains of interest, and in particular, polyhedral meshes have many advantages. One way to build such meshes consists of constructing Restricted Voronoi Diagrams (RVDs) whose boundaries respect the domain of interest. By minimizing a function defined for RVDs, the shapes of cells can be controlled, e.g., elongated according to user-defined directions or adjusted to comply with given aspect ratios (anisotropy) and density variations. In this paper, our contribution is threefold: First, we introduce a new gradient formula for the Voronoi tessellation energy under a continuous anisotropy field. Second, we describe a meshing algorithm based on the optimisation of this function that we validate against state-of-the-art approaches. Finally, we propose a hierarchical approach to speed up our meshing algorithm.

Keywords: anisotropic Voronoi diagrams, meshes for numerical simulations, optimisation, volumic polyhedral meshing

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Authors: Melkamu Tadesse Getachew

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Intelligent control systems have revolutionized biomedical engineering, advancing research and enhancing medical practice. This review paper examines the impact of intelligent control on various aspects of biomedical engineering. It analyzes how these systems enhance precision and accuracy in biomedical instrumentation, improving diagnostics, monitoring, and treatment. Integration challenges are addressed, and potential solutions are proposed. The paper also investigates the optimization of drug delivery systems through intelligent control. It explores how intelligent systems contribute to precise dosing, targeted drug release, and personalized medicine. Challenges related to controlled drug release and patient variability are discussed, along with potential avenues for overcoming them. The comparison of algorithms used in intelligent control systems in biomedical control is also reviewed. The implications of intelligent control in computational and systems biology are explored, showcasing how these systems enable enhanced analysis and prediction of complex biological processes. Challenges such as interpretability, human-machine interaction, and machine reliability are examined, along with potential solutions. Intelligent control in biomedical engineering also plays a crucial role in risk management during surgical operations. This section demonstrates how intelligent systems improve patient safety and surgical outcomes when integrated into surgical robots, augmented reality, and preoperative planning. The challenges associated with these implementations and potential solutions are discussed in detail. In summary, this review paper comprehensively explores the widespread impact of intelligent control on biomedical engineering, showing the future of human health issues promising. It discusses application areas, challenges, and potential solutions, highlighting the transformative potential of these systems in advancing research and improving medical practice.

Keywords: Intelligent control systems, biomedical instrumentation, drug delivery systems, robotic surgical instruments, Computational monitoring and modeling

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Authors: Lipeng Zhang, Limei Li, Yanming Pearl Zhang

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This paper takes a philosophical view as axiom, and reveals the relationship between information theory and Natural Intelligence and Artificial Intelligence under real world conditions. This paper also derives the relationship between natural intelligence and nature. According to communication principle of information theory, Natural Intelligence can be divided into real part and virtual part. Based on information theory principle that Information does not increase, the restriction mechanism of Natural Intelligence creativity is conducted. The restriction mechanism of creativity reveals the limit of natural intelligence and artificial intelligence. The paper provides a new angle to observe natural intelligence and artificial intelligence.

Keywords: natural intelligence, artificial intelligence, creativity, information theory, restriction of creativity

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Authors: D. Diaz, C. Guevara, P. Rey

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This paper contains information about designing, shielding and protocols building in order to avoid ionizing radiation in X-Rays imaging areas as generated by X-Ray, mammography equipment, computed tomography equipment and digital subtraction angiography equipment, according to global standards. Furthermore, tools and elements about infrastructure to improve protection over patients, physicians and staff involved in a diagnostic imaging area are presented. In addition, technical parameters about each machine and the architecture designs and maps are described.

Keywords: imaging area, X-ray, shielding, dose

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Authors: N. Seemuang, T. McLeay, T. Slatter

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Worn tools have a direct effect on the surface finish and part accuracy. Tool condition monitoring systems have been developed over a long period and used to avoid a loss of productivity resulting from using a worn tool. However, the majority of tool monitoring research has applied expensive sensing systems not suitable for production. In this work, the cutting sound in turning machine was studied using microphone. Machining trials using seven cutting conditions were conducted until the observable flank wear width (FWW) on the main cutting edge exceeded 0.4 mm. The cutting inserts were removed from the tool holder and the flank wear width was measured optically. A microphone with built-in preamplifier was used to record the machining sound of EN24 steel being face turned by a CNC lathe in a wet cutting condition using constant surface speed control. The sound was sampled at 50 kS/s and all sound signals recorded from microphone were transformed into the frequency domain by FFT in order to establish the frequency content in the audio signature that could be then used for tool condition monitoring. The extracted feature from audio signal was compared to the flank wear progression on the cutting inserts. The spectrogram reveals a promising feature, named as ‘spindle noise’, which emits from the main spindle motor of turning machine. The spindle noise frequency was detected at 5.86 kHz of regardless of cutting conditions used on this particular CNC lathe. Varying cutting speed and feed rate have an influence on the magnitude of power spectrum of spindle noise. The magnitude of spindle noise frequency alters in conjunction with the tool wear progression. The magnitude increases significantly in the transition state between steady-state wear and severe wear. This could be used as a warning signal to prepare for tool replacement or adapt cutting parameters to extend tool life.

