Application In Real Systems Research Articles

Praseodymium doping in ceria-supported palladium nanocatalysts as an effective strategy to minimize the inhibiting effects of water during methane oxidation

The present study reports an improved design for Pd/Ce-Pr catalysts. Pd-impregnated nanostructured ceria-praseodymia catalysts with different compositions were comprehensively characterized and tested for dry and wet methane oxidation. The strong PdO-PrOx interaction, detected via XRD, TPR/TPO, Raman and HRTEM analyses, retains Pd mainly in its oxidized form in the materials with high praseodymium content, thus resulting in a lower activity. Conversely, the introduction of a limited amount of Pr in ceria allows to obtain a more active catalyst (2% of Pd supported on a mixed oxide with 10% of Pr) than the typical Pd/CeO2 systems. Hence, the simultaneous presence of Pd in its reduced and oxidized forms results to be a key factor for high activity. Additionally, the higher hydrophobicity of this sample, investigated through NMR and in situ FTIR, markedly reduces the H2O inhibition effect typical of Pd-based materials, paving the way for using this system in real applications.

A Joint estimation approach to sparse additive ordinary differential equations.

Ordinary differential equations (ODEs) are widely used to characterize the dynamics of complex systems in real applications. In this article, we propose a novel joint estimation approach for generalized sparse additive ODEs where observations are allowed to be non-Gaussian. The new method is unified with existing collocation methods by considering the likelihood, ODE fidelity and sparse regularization simultaneously. We design a block coordinate descent algorithm for optimizing the non-convex and non-differentiable objective function. The global convergence of the algorithm is established. The simulation study and two applications demonstrate the superior performance of the proposed method in estimation and improved performance of identifying the sparse structure.

Probabilistic Prediction Intervals of Wind Speed Based on Explainable Neural Network

With the rapid growth of wind power penetration into modern power grids, wind speed forecasting plays an increasingly significant role in the planning and operation of electric power and energy systems. However, the existing wind speed forecasting methods are modeled as black boxes, which are very complicated and cannot be written down explicitly due to the complex fluctuation characteristics of wind speed series. To this end, this study proposes a novel direct method based on an explainable neural network (xNN) for deterministic and probabilistic wind speed forecasting. It can theoretically extract the nonlinear mapping features in wind speed, thereby providing a clear explanation of the relationship between the input and the output of the forecasting model. Then, the uncertainties in wind speed are statistically synthesized via the kernel density estimation method. Finally, we use wind speed data from real wind farms in Belgium to verify the feasibility and effectiveness of the proposed method. The simulation results demonstrate that it is not only able to accurately extract the non-stationary feature in the wind speed series but also superior to other benchmark algorithms in prediction accuracy. Therefore, the proposed method has a high potential for practical applications in real electric power and energy systems.

Li+ Ion Transport at the Lithium Metal Anode: A Fundamental Picture and Future Perspectives

The unsurpassed energy density of lithium metal batteries (LMBs) offers a promising route to meet increasing clean energy demand. Nonetheless, their instability creates critical safety and performance concerns that prevent their effective implementation. Careful design of the solid-electrolyte interphase (SEI) that passivates the lithium metal anode could allow for stable, efficient cycling, unlocking the commercialization of LMBs. However, the complexity of the SEI, both formed in situ through electrolyte decomposition and fabricated ex situ, introduces significant ambiguity. Here, we present several mechanisms that define ionic transport through the SEI, grounded in studies that concretely relate structure to ionic conductivity. We build on these mechanisms with examples of their application in real battery systems. Overall, we seek to lay a foundation of structure–property relationships that will guide the rational design of stable SEIs that efficiently transport Li+ ions.

