Search results for: Automation.

Authors: Sezen Gurdag, Ayse Ebru Akin

Abstract:

There is a growing concern all over the world on the health effect of the formaldehyde emission coming from the adhesive used in the MDF production. In this research, we investigated the effect of nanoparticle addition such as nanoclay and halloysite into urea-formadehyde resin on the total emitted formaldehyde from MDF plates produced using the resin modified as such. First, the curing behavior of the resin was studied by monitoring the pH, curing time, solid content, density and viscosity of the modified resin in comparison to the reference resin with no added nanoparticle. The dosing of the nanoparticle in the dry resin was kept at 1wt%, 3wt% or 5wt%. Consecutively, the resin was used in the production of 50X50 cm MDF samples using laboratory scale press line with full automation system. Modulus of elasticity, bending strength, internal bonding strength, water absorption were also measured in addition to the main interested parameter formaldehyde emission levels which is determined via spectrometric technique following an extraction procedure. Threshold values for nanoparticle dosing levels were determined to be 5wt% for both nanoparticles. However, the reinforcing behavior was observed to be occurring at different levels in comparison to the reference plates with each nanoparticle such that the level of reinforcement with nanoclay was shown to be more favorable than the addition of halloysite due to higher surface area available with the former. In relation, formaldehyde emission levels were observed to be following a similar trend where addition of 5wt% nanoclay into the urea-formaldehyde adhesive helped decrease the formaldehyde emission up to 40% whereas addition of halloysite at its threshold level demonstrated as the same level, i.e., 5wt%, produced an improvement of 18% only.

Keywords: halloysite, nanoclay, fiberboard, urea-formaldehyde adhesive

Procedia PDF Downloads 138

Authors: J. C. Cabanillas-Noris, M. I. Martínez-Hernández, I. León-Monzón

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The selection of diffractive events in the ALICE experiment during the first data taking period (RUN-I) of the Large Hadron Collider (LHC) was limited by the range over which rapidity gaps occur. It would be possible to achieve better measurements by expanding the range in which the production of particles can be detected. For this purpose, the ALICE Diffractive (AD0) detector has been installed and commissioned for the second phase (RUN-II). Any new detector should be able to take the data synchronously with all other detectors and be operated through the ALICE central systems. One of the key elements that must be developed for the AD0 detector is the Detector Control System (DCS). The DCS must be designed to operate safely and correctly this detector. Furthermore, the DCS must also provide optimum operating conditions for the acquisition and storage of physics data and ensure these are of the highest quality. The operation of AD0 implies the configuration of about 200 parameters, from electronics settings and power supply levels to the archiving of operating conditions data and the generation of safety alerts. It also includes the automation of procedures to get the AD0 detector ready for taking data in the appropriate conditions for the different run types in ALICE. The performance of AD0 detector depends on a certain number of parameters such as the nominal voltages for each photomultiplier tube (PMT), their threshold levels to accept or reject the incoming pulses, the definition of triggers, etc. All these parameters define the efficiency of AD0 and they have to be monitored and controlled through AD0 DCS. Finally, AD0 DCS provides the operator with multiple interfaces to execute these tasks. They are realized as operating panels and scripts running in the background. These features are implemented on a SCADA software platform as a distributed control system which integrates to the global control system of the ALICE experiment.

Keywords: AD0, ALICE, DCS, LHC

Procedia PDF Downloads 283

Authors: Abu Zafor Sadek

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Pharmaceuticals were one of the least impacted business sectors during the corona pandemic as they are the center point of Covid-19 fight. Emergency use authorization, unproven indication of some commonly used drugs, self-medication, research and production capacity of an individual country, capacity of producing vaccine by many countries, Active Pharmaceutical Ingredients (APIs) related uncertainty, information gap among manufacturer, practitioners and user, export restriction, duration of lock-down, lack of harmony in transportation, disruption in the regulatory approval process, sudden increased demand of hospital items and protective equipment, panic buying, difficulties in in-person product promotion, e-prescription, geo-politics and associated issues added a new dimension to this industry. Although the industry maintains a reasonable growth throughout Covid-19 days; however, it has been characterized by both long- and short-term effects. Short-term effects have already been visible to so many countries, especially those who are import-dependent and have limited research capacity. On the other hand, it will take a few more time to see the long-term effects. Nevertheless, supply chain disruption, changes in strategic planning, new communication model, squeezing of job opportunity, rapid digitalization are the major short-term effects, whereas long-term effects include a shift towards self-sufficiency, growth pattern changes of certain products, special attention towards clinical studies, automation in operations, the increased arena of ethical issues etc. Therefore, this qualitative and exploratory study identifies the post-covid-19 landscape of the global pharmaceutical industry.

