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This paper presents a new transistor self-oscillating inverter suitable for high efficiency switch mode power supplies, which can operate directly from high voltages and is free from output transformer saturation problems due to transistor characteristic mismatch. It uses a half-bridge power circuit, consisting of two transistors in one limb and two capacitors in the other. Amongst several advantages of this configuration, the switching transistors need to withstand a minimum voltage equal to the dc supply only and not higher as in conventional circuits, while using only two transistors.

In this paper, an overview of the field of wireless powering is presented with an emphasis on low-power applications. Several rectenna elements and arrays are discussed in more detail: (1) a 10-GHz array for powering sensors in aircraft wings; (2) a single antenna in the 2.4-GHz ISM band for low-power assisted-living sensors; and (3) a broadband array for power harvesting in the 2-18GHz frequency range.

Internet of Things technologies open up new applications for remote monitoring of forests, fields, etc. These networks require autonomous operation: combining ultra-long-range connectivity with low energy consumption. While typical low-power wide-area networks offer long-range characteristics, they fall short in providing coverage for environmental tracking in ultra-remote areas spanning hundreds of square kilometers. This paper presents a multi-hop protocol to extend the sensor's range, whilst still enabling low-power operation: maximizing sleep time by employing prolonged preamble sampling, and minimizing the transmit energy per actual payload bit through forwarded data aggregation. Real-life experiments, as well as large-scale simulations, prove the capabilities of the proposed multi-hop network protocol. By employing prolonged preamble sampling a node's lifespan can be increased to up to 4 years when transmitting packages every 6 h, a significant improvement compared to only 2 days when continuously listening for incoming packages. By aggregating forwarded data, a node is able to further reduce its energy consumption by up to 61%. The reliability of the network is proven: 90% of nodes achieve a packet delivery ratio of at least 70%. The employed hardware platform, network protocol stack and simulation framework for optimization are released in open access. ispartof: SENSORS vol:23 issue:11 ispartof: location:Switzerland status: published

In this paper, we investigated the applicability of ChatGPT AI in electronics research and development via a case study of applied sensors in embedded electronic systems, a topic that is rarely mentioned in the recent literature, thus providing new insight for professionals and academics. The initial electronics-development tasks of a smart home project were prompted to the ChatGPT system to find out its capabilities and limitations. We wanted to obtain detailed information on the central processing controller units and the actual sensors usable for the specific project, their specifications and recommendations on the hardware and software design flow additionally. Furthermore, an extensive literature survey was requested to see if the bot could offer scientific papers covering the given topic. It was found that the ChatGPT responded with proper recommendations on controllers. However, the suggested sensor units, the hardware and software design were only partially acceptable, with occasional errors in specifications and generated code. The results of the literature survey showed that non-acceptable, fabricated citations (fake authors list, title, journal details and DOI—Digital Object identifier) were presented by the bot. The paper provides a detailed qualitative analysis, a performance analysis and critical discussion of the aforementioned aspects while providing the query set, the generated answers and codes as supplied data with the goal to give added value to electronics researchers and developers if trying to reach out for the tools in their profession.

In the above titled paper (ibid., vol. 56, no. 1, pp. 85-93, Mar 07), there were a number of errors. A list of corrections is presented here.

A new magnetorheological piston head design inspired by toroidal electromagnets was proposed in previous work as an alternative to conventional annular dampers. A prototype with a circular valve array integrated inside the piston head was built and tested to measure its passive performance. The mechanical, electromagnetic, and hydraulic models used in the new design were explained, and the relevant parameters of the actuator were analyzed to construct a mathematical model to estimate its performance. These works showed the feasibility of the concept and its potential as an alternative to current damper technology, but lacked a baseline for comparison. This paper reviews and widens this groundwork. It adds a magnetic finite element method study to address the previously found leakage, and a new set of experiments, including a force controller, to compare its performance against a conventional annular piston head. The new findings show how the current force limitations can be overcome by striking a balance between the coil space and the size of electromagnet cores to achieve the performance of current dampers. They also highlight its potential in force control applications to provide a wider range of customization options, such as number and size of holes, electromagnets, and coils, and a better use of the active area of the gap.

