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Facial mask detection technology has become increasingly important even beyond the context of the COVID-19 pandemic. Along with the advancement in facial recognition technology, face mask detection has become a crucial feature for various applications. This paper introduces an Internet of Things (IoT) architecture based on a developed deep learning algorithm named You Only Look Once (YOLO) to keep society healthy, and secured, and collect data for future research. The proposed paradigm is built on the basis of economic consideration and is easy to implement. Yet, the used YOLOv4-tiny is one of the fastest object detection models to exist. A mask detection camera (MaskCam) that leverages the computing power of NVIDIA's Jetson Nano edge nanodevices was built side by side with a smart camera application to detect a mask on the face of an individual. MaskCam distinguishes between mask wearers, those who are not wearing masks, and those who are not wearing masks properly according to MQTT protocol. Furthermore, a self-developed web browsing application comes with the MaskCam system to collect and visualize statistics for qualitative and quantitative analysis. The practical results demonstrate the superiority and effectiveness of the proposed smart mask detection system. On the one hand, YOLOv4-full obtained the best results even at smaller resolutions, although the frame rate is too small for real-time use. On the other hand, it is twice as fast as the other detection models, regardless of the quality of detection. Consequently, inferences may be run more frequently over the entire video sequence, resulting in more accurate output. [ABSTRACT FROM AUTHOR]

The European 1 MW, 170 GHz industrial CW prototype gyrotron has been designed within EGYC (European GYrotron Consortium) in collaboration with the industrial partner Thales Electron Devices (TED) and under the coordination of Fusion for Energy (F4E). This is a conventional (hollow) cavity gyrotron that is based on the 1 MW, 170 GHz short-pulse (SP) modular gyrotron, which has been designed and manufactured by KIT in collaboration with TED. The SP prototype has been tested in multiple experimental campaigns since 2015 and the nominal cavity mode TE32,9 is exited at 170.1 GHz, producing RF power above 1 MW with 35 % interaction efficiency. The first phase of the experiments with the CW industrial gyrotron was successfully completed at KIT in 2016, verifying most of the ITER specifications. Short pulses (

The magnetron injection gun (MIG) is one of the most critical subcomponents in gyrotrons. The electron beam, which has the primary role on the gyrotron operation, is generated and configured at this part of the tube. The electron beam properties determine the excitation mode in the cavity, the power of the generated microwaves and the gyrotron efficiency. The operation of MIGs could be influenced by several factors such as trapped electrons, manufacturing tolerances, roughness of the emitter ring, emitter temperature inhomogeneity, electron beam neutralization effect, etc. The influence of many of these factors on the electron beam quality has been systematically investigated during the last years. Several novelties have been proposed in order to limit the influence of these factors on the gyrotron operation. In particular, new design criteria have been proposed for the suppression of electron trapping mechanisms, a new type of the emitter ring has been proposed to minimize the influence of the manufacturing tolerances and edge effects on the beam quality, alternative MIG design approaches have been proposed, etc. An overview of all these works will be presented here.

During the ongoing pre-concept phase (2014 – 2020) for a possible future European DEMOnstration Fusion Power Plant (DEMO) the activities within EUROfusion WP HCD EC Gyrotron R&D and Advanced Developments are focusing on options for near-term solutions, and, at the same time, on long-term even more advanced options. The near-term target for DEMO is to realize pulsed operation. According to the current baseline it will probably use an EC system operating at 170 GHz and 204 GHz is being assessed, whereas the long-term target aims for steady-state operation and frequencies for current drive up to 240 GHz. Common targets for both are an RF output power per unit of significantly above 1 MW (target: 2 MW) and a total gyrotron efficiency of significantly higher than 60 %. Frequency step-tunability and multi-purpose/multi-frequency operation have to be considered. Those targets shall be achieved by considering the coaxial-cavity gyrotron technology and advanced technologies for key components (e.g. CVD diamond-disk Brewster angle window). Advanced simulation and test tools are complementing the research and developments. Gyrotron development is additionally supported by a significant investment into a new multi-megawatt long-pulse gyrotron test stand which is under final installation at KIT currently.

