Search results for: zero knowledge Ethereum virtual machine

Authors: Chhavi Rana

Abstract:

The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, healthcare, and business intelligence contain voluminous and incremental data which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organisation decision-making strategy can be enhanced by using big data analytics and applying different machine learning techniques and statistical tools to such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates various frameworks in the process of analysis using different machine learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

Procedia PDF Downloads 95

Authors: Mohan Li

Abstract:

Nowadays, intelligent research technology is more and more important than traditional research methods in urban research work, and this proportion will greatly increase in the next few decades. Frequently such analyzing work cannot be carried without some software engineering knowledge. And here, domain models of urban research will be necessary when applying software engineering knowledge to urban work. In many urban plan practice projects, making rational models, feeding reliable data, and providing enough computation all make indispensable assistance in producing good urban planning. During the whole work process, domain models can optimize workflow design. At present, human beings have entered the era of big data. The amount of digital data generated by cities every day will increase at an exponential rate, and new data forms are constantly emerging. How to select a suitable data set from the massive amount of data, manage and process it has become an ability that more and more planners and urban researchers need to possess. This paper summarizes and makes predictions of the emergence of technologies and technological iterations that may affect urban research in the future, discover urban problems, and implement targeted sustainable urban strategies. They are summarized into seven major domain models. They are urban and rural regional domain model, urban ecological domain model, urban industry domain model, development dynamic domain model, urban social and cultural domain model, urban traffic domain model, and urban space domain model. These seven domain models can be used to guide the construction of systematic urban research topics and help researchers organize a series of intelligent analytical tools, such as Python, R, GIS, etc. These seven models make full use of quantitative spatial analysis, machine learning, and other technologies to achieve higher efficiency and accuracy in urban research, assisting people in making reasonable decisions.

Keywords: big data, domain model, urban planning, urban quantitative analysis, machine learning, workflow design

Procedia PDF Downloads 177

Authors: Chyong-Ru Liu, Wen-Shiung Huang, Wan-Ching Tang, Shan-Pei Chen

Abstract:

Two of the biggest challenges companies involved in sports and exercise information services face are how to strengthen participation in virtual sports/exercise communities and how to increase the ongoing participatoriness of those communities. In the past, relatively little research has explored mechanisms for strengthening alignment practice and community response from the perspective of self-construal, and as such this study seeks to explore the self-construal of virtual sports/exercise communities, the role it plays in the emotional commitment of forming communities, and the factor that can strengthen alignment practice. Moreover, which factor of the emotional commitment of forming virtual communities have the effect of strengthening interference in the process of transforming customer citizenship behaviors? This study collected 625 responses from the two leading websites in terms of fan numbers in the provision of information on road race and marathon events in Taiwan, with model testing conducted through linear structural equation modelling and the bootstrapping technique to test the proposed hypotheses. The results proved independent construal had a stronger positive direct effect on affective commitment to fellow customers than did interdependent construal, and the influences of affective commitment to fellow customers in enhancing customer citizenship behavior. Public self-consciousness moderates the relationships among independent self-construal and interdependent self-construal on effective commitment to fellow customers. Perceived playfulness moderates the relationships between effective commitment to fellow customers and customer citizenship behavior. The findings of this study provide significant insights for the researchers and related organizations. From the theoretical perspective, this is empirical research that investigated the self-construal theory and responses (i.e., affective commitment to fellow customers, customer citizenship behavior) in virtual sports/exercise communities. We further explore how to govern virtual sports/exercise community participants’ heterogeneity through public self-consciousness mechanism to align participants’ affective commitment. Moreover, perceived playfulness has the effect of strengthening effective commitment to fellow customers with customer citizenship behaviors. The results of this study can provide a foundation for the construction of future theories and can be provided to related organizations for reference in their planning of virtual communities.

Keywords: self-construal theory, public self-consciousness, affective commitment, customer citizenship behavior

Procedia PDF Downloads 106

Authors: Dewi Indah Irianti

Abstract:

Introduction: Street children include one of the populations at risk of HIV infection. Their vulnerability to these situations is increased by their lack of understanding of the changes associated with adolescence, the lack of knowledge and skills which could help them to make healthy choices. Social integration increased AIDS knowledge among migrant workers in Thailand. Although social integration has been incorporated into health research in other areas, it has received less attention in AIDS prevention research. This factor has not been integrated into models for HIV prevention. Objectives: The goal of this review is to summarize available knowledge about factors related to HIV/AIDS knowledge and to examine whether social integration was reviewed among street children. Methodology: This study performed a systematic search for English language articles published between January 2006 and March 2016 using the following keywords in various combination: street children, HIV/AIDS knowledge and social integration from the following bibliographic databases: Scopus, ProQuest, JSTOR, ScienceDirect, SpringerLink, EBSCOhost, Sage Publication, Clinical Key, Google Web, and Google Scholar . Results: A total of 10 articles met the inclusion criteria were systematically reviewed. This study reviews the existing quantitative and qualitative literature regarding the HIV/AIDS knowledge of street children in many countries. The study locations were Asia, the Americas, Europe, and Africa. The most determinants associated with HIV/AIDS knowledge among street children are age and sex. In this review, social integration that may be associated with HIV/AIDS knowledge among street children has not been investigated. Conclusion: To the best of the author’s knowledge, this study found that there is no research examining the relationship of social integration with the HIV knowledge among street children. This information may assist in the development of relevant strategies and HIV prevention programs to improve HIV knowledge and decrease risk behaviors among street children.

