Different Types Of Components Research Articles

Multitype Optimal Component Allocation of Multicomponent Systems Considering Fuzzy State

A real system may go through several states ranging from full performance to complete failure, carrying out infinite partial performances during service time. This paper portrays such a non-binary state of systems via the fuzzy membership function. Then, the relation between the system reliability under the binary state and that under the fuzzy state is deduced, on which a multi-type component allocation problem (MCAP) is investigated to search for the optimal permutation of different types of components to maximize the fuzzy system reliability. After that, we inherit the exploration ability of genetic algorithm (GA) and the exploitation ability of Birnbaum importance (BI) to propose a fuzzy-BI-based two-stage approach combined with GA (FBITS-GA) in order to deal with the MCAP under fuzzy state assumption efficiently and accurately. The k-out-of-n systems in both low and high dimensions are presented to illustrate the effectiveness of the proposed algorithm and demonstrate the similarity and difference between the MCAP under binary state and that under fuzzy state. The experimental results showcase that the proposed approach outperforms the existing state-of-the-art approaches for MCAP.

Automatic Street Light

Abstract: Traditional street lighting systems consume a significant amount of energy, leading to increased costs and negative environmental impacts. The demand for energy-efficient and cost-effective lighting solutions for streets and highways has increased in recent years. The given project focuses on to manage the amount of electricity used in public areas and to reduce labor. The research paper presents the design and working of an automatic street light system that uses LDR which detects the intensity of light and according to that it adjusts the lightening. The system decreases the costs by only lighting the areas where it is needed. The given paper offers a sketch of the different types of components used in the system such as LDR and microcontroller during day and night. The paper presents the results of experiments conducted to evaluate the performance of the system. The results showed that the system was able to accurately detect the intensity of sunlight and adjust the lighting accordingly. Ultimately, the paper focuses on the potential benefits of the system which includes improved safety for vehicles and reduced energy consumption. It also focus on some of the challenges that need to be addressed to deploy the system on a larger scale, such as the cost of the sensors and the need for maintenance. Overall, this research paper highlights on the feasibility and effectiveness of using automatic street lights controlled by a microcontroller to improve energy efficiency and reduce costs in urban areas

Augmentation of heat transfer through passive techniques

AbstractThe thermal performance of energy preservation systems is greatly improved by increasing miniaturization and boosting. These are imaginative (or Promethean) techniques to enhance heat transfer. Enhancement methods of heat transfer draw great attention in front of the industrial sector because of their ability to provide energy savings and raise the economic efficiency of thermal systems. Three techniques these methods are categorized; those are active, passive, and compound. Different types of components are used in passive methods because of the transfer/working fluid flow path to the enhancement of the heat transfer rate. In this article, the subject of the review was the passive heat transfer enhancement methods including inserts (conical strips, winglets, twisted tapes, baffles), porous materials, coil/helical/spiral tubes, rough surfaces (corrugated/ribbed surfaces), extended surfaces (fins) and nanofluids (mono and hybrid nanofluid). Recent passive heat transfer enhancement techniques are studied in this article as they are cost‐effective and reliable, and also comparably passive methods do not need any extra power to promote the energy conversion systems' thermal efficiency than active methods. In the passive approaches, various components are applied to the heat transfer/working fluid flow path to improve the heat transfer rate. The passive heat transfer enhancement methods studied in this article include inserts (twisted tapes, conical strips, baffles, winglets), extended surfaces (fins), porous materials, coil/helical/spiral tubes, rough surfaces (corrugated/ribbed surfaces), and nanofluids (mono and hybrid nanofluid). From the pioneers' research work, it is clear that a lower twist ratio and lower pitch, lesser winglet angles can provide more heat transfer rate and a little bit more friction factor. In the case of nanofluids, a little bit of pumping power is enhanced. Finally, heat transfer enhancement is compared with the thermal performance factor, which is more than unity.

