The paper presents a metamaterial with the reconfigurable effective properties, intended to operate in alternating magnetic fields. The structure of a resonator, based on a series connection of a planar inductor and a lumped capacitor, is expanded using an additional capacitor with a MOSFET transistor. Due to the presence of the controllable active element, it is possible to dynamically change the phase of a current flowing through a meta-cell and shift a frequency response within an assumed range. Since the transistor is driven by the unipolar square wave with a changeable duty cycle and time delay, two closed-loop controllers were utilized to achieve a smart material, able to automatically attain and maintain the imposed resonant frequency. As a result, the complex effective magnetic permeability of the metamaterial can be smoothly changed, during its operation, via an electrical signal, that is, by adjusting the parameters of a control signal of the active element. The design of the meta-cell, as well as the measuring, data operation and control part, are presented in detail. An illustrative system is examined in terms of achieving the user-defined resonance point of the metamaterial. The transient responses with estimated settling times and steady-state errors and the effective permeability characteristics for the exemplary cases are shown. The meta-cell is also tested experimentally to validate the theoretically determined effective properties.
Components used for the structure of the GLObal Solar Wind Structure experiment in the NASA Interstellar Mapping and Acceleration Probe space mission, made of AA6061-T6 alloy, are subjected to the coating process, where the temperature affects its mechanical properties. The aim of this paper is to examine the impact of the coating thermal cycle on the mechanical properties of AA6061-T6 alloy, which is the load-carrying material in a spaceborne instrument. As a part of the manufacturing process, the parts made of AA6061-T6 are subjected to a coating process at a temperature of about 220°C for a time longer than 1h. This treatment modifies the mechanical properties of the alloy. To evaluate the consequences of this change for spaceborne components, mechanical testing and numerical simulation were carried out. It was found that as a result of the coating process, the reduction in AA6061-T6 yield strength is about 16%, which entails a decrease in the margins of safety by 25% at its maximum.
In this paper, a robust fault-tolerant control with dynamic event-triggered mechanism based on observer is proposed for nonlinear switched system with faults, external disturbances and uncertainties. A first-order filter is utilized to equate sensor faults to actuator faults, and the augmented system is constructed. An adaptive observer with H∞ performance is designed based on the augmented system. The condition that the state error and fault error of the adaptive observer are uniformly bounded is given. In order to save communication resources and reduce the transmission of unnecessary information, an improved dynamic event-triggered mechanism is designed by introducing a fixed threshold and defining a sampling error function based on the observed state and the actual state. This mechanism can further expand the triggering time interval and effectively avoid the Zeno behavior. According to the observed state and real-time fault estimation information at the triggering moment, a faulttolerant controller for switched system based on the dynamic event-triggered mechanism is proposed, and the conditions for asymptotic stability of the closed-loop system are provided. Finally, the validity of the proposed method is verified by application simulation for the variant aircraft switched system.
Stall flutter is a serious threat to the operational integrity in turbomachinery, particularly in the final stage rotors of steam turbines and in compressors. Although computer science has developed rapidly and much of the research can be carried out using numerical tools, the simulation of some phenomena, such as stall flutter, is still very challenging and needs to be supported by experimental data. This paper presents an innovative experimental linear blade cascade design with five prismatic blades with pitch degrees of freedom, designed to be operated in a low subsonic wind tunnel. The geometry of the blade cascade was chosen on the basis of the experimental and numerical tests to allow stall flutter initiation. New suspension, measurement and electromagnetic excitation systems were developed and experimentally tested to allow accurate measurement of aerodynamic damping during controlled flutter tests. The novelty of the experimental blade cascade is the possibility of single pulse excitation of the blades. The cascade can be brought to the edge of stability by adjusting the angle of attack and flow velocity, and then the pulse can be used to induce stall flutter. Measurement of both mechanical and flow characteristics, also demonstrated in this paper, will provide data for in-depth analysis of stall flutter initiation and propagation.