Keywords: HIV/AIDS knowledge, review, social integration, street children

Procedia PDF Downloads 322

Authors: Shamatha Shetty, Sakshi Dhabadi, Prerana M., Indushree B.

Abstract:

About 66% of firms claim that insider attacks are more likely to happen. The frequency of insider incidents has increased by 47% in the last two years. The goal of this work is to prevent dangerous employee behavior by using keyloggers and the Machine Learning (ML) model. Every keystroke that the user enters is recorded by the keylogging program, also known as keystroke logging. Keyloggers are used to stop improper use of the system. This enables us to collect all textual data, save it in a CSV file, and analyze it using an ML algorithm and the VirusTotal API. Many large companies use it to methodically monitor how their employees use computers, the internet, and email. We are utilizing the SVM algorithm and the VirusTotal API to improve overall efficiency and accuracy in identifying specific patterns and words to automate and offer the report for improved monitoring.

Keywords: cyber security, machine learning, cyclic process, email notification

Procedia PDF Downloads 57

Authors: Ramachandra C. G., Amarnath. M., Prashanth Pai M., Nagesh S. N.

Abstract:

The machine's performance level drops down over a period of time due to the wear and tear of its components. The early detection of an emergent fault becomes very vital in order to obtain uninterrupted production in a plant. Maintenance is an activity that helps to keep the machine's performance at an anticipated level, thereby ensuring the availability of the machine to perform its intended function. At present, a number of modern maintenance techniques are available, such as preventive maintenance, predictive maintenance, condition-based maintenance, total productive maintenance, etc. Condition-based maintenance or condition monitoring is one such modern maintenance technique in which the machine's condition or health is checked by the measurement of certain parameters such as sound level, temperature, velocity, displacement, vibration, etc. It can recognize most of the factors restraining the usefulness and efficacy of the total manufacturing unit. This research work is conducted on a Batch Mill in a tire production unit located in the Southern Karnataka region. The health of the mill is assessed using amplitude of vibration as a parameter of measurement. Most commonly, the vibration level is assessed using various points on the machine bearing. The normal or standard level is fixed using reference materials such as manuals or catalogs supplied by the manufacturers and also by referring vibration standards. The Rio-Vibro meter is placed in different locations on the batch-off mill to record the vibration data. The data collected are analyzed to identify the malfunctioning components in the batch off the mill, and corrective measures are suggested.

Keywords: availability, displacement, vibration, rio-vibro, condition monitoring

Procedia PDF Downloads 91

Authors: Harshkumar K. Desai, Anuj K. Desai, Jay P. Patel, Snehal V. Trivedi, Yogendrasinh Parmar

Abstract:

The need of continuous improvement in a product or process in this era of global competition leads to apply value engineering for functional and aesthetic improvement in consideration with economic aspect too. Solar industries located at G.I.D.C., Makarpura, Vadodara, Gujarat, India; a manufacturer of variety of CNC Machines had a challenge to analyze the structural design of column, base, carriage and table of CNC Milling Machine in the account of reduction of overall weight of a machine without affecting the rigidity and accuracy at the time of operation. The identified task is the first attempt to validate and optimize the proposed design of ribbed structure statically using advanced modeling and analysis tools in a systematic way. Results of stress and deformation obtained using analysis software are validated with theoretical analysis and found quite satisfactory. Such optimized results offer a weight reduction of the final assembly which is desired by manufacturers in favor of reduction of material cost, processing cost and handling cost finally.

Keywords: CNC milling machine, optimization, finite element analysis (FEA), weight reduction

Procedia PDF Downloads 277

Authors: Heramb Somthankar, Anujoy Chakraborty

Abstract:

This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

Procedia PDF Downloads 109

Authors: Aditi Viswanathan, Shree Ranjani, Aruna Govada

Abstract:

With data sizes constantly expanding, and with classical machine learning algorithms that analyze such data requiring larger and larger amounts of computation time and storage space, the need to distribute computation and memory requirements among several computers has become apparent. Although substantial work has been done in developing distributed binary SVM algorithms and multi-class SVM algorithms individually, the field of multi-class distributed SVMs remains largely unexplored. This research seeks to develop an algorithm that implements the Support Vector Machine over a multi-class data set and is efficient in a distributed environment. For this, we recursively choose the best binary split of a set of classes using a greedy technique. Much like the divide and conquer approach. Our algorithm has shown better computation time during the testing phase than the traditional sequential SVM methods (One vs. One, One vs. Rest) and out-performs them as the size of the data set grows. This approach also classifies the data with higher accuracy than the traditional multi-class algorithms.