In this paper, defect detection and identification in aluminium joints is investigated based on guided wave monitoring. Guided wave testing is first performed on the selected damage feature from experiments, namely, the scattering coefficient, to prove the feasibility of damage identification. A Bayesian framework based on the selected damage feature for damage identification of three- dimensional joints of arbitrary shape and finite size is then presented. This framework accounts for both modelling and experimental uncertainties. A hybrid wave and finite element approach (WFE) is adopted to predict the scattering coefficients numerically corresponding to different size defects in joints. Moreover, the proposed approach leverages a kriging surrogate model in combination with WFE to formulate a prediction equation that links scattering coefficients to defect size. This equation replaces WFE as the forward model in probabilistic inference, resulting in a significant enhancement in computational efficiency. Finally, numerical and experimental case studies are used to validate the damage identification scheme. An investigation into how the location of sensors can impact the identified results is provided as well., European Union’s Horizon 2020 Marie Skłodowska-Curie 859957, Science and Technology Development Fund, Macau SAR (File No.: FDCT/0101/2021/A2, FDCT/001/2021/AGJ and SKL-IOTSC(UM)-2021-2023)

The paper proposes a multidisciplinary design optimization (MDO) of permanent magnet synchronous generators (PMSG) for wind energy conversion systems (WECS). Such a MDO seeks the best compromise between the PMSG cost and the lifetime energy production of the WECS. So far, most of the papers in this field presented design methods using quite a few optimization variables and requiring a lot of assumptions. In the proposed MDO the specifications consist of geometrical, economical, magnetic, electrical and thermal constraints. Several analytic and semi-analytic sub-models where used to deal with these constraints, resulting in a large non-linear optimization problem that uses a sequential quadratic programming optimization algorithm. The paper considers the annual wind profile to estimate the lifetime wind turbine energy production. Moreover, the best possible solutions are presented in a Pareto front showing the trade-off between the generator cost and the wind turbine energy production. Among them, one optimal solution machine was chosen to be further investigated and its results were verified by finite element analysis.

Two wheeled Self balancing robots have become more popular in recent times and used in many applications, commonly used in transportation as these balancing robots are faster and apparently stable. Balancing is achieved by using controllers tuned for the system. Inverted pendulum principle is used in these robots. This paper proposes the use of two control methods, namely Proportional-Integral-Derivative (PID) and Extended PID (EPID), for maintaining the balance of two-wheeled and self-balancing robots, even when slightly tilted, thus preventing them from falling. PID control theory generally depends up on transfer functions, this paper extends the theory using EPID. EPID provides a new perspective on the working principle of PID control and its relationship with other control theories, such as tracking control and disturbance observer. This approach is illustrated using the fundamental principle of inverted pendulum technology. The key advantage of these robots over other mobile robots is their increased stability due to zero turning radius, which allows for smoother rotations during changes in direction.

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the project being ongoing. Copyright © 2023 by the authors. Electric distribution grids are seeing an increased penetration of photovoltaic (PV) generation. High PV generation exceeding the grid load demand results in a reverse active power flow in the grid, which raises the voltage level. This paper presents a reactive power controller to overcome the overvoltage problem in the distribution system. A sequentially coordinated and cooperative volt/var control technique is presented. The proposed controller aims to use as low reactive power as possible while mitigating the voltage issues. Accordingly, it reduces the active power loss associated with reactive power flow and reduces the probability for active power curtailment of the PV system. The controller is developed for each lateral and is replicated for all laterals. The lateral controller coordinates the operation of the smart PV inverters in a sequential manner. Cooperative control is proposed between the laterals’ controllers as well and is engaged when the individual laterals’ controllers are unable to solve their overvoltage issues. The performance of the proposed controller is evaluated by comparing it to two other volt/var controllers, and it demonstrates better performance in terms of reactive power requirement. To conduct the simulation study, a modified version of the unbalanced IEEE 13-bus system is utilized, which includes an additional 44 low-voltage bus. The study involves simulating 720 operating points across daily time series. The results indicate that the proposed controller effectively addresses overvoltage problems that occur during periods of high PV generation. Newton Fund Institutional Links grant number 623801791, under the Newton-Katip Çelebi Fund partnership. The grant was funded by the UK Department for Business, Energy and Industrial Strategy and TUBITAK grant number 120N996 and delivered by the British Council.

