Classification Of Structural Changes Research Articles

THEORETICAL BASICS OF STUDYING STRUCTURAL CHANGES IN THE RESOURCE PROVISION OF THE BUSINESS SECTOR

Topicality. Ukraine is in difficult circumstances related to the military aggression of a neighboring country. These circumstances affected the socio-economic conditions: the fall in the level of GDP, the reduction of production processes, internal and external migration of the population, a decrease in the pace of technological development, a decrease in the number of business entities, a decrease in the capacities of enterprises, an increase in unemployment, a decrease in the welfare and quality of life of the population. Undoubtedly, this led to structural changes in the economy of Ukraine and its regions.Aim and tasks. The purpose of this scientific article is to develop the theoretical foundations of the study of structural changes in resource provision of the entrepreneurial sector. This goal made it possible to formulate the following tasks: define the concept of "resource provision of the entrepreneurial sector"; to improve the classification of structural changes: by levels of management (from the point of view of the types of structural changes and the potential capabilities of the system as a result of overcoming them); to systematize theories of economic growth and smoothing of structural changes in the economy; generalize the principles of overcoming structural changes in resource provision and the ways of their implementation in the economy of Ukraine.Materials and Methods. This study was carried out on the basis of the use of the comparative method, elementary theoretical analysis and synthesis, methods of empirical research, the method of scientific knowledge, classification and analogy, the method of scientific knowledge. Data of the Statistical Service of Ukraine, materials of scientific publications and researches, own scientific developments were also used.Research results. The impact of structural changes on such areas as: socio-societal; spatial and territorial; technological and production; organizational and managerial; foreign economic/international. The changes themselves are considered at the following levels: global, state, regional, branch, local, entrepreneurial (firm), as well as in various spheres of activity (economy, society, resource provision, etc.). The work classifies structural changes by management levels: global (world) changes; national (macroeconomic) changes; regional (territorial) changes; sectorial (industry) changes and entrepreneurial (micro) changes. In addition, structural changes are classified by functional component: technical and technological; institutional; reproductive; territorial and spatial. The main challenges of the Ukrainian economy in peacetime and wartime have been identified. The main methods ("cost-output", structural analysis, portfolio analysis, econometric analysis, cluster analysis, analysis of the dynamics of transactional and transformational costs) and structural change approaches (evolutionary, institutional, synergistic, cybernetic, structural, neoclassical, innovative-technological) are considered. Let's consider the main theories of the growth of the economy of the country and its regions according to the feature of the unification of territories (cumulative growth, the natural occurrence of negative structural changes, the relationship between the occurrence of negative structural changes and economic growth, the regulation of negative structural changes, technological growth, the impact of innovative development on negative structural changes).Conclusions. The effectiveness and efficiency of business activity consists in the identification of negative structural changes, the development of a methodological apparatus for their elimination, the regulatory and legal provision of these processes, the development of regulatory mechanisms, the identification of responsible persons and a clear communication process between them. Thus, the key to overcoming negative structural changes in the economy can be: strengthening of state management and regulation; balancing market self-regulation at the national, regional and local levels; state initiative and participation in regional transformation processes; development of theoretical and methodological approaches and principles to overcome negative shifts in the development of the country and its regions.This scientific article was carried out within the framework of the NDR "Mechanisms of structural transformation of the entrepreneurial sector of Ukraine on the basis of resilient economic development" (code: 3.1.8.84/B).

Vibration-Based Structural Health Monitoring Using Piezoelectric Transducers and Parametric t-SNE.