A demonstration fusion power plant (DEMO) producing electricity for the grid at the level of a few hundred megawatts is included in the European Roadmap [1]. The engineering design and R&D for the electron cyclotron (EC), ion cyclotron and neutral beam systems for the DEMO reactor is being performed by Work Package Heating and Current Drive (WPHCD) in the framework of EUROfusion Consortium activities. The EC target power to the plasma is about 50 MW, in which the required power for NTM control and burn control is included. EC launcher conceptual design studies are here presented, showing how the main design drivers of the system have been taken into account (physics requirements, reactor relevant operations, issues related to its integration as in-vessel components). Different options for the antenna are studied in a parameters space including a selection of frequencies, injection angles and launch points to get the best performances for the antenna configuration, using beam tracing calculations to evaluate plasma accessibility and deposited power. This conceptual design studies comes up with the identification of possible limits, constraints and critical issues, essential in the selection process of launcher setup solution.

The upgrade of the EC-system of the TCV tokamak has entered in its realization phase and is part of a broader upgrade of TCV. The MW-class dual-frequency gyrotrons (84 or 126GHz/2s/1MW) are presently being manufactured by Thales Electron Devices with the first gyrotron foreseen to be delivered at SPC by the end of 2017. In parallel to the gyrotron development, for extending the level of operational flexibility of the TCV EC-system the integration of the dual-frequency gyrotrons adds a significant complexity in the evacuated 63.5mm-diameter HE11 transmission line system connected to the various TCV low-field side and top launchers. As discussed in [1], an important part of the present TCV-upgrade consists in inserting a modular closed divertor chamber. This will have an impact on the X3 top-launcher which will have to be reduced in size. For using the new compact launcher we are considering employing a Fast Directional Switch (FADIS), combining the two 1MW/126GHz/2s rf-beams into a single 2MW rf-beam.

This study aims to develop an accurate dynamic cutting force model in the milling process. In the proposed model, the estimated cutting force tackles the effect of the self-excited vibration that causes machining instability during the cutting process. In particular, the square root of the residual cutting force between the prediction and the actual cutting force is considered as an objective function for optimizing the cutting force coefficients using the equilibrium optimizer (EO) approach instead of the trial-and-error approach. The results confirm that the proposed model can provide higher prediction accuracy when the EO is applied. In addition, the proposed EO has a minimum integral square error (ISE) of around 1.12, while the genetic algorithm (GA) has an ISE of around 1.14 and the trial-and-error method has an ISE of around 2.4. Moreover, the proposed method can help to investigate the cutting stability and to suspend the chatter phenomenon by selecting an optimal set of cutting parameters. [ABSTRACT FROM AUTHOR]

The first series tube of the gyrotrons for the 10-MW electron cyclotron resonance heating system of the stellarator W7-X was tested at Forschungszentrum Karlsruhe (FZK) and yielded a total output power of 0.98 MW, with an efficiency of 31% (without a single-stage depressed collector) in short-pulse operation and of 0.92 MW in pulses of 180 s (efficiency of almost 45% at a depression voltage of 29 kV). The Gaussian mode output power was 0.91 MW. The pulselength at full power (1 MW) is limited at FZK by the available power supply. At a reduced electron beam current, it is possible to operate at longer pulselengths. At an output power of 0.57 MW (electron beam current of 29 A), the pulselength was increased to 1893 s. There was no physical reason for a limitation of this pulse: The pressure increase during the pulse was less than a factor of two and ended up at a very low value in the [10.sup.-9] mbar range. The tube was delivered to Max-Planck-Institut fur Plasmaphysik Greifswald for tests at full power and up to 30-min pulselength. The Gaussian mode RF output power, measured in a calorimetric load after a 25-m-long quasi-optical transmission line (seven mirrors), was 0.87 MW at a total output power of 0.92 MW in 30-min pulses. Again, no indications for a limitation in pulselength were found. The second series tube was tested in short-pulse operation and showed a strange behavior concerning a mode hopping which has not yet been understood. The third series gyrotron delivers up to now 0.65 MW at a pulse duration of 180 s. Preliminary operation of the prototype tube as a two-frequency gyrotron delivered 0.41 MW in 10-s pulses at 103.8 GHz (T[E.sub.21,6] mode). Index Terms--Chemical-vapor deposition (CVD)-diamond window, frequency step tuning, gyrotron, high-power microwaves, quasi-optical (q.o.) mode converter, single-stage depressed collector (SDC), stray radiation.