THE STATES’ FINAL PROBABILITIES ANALYTICAL DESCRIPTION IN AN INCOMPLETELY ACCESSIBLE QUEUING SYSTEM WITH REFUSALS

Context. There is a problem of forecasting the efficiency of real queuing systems with refusals in the case of incomplete accessibility of service devices for the input flow of requirements. The solution of problem is necessary to create the possibility of more accurate design and control of such systems operation in real time. Objective. The aim of the research is to obtain an analytical description of the state’s final probabilities in a Markov queuing system with refusals and with incomplete accessibility of service devices for the input flow of requirements that is necessary to forecast the values of the queuing system performance indicators. Method. The probabilities of queuing systems’ states with refusals in the case of incomplete accessibility of service devices for the input flow of requirements are described by Kolmogorov differential equations. In a stationary state, these equations are transformed into a linearly dependent homogeneous system of algebraic equations. The number of equations is determined by the setdegree and for modern queuing and communication systems can be in the thousands, millions and more. Therefore, an attempt to predict the efficiency of a system is faced with the need to write down and numerically solve a countable set of algebraic equations systems that is quite difficult. The key idea of the proposed method for finding an analytical description of final probabilities for a given queuing system was the desire to move from the description of individual states (of 2n amount) to the description of groups of system states (of n+1 number) and to localize the influence of incomplete accessibility of service devices for the input flow of requirements in multiplicative functions of incomplete accessibility. Such functions allow obtaining the required analytical description and assessing the degree of the final probabilities transformation, in comparison with known systems, as well as assessing the forecasted values of the noted queuing system’s efficiency indicators when building a system and choosing the parameters for its controlling. Results. For the first time analytical expressions are obtained for the final probabilities of the queuing system states with refusals and with incomplete accessibility of service devices for the input flow of requirements, which makes it possible to evaluate as well as forecast values of all known system efficiency indicators. Conclusions. The resulting description turned out to be a general case for well-known type of Markov queuing systems with refusals. The results of the numerical experiment testify in favor of correctness the obtained analytical expressions for the final probabilities and in favor of possibility for their practical application in real queuing systems when solving problems of forecasting efficiency, as well as analyzing and synthesizing the parameters of real queuing systems.

Constructing Dixon Matrix for Sparse Polynomial Equations Based on Hybrid and Heuristics Scheme

Solving polynomial equations inevitably faces many severe challenges, such as easily occupying storage space and demanding prohibitively expensive computation resources. There has been considerable interest in exploiting the sparsity to improve computation efficiency, since asymmetry phenomena are prevalent in scientific and engineering fields, especially as most of the systems in real applications have sparse representations. In this paper, we propose an efficient parallel hybrid algorithm for constructing a Dixon matrix. This approach takes advantage of the asymmetry (i.e., sparsity) in variables of the system and introduces a heuristics strategy. Our method supports parallel computation and has been implemented on a multi-core system. Through time-complexity analysis and extensive benchmarks, we show our new algorithm has significantly reduced computation and memory overhead. In addition, performance evaluation via the Fermat–Torricelli point problem demonstrates its effectiveness in combinatorial geometry optimizations.

구명환 발사기를 갖는 쌍동형 구조로봇의 메커니즘 개발

In recent years, the need for surface life-saving is gradually increasing as a response to accidents such as ship collisions in the marine environment. To solve this problem, there were approaches that launched life-saving rings or approached humans closely using rescue robots. However, they had limitations in terms of stability, safety and rapidity. To resolve this problem, a catamaran-type rescue robot with a life-saving ring launcher is proposed and its mechanism is developed. Straightness, turning, and launching tests were performed to verify the validity of the proposed rescue robot mechanism. Experimental results show that the proposed rescue robot mechanism enables stable and rapid surface life-saving. Therefore, it shows that the mechanism has meaningful components in surface life-saving and has the possibility of real system application.

Selectively Growing a Highly Active Interface of Mixed Nb-Rh Oxide/2D Carbon for Electrocatalytic Hydrogen Production.