Keywords: distributed algorithm, MapReduce, multi-class, support vector machine

Procedia PDF Downloads 401

Authors: Beverly Komen

Abstract:

Online crimes have been on the rise in the recent days, especially with the hit of the covid-19 pandemic. The coronavirus pandemic has led to an unprecedented rise in screen time, this means more families are relying on technology and digital solutions to keep children learning, spending more time on the virtual platforms can leave children vulnerable to online abuse and exploitation. With ease access of affordable phones, internet, and increased online activities, all children are at risk of being abused online hence making the digital space unsafe for children. With these increased use of technology and its accessibility, children are at risk of facing challenges such as access to inappropriate content, online grooming, identity theft, cyber bullying, among other risks. The big question is; as we enjoy the benefits brought in by technology, how do we ensure that our children are save in this digital space? With the analysis of the current trends, there is a gap in knowledge on people’s understanding on child online protection and safety measures when using the digital space. A survey conducted among 50 parents in Nairobi in Kenya indicated that there is a gap in knowledge on online protection of children and over 50 % of the participants shared that for sure they have no idea on how to protect children online. This paper seeks to address the concept of child protection in the digital space and come up with viable solutions in protecting children from online vices.

Keywords: child protection, digital space, online risks, online grooming, cyber bulying, online child sexual exploitation, and abuse

Procedia PDF Downloads 187

Authors: Patrick Mbassegue, Mickael Gardoni

Abstract:

In a market environment centered more and more on services and the digital economy, knowledge management becomes a framework that can help organizations to create value and to improve their overall performance. Based on an optimal allocation of scarce resources, managers are interested in demonstrating the added value generated by knowledge management projects. One of the challenges faced by organizations is the difficulty in measuring impacts and concrete results of knowledge management initiatives. The present article concerns the measure of concrete results coming from knowledge management projects based on balance scorecard model. One of the goals is to underline what can be done based on this model but also to highlight the limits associated. The present article is structured in five parts; 1-knowledge management projects and organizational impacts; 2- a framework and a methodology to measure organizational impacts; 3- application illustrated in two case studies; 4- limits concerning the proposed framework; 5- the proposal of a new framework to measure organizational impacts.

Keywords: knowledge management, project, balance scorecard, impacts

Procedia PDF Downloads 262

Authors: Sadeeqa Khan, Muhammad Usman

Abstract:

Employees’ knowledge hiding behaviors can be detrimental to employees’ interpersonal relationships and individual and organizational learning and creativity. However, to the best of the authors’ knowledge, the literature on the contingencies, antecedents and outcomes of employees’ knowledge hiding behaviors is still in its infancy. On the other hand, not everyone who hides knowledge hides it the same way, as individuals are different, so do their behaviors. This study explores the links between employees’ personality traits and their knowledge hiding behaviors. By using a single case study as a research methodology and collecting data through 28 semi-structured interviews from employees working in Alpha Electronics (the pseudo name of the company to ascertain anonymity) operating in Pakistan, we foreground the patterns of relationships between employees’ personality traits and knowledge hiding behaviors – rationalized hiding, evasive hiding and playing dumb. Our findings suggest that employees high on extraversion involve in evasive knowledge hiding; while employees low on extraversion (introverts) demonstrate rationalized hiding. Moreover, both extrovert and introvert employees involve in playing dumb in situations that involve risk for their jobs and careers. For instance, when knowledge is requested from their managers, both extrovert and introvert employees tend to play dumb, as in such cases, evasive and rationalized hiding can be harmful to their job and career-related interests and motives. Other than theoretical contributions, the study offers important implications for organizations faced with the challenges of shortage of skills and knowledge.

Keywords: knowledge hiding, personality, rationalized hiding, playing dumb, evasive hiding

Procedia PDF Downloads 215

Authors: P. Hajek, J. Stejskal

Abstract:

The aim of this paper is to analyze the ability to identify and acquire knowledge from external sources at the regional level in the Czech Republic. The results show that the most important sources of knowledge for innovative activities are sources within the businesses themselves, followed by customers and suppliers. Furthermore, the analysis of relationships between the objective of the innovative activity and the ability to identify and acquire knowledge implies that knowledge obtained from a) customers aims at replacing outdated products and increasing product quality; b) suppliers aims at increasing capacity and flexibility of production; and c) competing businesses aims at growing market share and increasing the flexibility of production and services. Regions should therefore direct their support especially into development and strengthening of networks within the value chain.

