Conditions Of Incomplete Data Research Articles

Study of patterns of PM2.5 and PM10 changes in the atmospheric air of Ivano-Frankivsk region

The purpose of this research is to analyse the temporal patterns of changes in concentrations of dust particles – aerosols, regression modeling of the interdependence of PM2.5 and PM10 at the level of instantaneous, average hourly, average daily and average weekly concentrations, and assessment of the impact of the anthropogenic component of dust pollution of atmospheric air in the Ivano-Frankivsk region on regularities of the temporal distribution of these shares.The database for the study included measurements of the concentration of PM10, PM2.5 every hour at four Ecocity public monitoring stations: in the central part of the city of Ivano-Frankivsk, in the village of Bovshiv within the influence of the Burshtyn TPP, in the village of Broshniv-Osada within the influence of the woodworking enterprise "SVISS KRONO" and within the recreational territory of the village of Mykulychyn, Nadvirnyan district. The general regularity of the temporal distribution of PM10, PM2.5 was established for all posts for daily fluctuations – the highest concentrations are observed at night, the lowest – during the period of maximum daily temperatures. It has been proven by actual data that the greater the level of atmospheric air pollution (the greater the anthropogenic component of PM), the higher the daily concentrations of PM10, PM2.5 and the more often the one-time standards of solid suspended particles are exceeded.According to the data of monitoring stations from the territories with different anthropogenic influence, functional dependences of PM2.5 content on PM10 content were obtained for instantaneous values, hourly average values, daily average values, and weekly average values. The scientific novelty of the conducted research consists in the establishment of identical functional interdependence of temporal changes in concentrations of PM2.5 and PM10 dust particles within a conditionally clean area and within the limits of the influence of stationary sources of pollution with a high level of direct regression dependence and a coefficient of determination greater than 0.9 in all cases. This allows us to conclude that the temporal patterns of changes in both PM2.5 and PM10 will not differ in clean and polluted air conditions for the Ivano-Frankivsk region. In conditions of incomplete data on the temporal distribution of the concentration of PM10, the obtained equations can be used to forecast the temporal distribution of the concentration of PM2.5.

Stochastic model for product supplier selection

The aim of the article is to develop a model of rational choice of technology and equipment supplier under conditions of external environment uncertainty. The technical and economic requirements of the customer are considered as operational, i.e. each requirement can be evaluated by expert judgement or measured using one or another means of measurement. This problem belongs to the multi-criteria ones with diverse indicators, for which traditional methods of linear convolution of criteria do not function correctly. We present a stochastic model of product supplier selection based on the concept of suitability and stochastic dominance, as well as the principle of guaranteed result in the conditions of incomplete data represented by finite limited samples of supplier characteristics. Both univariate and multivariate cases of supplier selection are addressed within the model. Ultimately, the formulated models are converted into linear Boolean programming models, which can be effectively solved using conventional linear optimization software packages.

Features of using Cox regression in various instrumental environments

The presence of large amounts of data in information and analytical systems makes it necessary to study them using machine learning and artificial intelligence methods. These models require the definition of tuning parameters related to the specifics of the subject area. The article presents a Cox regression model to solve the problem of customer churn. Cox regression is recognized as a model with high accuracy of predictions in healthcare. Therefore, it is interesting to use the model in other industries. The paper presents the results and comparative analysis of calculations on the Cox model using three tools: Statistical Package for the Social Sciences, programming language R and Russian software – analytical platform Loginom. A distinctive feature of the developed probabilistic model is the determination of the risk of event occurrence in conditions of incomplete data, as well as the identification of indicators that have a significant impact on the degree of its manifestation.

Prediction of Duration of Traffic Incidents by Hybrid Deep Learning Based on Multi-Source Incomplete Data.

Traffic accidents causing nonrecurrent congestion and road traffic injuries seriously affect public safety. It is helpful for traffic operation and management to predict the duration of traffic incidents. Most of the previous studies have been in a certain area with a single data source. This paper proposes a hybrid deep learning model based on multi-source incomplete data to predict the duration of countrywide traffic incidents in the U.S. The text data from the natural language description in the model were parsed by the latent Dirichlet allocation (LDA) topic model and input into the bidirectional long short-term memory (Bi-LSTM) and long short-term memory (LSTM) hybrid network together with sensor data for training. Compared with the four benchmark models and three state-of-the-art algorithms, the RMSE and MAE of the proposed method were the lowest. At the same time, the proposed model performed best for durations between 20 and 70 min. Finally, the data acquisition was defined as three phases, and a phased sequential prediction model was proposed under the condition of incomplete data. The results show that the model performance was better with the update of variables.