Keywords: covid-19, pharmaceutical, businees, landscape

Procedia PDF Downloads 79

Authors: John Michael Russo

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Honeybees are important to our food system and continue to suffer from high rates of colony loss. Precision feed has brought many benefits to livestock cultivation and these should transfer to apiculture. However, apiculture has unique challenges. The objective of this research is to understand how principles of precision agriculture, applied to apiculture and feed specifically, might effectively improve state-of-the-art cultivation. The methodology surveys apicultural practice to build a model for assessment. First, a review of apicultural motivators is made. Feed method is then evaluated. Finally, precision feed methods are examined as accelerants with potential to advance the effectiveness of feed practice. Six important motivators emerge: colony loss, disease, climate change, site variance, operational costs, and competition. Feed practice itself is used to compensate for environmental variables. The research finds that the current state-of-the-art in apiculture feed focuses on critical challenges in the management of feed schedules which satisfy requirements of the bees, preserve potency, optimize environmental variables, and manage costs. Many of the challenges are most acute when feed is used to dispense medication. Technology such as RNA treatments have even more rigorous demands. Precision feed solutions focus on strategies which accommodate specific needs of individual livestock. A major component is data; they integrate precise data with methods that respond to individual needs. There is enormous opportunity for precision feed to improve apiculture through the integration of precision data with policies to translate data into optimized action in the apiary, particularly through automation.

Keywords: precision agriculture, precision feed, apiculture, honeybees

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Authors: Nadhifah A. Nugraha, Dida D. Damayanti, Widia Juliani

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Airplanes manufacturing is growing along with the increasing demand from consumers. The helicopter's tail called Tailboom is a product of the helicopter division at XYZ Corp, where the Tailboom assembly line is a pull system. Based on observations of existing conditions that occur at XYZ Corp, production is still unable to meet the demands of consumers; lead time occurs greater than the plan agreed upon by the consumers. In the assembly process, each work station experiences a lack of parts and components needed to assemble components. This happens because of the delay in getting the required part information, and there is no warning about the availability of parts needed, it makes some parts unavailable in assembly warehouse. The lack of parts and components from the previous work station causes the assembly process to stop, and the assembly line also stops at the next station. In its completion, the production time was late and not on the schedule. In resolving these problems, the controlling process is needed, which is controlling the assembly line to get all components and subassembly in the right amount and at the right time. This study applies one of Just In Time tools, namely Kanban and automation, should be added as efficiently and effectively communication line becomes electronic Kanban. The problem can be solved by reducing non-value added time, such as waiting time and idle time. The proposed results of controlling the assembly line of Tailboom result in a smooth assembly line without waiting, reduced lead time, and achieving production time according to the schedule agreed with the consumers.

Keywords: kanban, e-Kanban, lead time, pull system

Procedia PDF Downloads 89

Authors: Nathaniel Carpenter

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Web-enabled systems for supporting youth mental health management in Australia are pioneering in their field; however, with their success, these systems are experiencing exponential growth in demand which is straining an already stretched service. Supporting youth mental is critical as the lack of support is associated with significant and lasting negative consequences. To meet this growing demand, and provide critical support, investigations are needed on evaluating and improving existing online support services. Improvements should focus on developing frameworks capable of augmenting and scaling service provisions. There are few investigations informing best-practice frameworks when implementing e-mental health support systems for youth mental health; there are fewer which implement machine learning or artificially intelligent systems to facilitate the delivering of services. This investigation will use a case study methodology to highlight the design features which are important for systems to enable young people to self-manage their mental health. The investigation will also highlight the current information system challenges, to include challenges associated with service quality, provisioning, and scaling. This work will propose methods of meeting these challenges through improved design, service augmentation and automation, service quality, and through artificially intelligent inspired solutions. The results of this study will inform a framework for supporting youth mental health with intelligent and scalable web-enabled technologies to support an ever-growing user base.

Keywords: artificial intelligence, information systems, machine learning, youth mental health

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Authors: Iqraq Kamal, Akmal Razif, Sivadas Chandra Sekaran, Ahmad Syazwan Hisaburi

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Painting plays a crucial role in the aerospace manufacturing industry, serving both protective and cosmetic purposes for components. However, the traditional manual painting method is time-consuming and labor-intensive, posing challenges for the sector in achieving higher efficiency. Additionally, the current automated robot path planning has been a bottleneck for spray painting processes, as typical manual teaching methods are time-consuming, error-prone, and skill-dependent. Therefore, it is essential to develop automated tool path planning methods to replace manual ones, reducing costs and improving product quality. Focusing on flat panel painting in aerospace manufacturing, this study aims to address issues related to unreliable part identification techniques caused by the high-mixture, low-volume nature of the industry. The proposed solution involves using a spray gun and a UR10 robotic arm with a vision system that utilizes one-shot object detection (OS2D) to identify parts accurately. Additionally, the research optimizes path planning by concentrating on the region of interest—specifically, the identified part, rather than uniformly covering the entire painting tray.

Keywords: aerospace manufacturing, one-shot object detection, automated spray painting, vision-based path optimization, deep learning, automation, robotic arm

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Authors: Sagar Jitendra Mahendrakar

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Exploratory testing is a dynamic and adaptable method of software quality assurance that is frequently praised for its ability to find hidden flaws and improve the overall quality of the product. Instead of using preset test cases, exploratory testing allows testers to explore the software application dynamically. This is in contrast to scripted testing methodologies, which primarily rely on tester intuition, creativity, and adaptability. There are several tools and techniques that can aid testers in the exploratory testing process which we will be discussing in this talk.Tests of this kind are able to find bugs of this kind that are harder to find during structured testing or that other testing methods may have overlooked.The purpose of this abstract is to examine the nature and importance of exploratory testing in modern software development methods. It explores the fundamental ideas of exploratory testing, highlighting the value of domain knowledge and tester experience in spotting possible problems that may escape the notice of traditional testing methodologies. Throughout the software development lifecycle, exploratory testing promotes quick feedback loops and continuous improvement by giving testers the ability to make decisions in real time based on their observations. This abstract also clarifies the unique features of exploratory testing, like its non-linearity and capacity to replicate user behavior in real-world settings. Testers can find intricate bugs, usability problems, and edge cases in software through impromptu exploration that might go undetected. Exploratory testing's flexible and iterative structure fits in well with agile and DevOps processes, allowing for a quicker time to market without sacrificing the quality of the final product.

