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Production activities are generating a large amount of data in different types (i.e., text, images), that is not well exploited. This data can be translated easily to knowledge that can help to predict all the risks that can impact the business, solve problems, promote efficiency of the manufacturing to the maximum, make the production more flexibl...
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... main goal of smart manufacturing is to collect data in real time, analyze it in an automatic way, and use the results to improve the manufacturing process. Therefore, we can start by eliminating all hidden areas in the factory (Fig. 1.) through connecting all the tools, machines, equipment, workers, environment and supply chain to the cyberspace of computing platforms across IIoT technologies. Besides, Sensors, actuators, controllers, RFID tags and smart meters (IIoT equipment) that are connected through network, will generate a huge volume of different type of data. ...Similar publications
Production activities is generating a large amount of data in different types (i.e., text, images), that is not well exploited. This data can be translated easily to knowledge that can help to predict all the risks that can impact the business, solve problems, promote efficiency of the manufacture to the maximum, make the production more flexible a...
El incesante avance tecnológico producto del internet e Internet
de las Cosas, ha traído grandes avances informáticos, así como diferentes retos
en términos de ciberseguridad, razón por la cual el presente estudio tuvo como
objetivo identificar las principales tendencias investigativas alrededor de estudios
que abordaran el uso de tecnologías d...
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... Seamless connectivity of all sensors and actuators in the plant, even those in remote locations, is possible using Big Data. Through the dematerialization of activities and the widespread interconnection of objects, machines, and people, access is opened to the collection and analysis of massive data in manufacturing units [5], [25], [43], [155]. ...
With growing evidence of advanced technologies-assisted smart processes, it is fundamental to comprehend whether manufacturing systems are adequate to manage flexibility and complexity to enhance the monitoring of smart factories. Smart manufacturing (SM) is evolving as a new version of traditional manufacturing, revealing the magnitude and power of smart technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT). The wide applicability of these technologies is allowing important innovations across all industries. As the manufacturing industry has gained benefits, the current boosts of Smart Manufacturing are experiencing exceptional levels of interest. However, providing suitable SM systems and identifying the priority of requirements may vary according to different scenarios. To this end, this study presents a systematic survey of the current SM research trends. Furthermore, this paper aims to present a consistent and comprehensive vision of existing efforts in smart manufacturing and discussed the remaining open issues.
... There are many types of emerging methods such as SHM, General smart monitoring (GSM) in which IoMT are helpful in various streams to minimize direct, indirect costs and non-distinctive evolution such as electrical resistivity, ultrasonic surface waves, ground penetrating radar techniques are to name a few. IoTs are the cascade of various types of electrical and wireless devices, all are inter connected and share their patient data for the diagnosis and analysis and also used for saving the data of patients [4]. In wireless communications there are various sensors and devices which collect the patient data. ...
In the rapidly growing world of technology and evolution, the outbreak and emergences diseases have become a critical issue. Precaution, prevention and controlling the diseases by technology has become the major challenge for healthcare professionals and health care industries. Maintaining a healthy lifestyle has become impossible in the busy work schedules. Smart health monitoring system is the solution to the above poses challenges. The recent revolution of industry 5.0 and 5G has led to development of smart cum cost effective sensors which help in real time health monitoring or individuals. The SHM has led to fast, cost effective, and reliable health monitoring services from remote locations which was not possible with traditional health care systems. The integration of blockchain framework improved data security and data privacy of confidential data of patient to prevent the data misuse against patients. Involvement of Deep Learning and Machine learning to analyze health data to achieve multiple targets has helped attain preventive healthcare and fatality management in patients. This has helped in the early detection of chronic diseases which was not possible recently. To make the services more cost effective and real time, the integration of cloud computing and cloud storage has been implemented. The work presents the systematic review of SHM along with recent advancements in SHM with existing challenges.
State Vocational High School (SMK) 1 Perhentian Raja is a school located in Hangtuah village which is known as a catfish-producing village in Kampar Riau Regency, which has a major in Computer Network Engineering (TKJ) and Fisheries Cultivation. After making direct observations to the location of the Community Partnership Program (PKM), we found various forms of partner problems in education services, including student absenteeism which is still manual. The teacher will call the students one by one and then marked by the teacher on the attendance sheet. This will use teaching time in the classroom and laboratory, as a result learning activities will be less effective. This problem is also faced by librarians, with low student visits to the library. Meanwhile, other problems are parking services and school security. the condition of the school is still new, the condition of the school is still open will have an impact on the weakness of school security. especially with parking conditions that are still scattered and not neatly arranged, it will provoke crime in schools. 95 percent of students and teachers using motorized vehicles to school will make it difficult for school guards to maintain the vehicle. The solutions offered to achieve this PKM goal are Smart School Socialization, Internet of Things (IoT) training, Radio Frequency Identification (RFID) technology workshops, RFID Technology Training for Student Attendance and School Libraries, Parking RFID Assembly Training, and School CCTV Installation Training. , RFID and CCTV Maintenance Workshop. The output targets to be achieved from this PKM can be felt by students, teachers and schools. RFID and CCTV technology will provide educational services and school security. This PKM activity will have an impact on improving the quality of education and become one of the reference schools in the Perhentian Raja sub-district, Kampar district.