Keywords: tool wear, flank wear, condition monitoring, spindle noise

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Authors: Pei-Chun Lee, Sheng-Shih Wang

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This paper presents a route guidance system for car owners to find their cars in parking garages. The presents system comprises a positioning-assisting subsystem and a car-finding mobile app. The positioning-assisting subsystem mainly uses the iBeacon technology for indoor positioning. The car-finding mobile app guides car owners to their cars based on a non-map navigation strategy. This study also designs a virtual coordinate system to support identifying the locations of parking spaces and iBeacon devices. We use Arduino and Android as the platforms to implement the proposed positioning-assisting subsystem and car-finding mobile app, respectively. We have also deployed the system in a parking garage in our campus for testing. Experimental results verify that our system can efficiently and correctly guide car owners to the parking spaces of their cars.

Keywords: guidance, iBeacon, mobile app, navigation

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Authors: Rui Li, Min Wen, Kim Bang Salling

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For the modern railways, maintenance is critical for ensuring safety, train punctuality and overall capacity utilization. The cost of railway maintenance in Europe is high, on average between 30,000 – 100,000 Euros per kilometer per year. In order to reduce such maintenance expenditures, this paper presents a mixed 0-1 linear mathematical model designed to optimize the predictive railway tamping activities for ballast track in the planning horizon of three to four years. The objective function is to minimize the tamping machine actual costs. The approach of the research is using the simple dynamic model for modelling condition-based tamping process and the solution method for finding optimal condition-based tamping schedule. Seven technical and practical aspects are taken into account to schedule tamping: (1) track degradation of the standard deviation of the longitudinal level over time; (2) track geometrical alignment; (3) track quality thresholds based on the train speed limits; (4) the dependency of the track quality recovery on the track quality after tamping operation; (5) Tamping machine operation practices (6) tamping budgets and (7) differentiating the open track from the station sections. A Danish railway track between Odense and Fredericia with 42.6 km of length is applied for a time period of three and four years in the proposed maintenance model. The generated tamping schedule is reasonable and robust. Based on the result from the Danish railway corridor, the total costs can be reduced significantly (50%) than the previous model which is based on optimizing the number of tamping. The different maintenance strategies have been discussed in the paper. The analysis from the results obtained from the model also shows a longer period of predictive tamping planning has more optimal scheduling of maintenance actions than continuous short term preventive maintenance, namely yearly condition-based planning.

Keywords: integer programming, railway tamping, predictive maintenance model, preventive condition-based maintenance

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Authors: Adel Khorramrouz, Monireh Ahmadi Bani, Ehsan Norouzi, Morvarid Lalenoor

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Background: Plantar pressure distribution measurement has been used for a long time to assess foot disorders. Plantar pressure is an important component affecting the foot and ankle function and Changes in plantar pressure distribution could indicate various foot and ankle disorders. Morphologic and mechanical properties of the foot may be important factors affecting the plantar pressure distribution. Accurate and early measurement may help to reduce the prevalence of pes planus. With recent developments in technology, new techniques such as machine learning have been used to assist clinicians in predicting patients with foot disorders. Significance of the study: This study proposes a neural network learning-based flat foot classification methodology using static foot pressure distribution. Methodologies: Data were collected from 895 patients who were referred to a foot clinic due to foot disorders. Patients with pes planus were labeled by an experienced physician based on clinical examination. Then all subjects (with and without pes planus) were evaluated for static plantar pressures distribution. Patients who were diagnosed with the flat foot in both feet were included in the study. In the next step, the leg length was normalized and the network was trained for plantar pressure mapping images. Findings: From a total of 895 image data, 581 were labeled as pes planus. A computational neural network (CNN) ran to evaluate the performance of the proposed model. The prediction accuracy of the basic CNN-based model was performed and the prediction model was derived through the proposed methodology. In the basic CNN model, the training accuracy was 79.14%, and the test accuracy was 72.09%. Conclusion: This model can be easily and simply used by patients with pes planus and doctors to predict the classification of pes planus and prescreen for possible musculoskeletal disorders related to this condition. However, more models need to be considered and compared for higher accuracy.