In the first part of this paper, the status of the 140-GHz continuously operated gyrotrons with an output power of 1 MW or the stellarator Wendelstein 7-X will be described. With the first series tube, an output power of 1000 kW has been achieved in short pulse operation (milliseconds) with an electron beam current of 40 A, and of 1150 kW at 50 A. With a pulse length of 3 min limted by the available high-voltage (HV) power supply, an output power of 920 kW at an electron beam current of about 40 A with an efficiency of 45% and a mode purity of 97.5% has been obained. At a reduced beam current of 29 A, an output power of 570 kW was measured with a pulse length of 1893 s without significant increase in tube pressure. The energy content of this pulse is almost 1.1 GJ. For the next fusion plasma device, International Thermonuclear Experimental Reactor (ITER), gyrotrons with a higher output power of about 2 MW are desirable. In short-pulse experiments, the feasibility of the fabrication of coaxial cavity gyrotrons with an output power up to 2-MW, continuous wave (CW), has been demonstrated, and the information necessary for a technical design has been obtained. The development of a long-pulse 2-MW coaxial cavity gyrotron started within a European coopera tion. In parallel to the design and fabrication of an industrial prototype gyrotron, a short-pulse preprototype gyrotron has been operated to verify the design of critical components. An output power of 1.2 MW with an efficiency of 20% has been achieved. The development of frequency tunable gyrotrons operating in the range from 105 to 140 GHz for stabilization of current driven plasma instabilities in fusion plasma devices (neoclassical tearing modes) is another task in the development of gyrotrons at the Forschungszentrum Karlsruhe. Index Terms--Coaxial gyrotron, diamond window, frequency tuning, gyrotron, high-power microwaves, quasi-optical mode converter, single-stage depressed collector, step tunability, stray radiation.

Induction motors are important equipment in modern industry. However, the occurrence of fatigue failure following an extended period of operation invariably results in a catastrophic failure. As a result, monitoring and diagnosing induction motors is critical to avoiding unplanned shutdowns caused by premature failures. This article aims to develop an effective method for motor fault detection using time–frequency contents of vibration signals and an attention-based convolutional neural network model. First, the vibration signals are collected and labeled into five different categories: normal condition, outer ring fault, inner ring fault, misalignment condition, and broken rotor bar. Then, using the Morlet function, continuous wavelet transform (CWT) converts the vibratory time-series signals to the scalogram feature images. The time–frequency feature images are created after downsampling and converting the measured vibration signals to the frequency domain. These images are then resized and fed into the proposed convolutional attention neural network (CANN) to identify various induction motor failures. The experimental results demonstrate that the suggested model can provide an excellent diagnosis accuracy of 99.43%, significantly better than the state-of-the-art deep learning approaches for fault diagnosis. Moreover, the developed model’s robustness is validated against adversarial attacks based on the fast gradient sign method (FGSM) by including white Gaussian noise. [ABSTRACT FROM AUTHOR]

An electron cyclotron system (ECS) is required in the European DEMO fusion reactor for plasma heating, magnetohydrodynamic control, especially the suppression of neoclassical tearing modes (NTMs), and thermal instability control. Microwave beams of 2 MW, generated by gyrotrons, shall propagate through corrugated HE11 waveguides (WGs) of 63.5-mm inner diameter and finally reach specific locations in the plasma by a dedicated set of mirrors. The deployment of the beam may be done either by angular steering, i.e., tilting moveable mirrors, or by frequency steering, i.e., tuning the gyrotron frequency. In the last option, for long pulse operations, broadband optical grade diamond windows are required as confinement and/or vacuum barriers both at the torus and the gyrotron side. The Brewster-angle window represents a promising solution due to its intrinsic simplicity. However, the WG aperture imposes a minimum Ø180 mm for the disk at the Brewster-angle of 67.2° for diamond. This is an ambitious target, considering 140 mm as the state of the art. First, this article shows the very promising results of diamond growth experiments carried out by Diamond Materials GmbH with the first worldwide free-standing optical grade Ø180-mm disk having an average unpolished thickness of 1.3 mm. For the first time, one side of this large disk was polished and loss tangent measurements were performed at the center and over a large area. Then, the performance of the window featuring these large disks was characterized by a consistent set of thermomechanical analyses in view of the forthcoming window prototyping activity. [ABSTRACT FROM AUTHOR]

