Search results for: lidar Radar data fusion

Authors: Chuping Lo

Abstract:

This paper presents a simple model demonstrating that ceteris paribus countries with lower barriers to accessing global data tend to earn higher incomes than other countries. Therefore, large countries that inherently have greater data resources tend to have higher incomes than smaller countries, such that the former may be more hesitant than the latter to liberalize cross-border data flows to maintain this advantage. Furthermore, countries with higher artificial intelligence (AI) intensity in production technologies tend to benefit more from economies of scale in data aggregation, leading to higher income and more trade as they are better able to utilize global data.

Keywords: digital intensity, digital divide, international trade, scale of economics

Procedia PDF Downloads 51

Authors: G. R. Navaneesh, E. Nagarajan, C. H. Rajam Raju

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Nowadays the multimedia data are used to store some secure information. All previous methods allocate a space in image for data embedding purpose after encryption. In this paper, we propose a novel method by reserving space in image with a boundary surrounded before encryption with a traditional RDH algorithm, which makes it easy for the data hider to reversibly embed data in the encrypted images. The proposed method can achieve real time performance, that is, data extraction and image recovery are free of any error. A secure transmission process is also discussed in this paper, which improves the efficiency by ten times compared to other processes as discussed.

Keywords: secure communication, reserving room before encryption, least significant bits, image encryption, reversible data hiding

Procedia PDF Downloads 397

Authors: Fuad M. Alkoot

Abstract:

DNA data have been used in forensics for decades. However, current research looks at using the DNA as a biometric identity verification modality. The goal is to improve the speed of identification. We aim at using gene data that was initially used for autism detection to find if and how accurate is this data for identification applications. Mainly our goal is to find if our data preprocessing technique yields data useful as a biometric identification tool. We experiment with using the nearest neighbor classifier to identify subjects. Results show that optimal classification rate is achieved when the test set is corrupted by normally distributed noise with zero mean and standard deviation of 1. The classification rate is close to optimal at higher noise standard deviation reaching 3. This shows that the data can be used for identity verification with high accuracy using a simple classifier such as the k-nearest neighbor (k-NN). 

Keywords: biometrics, genetic data, identity verification, k nearest neighbor

Procedia PDF Downloads 235

Authors: R Palson Kennedy, P.Kiran Sai

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This article introduces machine learning (ML) researchers to the hurdles that geoscience problems present, as well as the opportunities for improvement in both ML and geosciences. This article presents a review from the data life cycle perspective to meet that need. Numerous facets of geosciences present unique difficulties for the study of intelligent systems. Geosciences data is notoriously difficult to analyze since it is frequently unpredictable, intermittent, sparse, multi-resolution, and multi-scale. The first half addresses data science’s essential concepts and theoretical underpinnings, while the second section contains key themes and sharing experiences from current publications focused on each stage of the data life cycle. Finally, themes such as open science, smart data, and team science are considered.

Keywords: Data science, intelligent system, machine learning, big data, data life cycle, recent development, geo science

Procedia PDF Downloads 123

Authors: Marc Bachelet, Abhijit Kumar Chatterjee, José Manuel Avila

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Data quality is a key component of any data-driven organization. Without data quality, organizations cannot effectively make data-driven decisions, which often leads to poor business performance. Therefore, it is important for an organization to ensure that the data they use is of high quality. This is where the concept of data mesh comes in. Data mesh is an organizational and architectural decentralized approach to data management that can help organizations improve the quality of data. The concept of data mesh was first introduced in 2020. Its purpose is to decentralize data ownership, making it easier for domain experts to manage the data. This can help organizations improve data quality by reducing the reliance on centralized data teams and allowing domain experts to take charge of their data. This paper intends to discuss how a set of elements, including data mesh, are tools capable of increasing data quality. One of the key benefits of data mesh is improved metadata management. In a traditional data architecture, metadata management is typically centralized, which can lead to data silos and poor data quality. With data mesh, metadata is managed in a decentralized manner, ensuring accurate and up-to-date metadata, thereby improving data quality. Another benefit of data mesh is the clarification of roles and responsibilities. In a traditional data architecture, data teams are responsible for managing all aspects of data, which can lead to confusion and ambiguity in responsibilities. With data mesh, domain experts are responsible for managing their own data, which can help provide clarity in roles and responsibilities and improve data quality. Additionally, data mesh can also contribute to a new form of organization that is more agile and adaptable. By decentralizing data ownership, organizations can respond more quickly to changes in their business environment, which in turn can help improve overall performance by allowing better insights into business as an effect of better reports and visualization tools. Monitoring and analytics are also important aspects of data quality. With data mesh, monitoring, and analytics are decentralized, allowing domain experts to monitor and analyze their own data. This will help in identifying and addressing data quality problems in quick time, leading to improved data quality. Data culture is another major aspect of data quality. With data mesh, domain experts are encouraged to take ownership of their data, which can help create a data-driven culture within the organization. This can lead to improved data quality and better business outcomes. Finally, the paper explores the contribution of AI in the coming years. AI can help enhance data quality by automating many data-related tasks, like data cleaning and data validation. By integrating AI into data mesh, organizations can further enhance the quality of their data. The concepts mentioned above are illustrated by AEKIDEN experience feedback. AEKIDEN is an international data-driven consultancy that has successfully implemented a data mesh approach. By sharing their experience, AEKIDEN can help other organizations understand the benefits and challenges of implementing data mesh and improving data quality.

