Search results for: operational data
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 26147

Search results for: operational data

25367 Cryptosystems in Asymmetric Cryptography for Securing Data on Cloud at Various Critical Levels

Authors: Sartaj Singh, Amar Singh, Ashok Sharma, Sandeep Kaur

Abstract:

With upcoming threats in a digital world, we need to work continuously in the area of security in all aspects, from hardware to software as well as data modelling. The rise in social media activities and hunger for data by various entities leads to cybercrime and more attack on the privacy and security of persons. Cryptography has always been employed to avoid access to important data by using many processes. Symmetric key and asymmetric key cryptography have been used for keeping data secrets at rest as well in transmission mode. Various cryptosystems have evolved from time to time to make the data more secure. In this research article, we are studying various cryptosystems in asymmetric cryptography and their application with usefulness, and much emphasis is given to Elliptic curve cryptography involving algebraic mathematics.

Keywords: cryptography, symmetric key cryptography, asymmetric key cryptography

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25366 Early Evaluation of Long-Span Suspension Bridges Using Smartphone Accelerometers

Authors: Ekin Ozer, Maria Q. Feng, Rupa Purasinghe

Abstract:

Structural deterioration of bridge systems possesses an ongoing threat to the transportation networks. Besides, landmark bridges’ integrity and safety are more than sole functionality, since they provide a strong presence for the society and nations. Therefore, an innovative and sustainable method to inspect landmark bridges is essential to ensure their resiliency in the long run. In this paper, a recently introduced concept, smartphone-based modal frequency estimation is addressed, and this paper targets to authenticate the fidelity of smartphone-based vibration measurements gathered from three landmark suspension bridges. Firstly, smartphones located at the bridge mid-span are adopted as portable and standalone vibration measurement devices. Then, their embedded accelerometers are utilized to gather vibration response under operational loads, and eventually frequency domain characteristics are deduced. The preliminary analysis results are compared with the reference publications and high-quality monitoring data to validate the usability of smartphones on long-span landmark suspension bridges. If the technical challenges such as high period of vibration, low amplitude excitation, embedded smartphone sensor features, sampling, and citizen engagement are tackled, smartphones can provide a novel and cost-free crowdsourcing tool for maintenance of these landmark structures. This study presents the early phase findings from three signature structures located in the United States.

Keywords: smart and mobile sensing, structural health monitoring, suspension bridges, vibration analysis

Procedia PDF Downloads 293
25365 Application of Medical Information System for Image-Based Second Opinion Consultations–Georgian Experience

Authors: Kldiashvili Ekaterina, Burduli Archil, Ghortlishvili Gocha

Abstract:

Introduction – Medical information system (MIS) is at the heart of information technology (IT) implementation policies in healthcare systems around the world. Different architecture and application models of MIS are developed. Despite of obvious advantages and benefits, application of MIS in everyday practice is slow. Objective - On the background of analysis of the existing models of MIS in Georgia has been created a multi-user web-based approach. This presentation will present the architecture of the system and its application for image based second opinion consultations. Methods – The MIS has been created with .Net technology and SQL database architecture. It realizes local (intranet) and remote (internet) access to the system and management of databases. The MIS is fully operational approach, which is successfully used for medical data registration and management as well as for creation, editing and maintenance of the electronic medical records (EMR). Five hundred Georgian language electronic medical records from the cervical screening activity illustrated by images were selected for second opinion consultations. Results – The primary goal of the MIS is patient management. However, the system can be successfully applied for image based second opinion consultations. Discussion – The ideal of healthcare in the information age must be to create a situation where healthcare professionals spend more time creating knowledge from medical information and less time managing medical information. The application of easily available and adaptable technology and improvement of the infrastructure conditions is the basis for eHealth applications. Conclusion - The MIS is perspective and actual technology solution. It can be successfully and effectively used for image based second opinion consultations.