The paper describes a novel, simple servo drive position controller, using only knowledge about the structure of the nonlinear model and the constraints met by individual components of the model. The desired behaviour of the position and velocity signals is obtained by imposing a time-varying constraint on the signal aggregating information about position and velocity tracking errors. The method allows you to determine the maximum control (servo drive current) necessary to achieve the control goal under the existing initial conditions and the selected reference trajectory. The control is constrained and consists in appropriate reaction when the trajectory approaches the barrier, the shape of which is responsible for the imposed properties of the transient and quasisteady state tracking error. In addition to the derivation of the control, a discussion of its possible variants and basic properties is presented. Control with time-varying constraints has been introduced, which allows the control objectives to be met with limited conservatism of the imposed constraints. The influence of technical factors related to actual speed and position measurements was discussed and the operation of the real drive on a laboratory stand was presented.
The field of ophthalmic surgery demands accurate identification of specialized surgical instruments. Manual recognition can be time-consuming and prone to errors. In recent years neural networks have emerged as promising techniques for automating the classification process. However, the deployment of these advanced algorithms requires the collection of large amounts of data and a painstaking process of tagging selected elements. This paper presents a novel investigation into the application of neural networks for the detection and classification of surgical instruments in ophthalmic surgery. The main focus of the research is the application of active learning techniques, in which the model is trained by selecting the most informative instances to expand the training set. Various active learning methods are compared, with a focus on their effectiveness in reducing the need for significant data annotation - a major concern in the field of surgery. The use of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to achieve high performance in the task of surgical tool detection is outlined. The combination of Artificial Intelligence (AI), Machine Learning, and Active Learning approaches, specifically in the field of ophthalmic surgery, opens new perspectives for improved diagnosis and surgical planning, ultimately leading to an improvement in patient safety and treatment outcomes. The article consists of seven sections. After the introduction, the classic approach to surgical instrument recognition is presented. In sections 3-4 the neural networks and active learning techniques are described. Our algorithm for surgical tool recognition is presented in sec. 5. The article concludes with a discussion.
Forecasting future trajectories of intelligent agents presents a formidable challenge, necessitating the analysis of intricate scenarios and uncertainties arising from agent interactions. Consequently, it is judicious to contemplate the establishment of inter-agent relationships and the assimilation of contextual semantic information. In this manuscript, we introduce HTTNet, a comprehensive framework that spans three dimensions of information modeling: (1) the temporal dimension, where HTTNet employs a Time Encoder to articulate time sequences, comprehending the influences of past and future trajectories; (2) the social dimension, where the Trajectory Encoder facilitates the input of trajectories from multiple agents, thereby streamlining the modeling of interaction information among intelligent agents; (3) the contextual dimension, where the TF-Map Encoder integrates semantic scene input, amplifying HTTNet's cognitive grasp of scene information. Furthermore, HTTNet integrates a hybrid modeling paradigm featuring CNN and Transformer, transmuting map scenes into feature information for the Transformer. Qualitative and quantitative analyses on the nuScenes and interaction datasets highlight the exceptional performance of HTTNet, achieving 1.03 minADE10 and a 0.31 Miss Rate on nuScenes, underscoring its effectiveness in multi-agent trajectory prediction in complex scenarios.
This paper proposes a new Principal Component Analysis (PCA) scheme to perform fault detection and identification (FDI) for systems affected by process faults. In this scheme, a new modeling method which maximizes the model sensitivity to a certain process fault type is proposed. This method uses normal operating or known faulty data to build the PCA model and other faulty data to fix its structure. A new structuration method is proposed to identify the process fault. This method computes the common angles between the residual subspaces of the different modes. It generates a reduced set of detection indices that are sensitive to certain process fault and unsensitive to others. The proposed FDI scheme is successfully applied to the Tenessee Eastman Process (TEP) supposedly affected by several process faults.
Vector controlled drives require stator current information for use in current feedback and/or state variable estimators. That is why the detection and compensation of possible CS damage is so important. This article focuses on current sensor (CS) fault-tolerant control (FTC) in induction motor (IM) drive systems. In contrast to solutions known from the literature, two Modified Luenberger Observers (MLO) were applied, allowing for high-quality estimation of currents used in the detector and fault compensator. In a simple implementation of a detection algorithm based on residuals, an adaptive threshold coefficient was employed, enabling effective detection of various types of faults, regardless of whether the second current sensor was faulty or intact. The presented solution was evaluated during both motor and regenerative operation, with faults occurring in transient states, unlike solutions known in the literature.