Keywords: exploratory, testing, automation, quality

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Authors: Muhammad Umair, Yuri Nikolaev, Denis Artemov, Ighor Uzhinsky

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A flexible manufacturing system is an integral part of a smart factory of industry 4.0 in which every machine is interconnected and works autonomously. Robots are in the process of replacing humans in every industrial sector. As the cyber-physical-system (CPS) and artificial intelligence (AI) are advancing, the manufacturing industry is getting more dependent on computers than human brains. This modernization has boosted the production with high quality and accuracy and shifted from classic production to smart manufacturing systems. However, material handling for such automated productions is a challenge and needs to be addressed with the best possible solution. Conventional clamping systems are designed for manual work and not suitable for highly automated production systems. Researchers and engineers are trying to find the most economical solution for loading/unloading and transportation workpieces from a warehouse to a machine shop for machining operations and back to the warehouse without human involvement. This work aims to propose an advanced multi-shape tooling solution for highly automated manufacturing systems. The currently obtained result shows that it could function well with automated guided vehicles (AGVs) and modern conveyor belts. The proposed solution is following requirements to be automation-friendly, universal for different part geometry and production operations. We used a bottom-up approach in this work, starting with studying different case scenarios and their limitations and finishing with the general solution.

Keywords: artificial intelligence, cyber physics system, Industry 4.0, material handling, smart factory, flexible manufacturing system

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Authors: Eden Teshome Hunde

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Wireless sensor and actuator networks (WSANs) are of great significance in the realm of industrial automation systems. However, the aspect of security in WSANs has been somewhat overlooked. One particular security concern is the rank attack, where malicious actors actively manipulate the transmission of messages from neighboring nodes. This undermines the entire network's data collection and routing operations, resulting in a significant degradation of network performance. This attack adversely affects crucial metrics such as packet delivery ratio (PDR), latency, and power consumption, ultimately reducing the network's overall lifespan. In order to foster trust among nodes, ensure accurate delivery of data to end users, safeguard shared data in the cloud from security breaches, and prevent rank attacks within the network, it is crucial to protect the network against such malicious activities. This research paper aims to introduce an enhanced version of the Routing Protocol for Low-Power and Lossy Networks (RPL) protocol, specifically tailored to identify and eliminate rank attacks within existing WSANs. The effectiveness of the new protocol will be assessed through experimentation using Zolertia (Z1) sensors in the Cooja network simulator. To minimize network overhead on the sensors' side, the proposed scheme limits cryptographic operations to symmetric key-based mechanisms such as XORing, hash functions, and encryption. These operations will be implemented using a C-compiler and verified through the ModelSIM Altera SE edition 11.0 simulator.

Keywords: ModelSIM Altera SE, RPL, WSANs, Zolertia

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Authors: Hung-Jen Lin, Chien-Hao Wang, Chien-Peng Huang, Yu-Sheng Tseng, En-Cheng Yang, Joe-Air Jiang

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Bees’ incoming and outgoing behavior is a decisive index which can indicate the health condition of a colony. Traditional methods for monitoring the behavior of honey bees (Apis mellifera) take too much time and are highly labor-intensive, and the lack of automation and synchronization disables researchers and beekeepers from obtaining real-time information of beehives. To solve these problems, this study proposes to use an Internet of Things (IoT)-based system for counting honey bees’ incoming and outgoing activities using an infrared interruption technique, while environmental factors are recorded simultaneously. The accuracy of the established system is verified by comparing the counting results with the outcomes of manual counting. Moreover, this highly -accurate device is appropriate for providing quantitative information regarding honey bees’ incoming and outgoing behavior. Different statistical analysis methods, including one-way ANOVA and two-way ANOVA, are used to investigate the influence of environmental factors, such as temperature, humidity, illumination and ambient pressure, on bees’ incoming and outgoing behavior. With the real-time data, a standard model is established using the outcomes from analyzing the relationship between environmental factors and bees’ incoming and outgoing behavior. In the future, smart control systems, such as a temperature control system, can also be combined with the proposed system to create an appropriate colony environment. It is expected that the proposed system will make a considerable contribution to the apiculture and researchers.

Keywords: ANOVA, environmental factors, honey bee, incoming and outgoing behavior

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Authors: Toshifumi Hiramatsu, Satoshi Morita, Manuel Pencelli, Marta Niccolini, Matteo Ragaglia, Alfredo Argiolas

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Automation technologies for agriculture field are needed to promote labor-saving. One of the most relevant problems in automated agriculture is represented by controlling the robot along a predetermined path in presence of rough terrain or incline ground. Unfortunately, disturbances originating from interaction with the ground, such as slipping, make it quite difficult to achieve the required accuracy. In general, it is required to move within 5-10 cm accuracy with respect to the predetermined path. Moreover, lateral velocity caused by gravity on the incline field also affects slipping. In this paper, a path-tracking controller for tracked mobile robots moving on rough terrains of incline field such as vineyard is presented. The controller is composed of a disturbance observer and an adaptive controller based on the kinematic model of the robot. The disturbance observer measures the difference between the measured and the reference yaw rate and linear velocity in order to estimate slip. Then, the adaptive controller adapts “virtual” parameter of the kinematics model: Instantaneous Centers of Rotation (ICRs). Finally, target angular velocity reference is computed according to the adapted parameter. This solution allows estimating the effects of slip without making the model too complex. Finally, the effectiveness of the proposed solution is tested in a simulation environment.