Keywords: digital images, medical information system, second opinion consultations, electronic medical record

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25364 Data Recording for Remote Monitoring of Autonomous Vehicles

Authors: Rong-Terng Juang

Abstract:

Autonomous vehicles offer the possibility of significant benefits to social welfare. However, fully automated cars might not be going to happen in the near further. To speed the adoption of the self-driving technologies, many governments worldwide are passing laws requiring data recorders for the testing of autonomous vehicles. Currently, the self-driving vehicle, (e.g., shuttle bus) has to be monitored from a remote control center. When an autonomous vehicle encounters an unexpected driving environment, such as road construction or an obstruction, it should request assistance from a remote operator. Nevertheless, large amounts of data, including images, radar and lidar data, etc., have to be transmitted from the vehicle to the remote center. Therefore, this paper proposes a data compression method of in-vehicle networks for remote monitoring of autonomous vehicles. Firstly, the time-series data are rearranged into a multi-dimensional signal space. Upon the arrival, for controller area networks (CAN), the new data are mapped onto a time-data two-dimensional space associated with the specific CAN identity. Secondly, the data are sampled based on differential sampling. Finally, the whole set of data are encoded using existing algorithms such as Huffman, arithmetic and codebook encoding methods. To evaluate system performance, the proposed method was deployed on an in-house built autonomous vehicle. The testing results show that the amount of data can be reduced as much as 1/7 compared to the raw data.

Keywords: autonomous vehicle, data compression, remote monitoring, controller area networks (CAN), Lidar

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25363 Multimedia Data Fusion for Event Detection in Twitter by Using Dempster-Shafer Evidence Theory

Authors: Samar M. Alqhtani, Suhuai Luo, Brian Regan

Abstract:

Data fusion technology can be the best way to extract useful information from multiple sources of data. It has been widely applied in various applications. This paper presents a data fusion approach in multimedia data for event detection in twitter by using Dempster-Shafer evidence theory. The methodology applies a mining algorithm to detect the event. There are two types of data in the fusion. The first is features extracted from text by using the bag-ofwords method which is calculated using the term frequency-inverse document frequency (TF-IDF). The second is the visual features extracted by applying scale-invariant feature transform (SIFT). The Dempster - Shafer theory of evidence is applied in order to fuse the information from these two sources. Our experiments have indicated that comparing to the approaches using individual data source, the proposed data fusion approach can increase the prediction accuracy for event detection. The experimental result showed that the proposed method achieved a high accuracy of 0.97, comparing with 0.93 with texts only, and 0.86 with images only.

Keywords: data fusion, Dempster-Shafer theory, data mining, event detection

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25362 Legal Issues of Collecting and Processing Big Health Data in the Light of European Regulation 679/2016

Authors: Ioannis Iglezakis, Theodoros D. Trokanas, Panagiota Kiortsi

Abstract:

This paper aims to explore major legal issues arising from the collection and processing of Health Big Data in the light of the new European secondary legislation for the protection of personal data of natural persons, placing emphasis on the General Data Protection Regulation 679/2016. Whether Big Health Data can be characterised as ‘personal data’ or not is really the crux of the matter. The legal ambiguity is compounded by the fact that, even though the processing of Big Health Data is premised on the de-identification of the data subject, the possibility of a combination of Big Health Data with other data circulating freely on the web or from other data files cannot be excluded. Another key point is that the application of some provisions of GPDR to Big Health Data may both absolve the data controller of his legal obligations and deprive the data subject of his rights (e.g., the right to be informed), ultimately undermining the fundamental right to the protection of personal data of natural persons. Moreover, data subject’s rights (e.g., the right not to be subject to a decision based solely on automated processing) are heavily impacted by the use of AI, algorithms, and technologies that reclaim health data for further use, resulting in sometimes ambiguous results that have a substantial impact on individuals. On the other hand, as the COVID-19 pandemic has revealed, Big Data analytics can offer crucial sources of information. In this respect, this paper identifies and systematises the legal provisions concerned, offering interpretative solutions that tackle dangers concerning data subject’s rights while embracing the opportunities that Big Health Data has to offer. In addition, particular attention is attached to the scope of ‘consent’ as a legal basis in the collection and processing of Big Health Data, as the application of data analytics in Big Health Data signals the construction of new data and subject’s profiles. Finally, the paper addresses the knotty problem of role assignment (i.e., distinguishing between controller and processor/joint controllers and joint processors) in an era of extensive Big Health data sharing. The findings are the fruit of a current research project conducted by a three-member research team at the Faculty of Law of the Aristotle University of Thessaloniki and funded by the Greek Ministry of Education and Religious Affairs.