In 2019, the world had been attacked with a severe situation by the new version of the SARSCOV- 2 virus, which is later called COVID-19. One can use artificial intelligence techniques to reduce time consumption and find safe solutions that have the ability to handle huge amounts of data. However, in this article, we investigated the classification performance of eight deep transfer learning methodologies involved (GoogleNet, AlexNet, VGG16, MobileNet-V2, ResNet50, DenseNet201, ResNet18, and Xception). For this purpose, we applied two types of radiographs (X-ray and CT scan) datasets with two different classes: non-COVID and COVID-19. The models are assessed by using seven types of evaluation metrics, including accuracy, sensitivity, specificity, negative predictive value (NPV), F1- score, and Matthew\"s correlation coefficient (MCC). The accuracy achieved by the X-ray was 99.3%, and the evaluation metrics that were measured above were (98.8%, 99.6%, 99.6%, 99.0%, 99.2%, and 98.5%), respectively. Meanwhile, the CT scan model classified the images without error. Our results showed a remarkable achievement compared with the most recent papers published in the literature. To conclude, throughout this study, it has been shown that the perfect classification of the radiographic lung images affected by COVID- 19.

Most major appliances are produced in manned assembly lines consisting of multiple workstations controlled by human labour. Since, human workers are very flexible in adapting to components’ major and minor changes, assembly process planning for manned assembly lines depends on products’ component structure than on the features of each component unlike automated systems. This paper proposes a framework for a variant process planning system for manned assembly lines comprising two phases: (1) the preparatory phase for building a database of standard process plans, and (2) the development phase for creating a new process plan through the modification of existing standard process plans. In the preparatory phase, this research develops a ‘work process analysis template’ that describes the task of human labour as a sequence of standard work processes. As the development phase requires an efficient method for evaluating the performance of assembly process plans, this research proposes an ‘assembly process analys...

International audience; Wireless power transfer (WPT) is the transmission of electrical energy to other external/internal devices without the need for wire connection. Such a system is useful to power electrical devices as a promising technology for various emerging applications. The implementation of devices integrated with WPT alters the existing technologies and enhance the theoretical concept for future works. Over the last decade, various studies have been conducted on the applications of magnetically coupled WPT systems, where a general overview over such devices would be beneficial. Hence, this paper presents a comprehensive review over various WPT systems developed for commercially existing applications. The importance of WPT systems is first reported from the engineering point of view, followed by their uses in biomedical devices.

Wireless sensor networks collect data from several nodes dispersed at remote sites. Sensor nodes can be installed in harsh environments such as deserts, cities, and indoors, where the link quality changes considerably over time. Particularly, changes in transmission power may be caused by temperature, humidity, and other factors. In order to compensate for link quality changes, existing schemes detect the link quality changes between nodes and control transmission power through a series of feedback processes, but these approaches can cause heavy overhead with the additional control packets needed. In this paper, the change of the link quality according to temperature is examined through empirical experimentation. A new power control scheme combining both temperature-aware link quality compensation and a closed-loop feedback process to adapt to link quality changes is proposed. We prove that the proposed scheme effectively adapts the transmission power to the changing link quality with less control overhead and energy consumption.

This paper presents a novel optimization approach to find the optimal STATCOM installation in the power system using Mixed Integer Distributed Ant Colony optimization (MIDACO). The proposed method is mathematically formulated based on minimizing the Voltage Deviation Index (VDI) and the installation cost of STATCOM. The ability of STATCOM to provide shunt compensation to maintain bus voltage is exploited here. The proposed approach has been tested on the standard IEEE 14-bus system.

