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1
Höhn, B. R., and H. Winter. "Laboratories at work: Institute for machine elements, Gear Research Centre (FZG)." Tribotest 3, no. 3 (March 1997): 325–40. http://dx.doi.org/10.1002/tt.3020030306.
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Hargreaves, D. J., and Anton Planitz. "Assessing the energy efficiency of gear oils via the FZG test machine." Tribology International 42, no. 6 (June 2009): 918–25. http://dx.doi.org/10.1016/j.triboint.2008.12.016.
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Winter, H. "Integrating Universities and Industry—A German Approach." Proceedings of the Institution of Mechanical Engineers, Part B: Management and engineering manufacture 202, no. 1 (February 1988): 9–17. http://dx.doi.org/10.1243/pime_proc_1988_202_041_02.
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Starting from a list of topics which were suggested by the Institution of Mechanical Engineers this paper surveys the situation at German technical universities in the field of mechanical engineering. The details of teaching and research activities described here refer to the Institute for Machine Elements, Technical University of Munich, but the principles of the organization and the structure are mostly comparable with corresponding institutes at other universities in the Federal Republic of Germany. The following subjects will be discussed: 1. The organization of German technical universities, in particular the Institute's structure of a Faculty of Mechanical Engineering. 2. Undergraduate courses in engineering based on ‘vocational’ education; the means to ensure an education of approximately equal academic standard at different universities. 3. Machine element teaching at undergraduate level; efforts to ensure an equal level of knowledge in this field. 4. The structure and funding of postgraduate engineering research centres and institutes. For example the relationship between the Gear Research Centre (FZG) and the gearing and transmission industry in Germany will be discussed. 5. A summary of the research carried out at the FZG (gears, clutches, tribology)
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Massocchi, Davide, Marco Lattuada, Steven Chatterton, and Paolo Pennacchi. "SRV Method: Lubricating Oil Screening Test for FZG." Machines 10, no. 8 (July 28, 2022): 621. http://dx.doi.org/10.3390/machines10080621.
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Governments and institutions have the following sustainable development goals: the improvement of energy efficiency and the reduction of CO2 emissions, in a “green economy” approach, have currently become the fundamental drivers that push research and development activity toward the optimization of rotating machine components in the industrial sector, with a special focus on lubrication systems too. The activity is directed towards the optimization of tribological testing methods and equipment to better discriminate the performance of lubricants in operating conditions as predictive as possible of real applications. In this context, the present paper describes the results of an experimental campaign based on the use of a well-selected linear oscillation SRV * (Schwingung, Reibung, Verschleiss) tribometer procedure as a screening of a rig test, the FZG ** (Forschungsstelle für Zahnräder und Getreibebau (German: Research Centre for Gears and Gear; University of Munich; Munich, Germany)) test, leading to concrete benefits such as saving time (time duration is 76% less without mentioning visual inspection and mounting/dismounting phase) and operative costs. Four cases for the determination of the failure load stage of SRV have been defined as links to seizure and microseizure phenomena. The procedure was tested for ten oils differing in scope (gas turbine oil, turbine oil, gear oil and circulating oil). The tests have been repeated three times and a procedure was defined for repeatability (± 1 stage difference between the minimum and maximum) for nine out of ten cases a failure stage could be defined. The same oils were also tested using the FZG scuffing test, and it can be seen that the results are very comforting as follows: a good correlation with the FZG rig test has been found for eight out of ten oils.
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Ayel, J., Y. Kraus, and J. P. Michel. "Séverisation de l'essai de capacité de charge des lubrifiants sur machine a engrenages FZG." Revue de l'Institut Français du Pétrole 40, no. 6 (November 1985): 831–42. http://dx.doi.org/10.2516/ogst:1985049.
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Durand de Gevigney, J., C. Changenet, F. Ville, and P. Velex. "Thermal modelling of a back-to-back gearbox test machine: Application to the FZG test rig." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 226, no. 6 (January 16, 2012): 501–15. http://dx.doi.org/10.1177/1350650111433243.
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A thermal model of a back-to-back gear test rig relying on a network approach is presented in which the predictions of temperatures and power losses are coupled. The numerical findings are in good agreement with the measurements for transient regimes on a FZG test rig and it is demonstrated that the proposed simulation is reliable. A number of results are presented which illustrate the influence of the pinion and gear immersion depths. It is found that, in certain conditions, the classic isothermal method for estimating integral temperatures is questionable because the actual bulk temperature can substantially deviate from that of the oil sump. The practical consequences in terms of scuffing capacity are emphasised.
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Tao, J., T. G. Hughes, H. P. Evans, R. W. Snidle, N. A. Hopkinson, M. Talks, and J. M. Starbuck. "Elastohydrodynamic Lubrication Analysis of Gear Tooth Surfaces From Micropitting Tests." Journal of Tribology 125, no. 2 (March 19, 2003): 267–74. http://dx.doi.org/10.1115/1.1510881.
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The paper presents numerical results for the elastohydrodynamic lubrication of gear teeth using real surface roughness data taken from micropitting tests carried out on an FZG gear testing machine. Profiles and load conditions corresponding to four load stages in the micropitting test protocol are considered. Elastohydrodynamic film thickness and pressure analyses are presented for conditions having a slide/roll ratio of 0.3 during the single tooth contact phase of the meshing cycle. Comparisons are also included showing the elastohydrodynamic response of the tooth contacts at different times in the meshing cycle for one of the load stages. The rheological model adopted is based on Ree-Eyring non-Newtonian shear thinning, and comparisons are also included of models having constant and different pressure-dependent specifications of the Eyring shear stress parameter τ0. Parameters obtained from the micro EHL analyses are presented that quantify the degree of adversity experienced by the surfaces in elastohydrodynamic contact. These quantify extreme pressure behavior, extreme proximity of surfaces, and pressure cycling within the overall contact and indicate that the different fluid models considered lead to significantly different pressure and film thickness behavior within the contact.
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Hlebanja, Gorazd. "Gradual development of S-shaped gears." MATEC Web of Conferences 366 (2022): 01001. http://dx.doi.org/10.1051/matecconf/202236601001.
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Search for an improved gear tooth flank shape arose from heavy industry problems rolling mills. Original involute gears suffered severe flank damages. So, better gear teeth flanks should improve contact circumstances, decrease the flank pressure, and enhance a lubrication film. This was achieved by a curved, pole symmetric path of contact by purely graphical methods. And the developed gears, proven in heavy industry applications, showed highly improved properties. Specimens of both gear geometries, which were made of tempered and nitrided alloy steel, were tested on an FZG testing machine, and results confirmed the theoretical foundations of S-gears. Then it was necessary to replace the graphical method by a numerical one and to define the tool. So, the rack profile was defined by a pole symmetric parabolic-type function, which in turn defined the path of contact and finally gears with an arbitrary number of teeth. Many applications were developed with S-gear shape, e.g., helical, crossed, and planetary gears, various worm drives, etc. S-gear concept was also used with polymer gears and high transmission ratio planetary gears. Lately, this concept was used to develop crossed helical gear drive with perpendicular shafts. Such drives are often used in centrifuge drives (e. g. Alfa-Laval) and this implementation with the module m = 5 mm uses a large driving gear with 60 teeth (with the left-handed helix angle of 30°) on the horizontal shaft and a smaller driven gear with 20 teeth (with the right-handed helix angle of 60°) on the vertical shaft. This paper is a tribute to work of Professor Jože Hlebanja (1926-2022) whose research was dedicated to gears with improved properties, namely S-gears.