Keywords: the agricultural robot, autonomous control, path-tracking control, tracked mobile robot

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Authors: Enido Fabiano de Ramos, Ieda Kanashiro Makiya, Francisco I. Giocondo Cesar

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This study presents an analysis of the Digital Twin concept applied to the academic environment, focusing on the development of a Digital Twin Smart Campus Framework. Using bibliometric analysis methodologies and literature review, the research investigates the evolution and applications of the Digital Twin in educational contexts, comparing these findings with the advances of Industry 4.0. It was identified gaps in the existing literature and highlighted the need to adapt Digital Twin principles to meet the specific demands of a smart campus. By integrating Industry 4.0 concepts such as automation, Internet of Things, and real-time data analytics, we propose an innovative framework for the successful implementation of the Digital Twin in academic settings. The results of this study provide valuable insights for university campus managers, allowing for a better understanding of the potential applications of the Digital Twin for operations, security, and user experience optimization. In addition, our framework offers practical guidance for transitioning from a digital campus to a digital twin smart campus, promoting innovation and efficiency in the educational environment. This work contributes to the growing literature on Digital Twins and Industry 4.0, while offering a specific and tailored approach to transforming university campuses into smart and connected spaces, high demanded by Society 5.0 trends. It is hoped that this framework will serve as a basis for future research and practical implementations in the field of higher education and educational technology.

Keywords: smart campus, digital twin, industry 4.0, education trends, society 5.0

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Authors: Julian Wise

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Newcrest Mining is one of the world’s top five gold and rare earth mining organizations by production, reserves and market capitalization in the world. This paper elaborates on the data acquisition processes employed by Newcrest in collaboration with Fortune 500 listed organization, Insight Enterprises, to standardize machine learning solutions which process data from over a hundred thousand distributed Internet of Things (IoT) devices located at mine sites globally. Through the utilization of software architecture cloud technologies and edge computing, the technological developments enable for standardized processes of machine learning applications to influence the strategic optimization of mineral processing. Target objectives of the machine learning optimizations include time savings on mineral processing, production efficiencies, risk identification, and increased production throughput. The data acquired and utilized for predictive modelling is processed through edge computing by resources collectively stored within a data lake. Being involved in the digital transformation has necessitated the standardization software architecture to manage the machine learning models submitted by vendors, to ensure effective automation and continuous improvements to the mineral process models. Operating at scale, the system processes hundreds of gigabytes of data per day from distributed mine sites across the globe, for the purposes of increased improved worker safety, and production efficiency through big data applications.

Keywords: mineral technology, big data, machine learning operations, data lake

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Authors: Akaki Qatamidze

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Implementation of the project will be a step forward in terms of reliability in Georgia and the improvement of the construction and the development of construction. Completion of the project is expected to result in a complete knowledge, which is expressed in concrete and stone structures of assessing the technical condition of the processing. This method is based on a detailed examination of the structure, in order to establish the injuries and the elimination of the possibility of changing the structural scheme of the new requirements and architectural preservationists. Reinforced concrete and stone structures research project carried out in a systematic analysis of the important approach is to optimize the process of research and development of new knowledge in the neighboring areas. In addition, the problem of physical and mathematical models of rational consent, the main pillar of the physical (in-situ) data and mathematical calculation models and physical experiments are used only for the calculation model specification and verification. Reinforced concrete and stone construction defects and failures the causes of the proposed research to enhance the effectiveness of their maximum automation capabilities and expenditure of resources to reduce the recommended system analysis of the methodological concept-based approach, as modern science and technology major particularity of one, it will allow all family structures to be identified for the same work stages and procedures, which makes it possible to exclude subjectivity and addresses the problem of the optimal direction. It discussed the methodology of the project and to establish a major step forward in the construction trades and practical assistance to engineers, supervisors, and technical experts in the construction of the settlement of the problem.

Keywords: building, reinforced concrete, expertise, stone structures

Procedia PDF Downloads 316

Authors: Enido Fabiano de Ramos, Ieda Kanashiro Makiya, Francisco I. Giocondo Cesar

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This study investigates the application of Digital Twins (DT) in Smart Hospital Environments (SHE), through a bibliometric study and literature review, including comparison with the principles of Industry 4.0. It aims to analyze the current state of the implementation of digital twins in clinical and non-clinical operations in healthcare settings, identifying trends and challenges, comparing these practices with Industry 4.0 concepts and technologies, in order to present a basic framework including stages and maturity levels. The bibliometric methodology will allow mapping the existing scientific production on the theme, while the literature review will synthesize and critically analyze the relevant studies, highlighting pertinent methodologies and results, additionally the comparison with Industry 4.0 will provide insights on how the principles of automation, interconnectivity and digitalization can be applied in healthcare environments/operations, aiming at improvements in operational efficiency and quality of care. The results of this study will contribute to a deeper understanding of the potential of Digital Twins in Smart Hospitals, in addition to the future potential from the effective integration of Industry 4.0 concepts in this specific environment, presented through the practical framework, after all, the urgent need for changes addressed in this article is undeniable, as well as all their value contribution to human sustainability, designed in SDG3 – Health and well-being: ensuring that all citizens have a healthy life and well-being, at all ages and in all situations. We know that the validity of these relationships will be constantly discussed, and technology can always change the rules of the game.

