Data Modeling Problems Research Articles

An information entropy-based fuzzy stochastic configuration network for robust data modeling

In data-driven modeling, the generalization performance of a neural network based on the minimum mean square error criterion may be affected when the samples contain noise. To handle uncertain data modeling problems, this study contributes a novel robust data modeling method called information entropy-based fuzzy stochastic configuration networks (IEFSCNs), which incorporates the maximum correntropy criterion (MCC) to reflect the squared error of the training sample set and uses the quantized minimum error entropy (QMEE) criterion to mine the distribution structure of the modeling data and suppress outlier data. Fuzzy systems are designed to generate nodes in the fuzzy transformation layer. A nonlinear enhancement layer is established as a hidden layer to enhance the representational capacity of the target system. The fuzzy transformation and nonlinear enhancement layers are fully connected to determine the outputs of the IEFSCNs. In addition, an incremental parameter learning algorithm is developed based on a supervision mechanism for optimizing the cost function, and its convergence property is demonstrated. Owing to the superiorities of the cost function by synthesizing MCC and QMEE, IEFSCNs can improve the modeling accuracy and reduce noise suppression. The performances are evaluated using various regression and classification tasks. Compared with existing methods, including SCNs, RVFL, F-SCNs, RSC-KDE, RSC-MCC, and SM-RSC, the proposed method demonstrates an effective robust modeling performance by reducing network structure redundancy while enhancing its ability to suppress noisy data.

Hybrid Model of Natural Time Series with Neural Network Component and Adaptive Nonlinear Scheme: Application for Anomaly Detection

It is often difficult to describe natural time series due to implicit dependences and correlated noise. During anomalous natural processes, anomalous features appear in data. They have a nonstationary structure and do not allow us to apply traditional methods for time series modeling. In order to solve these problems, new models, adequately describing natural data, are required. A new hybrid model of a time series (HMTS) with a nonstationary structure is proposed in this paper. The HMTS has regular and anomalous components. The HMTS regular component is determined on the basis of an autoencoder neural network. To describe the HMTS anomalous component, an adaptive nonlinear approximating scheme (ANAS) is used on a wavelet basis. HMTS is considered in this investigation for the problem of neutron monitor data modeling and anomaly detection. Anomalies in neutron monitor data indicate negative factors in space weather. The timely detection of these factors is critically important. This investigation showed that the developed HMTS adequately describes neutron monitor data and has satisfactory results from the point of view of numeric performance. The MSE model values are close to 0 and errors are white Gaussian noise. In order to optimize the estimate of the HMTS anomalous component, the likelihood ratio test was applied. Moreover, the wavelet basis, giving the least losses during ANAS construction, was determined. Statistical modeling results showed that HMTS provides a high accuracy of anomaly detection. When the signal/noise ratio is 1.3 and anomaly durations are more than 60 counts, the probability of their detection is close to 90%. This is a high rate in the problem domain under consideration and provides solution reliability of the problem of anomaly detection in neutron monitor data. Moreover, the processing of data from several neutron monitor stations showed the high sensitivity of the HMTS. This shows the possibility to minimize the number of engaged stations, maintaining anomaly detection accuracy compared to the global survey method widely used in this field. This result is important as the continuous operation of neutron monitor stations is not always provided. Thus, the results show that the developed HMTS has the potential to address the problem of anomaly detection in neutron monitor data even when the number of operating stations is small. The proposed HMTS can help us to decrease the risks of the negative impact of space weather anomalies on human health and modern infrastructure.

Improvement of the recommendation system based on the multi-armed bandit algorithm

In order to effectively solve common problems of the recommendation system, such as the cold start problem and dynamic data modeling problem, the multi-armed bandit (MAB) algorithm, the collaborative filtering (CF) algorithm, and the user information feedback are applied by researchers to update the recommendation model online and in time. In other words, the cold start problem of the recommendation system is transformed into an issue of exploration and utilization. The MAB algorithm is used, user features are introduced as content, and the synergy between users is further considered. In this paper, the author studies the improvement of the recommendation system based on the multi-armed bandit algorithm. The Liner Upper Confidence Bound (LinUCB), Collaborative Filtering Bandits (COFIBA), and Context-Aware clustering of Bandits (CAB) algorithms are analyzed. It is found that the MAB algorithm can get a good maximum total revenue regardless of the content value after going through the cold start stage. In the case of a particularly large amount of content, the CAB algorithm achieves the greatest effect.

Learning consistent representations with temporal and causal enhancement for knowledge tracing

Knowledge tracing is a crucial component of intelligent educational systems and deep learning technologies have significantly propelled its advancement. However, most existing models suffer from the incomplete data modeling problem of knowledge tracing, leading to inconsistent representations of students’ actual knowledge states. This paper proposes a new approach called Temporal- and Causal-enhanced Knowledge Tracing to improve the consistency of students’ knowledge state representations in intelligent educational systems. In particular, our method introduces a causal self-attention mechanism based on front-door adjustment theory, which improves interaction representation and reduces prediction errors caused by dataset bias. To effectively integrate the features of interval and response times into our model, we further use forget and input gates to simulate knowledge forgetting and acquisition, respectively. This maintains consistent learning behavior representations and improves model predictions. The results of our extensive experiments on the three datasets demonstrate that our method outperforms previous knowledge-tracing methods in predicting student scores.