A novel quality‐related process monitoring method for multi‐unit industrial processes under incomplete data conditions

AbstractModern industrial processes usually consist of multiple local units. With the wide application of distributed control systems, a large amount of industrial data is collected. During data acquisition and transmission, outliers and missing data commonly exist in different local units. Under such incomplete data conditions, the detection performance of traditional quality‐related process monitoring methods may be seriously degraded. This study develops a novel quality‐related process monitoring method for multi‐unit industrial processes. Considering the existence of outliers and missing data, incomplete data preprocessing is first performed on raw process data and quality data. Then, process variables are divided into several blocks according to the process knowledge. Afterwards, the quality‐related features extracted by variational information bottleneck (VIB) are used to train deep support vector data description (DSVDD). Finally, a quality‐related process monitoring strategy is designed at both block and global levels. Experimental results on the revised Tennessee Eastman process demonstrate that the proposed method has better monitoring performance under incomplete data conditions than other state‐of‐the‐art methods.

Robust correlation mapping of train-induced stresses for high-speed railway bridge using convolutional denoising autoencoder

Train-induced stresses in different monitoring points not only reflects local mechanical characteristics of the structural components but also has inherent spatiotemporal correlations in the high-speed railway bridges. Mapping correlations among the stress responses can assist recognizing train-induced stress pattern and lay foundation for structural health diagnosis. However, correlation mapping and feature extraction of structural responses rely heavily on the data integrity, the pre-constructed model may be out of action due to data acquisition/transmission error, sensor faults, etc., commonly exists in the structural health monitoring system. Considering the characteristics of various incomplete train-induced stresses, this study presents a robust correlation mapping of incomplete data to complete data using one-dimensional convolutional denoising autoencoder. Stacked convolutional layers are employed as encoder to extract robust spatiotemporal feature of incomplete stresses, and transposed convolutional layers served as decoder to reconstruct denoised and complete stresses. In the training strategy, various incomplete data conditions, where stress data are lost continuously or discretely with different missing rates, are considered as training samples, making the established correlation mapping robust, accurate, and adaptive. The application on a high-speed railway truss bridge demonstrates that the proposed method can robustly reconstruct the complete stress data under different data loss conditions. The method can also be employed to assess the importance of any sensor combinations to the monitoring item, which shed light on the maintenance of sensor network.

Research on short-term wind power forecasting method based on incomplete data

In accordance with the topographic features and other relevant factors, data affecting wind power generation in engineering practice may be difficult to obtain, and three short-term wind power forecasting methods are proposed based on incomplete data. Most wind power forecasting studies are based on wind speed data, but wind power forecasting needs more influential factors in addition to wind speed data, and it is difficult to obtain these data in actual production and life. Therefore, this paper proposes the wind power forecasting under the condition of incomplete data. First, this paper makes theoretical analysis on multi-state space Markov chain wind power forecasting, backpropagation (BP) neural network wind power forecasting, and genetic algorithm (GA)-BP neural network wind power forecasting, and the corresponding wind power forecasting models are constructed. Second, with the actually measured data of a wind farm in the Heilongjiang province as the research object, the historical data are preprocessed first and then imported into three wind power forecasting models for simulation, and the curves of relative error and absolute error of total wind power forecasting in this area are obtained. Finally, the simulation results of three methods based on incomplete data are analyzed and evaluated. The results show that under the condition of incomplete data, the maximum error of the GA-BP neural network wind power forecasting model improved by the genetic algorithm is reduced from 6.8% to 1.6%, and the forecasting accuracy is greatly improved.

Research on the Coupling and Coordinated Development of Sports Tourism and Cultural Industry under the Background of Artificial Intelligence Era

In the new era of rapid development of intelligent information, sports tourism and cultural industry have become a new goal to improve China’s urban competitiveness and realize the development of urban industry. Based on the integration of sports tourism industry and cultural industry, this paper establishes the relevant information data interface mode. This paper constructs the information coupling system model of sports tourism industry and cultural industry under the background of artificial intelligence era and obtains the corresponding data results by using the invisible statistical logic computer independent judgment (IDIJA) method of artificial intelligence system under the condition of incomplete data. The application of the coordinated development system of coupling industry can realize the rapid development of sports tourism and cultural reconstruction mode and achieve the new effect of intelligent sports tourism cultural comprehensive experience. It can not only improve the people’s quality of life but also provide feasible suggestions for improving the continuous development of the coupling and coordination of sports tourism and cultural industry.