Internet of Things (IoT) nodes get deployed for a variety of applications and often need to operate on batteries. This restricts their autonomy and/or can have a major ecological impact. The core idea of this paper is to use a unmanned aerial vehicle (UAV) to provide energy to IoT nodes, and hence prolong their autonomy. In particular, the objective is to perform a comparison of the total energy consumption resulting from UAV-based recharging or battery replacement versus full provisioning at install time or remote RF-based wireless power transfer. To that end, an energy consumption model for a small license-free UAV is derived, and expressions for system efficiencies are formulated. An exploration of design and deployment parameters is performed. Our assessment shows that UAV-based servicing of IoT nodes is by far more beneficial in terms of energy efficiency when nodes at distances further than a few meters are serviced, with the gap increasing to orders of magnitude with the distance. Our numerical results also show that battery swapping from an energy perspective outperforms recharging in the field, as the latter increases hovering time and the energy consumption related to that considerably. The ecological aspects of the proposed methods are further evaluated, e.g., considering toxic materials and e-waste. article-number: 2291 ispartof: Sensors vol:23 issue:4 ispartof: location:Switzerland status: Published online

Recent advances in guaranteed stable generalized predictive control algorithms are applied to vertical positioning of plasma in the COMPASS-D tokamak, a test device used to study instabilities and control techniques necessary for fusion power plants in the future. The tokamak current P+D controller cannot stabilize the plasma vertical position without internal sensors which will not be available in larger devices. A controller which is derived with the stable predictive control algorithms described in this paper stabilizes the plasma with only external sensors and also solves noise and robustness problems. It compares favorably to the one produced with standard H/sub /spl infin// design techniques.

This paper describes a strategy of backward motion control in a tractor-trailer system. The backward motion of a tractor-trailer requires skilled control, because the position and orientation of the trailer can only be controlled by an input to the tractor. When a driver changes orientation while driving backward, the steering angle must be skillfully controlled. This operation is different in the case of a single vehicle moving backward. An uncontrollable situation of a tractor-trailer motion often happens, for example, the "jackknife phenomenon." Such a situation is often induced by the nonholonomic constraint of a wheeled system. In this research, virtual steering is introduced to a trailer. This virtual steering controls the orientation of the trailer and suppresses the nonholonomic equivalent disturbance. With this virtual steering, the tractor-trailer is able to track a straight trajectory in backward driving independently of the nonholonomic constraint. Several numerical and experimental results are shown to confirm the validity of the proposed approach.

The manufacturing era is going through an evolutionary phase from Industry 3.0 to Industry 4.0 (I4.0). In 2011, Germany initiated I4.0 during the Hanover fair in collaboration with industrialists, academicians and researchers. The benefits of implementing I4.0 are attracting the curiosity of practitioners. In current academic literature, there is little discussion related to analysing potential roadblocks of I4.0 implementation using mathematical modelling. This paper has identified roadblocks through an extensive literature review and validation has been carried out by gathering experts’ opinions. As per the findings, the identified roadblocks have been categorized into five sections: management roadblocks, operational roadblocks, human resource roadblocks, procedural and behavioural roadblocks. A case study of the automobile industry has been discussed. The data was collected from experts in the area of I4.0. A Graph Theory and Matrix Approach (GTMA) was applied to evaluate the relative intensity of the roadblocks, Comprehensive I4.0 Index (CII) and its range for each category of roadblock. Implications for industrialists, practitioners and academicians are provided.

This paper describes a statistical and mathematical approach to optimize the mechanical properties of a wood plastic composite with Polyethylene Terephthalate (PET) as polymeric matrix. Wood plastic composite are materials that consist of a primary continuous polymer phase, where a secondary filler dispersed phase is embedded, the filler generally is wood fibers or sawdust. The slack variable approach in mixture experiments, consist in selecting a component of the mixture as slack variable, to subsequently design and analyze the experiment in terms of the remaining components. With the experimental design information three slack variable model were fit. Using response surface graphs, we show how different compositions modify the mechanical properties of wood plastic composite. Besides, by the desirability function, the optimal formulation of the compound that simultaneously maximizing the mechanical properties of wood plastic composite, was obtained. Finally, the components proportions that provides the best tensile, flexural and compression strength are presented.