Keywords: data culture, data-driven organization, data mesh, data quality for business success

Procedia PDF Downloads 118

Authors: Yansong Chen

Abstract:

In the age of media convergence, new media technology is constantly impacting, changing, and even reshaping the limits of Art. From the technological ontology of the new media art, the concept of interaction design has always been dominated by I/O (Input/Output) systems through the ages, which ignores the content of systems and kills the aura of art. Light, as a fusion media, basically comes from the extension of some human feelings and can be the content of the input or the effect of output. In this paper, firstly, on the basis of literature review, the interaction characteristics research was conducted on light. Secondly, starting from discourse patterns of people and machines, people and people, people, and imagining things, we propose three light modes: object-oriented interaction, Immersion interaction, Tele-Presence interaction. Finally, this paper explains how to regain the aura of art through light elements in new media art and understand multiple levels of 'Interaction design'. In addition, the new media art, especially the light-based interaction art, enriches the language patterns and motivates emerging art forms to be more widespread and popular, which achieves its aesthetics growth.

Keywords: new media art, interaction design, light art, immersion

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Authors: Ji Eun Shin, Byung Ho Jung, Dong Hoon Lim

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It is almost impossible to store or analyze big data increasing exponentially with traditional technologies. Hadoop is a new technology to make that possible. R programming language is by far the most popular statistical tool for big data analysis based on distributed processing with Hadoop technology. With RHadoop that integrates R and Hadoop environment, we implemented parallel multiple regression analysis with different sizes of actual data. Experimental results showed our RHadoop system was much faster as the number of data nodes increases. We also compared the performance of our RHadoop with lm function and big lm packages available on big memory. The results showed that our RHadoop was faster than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases.

Keywords: big data, Hadoop, parallel regression analysis, R, RHadoop

Procedia PDF Downloads 421

Authors: Haojie Wang, Xun Li, Rui Yin

Abstract:

In order to solve the memorization overfitting in the meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels, so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to exponential growth of computation, this paper also proposes a key data extraction method, that only extracts part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: data augmentation, mutex task generation, meta-learning, text classification.

Procedia PDF Downloads 79

Authors: Sifat Rahman Ahona, Rifat Tasnim, Naima Hassan

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Data aggregation is a helpful technique for reducing the data communication overhead in wireless sensor network. One of the important tasks of data aggregation is positioning of the aggregator points. There are a lot of works done on data aggregation. But, efficient positioning of the aggregators points is not focused so much. In this paper, authors are focusing on the positioning or the placement of the aggregation points in wireless sensor network. Authors proposed an algorithm to select the aggregators positions for a scenario where aggregator nodes are more powerful than sensor nodes.