In this paper, we evaluate the performance of the so-called parametric t-distributed stochastic neighbor embedding (P-t-SNE), comparing it to the performance of the t-SNE, the non-parametric version. The methodology used in this study is introduced for the detection and classification of structural changes in the field of structural health monitoring. This method is based on the combination of principal component analysis (PCA) and P-t-SNE, and it is applied to an experimental case study of an aluminum plate with four piezoelectric transducers. The basic steps of the detection and classification process are: (i) the raw data are scaled using mean-centered group scaling and then PCA is applied to reduce its dimensionality; (ii) P-t-SNE is applied to represent the scaled and reduced data as 2-dimensional points, defining a cluster for each structural state; and (iii) the current structure to be diagnosed is associated with a cluster employing two strategies: (a) majority voting; and (b) the sum of the inverse distances. The results in the frequency domain manifest the strong performance of P-t-SNE, which is comparable to the performance of t-SNE but outperforms t-SNE in terms of computational cost and runtime. When the method is based on P-t-SNE, the overall accuracy fluctuates between 99.5% and 99.75%.

Vibration‐based detection and classification of structural changes using principal component analysis and ‐distributed stochastic neighbor embedding

This paper describes a structural health monitoring strategy to detect and classify structural changes in structures that can be equipped with sensors. The proposed approach is based on the t-distributed stochastic neighbor embedding (t-SNE), a nonlinear technique that can represent the local structure of high-dimensional data collected from multiple sensors in a plane or spatial representation. We propose the following basic steps for the detection and classification. First, the raw data are preprocessed: We scale the data using the mean-centered group scaling and apply principal component analysis to reduce the dimensionality of the scaled data. Second, t-SNE is applied to represent the scaled and reduced data as points in a plane, defining a cluster for each structural state. Finally, the current structure to be diagnosed is associated with a cluster (or structural state) using three different strategies: (a) the smallest point-centroid distance; (b) the majority voting; and (c) the sum of the inverse distances. The combination of t-SNE with our preprocessing and the three proposed classification strategies signif- icantly improves the quality of the clusters that represent different structural states. We evaluate the performance of our method using experimental data from an aluminum plate instrumented with piezoelectric transducers. Results are presented in the time domain, and they reveal the high classification accuracy and strong performance of this method, with a percentage of correct decisions close to 100% in several scenarios.

A Frequency-Based Approach for the Detection and Classification of Structural Changes Using t-SNE †.

This work presents a structural health monitoring (SHM) approach for the detection and classification of structural changes. The proposed strategy is based on t-distributed stochastic neighbor embedding (t-SNE), a nonlinear procedure that is able to represent the local structure of high-dimensional data in a low-dimensional space. The steps of the detection and classification procedure are: (i) the data collected are scaled using mean-centered group scaling (MCGS); (ii) then principal component analysis (PCA) is applied to reduce the dimensionality of the data set; (iii) t-SNE is applied to represent the scaled and reduced data as points in a plane defining as many clusters as different structural states; and (iv) the current structure to be diagnosed will be associated with a cluster or structural state based on three strategies: (a) the smallest point-centroid distance; (b) majority voting; and (c) the sum of the inverse distances. The combination of PCA and t-SNE improves the quality of the clusters related to the structural states. The method is evaluated using experimental data from an aluminum plate with four piezoelectric transducers (PZTs). Results are illustrated in frequency domain, and they manifest the high classification accuracy and the strong performance of this method.

Detection and classification of structural changes using artificial immune systems and fuzzy clustering

Among all the elements that are integrated into a structural health monitoring (SHM) system, methods or strategies for damage detection and classification are nowadays playing a key role in enhancing the operational reliability of critical structures in several industrial sectors. The main contribution of this paper is the application of a new methodology to detect and classify structural changes. The methodology is based on: 1) an artificial immune system (AIS) and the notion of affinity is used for the sake of damage detection; 2) a fuzzy c-means algorithm is used for damage classification. One of the advantages of the proposed methodology is the fact that to develop and validate the strategy, a model is not needed. Additionally, and in contrast to standard Lamb waves-based methods, there is no need to directly analyse the complex time-domain traces containing overlapping, multimodal and frequency dispersive wave propagation that distorts the signals and difficult the analysis. The proposed methodology is applied to data coming from two sections of an aircraft skin panel. The results indicate that the proposed methodology is able to accurately detect damage as well as classify those damages.