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Arri, Harwant Singh, Ramandeep Singh, Sudan Jha, Deepak Prashar, Gyanendra Prasad Joshi, and Ill Chul Doo. "Optimized Task Group Aggregation-Based Overflow Handling on Fog Computing Environment Using Neural Computing." Mathematics 9, no. 19 (October 7, 2021): 2522. http://dx.doi.org/10.3390/math9192522.
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It is a non-deterministic challenge on a fog computing network to schedule resources or jobs in a manner that increases device efficacy and throughput, diminishes reply period, and maintains the system well-adjusted. Using Machine Learning as a component of neural computing, we developed an improved Task Group Aggregation (TGA) overflow handling system for fog computing environments. As a result of TGA usage in conjunction with an Artificial Neural Network (ANN), we may assess the model’s QoS characteristics to detect an overloaded server and then move the model’s data to virtual machines (VMs). Overloaded and underloaded virtual machines will be balanced according to parameters, such as CPU, memory, and bandwidth to control fog computing overflow concerns with the help of ANN and the machine learning concept. Additionally, the Artificial Bee Colony (ABC) algorithm, which is a neural computing system, is employed as an optimization technique to separate the services and users depending on their individual qualities. The response time and success rate were both enhanced using the newly proposed optimized ANN-based TGA algorithm. Compared to the present work’s minimal reaction time, the total improvement in average success rate is about 3.6189 percent, and Resource Scheduling Efficiency has improved by 3.9832 percent. In terms of virtual machine efficiency for resource scheduling, average success rate, average task completion success rate, and virtual machine response time are improved. The proposed TGA-based overflow handling on a fog computing domain enhances response time compared to the current approaches. Fog computing, for example, demonstrates how artificial intelligence-based systems can be made more efficient.
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Alalibo, Belema P., Bing Ji, and Wenping Cao. "Short Circuit and Broken Rotor Faults Severity Discrimination in Induction Machines Using Non-invasive Optical Fiber Technology." Energies 15, no. 2 (January 14, 2022): 577. http://dx.doi.org/10.3390/en15020577.
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Multiple techniques continue to be simultaneously utilized in the condition monitoring and fault detection of electric machines, as there is still no single technique that provides an all-round solution to fault finding in these machines. Having various machine fault-detection techniques is useful in allowing the ability to combine two or more in a manner that will provide a more comprehensive application-dependent condition-monitoring solution; especially, given the increasing role these machines are expected to play in man’s transition to a more sustainable environment, where many more electric machines will be required. This paper presents a novel non-invasive optical fiber using a stray flux technique for the condition monitoring and fault detection of induction machines. A giant magnetostrictive transducer, made of terfenol-D, was bonded onto a fiber Bragg grating, to form a composite FBG-T sensor, which utilizes the machines’ stray flux to determine the internal condition of the machine. Three machine conditions were investigated: healthy, broken rotor, and short circuit inter-turn fault. A tri-axial auto-data-logging flux meter was used to obtain stray magnetic flux measurements, and the numerical results obtained with LabView were analyzed in MATLAB. The optimal positioning and sensitivity of the FBG-T sensor were found to be transverse and 19.3810 pm/μT, respectively. The experimental results showed that the FBG-T sensor accurately distinguished each of the three machine conditions using a different order of magnitude of Bragg wavelength shifts, with the most severe fault reaching wavelength shifts of hundreds of picometres (pm) compared to the healthy and broken rotor conditions, which were in the low-to-mid-hundred and high-hundred picometre (pm) range, respectively. A fast Fourier transform (FFT) analysis, performed on the measured stray flux, revealed that the spectral content of the stray flux affected the magnetostrictive behavior of the magnetic dipoles of the terfenol-D transducer, which translated into strain on the fiber gratings.
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Lim, Jongbeom. "Scalable Fog Computing Orchestration for Reliable Cloud Task Scheduling." Applied Sciences 11, no. 22 (November 19, 2021): 10996. http://dx.doi.org/10.3390/app112210996.
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As Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices are becoming increasingly popular in the era of the Fourth Industrial Revolution, the orchestration and management of numerous fog devices encounter a scalability problem. In fog computing environments, to embrace various types of computation, cloud virtualization technology is widely used. With virtualization technology, IoT and IIoT tasks can be run on virtual machines or containers, which are able to migrate from one machine to another. However, efficient and scalable orchestration of migrations for mobile users and devices in fog computing environments is not an easy task. Naïve or unmanaged migrations may impinge on the reliability of cloud tasks. In this paper, we propose a scalable fog computing orchestration mechanism for reliable cloud task scheduling. The proposed scalable orchestration mechanism considers live migrations of virtual machines and containers for the edge servers to reduce both cloud task failures and suspended time when a device is disconnected due to mobility. The performance evaluation shows that our proposed fog computing orchestration is scalable while preserving the reliability of cloud tasks.
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Zenkert, Johannes, Christian Weber, Mareike Dornhöfer, Hasan Abu-Rasheed, and Madjid Fathi. "Knowledge Integration in Smart Factories." Encyclopedia 1, no. 3 (August 16, 2021): 792–811. http://dx.doi.org/10.3390/encyclopedia1030061.
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Knowledge integration is well explained by the human–organization–technology (HOT) approach known from knowledge management. This approach contains the horizontal and vertical interaction and communication between employees, human-to-machine, but also machine-to-machine. Different organizational structures and processes are supported with the help of appropriate technologies and suitable data processing and integration techniques. In a Smart Factory, manufacturing systems act largely autonomously on the basis of continuously collected data. The technical design concerns the networking of machines, their connectivity and the interaction between human and machine as well as machine-to-machine. Within a Smart Factory, machines can be considered as intelligent manufacturing systems. Such manufacturing systems can autonomously adapt to events through the ability to intelligently analyze data and act as adaptive manufacturing systems that consider changes in production, the supply chain and customer requirements. Inter-connected physical devices, sensors, actuators, and controllers form the building block of the Smart Factory, which is called the Internet of Things (IoT). IoT uses different data processing solutions, such as cloud computing, fog computing, or edge computing, to fuse and process data. This is accomplished in an integrated and cross-device manner.
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Xin, Si Jin, and Zhen Tong. "Vibration Fatigue Test Based on Fiber Bragg Grating Sensors and HHT." Applied Mechanics and Materials 328 (June 2013): 193–97. http://dx.doi.org/10.4028/www.scientific.net/amm.328.193.