Keywords: foot disorder, machine learning, neural network, pes planus

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Authors: Pimploi Tirastittam, Suppara Charoenpoom

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Nowadays the asynchronous learning has granted the permission to the anywhere and anything learning via the technology and E-media which give the learner more convenient. This research is about the design of the blended and online learning for the asynchronous learning of the process management subject in order to create the prototype of this subject asynchronous learning which will create the easiness and increase capability in the learning. The pattern of learning is the integration between the in-class learning and online learning via the internet. This research is mainly focused on the online learning and the online learning can be divided into 5 parts which are virtual classroom, online content, collaboration, assessment and reference material. After the system design was finished, it was evaluated and tested by 5 experts in blended learning design and 10 students which the user’s satisfaction level is good. The result is as good as the assumption so the system can be used in the process management subject for a real usage.

Keywords: blended learning, asynchronous learning, design, process management

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Authors: Muhammmad Riandhy Anindika Yudhy, Harry Patria, Ramadhani Santoso

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Loss of containment is the primary hazard that process safety management is concerned within the oil and gas industry. Escalation to more serious consequences all begins with the loss of containment, starting with oil and gas release from leakage or spillage from primary containment resulting in pool fire, jet fire and even explosion when reacted with various ignition sources in the operations. Therefore, the heart of process safety management is avoiding loss of containment and mitigating its impact through the implementation of safeguards. The most effective safeguard for the case is an early detection system to alert Operations to take action prior to a potential case of loss of containment. The detection system value increases when applied to a long surface pipeline that is naturally difficult to monitor at all times and is exposed to multiple causes of loss of containment, from natural corrosion to illegal tapping. Based on prior researches and studies, detecting loss of containment accurately in the surface pipeline is difficult. The trade-off between cost-effectiveness and high accuracy has been the main issue when selecting the traditional detection method. The current best-performing method, Real-Time Transient Model (RTTM), requires analysis of closely positioned pressure, flow and temperature (PVT) points in the pipeline to be accurate. Having multiple adjacent PVT sensors along the pipeline is expensive, hence generally not a viable alternative from an economic standpoint.A conceptual approach to combine mathematical modeling using computational fluid dynamics and a supervised machine learning model has shown promising results to predict leakage in the pipeline. Mathematical modeling is used to generate simulation data where this data is used to train the leak detection and localization models. Mathematical models and simulation software have also been shown to provide comparable results with experimental data with very high levels of accuracy. While the supervised machine learning model requires a large training dataset for the development of accurate models, mathematical modeling has been shown to be able to generate the required datasets to justify the application of data analytics for the development of model-based leak detection systems for petroleum pipelines. This paper presents a review of key leak detection strategies for oil and gas pipelines, with a specific focus on crude oil applications, and presents the opportunities for the use of data analytics tools and mathematical modeling for the development of robust real-time leak detection and localization system for surface pipelines. A case study is also presented.

Keywords: pipeline, leakage, detection, AI

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Authors: Abdulaziz Alsadhan, Naveed Khan

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In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP)

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Authors: Sheng-Shih Wang, Wei-Ting Wang

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This paper designs an intelligent guidance system, based on wireless sensor networks, for efficient parking in parking lots. The proposed system consists of a parking space allocation subsystem, a parking space monitoring subsystem, a driving guidance subsystem, and a vehicle detection subsystem. In the system, we propose a novel and effective virtual coordinate system for sensing and displaying devices to determine the proper vacant parking space and provide the precise guidance to the driver. This study constructs a ZigBee-based wireless sensor network on Arduino platform and implements the prototype of the proposed system using Arduino-based complements. Experimental results confirm that the proposed prototype can not only work well, but also provide drivers the correct parking information.