Description of the method of fuzzy situational control based on a composite hybrid model of a complex technical system

Objective. The aim of the study is to generalize the accumulated experience of fuzzy situational control based on a compositional hybrid model of a complex technical system in the form of an algorithm and, on this basis, to form recommendations on the methodology for the formation and identification of situations, determining parameters and solutions for managing a complex technical system under conditions incomplete data to improve the accuracy of control decisions. Method. The use of a compositional hybrid model solves the problem of describing and modeling the system in conditions of incomplete data and the impossibility of obtaining information about the entire range of the system's operation. Fuzzy situational control makes it possible to develop control decisions in accordance with the chosen control strategy and take into account the specifics of the system thanks to the compositional model. Result. An algorithm for fuzzy situational control of complex technical systems based on compositional hybrid models is proposed. The stages, features, advantages and disadvantages of fuzzy situational control for this type of systems are considered. The procedure for determining and unambiguously identifying emerging fuzzy situations for the system is given, and a method for analyzing and developing typical control strategies is also considered. The compositional hybrid model of a complex technical system considered in the article describes the operation of the experimental compressor unit ETsK-55. Conclusion. The main advantages of the developed fuzzy situational method for managing complex technical systems include: integration of the control system with existing elements of the system; better use of available resources; adaptability and reliability of a control method based on fuzzy situational networks and a composite hybrid model of the system. Management strategies have been defined to meet the customer's requirements for product quality, as well as the safety of personnel and equipment, trouble-free production and saving resources.

About the full prediction approximation by a lot of partial predictions in case of incomplete data

In this article, we are talking about the random subspaces method in forecasting under the condition of incomplete data and about estimation of a full forecast based on a set of partial predictions. Centered partial predictions are considered without loss of generality. According to the statistical model, off-diagonal elements in the correlation matrix of partial predictions are considered random with known mathematical expectation and variance. In case of this random matrix, analytical expressions are obtained for the mathematical expectation of the determinant and minors. Based on these results, a class of more accurate estimates of the full prediction is constructed, which differ from the mean partial prediction by a multipliers that depend on the statistical parameters of the correlation matrix of partial predictions. The results of modeling and practical forecasting based on incomplete biogeographic data are presented.

Modeling the risks of the confession process of the accused of criminal offenses based on survival concept

Based on statistical survival analysis, the assessment and forecasting of the risks of pleading guilty to criminal offenses in conditions of incomplete data are carried out. Risk function is constructed to estimate the probability of confession of suspects at certain stages (time periods) of the trial. The Kaplan-Meier model is applied to calculate the chances of obtaining confession evidence after the end of the trial in criminal proceedings. Differences in the decision to admit guilt for two groups of defendants: in the commission of a criminal offense by one person and a group of persons are investigated. Cox regression model is constructed to establish the interconnection between the stages of the pre-trial investigation, at which the accused gives confessions, with the duration of the investigation and the method of prosecution.

Neuro-fuzzy algorithm for clustering multidimensional objects in conditions of incomplete data

The paper is considered development of fuzzy expert system model for identifying faults in complex systems using data mining methods based on searching for hidden patterns in databases. The use of neural network technologies makes it possible to detect nonlinear dependencies of input and output data, improve the quality of an objective assessment of the state of complex technical objects, which ultimately will reduce the number of emergency situations during operation. A method is proposed for identifying the optimal number of fuzzy clusters in the space of training examples and determining, on their basis, the parameters of the membership functions for the input variables and inference results. Considered a neuro-fuzzy algorithm for clustering multidimensional objects in conditions of incompleteness and fuzzy initial information.

Assessment of Reliability Indicators of Space Vehicles under Conditions of Incomplete Data

Assessment of Reliability Indicators of Space Vehicles under Conditions of Incomplete Data

Mixed integer optimization approach to groundwater pollution source identification problems

ABSTRACTIn this study, we consider an approach that employs 0–1 type integer programming in a simulation-optimization model to identify the characteristics of groundwater pollutant sources. The advantage of the proposed solution model is that it reduces the dimensions of the unknown variable, where 0–1 type integer variables denote the source position. If the estimated location variable is equal to 0, this means that the corresponding location is not the actual source location and there is no need to estimate its source flux. If the estimated location variable is equal to 1, this means that the corresponding location is the actual source location and the source flux needs to be identified. We evaluated the performance of the proposed model based on four scenarios with different potential source location sets, incomplete data conditions, and noisy data conditions. The results obtained indicated that the proposed solution model is an effective approach for solving groundwater pollution source identification problems.

Demonstration of the Cascadia G‐FAST Geodetic Earthquake Early Warning System for the Nisqually, Washington, Earthquake

ABSTRACT A prototype earthquake early warning (EEW) system is currently in development in the Pacific Northwest. We have taken a two‐stage approach to EEW: (1) detection and initial characterization using strong‐motion data with the Earthquake Alarm Systems (ElarmS) seismic early warning package and (2) the triggering of geodetic modeling modules using Global Navigation Satellite Systems data that help provide robust estimates of large‐magnitude earthquakes. In this article we demonstrate the performance of the latter, the Geodetic First Approximation of Size and Time (G‐FAST) geodetic early warning system, using simulated displacements for the 2001 M w 6.8 Nisqually earthquake. We test the timing and performance of the two G‐FAST source characterization modules, peak ground displacement scaling, and Centroid Moment Tensor‐driven finite‐fault‐slip modeling under ideal, latent, noisy, and incomplete data conditions. We show good agreement between source parameters computed by G‐FAST with previously published and postprocessed seismic and geodetic results for all test cases and modeling modules, and we discuss the challenges with integration into the U.S. Geological Survey’s ShakeAlert EEW system.