Keywords: knowledge, acquisition, innovative business, Czech republic, region

Procedia PDF Downloads 372

Authors: Jacob Green, Cecilia Cabrera, Maximilian Jakobs, Andrea Dimitracopoulos, Mark van der Wilk, Ryan Greenhalgh

Abstract:

Predicting drug properties is key in drug discovery to enable de-risking of assets before expensive clinical trials and to find highly active compounds faster. Interest from the machine learning community has led to the release of a variety of benchmark datasets and proposed methods. However, it remains unclear for practitioners which method or approach is most suitable, as different papers benchmark on different datasets and methods, leading to varying conclusions that are not easily compared. Our large-scale empirical study links together numerous earlier works on different datasets and methods, thus offering a comprehensive overview of the existing property classes, datasets, and their interactions with different methods. We emphasise the importance of uncertainty quantification and the time and, therefore, cost of applying these methods in the drug development decision-making cycle. To the best of the author's knowledge, it has been observed that the optimal approach varies depending on the dataset and that engineered features with classical machine learning methods often outperform deep learning. Specifically, QSAR datasets are typically best analysed with classical methods such as Gaussian Processes, while ADMET datasets are sometimes better described by Trees or deep learning methods such as Graph Neural Networks or language models. Our work highlights that practitioners do not yet have a straightforward, black-box procedure to rely on and sets a precedent for creating practitioner-relevant benchmarks. Deep learning approaches must be proven on these benchmarks to become the practical method of choice in drug property prediction.

Keywords: activity (QSAR), ADMET, classical methods, drug property prediction, empirical study, machine learning

Procedia PDF Downloads 81

Authors: Juan-Gabriel Cegarra-Navarro, Alexeis Garcia-Perez, Denise Bedford

Abstract:

This paper provides guidelines for what constitutes a knowledge worker. Many graduates from non-managerial domains adopt, at some point in their professional careers, management roles at different levels, ranging from team leaders through to executive leadership. This is particularly relevant for professionals from an engineering background. Moving from a technical to an executive-level requires an understanding of those behaviour management techniques that can motivate and support individuals and their performance. Further, the transition to management also demands a shift of contextual enablers from tangible to intangible resources, which allows individuals to create new capacities, competencies, and capabilities. In this dynamic process, the knowledge worker becomes that key individual who can help members of the management board to transform information into relevant knowledge. However, despite its relevance in shaping the future of the organization in its transition to the knowledge economy, the role of a knowledge worker has not yet been studied to an appropriate level in the current literature. In this study, the authors review both the contextual enablers and behaviour outputs related to the role of the knowledge worker and relate these to their ability to deal with everyday management issues such as knowledge heterogeneity, varying motivations, information overload, or outdated information. This study highlights that the aggregate of capacities, competences and capabilities (CCCs) can be defined as knowledge structures, the study proposes several contextual enablers and behaviour outputs that knowledge workers can use to work cooperatively, acquire, distribute and knowledge. Therefore, this study contributes to a better comprehension of how CCCs can be managed at different levels through their contextual enablers and behaviour outputs.

Keywords: knowledge workers, capabilities, capacities, competences, knowledge structures

Procedia PDF Downloads 156

Authors: Sanja Seljan, Ivan Dunđer

Abstract:

The paper presents combined automatic speech recognition (ASR) for English and machine translation (MT) for English and Croatian in the domain of business correspondence. The first part presents results of training the ASR commercial system on two English data sets, enriched by error analysis. The second part presents results of machine translation performed by online tool Google Translate for English and Croatian and Croatian-English language pairs. Human evaluation in terms of usability is conducted and internal consistency calculated by Cronbach's alpha coefficient, enriched by error analysis. Automatic evaluation is performed by WER (Word Error Rate) and PER (Position-independent word Error Rate) metrics, followed by investigation of Pearson’s correlation with human evaluation.

Keywords: automatic machine translation, integrated language technologies, quality evaluation, speech recognition

Procedia PDF Downloads 484

Authors: Zheng Baichuan, Li Shenghui, Li Bingqian, Zhang Ning, Chen Kai

Abstract:

Entity alignment (EA) tasks in knowledge graphs often play a pivotal role in the integration of knowledge graphs, where structural differences often exist between the source and target graphs, such as the presence or absence of attribute information and the types of attribute information (text, timestamps, images, etc.). However, most current research efforts are focused on improving alignment accuracy, often along with an increased reliance on specific structures -a dependency that inevitably diminishes their practical value and causes difficulties when facing knowledge graph alignment tasks with varying structures. Therefore, we propose a universal knowledge graph alignment approach that only utilizes the common basic structures shared by knowledge graphs. We have demonstrated through experiments that our method achieves state-of-the-art performance in fair comparisons.

Keywords: knowledge graph, entity alignment, transformer, deep learning

Procedia PDF Downloads 45

Authors: Antony Thomas Vanchipurrakkal

Abstract:

Common property resource like fisheries is conserved and managed by fishermen with the help of Local Ecological Knowledge system. Various forms of Social and Ecological elements adapted to formularize management of Chakara fishery. This study tries for a better understanding of elements involved in fishery management in India, such traditional knowledge system practicing within the fishing communities for management and conservation of the marine resources. Participatory Rural Appraisal technique is applied to seize the traditional knowledge system in central Kerala coastal region, India. Socio-Ecological Analysis framework is used for the study. This paper discusses that traditional knowledge systems of chakara fishery and discloses need for inclusive governance system. The paper also discusses adaptation of different elements of the ecological, biological and institutional knowledge system in local ecological knowledge for sustain the fishery. A framework is formulized based on elements operating in chakara fishery management.