Keywords: digital twin, smart hospital, healthcare operations, industry 4.0, SDG3, technology

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Authors: Ruvaitha Banu, Amaladhithyan Krishnamoorthy

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IoT networks today solve various consumer problems, from home automation systems to aiding in driving autonomous vehicles with the exploration of multiple devices. For example, in an autonomous vehicle environment, multiple sensors are available on roads to monitor weather and road conditions and interact with each other to aid the vehicle in reaching its destination safely and timely. IoT systems are predominantly dependent on the cloud environment for data storage, and computing needs that result in latency problems. With the advent of Fog networks, some of this storage and computing is pushed to the edge/fog nodes, saving the network bandwidth and reducing the latency proportionally. Managing the data stored in these fog nodes becomes crucial as it might also store sensitive information required for a certain application. Data management in fog nodes is strenuous because Fog networks are dynamic in terms of their availability and hardware capability. It becomes more challenging when the nodes in the network also live a short span, detaching and joining frequently. When an end-user or Fog Node wants to access, read, or write data stored in another Fog Node, then a new protocol becomes necessary to access/manage the data stored in the fog devices as a conventional static way of managing the data doesn’t work in Fog Networks. The proposed solution discusses a protocol that acts by defining sensitivity levels for the data being written and read. Additionally, a distinct data distribution and replication model among the Fog nodes is established to decentralize the access mechanism. In this paper, the proposed model implements stewardship towards the data stored in the Fog node using the application of Reinforcement Learning so that access to the data is determined dynamically based on the requests.

Keywords: IoT, fog networks, data stewardship, dynamic access policy

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Authors: Sanchita Abrol, Kunal Rana, Ankit Dhir, S. K. Gupta

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According to N.R.E.L.’s findings, 700 crore gallons of petrol is used annually to run the air conditioners of passenger vehicles (nearly 6% of total fuel consumption in the USA). Beyond fuel use, the Environmental Protection Agency reported that refrigerant leaks from auto air conditioning units add an additional 5 crore metric tons of carbon emissions to the atmosphere each year. The objective of our project is to deal with this vital issue by carefully modifying the interiors of a car thereby increasing its mileage and the efficiency of its engine. This would consequently result in a decrease in tail emission and generated pollution along with improved car performance. An automatic mechanism, deployed between the front and the rear seats, consisting of transparent thermal insulating sheet/curtain, would roll down as per the requirement of the driver in order to optimize the volume for effective air conditioning, when travelling alone or with a person. The reduction in effective volume will yield favourable results. Even on a mild sunny day, the temperature inside a parked car can quickly spike to life-threatening levels. For a stationary parked car, insulation would be provided beneath its metal body so as to reduce the rate of heat transfer and increase the transmissivity. As a result, the car would not require a large amount of air conditioning for maintaining lower temperature, which would provide us similar benefits. Authors established the feasibility studies, system engineering and primarily theoretical and experimental results confirming the idea and motivation to fabricate and test the actual product.

Keywords: automation, car, cooling insulating curtains, heat optimization, insulation, reduction in tail emission, mileage

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Authors: Mehrshad Khosraviani, Sepehr Najjarpour

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Considering the usage of graph-based approaches in systems and synthetic biology, and the various types of ‎the graphs employed by them, a comprehensive graph library based ‎on the three-tier architecture (3TA) was previously introduced for full representation of the biological systems. Although proposing a 3TA-based graph library, three following reasons motivated us to redesign the graph ‎library based on the service-oriented architecture (SOA): (1) Maintaining the accuracy of the data related to an input graph (including its edges, its ‎vertices, its topology, etc.) without involving the end user:‎ Since, in the case of using 3TA, the library files are available to the end users, they may ‎be utilized incorrectly, and consequently, the invalid graph data will be provided to the ‎computer algorithms. However, considering the usage of the SOA, the operation of the ‎graph registration is specified as a service by encapsulation of the library files. In other words, overall control operations needed for registration of the valid data will be the ‎responsibility of the services. (2) Partitioning of the library product into some different parts: Considering 3TA, a whole library product was provided in general. While here, the product ‎can be divided into smaller ones, such as an AND/OR graph drawing service, and each ‎one can be provided individually. As a result, the end user will be able to select any ‎parts of the library product, instead of all features, to add it to a project. (3) Reduction of the complexities: While using 3TA, several other libraries must be needed to add for connecting to the ‎database, responsibility of the provision of the needed library resources in the SOA-‎based graph library is entrusted with the services by themselves. Therefore, the end user ‎who wants to use the graph library is not involved with its complexity. In the end, in order to ‎make ‎the library easier to control in the system, and to restrict the end user from accessing the files, ‎it was preferred to use the service-oriented ‎architecture ‎‎(SOA) over the three-tier architecture (3TA) and to redevelop the previously proposed graph library based on it‎.