Tailorable electron distribution of the active sites is widely regarded as the key issue to boost the catalytic activity and provide mechanistic insights into the structure–property–performance relationship. Here, a selective metal atom in situ growth strategy to construct highly active interface of mixed metal atom with different Nb y RhO x species on sp‐/sp2‐cohybridized graphdiyne (Nb y RhO x /GDY) is reported. With this innovative idea implemented, experimental results show that the asymmetric electron distribution and the variation of coordination environment of bimetallic species significantly improve the electrocatalytic activity of Nb y RhO x /GDY. Optimal hydrogen evolution reaction (HER) activity is achieved at the Nb/Rh ratio of 0.23, exhibiting excellent HER activity with the small overpotentials of 14 and 10 mV at 10 mA cm−2 in alkaline and neutral electrolytes. The data show the strong potential for real‐system application of such catalysts, which outperform commercial Pt/C (20 wt%). These results shown in this study represent a platform for designing novel catalytic materials by selectively introducing metal atoms on different supports, which can be used as a general method extended to other catalytic systems.

SIGW Based MIMO Antenna for Satellite Down-Link Applications

In this paper, a 4-port multi-input multi-output (MIMO) antenna based on substrate integrated gap wave guide (SIGW) is presented for down-link satellite applications. The proposed MIMO antenna consists of four rectangular-shaped radiating slots, which are fed via SIGW to minimize dispersion and insertion loss. Firstly, single antenna element (reference antenna) is designed to have impedance matching and stable radiation pattern over the bandwidth 13.4–13.65 GHz. Afterward, different antenna configurations are studied to combine four identical elements of the reference one for the sake of minimizing mutual coupling between them. In this work, the edge-to-edge separation between the antenna elements for orthogonal and opposite orientations are 9.3mm and 17.3mm, respectively. The mutual coupling between antenna elements is suppressed by etching the upper ground in the region between the antenna elements. Moreover, further optimization is carried out by changing the dimensions of the etched slot to reach higher isolation between radiating slots for better MIMO diversity performance. With this approach, the realized gain is increased by 3-dBi and the maximum mutual coupling achieved at 13.5 GHz between port (1,2) and port (1,3) are −63 dB and −78 dB, respectively with isolation enhancement of 38 dB between port (1,2) and 48 dB between port (1,3). Both simulated and measured results indicate that the proposed antenna has a bandwidth of 13.3–13.8 GHz, with a high isolation greater than 40 dB. Furthermore, the realized antenna gain is around 9-dBi with radiation efficiency of 86%. In addition, the designed MIMO antenna offers excellent radiation characteristics and stable gain over the whole operating band. To explore the diversity of the MIMO system the envelope correlation coefficient (ECC), multiplexing efficiency (ME), diversity gain (DG), channel capacity loss (CCL) and total active reflection coefficient (TARC) are studied. Finally, good consistency between simulation and measured outcomes is obtained confirming the validity of the MIMO antenna for real life satellite application systems.

Visually Impaired Person Assistance Based on Tensor FlowLite Technology

The most exciting thing about computer visualization is to detect a Real time object application system. This is abundantly used in many areas. With the more increase of development of deep learning such as self-driving cars, robots, safety tracking, and guiding visually impaired people, many algorithms have improved to find the relationship between video analysis and images analysis. Entire algorithms behave uniquely in the network architecture, and they have the same goal of detecting numerous objects in a composite image. It is very important to use our technology to train visually impaired people whenever they need them, as they are visually impaired and limit the movement of people in unknown places. This paper offers an application system that will identify all the possible day-to-day objects of our surroundings, and on the other side, it promotes speech feedback to the person about the sudden as well as far objects around them. This project was advanced using two different algorithms: Yolo and Yolo-v3, tested to the same criteria to measure its accuracy and performance. The SSD_MobileNet model is used in Yolo Tensor Flow and the Darknet model is used in Yolo_v3. Speech feedback: A Python library incorporated to convert statements to speech-to-speech. Both algorithms are analyzed using a web camera in a variety of circumstances to measure the correctness of the algorithm in every aspect.