IEC 61850 is emerging as a popular communication standard for smart grids. Standardized communication in smart grids has an unwanted consequence of higher vulnerability to cyber-attacks. Attackers exploit the standardized semantics of the communication protocols to launch different types of attacks such as false data injection (FDI) attacks. Hence, there is a need to develop a cybersecurity testbed and novel mitigation strategies to study the impact of attacks and mitigate them. This paper presents a testbed and methodology to simulate FDI attacks on IEC 61850 standard compliant Generic Object-Oriented Substation Events (GOOSE) protocol using real time digital simulator (RTDS) together with open-source tools such as Snort and Wireshark. Furthermore, a novel hybrid cybersecurity solution by the name of sequence content resolver is proposed to counter such attacks on the GOOSE protocol in smart grids. Utilizing the developed testbed FDI attacks in the form of replay and masquerade attacks on are launched and the impact of attacks on electrical side is studied. Finally, the proposed hybrid cybersecurity solution is implemented with the developed testbed and its effectiveness is demonstrated.

The computational cloud aims to move traditional computing from personal computers to cloud providers on the internet. Cloud security represents an important research area. Confidentiality, integrity, and availability are the main cloud security characteristics addressed. Cloud providers apply dynamic load balancing and reactive fault tolerance techniques to build secure cloud services to achieve high service availability. Dynamic cloud load balancing approaches distribute submitted tasks to virtual machines during tasks execution and the load of virtual machines is updated based on the system’s state. Reactive cloud fault tolerance is activated for system process failures after failure effectively happens. Reactive cloud fault tolerance handles failure after the fault has occurred. Despite the significance of dynamic load balancing and reactive fault tolerance techniques and mechanisms, few reviews focus on these approachesin a systematic, unbiased method focusing on integrating cloud dynamic load balancing and reactive fault tolerance techniques. This paper conducts a systematic literature review of the existing literature concerning reactive fault tolerance, dynamic load balancing, and their integration in their basic approaches, types,frameworks, and future directions.

Cognitive Radio (CR) presents a lot of challenges with regard to developing the techniques and methodologies to dynamically utilize the unused or underused spectrum. The issue of efficient utilization of radio resources can be implemented unitedly through dynamic waveform design and an efficient spectrum sensing. In this paper, waveform design using spectrally modulated spectrally encoded (SMSE) framework is conjoined with the cyclostationary based sensing technique (CSS) for CR. To identify the free spectrum, an analytical expression of waveform design variables in terms of sensing parameters has been derived. Further, to find the channel occupancy/spectrum holes, a joint CSS and SMSE based test stat has been proposed. The joint test stat offers a better and clear decision by providing a large gap between test stat and the required threshold setting. Finally, to evaluate the receiver performance, receiver operating characteristic (ROC) curves have been plotted which show that the proposed joint scheme achieves a significant performance improvement at lower SNR values when compared with traditionally available detectors.

Networks of Unmanned Aerial Vehicles (UAVs) are emerging in many application domains, e.g., military surveillance. To perform collaborative tasks, the involved UAVs exchange several types of information, e.g., sensor data and commands. The major question here is how to schedule the tasks under dynamic traffic flows to provide network services. Existing solutions use the Round-Robin Strategy (RRS), where the tasks are scheduled statistically by dividing the time into fixed-length rounds. However, the RRS wastes significant network and device resources due to task scheduling in each round. This paper proposes DROVE – a novel clustering approach that allows the UAVs for dynamic task scheduling. However, determining the task scheduling is crucial, as it significantly affects several network parameters, e.g., throughput. Therefore, we devise the problem of distributed task scheduling under dynamic traffic flow scenarios to optimize the throughput. We propose a clustering task scheduling algorithm to serve dynamic traffic flows. Particularly, we integrate the dynamic traffic flows into the Lyapunov drift analysis framework, and determine the throughput optimality of our proposed scheduling algorithm. We perform extensive simulations to validate the effectiveness of DROVE. The results show that DROVE outperforms the state-of-the-art solutions in terms of energy consumption, clustering overhead, throughput, end-to-end delay, flow success rate and packet drop rate.