Keywords: Arduino, parking guidance, wireless sensor network, ZigBee

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Authors: Fils Olivier Kamanzi

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This research Paper shows that materials CFRP were used to rehabilitate 9 Beams and retrofitting of 9 Beams with size (125x250x2300) mm each for M50 grade of concrete with 20% of Volume of Cement replaced by GGBS as a mineral Admixture. Superplasticizer (ForscoConplast SP430) used to reduce the water-cement ratio and maintaining good workability of fresh concrete (Slump test 57mm). Concrete Mix ratio 1:1.56:2.66 with a water-cement ratio of 0.31(ACI codebooks). A sample of 6cubes sized (150X150X150) mm, 6cylinders sized (150ФX300H) mm and 6Prisms sized (100X100X500) mm were cast, cured, and tested for 7,14&28days by compressive, tensile and flexure test; finally, mix design reaches the compressive strength of 59.84N/mm2. 21 Beams were cast and cured for up to 28 days, 3Beams were tested by a two-point loading machine as Control beams. 9 Beams were distressed in flexure by adopting failure up to final Yielding point under two-point loading conditions by taking 90% off Ultimate load. Three sets, each composed of three distressed beams, were rehabilitated by using CFRP sheets, one, two & three layers, respectively, and after being retested up to failure mode. Another three sets were freshly retrofitted also by using CFRP sheets one, two & three layers, respectively, and being tested by a two-point load method of compression strength testing machine. The aim of this study is to determine the flexural Strength & behaviors of repaired and retrofitted Beams by CFRP sheets for gaining good strength and considering economic aspects. The results show that rehabilitated beams increase its strength 47 %, 78 % & 89 %, respectively, to thickness of CFRP sheets and 41%, 51 %& 68 %, respectively too, for retrofitted Beams. The conclusion is that three layers of CFRP sheets are the best applicable in repairing and retrofitting the bonded beams method.

Keywords: retrofitting, rehabilitation, cfrp, rcc beam, flexural strength and behaviors, ggbs, and epoxy resin

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

Procedia PDF Downloads 116

Authors: Gihan Thilakarathna, Fernando Ishara, Rathnayake Yasith, Bandara A. M. R. Y.

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E-learning is a crucial component of intelligent education. Even in the midst of a pandemic, E-learning is becoming increasingly important in the educational system. Several e-learning programs are accessible for students. Here, we decided to create an e-learning framework for children. We've found a few issues that teachers are having with their online classes. When there are numerous students in an online classroom, how does a teacher recognize a student's focus on academics and below-the-surface behaviors? Some kids are not paying attention in class, and others are napping. The teacher is unable to keep track of each and every student. Key challenge in e-learning is online exams. Because students can cheat easily during online exams. Hence there is need of exam proctoring is occurred. In here we propose an automated online exam cheating detection method using a web camera. The purpose of this project is to present an E-learning platform for math education and include games for kids as an alternative teaching method for math students. The game will be accessible via a web browser. The imagery in the game is drawn in a cartoonish style. This will help students learn math through games. Everything in this day and age is moving towards automation. However, automatic answer evaluation is only available for MCQ-based questions. As a result, the checker has a difficult time evaluating the theory solution. The current system requires more manpower and takes a long time to evaluate responses. It's also possible to mark two identical responses differently and receive two different grades. As a result, this application employs machine learning techniques to provide an automatic evaluation of subjective responses based on the keyword provided to the computer as student input, resulting in a fair distribution of marks. In addition, it will save time and manpower. We used deep learning, machine learning, image processing and natural language technologies to develop these research components.

Keywords: math, education games, e-learning platform, artificial intelligence

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Authors: Audrey Ngambia, Ondrej Masek, Valentina Erastova

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Biochar is an amorphous carbon-rich material generated from the pyrolysis of biomass with multifarious properties and functionality. Biochar has shown proven applications in the treatment of flue gas and organic and inorganic pollutants in soil and water/wastewater as a result of its multiple surface functional groups and porous structures. These properties have also shown potential in energy storage and carbon capture. The availability of diverse sources of biomass to produce biochar has increased interest in it as a sustainable and environmentally friendly material. The properties and porous structures of biochar vary depending on the type of biomass and high heat treatment temperature (HHT). Biochars produced at HHT between 400°C – 800°C generally have lower H/C and O/C ratios, higher porosities, larger pore sizes and higher surface areas with temperature. While all is known experimentally, there is little knowledge on the porous role structure and functional groups play on processes occurring at the atomistic scale, which are extremely important for the optimization of biochar for application, especially in the adsorption of gases. Atomistic simulations methods have shown the potential to generate such amorphous materials; however, most of the models available are composed of only carbon atoms or graphitic sheets, which are very dense or with simple slit pores, all of which ignore the important role of heteroatoms such as O, N, S and pore morphologies. Hence, developing realistic models that integrate these parameters are important to understand their role in governing adsorption mechanisms that will aid in guiding the design and optimization of biochar materials for target applications. In this work, molecular dynamics simulations in the isobaric ensemble are used to generate realistic biochar models taking into account experimentally determined H/C, O/C, N/C, aromaticity, micropore size range, micropore volumes and true densities of biochars. A pore generation approach was developed using virtual atoms, which is a Lennard-Jones sphere of varying van der Waals radius and softness. Its interaction via a soft-core potential with the biochar matrix allows the creation of pores with rough surfaces while varying the van der Waals radius parameters gives control to the pore-size distribution. We focused on microporosity, creating average pore sizes of 0.5 - 2 nm in diameter and pore volumes in the range of 0.05 – 1 cm3/g, which corresponds to experimental gas adsorption micropore sizes of amorphous porous biochars. Realistic biochar models with surface functionalities, micropore size distribution and pore morphologies were developed, and they could aid in the study of adsorption processes in confined micropores.