In modern drive systems, the aim is to ensure their operational safety. Damage can occur not only to the components of the motor itself but also to the power electronic devices included in the frequency converter and the sensors in the measurement circuit. Critical damage to the electric drive that makes its further exploitation impossible can be prevented by using fault-tolerant control (FTC) algorithms. These algorithms are very often combined with diagnostic methods that assess the degree and type of damage. In this paper, a fault classification algorithm using an artificial neural network (ANN) is analysed for stator phase current sensors in AC motor drives. The authors confirm that the investigated classification algorithm works equally well on two different AC motors without the need for significant modifications, such as retraining the neural network when transferring the algorithm to another object. The method uses a stator current estimator to replace faulty sensor measurements in a vector control structure. The measured and estimated currents are then subjected to a classification process using a multilayer perceptron (MLP), which has the advantage of a small structure size compared to deep learning structures. The uniqueness of the method lies in the use of data in the training set that are not dependent on the parameters of a specific motor. Four types of current sensor faults were studied, namely total signal loss, gain error, offset, and signal saturation. Simulations were performed in a MATLAB/SIMULINK environment for drive systems with an induction motor (IM) and a permanent magnet synchronous motor (PMSM). The results show that the algorithm correctly evaluates the type of damage in more than 99.6% of cases regardless of the type of motor. Therefore, the results presented here may help to develop universal diagnostic methods that will work on a wide variety of motors.
The publication addresses the dynamic state challenges encountered during development of a Dual Active Bridge (DAB) converter within DC microgrid systems. The conventional startup method is identified as instigating a cascade of unfavorable outcomes, encompassing elevated starting current, transformer current asymmetry, DC voltage distortions, EMI and heightened thermal stress on semiconductor components. Additionally, it necessitates precise calibration of magnetic components and diodes. A proposed remedy to these issues is introduced, involving a control method based on an additional phase shift to modulate the current of the primary H bridge. This novel control methodology is posited as a means to mitigate the aforementioned undesirable effects associated with traditional converter initiation techniques. The research also delves into considerations of proper design procedure for the converter. Emphasis is placed on integrating the novel control methodology into the design framework to effectively address challenges arising during transient states. Validation of the proposed solution is substantiated through a series of laboratory tests, the results of which are comprehensively presented in the article. These tests affirm the efficiency of the system when incorporating the novel control methodology, thereby substantiating its practical utility in mitigating the identified issues during the initiation phase of the DAB converter in DC microgrid systems.
Fluid-flow approximation is an approach to modeling and evaluating the performance of vast computer networks. Due to varying traffic and performance of transmission protocols reacting to traffic overloads, computer networks are in a permanent transient state. The fluidflow method’s main advantage is its ability to analyse these transient states. The article reviews and organises several versions of this approach, indicating a few errors. The main reason for these errors is confusion or lack of distinction between the two versions of the Internet Protocol - when the queue of packets at a node is too long, they may be destroyed or only marked as redundant. The paper compares and evaluates these fluid-flow approximation models with mild and aggressive settings of RED parameters. The authors build a software system with hitherto unprecedented capabilities regarding the size of the networks to be analysed and with innovative way of organising the calculations. The paper shows how large differences imprecise assumptions can introduce in quantitative results.
The quality parameters of surface layers synthesised using electrospark alloying (ESA) technology were analysed in this paper. The main focus was on the influence of equipment energy parameters on structure formation, specifically the effect of discharge energy and productivity. Microstructural analysis of the modified surface of C40 steel after nitriding by ESA using a paste containing nitrogen compounds injected into the interelectrode gap was conducted. The layer structure for all studied ESA parameters includes three areas: the upper "white layer", the diffusion zone below it and the substrate. The roughness of the surface is Ra ~ 0.9 μm at low discharge energy Wp = 0.13 J and Ra ~ 6 μm at Wp = 3.4 J. The microhardness, continuity, and surface roughness of the layers varied with Wp. The influence of ESA productivity on the structure was studied. The thickness of the hardened layer and the diffusion zone, as well as the microhardness and continuity, are affected by reduced productivity. For the same discharge energy, the thickness of the hardened layer increases by 10-18% with decrease in productivity compared to the classical mode. Studies have shown that a productivity loss has a positive effect on the quality characteristics of the coatings produced by the ESA process.