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The metal fatigue is an important factor to cause an accident in machine operation, so metal fatigue test is a significant procedure in manufacturing. Fiber Bragg Grating (FBG), as an innovative sensor, has been applied to the measurement of various rotating machines. In this paper, the time-frequency analysis is used to detect the fatigue feature of a titanium alloy measured by FBG sensors. Furthermore, the Hilbert-Huang transform (HHT) is more effective to observe the fatigue limit of the titanium alloy sheet, compared to the Wavelet transform (WT).
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Pramuhadi, Gatot, Zavira Mega Ayu, Muhammad Haikal Kusdian, Riza Fahri, Raesa Firdiansyah Pratama, and Anik Rahayu. "Pengabut Semprot Bergerak untuk Pemberantasan Hama Kelapa Sawit." Jurnal Ilmu Pertanian Indonesia 27, no. 4 (September 21, 2022): 481–87. http://dx.doi.org/10.18343/jipi.27.4.487.
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Palm oil (Elaeis guineensis) is a tropical plant that can be attacked by various pests, start from nurseries to plantations, so it is necessary to eradicate pests. Generally, the pests is eradicated by applying pesticides using fogging machines brought down by farmers so that they are ineffective in reaching high plants, and hot smoke from fogging machines can damage oil palm leaves. This study aims to design a tool that can optimize the application of pesticide fog to oil palm plants at a certain height. The tool's design in the form of a mobile spray fogger also aims to facilitate the operator in applying pesticide fog at various heights of oil palm and, simultaneously, can reduce the impact of damage to oil palm leaves. The research method was carried out by testing the performance of pesticide fog spraying in the laboratory and the performance test of applying pesticide fog using mobile spray foggers on the land. Furthermore, pesticide smoke from fogging machines was optimized by combining electric sprayers and air blowers so that pesticide fog is formed, reducing the temperature of pesticide smoke, and increasing the range of fogging. The performance of the combination of outputs from fogging machines, electric sprayers, and air blowers on mobile spray fogger produces a droplet diameter of 94.41 μm, droplet density of 365.44 droplet/cm2, effective fogging range of 8.63 m, effective fogging width of 0.91 m, and an average temperature decrease of 4°C.
Keywords: air blower, electric sprayer, fogging machine, palm oil, pests
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Rouvinen, A., T. Lehtinen, and P. Korkealaakso. "Container Gantry Crane Simulator for Operator Training." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 219, no. 4 (December 1, 2005): 325–36. http://dx.doi.org/10.1243/146441905x63322.
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Real-time simulators have become more popular in the field of user training. This is due to the possibility to give basic training and knowledge of machines and their operation environment to the operator even when the machine is not actually present. The use of simulators instead of actual machines has several advantages. First of all, the available machine capacity is not tied to training and can be used in productive work. Secondly, using a simulator helps to avoid accidents that may occur using real machines. Using a simulator also enables different environmental aspects, such as lighting conditions, fog, wind, and so on, to be taken into account in the training of all operators alike. Real-time training simulators are complicated machine systems, which consist of a user interface, an I/O-system, a real-time simulation model describing the dynamics of the machine in question and its connections to the environment, a visualization of the operational environment, and a possible motion platform. The user interface is usually taken directly from the simulated machine. Consequently, the user has the possibility to become familiar with the operating interface in an early phase of training. In this article, the development of a gantry crane operator-training simulator, including all the earlier mentioned components, is presented. The aim of this article is to present an example of methods used in the development of the separate areas of a training simulator.
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Cao, Wenping, Belema P. Alalibo, Bing Ji, Xiangping Chen, and Cungang Hu. "Optical FBG-T Based Fault Detection Technique for EV Induction Machines." Journal of Physics: Conference Series 2195, no. 1 (February 1, 2022): 012045. http://dx.doi.org/10.1088/1742-6596/2195/1/012045.
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Abstract Electric vehicles (EV) represent a key technology to achieve a low-carbon transportation objective, whist induction motors are one of the promising topologies. The reliability of these machines is crucial to minimize the downtime, cost and unwanted human lives. Although several techniques are utilized in the condition monitoring and fault detection of electrical machines, there is still no single technique that provides an all-round solution to fault detection in these machines and thus hybrid techniques are used widely. This paper presents a novel non-invasive optical fiber technique in condition monitoring of induction machines and in the process detecting inter-turn short circuit faults. Owing to optical fiber’s immunity to magnetic flux, a composite FBG-T sensor formed by bonding a giant magnetostrictive transducer, Terfenol-D, onto a fiber Bragg grating is utilized to sense machines’ stray flux as a signature to determine the internal winding condition of the machines. A tri-axial auto datalogging flux meter was used to obtain the stray magnetic flux and test results obtained via LabView were analyzed in MatLab. Experimental and numerical results agree with each other and how that the FBG-T sensor accurately and reliably detected the short-circuit faults. Bragg shifts observed under short-circuit faults were in 100s of picometre range under various operating frequencies compared to the mid-10s of picometre obtained under healthy machine condition. These provide much promise for future EVs.
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Lipow, Gar W. "Shutting Down the Fog Machine." Review of Radical Political Economics 47, no. 2 (January 20, 2015): 231–42. http://dx.doi.org/10.1177/0486613414555106.
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Yasniy, Oleh, Iryna Didych, and Yuri Lapusta. "PREDICTION OF FATIGUE CRACK GROWTH DIAGRAMS BY METHODS OF MACHINE LEARNING UNDER CONSTANT AMPLITUDE LOADING." Acta Metallurgica Slovaca 26, no. 1 (March 19, 2020): 31–33. http://dx.doi.org/10.36547/ams.26.1.346.
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Important structural elements are often under the action of constant amplitude loading. Increasing their lifetime is an actual task and of great economic importance. To evaluate the lifetime of structural elements, it is necessary to be able to predict the fatigue crack growth rate (FCG). This task can be effectively solved by methods of machine learning, in particular by neural networks, boosted trees, support-vector machines, and k -nearest neighbors. The aim of the present work was to build the fatigue crack growth diagrams of steel 0.45% C subjected to constant amplitude loading at stress ratios R = 0, and R = –1 by the methods of machine learning. The obtained results are in good agreement with the experimental data.
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Juraszek, Janusz. "Application of fiber optic FBG techniques in analysis of strain in engineering machines." New Trends in Production Engineering 2, no. 1 (October 1, 2019): 480–85. http://dx.doi.org/10.2478/ntpe-2019-0051.