This article proposes a novel approach to replace the fixed projection planes existed in the previous researches to reduce motion artifacts obtained from the human face by a normal webcam for monitoring heart rate in a real-time fashion. The novel projection plane is adaptively changed with the light intensity change to eliminate the color distortion induced by motion. In this article, the state-of-the-art semantic segmentation Deeplabv3+ is implemented to segment the skin pixels from the facial region that is detected by several trackers (Boosting, MIL, TLD, Median Flow, Mosse, and CSRT) to boost the computational time compared to the conventional face detection by Haar-Like features. Image and digital signal processing techniques are also applied to eliminate possible noise for obtaining a clean pulse signal. The proposed approach is compared with other existing approaches (Green, PCA, Chrom, and POS) in multiple challenges. From the experiments conducted, the Deeplabv3+ outperforms the conventional K-means for different kinds of skin segmentation. Moreover, the proposed approach is quite robust and stable in the stationary case (with the accuracy 96%), dim-lighting environment and the long-distance up to 4-m away without zooming in camera. Besides, multiple head-movement simulations and motions of fitness are conquered by the APP approach as shown in the experiments. Thus, it can be concluded that the proposed approach is applicable to surveillance or healthcare applications. [ABSTRACT FROM AUTHOR]

Induction machines are widely used in the industry as one of the major actuators, such as water pumps, air compressors, and fans. It is necessary to monitor and diagnose these induction motors to prevent any sudden shut downs caused by premature failures. Numerous fault detection and isolation techniques for the diagnosis of induction machines have been proposed over the past few decades. Among these techniques, motor current signature analysis (MCSA) and vibration analysis are two of the most common signal-based condition monitoring methods. They are often adopted independently, but each method has its strengths and weaknesses. This research proposed a systemic method to integrate the information received from the vibration and current measurements. We applied the wavelet packet decomposition to extract the time-frequency features of the vibration and current measurements and used the support vector machines as classifiers for the initial decision-making. The significant features were identified, and the performances of several classifiers were compared. As a result, the decision-level sensor fusion based on the Sugeno fuzzy integral was proposed to integrate the vibration and current information to improve the accuracy of the diagnosis. [ABSTRACT FROM AUTHOR]

A huge amount of smart devices which have capacity of computing, storage, and communication to each other brings forth fog computing paradigm. Fog computing is a model in which the system tries to push data processing from cloud servers to "near" IoT devices in order to reduce latency time. The execution orderings and the deployed places of services make significant effect on the overall response time of an application. Beside new research directions in fog computing, e.g., fog-cloud collaboration, service scalability, fog scalability, mobile fog computing, fog federation, trade-off between energy consumption and communication efficiency, duration of storing data locally, storage security and communication security, and semantic-aware fog computing, the service deployment problem is one of the attractive research fields of fog computing. The service deployment is a multiobjective optimization problem; there are so many proposed solutions for various targets, such as response time, communication cost, and energy consumption. In this paper, we focus on the optimization problem which minimizes the overall response time of an application with awareness of network usage and server usage. Then, we have conducted experiments on two service deployment strategies, called cloudy and foggy strategies. We analyze numerically the overall response time, network usage, and server usage of those two strategies in order to prove the effectiveness of our proposed foggy service deployment strategy. [ABSTRACT FROM AUTHOR]

Fog computing is one of the promising technologies for realizing global-scale Internet of Things (IoT) applications as it allows moving compute and storage resources closer to IoT devices, where data is generated, in order to solve the limitations in cloud-based technologies such as communication delay, network load, energy consumption, and operational cost. However, this technology is still in its infancy stage containing essential research challenges. For instance, what is a suitable fog computing scheme where effective service provision models can be deployed is still an open question. This paper proposes a novel multitier fog computing architecture that supports IoT service provisioning. Concretely, a solid service placement mechanism that optimizes service decentralization on fog landscape leveraging context-aware information such as location, response time, and resource consumption of services has been devised. The proposed approach optimally utilizes virtual resources available on the network edges to improve the performance of IoT services in terms of response time, energy, and cost reduction. The experimental results from both simulated data and use cases from service deployments in real-world applications, namely, the intelligent transportation system (ITS) in Ho Chi Minh City, show the effectiveness of the proposed solution in terms of maximizing fog device utilization while reducing latency, energy consumption, network load, and operational cost. The results confirm the robustness of the proposed scheme revealing its capability to maximize the IoT potential. [ABSTRACT FROM AUTHOR]