Keywords: Bio-Design Automation, Biological System, Graph Library, Service-Oriented Architecture, Systems and Synthetic Biology

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Authors: Edward Nartey

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Although the boundaries of sustainable performance and growth in the field of service supply chains (SCs) have been broadened by scholars in recent years, research on the impact and promises of Industry 4.0 Destructive Technologies (IDTs) on sustainability performance under disruptive events is still scarce. To mitigate disruptions in the SC and improve efficiency by identifying areas for cost savings, organizations have resorted to investments in digitalization, automation, and control mechanisms in recent years. However, little is known about the sustainability implications for IDT adoption and controls in service SCs, especially during disruptive events. To investigate this paradox, survey data were sought from 223 public health managers across Ghana and analyzed via covariance-based structural equations modelling. The results showed that both formal and informal control have a positive and significant relationship with IDT adoption. In addition, formal control has a significant and positive relationship with environmental and economic sustainability but an insignificant relationship with social sustainability. Furthermore, informal control positively impacts economic performance but has an insignificant relationship with social and environmental sustainability. While the findings highlight the prevalence of the IDTs being initiated by Ghanaian public health institutions (PHIs), this study concludes that the installed control systems in these organizations are inadequate for promoting sustainable SC behaviors under destructive events. Thus, in crisis situations, PHIs need to redesign their control systems to facilitate IDT integration towards sustainability issues in SCs.

Keywords: industry 4.0 destructive technologies, formal control, informal control, sustainable supply chain performance, public health organizations

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Authors: Joshua Loiselle

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This paper discusses the challenges engineering teams face in conducting Failure Modes and Effects Analysis (FMEA) studies. This paper focuses on the specific topic of improving the efficiency and effectiveness of FMEA studies. Modern economic needs and increased business competition require engineers to constantly develop newer and better solutions within shorter timeframes and tighter margins. In addition, documentation requirements for meeting standards/regulatory compliance and customer needs are becoming increasingly complex and verbose. Managing open actions and continuous improvement activities across all projects, product variations, and processes in addition to daily engineering tasks is cumbersome, time consuming, and is susceptible to errors, omissions, and non-conformances. FMEA studies are proven methods for improving products and processes while subsequently reducing engineering workload and improving machine and resource availability through a pre-emptive, systematic approach of identifying, analyzing, and improving high-risk components. If implemented correctly, FMEA studies significantly reduce costs and improve productivity. However, the value of an effective FMEA is often shrouded by a lack of clarity and structure, misconceptions, and previous experiences and, as such, FMEA studies are frequently grouped with the other required information and documented retrospectively in preparation of customer requirements or audits. Performing studies in this way only adds cost to a project and perpetuates the misnomer that FMEA studies are not value-added activities. This paper discusses the benefits of effective FMEA studies, the challenges related to conducting FMEA studies, best practices for efficiently overcoming challenges via structure and automation, and the benefits of implementing those practices.

Keywords: FMEA, quality, APQP, PPAP

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Authors: Andrew Couch, Nicholas Loyd, Nathan Tenhundfeld

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The rapid emergence of technology observably induces disruptive effects that carry implications for internal organizational dynamics as well as external market opportunities, strategic pressures, and threats. An examination of the historical tendencies of technology innovation shows that the body of managerial knowledge for addressing such disruption is underdeveloped. Fundamentally speaking, the impacts of innovation are unique and situationally oriented. Hence, the appropriate managerial response becomes a complex function that depends on the nature of the emerging technology, the posturing of internal organizational dynamics, the rate of technological growth, and much more. This research considers a particular case of mismanagement, the BP Texas City Refinery explosion of 2005, that carries notable discrepancies on the basis of human factors principles. Moreover, this research considers the modern technological climate (shaped by Industry 4.0 technologies) and seeks to arrive at an appropriate conceptual lens by which human factors principles and Industry 4.0 may be favorably integrated. In this manner, the careful examination of these phenomena helps to better support the sustainment of human factors principles despite the disruptive impacts that are imparted by technological innovation. In essence, human factors considerations are assessed through the application of principles that stem from usability engineering, the Swiss Cheese Model of accident causation, human-automation interaction, signal detection theory, alarm design, and other factors. Notably, this stream of research supports a broader framework in seeking to guide organizations amid the uncertainties of Industry 4.0 to capture higher levels of adoption, implementation, and transparency.

Keywords: Industry 4.0, human factors engineering, management, case study

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Authors: Ian Omung'a

Abstract:

Breast cancer remains to be one of the deadliest cancers for women worldwide, with the risk of developing tumors being as high as 50 percent in Sub-Saharan African countries like Kenya. With as many as 42 percent of these cases set to be diagnosed late when cancer has metastasized and or the prognosis has become terminal, Full Field Digital [FFD] Mammography remains an effective screening technique that leads to early detection where in most cases, successful interventions can be made to control or eliminate the tumors altogether. FFD Mammograms have been proven to multiply more effective when used together with Computer-Aided Detection and Diagnosis [CADe] systems, relying on algorithmic implementations of Deep Learning techniques in Computer Vision to carry out deep pattern recognition that is comparable to the level of a human radiologist and decipher whether specific areas of interest in the mammogram scan image portray abnormalities if any and whether these abnormalities are indicative of a benign or malignant tumor. Within this paper, we review emergent Deep Learning techniques that will prove relevant to the development of State-of-The-Art FFD Mammogram CADe systems. These techniques will span self-supervised learning for context-encoded occlusion, self-supervised learning for pre-processing and labeling automation, as well as the creation of a standardized large-scale mammography dataset as a benchmark for CADe systems' evaluation. Finally, comparisons are drawn between existing practices that pre-date these techniques and how the development of CADe systems that incorporate them will be different.