Industry 5.0 is projected to be an exemplary improvement in digital transformation allowing for mass customization and production efficiencies using emerging technologies such as universal machines, autonomous and self-driving robots, self-healing networks, cloud data analytics, etc., to supersede the limitations of Industry 4.0. To successfully pave the way for acceptance of these technologies, we must be bound and adhere to ethical and regulatory standards. Presently, with ethical standards still under development, and each region following a different set of standards and policies, the complexity of being compliant increases. Having vague and inconsistent ethical guidelines leaves potential gray areas leading to privacy, ethical, and data breaches that must be resolved. This paper examines the ethical dimensions and dilemmas associated with emerging technologies and provides potential methods to mitigate their legal/regulatory issues.

Ambipolar carbon nanotube based field-effect transistors (AP-CNFETs) exhibit unique electrical characteristics, such as tri-state operation and bi-directionality, enabling systems with complex and reconfigurable computing. In this paper, AP-CNFETs are used to design a mixed-signal machine learning logistic regression classifier. The classifier is designed in SPICE with feature size of 15 nm and operates at 250 MHz. The system is demonstrated in SPICE based on MNIST digit dataset, yielding 90% accuracy and no accuracy degradation as compared with the classification of this dataset in Python. The system also exhibits lower power consumption and smaller physical size as compared with the state-of-the-art CMOS and memristor based mixed-signal classifiers.

This paper presents a deployment method of various test maneuver scenarios for 2 degree of freedom (2 DoF) vehicle simulator by using feature extraction and neural networks (NN). A prototype version has been set up for the 2 DoF vehicle simulator. Then, a hardware in the loop (HIL) model with 2 inputs (torque, τ1-τ2) and 3 outputs (acceleration, ax-ay-az) is created. System identification is performed to obtain the training data of NNs to be used for the deployment of test maneuvers. In the system identification process, 2 arbitrary sinusoidal torque signals (τ1-τ2) are generated by using the actuator specs of the 2 DoF vehicle simulator. By applying the generated torque signals to the actuators, acceleration (ax-ay-az) data are collected from the inertial measurement sensor (IMU) on the 2 DoF vehicle simulator. It is determined to create 3 different NN models for the obtained data. The 1st NN model is trained with 3 inputs (ax-ay-az) and 2 targets (τ1-τ2) training data. The 2nd NN model is trained with 6 inputs (amplitudes and phases of ax-ay-az) and 2 targets (τ1-τ2) training data. The input data features for the 2nd NN model is extracted by using the Fast Fourier Transform (FFT). The 3rd NN model is trained with 6 inputs (amplitudes and phases of ax-ay-az) and 4 targets (amplitudes and phases of τ1-τ2) training data. For the 3rd NN model, the features of input and target data are extracted by using the FFT. The NN training process continues until acceptable performance criteria are reached. Then, 3 NN models are run and analyzed under various test scenarios such as Double Lane Change, Constant Radius, Increase Steer, Fish Hook, Sine with Dwell and Swept Sine. Only for the 3rd NN, the actuator signals (τ1-τ2) are recomposed by applying an inverse FFT process to the 4 targets (amplitudes and phases of τ1-τ2). Finally, the reference trajectory tracking performances are evaluated by comparing the NN models that are run under the test scenarios.

International audience; This paper presents a visual servoing method based on 2D ultrasound images. The main goal is to guide a robot actuating a 2D ultrasound probe in order to reach a desired cross-section image of an object of interest. The method we propose allows the control of both in-plane and out-of-plane probe motions. Its feedback visual features are combinations of moments extracted from the observed image. The exact analytical form of the interaction matrix that relates the image moments time variation to the probe velocity is developed, and six independent visual features are proposed to control the 6 degrees of freedom of the robot. In order to endow the system with the capability of automatically interacting with objects of unknown shape, a model-free visual servoing is developed. For that, we propose an efficient on-line estimation method to identify the parameters involved in the interaction matrix. Results obtained in both simulations and experiments validate this work and show the robustness of the developed servo method to different errors and perturbations, especially those inherent to the noisy ultrasound images.

