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Soil tillage is the most energy-intensive and costly process. Therefore, the creation of combined machines that perform multiple operations in one workflow is a pressing issue for today, the solution of which is inextricably linked to the integration of agricultural operations into one technological process that brings economic, organizational and agrobiological benefits. Based on the peculiarities of soil preparation for planting or planting crops as an object, and the need for further scientific design of the combined technological process of soil application of fertilizers, active crumbling and cultivation of root, plant residues and fertilizers as a subject, the purpose of scientific research analysis of the interaction of the rotor with the reservoir, determination and optimization of the basic parameters of knives, radius, rotational speed with a view to providing classic soil cutting and mixing with fertilizers. At State Agrarian and Engineering University in Podiya the design of the combined plow was developed. The technological process of their work combines the operations of deep plowing, the active grinding of the formation and the earning of root and crop residues, as well as the introduction of mineral fertilizers. As a result of researches, the angles of installation of the knife with respect to the radius of the rotor α, the angle of sharpening γ, the cutting angle ψ, the occipital angle ζ, the angle of conicity of the rotor β, the angle of displacement of the blades (rays) of the attachment, the magnitude of the transformation of the angle of sharpening, the analysis of the part of the sharpening , and according to its results the required speed of rotation at which the soil passes on the surface of the knife without loading is determined and the effect of its unloading is provided

For cloud network performance profiling, network tomography based on end-to-end measurement is often used in deducing the network performance for its efficiency. However, most tomography problems are under-constrained, which require additional assumptions or probing monitors planted among network switches, which are often unavailable in software-defined networking (SDN) environment. On the other hand, SDN based flow mirroring could provide accurate flow information, but the cost of both gathering and analysing the packet traces is tremendous that it is impossible to cover the whole network. We propose ScoutFlow, a method combining SDN flow measurement and end-to-end performance tomography, to achieve accurate performance profiling for cloud network while keeping low monitoring overhead. We evaluate ScoutFlow in our campus data center cloud, the experiment shows good scalability and accuracy.

High system efficiency at all power levels is desired for electric vehicle (EV) propulsion systems. Conventional boost converters employed in EV traction architectures exhibit significantly lower efficiencies at low power levels when operated under traditional fixed-frequency pulse-width modulation (PWM) techniques. Also, low system power densities are associated with conventional boost converters due to their bulky inductors. These two shortcomings are addressed here through a comprehensive design methodology for an 80-kW interleaved boost converter stepping a low battery voltage up to an 800-V DC link. The designed converter employs a discontinuous conduction mode (DCM) variable-frequency control strategy to achieve an efficiency increase at low power levels, and thus, a fairly constant efficiency throughout the entire drive schedule. A system loss model is developed to decompose analytically the loss mechanisms, and thus, determine the optimized design parameters for the variable-frequency control strategy and converter magnetics. Moreover, a custom inductor design based on distributed airgaps and aggressive thermal management through potting is integrated into the design methodology to further increase the converter power density. An 80-kW prototype is fabricated achieving a total power density of 55.6 kW/L, and experimentally tested, yielding system efficiencies above 96% for all power levels.

We will discuss the automated electrical control panel in this project, with the primary goal of reducing road accidents and conserving electrical resources. I used a timer switch in the Electrical Control Panel, believing that this would enable the circuit to complete only at a specific time. If the time transfer is set to 7:00p.m., for example, the current would pass through the contactor at 7:00p.m. This breakthrough would aid in the reduction of energy waste. Because of workers incompetence, street lights are not turned on at the proper times, resulting in traffic accidents. This breakthrough would also help to minimise road accidents since it will turn on at a predetermined time, allowing drivers to see clearly on the road.

Light metals such as aluminum alloys which are being broadly used in automobile sideways, aerospace production as well as segments in military. A light-weight metals prominent for energy consume reduction, efficiency of gasoline and oil increment and superior security to environment. The main utilization of aluminum matrix alloy components have been manufactured by the appropriate behaviors of high tensile strength, hardness, decent machinability, great cast-ability and excessive resistance of corrosion. In the present investigation dealing with casting and heat treatment processes of aluminum alloys 6063 by two different methods such as Sand casting and Die casting. The characterization of AA6063 are analyzed with help of destructive and non-destructive techniques. Finally the results indicates that die casted material gives better results when it is heat treated than the sand casted material.

Idiyaapam Making Machine is used to make the favourite dish of Tamil Nadu idiyaapam automatically. Nowadays, in homes manual hand operated idiyaapam making machines are employed and it has string hopper to extrude idiyaapam flour. It takes more time to make the dish and it is hard to turn the hand lever when the density of rice flour mixture is high. This is an era of automation and needs simple and automatic idiyaapam maker in which it extrudes the flour in the form of strings. In this work, the fabrication of automatic idiyaapam maker is carried and the rice flour would be extruded. Initially the wet flour composition should be fed into the machine and a collecting plate is kept at proper place in which strings are extruded. While the machine gets started, the flour composition is extruded the flour strings are formed. While accumulating strings, the collecting plate should be rotated to avoid overlapping of strings. Whenever the plate is filled up to the required amount, further forming of the strings should be stopped. Then the plate with the flour strings is shifted away from the extruding unit.

Today our society needs more energy for day-to-day activities due to rapid globalization and industrialization. In order to minimize the stress and dependency on fossil fuel, the most sustainable way is to harness suns energy. Solar energy is characterized by low cost, environment friendly, does not require frequent maintenance and most importantly, negligible maintenance cost. However, there is a requirement of monitoring solar installation in order to maximize the output power by setting real-time angles with the suns position. This can be easily done by the adoption of IoT technologies. The Internet of Things is often utilized in the measurement of solar energy for efficiency. It's also utilized to maintain the solar plant's health. The cost of renewable energy technology is decreasing throughout the globe, which encourages the construction of large-scale solar plants. Automation of plant observation on such a large scale of preparatory deployments requires sophisticated systems reliant on Internet connections since the majority of field units are situated in isolated, remote locations and therefore are not overseen from a central office. The project is based on the use of the most up-to-date, cost-effective method for remotely monitoring a solar plant performance by the inclusion of IoT. It can assist with plant maintenance, problem diagnostics, and real-time monitoring.