Reliability modeling of modular k-out-of-n systems with functional dependency: A case study of radar transmitter systems

The k-out-of-n systems are among the most important redundancy structures in engineering practices, and their reliability assessment has been extensively studied in the past decades. However, components in a k-out-of-n structure are often subject to functional dependency (FDEP), in which component states are affected by other components’ states in the system. In this article, we study a new system structure, namely modular k-out-of-n system with FDEP. In such a system, the failure of some specific components will disable some components in the k-out-of-n structure. Bayesian network (BN) models are used to construct the structure function of modular k-out-of-n systems. The parameters encoded in the graphical structure of the modular k-out-of-n system are automatically generated by a customized algorithm. Furthermore, a dynamic BN (DBN) is developed to update the reliability of modular k-out-of-n system dynamically when observation data are collected from either component or system level. The Birnbaum importance measure of the different types of components in the modular k-out-of-n system is also evaluated by the DBN model via inserting evidence of the components’ states overtime. A real-world case of a radar transmitter system in the space launch site is studied to demonstrate the effectiveness of the proposed method.

Analysis of chemical composition of ancient glass products based on grey correlation method

The Silk Road was the channel of cultural exchange between China and the West in ancient times, and the glass is the precious material evidence of early trade. Early glass was often made into bead-shaped ornaments and introduced into our country in West Asia and Egypt. After absorbing the technology, the ancient glass in our country was made locally. Although the appearance of the glass products was similar to the foreign ones, the chemical composition was different. Therefore, the analysis and identification of chemical composition is an important aspect to identify and study ancient glass products. In view of the different contents of weathering or non-weathering chemical components of different types of glass relics, this paper considers the types, patterns, colors, chemical components and other factors, and ignores environment-related factors, such as atmospheric environment, pH value, burial time, depth and other factors. Based on the existing data and relevant research results of domestic and foreign scholars, the classification rules of chemical components of high-potassium glass and lead-barium glass were obtained by using grey correlation analysis model, Chi-square test and improved weighted average algorithm. It is pointed out that the contents of , , and should be observed in order to analyze and identify ancient glass products.

Composition Analysis of Glass Artefacts Based on Spearman's Coefficient and Fuzzy Correlation

The identification and analysis of the composition of glass artefacts can support the related fields in exploring the history of glass and its processes, as well as the development and progress of glass composition. This paper identifies and analyses the composition of glass artefacts by collecting relevant data and information on glass artefacts through the Spearman correlation coefficient method and fuzzy correlation. In terms of data pre-processing, data with component ratios that did not add up to between 85% and 105% were removed, and then the degree of weathering on the surface of the glass artefacts was initially determined to be closely related to the glass type by applying a chi-square test, and then the highest correlation between the weathered surface of the glass and the glass type was determined by excluding possible interference caused by ornamentation and colour through partial correlation analysis. Fuzzy correlation analysis was then applied to remove abnormal data and calculate the mean values of each chemical composition for different types of glass at different degrees of weathering to discover statistical patterns. Finally, the mean values calculated by the fuzzy correlation analysis were summed using the difference to obtain the predicted results. Six chemical components were also screened out by the Spearman correlation coefficient method, and then the difference quotients of the same components were calculated by averaging these six components for different types of glass respectively. Finally, it was found that in addition to the three elements of potassium, lead and barium, calcium and strontium also have a greater influence on glass classification. The sample data were then clustered by the hierarchical clustering method based on the correlation coefficient method of calculating distances, and the optimal number of clusters was found by calculating the clustering contour coefficients. Then, the data of the clustered subclasses were analysed and subclasses such as soda-lime glass, sulphide glass and aluminosilicate glass were classified. Finally, the clustering clarity values were defined using the entropy weighting method to measure the clustering results, and the Held mean values at different p-values were adjusted for sensitivity analysis of the clustering clarity values.

Consecutive-k1 and k2-out-of-n: F Structures with a Single Change Point

In the present paper, we establish a new consecutive-type reliability model with a single change point. The proposed structure has two common failure criteria and consists of two different types of components. The general framework for constructing the so-called consecutive-k1 and k2-out-of-n: F system with a single change point is launched. In addition, the number of path sets of the proposed structure is determined with the aid of a combinatorial approach. Moreover, two crucial performance characteristics of the proposed model are studied. The numerical investigation carried out reveals that the behavior of the new structure is outperforming against its competitors.