This review paper discusses the concept of a bidirectional Dual Active Bridge (DAB) DC/DC converter. Practical applications and control methods are explored, and various types of DAB converters are introduced and characterized. Aspects of operation are discussed, enriched by the results of theoretical analyses, simulations, and experimental measurements of the original authors works.
The growing number of distributed renewable energy sources and dynamic constant-power loads (e.g. electric vehicle charging stations) pose new challenges for network operators. These changes result in alterations to network load profiles and load flows, leading to greater voltage volatility. One effective solution to these problems can be the use of automatic voltage regulators (AVRs), which stabilize and symmetrize voltage output, whether at distribution transformers (DTs) or elsewhere in the distribution network. The device developed by the authors consists of two bidirectional power converters and three single-phase transformers connected in series to the low-voltage grid as a stabilizer. The proposed control system provides accurate and fast regulation of the AVR’s output voltage (within the range of ±10% of the nominal grid voltage), with each phase being independently adjusted, regardless of the type of power load. The article includes test results demonstrating selected functionalities of the developed AVR. The physical model of the device discussed in the article is a research component of the LINTE2 laboratory of the Gdansk University of Technology.
Gear systems in today’s industry are one of the critical pillars providing power transformation and matching speed and torque to application requirements. Reliability, high efficiency, and low operating costs are desirable due to the prevalence of gearboxes. This paper provides a comparative analysis of selected mechanical and magnetic gears, using a specific example to point out the advantages and disadvantages of contactless power conversion. After determining the essence of the operation of the magnetic gear on the base model, two magnetic gear optimization cases are presented, testifying to the application potential. An analysis of the stress distribution in the area of the teeth of the mechanical gearbox and the most stressed element of the magnetic gear - the modulator, was carried out. The effects of temperature and load on losses were measured and simulated, and ultimately, the efficiency characteristics of the two gears were also compared.
Efficiency, reliability, and durability play a key role in modern drive systems in line with the Industry 4.0 paradigm and the sustainability trend. To ensure this, highly efficient motors and appropriate systems must be deployed to monitor their condition and diagnose faults during the operation. For these reasons, in recent years, more and more research has been focused on developing new methods for fault diagnosis of permanent magnet synchronous motors (PMSMs). This paper proposes a novel hybrid method for the automatic detection and classification of PMSM stator winding faults based on combining the continuous wavelet transform (CWT) analysis of the negative sequence component of the stator phase currents with a convolutional neural network (CNN). CWT scalogram images are used as the inputs of the CNN-based interturn short circuits fault classifier model. Experimental tests were carried out to verify the effectiveness of the proposed approach under various motor operating conditions and at an incipient stage of fault propagation. In addition, the effects of the input image format, CNN structure, and training process parameters on model accuracy and classification effectiveness were investigated. The results of the experimental tests confirmed the high effectiveness of fault detection (99.4%) and classification (97.5%), as well as other important advantages of the developed method.
In the paper results of the operation and efficiency of a DC-DC resonant converter with a switched capacitor topology, equipped with GaN transistors and SiC diodes are presented. Investigated problems are related to the optimization of the DC- DC power electronic converter in order to achieve miniaturization, a simplified design and high efficiency. The proposed system operates at a high frequency with low switching losses. The proposed design helps to achieve uniform heating of the transistors and diodes, as demonstrated by the results of the thermal imaging measurements. The GaN transistors are integrated in one package with dedicated gate drivers and used to simplify the drivers circuitry and increase the power density factor of the proposed device. In the high-frequency design presented in the paper, the converter is implemented without electrolytic capacitors. The results included in the paper contain waveforms recorded in the power circuit at ZVS operation when switching on the transistors. It occurs when the system operates above the frequency of current oscillations in the resonant circuit of the switched capacitor. Efficiency characteristics and a voltage gain curve of the converter versus its output power are presented as well. Results of efficiency and quality of waveforms are important because they allow to characterize the tested system for the implementation using WBG devices. The use of integrated GaN modules to minimize elements in the physical system is also unique to this model and it allows for very short dead-time use, operation in ZVS mode at low reverse-conduction losses.