Keywords: aggregation point, data communication, data aggregation, wireless sensor network

Procedia PDF Downloads 144

Authors: Paula Simões, Isabel Natário

Abstract:

This paper reviews a number of theoretical aspects for implementing an explicit spatial perspective in econometrics for modelling non-continuous data, in general, and count data, in particular. It provides an overview of the several spatial econometric approaches that are available to model data that are collected with reference to location in space, from the classical spatial econometrics approaches to the recent developments on spatial econometrics to model count data, in a Bayesian hierarchical setting. Considerable attention is paid to the inferential framework, necessary for structural consistent spatial econometric count models, incorporating spatial lag autocorrelation, to the corresponding estimation and testing procedures for different assumptions, to the constrains and implications embedded in the various specifications in the literature. This review combines insights from the classical spatial econometrics literature as well as from hierarchical modeling and analysis of spatial data, in order to look for new possible directions on the processing of count data, in a spatial hierarchical Bayesian econometric context.

Keywords: spatial data analysis, spatial econometrics, Bayesian hierarchical models, count data

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Authors: Svanik Garg

Abstract:

Static Test Pads are stationary mechanisms that hold a solid rocket motor, measuring the different parameters of its operation including thrust and temperature to better calibrate it for launch. This paper outlines a specific STP designed to test high powered rocket motors with a thrust upwards of 4000N and limited to 6500N. The design includes a specific portable mechanism with cost an integral part of the design process to make it accessible to small scale rocket developers with limited resources. Using curved surfaces and an ergonomic design, the STP has a delicately engineered façade/case with a focus on stability and axial calibration of thrust. This paper describes the design, operation and working of the STP and its widescale uses given the growing market of aviation enthusiasts. Simulations on the CAD model in Fusion 360 provided promising results with a safety factor of 2 established and stress limited along with the load coefficient A PCB was also designed as part of the test pad design process to help obtain results, with visual output and various virtual terminals to collect data of different parameters. The circuitry was simulated using ‘proteus’ and a special virtual interface with auditory commands was also created for accessibility and wide-scale implementation. Along with this description of the design, the paper also emphasizes the design principle behind the STP including a description of its vertical orientation to maximize thrust accuracy along with a stable base to prevent micromovements. Given the rise of students and professionals alike building high powered rockets, the STP described in this paper is an appropriate option, with limited cost, portability, accuracy, and versatility. There are two types of STP’s vertical or horizontal, the one discussed in this paper is vertical to utilize the axial component of thrust.

Keywords: static test pad, rocket motor, thrust, load, circuit, avionics, drag

Procedia PDF Downloads 350

Authors: Gueidi Afef, Gharsellaoui Hamza, Ben Ahmed Samir

Abstract:

This paper deals with the secret of the continual progression data that new data management solutions have been emerged: The NoSQL databases. They crossed several areas like personalization, profile management, big data in real-time, content management, catalog, view of customers, mobile applications, internet of things, digital communication and fraud detection. Nowadays, these database management systems are increasing. These systems store data very well and with the trend of big data, a new challenge’s store demands new structures and methods for managing enterprise data. The new intelligent machine in the e-learning sector, thrives on more data, so smart machines can learn more and faster. The robotics are our use case to focus on our test. The implementation of NoSQL for Robotics wrestle all the data they acquire into usable form because with the ordinary type of robotics; we are facing very big limits to manage and find the exact information in real-time. Our original proposed approach was demonstrated by experimental studies and running example used as a use case.

Keywords: NoSQL databases, database management systems, robotics, big data

Procedia PDF Downloads 333

Authors: C. B. Le, V. N. Pham

Abstract:

In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

Procedia PDF Downloads 166

Authors: Shahrazed Medeghri, Saad Hamzaoui, Mokhtar Zerdali

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The metallurgical grade silicon (MG-Si) is obtained from the reduction of silica (SiO₂) in an induction furnace or an electric arc furnace. Impurities inherent in reduction process also depend on the quality of the raw material used. Among the applications of the silicon, it is used as a substrate for the photovoltaic conversion of solar energy and this conversion is wider as the purity of the substrate is important. Research is being done where the purpose is looking for new methods of manufacturing and purification of silicon, as well as new materials that can be used as substrates for the photovoltaic conversion of light energy. In this research, the technique of production of silicon in an induction furnace, using a high vacuum for fusion. Diatomaceous Silica (SiO2) used is 99 mass% initial purities, the carbon used is 6N of purity and the particle size of 63μm as starting materials. The final achieved purity of the material was above 50% by mass. These results demonstrate that this method is a technically reliable, and allows obtaining a better return on the amount 50% of silicon.