Keywords: biochar, heteroatoms, micropore size, molecular dynamics simulations, surface functional groups, virtual atoms

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Authors: Yuri Bizzoni, Marianne Reboul

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Our program compares French and Italian translations of Homer’s Odyssey, from the XVIth to the XXth century. We focus on the third point, showing how distributional semantics systems can be used both to improve alignment between different French translations as well as between the Greek text and a French translation. Although we focus on French examples, the techniques we display are completely language independent.

Keywords: classical receptions, computational linguistics, distributional semantics, Homeric poems, machine translation, translation studies, text alignment

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Authors: Ion Smeureanu, Adriana Reveiu, Marian Dardala, Titus Felix Furtuna, Roman Kanala

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This paper proposes a linear model for optimizing domestic energy consumption, in Romania. Both techno-economic and consumer behavior approaches have been considered, in order to develop the model. The proposed model aims to reduce the energy consumption, in households, by assembling in a unitary model, aspects concerning: residential lighting, space heating, hot water, and combined space heating – hot water, space cooling, and passenger transport. This paper focuses on space heating domestic energy consumption model, and quantify not only technical-economic issues, but also consumer behavior impact, related to people decision to envelope and insulate buildings, in order to minimize energy consumption.

Keywords: consumer behavior, open source energy modeling system (OSeMOSYS), MARKAL/TIMES Romanian energy model, virtual technologies

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Authors: Douglas A. Menezes, Isabel D. Nunes, Ulrich Schiel

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Most Virtual Learning Environments do not provide support mechanisms for the integrated planning, construction and follow-up of Instructional Design supported by Learning Analytic results. The present work aims to present an authoring tool that will be responsible for constructing the structure of an Instructional Design (ID), without the data being altered during the execution of the course. The visual interface aims to present the critical situations present in this ID, serving as a support tool for the course follow-up and possible improvements, which can be made during its execution or in the planning of a new edition of this course. The model for the ID is based on High-Level Petri Nets and the visualization forms are determined by the specific kind of the data generated by an e-course, a population of students generating sequentially dependent data.

Keywords: educational data visualization, high-level petri nets, instructional design, learning analytics

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Authors: Akhila Potluru

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Worldwide, more than 250 bodies of water are transboundary, meaning they cross the political boundaries of multiple countries. This creates a system of hydrological, economic, and social interdependence between communities reliant on these water sources. Transboundary water conflicts can occur as a result of this intense interdependence. Many factors contribute to the sparking of transboundary water conflicts, ranging from natural hydrological factors to hydro-political interactions. Previous attempts to predict transboundary water conflicts by analysing changes or trends in the contributing factors have typically failed because patterns in the data are hard to identify. However, there is potential for artificial intelligence and machine learning to fill this gap and identify future ‘hotspots’ up to a year in advance by identifying patterns in data where humans can’t. This research determines the extent to which AI can be used to predict and prevent transboundary water conflicts. This is done via a critical literature review of previous case studies and datasets where AI was deployed to predict water conflict. This research not only delivered a more nuanced understanding of previously undervalued factors that contribute toward transboundary water conflicts (in particular, culture and disinformation) but also by detecting conflict early, governance bodies can engage in processes to de-escalate conflict by providing pre-emptive solutions. Looking forward, this gives rise to significant policy implications and water-sharing agreements, which may be able to prevent water conflicts from developing into wide-scale disasters. Additionally, AI can be used to gain a fuller picture of water-based conflicts in areas where security concerns mean it is not possible to have staff on the ground. Therefore, AI enhances not only the depth of our knowledge about transboundary water conflicts but also the breadth of our knowledge. With demand for water constantly growing, competition between countries over shared water will increasingly lead to water conflict. There has never been a more significant time for us to be able to accurately predict and take precautions to prevent global water conflicts.