This paper targets to transmit industrial data from one sensor node to another sensor node, wirelessly and securely. However as the nodes have Internet access and are typically positioned in an unsupervised context with minimal individual intervention; security and seclusion are the prime challenges in Industrial Wireless Sensor Networks (IWSN). To use IWSN, a user must be able to get real time information straight from the source nodes and this is accomplished by Radio Frequency Identification (RFID). It yields a cost-effective solution of identification problems, but it frequently suffers from diverse safety and privacy difficulties. This paper resolves the issues of RFID systems using Global System for Mobile Communications (GSM) module that generates a One-Time Password (OTP) for accessing the data, and thereby ensures user's privacy and security in Industrial Wireless Sensor Network. Furthermore, eight evaluation criteria have been met by the proposed system surpassing the existing systems.

Stealth aircraft use Stealth Technology in order to evade recognition by utilizing mix of highlights with the goal that Aircraft can invade with RADAR and furthermore decrease the deceivability in “radio frequency (RF) range”, sound and infrared visual. During the most recent decades, stealth technology has demonstrated to be one of the best methodologies to the extent the undertaking to escape radar systems is concerned. Stealth intends to evade the recognition or attempt to cover up, for planes, avoiding radar implied stealth. The idea utilized in the stealth innovation isn’t so difficult. It utilizes the guideline to retain and mirror the radar waves. Airplane redirects the radar waves in different ways and limits the radar waves which return back to radar. Another marvel which permits the air ship maintain completely the incoming radar wavelengths to inhabit the electromagnetic reincorporated energy in various way. The structure and material used to manufacture air ship choose the degree of stealth accomplish via air ship. The thought for the radar radio wire to release Radio energy that is then returned through any item that occurs to recognize. The time taken for the reflection to come is measure by the radar radio wire and it can tell how far off an object is.

The dominant characteristics of the human foot are its shock-absorbing capability during walking or gait cycle and its adaptation to uneven surfaces. On the stance phase of the gait, the foot has to be flexible at first for shock absorption and adapt to the terrain; whereas, during the propulsive phase, it has to be dynamically rigid to function as a lever. Foot flexibility and rigidity are mainly controlled at the subtalar and midtarsal joints by tendons and ligaments. The subtalar joint is part of the longitudinal arch, but the midtarsal joint along with the tarsometatarsal joint are components of the transverse arch. However, the existence and functional role of transverse arch in human was challenged by some authors. But recent studies have revealed that the transverse arch has a predominant role in midfoot stiffness (Venkadeshan et al., 2020, & Holowoka et al., 2017). This midfoot stiffness allows the human foot to store elastic energy at the time of heel strike, which is utilized during the push-off mechanism for propulsion, thus making bipedalism more energy-efficient. Moreover, the transverse arch allows the longitudinal arch to be flexible like a lever and, at the same time, makes the arch of the foot rigid to behave like a stiff spring lever. Understanding the role of the transverse arch is obligatory to study the biomechanics of foot injuries and Charcot or diabetic foot. Studies on diabetic foot have shown that the modulation of transverse arch biomechanics and off-loading modalities would improve outcomes in the form of wound-healing and prevention of re-ulceration.

Battery storage system (BSS) integration in fast charging station (FCS) is becoming popular to achieve higher charging rates with peak-demand shaping possibility. However, the additional conversion stage for integrating the BSS increases the system losses, size and cost. The concept of partial power processing converter (PPPC), can mitigate this effect. Compared to conventional used full power processing converter, PPPC reduces the amount of transferred power from the BSS to the electric vehicle by the converter. As a consequence, the power losses generated by the converter are reduced, leading to lower sized converters and higher system efficiencies. This paper proposes a DC/DC multiport converter which allows the integration of battery storage in FCS based on a partial power processing concept, while maintaining the specific requirements in terms of isolation for FCS. The proposed three-port partial power processing converter (3P-PPPC) is derived from the commonly used triple active bridge (TAB) converter. The resulting design trade-offs, the dynamic behavior and limitations of the topology are investigated. Furthermore, the round-trip efficiency of the 3P-PPPC for integrating BSS in FCS is compared with conventional full power processing converter solutions, highlighting the superiority of the proposed topology. A prototype has been built to validate the 3P-PPPC.

The paper proposes an integrated power converter (IPC) with driving/charging capabilities for 4-phase switched reluctance motor (SRM) drive based electric vehicle (EV) application. With the proposed IPC, only two current sensors are employed for measuring phase currents under all operating conditions. The proposed IPC integrates three circuits. Firstly, during the SRM driving mode, the proposed IPC is utilized as an improved miller converter. Secondly, during battery charging mode, the proposed IPC is utilized as an on-board battery charger which is based on a bridgeless boost power factor correction circuit (PFCC) that incorporates two DC-DC boost converters. The on-board battery charger is realized by utilizing the existing power electronic switches in the proposed IPC and all the phase windings as charging inductors. Third, a front-end bidirectional DC-DC converter for maintaining voltage and current balance between IPC DC-bus and battery. During driving mode, the front-end converter boosts the battery voltage to the IPC DC bus voltage. And during regeneration/braking, the stored magnetic energy is used to charge the battery in constant-current (CC) mode via the front-end converter. During battery charging via AC grid, the bridgeless boost PFCC and the front-end converter operating in buck mode charges the battery in CC/ constant-voltage (CV) modes without stepping down the AC grid voltage. The claims of the proposed IPC are validated through simulation and experimental verifications.