The relationship between high potassium and lead-barium glass after subclassification based on SPSS

During the weathering process, the internal elements of the glass are exchanged with the environmental elements in large quantities, and the composition changes. In order to classify the glass, we select unweathered samples, classify the representative elements, binary classify the representative elements through systematic clustering, and finally judge the rationality by the standard error of the line chart at the classification. In order to further analyze the correlation relationship and difference between the chemical components of different types of glass cultural relics, combined with the grade division of the degree of correlation, the correlation of the chemical composition of the two types of glass was judged by comparing the colors of the correlation coefficient matrix, and the overall color of the high potassium glass was darker for the overall color of the two coefficient matrices, and the difference between its chemical components was considered to be greater.

Study on the composition of glass artifacts based on PSO particle swarm neural network

The analysis of the composition of glass artifacts has always been the focus of research in related fields. By analyzing and identifying the different compositions of glass artifacts from different eras and fields can support the understanding of the history of glass artifacts, their achievements and thus their exit from human civilization. In this paper, we use multiple correlation analysis, kernel principal component analysis, BP neural network model optimized by PSO particle swarm algorithm to study the composition of ancient glass artifacts.To address the shortcomings of the conventional BP neural network model, this paper adopts the BP neural network model under the optimization of PSO particle swarm algorithm to identify and study the unknown artifact types. And different subjective assignments are made to glass artifacts with different weathering degrees to discover the level of model sensitivity.Then the S-W normal distribution test was used and it was found that most of the chemical components did not obey normal distribution. Therefore, nuclear principal component analysis was chosen to find out the correlation between different types of components. On the basis of the identified two correlation coefficients and the paired data were normally distributed, by paired sample t-test, it was found that the magnitude of the differences between the chemical components of different categories was large. This was supplemented by correlation analysis and kernel principal component analysis. Finally, the composition identification study of glass artifacts was realized, which provided a new methodological path for the composition study of glass artifacts.

Ecological and Human Health Risks of Metal–PAH Combined Pollution in Riverine and Coastal Soils of Southern Russia

The floodplains and seacoasts of southern Russia are characterized by urbanization, developed agriculture, and rapidly developing industries. Anthropogenic activity leads to the long-term release of pollutants into the environment, which threatens the stability of ecosystems and public health. The study aimed to assess the ecological and human health risks posed by potentially toxic elements (PTEs) and polycyclic aromatic hydrocarbons (PAHs) in the topsoils of the Taganrog Bay coast and the Lower Don floodplain. Concentrations of PTEs and PAHs were measured using X-ray fluorescence and high-performance liquid chromatography, respectively. Except for the comparatively most toxic Cd, which ranged from low to moderate, ecological risk factors indicated a low risk for PTEs. The cumulative ecological risk of PTEs was low. Zn, As, Cd, and benzo[a]pyrene (BaP) were the most dangerous pollutants, with concentrations 1–2 orders of magnitude higher than the maximum permissible concentrations (MPCs). Mostly sandy soils were characterized by high and very high individual pollution since they have more stringent quality standards due to their lower resistance to contamination. Significant concern is caused by the total contamination of soils with PAHs. A comparison of the toxic equivalent quotient of PAHs with the MPC of BaP showed high or very high contamination in two-thirds of the samples. The non-carcinogenic risk for adults in the region was negligible, whereas the risk for children was low. Dermal contact with PTEs and PAHs contributed to a significant non-carcinogenic risk. Only the combined intake of pollutants poses a substantial risk for children. Over most of the research area, total carcinogenic risk surpasses the threshold, indicating a low risk, with As being the most important contributor. The results of the study showed that PAHs pose a greater potential ecological risk than PTEs, and the opposite trend was observed in relation to the risk of negative impacts on human health. In this regard, taking into account the combined influence of different types of components allows for a more comprehensive risk assessments.

