Hasil untuk "Electronics"

Abhiraam Eranti, Yogesh Tewari, Rafael Palacios et al.

Deep-learning methods have boosted the analytical power of Raman spectroscopy, yet they still require large, task-specific, labeled datasets and often fail to transfer across application domains. The study explores pre-trained encoders as a solution. Pre-trained encoders have significantly impacted Natural Language Processing and Computer Vision with their ability to learn transferable representations that can be applied to a variety of datasets, significantly reducing the amount of time and data required to create capable models. The following work puts forward a new approach that applies these benefits to Raman Spectroscopy. The proposed approach, RSPTE (Raman Spectroscopy Pre-Trained Encoder), is designed to learn generalizable spectral representations without labels. RSPTE employs a novel domain adaptation strategy using unsupervised Barlow Twins decorrelation objectives to learn fundamental spectral patterns from multi-domain Raman Spectroscopy datasets containing samples from medicine, biology, and mineralogy. Transferability is demonstrated through evaluation on several models created by fine-tuning RSPTE for different application domains: Medicine (detection of Melanoma and COVID), Biology (Pathogen Identification), and Agriculture. As an example, using only 20% of the dataset, models trained with RSPTE achieve accuracies ranging 50%–86% (depending on the dataset used) while without RSPTE the range is 9%–57%. Using the full dataset, accuracies with RSPTE range 81%–97%, and without pre-training 51%–97%. Current methods and state-of-the-art models in Raman Spectroscopy are compared to RSPTE for context, and RSPTE exhibits competitive results, especially with less data as well. These results provide evidence that the proposed RSPTE model can effectively learn and transfer generalizable spectral features across different domains, achieving accurate results with less data in less time (both data collection time and training time).

Jefferson S. Costa, Angelo Lunardi, Alessio Iovine et al.

Model predictive control (MPC) has emerged as a highly regarded control strategy in power electronics for renewable energy applications. While it minimizes tracking errors and control effort, a significant challenge is the lack of systematic tuning strategies to meet these systems’ energy quality performance requirements. This paper proposes a comprehensive MPC tuning methodology for grid-integrating converters with LCL filters, incorporating modulation and delay compensation. We conduct a stability analysis to define precise constraints for cost function weights. The fine-tuning strategy systematically maps a Figure of Merit (FoM) for system performance using an advanced computational model, revealing that optimal tunings reside in narrow parameter regions. Experimental validation on a 2 kW workbench confirmed that all proposed MPC tunings met IEEE Std. 519-2014 power quality criteria and consistently outperformed a two-sample deadbeat controller, exhibiting enhanced dynamic response and power quality.

Sayan Majumder, Debika Bhattacharyya, Swati Chowdhuri

Abstract Mobile ad hoc networks (MANETs) facilitate data communication across multiple nodes and hop stations, characterized by their dynamic topology. This inherent flexibility, however, makes MANETs vulnerable to various security threats, notably blackhole and wormhole attacks, where malicious nodes can intercept and manipulate data. This study investigates the security vulnerabilities of MANETs, particularly against blackhole, Sybil, and wormhole attacks, and introduces the Advanced Blockchain Dynamic Source Routing (ABCD) algorithm to address these challenges. Motivated by the need for robust and decentralized security solutions in MANETs, the proposed algorithm integrates blockchain technology and homomorphic encryption to secure data communication without intermediate decryption. The ABCD algorithm leverages Dijkstra’s algorithm for optimal routing and employs a tamper-proof, decentralized data storage approach. Comparative analysis under attack scenarios reveals that the ABCD algorithm outperforms the standard DSR protocol across multiple quality of service metrics, demonstrating a significant improvement in MANET security over equivalent studies. The packet delivery rate is also improved from 81 to 92% using the modified ABCD algorithm.

Emanuele Aiello, Mirko Agarla, Diego Valsesia et al.