The governmental subsidy is an important instrument for catalyzing the early adoption of Electric Vehicles (EVs)}. A sustainable subsidy policy must balance between promoting EVs and expanding the charging infrastructure. This paper proposes a dynamic game approach to accelerate the adoption of EV technology. The government observes two correlated processes - the penetration of the EV market and the expansion of the charging infrastructure. It finds an optimal strategy that maximizes the cumulative social benefit in the face of uncertainty. A case study of China's EV industry finds that \blue{the established EV subsidy policies are inadequate to support EVs' growth over the next decades}. In contrast, the optimal subsidies will achieve \blue{the target EV market share and ensure the accessibility to charging infrastructure by 2030}. This non-myopic strategy can prioritize the infrastructure expansion, eliminate policy enforcement's monitoring costs, prevent compensation evasion, and help achieve long-term targets by making proactive regulatory policies.

Analyzing user behavior characteristics in a complex power grid environment is essential for user behavior planning and resource coordination optimization. Traditional user behavior analysis methods based on model-driven and causal analysis have the disadvantages of strong subjectivity and physical models that are difficult to deal with the randomness and uncertainty of user behavior in complex grid environments. In this paper, we use unsupervised learning methods to analyze user behavior in complex power grid environments, and propose user behavior analysis methods based on stacked autoencoder and clustering. We first reduce the complexity of user behavior data by proposing adaptive feature selection algorithm of user behavior based on stacked autoencoder and unsupervised learning (AFS-SAEUL). Finally, we build a user behavior analysis model based on adaptive feature selection and improved clustering (UBA-AFSIC). The model improved the performance of unsupervised classification of user behavior by fusing the adaptive generation strategy of the initial cluster centers. The simulation experiment results on two real electricity datasets and one public electric vehicle charging dataset show that compared with the existing feature selection algorithm and clustering algorithm, the algorithms proposed in this paper have higher feature selection rate and better clustering performance.

Particle Impact Drilling (PID) is a new technology to effectively improve the rate of penetration (ROP) for oil and gas drilling in hard and strongly abrasive formations. In this paper, numerical simulation method is used to analyse the motion characteristics and the modulation method of particle swarm in high-pressure tank for the particle injection system based on differential pressure ejection in PID. The numerical simulation results show that: when there is no modulation elements, the motion of particle swarm in the high-pressure tank follows an asymmetric funnel flow with pulsating state, which could be divided into vertical flow domain, fast flow domain, slow flow domain and stagnation domain. The unstable dynamic arching effect of the funnel flow, the viscous effect of the liquid bridge force and the collapsing effect of the particle swarm could probably lead to the blockage of the discharge port of the high-pressure tank. When the semiapex angles of the high-pressure tank decreases, the volume flow rate of particles increases and the stagnation domain becomes smaller, but it becomes easier to form arching and blockage. The modelling results indicate that the pulsation of the funnel flow is minimum when the semiapex angle is 45° without the mutilation element, which means the funnel flow of the particle swarm is relatively stable. By introducing a conical modulating element above the discharge port, the unstable funnel flow of the particle swarm could be transformed to an overall uniform flow. The modelling results indicate that the installation height of the modulation element has the greatest influence on the pulsation degree. The optimized parameters for the conical modulation element based on numerical modelling tests are 70° for the vertex angle, 35 mm for the length of the flank and 70 mm for the installation heigh.

This paper presents a new interior permanent magnet (IPM) rotor structure/ assembly which inherits from the Halbach magnet configuration and spoke-type IPM rotor to achieve a high saliency ratio and high torque/power density. A higher air-gap flux density and a higher saliency ratio are expected to improve torque density, while the rotor inner diameter can be maximized as much as possible to reduce core material consumption and increase internal space of the rotor. Accordingly, the electromagnetic performance of three motors consists of a unified stator but three different rotors, namely flux-concentrating rotor, Halbach array PM rotor and spoke-type IPM rotor is compared by finite element analysis (FEA). Due to the segmented structure of flux-concentrating PM rotor without magnetic bridges, the mechanical challenges are analyzed in detail for electric vehicle application. The stress analysis on the prototype design with fixing pins to hold the pole pieces validates the structural feasibility at 7200 r/min. Finally, a 72-slot/ 16-pole fractional-slot PM motor with new flux-concentrating rotor is manufactured and tested to verify its performance and feasibility. It can be concluded that the new rotor is superior to the spoke-type IPM and Halbach array PM rotor in terms of torque density and PM utilization.

Vehicle to Vehicle (V2V) energy sharing is emerging as an alternate solution to range anxiety and limited charging infrastructure challenges associated with electric vehicles (EVs). The existing off-board DC fast charging options for V2V ap- plication found in the literature are not effective due to the additional weight, size and cost of the external charger or interface. In this paper, a new V2V interface is proposed in which both EVs motor winding neutrals and negative rails of on-board drivetrains are directly connected to each other. This further helps in forming an integrated dual bidirectional DC-DC converter to control the direction of power flow. The proposed approach does not require reconfiguration of motor winding or mechanical clutch to arrest the motor from rotating and/or the reconfiguration of drivetrain inverter connections. The major hardware requirement for the proposed approach is, access to the already existing neutral connection of the EV motor phase windings. Furthermore, Finite Element Analysis is presented for 150 kW EV motor model and its performance validation with the high frequency DC current flowing in the motor windings during the V2V operation. A scaled lab prototype is developed to demonstrate the energy sharing between two Lithium-ion battery banks through proposed direct V2V power transfer concept.