Data support of design of details production techniques

The issues of the computer-aided design of technological processes of parts and products production under the conditions of incomplete data are considered. The research aim is to define the structure of the data support of the technological design process in order to increase the automation level of the technological production preparation. The methods of the computer-aided design are analyzed and the areas of its implementation are defined. It is proved that under the conditions of job production the synthesis methods (design methods) predominate. Their efficiency depends on the completeness of products and parts information model. The composition and content of the information models for the automation of the technological design are suggested. It is demonstrated that the extent of the data granularity affects the choice of design mechanism. As a result, the data support conceptual model of the design of the technological processes is developed. This model includes the data streams, managing streams and implementation mechanisms. The results of this research can serve as a basis for the development of the support systems of the decision-making under the technological design. The demonstrated data composition of the data support will allow creating a consolidated information space of the design and production stages. As a result, it is possible to achieve the increased level of the technological design automation and, thus, the high quality of technological processes.

Evaluation of Metrological Reliability of Measuring Instruments under the Conditions of Incomplete Data

The problem of estimating the metrological reliability of measuring instruments under conditions of incomplete data, presented by small samples of metrological resource values is considered. An approach to metrological reliability assessment based on the method of moments is suggested. Analytical expressions and examples of metrological reliability estimation are presented.

Multivariate regression analytical method based on heuristic constructed variable under condition of incomplete data

Regression analysis is often used for filling and predicting incomplete data,whereas it has some flaws when constructing regression equation,the independent variable form is fixed and single.In order to solve the problem,the paper proposed an improved multivariate regression analytical method based on heuristic constructed variable.Firstly,the existing variables' optimized combination forms were found by means of greedy algorithm,then the new constructed variable for multivariate regression analysis was chosen to get a better goodness of fit.Results of calculating and estimating incomplete data of wheat stalks' mechanical strength prove that the proposed method is feasible and effective,and it can get a better goodness of fit when predicting incomplete data.

Iterative Methods in Large Field Electron Microscope Tomography

Electron tomography (ET) is a powerful technology allowing the three-dimensional (3D) imaging of cellular ultrastructure. These structures are reconstructed from a set of micrographs taken at different sample orientations, the final volume being the solution of a general inverse problem. Two different approaches are used in this context: iterative methods and filtered backprojection. Iterative methods are known to provide high-resolution 3D reconstructions for ET under noisy and incomplete data conditions. However, all previous implementations have been restricted to the straight-line projection models. This is not accurate since electron trajectories in electron microscopes do not follow the straight-line optics assumed for X-rays, and biological samples may warp as a result of being exposed to an electron beam. Compensation for curvilinear trajectories, nonlinear electron optics, and sample warping constitutes a major advance in large-field ET and has made possible resolution down to the molecular level...

A fuzzy intelligent approach to the classification problem in gene expression data analysis

Classification is an important data mining task that widely used in several different real world applications. In microarray analysis, classification techniques are applied in order to discriminate diseases or to predict outcomes based on gene expression patterns, and perhaps even to identify the best treatment for given genetic signature. The most important challenge in gene expression data analysis lies in how to deal with its unique “high dimension small sample” characteristic, which makes many traditional classification techniques non-applicable or inefficient; and hence, more dedicated techniques are nowadays needed in order to approach this problem. Fuzzy logic is recently shown that is a powerful and suitable soft computing tool for handling the complex problems under incomplete data conditions. In this paper, a new hybrid model is proposed that combines artificial intelligence with fuzzy in order to benefit from unique advantages of both fuzzy logic and the classification power of the artificial neural networks (ANNs), to construct an efficient and accurate hybrid classifier in less available data situations. The proposed model, because of using the fuzzy parameters instead of the crisp parameters, will need less data set in comparing with traditional nonfuzzy neural networks in its training process or with same training sample can better learn and hence can yield more accurate results than traditional neural networks. In addition of theoretical evidence of using fuzzy logic, empirical results of gene expression classification indicate that the proposed model exhibits effectively improved classification accuracy in comparison with traditional artificial neural networks (ANNs) and also some other well-known statistical and intelligent classification models such as the linear discriminant analysis (LDA), the quadratic discriminant analysis (QDA), the K-nearest neighbor (KNN), and the support vector machines (SVMs). Therefore, the proposed model can be applied as an appropriate alternate approach for solving problems with scant data such as gene expression data classification, specifically when higher classification accuracy is needed.