In this paper, a voltage control system for a PMSM motor based on the QZSDMC converter is proposed, which allows operation in both buck and boost modes as a possible method to make the drive resistant to power grid voltage sags. The authors presented a method for measuring and transforming the output voltage from QZS, enabling the use of a PI controller to control the voltage supplied to the DMC converter. The publication includes simulation and experimental studies comparing the operation of a PMSM motor powered by DMC and the proposed QZSDMC with voltage regulation. Simulation studies confirm the drive with QZSDMC resistance to voltage sags up to 80% of the rated value. Experimental studies demonstrate the correct operation of PMSM even with a power grid voltage amplitude equal to 40% of the rated value.
The study aims to analyse the dynamic buckling phenomenon and assess the role of the stress tensor components in the failure process of a short Fiber Metal Laminate column under axial compressive dynamic loading. The investigation is focused on a channel-section profile composed of three aluminium layers and two doubled composite plies [Al/0/90/Al/90/0/Al]. The numerical analysis was performed on the finite element model, which was validated by experimental static buckling tests. Employing a progressive failure algorithm, this analysis incorporated the material property degradation method and Hashin’s criterion as the damage initiation criterion. Failure initiation in metal layers was based on the HuberMises-Hencky failure criterion. Based on the conducted analyses, it was concluded that the dominant forms of destruction in the FML structure are yielding in the metal layers due to excessive compressive stresses and the failure of the matrix in composite plies as a result of compressive and shear stresses. Through a thorough examination of the stress tensor components, critical stresses contributing to aluminum plastic deformation and laminate failure mechanisms were identified.
The study reports the results of a comparative analysis of advanced high-accuracy Stewart-Lift platform along with a comparative study of dynamic control. A control system powered by a programmable logic controller (PLC) was used. The properties of the system were described using a dynamic model using the Lagrange method. The real object was verified by performing several tests and comparing them using quality indicators. The results of verification tests conclusively demonstrate the system’s suitability for applications within industrial automation and robotics systems.
In the paper, a design method of a static anti-windup compensator for systems with input saturations is proposed. First, an antiwindup controller is presented for system with cut-off saturations, and, secondly, the design problem of the compensator is presented to be a non-convex optimization problem easily solved using bilinear matrix inequalities formulation. This approach guarantees stability of the closedloop system against saturation nonlinearities and optimizes the robust control performance while the saturation is active.
In Moncongloe area, Maros Regency, South Sulawesi Province, Indonesia, zeolite mineralization in porphyritic rhyolite and green tuff was identified occurred in a volcano-sedimentary sequence, members of the Miocene Camba Formation. This paper describes a recent study of the zeolite mineralization on the basis of field and laboratory data, which focused on its genetic aspects and potential utilizations based on its mineralogical and chemical characteristics. The laboratory works applied in this study include mineralogical analysis (petrography and XRD) and bulk chemical analysis (XRF for major oxides, ICP-OES and ICP-MS for trace elements). Microscopic and XRD studies indicate that the zeolite is a phillipsite type, which is associated with smectite, and was formed as an alteration product of the primary K-feldspar phenocrysts as well as fine crystalline ground mass and volcanic glass. The presence of phillipsite-type zeolite associated with smectite were also confirmed by the chemical compositions. The zeolite mineralization associated with smectite in the study area were formed by alteration process by hydrothermal fluid in alkaline seawater condition, during Late Miocene to Pliocene. So, it can be concluded that the hydrothermal alteration is occurred in that time range. The magma that forms the volcanic host rocks is an alkaline series magma, intermediate to acid in composition, and originates from subduction zone.The K-rich phillipsite-type zeolite in the study area can be used for, among other things: to remove lead from water, remove paraquat from wastewater, extract potassium from seawater, remove thorium from carbonate solutions, as catalyst in knoevenagel, as dietary supplementation for pets, to uptake ammonia in water, and for colorectal cancer therapy.
In this article, we propose a data-driven method for short-circuit fault detection in transmission lines that exploits the capabilities of convolutional neural networks (CNNs). CNNs, a class of deep feedforward neural networks, can autonomously detect different features from data, eliminating the need for manual intervention. To mitigate the effects of noise and increase network robustness, we present a CNN architecture with six convolutional layers. The study uses a single busbar power system model developed with the PSCAD simulation program to evaluate the performance of the proposed method. The proposed CNN method is also compared with machine learning methods such as LSTM, SVM and ELM. Our results show a high success rate of 98.4% across all fault impedances, confirming the effectiveness of the proposed CNN methods in accurately detecting short-circuit faults based on current and voltage measurements.