Keywords: common property, fisheries, India, local ecological knowledge, management

Procedia PDF Downloads 413

Authors: Nishaben Desai Dholakiya, Anirban Roy, Ranjan Dey

Abstract:

Understanding ocean thermodynamics has become increasingly critical as Earth's oceans serve as the primary planetary heat regulator, absorbing approximately 93% of excess heat energy from anthropogenic greenhouse gas emissions. This investigation presents a comprehensive analysis of Arabian Sea surface seawater thermodynamics, focusing specifically on heat capacity (Cp) and thermal expansion coefficient (α) - parameters fundamental to global heat distribution patterns. Through high-precision experimental measurements of ultrasonic velocity and density across varying temperature (293.15-318.15K) and salinity (0.5-35 ppt) conditions, it characterize critical thermophysical parameters including specific heat capacity, thermal expansion, and isobaric and isothermal compressibility coefficients in natural seawater systems. The study employs advanced machine learning frameworks - Random Forest, Gradient Booster, Stacked Ensemble Machine Learning (SEML), and AdaBoost - with SEML achieving exceptional accuracy (R² > 0.99) in heat capacity predictions. the findings reveal significant temperature-dependent molecular restructuring: enhanced thermal energy disrupts hydrogen-bonded networks and ion-water interactions, manifesting as decreased heat capacity with increasing temperature (negative ∂Cp/∂T). This mechanism creates a positive feedback loop where reduced heat absorption capacity potentially accelerates oceanic warming cycles. These quantitative insights into seawater thermodynamics provide crucial parametric inputs for climate models and evidence-based environmental policy formulation, particularly addressing the critical knowledge gap in thermal expansion behavior of seawater under varying temperature-salinity conditions.

Keywords: climate change, arabian sea, thermodynamics, machine learning

Procedia PDF Downloads 8

Authors: Swee Kun Yap, Sachin Jangam, Suraj Vasudevan

Abstract:

Experiential learning is dubbed as a highly effective way to enhance learning. Virtual reality (VR) is thus a helpful tool in providing a safe, memorable, and interactive learning environment. A class of 49 fluid mechanics students participated in starting up a pump, one of the most used equipment in the chemical industry, in VR. They experience the process in VR to familiarize themselves with the safety training and the standard operating procedure (SOP) in guided mode. Students subsequently observe their peers (in groups of 4 to 5) complete the same training. The training first brings each user through the personal protection equipment (PPE) selection, before guiding the user through a series of steps for pump startup. One of the most common feedback given by industries include the weakness of our graduates in pump design and operation. Traditional fluid mechanics is a highly theoretical module loaded with engineering equations, providing limited opportunity for visualization and operation. With VR pump, students can now learn to startup, shutdown, troubleshoot and observe the intricacies of a centrifugal pump in a safe and controlled environment, thereby bridging the gap between theory and practical application. Following the completion of the guided mode operation, students then individually complete the VR assessment for pump startup on the same day, which requires students to complete the same series of steps, without any cues given in VR to test their recollection rate. While most students miss out a few minor steps such as the checking of lubrication oil and the closing of minor drain valves before pump priming, all the students scored full marks in the PPE selection, and over 80% of the students were able to complete all the critical steps that are required to startup a pump safely. The students were subsequently tested for their recollection rate by means of an online quiz 3 weeks later, and it is again found that over 80% of the students were able to complete the critical steps in the correct order. In the survey conducted, students reported that the VR experience has been enjoyable and enriching, and 79.5% of the students voted to include VR as a positive supplementary exercise in addition to traditional teaching methods. One of the more notable feedback is the higher ease of noticing and learning from mistakes as an observer rather than as a VR participant. Thus, the cycling between being a VR participant and an observer has helped tremendously in their knowledge retention. This reinforces the positive impact VR has on learning.

Keywords: experiential learning, learning by doing, pump, unit operations, virtual reality

Procedia PDF Downloads 138

Authors: Chih-Jer Lin, Jian-Hong Hou

Abstract:

Industry 4.0 is a vision of integrated industry implemented by artificial intelligent computing, software, and Internet technologies. The main goal of industry 4.0 is to deal with the difficulty owing to competitive pressures in the marketplace. For today’s manufacturing factories, the type of production is changed from mass production (high quantity production with low product variety) to medium quantity-high variety production. To offer flexibility, better quality control, and improved productivity, robot manipulators are used to combine material processing, material handling, and part positioning systems into an integrated manufacturing system. To implement the automated system for sheet metal bending operations, motion planning of a 7-degrees of freedom (DOF) robot is studied in this paper. A virtual reality (VR) environment of a bending cell, which consists of the robot and a bending machine, is established using the virtual robot experimentation platform (V-REP) simulator. For sheet metal bending operations, the robot only needs six DOFs for the pick-and-place or tracking tasks. Therefore, this 7 DOF robot has more DOFs than the required to execute a specified task; it can be called a redundant robot. Therefore, this robot has kinematic redundancies to deal with the task-priority problems. For redundant robots, Pseudo-inverse of the Jacobian is the most popular motion planning method, but the pseudo-inverse methods usually lead to a kind of chaotic motion with unpredictable arm configurations as the Jacobian matrix lose ranks. To overcome the above problem, we proposed a method to formulate the motion planning problems as optimization problem. Moreover, a genetic algorithm (GA) based method is proposed to deal with motion planning of the redundant robot. Simulation results validate the proposed method feasible for motion planning of the redundant robot in an automated sheet-metal bending operations.

Keywords: redundant robot, motion planning, genetic algorithm, obstacle avoidance

Procedia PDF Downloads 146

Authors: Pejman Hosseinioun, Hasan Shakeri, Ghasem Ghorbanirostam

Abstract:

In recent years, we have seen an increasing importance of research and study on knowledge source, decision support systems, data mining and procedure of knowledge discovery in data bases and it is considered that each of these aspects affects the others. In this article, we have merged information source and knowledge source to suggest a knowledge based system within limits of management based on storing and restoring of knowledge to manage information and improve decision making and resources. In this article, we have used method of data mining and Apriori algorithm in procedure of knowledge discovery one of the problems of Apriori algorithm is that, a user should specify the minimum threshold for supporting the regularity. Imagine that a user wants to apply Apriori algorithm for a database with millions of transactions. Definitely, the user does not have necessary knowledge of all existing transactions in that database, and therefore cannot specify a suitable threshold. Our purpose in this article is to improve Apriori algorithm. To achieve our goal, we tried using fuzzy logic to put data in different clusters before applying the Apriori algorithm for existing data in the database and we also try to suggest the most suitable threshold to the user automatically.

Keywords: decision support system, data mining, knowledge discovery, data discovery, fuzzy logic

Procedia PDF Downloads 335

Authors: Gor Muradyan

Abstract:

The paper is about friction and wear, including mechanisms, modeling, characterization, measurement and testing case in Bangladesh. Bangladesh is a country under development, A lot of people live here, approximately 145 million. The territory of this country is very small. Therefore buildings are very close to each other. As the pipe lines are very old, and people get almost dirty water, there are a lot of ongoing projects under ADB. In those projects the contractors using HDD machines (Horizontal Directional Drilling ) and grundoburst. These machines are working underground. As ground in Bangladesh is very sludge, machine can't work relevant because of big friction in the soil. When drilling works are finished machine is pulling the pipe underground. Very often the pulling of the pipes becomes very complicated because of the friction. Therefore long section of the pipe laying can’t be done because of a big friction. In that case, additional problems rise, as well as additional work must be done. As we mentioned above it is not possible to do big section of the pipe laying because of big friction in the soil, Because of this it is coming out that contractors must do more joints, more pressure test. It is always connected with additional expenditure and losing time. This machine can pull in 75 mm to 500 mm pipes connected with the soil condition. Length is possible till 500m related how much friction it will had on the puller. As less as much it can pull. Another machine grundoburst is not working at this soil condition at all. The machine is working with air compressor. This machine are using for the smaller diameter pipes, 20 mm to 63 mm. Most of the cases these machines are being used for the installing of the house connection pipes, for making service connection. To make a friction less contractors using bigger pulling had then the pipe. It is taking down the friction, But the problem of this machine is that it can't work at sludge. Because of mentioned reasons the friction has a big mining during this kind of works. There are a lot of ways to reduce the friction. In this paper we'll introduce the ways that we have researched during our practice in Bangladesh.

Keywords: Bangladesh, friction and wear, HDD machines, reducing friction

Procedia PDF Downloads 317

Authors: Michael Zimba

Abstract:

A novel set of features for copy-move image forgery, CMIF, detection method is proposed. The proposed set presents a new approach which relies on electrostatic field theory, EFT. Solely for the purpose of reducing the dimension of a suspicious image, firstly performs discrete wavelet transform, DWT, of the suspicious image and extracts only the approximation subband. The extracted subband is then bijectively mapped onto a virtual electrostatic field where concepts of EFT are utilised to extract robust features. The extracted features are shown to be invariant to additive noise, JPEG compression, and affine transformation. The proposed features can also be used in general object matching.