Large-scale self-supervised pretraining of deep learning models is known to be critical in several fields, such as language processing, where its has led to significant breakthroughs. Indeed, it is often more impactful than architectural designs. However, the use of self-supervised pretraining lags behind in several domains, such as hyperspectral images, due to data scarcity. This paper addresses the challenge of data scarcity in the development of methods for spatial super-resolution of hyperspectral images (HSI-SR). We show that state-of-the-art HSI-SR methods are severely bottlenecked by the small paired datasets that are publicly available, also leading to unreliable assessment of the architectural merits of the models. We propose to capitalize on the abundance of high resolution (HR) RGB images to develop a self-supervised pretraining approach that significantly improves the quality of HSI-SR models. In particular, we leverage advances in spectral reconstruction methods to create a vast dataset with high spatial resolution and plausible spectra from RGB images, to be used for pretraining HSI-SR methods. Experimental results, conducted across multiple datasets, report large gains for state-of-the-art HSI-SR methods when pretrained according to the proposed procedure, and also highlight the unreliability of ranking methods when training on small datasets.

Saman Mahmoodi, Hadi Tarimoradi

The growing emphasis on power quality has posed significant challenges for distribution system operators (DSOs). Among these challenges, short-term voltage fluctuations, specifically voltage sag, have drawn considerable attention. In this study, three concepts of average edge (AE), lower average edge (LAE), and upper average edge (UAE) based on the electrical connection matrix and voltage-magnitude sensitivity matrix are defined and used as the partitioning first level. At the second level, a kernel smoothing function is employed to refine the zoning process. Subsequently, strategic locations within each zone are identified: the vertex and middle buses. These carefully selected buses serve as installation points for dynamic voltage restorers (DVRs). In response, this study proposes a novel solution by partitioning the distribution network into distinct zones. The focus lies in developing a two-level offline partitioning approach for active distribution networks (ADNs) that incorporate photovoltaic (PV) systems. To evaluate the effectiveness of the proposed method, numerical studies were conducted on modified IEEE 33-bus, IEEE 69-bus, and Iranian 95-bus systems, with simulations performed using MATLAB/Simulink. The proposed method provides good performance and fast calculation speed for distribution network partitioning, as confirmed by the results. Test results show improved bus voltage with PV unit integration. Additionally, power loss in the IEEE 33-bus, IEEE 69-bus, and Iranian 95-bus networks decreased by 47.73 kW, 56.87 kW, and 69.63 kW, respectively. Furthermore, the voltage profile improved from 0.75 p.u. to 0.928 p.u. during a voltage sag in the IEEE 33-bus system, and in steady state, the voltage increased from 0.933 to 0.959 p.u.

Yao Meng, Xinyu Yang, Haitao Wang et al.

This paper proposes a new dual-stator hybrid-magnet flux modulation machine (DS-FMHMM) for direct-drive applications, which employs NdFeB magnet excitation and Ferrite magnet excitation on the rotor and outer stator sides, respectively. With this design, the proposed DS-FMHMM can not only fully use the bidirectional flux modulation effect, but also effectively alleviate the magnetic saturation issue. The machine configuration is described, together with the operating principle. Then, the design parameters of DS-FMHMM are globally optimized for obtaining high torque quality, and the influence of magnet dimensions on torque is analyzed. To evaluate the merits of the proposed DS-FMHMM, the electromagnetic performances of machines under different magnet excitation sources are analyzed, and a comprehensive electromagnetic performance comparison of DS-FMHMM and two existing dual-stator flux modulation machines (DSFMMs) is developed.

Julles Mitoura dos Santos Junior, Adriano Pinto Mariano

As one of the main industrial segments of the current geoeconomics scenario, agro-industrial activities generate excessive amounts of waste. The gasification of such waste using supercritical water (SCWG) has the potential to convert the waste and generate products with high added value, hydrogen being the product of greatest interest. Within this context, this article presents studies on the SCWG processes of lignocellulosic residues from cotton, rice, and mustard husks. The Gibbs energy minimization (minG) and entropy maximization (maxS) approaches were applied to evaluate the processes conditioned in isothermal and adiabatic reactors, respectively. The thermodynamic and phase equilibria were written as a nonlinear programming problem using the <i>Peng–Robinson</i> state solution for the prediction of fugacity coefficients. As an optimization tool, TeS (Thermodynamic Equilibrium Simulation) software v.10 was used with the help of the <i>trust-constr</i> algorithm to search for the optimal point. The simulated results were validated with experimental data presenting surface coefficients greater than 0.99, validating the use of the proposed modeling to evaluate reaction systems of interest. It was found that increases in temperature and amounts of biomass in the process feed tend to maximize hydrogen formation. In addition to these variables, the H₂/CO ratio is of interest considering that these processes can be directed toward the production of synthesis gas (syngas). The results indicated that the selected processes can be directed to the production of synthesis gas, including the production of chemicals such as methanol, dimethyl ether, and ammonia. Using an entropy maximization approach, it was possible to verify the thermal behavior of reaction systems. The maxS results indicated that the selected processes have a predominantly exothermic character. The initial temperature and biomass composition had predominant effects on the equilibrium temperature of the system. In summary, this work applied advanced optimization and modeling methodologies to validate the feasibility of SCWG processes in producing hydrogen and other valuable chemicals from agro-industrial waste.