Observability of a hybrid system is defined as the ability to determine the continuous state of the system. Whether a hybrid system is observable or not depends on which events can be disabled, which events can be forced, and the connectivity of the discrete states, as well as its continuous dynamics. We model a hybrid system using a hybrid machine that takes into consideration both continuous variables and discrete events. We classify hybrid systems into four classes based on their discrete-event parts. For each class, conditions are derived to check observability. If a hybrid system is not observable, then we check if a weaker version of observability, called B-observability, is satisfied. B-observability requires that a hybrid system become observable after some finite occurrences of events. Conditions are derived to check B-observability. These conditions involve both the discrete-event and continuous-variable parts of hybrid systems. If the continuous-variable part of a system has a constant A matrix, then the conditions for the continuous-variable part can be simplified. We illustrate the results by an example of a battery management system of an electric vehicle.

Deep learning-based methods have achieved remarkable success in object detection, but this success requires the availability of a large number of training images. Collecting sufficient training images is difficult in detecting damages of airplane engines. Directly augmenting images by rotation, flipping, and random cropping cannot further improve the generalization ability of existing deep models. We propose an interactive augmentation method for airplane engine damage images using a prior-guided GAN to augment training images. Our method can generate many types of damages on arbitrary image regions according to the strokes of users. The proposed model consists of a prior network and a GAN. The Prior network generates a shape prior vector, which is used to encode the information of user strokes. The GAN takes the shape prior vector and random noise vectors to generate candidate damages. Final damages are pasted on the given positions of background images with an improved Poisson fusion. We compare the proposed method with traditional data augmentation methods by training airplane engine damage detectors with state-of-the-art object detectors, namely, Mask R-CNN, SSD, and YOLO v5. Experimental results show that training with images generated by our proposed data augmentation method achieves a better detection performance than that by traditional data augmentation methods.

The development of electric vehicle (EV) techniques has led to a new vehicle routing problem (VRP) called the capacitated EV routing problem (CEVRP). Because of the limited number of charging stations and the limited cruising range of EVs, not only the service order of customers but also the recharging schedules of EVs should be considered. However, solving these two aspects of the problem together is very difficult. To address the above issue, we treat CEVRP as a bilevel optimization problem and propose a novel bilevel ant colony optimization algorithm in this article, which divides CEVRP into two levels of subproblem: 1) capacitated VRP and 2) fixed route vehicle charging problem. For the upper level subproblem, the electricity constraint is ignored and an order-first split-second max-min ant system algorithm is designed to generate routes that fulfill the demands of customers. For the lower level subproblem, a new effective heuristic is designed to decide the charging schedule in the generated routes to satisfy the electricity constraint. The objective values of the resultant solutions are used to update the pheromone information for the ant system algorithm in the upper level. Through good orchestration of the two components, the proposed algorithm can significantly outperform state-of-the-art algorithms on a wide range of benchmark instances.

To overcome the limitations of a traditional soft hip exosuit directly driven by Bowden cables or winding belts at two points, in this article, we propose a new soft hip exosuit design concept based on series-wedge-structures for energy-efficient walking and running during daily activities for elderly individuals. The series-wedge-structure is designed to improve the comfort of the wearer–exosuit interface and to passively adapt to the user's body curvature, with no hindrance during movement. The design and function, including the series-wedge-structure belt, series elastic cable drive actuator, and control scheme, are described. The presented soft exosuit, which is lightweight (1.4 kg) and comfortable, provides assistive torque for hip flexion by folding series-wedge-structures driven by Bowden cables. The series-wedge-structure-based hip exosuit adopts single actuation coupled to the Bowden cable pull mechanism with a series elastic actuator to drive both hip flexions based on the out-of-phase nature of the human legs during walking and running. The exosuit performance test results show that the proposed exosuit improves the comfort of the wearer–exoskeleton interface by minimizing the shear force and maximizing the exosuit–skin interaction force contact area. Three healthy subjects walked at a fixed speed of 1.25 m/s and ran at a fixed speed of 2.2 m/s while wearing the proposed exosuit with/without assistance and without wearing the exosuit to evaluate the biomechanical and physiological effects of the proposed exosuit assistance on locomotion. The metabolic rate with exosuit assistance was reduced by 10.9% for walking and by 6.2% for running compared with not wearing the exosuit.

Statement of problem The light source stability of digital cameras and smartphones is important in shade matching in restorative and prosthetic dentistry to communicate objectively with the dental laboratory. Techniques that standardize the light source of such devices are lacking, and this limitation can lead to color mismatches, difficulties in color communication, and treatment documentation. Purpose The purpose of this clinical study was to compare the magnitude of color difference (ΔE) among 3 shade selection methods during the fabrication of ceramic crowns: visual shade selection with a shade guide, digital shade selection with a digital camera and cross-polarizing filter, and digital shade selection with a smartphone and a light-correcting device. Material and methods Forty-five patients in need of ceramic crowns were enrolled, and shade selection was evaluated according to different protocols: visual shade selection (A-D shade guide and IPS Natural Die Material Shade Guide, sent to the dental laboratory technician via a laboratory prescription); digital shade selection with a digital camera (D7000; Nikon Corp) with an 85-mm lens and wireless close-up flash, with and without a cross-polarizing filter (Polar eyes); and digital shade selection with a smartphone and a light-correcting device (iPhone XS attached to Smile Lite MDP, with and without its cross-polarizing filter accessory). Information from the smartphone was imported to an app (IPS e.max Shade Navigation App; Ivoclar Vivadent AG) that converted the reading to a shade and level of translucency for the ceramic restoration. For all photographs, a gray reference card with known color values was positioned by the mandibular teeth and was used for white balancing of the digital photographs with a software program. All photographs were edited and sent to the dental laboratory: white-balanced with the shade guide; white-balanced with the substrate shade guide; black and white; saturated; and cross-polarized. Ceramic crowns were made with the same lithium disilicate material (IPS e.max CAD; Ivoclar Vivadent AG) and cemented with the same resin cement (RelyX Ultimate Clicker, A3 shade; 3M). The ΔE values between the crown and the adjacent tooth were determined. The data were analyzed by using a 1-way analysis of variance (ANOVA) and Tukey posthoc tests (α=.05). Results The mean ΔE between a cemented ceramic crown and the adjacent tooth in the visual shade selection group was 5.32, significantly different than both digital camera (ΔE=2.75; P=.002) and smartphone (ΔE=2.34; P=.001), which were not different from each other (P=.857). Conclusions The digital shade selection with photographs acquired with both a digital camera and a smartphone with a light-correcting device showed a threshold within the acceptable values (ΔE 3.7). The use of a gray reference card helped standardize the white balance from the digital images.