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Abstract The paper presents the use of fiber optic FBG strain sensors for analysis of deformations of machines and devices including those used in mining techniques. FBG strain sensors have many advantages over classic strain measurements using electro resistance strain gauges. They are characterized by a significant measurement accuracy of up to 1 mm, a service life of up to 30 years, the possibility of measuring large deformations of up to 8%, significant fatigue life, the possibility of building measurement networks and, something extremely important in mining – intrinsic safety, because the operating medium is white light. The entire measurement system based on the optical interrogator was also discussed. It enables conducting both static and dynamic measurements. The results of the strain research for an engineering machine, in which the loads had exceeded 800 T, were reported.
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Tag, Paul M., and James E. Peak. "Machine Learning of Maritime Fog Forecast Rules." Journal of Applied Meteorology 35, no. 5 (May 1996): 714–24. http://dx.doi.org/10.1175/1520-0450(1996)035<0714:mlomff>2.0.co;2.
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Liu, Zhaohui, Yongjiang He, Chao Wang, and Runze Song. "Analysis of the Influence of Foggy Weather Environment on the Detection Effect of Machine Vision Obstacles." Sensors 20, no. 2 (January 8, 2020): 349. http://dx.doi.org/10.3390/s20020349.
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This study is to analyze the influence of visibility in a foggy weather environment on the accuracy of machine vision obstacle detection in assisted driving. We present a foggy day imaging model and analyze the image characteristics, then we set up the faster region convolutional neural network (Faster R-CNN) as the basic network for target detection in the simulation experiment and use Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) data for network detection and classification training. PreScan software is used to build weather and traffic scenes based on a foggy imaging model, and we study object detection of machine vision in four types of weather condition—clear (no fog), light fog, medium fog, and heavy fog—by simulation experiment. The experimental results show that the detection recall is 91.55%, 85.21%, 72.54~64.79%, and 57.75% respectively in no fog, light fog, medium fog, and heavy fog environments. Then we used real scenes in medium fog and heavy fog environment to verify the simulation experiment. Through this study, we can determine the influence of bad weather on the detection results of machine vision, and hence we can improve the safety of assisted driving through further research.
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Behroozi-Khazaei, Nasser, Jalal Khodaei, and Ahmad Banakar. "Applied linear discriminant analysis and artificial neural network for sorting dried figs based on texture properties." Acta Scientiarum Polonorum Technica Agraria 12, no. 3-4 (December 31, 2013): 3–15. http://dx.doi.org/10.24326/aspta.2013.3-4.1.
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Dried figs are one of the horticultural products that require sorting in the postharvest stage in order to be presented to the market. In Iran, figs are graded manually by professional workers or automatically by mechanical machines. This paper presents a new algorithm based on machine vision technology applicable to be installed in the fig sorting machines. In the presented methodology, image texture properties of figs are extracted by an image processing algorithm. Some features selected by stepwise linear discriminant analysis were introduced as the superior ones for discriminating different classes of dried figs. Among the ten features, discriminant analysis selected six. The selected texture features were fed to artificial neural networks in order to implement the classification process. The image processing assisted neural networks methodology showed promising result as the total sorting accuracy was 100%.
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Azarkasb, Seyed Omid, and Seyed Hossein Khasteh. "Advancing Intrusion Detection in Fog Computing: Unveiling the Power of Support Vector Machines for Robust Protection of Fog Nodes against XSS and SQL Injection Attacks." Journal of Engineering Research and Reports 25, no. 3 (June 5, 2023): 59–84. http://dx.doi.org/10.9734/jerr/2023/v25i3892.
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Fog computing, characterized as a cloud infrastructure in close proximity to end devices, faces substantial security challenges that necessitate robust intrusion detection mechanisms for fog nodes. The resource-constrained nature of fog nodes renders them particularly susceptible to attacks, making the development of efficient intrusion detection systems imperative. In this study, we propose a comprehensive approach to protect fog nodes, taking into account their limited resources. Leveraging the power of Support Vector Machines (SVMs), a widely adopted machine learning technique in IoT security, our method overcomes challenges associated with local optima, overfitting, and high-dimensional data. A thorough literature review underscores the prevalent use of SVMs in IoT security research. Specifically, we focus on addressing two prevalent web attacks: Cross-Site Scripting (XSS) and SQL injection attacks, based on global statistical data. To evaluate our approach, we employ the CSE-CIC-IDS2018 dataset and a pseudo-real dataset. Precision, recall, and accuracy are employed as evaluation metrics, along with the Mean Average Precision (MAP). Our evaluation results demonstrate an exceptional level of accuracy, achieving an impressive 98.28% accuracy in terms of average performance when compared to existing methods. Comparative analysis with state-of-the-art approaches further validates the superior efficacy and efficiency of our proposed method.
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Moura, Leonel. "Notes on a New Kind of Art." Matlit Revista do Programa de Doutoramento em Materialidades da Literatura 3, no. 1 (October 28, 2015): 185–94. http://dx.doi.org/10.14195/2182-8830_3-1_11.
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I started working with robots applied to art around the turn of the century. Aiming at the most possible autonomy of the process, they were the next logical step after experimenting with algorithms confined to the computer environment. I was never interested in “digital art”. The first experiences, with an ant algorithm running on a computer connected to a robotic arm [fig. 1], showed the potential for a machine to create its own drawings and paintings as a kind of artificial creativity. The claim that these works represent a new kind of art, the art of machines, may be controversial in the context of the mainstream art world. But, actually, it is inscribed in the global evolution of robotics and artificial intelligence towards a greater autonomy of machines. Art announces what is about to arrive. DOI: http://dx.doi.org/10.14195/2182-8830_3-1_11 DOI: http://dx.doi.org/10.14195/2182-8830_3-1_11
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Tomer, Vikas, and Sachin Sharma. "Detecting IoT Attacks Using an Ensemble Machine Learning Model." Future Internet 14, no. 4 (March 24, 2022): 102. http://dx.doi.org/10.3390/fi14040102.
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Malicious attacks are becoming more prevalent due to the growing use of Internet of Things (IoT) devices in homes, offices, transportation, healthcare, and other locations. By incorporating fog computing into IoT, attacks can be detected in a short amount of time, as the distance between IoT devices and fog devices is smaller than the distance between IoT devices and the cloud. Machine learning is frequently used for the detection of attacks due to the huge amount of data available from IoT devices. However, the problem is that fog devices may not have enough resources, such as processing power and memory, to detect attacks in a timely manner. This paper proposes an approach to offload the machine learning model selection task to the cloud and the real-time prediction task to the fog nodes. Using the proposed method, based on historical data, an ensemble machine learning model is built in the cloud, followed by the real-time detection of attacks on fog nodes. The proposed approach is tested using the NSL-KDD dataset. The results show the effectiveness of the proposed approach in terms of several performance measures, such as execution time, precision, recall, accuracy, and ROC (receiver operating characteristic) curve.
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Bemani, Ali, and Niclas Björsell. "Aggregation Strategy on Federated Machine Learning Algorithm for Collaborative Predictive Maintenance." Sensors 22, no. 16 (August 19, 2022): 6252. http://dx.doi.org/10.3390/s22166252.