Keywords: big health data, data subject rights, GDPR, pandemic

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25361 Adaptive Data Approximations Codec (ADAC) for AI/ML-based Cyber-Physical Systems

Authors: Yong-Kyu Jung

Abstract:

The fast growth in information technology has led to de-mands to access/process data. CPSs heavily depend on the time of hardware/software operations and communication over the network (i.e., real-time/parallel operations in CPSs (e.g., autonomous vehicles). Since data processing is an im-portant means to overcome the issue confronting data management, reducing the gap between the technological-growth and the data-complexity and channel-bandwidth. An adaptive perpetual data approximation method is intro-duced to manage the actual entropy of the digital spectrum. An ADAC implemented as an accelerator and/or apps for servers/smart-connected devices adaptively rescales digital contents (avg.62.8%), data processing/access time/energy, encryption/decryption overheads in AI/ML applications (facial ID/recognition).

Keywords: adaptive codec, AI, ML, HPC, cyber-physical, cybersecurity

Procedia PDF Downloads 80
25360 Analysis of Maintenance Operations in an Industrial Bakery Line

Authors: Mehmet Savsar

Abstract:

This paper presents a practical case application of simulation modeling and analysis in a specific industrial setting. Various maintenance related parameters of the equipment in the system under consideration are determined and a simulation model is developed to study system behavior. System performance is determined based on established parameters and operational policies, which included system operation with and without preventive maintenance implementation. The results show that preventive maintenance practice has significant effects on improving system productivity. The simulation procedures outlined in this paper can be used by operation managers to perform production line analysis under different maintenance policies in various industrial settings.

Keywords: simulation, production line, machine failures, maintenance, industrial bakery

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25359 Real-Time Visualization Using GPU-Accelerated Filtering of LiDAR Data

Authors: Sašo Pečnik, Borut Žalik

Abstract:

This paper presents a real-time visualization technique and filtering of classified LiDAR point clouds. The visualization is capable of displaying filtered information organized in layers by the classification attribute saved within LiDAR data sets. We explain the used data structure and data management, which enables real-time presentation of layered LiDAR data. Real-time visualization is achieved with LOD optimization based on the distance from the observer without loss of quality. The filtering process is done in two steps and is entirely executed on the GPU and implemented using programmable shaders.

Keywords: filtering, graphics, level-of-details, LiDAR, real-time visualization

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25358 Identifying Issues of Corporate Governance and the Effect on Organizational Performance

Authors: Abiodun Oluwaseun Ibude

Abstract:

Every now and then we hear of companies closing down their operations due to unethical practices like an overstatement of company’s balance sheet, concealing company’s debt, embezzlement of company’s fund, declaring false profit and so on. This has led to the liquidation of companies and the loss of investments of shareholders as well as the interest of other stakeholders. As a result of these ugly trends, there is need to put in place a formidable mechanism that will ensure that business activities are conducted in a healthy manner. It should also promote good ethics as well as ensure that the interest of stakeholders and the objectives of any organization is achieved within the confines of the law; wherein law exists to provide criminal penalties for falsification of documents and for conducting other irregularities. Based on the foregoing, it becomes imperative to ensure that steps are taken to stop this menace and face the challenges ahead. This calls for the practice of good governance. The purpose of this study is to identify various components of corporate governance and determine the impact of it on the performance of established organizations. A survey method with the use of questionnaire was applied in collecting data useful for this study which were later analyzed using correlation co-efficiency statistical tools in generating finding, making a conclusion, and necessary recommendation. From the research conducted, it was discovered that there are systems within organizations apart from regulatory agencies that ensure effective control of activities, promote accountability, and operational efficiency. However, some members of organizations fail to explore the usage of corporate governance and impact negatively of an organization’s performance. In conclusion, good corporate governance will not be achieved unless there is openness, honesty, transparency, accountability, and fairness.

Keywords: corporate governance, formidable mechanism, company’s balance sheet, stakeholders

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25357 Pre- and Post-Analyses of Disruptive Quay Crane Scheduling Problem

Authors: K. -H. Yang

Abstract:

In the past, the quay crane operations have been well studied. There were a certain number of scheduling algorithms for quay crane operations, but without considering some nuisance factors that might disrupt the quay crane operations. For example, bad grapples make a crane unable to load or unload containers or a sudden strong breeze stops operations temporarily. Although these disruptive conditions randomly occur, they influence the efficiency of quay crane operations. The disruption is not considered in the operational procedures nor is evaluated in advance for its impacts. This study applies simulation and optimization approaches to develop structures of pre-analysis and post-analysis for the Quay Crane Scheduling Problem to deal with disruptive scenarios for quay crane operation. Numerical experiments are used for demonstrations for the validity of the developed approaches.

