Hasil untuk "Electronics"

PENG Heng, DONG Minghui, ZHANG Maoqi, BAI Mingxian, GUO Jianwei

Sealing is a core factor for the safe operation of high-pressure proton exchange membrane electrolysis cells(PEMECs).To investigate the corrosion behavior of sealing polymer materials，two experimental methods were established: an accelerated high-temperature evaluation(100 ℃ acidic environment with H2 or O2 for 100 h) and a long-term high-pressure evaluation(80 ℃ acidic environment with 6－7 MPa H2 or O2 for 1 000 h)，to evaluate the corrosion behavior of five sealing polymer materials.Results showed that ethylene-propylene-diene monomer(EPDM) and fluoroelastomer(FKM) had chemical degradation and localized corrosion.In contrast，polyimide(PI)，polyetheretherketone(PEEK) and polytetrafluoroethylene(PTFE) had low corrosion rates and showed uniform corrosion characteristics.Specifically，PTFE showed excellent corrosion resistance，and its composite structure may help address corrosion and assembly issues in high-pressure PEMEC systems.

The CMS Electromagnetic Calorimeter Group

The performance of the CMS electromagnetic calorimeter upgraded readout electronics, developed for the High-Luminosity phase of the LHC, is discussed. Data collected in two beam test campaigns conducted in 2018 and 2021 at the H4 and H2 beam lines of the CERN SPS are analyzed. Time and energy resolutions are measured on a $5\times 5$ matrix of lead tungstate crystals equipped with prototypes of the new front end readout electronics, using electron and pion beams of energies spanning from 25 to 250 GeV. In both campaigns the constant term of the energy resolution is measured to be better than 0.6% and the time resolution for electrons with energies above 50 GeV is measured to be better than 30 ps, fulfilling the design requirements.

Aleksanteri Hamalainen, Aku Karhinen, Jesse Miettinen et al.

Rotating machines are extremely common in many industries, and their maintenance involves substantial costs and labor. Most recent studies aiming to automate fault diagnosis have focused on deep learning, but industry adoption has been slow owing to the lack of well-curated datasets and the complexity of the methods. We propose a new method called Rapid Few-shot Condition Monitoring (Rapid-FSCM), which enables the rapid deployment of deep learning-based condition monitoring models and is readily extensible to future advancements in the field. This will make it simpler for the industry to conduct machine condition monitoring without the cost of an expert. Rapid-FSCM utilizes few-shot learning and the InceptionTime convolutional neural network to enable training on data from a related base domain more readily available than data from the target domain. In addition, the prototypical networks method for few-shot learning is modified to enable the deployment of the model as an anomaly detector, even before any fault samples have been recorded. After faults have occurred and been recorded, the model demonstrates the ability to initiate fault diagnosis without further retraining. Validated with three datasets, two gear datasets from a test bench with complex features, and the CWRU bearing dataset, the model was shown to have high accuracy in target domains containing unseen faults, sensors, operating conditions, and even entirely new components. The developed method can be used to rapidly deploy a condition monitoring model for any rotating machine without the need to first conduct a large data acquisition process.

Uttam U. Deshpande, Supriya Shanbhag, Ramesh Koti et al.

Phone calls are strictly forbidden in certain locations due to the potential security threats. Mobile phones’ growing capabilities have also increased the risk of their misuse in places that are restricted, like manufacturing plants. Unauthorized mobile phone use in these environments can lead to significant safety hazards, operational disruptions, and security breaches. There is an urgent need to develop an intelligent system that can identify the presence of individuals as well as cellphone usage. We propose an advanced Artificial Intelligence and Computer Vision-based real-time cell phone detection system to detect mobile phone usage in restricted zones. Modern deep learning approaches, such as YOLOv8 for real-time object detection to accurately detect cell phone usage, are combined with dense layers of ResNet-50 to perform image classification tasks. We highlight the critical need for such detection systems in manufacturing settings and discuss the specific challenges encountered. To support this research, we have developed a custom dataset of 2,150 images, which features a diverse array of images with varying foreground and background elements to reflect real-world conditions. Our experimental results demonstrate that YOLOv8 achieves a Mean Average Precision (mAP50) of 49.5% at 0.5 IoU for cellphone detection tasks and an accuracy of 96.03% for prediction tasks. These findings underscore the effectiveness of our AI and CV-based system in detecting unauthorized mobile phone usage in restricted zones.

Willian Borges De Lemos, Lucas De Angelo Martins Ribeiro, Vanessa Telles Da Silva et al.