Keywords: breast cancer diagnosis, computer aided detection and diagnosis, deep learning, whole mammogram classfication, ultrasound classification, computer vision

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Authors: Jigg L. Pelayo, Ricardo G. Villar, Einstine M. Opiso

Abstract:

The advances of aerial laser scanning in the Philippines has open-up entire fields of research in remote sensing and machine vision aspire to provide accurate timely information for the government and the public. Rapid mapping of polygonal roads and roof boundaries is one of its utilization offering application to disaster risk reduction, mitigation and development. The study uses low density LiDAR data and high resolution aerial imagery through object-oriented approach considering the theoretical concept of data analysis subjected to machine learning algorithm in minimizing the constraints of feature extraction. Since separating one class from another in distinct regions of a multi-dimensional feature-space, non-trivial computing for fitting distribution were implemented to formulate the learned ideal hyperplane. Generating customized hybrid feature which were then used in improving the classifier findings. Supplemental algorithms for filtering and reshaping object features are develop in the rule set for enhancing the final product. Several advantages in terms of simplicity, applicability, and process transferability is noticeable in the methodology. The algorithm was tested in the different random locations of Misamis Oriental province in the Philippines demonstrating robust performance in the overall accuracy with greater than 89% and potential to semi-automation. The extracted results will become a vital requirement for decision makers, urban planners and even the commercial sector in various assessment processes.

Keywords: feature extraction, machine learning, OBIA, remote sensing

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Authors: J.P. Henriques, E. R. Neto, G. Almeida, G. Ribeiro, J.V. Coutinho, A.B. Lugli

Abstract:

Industry 4.0, or the Fourth Industrial Revolution, is transforming the relationship between people and machines. In this scenario, some technologies such as Cloud Computing, Internet of Things, Augmented Reality, Artificial Intelligence, Additive Manufacturing, among others, are making industries and devices increasingly intelligent. One of the most powerful technologies of this new revolution is the Digital Twin, which allows the virtualization of a real system or process. In this context, the present paper addresses the linear and nonlinear dynamic study of a didactic level plant using Digital Twin. In the first part of the work, the level plant is identified at a fixed point of operation, BY using the existing method of least squares means. The linearized model is embedded in a Digital Twin using Automation Studio® from Famous Technologies. Finally, in order to validate the usage of the Digital Twin in the linearized study of the plant, the dynamic response of the real system is compared to the Digital Twin. Furthermore, in order to develop the nonlinear model on a Digital Twin, the didactic level plant is identified by using the method proposed by Hammerstein. Different steps are applied to the plant, and from the Hammerstein algorithm, the nonlinear model is obtained for all operating ranges of the plant. As for the linear approach, the nonlinear model is embedded in the Digital Twin, and the dynamic response is compared to the real system in different points of operation. Finally, yet importantly, from the practical results obtained, one can conclude that the usage of Digital Twin to study the dynamic systems is extremely useful in the industrial environment, taking into account that it is possible to develop and tune controllers BY using the virtual model of the real systems.

Keywords: industry 4.0, digital twin, system identification, linear and nonlinear models

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Authors: Nikolay P. Brayanov, Anna V. Stoynova

Abstract:

Model-based development approach is gaining more support and acceptance. Its higher abstraction level brings simplification of systems’ description that allows domain experts to do their best without particular knowledge in programming. The different levels of simulation support the rapid prototyping, verifying and validating the product even before it exists physically. Nowadays model-based approach is beneficial for modelling of complex embedded systems as well as a generation of code for many different hardware platforms. Moreover, it is possible to be applied in safety-relevant industries like automotive, which brings extra automation of the expensive device certification process and especially in the software qualification. Using it, some companies report about cost savings and quality improvements, but there are others claiming no major changes or even about cost increases. This publication demonstrates the level of maturity and autonomy of model-based approach for code generation. It is based on a real live automotive seat heater (ASH) module, developed using The Mathworks, Inc. tools. The model, created with Simulink, Stateflow and Matlab is used for automatic generation of C code with Embedded Coder. To prove the maturity of the process, Code generation advisor is used for automatic configuration. All additional configuration parameters are set to auto, when applicable, leaving the generation process to function autonomously. As a result of the investigation, the publication compares the quality of generated embedded code and a manually developed one. The measurements show that generally, the code generated by automatic approach is not worse than the manual one. A deeper analysis of the technical parameters enumerates the disadvantages, part of them identified as topics for our future work.

Keywords: embedded code generation, embedded C code quality, embedded systems, model-based development

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Authors: Adrian Ionita, Ana-Maria Ghimes

Abstract:

The lack of features, design and the lack of promoting an integrated booking application are some of the reasons why most online travel platforms only offer automation of old booking processes, being limited to the integration of a smaller number of services without addressing the user experience. This paper represents a practical study on how to improve travel applications creating user-profiles through data-mining based on neural networks and genetic algorithms. Choices made by users and their ‘friends’ in the ‘social’ network context can be considered input data for a recommendation engine. The purpose of using these algorithms and this design is to improve user experience and to deliver more features to the users. The paper aims to highlight a broader range of improvements that could be applied to travel applications in terms of design and service integration, while the main scientific approach remains the technical implementation of the neural network solution. The motivation of the technologies used is also related to the initiative of some online booking providers that have made the fact that they use some ‘neural network’ related designs public. These companies use similar Big-Data technologies to provide recommendations for hotels, restaurants, and cinemas with a neural network based recommendation engine for building a user ‘DNA profile’. This implementation of the ‘profile’ a collection of neural networks trained from previous user choices, can improve the usability and design of any type of application.