Keywords: disruptive quay crane scheduling, pre-analysis, post-analysis, disruption

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25356 Estimating Destinations of Bus Passengers Using Smart Card Data

Authors: Hasik Lee, Seung-Young Kho

Abstract:

Nowadays, automatic fare collection (AFC) system is widely used in many countries. However, smart card data from many of cities does not contain alighting information which is necessary to build OD matrices. Therefore, in order to utilize smart card data, destinations of passengers should be estimated. In this paper, kernel density estimation was used to forecast probabilities of alighting stations of bus passengers and applied to smart card data in Seoul, Korea which contains boarding and alighting information. This method was also validated with actual data. In some cases, stochastic method was more accurate than deterministic method. Therefore, it is sufficiently accurate to be used to build OD matrices.

Keywords: destination estimation, Kernel density estimation, smart card data, validation

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25355 'Enjoying the Czech Traditions with All Sences!': Tourism Product Promotion

Authors: Tomas Seidl

Abstract:

'Enjoy the Czech traditions with all sences!' is the main communication headline of one of the major current marketing project representing the intangible cultural heritage of the Czech Republic to its visitors. The project CZ.1.06/4.1.00/12.08915 and CZ.1.06/4.1.00/12.08916 which is solved in the period 2013-2015 is co-financed form the EU financial sources from the Integrated Operational Programme. The primary goal of the project was to analyze the dislocation and potential of the intangible cultural heritage in the Czech Republic. Further goal was to prepare a useful regionalization. An as solution based on the outcomes the creative and media strategy was created and prepared. The processor – CzechTourism expect the following web and mobile application development and successful marketing campaign in 2015.

Keywords: traditions, intangible cultural heritage, Czech Republic, CzechTourism, digital performance

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25354 Automation of Embodied Energy Calculations for Buildings through Building Information Modelling

Authors: Ahmad Odeh

Abstract:

Researchers are currently more concerned about the calculations of energy at the operational stage, mainly due to its larger environmental impact, but the fact remains, embodied energies represent a substantial contributor unaccounted for in the overall energy computation method. The calculation of materials’ embodied energy during the construction stage is complicated. This is due to the various factors involved. The equipment used, fuel needed, and electricity required for each type of materials varies with location and thus the embodied energy will differ for each project. Moreover, the method used in manufacturing, transporting and putting in place will have significant influence on the materials’ embodied energy. This anomaly has made it difficult to calculate or even bench mark the usage of such energies. This paper presents a model aimed at calculating embodied energies based on such variabilities. It presents a systematic approach that uses an efficient method of calculation to provide a new insight for the selection of construction materials. The model is developed in a BIM environment. The quantification of materials’ energy is determined over the three main stages of their lifecycle: manufacturing, transporting and placing. The model uses three major databases each of which contains set of the construction materials that are most commonly used in building projects. The first dataset holds information about the energy required to manufacture any type of materials, the second includes information about the energy required for transporting the materials while the third stores information about the energy required by machinery to place the materials in their intended locations. Through geospatial data analysis, the model automatically calculates the distances between the suppliers and construction sites and then uses dataset information for energy computations. The computational sum of all the energies is automatically calculated and then the model provides designers with a list of usable equipment along with the associated embodied energies.

Keywords: BIM, lifecycle energy assessment, building automation, energy conservation

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25353 Evaluated Nuclear Data Based Photon Induced Nuclear Reaction Model of GEANT4

Authors: Jae Won Shin

Abstract:

We develop an evaluated nuclear data based photonuclear reaction model of GEANT4 for a more accurate simulation of photon-induced neutron production. The evaluated photonuclear data libraries from the ENDF/B-VII.1 are taken as input. Incident photon energies up to 140 MeV which is the threshold energy for the pion production are considered. For checking the validity of the use of the data-based model, we calculate the photoneutron production cross-sections and yields and compared them with experimental data. The results obtained from the developed model are found to be in good agreement with the experimental data for (γ,xn) reactions.

Keywords: ENDF/B-VII.1, GEANT4, photoneutron, photonuclear reaction

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25352 Optimizing Communications Overhead in Heterogeneous Distributed Data Streams

Authors: Rashi Bhalla, Russel Pears, M. Asif Naeem

Abstract:

In this 'Information Explosion Era' analyzing data 'a critical commodity' and mining knowledge from vertically distributed data stream incurs huge communication cost. However, an effort to decrease the communication in the distributed environment has an adverse influence on the classification accuracy; therefore, a research challenge lies in maintaining a balance between transmission cost and accuracy. This paper proposes a method based on Bayesian inference to reduce the communication volume in a heterogeneous distributed environment while retaining prediction accuracy. Our experimental evaluation reveals that a significant reduction in communication can be achieved across a diverse range of dataset types.