Automated reasoning of vehicle brake-force: a fuzzy inference system model

Automatic regulation of vehicle braking system is very crucial to control vehicle speed, keep it on its track, and avoid any collision. A traditional binary control for brake system is not appropriate as the on-off conditions induce unpleasant feelings to the passengers. Control engineering technique may solve this problem but does not reflect artificial intelligence in terms of human reasoning behaviour. Although, a few study on fuzzy-based automated brake-force reasoning has been conducted showing the superiority over all the techniques, comparisons among various defuzzification methods are rarely found in the literature. Thus, the objective of this study is to design a fuzzy-based architecture for automated brake-force reasoning system through comparative analysis among various defuzzification approaches. The model takes two inputs as linguistic variables, the speed and the vehicular distance, and the output of the system is the required brake-force. The Mamdani fuzzy inference system (MFIS) is applied in designing the fuzzy architecture for automated brake-force prediction. The output fuzzy set is defuzzified with five defuzzification methods and analysed through observations. The results manifest that out of the five defuzzification approaches, the centroid and bisector methods demonstrate most satisfactory outputs and could be chosen for real system application.

The Practice of Implementing ML Service into an Internet Business Application

Currently, there are a large number of articles describing the theoretical aspects of development in the field of machine learning. However, the experience of their practical application in real systems is described much less often. Basically, authors describe the efficiency, accuracy, and other performance metrics of the resulting solution, but everything stops at the prototype stage. At the same time, how the trained model will behave not on test data, but in real conditions, can be very different from the indicators obtained at the development stage. This article describes the experience of the implementation and real use of a classification service based on machine learning techniques.

A hybrid threat model for system-centric and attack-centric for effective security design in SDLC

Threat modeling is an essential activity in the security development lifecycle. To provide security at the design phase of software development, Microsoft introduced threat modeling stride to identify the vulnerabilities and attacks of application. An efficient solution is necessary to deal with these issues in the software development life cycle. In this context, the paper focused on the analysis of threats and attack tree techniques that are traditionally available and frequently used. Automated Threat modeling enables to simulate attacks and visualized the existing vulnerabilities and misconfiguration. A hybrid model is proposed based on system-centric and attacker-centric to identify the threats in the software application during the software design phase. This model is built by STRIDE by defining security architecture and then analyzed the risks regarding its security characteristics and applied to its real application system. Our model is applied in a case study of the health center management system and shows a better result is identifying the threats and severity in the design phase. And also attack tree defines the stages of threats to understand the severity.

Two-phase flow pattern classification based on void fraction time series and machine learning

Two-phase flow is a complex phenomenon present in several industrial applications such as chemical reactors, power generation, and in the exploration, production, and transport of oil and natural gas. The classification of the flow pattern is a fundamental step in such applications, as it influences several derived parameters and sub-processes such as flow rate, void fraction, and pressure drop estimation. In this paper, we propose an objective approach for classifying flow patterns using time series of void fraction (from a wire-mesh sensor), signal processing and machine learning. As novel approach, the time series is modeled as a stochastic process of independent and identically distributed samples with probability density function described by a Gaussian mixture model. The estimated parameters of the mixture are then fed into a Support Vector Machine (SVM), yielding the flow pattern classification. Tests were performed with a vertical liquid–gas flow database from a 52.3-mm-diameter pipe and the results indicate a great potential for application in real systems. The average accuracy and F-score obtained was higher than 0.94 for different test sets, with standard deviation lower than 0.08 for accuracy a lower than 0.11 for F-Score, demonstrating the efficiency and generalization of the proposed method.

Validation of aflatoxin B1 MIP membrane-based smartphone sensor system for real sample applications

Validation of aflatoxin B1 MIP membrane-based smartphone sensor system for real sample applications

Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management.

Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prognostics and health management (PHM) frameworks. Several data-driven models (DDMs) have been proposed and applied for diagnostics and prognostics purposes in complex systems. However, many of these models are developed using simulated or experimental data sets, and there is still a knowledge gap for applications in real operating systems. Furthermore, little attention has been given to the required data preprocessing steps compared to the training processes of these DDMs. Up to date, research works do not follow a formal and consistent data preprocessing guideline for PHM applications. This paper presents a comprehensive step-by-step pipeline for the preprocessing of monitoring data from complex systems aimed for DDMs. The importance of expert knowledge is discussed in the context of data selection and label generation. Two case studies are presented for validation, with the end goal of creating clean data sets with healthy and unhealthy labels that are then used to train machinery health state classifiers.

Efficient degradation of tetracycline in real water systems by metal-free g-C3N4 microsphere through visible-light catalysis and PMS activation synergy

In this study, a porous C3N4 microsphere (MCN) with curved structure was fabricated through the preassembly-calcination method, and the visible-light catalysis and PMS activation synergy were studied. Different from the conventional bulk g-C3N4 (CN) with flat geometry, the curvature of the C3N4 microsphere has shown significantly enhanced PMS assisted visible-light catalysis efficiency. Besides, the synergy between PMS activation and photocatalysis on MCN is highly favorable for the application in real water systems. Quenching tests and electron paramagnetic resonance (EPR) analysis revealed that the visible-light catalysis and PMS activation synergy on MCN mainly enhanced the nonradical pathway. DFT calculation indicates that the enhanced performance of MCN than CN is due to the curvature induced spatially uneven distribution of electrons, which facilitates the dissociation of exciton and enhances the electron donation ability of N atoms.

Recent Advances in Biomedical Photonic Sensors: A Focus on Optical-Fibre-Based Sensing.

In this invited review, we provide an overview of the recent advances in biomedical photonic sensors within the last five years. This review is focused on works using optical-fibre technology, employing diverse optical fibres, sensing techniques, and configurations applied in several medical fields. We identified technical innovations and advancements with increased implementations of optical-fibre sensors, multiparameter sensors, and control systems in real applications. Examples of outstanding optical-fibre sensor performances for physical and biochemical parameters are covered, including diverse sensing strategies and fibre-optical probes for integration into medical instruments such as catheters, needles, or endoscopes.

Temperature Measurement and Control Application in a Laser Plastic Surgery Real Temperature Detection System

The purpose of this study was to grasp the development process of thermal image temperature measurement technology. It provides directional support for the optimization development of the thermal imagery and laser plastic surgery and laser treatment. This paper uses the infrared thermal image temperature measurement principle and performs infrared thermal image precise temperature measurement technology and its application research. The results showed that there was a correlation between 595 nm pulse dye laser, age, laser energy density, and skin temperature ( P < 0.05 ). There is a significant difference in the average ( P < 0.05 ). The infrared thermal imagery temperature monitoring system is a simple and relatively accurate temperature detection system that can be widely used in temperature measurement and control of laser plastic surgery.

Effects of wastewater type on stability and operating conditions control strategy in relation to the formation of aerobic granular sludge - a review.

Currently, research trends on aerobic granular sludge (AGS) have integrated the operating conditions of extracellular polymeric substances (EPS) towards the stability of AGS systems in various types of wastewater with different physical and biochemical characteristics. More attention is given to the stability of the AGS system for real site applications. Although recent studies have reported comprehensively the mechanism of AGS formation and stability in relation to other intermolecular interactions such as microbial distribution, shock loading and toxicity, standard operating condition control strategies for different types of wastewater have not yet been discussed. Thus, the dimensional multi-layer structural model of AGS is discussed comprehensively in the first part of this review paper, focusing on diameter size, thickness variability of each layer and diffusion factor. This can assist in facilitating the interrelation between disposition and stability of AGS structure to correspond to the changes in wastewater types, which is the main objective and novelty of this review.