Keywords: induction furnaces, amorphous silica, carbon microstructure, silicon

Procedia PDF Downloads 387

Authors: Sadman Durlov, Aditya Ganesh-Ram, Hamidreza Hekmatjou, Md Najmus Salehin, Nora Shayesteh Ameri

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In the field of additive manufacturing, tungsten stands out for its exceptional resistance to high temperatures, making it an ideal candidate for use in extreme conditions. However, its inherent brittleness and vulnerability to thermal cracking pose significant challenges to its manufacturability. This study explores the microstructural evolution of tungsten processed through layer-wise rolling in laser powder bed fusion additive manufacturing, utilizing a comprehensive approach that combines advanced simulation techniques with empirical research. We aim to uncover the complex processes of plastic deformation and microstructural transformations, with a particular focus on the dynamics of grain size, boundary evolution, and phase distribution. Our methodology employs a combination of simulation and experimental data, allowing for a detailed comparison that elucidates the key mechanisms influencing microstructural alterations during the rolling process. This approach facilitates a deeper understanding of the material's behavior under additive manufacturing conditions, specifically in terms of deformation and recrystallization. The insights derived from this research not only deepen our theoretical knowledge but also provide actionable strategies for refining manufacturing parameters to improve the tungsten components' mechanical properties and functional performance. By integrating simulation with practical experimentation, this study significantly enhances the field of materials science, offering a robust framework for the development of durable materials suited for challenging operational environments. Our findings pave the way for optimizing additive manufacturing techniques and expanding the use of tungsten across various demanding sectors.

Keywords: additive manufacturing, layer wise rolling, refractory materials, in-situ microstructure modifications

Procedia PDF Downloads 38

Authors: Haojie Wang, Xun Li, Rui Yin

Abstract:

In order to solve the memorization overfitting in the model-agnostic meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to an exponential growth of computation, this paper also proposes a key data extraction method that only extract part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: mutex task generation, data augmentation, meta-learning, text classification.

Procedia PDF Downloads 120

Authors: Bosiacki Mateusz, Anna Lubkowska, Dariusz Chlubek, Irena Baranowska-Bosiacka

Abstract:

Exposure to cold temperatures can be considered a stressor that can lead to adaptive responses. The present study hypothesized the possibility of a positive effect of cold water exercise on mitochondrial biogenesis and muscle energy metabolism in aging rats. The purpose of this study was to evaluate the effects of cold water exercise on energy status, purine compounds, and mitochondrial biogenesis in the muscles of aging rats as indicators of the effects of cold water exercise and their usefulness in monitoring adaptive changes. The study was conducted on 64 aging rats of both sexes, 15 months old at the time of the experiment. The rats (male and female separately) were randomly assigned to the following study groups: control, sedentary animals; 5°C groups animals - training swimming in cold water at 5°C; 36°C groups - animals training swimming in water at thermal comfort temperature. The study was conducted with the approval of the Local Ethical Committee for Animal Experiments. The animals in the experiment were subjected to swimming training for 9 weeks. During the first week of the study, the duration of the first swimming training was 2 minutes (on the first day), increasing daily by 0.5 minutes up to 4 minutes on the fifth day of the first week. From the second to the eighth week, the swimming training was 4 minutes per day, five days a week. At the end of the study, forty-eight hours after the last swim training, the animals were dissected. In the skeletal muscle tissue of the thighs of the rats, we determined the concentrations of ATP, ADP, AMP, Ado (HPLC), PGC-1a protein expression (Western blot), PGC1A, Mfn1, Mfn2, Opa1, and Drp1 gene expression (qRT PCR). The study showed that swimming in water at a thermally comfortable temperature improved the energy metabolism of the aging rat muscles by increasing the metabolic rate (increase in ATP, ADP, TAN, AEC) and enhancing mitochondrial fusion (increase in mRNA expression of regulatory proteins Mfn1 and Mfn2). Cold water swimming improved muscle energy metabolism in aging rats by increasing the rate of muscle energy metabolism (increase in ATP, ADP, TAN, AEC concentrations) and enhancing mitochondrial biogenesis and dynamics (increase in the mRNA expression of proteins of fusion-regulating factors – Mfn1, Mfn2, and Opa1, and the factor regulating mitochondrial fission – Drp1). The concentration of high-energy compounds and the expression of proteins regulating mitochondrial dynamics in the muscle may be a useful indicator in monitoring adaptive changes occurring in aging muscles under the influence of exercise in cold water. It represents a short-term adaptation to changing environmental conditions and has a beneficial effect on maintaining the bioenergetic capacity of muscles in the long term. Conclusion: exercise in cold water can exert positive effects on energy metabolism, biogenesis and dynamics of mitochondria in aging rat muscles. Enhancement of mitochondrial dynamics under cold water exercise conditions can improve mitochondrial function and optimize the bioenergetic capacity of mitochondria in aging rat muscles.