Graph embeddings map graph-structured data into vector spaces for machine learning tasks. In Graph Neural Networks (GNNs), these embeddings are computed through message passing and support tasks such as node classification, link prediction and community detection across several application domains. Prior reviews and benchmarking studies often focus on accuracy or scalability alone and do not examine structural preservation or the effect of model design on network distortion. This limits comparability and leaves open questions on how embeddings reflect graph topology in GNNs models. This topical review examines how graph embeddings are generated within GNN architectures and how model design choices affect both classification performance and graph structural preservation. The present work introduces a benchmarking framework that evaluates GNNs across different dimensions, including local and global structure preservation, complex relationships and scalability. The framework uses distortion metrics, automated parameter search and sensitivity analysis to provide a more complete view of model behavior. A controlled set of experiments was conducted using shared datasets and common hyperparameter spaces. The results show that GAT maintains stable performance across configurations, whereas GCN and GraphSAGE are more sensitive to parameter choices. The main takeaway is that reliable benchmarking of GNN-based embeddings requires a multidimensional analysis. Model behavior varies across datasets, and no architecture performs better across all conditions.

Urslla Uchechi Izuazu, Cosmas Ifeanyi Nwakanma, Dong-Seong Kim et al.

Abstract Deep learning-based intrusion detection systems (DL-IDS) have proven effective in detecting cyber threats. However, their vulnerability to adversarial attacks and environmental noise, particularly in industrial settings, limits practical application. Current IDS models often assume ideal conditions, overlooking noise and adversarial manipulations, leading to degraded performance when deployed in real-world environments. Additionally, the black-box nature of DL model complicates decision-making, especially in industrial control systems (ICS) network, where understanding model behavior is crucial. This paper introduces the eXplainable Cyber-Threat Detection Framework (XC-TDF), a novel solution designed to overcome these challenges. XC-TDF enhances robustness against noise and adversarial attacks using regularization and adversarial training respectively, and also improves transparency through an eXplainable Artificial Intelligence (XAI) module. Simulation results demonstrate its effectiveness, showing resilience to perturbation by achieving commendable accuracy of 100% and 99.4% on the Wustl-IIoT2021 and Edge-IIoT datasets, respectively.

Iason Tsardanidis, Alkiviadis Koukos, Vasileios Sitokonstantinou et al.

Uninterrupted optical image time series are crucial for the timely monitoring of agricultural land changes, particularly in grasslands. However, the continuity of such time series is often disrupted by clouds. In response to this challenge, we propose an innovative deep learning method that integrates cloud-free optical (Sentinel-2) observations and weather-independent (Sentinel-1) Synthetic Aperture Radar (SAR) data. Our approach employs a hybrid architecture combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to generate continuous Normalized Difference Vegetation Index (NDVI) time series, highlighting the role of NDVI in the synergy between SAR and optical data. We demonstrate the significance of observation continuity by assessing the impact of the generated NDVI time series on the downstream task of grassland mowing event detection. We conducted our study in Lithuania, a country characterized by extensive cloud coverage, and compared our approach with alternative interpolation techniques (i.e., linear, Akima, quadratic). Our method outperformed these techniques, achieving an average Mean Absolute Error (MAE) of 0.024 and a coefficient of determination R^2 of 0.92. Additionally, our analysis revealed improvement in the performance of the mowing event detection, with F1-score up to 84% using two widely applied mowing detection methodologies. Our method also effectively mitigated sudden shifts and noise originating from cloudy observations, which are often missed by conventional cloud masks and adversely affect mowing detection precision.

Dzihan Khilmi Ayu Firdausi, Muhammad Eka Mardyansyah Simbolon, Indra Dwisaputra et al.

This study aims to develop a novel "sit and reach test" flexometer device utilizing a linear potentiometer sensor to quantitatively evaluate an individual's body flexibility. The innovation in this research lies in the utilization of electronic means to measure flexibility, replacing conventional manual methods. The device employs a linear potentiometer sensor that translates analog voltage into a digital value (ADC) and subsequently converts the digital data into distance measurements in centimeters (cm). The processed data, representing the distance measurements, is wirelessly transmitted to a personal computer (PC) for automatic capture and analysis. The design considerations prioritize modernity, practicality, effectiveness, and efficiency. The experimental outcomes demonstrate the efficacy of the developed tool. The readings from the linear potentiometer sensor exhibit a high linearity, indicated by an R2 value of 0.9999. The average error percentage is minimal, measuring at 0.17%. Moreover, the device allows for direct wireless transmission of data to a PC immediately after assessing an athlete's flexibility. This study not only introduces a novel electronic approach for assessing body flexibility but also validates its accuracy and efficiency through comprehensive testing and analysis.

Ashish Majithia, Darshita Shah, Jatin Dave et al.