Objectives The deviation in gait cycle due to trunk acceleration and muscle activity on even and uneven inclined planes should be analyzed for the design of lower limb exoskeletons. This study compares the gait variability of gastrocnemius and medial hamstring muscle activity variation of twenty young male adults on inclined even and uneven planes. Material and methods The individuals walked on a long, 10° inclined even and uneven plane in both up-the-plane and down-the-plane directions at their preferred speed (average speed is 1.2 m/s). Gait variability during walking was calculated using an average standard deviation of trunk acceleration and the significance of change was calculated using two-way-ANOVA. For studying the difference between integrated electromyography (IEMG) values of walking on even and uneven planes, two parameters Normalized IEMG Percentage (NIP) and IEMG Variation Percentage (IVP) were chosen for the analysis. Results The results strongly agree with the hypothesis that gait variability hikes in the vertical direction of subject with a p-value of 0.04. The IEMG range of medial-hamstring muscle while walking on even and uneven plane is not highly significant for swing (0.44) as well as stance phase (0.47). While walking on an inclined uneven plane, the response of gastrocnemius muscle indicated the variation of NIP between 14.31% to 64.63%. It was observed that NIP and IEMG values of medial-hamstring muscles during backward walking have a resemblance. Conclusion Trunk variability had a significant change in the vertical direction (V) and was insignificant in medial-lateral (ML) and anterior-posterior (AP) orientations for both even and uneven inclined planes during forward and reverse walking. The muscle activity of gastrocnemius and medial-hamstring muscles does not have sound variations while walking on the inclined uneven plane.

Today, the rapid growth of the internet and advancements in mobile technology and increased internet connectivity have brought us to a data-driven economy where an enormous amount of data is being used to train machine learning models to make strategic decisions. However, in the aftermath of a data breach by Facebook in 2018, there are some serious concerns over user data privacy and security being used to train the Machine Learning models. In this context, a new approach, Federated Machine Learning is now one of the most talked-about and recent approaches. Current research primarily focuses on Federated Learning's advantages over the traditional methods and/or its classification. However, being in a nascent stage of development as a method, certain challenges need to be addressed. This paper intends to address the totality of federated learning with a complete vulnerability assessment. During the study of the literature, it is found that security being promised as one of the key advantages of federated learning can still not be guaranteed because of some issues inherently present, and this can lead to poisoning, inference attacks and insertion of backdoors, etc. This paper intends to provide a complete picture by giving an in-depth and comprehensive analysis of Federated Learning and its taxonomy. It also provides a detailed vulnerability assessment and highlights the challenges faced in the current setting and future research directions to make federated learning a more functional, robust and secure method to train machine learning models.

In the inductive charging scenario of an electric vehicle (EV), the charger should have the capability to flexibly regulate the output power regardless of the misalignment and EV battery voltage. This paper proposes a hybrid voltage and frequency tuning strategy for a series-series (SS) inductive power transfer (IPT) system to regulate power through 0 to 3.3kW at any battery voltages and misalignment conditions without using additional converters. A voltage tuning is used to achieve full-power of 3.3kW and partly contribute to reducing the power. When the input voltage achieves the lower limit, a frequency tuning is used to further decrease the power to 0W, achieving the regulation through 0 to full power. The practical charging region is specified by 110mm airgap, 100mm misalignment in both x and y directions, and 180V-240V battery voltage range. Experiments are carried out at various conditions, and it shows that with an input voltage range of 180V-380V and a frequency of 82.5kHz~87.9kHz, the charging power can be regulated between 100W and 3.3kW at any case in the specified working region. In addition, a peak dc-dc efficiency of 95.7% is achieved at 3.29 kW.