In as early as the 1980s, air traffic flow management actions (ATFM), as supplementary strategies to match the demand for air travel with the available resource capacities, have been widely discussed and evaluated based on its implementation and probable trade-offs between conflicting and diverse interests of stakeholders in the commercial aviation industry. Among the ATFM actions—ground holding, airborne holding, speed controlling, and rerouting—rerouting is found to be a viable recourse particularly when flights are already at its en-route phase, where the presumed and more favored based on safety considerations, holding of flights on the ground, becomes completely infeasible. Some research works put forward relevant solution approaches including deterministic and stochastic mathematical programming models, machine learning algorithms, and simulation models. Despite the relevance and validity demonstrated by such models in testbed environments, even on a large-scale basis, these models failed to sufficiently capture the individual and collective interests of stakeholders altogether. Considering that the decision process in the air transportation system is taken part by stakeholders (i.e., airlines, air traffic control), previous research works tend to satisfy only one stakeholder by incorporating one or more of its interests (e.g., cost minimization, reduction of distance traveled). Such a case does not take full regard to how a stakeholder-specific solution might affect another stakeholder’s preference. Therefore, this paper aims to address the post-departure aircraft rerouting problem by proposing a multiple stakeholder-based target-oriented robust-optimization (MS-TORO) approach that incorporates the individual interests of stakeholders. A hypothetical case study is conducted to illustrate the proposed model. It can be noted that a significant shift of route preference occurs as goals are aligned in terms of the individual interests of the stakeholders and that of their collective goal. The results of this work can provide practical insights to stakeholders in the course of decision-making in a particular area of the air transportation domain.
For achieving the ambitious objectives of intelligent production, the artificial intelligence through its machine learning algorithms represents one of the most promising technology. The employment of machine learning algorithms for the optimization of complex production processes faces the big challenge of selecting the suitable machine learning method which fits the optimization parameter objectives. This paper introduces our approach for automated selection of ML algorithms to be used for optimization of a specific production process. The approach and the corresponding method consists of the following main blocks: (a) production process definition, (b) ML performance, (c) selector constructor and (d) assessment and incremental improvement of selector performance. The first component defines a typical production process or domain based on a well-established set of features, e.g. product quality inspection through process features as accuracy, material characteristics, etc. The ML performance construct contains precise defined performance of well clustered ML algorithms based on established benchmarking. The third construct, the Selector, automatically realizes a perfect mapping between the production process features and the performance of the ML algorithms. The logic of this automated selection represents the innovation of our work. The last component assesses the selector algorithm based on a set of specific KPIs for each production domain or process. The incremental improvement of the selector is approached as well, closing the loop between all method components. The developed approach and method have as foundations our work on identifying critical production processes/domains as core of realizing the intelligent production and laborious developed collection of ML algorithms, based on their performance data. These foundations and a motivation scenario are presented inside our paper to highlight the relevance of our research work.
Innovative technologies enable businesses to stay competitive in the marketplace while also increasing profits in the manufacturing of nanocomposites for water treatment. The aforementioned driving factors resulted in the adoption of a number of innovative technologies, and no other trend has had a greater impact in recent years than Industry 4.0. Industry 4.0 is a comprehensive term that encompasses data management, manufacturing competitiveness, manufacturing processes, and efficiency. The term "Industry 4.0" refers to a group of key enabler technologies, such as cyber-physical models, Internet of Things (IoT), artificial intelligence (AI), and big data analytics, including embedded devices which are all significant components to the mechanized and digitized industrial environments. AI approaches have been used for water treatment processes as well as desalination in recent years for optimizing the process along with providing realistic answers to water scarcity and water pollution-related issues. AI applications have been used to predict and minimize water treatment process operational costs by lowering costs and optimizing chemical utilization. Several AI models are successful and accurate in predicting effectiveness of various adsorbents used in the removal process of a variety of contaminants from water. To identify the current level of research and future development prospects of smart manufacturing, this study uses a comprehensive literature review technique for manufacturing sustainability of nanocomposite fabrication for water treatment applications. The model provided will help to create a baseline for AI and hybrid models in the water treatment and wastewater management sectors, allowing for the increased performance and innovative growth. It will serve to provide the framework and give guidance for researchers interested in creating superior nanocomposites for waste and fresh water treatment and management using Industry 4.0. This study looks at a variety of AI approaches as well as how they may be used in water treatment, with an emphasis on pollutant adsorption. This assessment also identified certain obstacles and research gaps in the field of AI applications in water treatment processes.
Deep learning (DL) and machine learning (ML) have a pivotal role in logistic supply chain management and smart manufacturing with proven records. The ability to handle large complex data with minimal human intervention made DL and ML a success in the healthcare systems. In the present healthcare system, the implementation of ML and DL is extensive to achieve a higher quality of service and quality of health to patients, doctors, and healthcare professionals. ML and DL were found to be effective in disease diagnosis, acute disease detection, image analysis, drug discovery, drug delivery, and smart health monitoring. This work presents a state-of-the-art review on the recent advancements in ML and DL and their implementation in the healthcare systems for achieving multi-objective goals. A total of 10 papers have been thoroughly reviewed that presented novel works of ML and DL integration in the healthcare system for achieving various targets. This will help to create reference data that can be useful for future implementation of ML and DL in other sectors of healthcare system.