Keywords: big data, bayesian inference, distributed data stream mining, heterogeneous-distributed data

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25351 Data Privacy: Stakeholders’ Conflicts in Medical Internet of Things

Authors: Benny Sand, Yotam Lurie, Shlomo Mark

Abstract:

Medical Internet of Things (MIoT), AI, and data privacy are linked forever in a gordian knot. This paper explores the conflicts of interests between the stakeholders regarding data privacy in the MIoT arena. While patients are at home during healthcare hospitalization, MIoT can play a significant role in improving the health of large parts of the population by providing medical teams with tools for collecting data, monitoring patients’ health parameters, and even enabling remote treatment. While the amount of data handled by MIoT devices grows exponentially, different stakeholders have conflicting understandings and concerns regarding this data. The findings of the research indicate that medical teams are not concerned by the violation of data privacy rights of the patients' in-home healthcare, while patients are more troubled and, in many cases, are unaware that their data is being used without their consent. MIoT technology is in its early phases, and hence a mixed qualitative and quantitative research approach will be used, which will include case studies and questionnaires in order to explore this issue and provide alternative solutions.

Keywords: MIoT, data privacy, stakeholders, home healthcare, information privacy, AI

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25350 Influence of Parameters of Modeling and Data Distribution for Optimal Condition on Locally Weighted Projection Regression Method

Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

Abstract:

Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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25349 Big Data Strategy for Telco: Network Transformation

Authors: F. Amin, S. Feizi

Abstract:

Big data has the potential to improve the quality of services; enable infrastructure that businesses depend on to adapt continually and efficiently; improve the performance of employees; help organizations better understand customers; and reduce liability risks. Analytics and marketing models of fixed and mobile operators are falling short in combating churn and declining revenue per user. Big Data presents new method to reverse the way and improve profitability. The benefits of Big Data and next-generation network, however, are more exorbitant than improved customer relationship management. Next generation of networks are in a prime position to monetize rich supplies of customer information—while being mindful of legal and privacy issues. As data assets are transformed into new revenue streams will become integral to high performance.

Keywords: big data, next generation networks, network transformation, strategy

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25348 REDUCER: An Architectural Design Pattern for Reducing Large and Noisy Data Sets

Authors: Apkar Salatian

Abstract:

To relieve the burden of reasoning on a point to point basis, in many domains there is a need to reduce large and noisy data sets into trends for qualitative reasoning. In this paper we propose and describe a new architectural design pattern called REDUCER for reducing large and noisy data sets that can be tailored for particular situations. REDUCER consists of 2 consecutive processes: Filter which takes the original data and removes outliers, inconsistencies or noise; and Compression which takes the filtered data and derives trends in the data. In this seminal article, we also show how REDUCER has successfully been applied to 3 different case studies.

Keywords: design pattern, filtering, compression, architectural design

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25347 Fuzzy Expert Systems Applied to Intelligent Design of Data Centers

Authors: Mario M. Figueroa de la Cruz, Claudia I. Solorzano, Raul Acosta, Ignacio Funes

Abstract:

This technological development project seeks to create a tool that allows companies, in need of implementing a Data Center, intelligently determining factors for allocating resources support cooling and power supply (UPS) in its conception. The results should show clearly the speed, robustness and reliability of a system designed for deployment in environments where they must manage and protect large volumes of data.

Keywords: telecommunications, data center, fuzzy logic, expert systems

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25346 Genetic Testing and Research in South Africa: The Sharing of Data Across Borders

Authors: Amy Gooden, Meshandren Naidoo

Abstract:

Genetic research is not confined to a particular jurisdiction. Using direct-to-consumer genetic testing (DTC-GT) as an example, this research assesses the status of data sharing into and out of South Africa (SA). While SA laws cover the sending of genetic data out of SA, prohibiting such transfer unless a legal ground exists, the position where genetic data comes into the country depends on the laws of the country from where it is sent – making the legal position less clear.