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Industry 4.0 lets the industry build compact, precise, and connected assets and also has made modern industrial assets a massive source of data that can be used in process optimization, defining product quality, and predictive maintenance (PM). Large amounts of data are collected from machines, processed, and analyzed by different machine learning (ML) algorithms to achieve effective PM. These machines, assumed as edge devices, transmit their data readings to the cloud for processing and modeling. Transmitting massive amounts of data between edge and cloud is costly, increases latency, and causes privacy concerns. To address this issue, efforts have been made to use edge computing in PM applications., reducing data transmission costs and increasing processing speed. Federated learning (FL) has been proposed a mechanism that provides the ability to create a model from distributed data in edge, fog, and cloud layers without violating privacy and offers new opportunities for a collaborative approach to PM applications. However, FL has challenges in confronting with asset management in the industry, especially in the PM applications, which need to be considered in order to be fully compatible with these applications. This study describes distributed ML for PM applications and proposes two federated algorithms: Federated support vector machine (FedSVM) with memory for anomaly detection and federated long-short term memory (FedLSTM) for remaining useful life (RUL) estimation that enables factories at the fog level to maximize their PM models’ accuracy without compromising their privacy. A global model at the cloud level has also been generated based on these algorithms. We have evaluated the approach using the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset to predict engines’ RUL Experimental results demonstrate the advantage of FedSVM and FedLSTM in terms of model accuracy, model convergence time, and network usage resources.
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Powell, Gareth L. "Utility Fog." Engineer 302, no. 7935 (April 2022): 32. http://dx.doi.org/10.12968/s0013-7758(22)90208-9.
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Zhuravleva, Larisa Anatolievna, and Van Thuan Nguyen. "Experimental and theoretical studies of the system “irrigation rate – soil –sprinkling machine”." Agrarian Scientific Journal, no. 10 (November 17, 2021): 103–7. http://dx.doi.org/10.28983/asj.y2021i10pp103-107.
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To provide an efficient and reliable operation of sprinkler machines while observing erosion-safe irrigation technologies, you should take into account multiple passes on moist and waterlogged soil. It is a complicated technical problem requiring the solution of a set of scientific and practical problems based on studies of the relationship of the "irrigation rate - soil - sprinkler machine system. The article considers the wheel and soil interaction model (Fig. 1). The dependence of the depth and track width on the number of the support bogie and on the bearing, capacity is also shown. The research allowed us to determine the approximate zones of application of wheel systems depending on the bearing capacity of the soil.
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Vitz, Ed, and Kenneth S. Lyle. "Fog Machines, Vapors, and Phase Diagrams." Journal of Chemical Education 85, no. 10 (October 2008): 1385. http://dx.doi.org/10.1021/ed085p1385.
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Jeong, Su Young, Wook Kim, Byung Hyun Byun, Chang-Bae Kong, Won Seok Song, Ilhan Lim, Sang Moo Lim, and Sang-Keun Woo. "Prediction of Chemotherapy Response of Osteosarcoma Using Baseline 18F-FDG Textural Features Machine Learning Approaches with PCA." Contrast Media & Molecular Imaging 2019 (July 24, 2019): 1–7. http://dx.doi.org/10.1155/2019/3515080.
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Purpose. Patients with high-grade osteosarcoma undergo several chemotherapy cycles before surgical intervention. Response to chemotherapy, however, is affected by intratumor heterogeneity. In this study, we assessed the ability of a machine learning approach using baseline 18F-fluorodeoxyglucose (18F-FDG) positron emitted tomography (PET) textural features to predict response to chemotherapy in osteosarcoma patients. Materials and Methods. This study included 70 osteosarcoma patients who received neoadjuvant chemotherapy. Quantitative characteristics of the tumors were evaluated by standard uptake value (SUV), total lesion glycolysis (TLG), and metabolic tumor volume (MTV). Tumor heterogeneity was evaluated using textural analysis of 18F-FDG PET scan images. Assessments were performed at baseline and after chemotherapy using 18F-FDG PET; 18F-FDG textural features were evaluated using the Chang-Gung Image Texture Analysis toolbox. To predict the chemotherapy response, several features were chosen using the principal component analysis (PCA) feature selection method. Machine learning was performed using linear support vector machine (SVM), random forest, and gradient boost methods. The ability to predict chemotherapy response was evaluated using the area under the receiver operating characteristic curve (AUC). Results. AUCs of the baseline 18F-FDG features SUVmax, TLG, MTV, 1st entropy, and gray level co-occurrence matrix entropy were 0.553, 0538, 0.536, 0.538, and 0.543, respectively. However, AUCs of the machine learning features linear SVM, random forest, and gradient boost were 0.72, 0.78, and 0.82, respectively. Conclusion. We found that a machine learning approach based on 18F-FDG textural features could predict the chemotherapy response using baseline PET images. This early prediction of the chemotherapy response may aid in determining treatment plans for osteosarcoma patients.
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H, Sabireen, and Neelanarayanan Venkataraman. "Proactive Fault Prediction of Fog Devices Using LSTM-CRP Conceptual Framework for IoT Applications." Sensors 23, no. 6 (March 8, 2023): 2913. http://dx.doi.org/10.3390/s23062913.
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Technology plays a significant role in our daily lives as real-time applications and services such as video surveillance systems and the Internet of Things (IoT) are rapidly developing. With the introduction of fog computing, a large amount of processing has been done by fog devices for IoT applications. However, a fog device’s reliability may be affected by insufficient resources at fog nodes, which may fail to process the IoT applications. There are obvious maintenance challenges associated with many read-write operations and hazardous edge environments. To increase reliability, scalable fault-predictive proactive methods are needed that predict the failure of inadequate resources of fog devices. In this paper, a Recurrent Neural Network (RNN)-based method to predict proactive faults in the event of insufficient resources in fog devices based on a conceptual Long Short-Term Memory (LSTM) and novel Computation Memory and Power (CRP) rule-based network policy is proposed. To identify the precise cause of failure due to inadequate resources, the proposed CRP is built upon the LSTM network. As part of the conceptual framework proposed, fault detectors and fault monitors prevent the outage of fog nodes while providing services to IoT applications. The results show that the LSTM along with the CRP network policy method achieves a prediction accuracy of 95.16% on the training data and a 98.69% accuracy on the testing data, which significantly outperforms the performance of existing machine learning and deep learning techniques. Furthermore, the presented method predicts proactive faults with a normalized root mean square error of 0.017, providing an accurate prediction of fog node failure. The proposed framework experiments show a significant improvement in the prediction of inaccurate resources of fog nodes by having a minimum delay, low processing time, improved accuracy, and the failure rate of prediction was faster in comparison to traditional LSTM, Support Vector Machines (SVM), and Logistic Regression.
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Samuel, Urang Awajionyi, and Onuodu, Friday Eleonu. "Predictive Analysis of Mental Fog Using Machine Learning." IJARCCE 9, no. 1 (January 30, 2020): 191–96. http://dx.doi.org/10.17148/ijarcce.2020.9137.