A Salt Spray Corrosion Prediction Model for Aviation Aluminum Alloy Based on MVO-GRNN Algorithm

It is very important to focus on the corrosion failure of aluminum alloy materials for airframe because of the increasing of aircraft service time. However, due to the long corrosion test cycle, the number of samples for processing alloy corrosion related data with high cost is very small. In this paper, a corrosion rate prediction model for less sample data sets is proposed: a General Regression Neural Network which using Multi-Verse Optimizer to optimize prarmeters in order to improve accuracy. In this paper, according to the public data of China Corrosion and Protection Network, the content of Mg, Cu, Fe and other components in different types of aluminum alloys, the corrosion environment (Cl− concentration, pH value) and outdoor exposure time are taken as the input and the mass loss per unit area of aluminum alloys is taken as the output of the model. Moreover, using radial basis function for comparsion. The experimental results show that the model has a good fitting effect for predicting the salt spray corrosion of aviation aluminum alloy, and its performance is better than the traditional RBFnetwork.

ОСОБЕННОСТИ ПРАКТИЧЕСКОЙ РЕАЛИЗАЦИИ БЕСПИЛОТНОЙ АВИАЦИОННОЙ СИСТЕМЫ МУЛЬТИРОТОРНОГО ТИПА НА БАЗЕ 5-ТИ ДЮЙМОВОГО КВАДРОКОПТЕРА ДЛЯ ОТРАБОТКИ НАВЫКОВ ПИЛОТИРОВАНИЯ И АВИАМОНИТОРИНГА ОТ ПЕРВОГО ЛИЦА

In this article the basic elements of an unmanned aerial system of multirotor type, necessary for the assembly of a sports (racing) quadcopter for first-person piloting using a special camera and helmet, are considered. An analysis of classifications of components for different types of sports quadcopters was carried out, and in practice the optimal components for the assembly of an unmanned aircraft system based on a 5-inch frame with the final results of the practical application of the assembled design were selected. The window forms of the design and a comparison of the flight characteristics are given. The article provides a schematic diagram of the connection of electronic components of the quadcopter on-board system. As a result of the performed analysis of the components, the practical implementation of the racing quadcopter is performed and the practical study of flight modes in the laboratory of unmanned aircraft systems of the SUAI engineering school is presented.

Identification model of surface composition of glass relics based on K-means clustering and boosting learning strategy

As one of the main commodities circulated on the Silk Road, glass is valuable material evidence of early trade, but glass is vulnerable to external factors leading to weathering, so this paper establishes a comprehensive identification model to study the chemical composition of the surface of glass cultural relics. In this paper, the Chi-square independence test and fuzzy function are used to analyze the relationship between the type of glass, weathering degree and chemical composition. Then the principal component analysis is used to extract the principal components, and the clustering and algorithm analysis is carried out to identify the unknown glass cultural relics. Finally, the variance test method was used to test the difference between the correlation coefficients of glass types and chemical elements, and the correlation and difference between the chemical components of different types of glass cultural relics were analyzed.

Study on Composition Analysis and Identification of Ancient Glass Products

Glass is the precious material evidence of the early trade of the ancient Silk Road, but the ancient glass is easily weathered by the influence of burial, and its composition ratio changes, which affects the correct judgment of its category. The study of the composition analysis and identification of ancient glass products is of great help to understand the social culture and foreign trade civilization at that time. This paper mainly studies the composition analysis and identification of ancient glassware, to evaluate, predict and classify the ancient glassware, this paper establishes a comprehensive evaluation model, using the chi-square test, K-means clustering model, decision tree model, Lasso regression and grey correlation degree test. It helps archaeologists to analyze and predict the correlation between the weathering degree of cultural relics and their attributes and chemical composition content, and according to the existing classification standards of cultural relics. A labelled subclassification scheme is formulated to identify the types of unknown cultural relics. At the same time, the correlation between the chemical components of different types of cultural relics was analyzed.