Keywords: artificial intelligence, machine learning, transboundary water conflict, water management

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Authors: Kais Siala, Mohamed Kharrat, Mohamed Abid

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Exposure therapies encourage patients to gradually begin facing their painful memories of the trauma in order to reduce fear and anxiety. In this context, virtual reality techniques are widely used for treatment of different kinds of phobia. The particular case of fear of death phobia (thanataphobia) is addressed in this paper. For this purpose, we propose to make a simulation of Near Death Experience (NDE) using augmented reality techniques. We propose in particular to simulate the Out-of-Body experience (OBE) which is the first step of a Near-Death-Experience (NDE). In this paper, we present technical aspects of this simulation as well as short-term impact in terms of physiological measures. The non-linear Poincéré plot is used to describe the difference in Heart Rate Variability between In-Body and Out-Of-Body conditions.

Keywords: Out-of-Body simulation, physiological measure, augmented reality, phobia psychotherapy, HRV, Poincaré plot

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Authors: Thomas E. Portegys

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An animal behavior problem is presented in the form of a nest-building task that involves two cooperating virtual birds, a male and female. The female builds a nest into which she lays an egg. The male's job is to forage in a forest for food for both himself and the female. In addition, the male must fetch stones from a nearby desert for the female to use as nesting material. The task is completed when the nest is built, and an egg is laid in it. A goal-seeking neural network and a recurrent neural network were trained and tested with little success. The goal-seeking network was then enhanced with “place cells”, allowing the birds to spatially navigate the world, building the nest while keeping themselves fed. Place cells are neurons in the hippocampus that map space.

Keywords: artificial animal intelligence, artificial life, goal-seeking neural network, nest-building, place cells, spatial navigation

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Authors: Jimmy Eadie

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Convolutional Cross Synthesis (CCS) has emerged as a powerful technique for blending input signals to create hybrid sounds. It has significantly expanded the horizons of digital signal processing, enabling artists to explore audio effects. However, the conventional applications of CCS primarily revolve around reverberation and room simulation rather than being utilized as a creative synthesis method. In this paper, we present the design of a digital instrument called CrossSampler that harnesses a parametric approach to convolution cross-synthesis, which involves using adjustable parameters to control the blending of audio signals through convolution. These parameters allow for customization of the resulting sound, offering greater creative control and flexibility. It enables users to shape the output by manipulating factors such as duration, intensity, and spectral characteristics. This approach facilitates experimentation and exploration in sound design and opens new sonic possibilities.

Keywords: convolution, synthesis, sampling, virtual instrument

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Authors: Tarali Kalita, Karabi Dutta

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The North Eastern Region of India, with diverse climatic conditions and a wide range of ecological habitats, makes an ideal natural abode for a good number of silk-producing insects. Cocoon is the economically important life stage from where silk of economic importance is obtained. In recent years, silk-based biomaterials have gained considerable attention, which is dependent on the structure and properties of the silkworm cocoons as well as silk yarn. The present investigation deals with the morphological study of cocoons, including cocoon color, cocoon size, shell weight and shell ratio of eleven different species of silk insects collected from different regions of North East India. The Scanning Electron Microscopic study and X-ray photoelectron spectroscopy were performed to know the arrangement of silk threads in cocoons and the atomic elemental analysis, respectively. Further, collected cocoons were degummed and reeled/spun on a reeling machine or spinning wheel to know the filament length, linear density and tensile strength by using Universal Testing Machine. The study showed significant variation in terms of cocoon color, cocoon shape, cocoon weight and filament packaging. XPS analysis revealed the presence of elements (Mass %) C, N, O, Si and Ca in varying amounts. The wild cocoons showed the presence of Calcium oxalate crystals which makes the cocoons hard and needs further treatment to reel. In the present investigation, the highest percentage of strain (%) and toughness (g/den) were observed in Antheraea assamensis, which implies that the muga silk is a more compact packing of molecules. It is expected that this study will be the basis for further biomimetic studies to design and manufacture artificial fiber composites with novel morphologies and associated material properties.

Keywords: cocoon characterization, north-east India, prospects, silk characterization

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Authors: Zeynep Ayfer Bozat

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As traditional innovation has already taken its place in managers’ to do lists; managers and companies have started to look for new ways to go beyond the traditional innovation. Because of its cost, traditional innovation became a burden for companies since they only use inner sources. Companies have intended to use outer innovation sources to decrease the innovation costs and Open Innovation has become a new solution for companies at this point. Crowdsourcing is a tool of Open Innovation and it consists of two words: Outsourcing and crowd. Crowdsourcing aims to benefit from the efforts and ideas of a virtual crowd via Internet technologies. In addition to that, crowdsourcing can help entrepreneurs to innovate and grow their businesses. They can crowd source anything they can use to grow their businesses: Ideas, investment, new business, new partners, new solutions, new policies, data, insight, marketing or talent. Therefore, the aim of the study is to be able to show some possible ways for entrepreneurs to benefit from crowdsourcing to expand or foster their businesses. In the study, the term crowdsourcing has been given in details and these possible ways have been searched and given.