This paper introduces an integrated IoT architecture to handle the problem of cyber attacks based on a developed deep neural network (DNN) with a rectified linear unit in order to provide reliable and secure online monitoring for automated guided vehicles (AGVs). The developed IoT architecture based on a DNN introduces a new approach for the online monitoring of AGVs against cyber attacks with a cheap and easy implementation instead of the traditional cyber attack detection schemes in the literature. The proposed DNN is trained based on experimental AGV data that represent the real state of the AGV and different types of cyber attacks including a random attack, ramp attack, pulse attack, and sinusoidal attack that is injected by the attacker into the internet network. The proposed DNN is compared with different deep learning and machine learning algorithms such as a one dimension convolutional neural network (1D-CNN), a supported vector machine model (SVM), random forest, extreme gradient boosting (XGBoost), and a decision tree for greater validation. Furthermore, the proposed IoT architecture based on a DNN can provide an effective detection for the AGV status with an excellent accuracy of 96.77% that is significantly greater than the accuracy based on the traditional schemes. The AGV status based on the proposed IoT architecture with a DNN is visualized by an advanced IoT platform named CONTACT Elements for IoT. Different test scenarios with a practical setup of an AGV with IoT are carried out to emphasize the performance of the suggested IoT architecture based on a DNN. The results approve the usefulness of the proposed IoT to provide effective cybersecurity for data visualization and tracking of the AGV status that enhances decision-making and improves industrial productivity. [ABSTRACT FROM AUTHOR]

This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters' uncertainty compared with other techniques. [ABSTRACT FROM AUTHOR]

Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that establishes the core of Industry 4.0. The IoT enables the sharing of signals between devices and machines via the internet. Besides, the IoT system enables the utilization of artificial intelligence (AI) techniques to manage and control the signals between different machines based on intelligence decisions. The paper's innovation is to introduce a deep learning and IoT based approach to control the operation of air conditioners in order to reduce energy consumption. To achieve such an ambitious target, we have proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area. Accordingly, the operation of the air conditioners could be optimally managed in a smart building. Furthermore, the number of persons and the status of the air conditioners are published via the internet to the dashboard of the IoT platform. The proposed system enhances decision making about energy consumption. To affirm the efficacy and effectiveness of the proposed approach, intensive test scenarios are simulated in a specific smart building considering the existence of air conditioners. The simulation results emphasize that the proposed deep learning-based recognition algorithm can accurately detect the number of persons in the specified area, thanks to its ability to model highly non-linear relationships in data. The detection status can also be successfully published on the dashboard of the IoT platform. Another vital application of the proposed promising approach is in the remote management of diverse controllable devices. [ABSTRACT FROM AUTHOR]

A novel concept of wind farm fault detection by monitoring the wind speed in the wake region is proposed in this study. A wind energy dissipation model was coupled with a computational fluid dynamics solver to simulate the fluid field of a wind turbine array, and the wind velocity and direction in the simulation were exported for identifying wind turbine faults. The 3D steady Navier–Stokes equations were solved by using the cell center finite volume method with a second order upwind scheme and a k − ε turbulence model. In addition, the wind energy dissipation model, derived from energy balance and Betz's law, was added to the Navier–Stokes equations' source term. The simulation results indicate that the wind speed distribution in the wake region contains significant information regarding multiple wind turbine faults. A feature selection algorithm specifically designed for the analysis of wind flow was proposed to reduce the number of features. This algorithm proved to have better performance than fuzzy entropy measures and recursive feature elimination methods under a limited number of features. As a result, faults in the wind turbine array could be detected and identified by machine learning algorithms. [ABSTRACT FROM AUTHOR]

Titanium dioxide (TiO 2 ) has been proven to be an excellent system for wettability patterning purposes because of its super hydrophilic ability and its oxidative/reductive degradation of substances when exposed to UV radiation. TiO 2 substrates upon which was deposited a self-assembled monolayer (SAM) of n-octadecyltrimethoxysilane (ODS) shifts the surface to become super hydrophobic, which when subjected to UV irradiation causes the ODS compound to be degraded with the substrate turning back to be super hydrophilic. Such events allow wettability patterns to be easily created. The objective of this study was to reduce the time required to construct a wettability pattern. For this purpose, highly photoactive TiO 2 nanoparticles were coated onto a titanium plate whose surface had been previously oxidized at high temperatures in an electric furnace. The subsequent TiO 2 /Ti system was microwaved and simultaneously irradiated with ultraviolet light (UV) to further accelerate its photocatalytic activity. Using a set of photomasks and both UV and microwave irradiation, the generation of a pattern was achieved 15 times faster (2 min versus 30 min) compared to an earlier result that used only UV radiation.

The wettability efficiency of TiO 2 -coated Ti substrate wafers was improved using a microwave/UV pre-treatment method. With the assistance of microwave heating, TiO 2 substrates coating with P25 completely achieved super hydrophilic state within 5 min, which is twice as fast compared with only UV irradiation condition. Moreover, when the microwave power was increased, improvement in the wettability activity was observed. Apart from P25, coating with brookite also resulted in a good performance. The contact angle was 0° with only 10 min of irradiation.