Robots are becoming integral to society and industries due to their enormous advantages. Among the various categories of mobile robots, including wheeled robot, tracked robot, and legged robots, the latter stands out as a better choice for most field applications due to their adaptability across various terrains. The purpose of this review is to study the locomotion capabilities of quadruped robots and judge their suitability for climbing applications as most unexplored applications of automation and robotics are required to climb. This review explores the locomotion capabilities of quadruped robots. It covers different aspects of quadruped robots like types of legs, leg design, gait patterns, and their mathematical formulations, and types of motions like omnidirectional motion and body sway motion. It also emphasizes its fault-tolerant gait, adaptability, and reliability. The paper also focuses on slope and stair climbing, outlining design requirements and applications. The study includes an examination of the applicability of various gaits under different conditions and the methods for increasing stability without compromising speed. Overall, the review serves as a valuable resource for future research in this field.

Jyoti Bala Kaushal, Pratima Raut, Sanjay Kumar

The promising field of organic electronics has ushered in a new era of biosensing technology, offering a promising frontier for applications in both medical diagnostics and environmental monitoring. This review paper provides a comprehensive overview of the remarkable progress and potential of organic electronics in biosensing applications. It explores the multifaceted aspects of organic materials and devices, highlighting their unique advantages, such as flexibility, biocompatibility, and low-cost fabrication. The paper delves into the diverse range of biosensors enabled by organic electronics, including electrochemical, optical, piezoelectric, and thermo sensors, showcasing their versatility in detecting biomolecules, pathogens, and environmental pollutants. Furthermore, integrating organic biosensors into wearable devices and the Internet of Things (IoT) ecosystem is discussed, offering real-time, remote, and personalized monitoring solutions. The review also addresses the current challenges and prospects of organic biosensing, emphasizing the potential for breakthroughs in personalized medicine, environmental sustainability, and the advancement of human health and well-being.

J. Garza, Steven Swanson

Electronics prototyping using breakout boards allows designers with and without an engineering background to rapidly create interactive prototypes. However, when it comes to transition to a production-ready PCB design, stagnation exists due to the high skill floor required for PCB design. While PCB design automation has been used successfully in recent research tools to reduce the required expertise, little has been done to integrate these tools directly into the electronics prototyping cycle. This position paper aims to bring attention to the possibility of integrating recent PCB design automation paradigms into the electronics prototyping cycle for the creation of PCB-ready breakout boards: breakout boards whose designs would have the ability to be pipelined directly into new user interfaces that leverage the use of automation for the rapid creation of production-ready PCB designs.

Emma Batson, Marco Colangelo, John Simonaitis et al.

Absorption of light in superconducting electronics is a major limitation on the quality of circuit architectures that integrate optical components with superconducting components. A 10 nm thick film of a typical superconducting material like niobium can absorb over half of any incident optical radiation. We propose instead using superconductors which are transparent to the wavelengths used elsewhere in the system. In this paper we investigated reduced indium tin oxide (ITO) as a potential transparent superconductor for electronics. We fabricated and characterized superconducting wires of reduced indium tin oxide. We also showed that a $\SI{10}{nm}$ thick film of the material would only absorb about 1 - 20\% of light between 500 - 1700 nm.

Zhexin Li, Wenhao Ran, Yongxu Yan et al.

Abstract Noncontact interaction systems have attracted considerable research attention in recent years because of convenient operation, sterility, and injury prevention. However, the insufficient sensing distance and weak robustness of noncontact interaction systems for complex environments limit their practical applications. Here, we designed an integrated optical noncontact controlling system (ONCS) based on PtTex/Si optoelectronic heterojunction array. Broadband sensitive photoresponse is realized at zero bias voltage, with excellent detectivity and responsivity, boosting the noncontact sensing distance to at least 150 mm. Consequently, the system can perform noncontact detection, encoding, and control by recognizing shadow‐induced spatiotemporal sequence changes in heterojunction array photocurrents. As a proof of concept, different interactive functions have been demonstrated with good accuracy and robustness by encoding finger movement above the ONCS. This study provides a new perspective for constructing high‐performance noncontact interaction systems.

Zeying Cao, Ningyun Sun, Hongyu Sun et al.

The consistency evaluation, which is largely unexplored is crucial in regulating the quality and efficacy of generic drugs. In this report, after generic ibuprofen (IBU) sustained-release pellets were developed and validated as bioequivalent to the reference list drug (RLD) in 48 healthy human volunteers (p < 0.001***), three-dimensional (3D) structures of generic IBU pellets from bioequivalence tests, along with RLD were investigated and compared for architecture using Micro-computed tomography (Micro-CT). The surface and internal architectures of the pellets, sphericity, pellet volume, core volume and gray value have been evaluated in both static and dynamic conditions. The material distribution and composition of IBU pellets were characterized using synchrotron radiation-FTIR mapping. Although the structures of RLD and the generic products were not similar dynamically, identical release profiles and bioequivalence were obtained. Overall, micro-CT could be very useful for characterization of internal structure, material design and development of new dosage form.