OBJECTIVE Innovative strategies are needed to reduce young adult drinking. Real-time feedback via mobile health (mHealth) technology (e.g., smartphone devices/apps) may facilitate moderate drinking, yet requires evidence of feasibility, acceptability, and usability. METHOD Young adults reporting frequent heavy drinking (N = 99, Mage = 23, 51% male) participated in a manualized, brief, motivational interview on recent typical and peak blood alcohol concentration (BAC), then were randomized to use 1 of the 3 forms of technology: (a) smartphone breathalyzer device/app; (b) app that estimates BAC based on factors including sex, weight, number/types of drinks over time; or (c) self-text messaging after each drink. Technologies were tested initially in small-group laboratory alcohol self-administration sessions. Participants then completed a 2-week field test wherein they had free access to all three technologies. Participants reported on usability and acceptability. RESULTS Laboratory alcohol self-administration did not differ significantly by technology condition. The smartphone breathalyzer and BAC estimator app had favorable acceptability and usability. Participants used at least one form of technology on 67% of drinking days in the field period. In exploratory analyses, alcohol use during the field period was significantly lower than the baseline including a decrease of nearly one drink per drinking day. CONCLUSIONS These findings support the feasibility of research combining lab and field methods to test moderate drinking technologies in young adults. Findings further support the acceptability and usability of these technologies, along with young adults' openness to using them. Exploratory results suggest potential efficacy of combined mobile technology intervention to be tested in subsequent controlled studies. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Electrification of bus fleets in most cities is expected to rise due to its significant environmental benefits. However, electric buses have limited driving range and long recharging times. Additionally, electric buses require special charging infrastructure, which overall makes them less flexible than conventional diesel buses. Due to the limitations of the electric bus technologies, further adjustments have to be made to the current bus transport planning problems. The scheduling of electric vehicles is recognized as a fast-growing area of research. In this paper, we review 43 articles related to the electric bus technologies and give an overview of the different problems in the electric bus planning process (strategic, tactical and operational). The different problems are: 1) investment of electric bus fleet and charging infrastructure, 2) placement of charging infrastructure, 3) the electric vehicle scheduling problem (E-VSP) and 4) the charging scheduling problem. Given a set of timetabled trips and recharging stations, the E-VSP is concerned with finding a vehicle schedule that covers the trips and satisfies the driving range and recharging requirements of electric buses while minimizing operational cost. A detailed literature review of the constraints associated with the E-VSP and the solution approaches proposed to solve it is given. Rescheduling aspects or considerations of robustness for scheduling of electric vehicles is identified as a future area of research. Furthermore, integrated electric bus planning is considered as a crucial area of research and integrated approaches could further improve the efficiency of electric bus transport systems.

As the wind turbine operates in harsh conditions, numerous of its components are critical and present an important downtime for maintenance. In this paper, we propose a fault diagnosis algorithm to detect and locate the defects affecting the generator rotor and the pinion of the gearbox lay shaft in a real 750 kW wind turbine drivetrain. The induction generator was used as a fault sensor for gear teeth damage. Through the use of the wavelet packet transform, and the local mean decomposition combined with the Fast Fourier Transform, the detection of gear meshing frequency in the stator current reflects teeth faults. Hence, the principal component analysis of the stator current gives a suitable classification for the gearbox states under different working stages. The obtained results have been significant, despite the use of a short duration and a low sampling frequency of the experimental data.

Due to the resource-constraint nature and lack of lightweight computing solutions for diagnostic devices in healthcare IoT, provisioning time-critical responses is still challenging. In this paper, we propose DC-Health, a Dew Computing enabled IoT healthcare solution for offline and ultra-low latency decisions. The proposed solution connects a large number of healthcare devices and provisions user-specific services even when Internet connectivity is not available. The computation module is placed at the extreme edge rather than the cloud to reduce the complexity and to improve the user-specific services. In addition to the other computing facilities provided by the cloud, fog, and edge, our solution performs with a negligible dependency on the Internet. We develop a prototype of DC-Health, which monitors the heart condition using the ECG sensors with end-mile services, flexibility in terms of user-control, and mobility feature. The experimental implementations show that the proposed architecture minimizes the network response time by at least 92% and 98%, compared to the fog and cloud-based approaches, respectively. Along with this, the proposed technique also reduces the CPU and memory usages, and response time by around 30% compared to the conventional method.

In this paper, an integrated drive converter of segmented-double-stator switched reluctance machine (SDS-SRM) with isolation and non-isolation charging capabilities for electric vehicle (EV) is presented. The proposed converter can not only reduce the number of the power switches, but also implement multiple operation modes. In non-isolation charging mode, the motor windings and existing power switches constitute a buck/boost converter to charge the battery. The appropriate rotor positions are obtained to achieve torque-free control, and the interleaved phase currents control method is introduced to reduce the charging current ripples. In isolation charging mode, the inner-stator is powered by the utility grid to drive the middle-rotor, and converts electrical energy into mechanical energy. While, the outer-stator is operated in generation mode to convert mechanical energy into electrical energy. The SDS-SRM is served as a rotating transformer to transfer the electric power to charge the battery with the galvanic isolation function. A low power proof-of-concept prototype platform is built and experiments are carried out to verify the effectiveness of the proposed integrated drive converter and the corresponding control schemes.

The aggressive goal of having a 100% renewable energy system requires preparing an appropriate infrastructure for deploying as much distributed generation (DG) as possible into the electricity network. This article aims at proposing a new framework to maximize the hosting capacity (HC) of DGs in a distribution network. This operation-planning framework provides a suitable and interactive environment for the electricity market, distribution company (DISCO), electric vehicle (EV) aggregator, and rival DISCOs to increase the HC. The problem is modeled via a bilevel conditional-value-at-risk-constrained stochastic programming approach. The DISCO, in the upper level (UL), tries to maximize the HC and minimize the operating costs while interacting with a passive electricity market and an EV aggregator. The aggregator, in the lower level, aims to maximize their profit by interacting with the primary DISCO in the UL and the rival DISCO, to satisfy the EV owners. The Karush–Kuhn–Tucker conditions are used to recast the model into an equivalent single level. The proposed framework is tested on a 33-node distribution network. Results show how an interactive-based framework can contribute to maximizing the HC of DGs.

In the research of the electric vehicle charging safety evaluation model, the key problem is to determine the influencing factors of charging safety scientifically and ensure the accuracy and real-time performance of the model evaluation. This article takes the real-time charging message data of electric vehicles as the research object, analyzes and establishes the membership model of charging safety influencing factors, and proposes a real-time charging risk of electric vehicles based on the improved broad Back Propagation & Analytic Hierarchy Process (BBP-AHP) Evaluation method. Firstly, the characteristic membership degree of electric vehicle charging message data is calculated. Then the improved broad BP neural network based on compression factor is established to train the evaluation results of several experts, and the AHP model is optimized. Finally, the charging safety influencing factor system is constructed by AHP to evaluate the charging safety of real-time message. According to the accuracy comparison of the AHP model and improved broad BP-AHP model, the experimental results show that the improved broad BP-AHP model can more accurately evaluate the safety risk of electric vehicle real-time charging.