It is well known that activities in running water or wind and waves expose the Autonomous Surface Vessels (ASVs) to considerable challenges. Under these conditions, it is essential to develop a robust control system that can meet the requirements and ensure the safe and accurate execution of missions. In this context, this paper presents a new topology for controller design based on a combination of the Successive Loop Closure (SLC) method and optimal control. This topology enables the design of robust autopilots based on the Proportional-Integral-Derivative (PID) controller. The controllers are tuned from the solution of the optimal control problem, which aims to minimize the effects of model uncertainties. To verify the effectiveness of the proposed controller, a numerical case study of a natural ASV with 3 Degree of Freedom (DoF) is investigated. The results show that the methodology enabled the tuning of a PID controller capable of dealing with different parametric uncertainties, demonstrating robustness and applicability for different prototype scenarios.

Fully-supervised object detection and instance segmentation models have accomplished notable results on large-scale computer vision benchmark datasets. However, fully-supervised machine learning algorithms’ performances are immensely dependent on the quality of the training data. Preparing computer vision datasets for object detection and instance segmentation is a labor-intensive task requiring each instance in an image to be annotated. In practice, this often results in the quality of bounding box and polygon mask annotations being suboptimal. This paper quantifies empirically the ground truth annotation quality and COCO’s mean average precision (mAP) performance by introducing two separate noise measures, uniform and radial, into the ground truth bounding box and polygon mask annotations forthe COCO and Cityscapes datasets. Mask-RCNN models are trained on various levels of noise measures to investigate the performance of each level of noise. The results showed degradation of mAP as the level of both noise measures increased. For object detection and instance segmentation respectively, using the highest level of noise measure resulted in a mAP degradation of 0.185 & 0.208 for uniform noise with reductions of 0.118 & 0.064 for radial noise on the COCO dataset. As for the Cityscapes datasets, reductions of mAP performance of 0.147 & 0.142 for uniform noise and 0.101 & 0.033 for radial noise were recorded. Furthermore, a decrease in average precision is seen across all classes, with the exception of the class motorcycle. The reductions between classes vary, indicating the effects of annotation uncertainty are class-dependent.

This paper presents a methodology for generating geometries for interior permanent magnet (IPM) motors in electric vehicles (EVs) through the application of artificial intelligence (AI) and image processing (IP) techniques. Due to the implementation of green agreements and policies aimed at reducing greenhouse gas emissions, EVs have become popularity. As a consequence, the improvement studies on the powertrain and battery system of EVs are focused. Especially for the powertrain, design optimization studies of e-motor have increased in the literature. One of the most widely used e-motor topologies is interior permanent magnet (IPM) motor. However, designing the IPM motor presents a challenge due to the dynamic considerations with geometric constraints. Therefore, e-motor designers encounter challenges related to determining initial geometry and the long time of the optimization process. To address these challenges, a novel approach is proposed that leverages machine learning (ML) techniques in combination with IP to generate initial geometries and design parameters for IPM motors. The proposed approach generates images of the motor geometry and extract dimensional features from the resulting images by using artificial neural networks (ANNs). The proposed method performs an analysis of the input vectors to reduce their size using techniques such as Histogram, 2D Maximum, 2D Mean, 2D Minimum, 2D Standard Deviation, and 2D Variance to enhance feature extraction. Additionally, FFT (Fast Fourier Transform) and IFFT (Inverse Fast Fourier Transform) are used to improve the neural network process in generating the image geometry. Further, the generated image geometry is improved by applying digital filtering techniques such as Log, FFT, Log+FFT, Laplacian, Sobel, and Histogram Equalization. Finally, the trained ANNs are tested to validate the results by using Ansys RMXprt and Maxwell. Eventually, the proposed method represents an innovative solution to generating initial geometries for IPM motors in EVs that satisfies desired design requirements. This approach leverages the power of AI and image processing techniques, which could lead to significant improvements in the optimization process for IPM motors, accelerate the designer’s analysis process, and enhance the performance of EVs.