Keywords: crowdsourcing, entrepreneurship, innovation, open innovation

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Authors: D. Nam Kim, D. Jin Kim, Jeonghwan Jeon

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Defense Defensive Modeling and Simulation (M&S) is a system which enables impracticable training for reducing constraints of time, space and financial resources. The necessity of defensive M&S has been increasing not only for education and training but also virtual fight. Soldiers who are using defensive M&S for education and training will obtain empirical knowledge and know-how. However, the obtained knowledge of individual soldiers have not been managed and utilized yet since the nature of military organizations: confidentiality and frequent change of members. Therefore, this study aims to develop a management system for the experience of defensive M&S based on data mining approach. Since individual empirical knowledge gained through using the defensive M&S is both quantitative and qualitative data, data mining approach is appropriate for dealing with individual empirical knowledge. This research is expected to be helpful for soldiers and military policy makers.

Keywords: data mining, defensive m&s, management system, knowledge management

Procedia PDF Downloads 258

Authors: Basiru Amuneni

Abstract:

Astronomy is one domain with a rise in data. Traditional tools for data management have been employed in the quest for knowledge discovery. However, these traditional tools become limited in the face of big. One means of maximizing knowledge discovery for big data is the use of scientific visualisation. The aim of the work is to explore the possibilities offered by emerging creative technologies of Virtual Reality (VR) systems and game engines to visualize multiwavelength datasets. Game Engines are primarily used for developing video games, however their advanced graphics could be exploited for scientific visualization which provides a means to graphically illustrate scientific data to ease human comprehension. Modern astronomy is now in the era of multiwavelength data where a single galaxy for example, is captured by the telescope several times and at different electromagnetic wavelength to have a more comprehensive picture of the physical characteristics of the galaxy. Visualising this in an immersive environment would be more intuitive and natural for an observer. This work presents a standalone VR application that accesses galaxy FITS files. The application was built using the Unity Game Engine for the graphics underpinning and the OpenXR API for the VR infrastructure. The work used a methodology known as Design Science Research (DSR) which entails the act of ‘using design as a research method or technique’. The key stages of the galaxy modelling pipeline are FITS data preparation, Galaxy Modelling, Unity 3D Visualisation and VR Display. The FITS data format cannot be read by the Unity Game Engine directly. A DLL (CSHARPFITS) which provides a native support for reading and writing FITS files was used. The Galaxy modeller uses an approach that integrates cleaned FITS image pixels into the graphics pipeline of the Unity3d game Engine. The cleaned FITS images are then input to the galaxy modeller pipeline phase, which has a pre-processing script that extracts, pixel, galaxy world position, and colour maps the FITS image pixels. The user can visualise image galaxies in different light bands, control the blend of the image with similar images from different sources or fuse images for a holistic view. The framework will allow users to build tools to realise complex workflows for public outreach and possibly scientific work with increased scalability, near real time interactivity with ease of access. The application is presented in an immersive environment and can use all commercially available headset built on the OpenXR API. The user can select galaxies in the scene, teleport to the galaxy, pan, zoom in/out, and change colour gradients of the galaxy. The findings and design lessons learnt in the implementation of different use cases will contribute to the development and design of game-based visualisation tools in immersive environment by enabling informed decisions to be made.