Phytochemical and chemotaxonomic studies on four plants from genus of Urtica L. (Urticaceae)

The study aims to conduct phytochemical investigation on four different species of Urtica L., collected in China. The main components of different types of plants in this genus are flavonoids, coumarins, lignans and so on, which are compatible with this study. Eight substances were isolated from Urtica laetevirens Maxim., Urtica mairei Levl., Urtica fissa E. Pritz. and Urtica triangularis Hand-Mass.: one terpenoid (1), one coumarin (2), four lignans (3–6) and two flavonoids (7 and 8). Among them, compound 2 was isolated from all four plants, compounds 1 and 8 were first discovered from Urtica L. In addition, secolignans could be regarded as taxonomic markers for the genus of Urtica L., and its accumulation may be related to its location and altitude. All the above results of the research contributed to consolidating the chemotaxonomic knowledge of Urtica L..

Automation for structured cabling system in data centers using Building Information Modelling

Abstract Building Information Modelling (BIM) involves management efforts, tools and applications for enhancing information flow and, therefore, productivity of projects; adoption of BIM and the increasing use of digital technologies (e.g. data centers) in construction and operation of buildings, as well as in maintenance can transform the way how the industry works. Telecommunication cabinets equipped with large number of components of different types are inevitable parts of data centers having a significant influence on the efficiency and the reliability of data centers. Since the layout of equipment inside telecommunication cabinets is crucial in data centers and the ideal location of equipment inside each cabinet can be unique, an algorithmic and automated process is necessary for the cabinet façade scheme design; manually created diagrams can lead to errors. Furthermore, precise and up-to-date pieces of information about the special layout of equipment in each cabinet are also important for the further operation and maintenance. As an answer to this challenge, a BIM-based approach and method are presented in this article that can result in significant cost savings. When designing data centers, special attention must be paid to the location of telecommunication cabinets, the design of cable routes, and the layout of equipment inside. The novel cabinet family for BIM technology-based method has been developed for the improvement of data center design. Cable trays plug-in can lead to higher quality and efficiency of construction and the facility management as well. The main goal of the case study: optimization of the design process of structured cabling systems (SCS) in data centers (DC) using new additional BIM capabilities. The novel dynamic cabinet family have several distinctive features: the ability to select in the properties which equipment will be inside the cabinet and which unit will occupy; possibility to change and add equipment inside the cabinet; creation of cabinet façade scheme based on equipment inside the cabinet; automatically created specification; Cable tray plug-in can improve and automate the design process in the following areas: place trays depending on the installation method (wall, floor, ceiling); place elements of the trays (hangers, consoles, racks) in a user-defined interval; place tray connectors based on selected connector type and tray type at user-selectable spacing; adding a tray cover and a partition in the tray properties; automatically created specification of all cable trays elements. The model created in BIM can be used for further operation and maintenance. To reach it, each of the elements or groups of elements contains a unique number that allows to bind its parameters and geometry for further export and use in the production model.

Analysis of the thermal stability of residual stresses induced in Ti-6Al-4 V by high density LSP treatments

Laser Shock Processing (LSP) is increasingly applied as an effective technology for the improvement mechanical and surface properties of metallic materials for different types of components, mostly as a means of enhancement of their fatigue life behavior. As reported in previous contributions, a main effect resulting from the application of the LSP technique consists in the generation of relatively deep compression residual stresses fields allowing an improved mechanical behavior. In this paper, the special case of Ti-6Al-4 V alloy is considered with specific consideration of the microstructural changes and residual stresses fields justifying those macroscopic effects. In particular, the effect of the application of different typical LSP intensities on the microstructure and residual stresses fields introduced in this material and their possible correlation to the associated surface effects are analyzed. Emphasis is placed on the study of the thermal stability of these fields after an aging heat treatment at typical high temperature working conditions. The results show that, according to expectations, a certain level of residual stresses and dislocation density remains after in-work thermal aging. The combination of different material characterization techniques has made possible to obtain a correlated set of results highlighting the complementarity of different scale approaches from surface (X-ray) to bulk (neutrons) methods. A model based on the evolution of immobile dislocation density is proposed to rationalize the distribution of dislocation densities, pointing out that plastic deformation is retained. The results clearly show such material improvement stability under typical heavy-duty conditions, thus endorsing the use of the LSP for Ti-6Al-4 V as a suitable technology for industrial applications in relatively high temperature conditions.