Yiming Zhang, Zhongjin Huang, Ronghuan Xie et al.

Inductive power transfer (IPT), as a method of wireless power transfer (WPT) via magnetic induction, can be applied to electric vehicles (EVs) due to its convenience and automation. Interoperability and misalignment tolerance are both major research difficulties for WPT of EVs. This paper proposes a two-channel topology and a coil optimization method, which can improve misalignment tolerance for the unipolar coil (Q) and interoperate with the bipolar coil (DD). Firstly, a topology with phase shift strategy is constructed to increase output ability with Y misalignments and the mathematical model of the proposed topology is established. Secondly, a coil density optimization method is presented to smooth the transmitting mutual inductances fluctuation. Finally, a 1-kW prototype is built to verify the proposed system which can achieve load-independent constant-current charging. With the Y misalignment of 150 mm, the experimental results agree well with the theoretical analysis. The proposed system is able to interoperate with two types of coils and can achieve misalignment tolerance.

Mohd Izrul Izwan RAMLI, Siti Farahnabilah MUHD AMLI, Norainiza SAUD et al.

Hot Air Solder Leveling (HASL) is one of the most commonly used surface finishes in the industry. HASL is also one of the least expensive types of PCB surface finishes available. This study aims to examine the influence on the solder joint microstructure of dipping time and solder temperature. During soldering process, the temperature that used were 300°C and 400°C. The dipping time was split into three batches which is 20s, 60s, and 100s. The Sn-0.7Cu0.05Ni solder alloy was used in this analysis to shape the solder coating microstructure. In this analysis, an Optical Microscope (OM) was used to determine the microstructure of the shape of the solder coating microstructure. As dipping time and dipping speed increased, the interfacial IMC thickness was found to increase, grown up and getting thicker. This outcome results can be used as the basis in order to improve the solder joint properties.

HOU YanYan, CHANG Qing, HU XiWen et al.

Active gear design is a method that can pre-control gear meshing performance directly. In order to study the influence of active gear design on the bearing characteristics of tooth surface, the relationship between active modification parameters of the face gear and bearing characteristics of tooth surface is studied. Firstly, the contact model of the gear pair is established by the finite element method, and then different groups of active modification parameters are designed for simulation analysis. The results show that the geometric transmission error greatly influences gear transmission stability and contact strength, and controlling its amplitude can control bearing transmission error amplitude. Pre-designed contact path inclination angle within 30° neighborhood, bearing contact characteristics are better. The contact ellipse length mainly affects the contact strength of tooth surface and bending strength of tooth root. According to the simulation results, when it takes 0.5～0.75 times of tooth width, face gear pair has better bearing contact characteristics.

Muhammad Abdul Basit, Wenxi Tian, Ronghua Chen et al.

Study of single bubble behavior under rolling motions can prove useful for fundamental understanding of flow field inside the modern small modular nuclear reactors. The objective of the present study is to simulate the influence of rolling conditions on single rising bubble in a liquid using multiphase Moving Particle Semi-implicit (MPS) method. Rolling force term was added to 2D Navier-Stokes equations and a computer program was written using C language employing OpenACC to port the code to GPU. Computational results obtained were found to be in good agreement with the results available in literature. The impact of rolling parameters on trajectory and velocity of the rising bubble has been studied. It has been found that bubble rise velocity increases with rolling amplitude due to modification of flow field around the bubble. It has also been concluded that the oscillations of free surface, caused by rolling, influence the bubble trajectory. Furthermore, it has been discovered that smaller vessel width reduces the impact of rolling motions on the rising bubble. The effect of liquid viscosity on bubble rising under rolling was also investigated and it was found that effects of rolling became more pronounced with the increase of liquid viscosity.