Keywords: cross-border, data, genetic testing, law, regulation, research, sharing, South Africa

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25345 1-D Convolutional Neural Network Approach for Wheel Flat Detection for Freight Wagons

Authors: Dachuan Shi, M. Hecht, Y. Ye

Abstract:

With the trend of digitalization in railway freight transport, a large number of freight wagons in Germany have been equipped with telematics devices, commonly placed on the wagon body. A telematics device contains a GPS module for tracking and a 3-axis accelerometer for shock detection. Besides these basic functions, it is desired to use the integrated accelerometer for condition monitoring without any additional sensors. Wheel flats as a common type of failure on wheel tread cause large impacts on wagons and infrastructure as well as impulsive noise. A large wheel flat may even cause safety issues such as derailments. In this sense, this paper proposes a machine learning approach for wheel flat detection by using car body accelerations. Due to suspension systems, impulsive signals caused by wheel flats are damped significantly and thus could be buried in signal noise and disturbances. Therefore, it is very challenging to detect wheel flats using car body accelerations. The proposed algorithm considers the envelope spectrum of car body accelerations to eliminate the effect of noise and disturbances. Subsequently, a 1-D convolutional neural network (CNN), which is well known as a deep learning method, is constructed to automatically extract features in the envelope-frequency domain and conduct classification. The constructed CNN is trained and tested on field test data, which are measured on the underframe of a tank wagon with a wheel flat of 20 mm length in the operational condition. The test results demonstrate the good performance of the proposed algorithm for real-time fault detection.

Keywords: fault detection, wheel flat, convolutional neural network, machine learning

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25344 Generation of Roof Design Spectra Directly from Uniform Hazard Spectra

Authors: Amin Asgarian, Ghyslaine McClure

Abstract:

Proper seismic evaluation of Non-Structural Components (NSCs) mandates an accurate estimation of floor seismic demands (i.e. acceleration and displacement demands). Most of the current international codes incorporate empirical equations to calculate equivalent static seismic force for which NSCs and their anchorage system must be designed. These equations, in general, are functions of component mass and peak seismic acceleration to which NSCs are subjected to during the earthquake. However, recent studies have shown that these recommendations are suffered from several shortcomings such as neglecting the higher mode effect, tuning effect, NSCs damping effect, etc. which cause underestimation of the component seismic acceleration demand. This work is aimed to circumvent the aforementioned shortcomings of code provisions as well as improving them by proposing a simplified, practical, and yet accurate approach to generate acceleration Floor Design Spectra (FDS) directly from corresponding Uniform Hazard Spectra (UHS) (i.e. design spectra for structural components). A database of 27 Reinforced Concrete (RC) buildings in which Ambient Vibration Measurements (AVM) have been conducted. The database comprises 12 low-rise, 10 medium-rise, and 5 high-rise buildings all located in Montréal, Canada and designated as post-disaster buildings or emergency shelters. The buildings are subjected to a set of 20 compatible seismic records and Floor Response Spectra (FRS) in terms of pseudo acceleration are derived using the proposed approach for every floor of the building in both horizontal directions considering 4 different damping ratios of NSCs (i.e. 2, 5, 10, and 20% viscous damping). Several effective parameters on NSCs response are evaluated statistically. These parameters comprise NSCs damping ratios, tuning of NSCs natural period with one of the natural periods of supporting structure, higher modes of supporting structures, and location of NSCs. The entire spectral region is divided into three distinct segments namely short-period, fundamental period, and long period region. The derived roof floor response spectra for NSCs with 5% damping are compared with the 5% damping UHS and procedure are proposed to generate roof FDS for NSCs with 5% damping directly from 5% damped UHS in each spectral region. The generated FDS is a powerful, practical, and accurate tool for seismic design and assessment of acceleration-sensitive NSCs particularly in existing post-critical buildings which have to remain functional even after the earthquake and cannot tolerate any damage to NSCs.

Keywords: earthquake engineering, operational and functional components (OFCs), operational modal analysis (OMA), seismic assessment and design

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25343 Design of a Low Cost Motion Data Acquisition Setup for Mechatronic Systems

Authors: Baris Can Yalcin

Abstract:

Motion sensors have been commonly used as a valuable component in mechatronic systems, however, many mechatronic designs and applications that need motion sensors cost enormous amount of money, especially high-tech systems. Design of a software for communication protocol between data acquisition card and motion sensor is another issue that has to be solved. This study presents how to design a low cost motion data acquisition setup consisting of MPU 6050 motion sensor (gyro and accelerometer in 3 axes) and Arduino Mega2560 microcontroller. Design parameters are calibration of the sensor, identification and communication between sensor and data acquisition card, interpretation of data collected by the sensor.