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Samann, Fady Esmat Fathel, Adnan Mohsin Abdulazeez, and Shavan Askar. "Fog Computing Based on Machine Learning: A Review." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 12 (June 18, 2021): 21. http://dx.doi.org/10.3991/ijim.v15i12.21313.
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<p>Internet of Things (IoT) systems usually produce massive amounts of data, while the number of devices connected to the internet might reach billions by now. Sending all this data over the internet will overhead the cloud and consume bandwidth. Fog computing's (FC) promising technology can solve the issue of computing and networking bottlenecks in large-scale IoT applications. This technology complements the cloud computing by providing processing power and storage to the edge of the network. However, it still suffers from performance and security issues. Thus, machine learning (ML) attracts attention for enabling FC to settle its issues. Lately, there has been a growing trend in utilizing ML to improve FC applications, like resource management, security, lessen latency and power usage. Also, intelligent FC was studied to address issues in industry 4.0, bioinformatics, blockchain and vehicular communication system. Due to the ML vital role in the FC paradigm, this work will shed light on recent studies utilized ML in a FC environment. Background knowledge about ML and FC also presented. This paper categorized the surveyed studies into three groups according to the aim of ML implementation. These studies were thoroughly reviewed and compared using sum-up tables. The results showed that not all studies used the same performance metric except those worked on security issues. In conclusion, the simulations of proposed ML models are not sufficient due to the heterogeneous nature of the FC paradigm.</p>
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Shi, Xinghua, and Taekjip Ha. "Seeing a molecular machine self-renew: Fig. 1." Proceedings of the National Academy of Sciences 108, no. 9 (February 16, 2011): 3459–60. http://dx.doi.org/10.1073/pnas.1100150108.
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Zaharia, George-Eduard, Tiberiu-Alex-Irinel Şoşea, Radu-Ioan Ciobanu, and Ciprian Dobre. "Machine learning-Based traffic offloading in fog networks." Simulation Modelling Practice and Theory 101 (May 2020): 102045. http://dx.doi.org/10.1016/j.simpat.2019.102045.
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Zhou, Zude, Jianmin Hu, Quan Liu, Ping Lou, Junwei Yan, and Wenfeng Li. "Fog Computing-Based Cyber-Physical Machine Tool System." IEEE Access 6 (2018): 44580–90. http://dx.doi.org/10.1109/access.2018.2863258.
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An, Xingshuo, Xianwei Zhou, Xing Lü, Fuhong Lin, and Lei Yang. "Sample Selected Extreme Learning Machine Based Intrusion Detection in Fog Computing and MEC." Wireless Communications and Mobile Computing 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/7472095.
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Fog computing, as a new paradigm, has many characteristics that are different from cloud computing. Due to the resources being limited, fog nodes/MEC hosts are vulnerable to cyberattacks. Lightweight intrusion detection system (IDS) is a key technique to solve the problem. Because extreme learning machine (ELM) has the characteristics of fast training speed and good generalization ability, we present a new lightweight IDS called sample selected extreme learning machine (SS-ELM). The reason why we propose “sample selected extreme learning machine” is that fog nodes/MEC hosts do not have the ability to store extremely large amounts of training data sets. Accordingly, they are stored, computed, and sampled by the cloud servers. Then, the selected sample is given to the fog nodes/MEC hosts for training. This design can bring down the training time and increase the detection accuracy. Experimental simulation verifies that SS-ELM performs well in intrusion detection in terms of accuracy, training time, and the receiver operating characteristic (ROC) value.
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Xiao, Zhen Gang, and Carlo Menon. "A Review of Force Myography Research and Development." Sensors 19, no. 20 (October 20, 2019): 4557. http://dx.doi.org/10.3390/s19204557.
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Information about limb movements can be used for monitoring physical activities or for human-machine-interface applications. In recent years, a technique called Force Myography (FMG) has gained ever-increasing traction among researchers to extract such information. FMG uses force sensors to register the variation of muscle stiffness patterns around a limb during different movements. Using machine learning algorithms, researchers are able to predict many different limb activities. This review paper presents state-of-art research and development on FMG technology in the past 20 years. It summarizes the research progress in both the hardware design and the signal processing techniques. It also discusses the challenges that need to be solved before FMG can be used in an everyday scenario. This paper aims to provide new insight into FMG technology and contribute to its advancement.
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Zaoui, Chaimae, Faouzia Benabbou, and Abdelaziz Ettaoufik. "Edge-Fog-Cloud Data Analysis for eHealth-IoT." International Journal of Online and Biomedical Engineering (iJOE) 19, no. 07 (June 13, 2023): 184–99. http://dx.doi.org/10.3991/ijoe.v19i07.38903.
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Thanks to advancements in artificial intelligence and the Internet of Things (IoT), eHealth is becoming an increasingly attractive area for researchers. However, different challenges arise when sensor-generated information is stored and analyzed using cloud computing. Latency, response time, and security are critical concerns that require attention. Fog and Edge Computing technologies have emerged in response to the requirement for resources near the network edge where data is collected, to minimize cloud challenges. This paper aims to assess the effectiveness of Machine Learning (ML) and Deep Learning (DL) techniques when executed in Edge or Fog nodes within the eHealth data. We compared the most efficient baseline techniques from the state-of-the-art on three eHealth datasets: Human Activity Recognition (HAR), University of Milano Bicocca Smartphone-based Human Activity Recognition (UniMiB SHAR), and MIT-BIH Arrhythmia. The experiment showed that for the HAR dataset, the Support Vector Machines (SVM) model was the best performer among the ML techniques, with low processing time and an accuracy of 96%. In comparison, the K-Nearest Neighbors (KNN) performed 94.43, and 96%, respectively, for SHAR and MIT-BIH datasets. Among the DL techniques, the Convolutional Neural Network with Fourier (CNNF) model performed the best, with accuracies of 94.49% and 98.72% for HAR and MIT-BIH. In comparison, CNN achieved 96.90% for the SHAR dataset.
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Borzì, Luigi, Ivan Mazzetta, Alessandro Zampogna, Antonio Suppa, Gabriella Olmo, and Fernanda Irrera. "Prediction of Freezing of Gait in Parkinson’s Disease Using Wearables and Machine Learning." Sensors 21, no. 2 (January 17, 2021): 614. http://dx.doi.org/10.3390/s21020614.