G Carbajal-Franco, E Rendón-Lara, I M Abundez-Barrera et al.

The finding of a material with the precise properties needed to solve a specific issue is the first topic that needs unraveling when an application is projected. One approach to find a material with a specific property value is to study a different but linked property. The aim of this research is to find materials with similar Electronic Band Structures (EBS); which in a simulation typically contain more than 1,000 ordered pairs of data. Our approach is, instead of calculating the similarities between the EBS of different materials, to calculate the similarity between their crystalline structures, and then the similarity between the EBS of the resulting similar compounds is tested. The software system developed in this research finds materials with similar crystallography, then the similarity of the compounds is tested by comparing the DFT modeled Electronic Band Diagrams (EBDs). The crystallographic data was mined from the Crystallography Open Database (COD) in the form of CIF files; that were used to calculate the x-ray diffraction (XRD) data using REFLEX, a component of Materials Studio. The plane presence, position and intensity of the peaks from the XRD data, were used to calculate the similarity between materials. With the list of similar materials from the previous process and the correspondent CIF files, the CASTEP code (from Materials Studio) was used to calculate the EBDs. In this work, three different materials were analyzed: CdTe, CdSe and GaAs. As results, 2D maps showing 50 compounds with the highest similarities are shown and for the EBD analysis, the 6 + most similar compounds were computed and analyzed by means of the first derivative. It is shown that the EBDs of the similar materials share the same shape, but with different values, making the system a useful tool for Materials Integration.

Van Nhan Vo, Chakchai So-In, Hung Tran et al.

In this paper, we analyze the secrecy and throughput of multiple-input single-output (MISO) energy harvesting (EH) Internet of Things (IoT) systems, in which a multi-antenna base station (BS) transmits signals to IoT devices (IoTDs) with the help of relays. Specifically, the communication process is separated into two phases. In the first phase, the BS applies transmit antenna selection (TAS) to broadcast the signal to the relays and IoTDs by using non-orthogonal multiple access (NOMA). Here, the relays use power-splitting-based relaying (PSR) for EH and information processing. In the second phase, the selected relay employs the amplify-and-forward (AF) technique to forward the received signal to the IoTDs using NOMA. The information transmitted from the BS to the IoTD risks leakage by the relay, which is able to act as an eavesdropper (EAV) (i.e., an untrusted relay). To analyze the secrecy performance, we investigate three schemes: random-BS-best-relay (RBBR), best-BS-random-relay (BBRR), and best-BS-best-relay (BBBR). The physical layer secrecy (PLS) performance is characterized by deriving closed-form expressions of secrecy outage probability (SOP) for the IoTDs. A BS transmit power optimization algorithm is also proposed to achieve the best secrecy performance. Based on this, we then evaluate the system performance of the considered system, i.e., the outage probability and throughput. In addition, the impacts of the EH time, the power-splitting ratio, the numbers of BS antennas, and the numbers of untrusted relays on the SOP and throughput are investigated. The Monte Carlo approach is applied to verify our analytical results. Finally, the numerical examples indicate that the system performance of BBBR is greater than that of RBBR and BBRR.

Muhammad Kashif Ali, Asif Rajput, Muhammad Shahzad et al.

For autonomous robots, 3D perception of environment is an essential tool, which can be used to achieve better navigation in an obstacle rich environment. This understanding requires a huge amount of computational resources; therefore, the real-time 3D reconstruction of surrounding environment has become a topic of interest for countless researchers in the recent past. Generally, for the outdoor 3D models, stereo cameras and laser depth measuring sensors are employed. The data collected through the laser ranging sensors is relatively accurate but sparse in nature. In this paper, we propose a novel mechanism for the incremental fusion of this sparse data to the dense but limited ranged data provided by the stereo cameras, to produce accurate dense depth maps in real-time over a resource limited mobile computing device. Evaluation of the proposed method shows that it outperforms the state-of-the-art reconstruction frameworks which only utilizes depth information from a single source.