Keywords: cold water immersion, adaptive responses, muscle energy metabolism, aging

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Authors: Milind Chaudhari, Suhail Balasinor

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Due to massive deforestation and an ever-increasing population, the organic content of the soil is depleting at a much faster rate. Due to this, there is a big chance that the entire food production in the world will drop by 40% in the next two decades. Vertical farming can help in aiding food production by leveraging big data and cloud computing to ensure plants are grown naturally by providing the optimum nutrients sunlight by analyzing millions of data points. This paper outlines the most important parameters in vertical farming and how a combination of big data and AI helps in calculating and analyzing these millions of data points. Finally, the paper outlines how different organizations are controlling the indoor environment by leveraging big data in enhancing food quantity and quality.

Keywords: big data, IoT, vertical farming, indoor farming

Procedia PDF Downloads 159

Authors: Venkatesan Sundaramoorthy

Abstract:

The field of ceramics has undergone a remarkable transformation with the advent of additive manufacturing technologies. This comprehensive review explores the mechanical properties of additively manufactured ceramic components, focusing on key materials such as Alumina, Zirconia, and Silicon Carbide. The study delves into various authors' review technology into the various additive manufacturing techniques, including Stereolithography, Powder Bed Fusion, and Binder Jetting, highlighting their advantages and challenges. It provides a detailed analysis of the mechanical properties of these ceramics, offering insights into their hardness, strength, fracture toughness, and thermal conductivity. Factors affecting mechanical properties, such as microstructure and post-processing, are thoroughly examined. Recent advancements and future directions in 3D-printed ceramics are discussed, showcasing the potential for further optimization and innovation. This review underscores the profound implications of additive manufacturing for ceramics in industries such as aerospace, healthcare, and electronics, ushering in a new era of engineering and design possibilities for ceramic components.

Keywords: mechanical properties, additive manufacturing, ceramic materials, PBF

Procedia PDF Downloads 48

Authors: Aditi Chauhan, Neethu S. Mohan

Abstract:

In the modern era, the advancement and digitalization in technology have taken place during an investigation of crime scene. The rapid enhancement and investigative techniques have changed the mean of identification of suspect. Identification of the person is one of the significant aspects, and personal authentication is the key of security and reliability in society. Since early 90 s, people have relied on comparing handwriting through its class and individual characteristics. But in today’s 21st century we need more reliable means to identify individual through handwriting. An approach employing computer modalities have lately proved itself auspicious enough in exploration of hieroglyphics substantial in investigating the case. Various software’s such as FISH, WRITEON, and PIKASO, CEDAR-FOX SYSTEM identify and verify the associated quantitative measure of the similarity between two samples. The research till date has been confined to identify the authorship of the concerned samples. But prospects associated with the use of computational modalities might help to identify disguised writing, forged handwriting or say altered or modified writing. Considering the applications of such modal, similar work is sure to attract plethora of research in immediate future. It has a promising role in national security too. Documents exchanged among terrorist can also be brought under the radar of surveillance, bringing forth their source of existence.

Keywords: documents, identity, computational system, suspect

Procedia PDF Downloads 160

Authors: A. Anis, W. Bekheet, A. El Hakim

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Implementing a Road Safety Management System (SMS) in a crowded developing country such as Egypt is a necessity. Beginning a sustainable SMS requires a comprehensive reliable data system for all information pertinent to road crashes. In this paper, a survey for the available data in Egypt and validating it for using in an SMS in Egypt. The research provides some missing data, and refer to the unavailable data in Egypt, looking forward to the contribution of the scientific society, the authorities, and the public in solving the problem of missing or unreliable crash data. The required data for implementing an SMS in Egypt are divided into three categories; the first is available data such as fatality and injury rates and it is proven in this research that it may be inconsistent and unreliable, the second category of data is not available, but it may be estimated, an example of estimating vehicle cost is available in this research, the third is not available and can be measured case by case such as the functional and geometric properties of a facility. Some inquiries are provided in this research for the scientific society, such as how to improve the links among stakeholders of road safety in order to obtain a consistent, non-biased, and reliable data system.