This paper presents a simple nonlinear magnetic analysis for a surface-mounted permanent-magnet synchronous motor as an assistant design tool of finite-element analysis (FEA). The equivalent magnetic circuit of the motor used in the proposed analysis is composed of the saturable permeance tips in the stator teeth for considering the local magnetic saturation. As a result, the proposed analysis is capable of calculating the flux distribution and the torque characteristics in the presence of magnetic saturation. The effectiveness of the proposed analysis is verified by comparing with three-dimensional (3-D) FEA in terms of the analytical accuracy. The computation time is greatly reduced as short as 1.5 s while the 3-D FEA requires as much as 32 min for the same evaluation.

In semiconductor manufacturing, the accurate placement of circuit components ensures the proper functioning of microelectronic circuits. This is often subject to photolithography, an optical technique that transfers circuit patterns from photomasks to silicon wafers. Sources of placement error include aberration and misalignment between different levels, and we focus on the former. Aberration is an optical phenomenon that often degrades imaging system performance. Since aberration differs from one imaging system to another, a photomask design that minimizes the aberration-induced placement error is desired. In this paper, we discuss the optimization process of a general one-dimensional mask pattern under a general illumination condition. The constraint is a known population mean of the root mean square aberrations for the imaging systems under consideration. To apply the theory, we search for the optimal parameters for two common mask designs: alternating phase-shifting masks (PSMs) and attenuated PSMs. The theoretical results are compared with those from a Monte Carlo analysis on a large set of imaging systems. These results are indicative to mask manufacturers and circuit designers of increasing manufacturability of circuits

Interacting with other roads users is a challenge for an autonomous vehicle, particularly in urban areas. Existing vehicle systems behave in a reactive manner, warning the driver or applying the brakes when the pedestrian is already in front of the vehicle. The ability to anticipate a pedestrian’s crossing intention ahead of time will result in safer roads and smoother vehicle maneuvers. The problem of crossing intent forecasting at intersections is formulated in this paper as a classification task. A model that predicts pedestrian crossing behaviour at different locations around an urban intersection is proposed. The model not only provides a classification label (e.g., crossing, not-crossing), but a quantitative confidence level (i.e., probability). The training and evaluation are carried out using naturalistic trajectories provided by a publicly available dataset recorded from a drone. Results show that the model is able to predict crossing intention within a 3-s time window.

This paper introduces adaptive decision-making logic to improve the performance of distance protection of transmission lines. The proposed approach incorporates the trajectory of the measured impedance into the relay final decision-making logic through a weighting procedure. Two criteria are used to weigh the impedance plane appropriately, where higher weights are assigned to the faults occurring at the interior areas of the relay protective zone and low resistance faults. The weights assigned to impedance samples are added together considering the amount of smoothness of the measured impedance trajectory. An adaptive neurofuzzy inference system (ANFIS) is used to implement the proposed decision logic. Meanwhile, the fault current amplitude is considered as the other input of the ANFIS network to accelerate the relay operation for close-in faults and to incorporate an index for the source-to-line impedance ratio into the final decision-making logic. Extensive simulation studies using PSCAD/EMTDC software indicate that the proposed approach provides a very reliable technique to make the distance protective scheme faster and smarter. It helps to achieve the optimal compromise between the protective relay operation time and security.

In this paper, we study the heterogeneous consensus problem in directed networks consisting of first- and second-order agents that can only receive the position states of their neighbors. Necessary and sufficient conditions on the controller parameters are obtained in order to achieve consensus in the network. The mathematical expressions of the consensus equilibria are given for two different scenarios. Furthermore, we propose a systematic method for choosing controller parameters to ensure stability in a network of agents with heterogeneous dynamics. Several numerical examples are also provided to illustrate the theoretical results.