Keywords: astronomy, visualisation, multiwavelenght dataset, virtual reality

Procedia PDF Downloads 95

Authors: Xiaoyang Zhao, Kaiying Wang

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The choice of stocking density in poultry farming is a potential way for determining welfare level of poultry. However, it is difficult to measure stocking densities in poultry farming because of a lot of variables such as species, age and weight, feeding way, house structure and geographical location in different broiler houses. A method was proposed in this paper to measure the differences of young broilers reared under different stocking densities by sound analysis. Vocalisations of broilers were recorded and analysed under different stocking densities to identify the relationship between sounds and stocking densities. Recordings were made continuously for three-week-old chickens in order to evaluate the variation of sounds emitted by the animals at the beginning. The experimental trial was carried out in an indoor reared broiler farm; the audio recording procedures lasted for 5 days. Broilers were divided into 5 groups, stocking density treatments were 8/m², 10/m², 12/m² (96birds/pen), 14/m² and 16/m², all conditions including ventilation and feed conditions were kept same except from stocking densities in every group. The recordings and analysis of sounds of chickens were made noninvasively. Sound recordings were manually analysed and labelled using sound analysis software: GoldWave Digital Audio Editor. After sound acquisition process, the Mel Frequency Cepstrum Coefficients (MFCC) was extracted from sound data, and the Support Vector Machine (SVM) was used as an early detector and classifier. This preliminary study, conducted in an indoor reared broiler farm shows that this method can be used to classify sounds of chickens under different densities economically (only a cheap microphone and recorder can be used), the classification accuracy is 85.7%. This method can predict the optimum stocking density of broilers with the complement of animal welfare indicators, animal productive indicators and so on.

Keywords: broiler, stocking density, poultry farming, sound monitoring, Mel Frequency Cepstrum Coefficients (MFCC), Support Vector Machine (SVM)

Procedia PDF Downloads 164

Authors: Sahithi Yarlagadda

Abstract:

The design of antenna is constrained by mathematical and geometrical parameters. Though there are diverse antenna structures with wide range of feeds yet, there are many geometries to be tried, which cannot be customized into predefined computational methods. The antenna design and optimization qualify to apply evolutionary algorithmic approach since the antenna parameters weights dependent on geometric characteristics directly. The evolutionary algorithm can be explained simply for a given quality function to be maximized. We can randomly create a set of candidate solutions, elements of the function's domain, and apply the quality function as an abstract fitness measure. Based on this fitness, some of the better candidates are chosen to seed the next generation by applying recombination and permutation to them. In conventional approach, the quality function is unaltered for any iteration. But the antenna parameters and geometries are wide to fit into single function. So, the weight coefficients are obtained for all possible antenna electrical parameters and geometries; the variation is learnt by mining the data obtained for an optimized algorithm. The weight and covariant coefficients of corresponding parameters are logged for learning and future use as datasets. This paper drafts an approach to obtain the requirements to study and methodize the evolutionary approach to automated antenna design for our past work on Vivaldi antenna as test candidate. The antenna parameters like gain, directivity, etc. are directly caged by geometries, materials, and dimensions. The design equations are to be noted here and valuated for all possible conditions to get maxima and minima for given frequency band. The boundary conditions are thus obtained prior to implementation, easing the optimization. The implementation mainly aimed to study the practical computational, processing, and design complexities that incur while simulations. HFSS is chosen for simulations and results. MATLAB is used to generate the computations, combinations, and data logging. MATLAB is also used to apply machine learning algorithms and plotting the data to design the algorithm. The number of combinations is to be tested manually, so HFSS API is used to call HFSS functions from MATLAB itself. MATLAB parallel processing tool box is used to run multiple simulations in parallel. The aim is to develop an add-in to antenna design software like HFSS, CSTor, a standalone application to optimize pre-identified common parameters of wide range of antennas available. In this paper, we have used MATLAB to calculate Vivaldi antenna parameters like slot line characteristic impedance, impedance of stripline, slot line width, flare aperture size, dielectric and K means, and Hamming window are applied to obtain the best test parameters. HFSS API is used to calculate the radiation, bandwidth, directivity, and efficiency, and data is logged for applying the Evolutionary genetic algorithm in MATLAB. The paper demonstrates the computational weights and Machine Learning approach for automated antenna optimizing for Vivaldi antenna.

Keywords: machine learning, Vivaldi, evolutionary algorithm, genetic algorithm

Procedia PDF Downloads 111

Authors: Ida Srdić, Luka Mandić, LidijaMandić

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Digitization and intensive use of tablets, smartphones, the internet, mobile, and web applications have massively disrupted our habits, and the way audiences (especially youth) consume content. To introduce educational content in games and animations, and at the same time to keep it interesting and compelling for kids, is a challenge. In our work, we are comparing the different possibilities and potentials that digital games could provide to successfully mitigate direct connection with education. We analyze the main directions and educational methods in game-based learning and the possibilities of interactive modeling through questionnaires for user experience and requirements. A pre and post-quantitative survey will be conducted in order to measure levels of objective knowledge as well as the games perception. This approach enables quantitative and objective evaluation of the impact the game has on participants. Also, we will discuss the main barriers to the use of games in education and how games can be best used for learning.

Keywords: Bloom’s taxonomy, epistemic games, learning objectives, virtual learning environments

Procedia PDF Downloads 101