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Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson’s disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of our system to detect and predict FOG. Methods: A cohort of 11 Parkinson’s disease patients receiving (on) and not receiving (off) dopaminergic therapy was equipped with two inertial sensors placed on each shin, and asked to perform a timed up and go test. We performed a step-to-step segmentation of the angular velocity signals and subsequent feature extraction from both time and frequency domains. We employed a wrapper approach for feature selection and optimized different machine learning classifiers in order to catch FOG and pre-FOG episodes. Results: The implemented FOG detection algorithm achieved excellent performance in a leave-one-subject-out validation, in patients both on and off therapy. As for pre-FOG detection, the implemented classification algorithm achieved 84.1% (85.5%) sensitivity, 85.9% (86.3%) specificity and 85.5% (86.1%) accuracy in leave-one-subject-out validation, in patients on (off) therapy. When the classification model was trained with data from patients on (off) and tested on patients off (on), we found 84.0% (56.6%) sensitivity, 88.3% (92.5%) specificity and 87.4% (86.3%) accuracy. Conclusions: Machine learning models are capable of predicting FOG before its actual occurrence with adequate accuracy. The dopaminergic therapy affects pre-FOG gait patterns, thereby influencing the algorithm’s effectiveness.
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Bhatt, Chintan, and C. K. Bhensdadia. "Fog Computing." International Journal of Grid and High Performance Computing 9, no. 4 (October 2017): 105–13. http://dx.doi.org/10.4018/ijghpc.2017100107.
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The Internet of Things could be a recent computing paradigm, defined by networks of extremely connected things – sensors, actuators and good objects – communication across networks of homes, buildings, vehicles, and even individuals whereas cloud computing could be ready to keep up with current processing and machine demands. Fog computing provides architectural resolution to deal with some of these issues by providing a layer of intermediate nodes what's referred to as an edge network [26]. These edge nodes provide interoperability, real-time interaction, and if necessary, computational to the Cloud. This paper tries to analyse different fog computing functionalities, tools and technologies and research issues.
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Guan, Wei, Haolin Chen, Xuewei Li, Haijian Li, and Xin You. "Study on the Influence of Connected Vehicle Fog Warning Systems on Driving Behavior and Safety." Journal of Advanced Transportation 2022 (April 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/8436388.
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Driving speeds are higher on freeways, and the visibility reduction occurring in freeway fog zones often increases traffic accidents. This study aims at assessing the impact of different levels of connected vehicle fog warning systems on driving behavior and safety. A connected vehicle fog warning system is developed based on driving simulators, and virtual scenes are developed based on fog zones. The connected vehicle technology includes three levels: a normal level, a level including a human-machine interface, and a level with both a human-machine interface and dynamic message signs. Speed and lateral deviation are chosen as assessment indicators and combined with sample entropy to evaluate the impact of the connected vehicle fog warning system on safety. The deceleration ratio of the warning point is used to evaluate the efficiency of the connected vehicle fog warning system. Results show that the connected vehicle fog warning system can significantly reduce driving speed, and that there are differences in the speed-reduction effectiveness for different technical levels. The connected vehicle fog warning system can reduce the lateral deviation and improve the lateral driving safety. From the perspective of change stability, speed safety entropy and lateral deviation safety entropy are increased, which indicates that the connected vehicle fog warning system will negatively impact safety because of the additional workload. Drivers’ responses are more pronounced in the human-machine interface group compared to the group with dynamic message signs, where the drivers maintained a lower speed. This study provides a reference for the studies on connected vehicle technology based on driving simulators and supports the optimization, design, and evaluation of connected vehicle fog warning systems.
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Ahanger, Tariq Ahamed, Usman Tariq, Atef Ibrahim, Imdad Ullah, Yassine Bouteraa, and Fayez Gebali. "Securing IoT-Empowered Fog Computing Systems: Machine Learning Perspective." Mathematics 10, no. 8 (April 14, 2022): 1298. http://dx.doi.org/10.3390/math10081298.
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The Internet of Things (IoT) is an interconnected network of computing nodes that can send and receive data without human participation. Software and communication technology have advanced tremendously in the last couple of decades, resulting in a considerable increase in IoT devices. IoT gadgets have practically infiltrated every aspect of human well-being, ushering in a new era of intelligent devices. However, the rapid expansion has raised security concerns. Another challenge with the basic approach of processing IoT data on the cloud is scalability. A cloud-centric strategy results from network congestion, data bottlenecks, and longer response times to security threats. Fog computing addresses these difficulties by bringing computation to the network edge. The current research provides a comprehensive review of the IoT evolution, Fog computation, and artificial-intelligence-inspired machine learning (ML) strategies. It examines ML techniques for identifying anomalies and attacks, showcases IoT data growth solutions, and delves into Fog computing security concerns. Additionally, it covers future research objectives in the crucial field of IoT security.
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Bartok, Juraj, Peter Šišan, Lukáš Ivica, Ivana Bartoková, Irina Malkin Ondík, and Ladislav Gaál. "Machine Learning-Based Fog Nowcasting for Aviation with the Aid of Camera Observations." Atmosphere 13, no. 10 (October 14, 2022): 1684. http://dx.doi.org/10.3390/atmos13101684.
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In aviation, fog is a severe phenomenon, causing difficulties in airport traffic management; thus, accurate fog forecasting is always appreciated. The current paper presents a fog forecast at the Poprad-Tatry Airport, Slovakia, where various methods of machine learning algorithms (support vector machine, decision trees, k-nearest neighbors) are adopted to predict fog with visibility below 300 m for a lead time of 30 min. The novelty of the study is represented by the fact that beyond the standard meteorological variables as predictors, the forecast models also make use of information on visibility obtained through remote camera observations. Cameras observe visibility using tens of landmarks in various distances and directions from the airport. The best performing model reached a score level of 0.89 (0.23) for the probability of detection (false alarm ratio). One of the most important findings of the study is that the predictor, defined as the minimum camera visibilities from eight cardinal directions, helps improve the performance of the constructed machine learning models in terms of an enhanced ability to forecast the initiation and dissipation of fog, i.e., the moments when a no-fog event turns into fog and vice versa. Camera-based observations help to overcome the drawbacks of the automated sensors (predominantly point character of measurements) and the human observers (complex, but lower frequency observations), and offer a viable solution for certain situations, such as the recent periods of the COVID-19 pandemic.
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Yu, Dongmin, Zimeng Ma, and Rijun Wang. "Efficient Smart Grid Load Balancing via Fog and Cloud Computing." Mathematical Problems in Engineering 2022 (May 17, 2022): 1–11. http://dx.doi.org/10.1155/2022/3151249.
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As the cloud data centers size increases, the number of virtual machines (VMs) grows speedily. Application requests are served by VMs be located in the physical machine (PM). The rapid growth of Internet services has created an imbalance of network resources. Some hosts have high bandwidth usage and can cause network congestion. Network congestion affects overall network performance. Cloud computing load balancing is an important feature that needs to be optimized. Therefore, this research proposes a 3-tier architecture, which consists of Cloud layer, Fog layer, and Consumer layer. The Cloud serves the world, and Fog analyzes the services at the local edge of network. Fog stores data temporarily, and the data is transmitted to the cloud. The world is classified into 6 regions on the basis of 6 continents in consumer layer. Consider Area 0 as North America, for which two fogs and two cluster buildings are considered. Microgrids (MG) are used to supply energy to consumers. In this research, a real-time VM migration algorithm for balancing fog load has been proposed. Load balancing algorithms focus on effective resource utilization, maximum throughput, and optimal response time. Compared to the closest data center (CDC), the real-time VM migration algorithm achieves 18% better cost results and optimized response time (ORT). Realtime VM migration and ORT increase response time by 11% compared to dynamic reconFigure with load (DRL) with load. Realtime VM migration always seeks the best solution to minimize cost and increase processing time.