Keywords: road safety management system, road crash, road fatality, road injury

Procedia PDF Downloads 107

Authors: Oualid Walid Ben Ali

Abstract:

Big Data has become one of the buzzwords today. The recent explosion of digital data has led the organization, either private or public, to a new era towards a more efficient decision making. At some point, business decided to use that concept in order to learn what make their clients tick with phrases like ‘sales funnel’ analysis, ‘actionable insights’, and ‘positive business impact’. So, it stands to reason that Big Data was viewed through green (read: money) colored lenses. Somewhere along the line, however someone realized that collecting and processing data doesn’t have to be for business purpose only, but also could be used for other purposes to assist law enforcement or to improve policing or in road safety. This paper presents briefly, how Big Data have been used in the fields of policing order to improve the decision making process in the daily operation of the police. As example, we present a big-data driven system which is sued to accurately dispatch the patrol cars in a geographic environment. The system is also used to allocate, in real-time, the nearest patrol car to the location of an incident. This system has been implemented and applied in the Emirate of Abu Dhabi in the UAE.

Keywords: big data, big data analytics, patrol car allocation, dispatching, GIS, intelligent, Abu Dhabi, police, UAE

Procedia PDF Downloads 473

Authors: Xu Du, Aparna S. Varde

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In this research, we propose to conduct diagnostic and predictive analysis about the key factors and consequences of urban population relocation. To achieve this goal, urban simulation models extract the urban development trends as land use change patterns from a variety of data sources. The results are treated as part of urban big data with other information such as population change and economic conditions. Multiple data mining methods are deployed on this data to analyze nonlinear relationships between parameters. The result determines the driving force of population relocation with respect to urban sprawl and urban sustainability and their related parameters. Experiments so far reveal that data mining methods discover useful knowledge from the multicity urban data. This work sets the stage for developing a comprehensive urban simulation model for catering to specific questions by targeted users. It contributes towards achieving sustainability as a whole.

Keywords: data mining, environmental modeling, sustainability, urban planning

Procedia PDF Downloads 283

Authors: Aref Ghafouri, Mohammad javad Mollakazemi, Farhad Asadi

Abstract:

In this paper, model order reduction method is used for approximation in linear and nonlinearity aspects in some experimental data. This method can be used for obtaining offline reduced model for approximation of experimental data and can produce and follow the data and order of system and also it can match to experimental data in some frequency ratios. In this study, the method is compared in different experimental data and influence of choosing of order of the model reduction for obtaining the best and sufficient matching condition for following the data is investigated in format of imaginary and reality part of the frequency response curve and finally the effect and important parameter of number of order reduction in nonlinear experimental data is explained further.

Keywords: frequency response, order of model reduction, frequency matching condition, nonlinear experimental data

Procedia PDF Downloads 382

Authors: Thuan Nguyen

Abstract:

In the present time, data to a data-driven knowledge-based economy is the same as oil to the industrial age hundreds of years ago. Data is everywhere in vast volumes! Big data analytics is expected to help firms not only efficiently improve performance but also completely transform how they should run their business. However, employing the emergent technology successfully is not easy, and assessing the roles of big data in improving firm performance is even much harder. There was a lack of studies that have examined the impacts of big data analytics on organizational performance. This study aimed to fill the gap. The present study suggested using firms’ intellectual capital as a proxy for big data in evaluating its impact on organizational performance. The present study employed the Value Added Intellectual Coefficient method to measure firm intellectual capital, via its three main components: human capital efficiency, structural capital efficiency, and capital employed efficiency, and then used the structural equation modeling technique to model the data and test the models. The financial fundamental and market data of 100 randomly selected publicly listed firms were collected. The results of the tests showed that only human capital efficiency had a significant positive impact on firm profitability, which highlighted the prominent human role in the impact of big data technology.