Keywords: design, mechatronics, motion sensor, data acquisition

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25342 Artificial Intelligence in Management Simulators

Authors: Nuno Biga

Abstract:

Artificial Intelligence (AI) has the potential to transform management into several impactful ways. It allows machines to interpret information to find patterns in big data and learn from context analysis, optimize operations, make predictions sensitive to each specific situation and support data-driven decision making. The introduction of an 'artificial brain' in organization also enables learning through complex information and data provided by those who train it, namely its users. The "Assisted-BIGAMES" version of the Accident & Emergency (A&E) simulator introduces the concept of a "Virtual Assistant" (VA) sensitive to context, that provides users useful suggestions to pursue the following operations such as: a) to relocate workstations in order to shorten travelled distances and minimize the stress of those involved; b) to identify in real time existing bottleneck(s) in the operations system so that it is possible to quickly act upon them; c) to identify resources that should be polyvalent so that the system can be more efficient; d) to identify in which specific processes it may be advantageous to establish partnership with other teams; and e) to assess possible solutions based on the suggested KPIs allowing action monitoring to guide the (re)definition of future strategies. This paper is built on the BIGAMES© simulator and presents the conceptual AI model developed and demonstrated through a pilot project (BIG-AI). Each Virtual Assisted BIGAME is a management simulator developed by the author that guides operational and strategic decision making, providing users with useful information in the form of management recommendations that make it possible to predict the actual outcome of different alternative management strategic actions. The pilot project developed incorporates results from 12 editions of the BIGAME A&E that took place between 2017 and 2022 at AESE Business School, based on the compilation of data that allows establishing causal relationships between decisions taken and results obtained. The systemic analysis and interpretation of data is powered in the Assisted-BIGAMES through a computer application called "BIGAMES Virtual Assistant" (VA) that players can use during the Game. Each participant in the VA permanently asks himself about the decisions he should make during the game to win the competition. To this end, the role of the VA of each team consists in guiding the players to be more effective in their decision making, through presenting recommendations based on AI methods. It is important to note that the VA's suggestions for action can be accepted or rejected by the managers of each team, as they gain a better understanding of the issues along time, reflect on good practice and rely on their own experience, capability and knowledge to support their own decisions. Preliminary results show that the introduction of the VA provides a faster learning of the decision-making process. The facilitator designated as “Serious Game Controller” (SGC) is responsible for supporting the players with further analysis. The recommended actions by the SGC may differ or be similar to the ones previously provided by the VA, ensuring a higher degree of robustness in decision-making. Additionally, all the information should be jointly analyzed and assessed by each player, who are expected to add “Emotional Intelligence”, an essential component absent from the machine learning process.

Keywords: artificial intelligence, gamification, key performance indicators, machine learning, management simulators, serious games, virtual assistant

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25341 Speed Characteristics of Mixed Traffic Flow on Urban Arterials

Authors: Ashish Dhamaniya, Satish Chandra

Abstract:

Speed and traffic volume data are collected on different sections of four lane and six lane roads in three metropolitan cities in India. Speed data are analyzed to fit the statistical distribution to individual vehicle speed data and all vehicles speed data. It is noted that speed data of individual vehicle generally follows a normal distribution but speed data of all vehicle combined at a section of urban road may or may not follow the normal distribution depending upon the composition of traffic stream. A new term Speed Spread Ratio (SSR) is introduced in this paper which is the ratio of difference in 85th and 50th percentile speed to the difference in 50th and 15th percentile speed. If SSR is unity then speed data are truly normally distributed. It is noted that on six lane urban roads, speed data follow a normal distribution only when SSR is in the range of 0.86 – 1.11. The range of SSR is validated on four lane roads also.

Keywords: normal distribution, percentile speed, speed spread ratio, traffic volume

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25340 An Exploratory Analysis of Brisbane's Commuter Travel Patterns Using Smart Card Data

Authors: Ming Wei

Abstract:

Over the past two decades, Location Based Service (LBS) data have been increasingly applied to urban and transportation studies due to their comprehensiveness and consistency. However, compared to other LBS data including mobile phone data, GPS and social networking platforms, smart card data collected from public transport users have arguably yet to be fully exploited in urban systems analysis. By using five weekdays of passenger travel transaction data taken from go card – Southeast Queensland’s transit smart card – this paper analyses the spatiotemporal distribution of passenger movement with regard to the land use patterns in Brisbane. Work and residential places for public transport commuters were identified after extracting journeys-to-work patterns. Our results show that the locations of the workplaces identified from the go card data and residential suburbs are largely consistent with those that were marked in the land use map. However, the intensity for some residential locations in terms of population or commuter densities do not match well between the map and those derived from the go card data. This indicates that the misalignment between residential areas and workplaces to a certain extent, shedding light on how enhancements to service management and infrastructure expansion might be undertaken.