This article presents a dual-stage coordinated control approach for voltage regulation and congestion management of active distribution networks (ADN) in the presence of photovoltaic (PV) generators and electric vehicle stations (EVS). The proposed scheme operates on rule-based model predictive control (RBMPC) to optimally manage the settings of the regulating devices, i.e., on-load tap changer (OLTC), distribution static synchronous compensator (DSTATCOM), PV generators, and EV inverters that possess different temporal characteristics. The first (hourly time-scale) and the second (one-minute time-scale) stages of the dual-stage coordinated control mechanism are designed to correct the long-term and short-term voltage fluctuations, respectively. The objectives of the proposed approach are to minimize the number of OLTC operations (first stage) and changes in set-points of PV and EV inverters, and DSTATCOM (second stage) in addition to minimization of slack variables, energy loss, and voltage error at EV stations. In both the stages, predefined rules are set for MPC to optimize different objectives on the basis of the magnitudes of bus voltages. Simulations are performed on 33-bus and 38-bus distribution networks to test the efficacy of the control approach. It is observed that the proposed approach could mitigate the voltage variations as well as line congestion for different scenarios.

Adaptive output-feedback consensus with funnel performance is studied for nonlinear uncertain multi-agent systems (MASs). The nonlinear MASs contain unknown dynamics and only an output variable can be measured. The other states are not measured directly and are reconstructed via fuzzy state observers. The presence of output feedback makes the proposed design significantly different from the existing literature on control with funnel performance. In particular, appropriate adaptive laws must be defined to handle the presence of uncertain dynamics and local output information from a few neighboring nodes. Interestingly, with a suitable error transformation, it is shown that the proposed control law has a simpler structure than barrier function methods proposed in the literature to handle funnel-like performance. With respect to this point, simulation studies illustrate that the proposed method can dramatically reduce the control effort while satisfying transient and steady-state performance imposed by the funnel.

Due to current and future environmental and safety issues in space propulsion, typical propellants for upper stage or satellite rocket engines such as the toxic hydrazine are going to be replaced by green propellants like the combination of liquid oxygen and hydrogen or methane. The injection of that kind of cryogenic fluids into the vacuum atmosphere of space leads to a superheated state, which results in a sudden and eruptive atomization due to flash boiling. For a detailed experimental investigation of superheated cryogenic fluids, the new cryogenic test bench M3.3 with a temperature controlled injection system was built at DLR Lampoldshausen. After a first test campaign with high-speed shadowgraphy of flash boiling liquid nitrogen sprays, a laser-based Phase Doppler system was set-up to determine the spatial distributions of droplet velocities and diameters in highly superheated sprays. The spatial distributions revealed a core region with high mean velocities close to the injector orifice. With increasing distance from the injector orifice, the sprays develop a more and more monodisperse pattern. These distributions also showed that atomization due to flash boiling generates finer sprays with growing degrees of superheat. In certain spray regions, two droplet populations varying in their direction of motion, velocity and diameter due to possible recirculation zones were observed. The experimental data of flash boiling liquid nitrogen generated within this study provide a comprehensive data base for the validation of numerical models and further numerical investigations., Deutsche Forschungsgemeinschaft, Projekt DEAL

The relationship among the microstructure, hardness and electrical conductivity of the as-forged ZYK530 Mg alloy after heat treatment was analyzed and studied using a microscope, X-Ray Diffractometer, eddy current conductivity meter, and Vickers microhardness tester, to explore optimum heat treatment process of ZYK530 Mg alloy. The results show that: with the prolongation of holding time, the electrical conductivity and microhardness show the same change trend, both of which show an oscillatory upward trend, and then decrease in an oscillatory downward trend after reaching the peak value. There is a linear positive correlation between the conductivity and the hardness, and the fitting results of the conductivity and hardness are in good agreement with the measured results; combined with the actual production, when the heat-treatment is 480 ℃ × 8 h + 220 ℃ × 3 h, the highest hardness is 79.2 HV, the electroconductivity is 36.2%IACS, and the comprehensive performance is the best, which is the best heat treatment process.

A novel ultrasonic burnishing (UB) system with a flat tool using a piezoelectric longitudinal transducer (PLT) is proposed. High-precision no-feeding mark planar burnishing can be achieved by the compact system with a size of only 280 mm 224 mm 558 mm without the need for machine tools. Above all, the structure of the burnishing system is presented and the operation principle is analyzed in detail. A resonant frequency design method of the PLT with a tool head is discussed. Based on thisthe PLT achieves the expected resonant frequency precisely. Subsequently, the test system for the PLT is built and the resonant frequency and vibration amplitude are tested, which are in good agreement with the simulation results. Ultimately, the UB experiments are carried out and the results indicate that the surface roughness of the workpiece is reduced from Ra 1.6 m to Ra 0.182 m, which is reduced by 88.31%. Furthermore, the surface topography shows that the surface without the feeding mark is generated, which is beneficial to improve the burnishing accuracy and expand the application in ultra-precision processing.