Asadullah Kehar, Rafaqat Hussain Arain, Dr Riaz Ahmed Shaikh et al.

Bots are created to use the resources maliciously on World Wide Web. The misuse of the resources could be prevented by employing CAPTCHAs. Several types of CAPTCHAs are being used against the bots (robot) attacks but text-based CAPTCHA type is the most popular being very secured and easy to use. Latin language based text CAPTCHAs can be found ubiquitously on Internet but English text based CAPTCHAs are already decoded by many researchers. Thus, a novel Sindhi language based text CAPTCHA was proposed for regional websites where Arabic style script was utilized. This scheme offered two fold benefits: first, the proposed scheme could easily be understood by averagely literate person; second, this scheme paved a way for Arabic style OCR developers to understand Sindhi language specific features and facilitate Sindhi text recognition in future. A survey was also conducted to analyze the usability and strength of proposed CAPTCHA.

Peter H. Siegel

This is the fourth article in our continuing series of biographical pieces with a technical lean. The subject of this paper is Professor Arye Rosen, who &#x2013; while employed full time at RCA Sarnoff Research Laboratory as an engineer and working on the side more-or-less as a &#x201C;hobby&#x201D; - pioneered the use of microwave angioplasty and ablation techniques in cardiology, and later influenced the use of microwaves in the treatment of benign prostatic hyperplasia (BPH). Dr. Rosen&#x0027;s life story is as varied and interesting as his career path, and his contributions both to traditional microwave devices as well as microwaves in medicine will definitely inspire those of you who feel like they are pulled in more than one direction professionally. In Dr. Rosen&#x0027;s case, he did &#x201C;have it all,&#x201D; but he worked very hard to make it happen and he took lots of chances&#x2026;as you will hear&#x0021;

Honglin Zhang, Haibo Yang, Xianqin Li et al.

This article presents the readout electronics of a novel beam monitoring system for ion research facility accelerator. The readout electronics are divided into Front-end Card (FEC) and Readout Control Unit (RCU). FEC uses Topmetal II minus to processes the energy of the hitting particles and convert it into a voltage signal. The main function of RCU is to digitize the analog output signal of FEC and format the raw data. On the other hand, the RCU also processes the control commands from the host and distributes the commands according to the mapping. The readout electronic has been characterized and calibrated in the laboratory, and have been installed with the detector. Implementation and testing of readout electronics have been discussed.

Yanming Fu, Weizhi Zhang, Chiwen Qu et al.

The optimal foraging algorithm (OFA) was proposed by summarizing the rules of the animal foraging behavior in a group. Therefore OFA also has the defects of the swarm intelligence algorithm, such as easy to trap into local optimum and low convergence accuracy. In order to overcome these defects, an optimal foraging algorithm based on differential evolution (DEOFA) is proposed. The differential evolution mechanism contains mutation and crossover operators. The mutation and crossover operators are used to accelerate the convergence speed and global search capability of the OFA. The mutation operator is adopted to perform mutation operations centered on the optimal individual of each iteration to raise the convergence accuracy of the OFA. The test results of 30 benchmark functions show that the performance of DEOFA is better than nine compared algorithms in search accuracy, convergence speed and robustness. In order to verify the effectiveness of the DEOFA in solving practical problems, DEOFA is applied to solve the 0-1 knapsack problem. The test results in the six examples of 0-1 knapsack problems indicate that the DEOFA achieves better performance in accuracy, stability and high dimension.

Xiaolin Zhao, Yiman Zhang, Jingfeng Xue et al.

With the rapid development of networks, network security is a serious problem. To evaluate a network accurately, this paper proposes a network risk evaluation method based on a differential manifold (DM) and research on traditional methods. The DM divides the network risk evaluation into network structure risk and network behavior risk evaluations. Network structure risk evaluates the network identity, and network behavior risk evaluates the attack and defense of the network. Network assets and asset vulnerabilities characterize a network, and the analytic hierarchy process (AHP) and the Common Vulnerability Scoring System (CVSS) are combined to evaluate the network identity. Network behavior causes high-dimensional indicator changes, and DMs are used to measure network behavior. To examine the effectiveness and accuracy of DMs, two experiments were performed. The experimental results show that the DM method is valid and accurate for evaluating network risk.

Vivek Mange, Priyam Shah, Vishal Kothari

The paper deals with the software and the electronics unit for an autonomous underwater vehicle. The implementation in the electronics unit is the connection and communication between SBC, pixhawk controller and other sensory hardware and actuators. The major implementation of the software unit is the algorithm for object detection based on Convolutional Neural Network (CNN) and its models. The Hyperparameters were tuned according to Odroid Xu4 for various models. The maneuvering algorithm uses the MAVLink protocol of the ArduSub project for movement and its simulation.