A novel method for estimating subresolution porosity from CT images and its application to homogeneity evaluation of porous media

We propose a new method, i.e., the statistical phase fraction (SPF) method, to estimate the total porosity and spatial distribution of local porosities from subresolution pore-dominated X-ray microtomography images of porous materials. The SPF method assumes that a voxel in a CT image is composed of either a single or a maximum of three pure phases of matter (solid, liquid and air). Gaussian function (GF) fitting is conducted on the basis that the summation of the area of each GF curve is equal to the total area covered by the CT histogram. The volume fraction of each phase corresponding to each GF is calculated based on the mean value of the GF, the area of the GF, and the CT numbers for pure phases. The SPF method is verified on three different types of components containing only air and solid phases, i.e., alumina ceramic and two sintered lunar regolith simulants with relatively homogenous and inhomogeneous microstructures. The estimated porosities of a total of 15 specimens (the total porosity ranges from 0 to 51%) via the SPF method show an average error of 3.11% compared with the ground truth. Spatial distribution of local porosities in the defined representative element volume is investigated for homogeneity evaluation. Results show that the local porosity inhomogeneity in the sintered FJS-1 specimens is more prominent than that in the sintered KLS-1 specimens.

A study on the number of failed components in a failed coherent system consisting of different types of components

In this paper, we consider a coherent system consisting of different types of components. It is of interest to study the number of failed components of each type when the system fails. The obtained results are next used in the classical age replacement policy when the corrective replacement of the system is applied if a system has failed, while the preventive replacement is done before failure. We formulate the optimization problem. We wish to find the optimal replacement time. We provide a numerical example to illustrate the theoretical results.

RBM-MCDM framework for optimization of maintenance and inspection intervals of small unmanned aircrafts

PurposeUnmanned aircraft applications are quickly expanded in different fields. These systems are complex that include several subsystems with different types of technologies. Maintenance and inspection planning is necessary to obtain optimal performance and effectiveness. The failure rate in these systems is more than commercial and manned aircraft since they are usually cheaper. But maintenance and operation planning are difficult because we deal with a system that has multi-components, multi-failure models, and different dependencies between subsystems without any advanced health monitoring system. In this paper, this matter is considered and a framework to determine optimal maintenance and inspection plan for this type of system is proposed to improve system reliability and availability. The new criteria according to this field are proposed.Design/methodology/approachMaintenance of unmanned systems influences their readiness; also, according to the complexity of the system and different types of components, maintenance programming is a vital requirement. The plan should consider several criteria and disciplines; thus, multicriteria decision approaches may be useful. On another side, the reliability and safety of unmanned aircraft are the most important requirements in the design and operation phases. The authors consider these parameters and develop a framework based on risk-based maintenance to overcome the problems for unmanned systems. This framework consists of two stages: at the first stage, the critical components and failure modes are determined by FMEA, and in the second stage, the priority of maintenance tasks is determined by a fuzzy multicriteria weighted decision system. In this study, fourteen criteria with different levels of importance are developed and proposed to find the best plan for maintenance and inspection intervals. These criteria have been extracted from the literature review, the author's experience, and expert opinions.FindingsA novel framework for risk-based maintenance has been proposed. Risk determination and risk criteria are the most important factors in this framework. Risks are determined by FMEA, and new criteria are proposed that are used for decision-making. These criteria are proposed based on practical experience and experts' opinions for the maintenance process in the aeronautic industry. These are evaluated by industrial cases, and this framework capability has been demonstrated.Research limitations/implicationsThe proposed framework and criteria for small unmanned aircraft have been developed based on a practical point of view and expert opinion. Thus for implementation in other aeronautic industries, the framework may need a minor modification.Practical implicationsTwo important subsystems of an unmanned aircraft have been studied, and the capabilities of this method have been presented.Originality/valueThis research is original work to determine a maintenance program for unmanned aircraft that their application has rapidly grown up. Practical and design parameters have been considered in this work.