En-route charging stations are essential to ensure the adoption of electric vehicles. However, careful planning is necessary due to high cost in infrastructure and potentially long waiting queues. Existing literature on the placement of charging stations largely disregards competition, sets prices to cover costs and/or disregards queues. In contrast, this work models competing station investors who aim to maximise expected profit, while electric vehicle drivers aim to minimise expected travel costs including queues. Following a game-theoretic approach, investors strategically decide station capacities, locations and charging unit power outputs as well as fees, taking into consideration building and operational costs. Given the complexity of the problem, the solution involves a combination of theoretical and algorithmic techniques to obtain subgame-perfect equilibria of investor and driver choices. Subgame-perfect equilibria are found to be at least 92.85% efficient, for reasonable fluctuations of problem parameters. Furthermore, it is found that charging prices can be up to approximately 5 times higher than marginal cost due to long charging times, and also that better charging technology may not necessarily benefit drivers in the near future. Finally, subsidies towards the purchase of charging units are shown to be beneficial for both drivers and investors, being able to generate up to 14.3% additional value than the cost of the subsidy. In contrast, subsidies on the energy price for stations are found to have small effect and can be abused by investors.

Electric vehicles (EVs) are now widely acknowledged as ideal means of transportation in future, in terms of energy conservation and environmental protection for urban governance. EV lease service is an effective measure to promote the use of EVs and has gained the support of many countries. In this article, a comprehensive pricing scheme is developed to optimize the annual operations profit for EV lease service based on mileage-based pricing (MBP), where the profit is defined as the revenue subtracted by the maintenance cost. Different from the traditional MBP for vehicle lease service, our model characterizes how the grade level of EVs affects the maintenance cost after each failure. Moreover, the lease service is modeled as a queuing system where each consumer has the probability choosing the MBP by comparing the service value and his expense. An enumerative algorithm is developed to find the optimal pricing as well as analyze the influence of parameters on the optimal profit. The algorithm is implemented in the pricing of an EV lease company in China. The results show that the optimal pricing will increase with service rate, consumer arrival rate, and the service value from lessor, but decrease with waiting cost of consumers. Differential pricing strategies are also proposed to resolve the conflict goals of lessors and consumers, which can further boost lessors’ profit and raise consumers’ satisfaction level. The results show that MBP with differential pricing strategies will increase profit between 2.3% and 69.8%, comparing with cases without differential pricing strategies.

The bipolar pad is one of the most promising topologies in inductive power transfer systems for electric vehicles. However, there is scant literature on the analytical model of the bipolar pad. In this paper, a 3-D analytical model of the inductive power transfer system including a bipolar transmitter and a square receiver is developed based on the superposition of two 2-D subdomain analytical models. Ferrite and the aluminum shields with finite dimension are taken into account on the transmitter and receiver sides. An analytical calculation of the mutual inductance is then carried out with respect to the main parameters of the inductive power transfer system, namely the dimension of the coils, the conductivity and the permeability of the shield. Two study cases are demonstrated to highlight how the proposed method can accelerate speed up the pad design process. Calculation results of the proposed model are compared with both a finite element analysis model and experimental measurements, demonstrating that the proposed model is 9 times faster than the finite element analysis method. When comparing with the experimental results, computational error of the proposed model is less than 6% in most of the study cases

BackgroundIn the context of the SARS-CoV-2 pandemic, reuse of personal protective equipment, specifically that of medical face coverings, has been recommended. The reuse of these typically single-use only items necessitates procedures to inactivate contaminating human respiratory and gastrointestinal pathogens. We previously demonstrated decontamination of surgical masks and respirators contaminated with infectious SARS-CoV-2 and various animal coronaviruses via low concentration- and short exposure methylene blue photochemical treatment (10 µM methylene blue, 30 minutes of 12,500-lux red light or 50,000 lux white light exposure).MethodsHere, we describe the adaptation of this protocol to the decontamination of a more resistant, non-enveloped gastrointestinal virus and demonstrate efficient photodynamic inactivation of murine norovirus, a human norovirus surrogate.ResultsMethylene blue photochemical treatment (100 µM methylene blue, 30 minutes of 12,500-lux red light exposure) of murine norovirus-contaminated masks reduced infectious viral titres by over four orders of magnitude on surgical mask surfaces.Discussion and ConclusionsInactivation of a norovirus, the most difficult to inactivate of the respiratory and gastrointestinal human viruses, can predict the inactivation of any less resistant viral mask contaminant. The protocol developed here thus solidifies the position of methylene blue photochemical decontamination as an important tool in the package of practical pandemic preparedness.

In the fully mechanized mining face, the proper chain tension of chain transmission system is the key to reduce the failure rate of scraper conveyor and improve the service life of expensive chain. However, the interference of random load and variable stiffness in the chain transmission system of the scraper conveyor make it very difficult for tensionable tail drive (TTD) to control the chain tension. In this research, the mathematical model of the TTD based on catenary theory is established, and the dynamic characteristics are analyzed to investigate the influence of load and stiffness on the tension control. An adaptive neural command filtering backstepping algorithm with parameter identification is proposed to control the chain tension of the chain transmission system. The combination of stiffness identification with the linearized tension model weakens the influence of variable stiffness and load on the tension control. Based on state observation and stiffness estimation, radial basis function neural network is introduced into the tension controller to compensate the load randomness caused by tail sprocket drive. Comparative experimental results are completed to verify the high-accuracy tension tracking performance and a strong robustness of the proposed controller in the random load mode.

Scheduling the recharging of electric vehicle fleets under different scenarios is an important but open problem. One important scenario is that vehicles travel at different speeds in different periods since traffic congestion is common in urban areas nowadays. Therefore, in this article, a novel time-dependent electric vehicle routing problem with congestion tolls is proposed. If a vehicle enters a peak period, a fixed congestion toll needs to be paid in this problem. A mixed integer linear programming model is established and an adaptive large neighborhood search (ALNS) heuristic is designed to solve the model. The model and solving method are validated and evaluated extensively with benchmark instances. Results indicate that a certain level of congestion tolls could prevent vehicles from entering peak periods and relieve road congestions significantly. Furthermore, the ALNS heuristic could provide much better solutions for the problem than typical optimization software, such as Gurobi, in much shorter running time.