Detection and classification of structural changes using artificial immune systems and fuzzy clustering

Detection and classification of structural changes using artificial immune systems and fuzzy clustering

A Bioinspired Methodology Based on an Artificial Immune System for Damage Detection in Structural Health Monitoring

Among all the aspects that are linked to a structural health monitoring (SHM) system, algorithms, strategies, or methods for damage detection are currently playing an important role in improving the operational reliability of critical structures in several industrial sectors. This paper introduces a bioinspired strategy for the detection of structural changes using an artificial immune system (AIS) and a statistical data-driven modeling approach by means of a distributed piezoelectric active sensor network at different actuation phases. Damage detection and classification of structural changes using ultrasonic signals are traditionally performed using methods based on the time of flight. The approach followed in this paper is a data-based approach based on AIS, where sensor data fusion, feature extraction, and pattern recognition are evaluated. One of the key advantages of the proposed methodology is that the need to develop and validate a mathematical model is eliminated. The proposed methodology is applied, tested, and validated with data collected from two sections of an aircraft skin panel. The results show that the presented methodology is able to accurately detect damage.

Data-driven methodology to detect and classify structural changes under temperature variations

This paper presents a methodology for the detection and classification of structural changes under different temperature scenarios using a statistical data-driven modelling approach by means of a distributed piezoelectric active sensor network at different actuation phases. An initial baseline pattern for each actuation phase for the healthy structure is built by applying multiway principal component analysis (MPCA) to wavelet approximation coefficients calculated using the discrete wavelet transform (DWT) from ultrasonic signals which are collected during several experiments. In addition, experiments are performed with the structure in different states (simulated damages), pre-processed and projected into the different baseline patterns for each actuator. Some of these projections and squared prediction errors (SPE) are used as input feature vectors to a self-organizing map (SOM), which is trained and validated in order to build a final pattern with the aim of providing an insight into the classified states. The methodology is tested using ultrasonic signals collected from an aluminium plate and a stiffened composite panel. Results show that all the simulated states are successfully classified no matter what the kind of damage or the temperature is in both structures.

A Hierarchical Classification Scheme for Chemical Reactions

Several known approaches to classification of structural changes in the course of chemical reactions are briefly discussed. The hierarchical five-level classification scheme is suggested for organic interconversions in which the bond redistributions (summarizing all required steps of the reaction process) are exactly known. The main hierarchy levels, i.e., reaction category, reaction class, interconversion type, topological type, and reaction typeand also corresponding sublevelsare illustrated by numerous examples. The first two levels together with reaction subcategories and subclasses relate to the nature of educt and product species (neutral or dipolar molecules, ions, radicals, etc.) and to contours and graphs formed by bonds which change their multiplicity during the reaction. The interconversion, topological, and reaction types are uniquely characterized by classification, symbolic, and skeletal equations, respectively; the subtypes are also associated with specific kinds of equations. The central h...

Friedrich Daniel von Recklinghausen Biographie und Bedeutung seiner osteopathologischen Arbeiten nach mehr als 100 Jahren

Even today the name of F.D. von Recklinghausen is often mentioned in connection with primary and secondary hyperparathyroidism. He was one of the first physicians and scientists to render outstanding services to the classification of structural changes of bones, and in many aspects his work remains valid today. He succeeded through precise and comparative studies in making the disease and symptoms of primary hyperparathyroidism known and thus distinguishing it from other known structural bone diseases. He also described the phenomenon of "bone remodeling" which has become relevant today again. Although his written macroscopic and microscopic observations include meticulously precise differentiations which make it difficult to study them today, they are still of interest in order to follow his basic ideas. This article does not constitute a comprehensive biography, but it contains a description of F.D. von Recklinghausen's important work on bone pathology against the background of the main stages of his life.