Keywords: virtual electrostatic field, features, affine transformation, copy-move image forgery

Procedia PDF Downloads 543

Authors: Jonathan Martino, Kristof Harri

Abstract:

In some cases, the increase in computing resources makes simulation methods more affordable. The increase in processing speed also allows real time analysis or even more rapid tests analysis offering a real tool for test prediction and design process optimization. Vibration tests are no exception to this trend. The so called ‘Virtual Vibration Testing’ offers solution among others to study the influence of specific loads, to better anticipate the boundary conditions between the exciter and the structure under test, to study the influence of small changes in the structure under test, etc. This article will first present a virtual vibration test modeling with a main focus on the shaker model and will afterwards present the experimental parameters determination. The classical way of modeling a shaker is to consider the shaker as a simple mechanical structure augmented by an electrical circuit that makes the shaker move. The shaker is modeled as a two or three degrees of freedom lumped parameters model while the electrical circuit takes the coil impedance and the dynamic back-electromagnetic force into account. The establishment of the equations of this model, describing the dynamics of the shaker, is presented in this article and is strongly related to the internal physical quantities of the shaker. Those quantities will be reduced into global parameters which will be estimated through experiments. Different experiments will be carried out in order to design an easy and practical method for the identification of the shaker parameters leading to a fully functional shaker model. An experimental modal analysis will also be carried out to extract the modal parameters of the shaker and to combine them with the electrical measurements. Finally, this article will conclude with an experimental validation of the model.

Keywords: lumped parameters model, shaker modeling, shaker parameters, state-space, virtual vibration

Procedia PDF Downloads 270

Authors: Michael Wagner, Philipp Steinbauer, Andrea Katharina Lietz, Alexander Hoffelner, Johannes Fessler

Abstract:

Background: Cardiopulmonary resuscitations are stressful situations in which vital decisions must be made within seconds. Lack of routine due to the infrequency of pediatric emergencies can lead to serious medical and communication errors. Virtual reality can fundamentally change the way simulation training is conducted in the future. It appears to be a useful learning tool for technical and non-technical skills. It is important to investigate the use of VR in providing a strong sense of presence within simulations. Methods: In this randomized study, we will enroll doctors and medical students from the Medical University of Vienna, who will receive learning material regarding the resuscitation of a one-year-old child. The study will be conducted in three phases. In the first phase, 20 physicians and 20 medical students from the Medical University of Vienna will be included. They will perform simulation-based training with a standardized scenario of a critically ill child with a hypovolemic shock. The main goal of this phase is to establish a baseline for the following two phases to generate comparative values regarding cognitive load and stress. In phase 2 and 3, the same participants will perform the same scenario in a VR setting. In both settings, on three set points of progression, one of three predefined events is triggered. For each event, three different stress levels (easy, medium, difficult) will be defined. Stress and cognitive load will be analyzed using the NASA Task Load Index, eye-tracking parameters, and heart rate. Subsequently, these values will be compared between VR training and traditional simulation-based training. Hypothesis: We hypothesize that the VR training and the traditional training groups will not differ in physiological response (cognitive load, heart rate, and heart rate variability). We further assume that virtual reality training can be used as cost-efficient additional training. Objectives: The aim of this study is to measure cognitive load and stress level during a real-life simulation training and compare it with VR training in order to show that VR training evokes the same physiological response and cognitive load as real-life simulation training.

Keywords: virtual reality, cognitive load, simulation, adaptive virtual reality training

Procedia PDF Downloads 115

Authors: Yujiang Wu

Abstract:

As electrical machine (e-machine) power density re-querulents become more stringent in vehicle electrification, mounting a temperature sensor for e-machine stator windings becomes increasingly difficult. This can lead to higher manufacturing costs, complicated harnesses, and reduced reliability. In this paper, we propose a deep-learning method for predicting electric machine winding temperature, which can either replace the sensor entirely or serve as a backup to the existing sensor. We compare the performance of our method, the stateful long short-term memory networks (LSTM) with truncated backpropagation through time (TBTT), with that of linear regression, as well as stateless LSTM with/without residual connection. Our results demonstrate the strength of combining stateful LSTM and TBTT in tackling nonlinear time series prediction problems with long sequence lengths. Additionally, in industrial applications, high-temperature region prediction accuracy is more important because winding temperature sensing is typically used for derating machine power when the temperature is high. To evaluate the performance of our algorithm, we developed a temperature-stratified MSE. We propose a simple but effective data preprocessing trick to improve the high-temperature region prediction accuracy. Our experimental results demonstrate the effectiveness of our proposed method in accurately predicting winding temperature, particularly in high-temperature regions, while also reducing manufacturing costs and improving reliability.