Hong Pei, Changhua Hu, Xiaosheng Si et al.

As the fundamental and prerequisite work of remaining useful life (RUL) prediction, degradation modeling directly affects the accuracy of RUL prediction. Existing degradation models are all developed under a single time scale, and less research has been carried out to consider the impact of multiple time scales on the degradation model. Toward this end, we mainly study a nonlinear degradation modeling and RUL prediction method for nonlinear stochastic degraded equipment with bivariate time scales in this paper. Firstly, a nonlinear degradation model considering the influence of two time scales is constructed based on the diffusion process. At the same time, the relationship between the two scales is quantitatively characterized by random proportional coefficient. Then, the analytical expressions of the life and RUL for nonlinear degraded equipment are derived under the concept of first passage time (FPT). In order to realize the adaptive estimation of parameters, the model parameters estimation method based on maximum likelihood estimation (MLE) and Kalman filtering algorithm is developed in this paper. Finally, numerical simulation and the monitoring data of gyroscope verify the effectiveness and superiority of the proposed method. The experimental results show that the method proposed in this paper can effectively improve the accuracy of RUL prediction, and has a broad engineering application space.

Kazuhei AOYAGI, Eiichi ISHII, Tsuyoshi ISHIDA

In the construction of a deep underground facility, the hydromechanical properties of the rock mass around an underground opening are changed significantly due to stress redistribution. This zone is called an excavation damaged zone (EDZ). In high-level radioactive waste disposal, EDZs can provide a shortcut for the escape of radionuclides to the surface environment. Therefore, it is important to develop a method for predicting the detailed characteristics of EDZs. For prediction of the EDZ in the Horonobe Underground Research Laboratory of Japan, we conducted borehole televiewer surveys, rock core analyses, and repeated hydraulic conductivity measurements. We observed that niche excavation resulted in the formation of extension fractures within 0.2 to 1.0 m into the niche wall, i.e., the extent of the EDZ is within 0.2 to 1.0 m into the niche wall. These results are largely consistent with the results of a finite element analysis implemented with the failure criteria considering failure mode. The hydraulic conductivity in the EDZ was increased by 3 to 5 orders of magnitude compared with the outer zone. The hydraulic conductivity in and around the EDZ has not changed significantly in the two years following excavation of the niche. These results show that short-term unloading due to excavation of the niche created a highly permeable EDZ.

Shobha Rani Depuru, Muralidhar Mahankali

This paper proposes the utilization of a DC-DC boost converter as a mediator between a Solar Photovoltaic (SPV) array and the Voltage Source Inverters (VSI) in an SPV array powered air cooling system to attain maximum efficiency. The boost converter, over the various common DC-DC converters, offers many advantages in SPV based applications. Further, two Brushless DC (BLDC) motors are employed in the proposed air cooling system: one to run the centrifugal water pump and the other to run a fan-blower. Employing a BLDC motor is found to be the best option because of its top efficiency, supreme reliability and better performance over a wide range of speeds. The air cooling system is developed and simulated using the MATLAB/Simulink environment considering the steady state variation in the solar irradiance. Further, the efficiency of BLDC drive system is compared with a conventional Permanent Magnet DC (PMDC) motor drive system and from the simulated results it is found that the proposed system performs better.

Jagadeesh Babu Veluru, Satheesh K. K., Trivedi D.C. et al.

Electrospinning is one of the simplest techniques for obtaining polymer nano fibers. Nanofibers have large surface area to volume ratio and hence have excellent application potential in sensors, filter design etc. Polyaniline (PANI) is the well-known and widely studied conducting polymer, which however, is insoluble in many common organic solvents and hence difficult to process. PANI in its base form is non conductive but it can be made conducting by protonating with an acids such as hydrochloric acid (HCl) or camphor sulphonic acid (CSA). However, it is difficult to electrospin PANI by itself since we need preferably the polymer in solution form. In this study we have formed nanofibers of PANI (CSA) dispersed in Poly Methyl Methacrylate (PMMA) solution in chloroform. The morphology of the electrospun conducting PMMA-PANI composite fibers is studied using Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM). The DC and AC conductivities of these fibers are measured and the results are discussed.