Keywords: artificial intelligence, big data, cloud computing, DNA profile, genetic algorithms, machine learning, neural networks, optimization, recommendation system, user profiling

Procedia PDF Downloads 143

Authors: Pedro Ferreira, Alexandre Frugoli, Pedro Frugoli, Lucio Leonardo, Thais Cavalheri

Abstract:

The teaching of fluid mechanics in engineering courses is, in general, a source of great difficulties for learning. The possibility of the use of experiments with didactic wind tunnels can facilitate the education of future professionals. The objective of this proposal is the development of a low-cost aerodynamic balance to be used in a didactic wind tunnel. The set is comprised of an Arduino microcontroller, programmed by an open source software, linked to load cells built by students from another project. The didactic wind tunnel is 5,0m long and the test area is 90,0 cm x 90,0 cm x 150,0 cm. The Weq® electric motor, model W-22 of 9,2 HP, moves a fan with nine blades, each blade 32,0 cm long. The Weq® frequency inverter, model WEGCFW 08 (Vector Inverter) is responsible for wind speed control and also for the motor inversion of the rotational direction. A flat-convex profile prototype of airfoil was tested by measuring the drag and lift forces for certain attack angles; the air flux conditions remained constant, monitored by a Pitot tube connected to a EXTECH® Instruments digital pressure differential manometer Model HD755. The results indicate a good agreement with the theory. The choice of all of the components of this proposal resulted in a low-cost product providing a high level of specific knowledge of mechanics of fluids, which may be a good alternative to teaching in countries with scarce educational resources. The system also allows the expansion to measure other parameters like fluid velocity, temperature, pressure as well as the possibility of automation of other functions.

Keywords: aerodynamic balance, wind tunnel, strain gauge, load cell, Arduino, low-cost education

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Authors: Sugandhi, Parteek Kumar, Sanmeet Kaur

Abstract:

Sign Language (SL) is used by deaf and other people who cannot speak but can hear or have a problem with spoken languages due to some disability. It is a visual gesture language that makes use of either one hand or both hands, arms, face, body to convey meanings and thoughts. SL automation system is an effective way which provides an interface to communicate with normal people using a computer. In this paper, an avatar based dictionary has been proposed for text to Indian Sign Language (ISL) generation system. This research work will also depict a literature review on SL corpus available for various SL s over the years. For ISL generation system, a written form of SL is required and there are certain techniques available for writing the SL. The system uses Hamburg sign language Notation System (HamNoSys) and Signing Gesture Mark-up Language (SiGML) for ISL generation. It is developed in PHP using Web Graphics Library (WebGL) technology for 3D avatar animation. A multilingual ISL dictionary is developed using HamNoSys for both English and Hindi Language. This dictionary will be used as a database to associate signs with words or phrases of a spoken language. It provides an interface for admin panel to manage the dictionary, i.e., modification, addition, or deletion of a word. Through this interface, HamNoSys can be developed and stored in a database and these notations can be converted into its corresponding SiGML file manually. The system takes natural language input sentence in English and Hindi language and generate 3D sign animation using an avatar. SL generation systems have potential applications in many domains such as healthcare sector, media, educational institutes, commercial sectors, transportation services etc. This research work will help the researchers to understand various techniques used for writing SL and generation of Sign Language systems.

Keywords: avatar, dictionary, HamNoSys, hearing impaired, Indian sign language (ISL), sign language

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Authors: Tei Kim, Brooklynn McNeil, Kathryn Dunn, Douglas I. Walker

Abstract:

To better characterize the relationship between complex chemical exposures and disease, our laboratory uses an approach that combines low-cost, polydimethylsiloxane (silicone) wristband samplers that absorb many of the chemicals we are exposed to with untargeted high-resolution mass spectrometry (HRMS) to characterize 1000’s of chemicals at a time. In studies with human populations, these wristbands can provide an important measure of our environment: however, there is a need to use this approach in large cohorts to study exposures associated with the disease. To facilitate the use of silicone samplers in large scale population studies, the goal of this research project was to establish automated sample preparation methods that improve throughput, robustness, and scalability of analytical methods for silicone wristbands. Using the Opentron OT2 automated liquid platform, which provides a low-cost and opensource framework for automated pipetting, we created two separate workflows that translate the manual wristband preparation method to a fully automated protocol that requires minor intervention by the operator. These protocols include a sequence generation step, which defines the location of all plates and labware according to user-specified settings, and a transfer protocol that includes all necessary instrument parameters and instructions for automated solvent extraction of wristband samplers. These protocols were written in Python and uploaded to GitHub for use by others in the research community. Results from this project show it is possible to establish automated and open source methods for the preparation of silicone wristband samplers to support profiling of many environmental exposures. Ongoing studies include deployment in longitudinal cohort studies to investigate the relationship between personal chemical exposure and disease.

Keywords: bioinformatics, automation, opentrons, research

Procedia PDF Downloads 90