Keywords: deep learning, electrical machine, functional safety, long short-term memory networks (LSTM), thermal management, time series prediction

Procedia PDF Downloads 99

Authors: Enze Zhang

Abstract:

One of the most challenging steps in implementing virtual water content (VWC) analysis of crops is to get properly the total volume of consumptive water use (CWU) and, therefore, the choice of a reliable crop CWU estimation method. In practice, lots of previous researches obtaining CWU of crops follow a classical procedure for calculating crop evapotranspiration which is determined by multiplying reference evapotranspiration by appropriate coefficient, such as crop coefficient and water stress coefficients. However, this manner of calculation requires lots of field experimental data at point scale and more seriously, when current growing conditions differ from the standard conditions, may easily produce deviation between the calculated CWU and the actual CWU. Since evapotranspiration caused by crop planting always plays a vital role in surface water-energy balance in an agricultural region, this study decided to alternatively estimates crop evapotranspiration by Budyko framework. After brief introduce the development process of Budyko framework. We choose a modified Budyko framework under unsteady-state to better evaluated the actual CWU and apply it in an agricultural irrigation area in North China Plain which rely on underground water for irrigation. With the agricultural statistic data, this calculated CWU was further converted into VWC and its subdivision of crops at the annual scale. Results show that all the average values of VWC, VWC_blue and VWC_green show a downward trend with increased agricultural production and improved acreage. By comparison with the previous research, VWC calculated by Budyko framework agree well with part of the previous research and for some other research the value is greater. Our research also suggests that this methodology and findings may be reliable and convenient for investigation of virtual water throughout various agriculture regions of the world.

Keywords: virtual water content, Budyko framework, consumptive water use, crop evapotranspiration

Procedia PDF Downloads 333

Authors: Ehsan Tuzcuoğlu, Muhammed Emin Çoban, Songül Byraktar

Abstract:

One of the main targets of the washing machine is to remove any dirt and stains from the clothes. Especially, the stain removal is significantly important in the Far East market, where the high percentage of the consumers use the top load washing machines as washing appliance. They use all pretreatment methods (i.e. soaking, prewash, and heavy functions) to eliminate the stains from their clothes. Therefore, with this study it is aimed to study experimentally the stain removal performance of 3 different Top-Loading washing machines of the Far East market with 24 different types of stains which are mostly related to Far East culture. In the meanwhile, the mechanical damge on laundry is examined for each machine to see the mechanical effect of the related stain programs on the textile load of the machines. The test machines vary according to have a heater, moving part(s)on their impeller, and to be in different height/width ratio of the drum. The results indicate that decreasing the water level inside the washing machine might result in better soil removal as well as less textile damage. Beside this, the experimental results reveal that heating has the main effect on stain removal. Two-step (or delayed) heating and a lower amount of water can also be considered as the further parameters

Keywords: laundry, washing machine, top load washing machine, stain removal, textile damage, mechanical textile damage

Procedia PDF Downloads 124

Authors: Jacqueline A. G. Sachett, Cláudia S. Nogueira, Diana C. P. Lima, Jessica T. S. Oliveira, Guilherme K. M. Salazar, Lílian K. Aguiar

Abstract:

The Polo Amazon Telehealth offers several tools for professionals working in Primary Health Care as a second formative opinion, teleconsulting and training between the different areas, whether medicine, dentistry, nursing, physiotherapy, among others. These activities have a monthly schedule of free access to the municipalities of Amazonas registered. With this premise, and in partnership with the University of the State of Amazonas (UEA), is promoting the practice of the triad; teaching-research-extension in order to collaborate with the enrichment and acquisition of knowledge through educational practices carried out through teleconferences. Therefore, nursing is to join efforts and inserts as a collaborator of this project running, contributing to the education and training of these professionals who are part of the health system in full Amazon. The aim of this study is to report the experience of academic of Amazonas State University nursing course, about the experience in the extension project underway in Polo Telemedicine Amazon. This was a descriptive study, the experience report type, about the experience of nursing academic UEA, by extension 'Telenursing: teleconsulting and second formative opinion for FHS professionals in the state of Amazonas' project, held in Polo Telemedicine Amazon, through an agreement with the UEA and funded by the Foundation of Amazonas Research from July / 2012 to July / 2016. Initially developed active search of members of the Family Health Strategy professionals, in order to provide training and training teams to use the virtual clinic, as well as the virtual environment is the focus of this tool design. The election period was an aggravating factor for the implementation of teleconsulting proposal, due to change of managers in each municipality, requiring the stoppage until they assume their positions. From this definition, we established the need for new training. The first video conference took place on 03.14.2013 for learning and training in the use of Virtual Learning Environment and Virtual Clinic, with the participation of municipalities of Novo Aripuanã, São Paulo de Olivença and Manacapuru. During the whole project was carried out literature about what is being done and produced at the national level about the subject. By the time the telenursing project has received twenty-five (25) consultancy requests. The consultants sent by nursing professionals, all have been answered to date. Faced with the lived experience, particularly in video conferencing, face to cause difficulties issues, such as the fluctuation in the number of participants in activities, difficulty of participants to reconcile the opening hours of the units with the schedule of video conferencing, transmission difficulties and changes schedule. It was concluded that the establishment of connection between the Telehealth points is one of the main factors for the implementation of Telenursing and that this feature is still new for nursing. However, effective training and updating, may provide to these professional category subsidies to quality health care in the Amazon.

Keywords: Amazon, teleconsulting, telehealth, telenursing

Procedia PDF Downloads 310