Keywords: big data, big data analytics, intellectual capital, organizational performance, value added intellectual coefficient

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Authors: Hitesh Tahbildar

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The cost of test data generation for a program is computationally very high. In general case, no algorithm to generate test data for all types of algorithms has been found. The cost of generating test data for different types of algorithm is different. Till date, people are emphasizing the need to generate test data for different types of programming constructs rather than different types of algorithms. The test data generation methods have been implemented to find heuristics for different types of algorithms. Some algorithms that includes divide and conquer, backtracking, greedy approach, dynamic programming to find the minimum cost of test data generation have been tested. Our experimental results say that some of these types of algorithm can be used as a necessary condition for selecting heuristics and programming constructs are sufficient condition for selecting our heuristics. Finally we recommend the different heuristics for test data generation to be selected for different types of algorithms.

Keywords: ongest path, saturation point, lmax, kL, kS

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Authors: Hatice Kübra Koç

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For academia, collecting reliable and valid data is one of the most significant issues for researchers. However, it is not the same procedure for all different target groups; meanwhile, during data collection from teenagers, young adults, or adults, researchers can use common data collection tools such as questionnaires, interviews, and semi-structured interviews; yet, for young learners and very young ones, these reliable and valid data collection tools cannot be easily designed or applied by the researchers. In this study, firstly, common data collection tools are examined for ‘very young’ and ‘young learners’ participant groups since it is thought that the quality and efficiency of an academic study is mainly based on its valid and correct data collection and data analysis procedure. Secondly, two different data collection instruments for very young and young learners are stated as discussing the efficacy of them. Finally, a suggested data collection tool – a performance-based questionnaire- which is specifically developed for ‘very young’ and ‘young learners’ participant groups in the field of teaching English to young learners as a foreign language is presented in this current study. The designing procedure and suggested items/factors for the suggested data collection tool are accordingly revealed at the end of the study to help researchers have studied with young and very learners.

Keywords: data collection instruments, performance-based questionnaire, young learners, very young learners

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Authors: Yaxin Bi

Abstract:

Accurate prediction and understanding of the evolution mechanisms of earthquakes remain challenging in the fields of geology, geophysics, and seismology. This study leverages Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs), a generative model tailored to time-series data, for generating synthetic time series data based on Swarm satellite data, which will be used for detecting seismic anomalies. LSTMs demonstrated commendable predictive performance in generating synthetic data across multiple countries. In contrast, the GAN models struggled to generate synthetic data, often producing non-informative values, although they were able to capture the data distribution of the time series. These findings highlight both the promise and challenges associated with applying deep learning techniques to generate synthetic data, underscoring the potential of deep learning in generating synthetic electromagnetic satellite data.

Keywords: LSTM, GAN, earthquake, synthetic data, generative AI, seismic precursors

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Authors: Hyun-Woo Cho

Abstract:

For ensuring the safety of a manufacturing process one should quickly identify an assignable cause of a fault in an on-line basis. To this end, many statistical techniques including linear and nonlinear methods have been frequently utilized. However, such methods possessed a major problem of small sample size, which is mostly attributed to the characteristics of empirical models used for reference models. This work presents a new method to overcome the insufficiency of measurement data in the monitoring and diagnosis tasks. Some quasi-measurement data are generated from existing data based on the two indices of similarity and importance. The performance of the method is demonstrated using a real data set. The results turn out that the presented methods are able to handle the insufficiency problem successfully. In addition, it is shown to be quite efficient in terms of computational speed and memory usage, and thus on-line implementation of the method is straightforward for monitoring and diagnosis purposes.

Keywords: data analysis, diagnosis, monitoring, process data, quality control

Procedia PDF Downloads 463

Authors: Hsien-Tsen Wang

Abstract:

Business Intelligence (BI) has long helped organizations make informed decisions based on data-driven insights and gain competitive advantages in the marketplace. In the past two decades, businesses witnessed not only the dramatically increasing volume and heterogeneity of business data but also the emergence of new technologies, such as Artificial Intelligence (AI), Semantic Web (SW), Cloud Computing, and Big Data. It is plausible that the convergence of these technologies would bring more value out of business data by establishing linked data frameworks and connecting in ways that enable advanced analytics and improved data utilization. In this paper, we first review and summarize current BI applications and methodology. Emerging technologies that can be integrated into BI applications are then discussed. Finally, we conclude with a proposed synergy framework that aims at achieving a more flexible, scalable, and intelligent BI solution.

Keywords: business intelligence, artificial intelligence, semantic web, big data, cloud computing

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