Keywords: big data, smart card data, travel pattern, land use

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25339 Autism and Work, From the Perception of People Inserted in the Work

Authors: Nilson Rogério Da Silva, Ingrid Casagrande, Isabela Chicarelli Amaro Santos

Abstract:

Introduction: People with Autism Spectrum Disorder (ASD) may face difficulties in social inclusion in different segments of society, especially in entering and staying at work. In Brazil, although there is legislation that equates it to the condition of disability, the number of people at work is still low. The United Nations estimates that more than 80 percent of adults with autism are jobless. In Brazil, the scenario is even more nebulous because there is no control and tracking of accurate data on the number of individuals with autism and how many of these are inserted in the labor market. Pereira and Goyos (2019) found that there is practically no scientific production about people with ASD in the labor market. Objective: To describe the experience of people with ASD inserted in the work, facilities and difficulties found in the professional exercise and the strategies used to maintain the job. Methodology: The research was approved by the Research Ethics Committee. As inclusion criteria for participation, the professional should accept to participate voluntarily, be over 18 years of age and have had some experience with the labor market. As exclusion criteria, being under 18 years of age and having never worked in a work activity. Participated in the research of 04 people with a diagnosis of ASD, aged 22 to 32 years. For data collection, an interview script was used that addressed: 1) General characteristics of the participants; 2) Family support; 3) School process; 4) Insertion in the labor market; 5) Exercise of professional activity; (6) Future and Autism; 7) Possible coping strategies. For the analysis of the data obtained, the full transcription of the interviews was performed and the technique of Content Analysis was performed. Results: The participants reported problems in different aspects: In the school environment: difficulty in social relationships, and Bullying. Lack of adaptation to the school curriculum and the structure of the classroom; In the Faculty: difficulty in following the activities, ealizar group work, meeting deadlines and establishing networking; At work: little adaptation in the work environment, difficulty in establishing good professional bonds, difficulty in accepting changes in routine or operational processes, difficulty in understanding veiled social rules. Discussion: The lack of knowledge about what disability is and who the disabled person is leads to misconceptions and negatives regarding their ability to work and in this context, people with disabilities need to constantly prove that they are able to work, study and develop as a human person, which can be classified as ableism. The adaptations and the use of technologies to facilitate the performance of people with ASD, although guaranteed in national legislation, are not always available, highlighting the difficulties and prejudice. Final Considerations: The entry and permanence of people with ASD at work still constitute a challenge to be overcome, involving changes in society in general, in companies, families and government agencies.

Keywords: autism spectrum disorder (ASD), work, disability, autism

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25338 Analyzing the Support to Fisheries in the European Union: Modelling Budgetary Transfers in Wild Fisheries

Authors: Laura Angulo, Petra Salamon, Martin Banse, Frederic Storkamp

Abstract:

Fisheries subsidies are focus on reduce management costs or deliver income benefits to fishers. In 2015, total fishery budgetary transfers in 31 OECD countries represented 35% of their total landing value. However, subsidies to fishing have adverse effects on trade and it has been claimed that they may contribute directly to overfishing. Therefore, this paper analyses to what extend fisheries subsidies may 1) influence capture production facing quotas and 2) affect price dynamics. The study uses the fish module in AGMEMOD (Agriculture Member States Modelling, details see Chantreuil et al. (2012)) which covers eight fish categories (cephalopods; crustaceans; demersal marine fish; pelagic marine fish; molluscs excl. cephalopods; other marine finfish species; freshwater and diadromous fish) for EU member states and other selected countries developed under the SUCCESS project. This model incorporates transfer payments directly linked to fisheries operational costs. As aquaculture and wild fishery are not included within the WTO Agreement on Agriculture, data on fisheries subsidies is obtained from the OECD Fisheries Support Estimates (FSE) database, which provides statistics on budgetary transfers to the fisheries sector. Since support has been moving from budgetary transfers to General Service Support Estimate the last years, subsidies in capture production may not present substantial effects. Nevertheless, they would still show the impact across countries and fish categories within the European Union.

Keywords: AGMEMOD, budgetary transfers, EU Member States, fish model, fisheries support estimate

Procedia PDF Downloads 248