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Reches, Tal, Moria Dagan, Talia Herman, Eran Gazit, Natalia A. Gouskova, Nir Giladi, Brad Manor, and Jeffrey M. Hausdorff. "Using Wearable Sensors and Machine Learning to Automatically Detect Freezing of Gait during a FOG-Provoking Test." Sensors 20, no. 16 (August 10, 2020): 4474. http://dx.doi.org/10.3390/s20164474.
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Freezing of gait (FOG) is a debilitating motor phenomenon that is common among individuals with advanced Parkinson’s disease. Objective and sensitive measures are needed to better quantify FOG. The present work addresses this need by leveraging wearable devices and machine-learning methods to develop and evaluate automated detection of FOG and quantification of its severity. Seventy-one subjects with FOG completed a FOG-provoking test while wearing three wearable sensors (lower back and each ankle). Subjects were videotaped before (OFF state) and after (ON state) they took their antiparkinsonian medications. Annotations of the videos provided the “ground-truth” for FOG detection. A leave-one-patient-out validation process with a training set of 57 subjects resulted in 84.1% sensitivity, 83.4% specificity, and 85.0% accuracy for FOG detection. Similar results were seen in an independent test set (data from 14 other subjects). Two derived outcomes, percent time frozen and number of FOG episodes, were associated with self-report of FOG. Bother derived-metrics were higher in the OFF state than in the ON state and in the most challenging level of the FOG-provoking test, compared to the least challenging level. These results suggest that this automated machine-learning approach can objectively assess FOG and that its outcomes are responsive to therapeutic interventions.
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Jansi Rani, S., Dr Selvakani, and K. Vasumathi. "Improvement and Survey of Fog Computing Using Encryption." YMER Digital 21, no. 05 (May 28, 2022): 1254–64. http://dx.doi.org/10.37896/ymer21.05/d9.
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This research develops a fog computing-based encrypted manipulate machine in a practical industrial placing. The evolved system conceals controller profits and signals over communique links the usage of multiplicative holomorphic encryption to prevent eavesdropping assaults. Experimental validation confirms the feasibility of function servo manipulate for the motor driven degree with the developed machine in phrases of overall performance degradation, parameter version, and processing time. The evolved machine inherits its stability no matter whether plant parameters range or no longer even after the controller profits and indicators are encrypted. Furthermore, despite the fact that processing time becomes longer by way of increasing a key length of encryption, degradation of control performance is advanced simultaneously. Keywords: Networked robots, robot safety, motion control, encryption control, fog computing.
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Akinin, K. P., and V. G. Kireyev. "TWO- DEGREE-OF-FREEDOM ELECTRIC MACHINE AND ITS OPERATION MODES." Praci Institutu elektrodinamiki Nacionalanoi akademii nauk Ukraini 2023, no. 65 (August 28, 2023): 145–54. http://dx.doi.org/10.15407/publishing2023.65.145.
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This paper is devoted to the problems of developing an electric machine with two-degrees-of-freedom (2-DOF) of rotor movement and its control system. The structure of the machine with the possibility of rotation of the rotor along two angular coordinates in a limited range of rotation angles is considered. The machine is designed to control the position of the axis of the optical beam along the line and frame trajectories. Based on the electrodynamic state model of the 2-DOF electric machine, a block diagram of the servo system was developed to control the trajectory of the rotor in two coordinates. Relationships between the time constant of the angle controller and the time constants of the high-frequency part of the amplitude-frequency characteristic of an open-loop system are determined. The dependences of the effective values of the currents in the control windings on the frequency of the frames and the duration of the linear part of the triangular signal are obtained. The dependences of the modules of relative accuracy of the rotor movement along a given trajectories on the system tunings are obtained. Ref. 12, fig. 8. Key words: two-degree-of-freedom electric machine, control system, scanning device, line trajectory, frame trajectory.
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Fitriyani, Norma Latif, Muhammad Syafrudin, Siti Maghfirotul Ulyah, Ganjar Alfian, Syifa Latif Qolbiyani, and Muhammad Anshari. "A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and Prediction." Mathematics 10, no. 21 (October 30, 2022): 4027. http://dx.doi.org/10.3390/math10214027.
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Risk assessment and developing predictive models for diabetes prevention is considered an important task. Therefore, we proposed to analyze and provide a comprehensive analysis of the performance of diabetes screening scores for risk assessment and prediction in five populations: the Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian populations, utilizing statistical and machine learning (ML) methods. Additionally, due to the present COVID-19 epidemic, it is necessary to investigate how diabetes and COVID-19 are related to one another. Thus, by using a sample of the Korean population, the interrelationship between diabetes and COVID-19 was further investigated. The results revealed that by using a statistical method, the optimal cut points among Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian populations were 6.205 mmol/L (FPG), 5.523 mmol/L (FPG), and 5.375% (HbA1c), 150.50–106.50 mg/dL (FBS), 123.50 mg/dL (2hPG), and 107.50 mg/dL (FBG), respectively, with AUC scores of 0.97, 0.80, 0.78, 0.85, 0.79, and 0.905. The results also confirmed that diabetes has a significant relationship with COVID-19 in the Korean population (p-value 0.001), with an adjusted OR of 1.21. Finally, the overall best ML models were performed by Naïve Bayes with AUC scores of 0.736, 0.75, and 0.83 in the Japanese, Korean, and Trinidadian populations, respectively.
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Shang, Qiufeng, and Wenjie Qin. "Fiber Bragg Grating Dynamic Calibration Based on Online Sequential Extreme Learning Machine." Sensors 20, no. 7 (March 26, 2020): 1840. http://dx.doi.org/10.3390/s20071840.
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The fiber Bragg grating (FBG) sensor calibration process is critical for optimizing performance. Real-time dynamic calibration is essential to improve the measured accuracy of the sensor. In this paper, we present a dynamic calibration method for FBG sensor temperature measurement, utilizing the online sequential extreme learning machine (OS-ELM). During the measurement process, the calibration model is continuously updated instead of retrained, which can reduce tedious calculations and improve the predictive speed. Polynomial fitting, a back propagation (BP) network, and a radial basis function (RBF) network were compared, and the results showed the dynamic method not only had a better generalization performance but also had a faster learning process. The dynamic calibration enabled the real-time measured data of the FBG sensor to input calibration models as online learning samples continuously, and could solve the insufficient coverage problem of static calibration training samples, so as to improve the long-term stability, accuracy of prediction, and generalization ability of the FBG sensor.