THE ROLE OF CONTEXTS IN SUPPORTING EARLY STATISTICAL REASONING IN DATA MODELING

Data modeling is an essential activity in a data-driven society, but such a topic and how the context shapes it has received limited attention. This paper reports on research that investigated the role of context in supporting early statistical reasoning in the data modeling process. The data were collected throughout sessions in which young children (7 year-old) worked out problem activities designed to stimulate data modeling. The problem activities started by reading children’s story books purposefully created as a strategy to provide contexts of interest. The stories were inscribed within culturally relevant contexts in which the characters deal with data in different formats. The data modeling problem activities were closely related to the stories described within the books. Special attention was put into the actions of organizing, structuring, visualizing, and representing data and the role of the context in the data modeling process. The main results suggest that the context of the problem activities for the data modeling process seems to facilitate statistical reasoning in young children. Additionally, the context of the problem activities helped participants to develop strategies to identify attributes of data, assess the model created, make sense of the data, and make informal inferences.

Fusion of heterogeneous industrial data using polygon generation & deep learning

Analysis of industrial data imposes several challenges. These data are acquired from heterogeneous sources such as sensors, cameras, IoT, etc, and are stored in different structures and formats with different sampling frequencies. They are also stored in isolated silos in different locations which hinders their exploitation. Therefore, there is a clear need to integrate these disconnected data silos at different processing levels and make them clean, easily accessible, and fully exploitable. This paper proposes a data fusion method that merges heterogeneous sources of data at raw, information, and decision levels using polygon generation and deep learning (DL) techniques. An innovative polygon generation technique is proposed to preprocess each data source and convert it into powerful representations that capture all possible relationships in the data, thus extracting the maximum knowledge and achieving better prediction accuracy of the corresponding DL method. The proposed method is targeting challenging data modeling problems found in industrial processes. It is validated successfully using a case study in the realm of process system engineering. The results obtained demonstrate that the proposed fusion method is more accurate, with a minimum of 20% improvement, compared to other methods previously used in the literature.

A Hybrid Approach based on PSO and Boosting Technique for Data Modeling in Sensor Networks

An efficient data aggregation approach in wireless sensor networks (WSNs) is to abstract the network data into a model. In this regard, regression modeling has been addressed in many studies recently. If the limited characteristics of the sensor nodes are omitted from consideration, a common regression technique could be employed after transmitting all the network data from the sensor nodes to the fusion center. However, it is not practical nor efferent. To overcome this issue, several distributed methods have been proposed in WSNs where the regression problem has been formulated as an optimization based data modeling problem. Although they are more energy efficient than the centralized method, the latency and prediction accuracy needs to be improved even further. In this paper, a new approach is proposed based on the particle swarm optimization (PSO) algorithm. Assuming a clustered network, firstly, the PSO algorithm is employed asynchronously to learn the network model of each cluster. In this step, every cluster model is learnt based on the size and data pattern of the cluster. Afterwards, the boosting technique is applied to achieve a better accuracy. The experimental results show that the proposed asynchronous distributed PSO brings up to 48% reduction in energy consumption. Moreover, the boosted model improves the prediction accuracy about 9% on the average.

Rapid Surrogate Modeling of Electromagnetic Data in Frequency Domain Using Neural Operator

The efficiency of solving geophysical inverse problem largely relies on the efficiency of solving the corresponding forward problem. As for electromagnetic (EM) data forward modeling in frequency domain, the conventional numerical methods, e.g. finite difference method (FDM), discretize the governing equations resulting a large linear system which is usually expensive to solve. Meanwhile, for inversion iteration we normally do not need to solve the forward problem in high precision. Thus a rapid surrogate modeling approach which uses the neural network is promising for replacing the forward modeling module in the inversion scheme. Here we proposed an algorithm which uses the neural operator to solve the EM data modeling problem in the frequency domain. To develop a surrogate model for EM data forward problem, we introduce an extended Fourier neural operator (EFNO) that enables the calculation at least 100 times faster than the conventional FDM solver while maintaining good precision. Moreover, by adding a sub-network the proposed neural operator has good generalization which has the capacity of predicting solution at any site locations and frequencies. Due to the discretization-invariance of Fourier neural operator, the neural operator trained on coarse grids can easily transfer to fine grids with only retraining part of parameters, resulting in a super-resolution prediction capability. We test our proposed method with 2-D and 3-D magnetotelluric (MT) data modeling problems, demonstrating that the EFNO has great potentials for severing as a general rapid surrogate forward solver in EM data inversion scheme.

Bayesian stochastic configuration networks for robust data modeling

AbstractThe SCN networks is incrementally generated by stochastic configuration (SC) algorithms. It randomly assigns the input weights and deviations of hidden nodes through a supervisory mechanism, which can be trained by solving linear modeling problems. The version that uses least squares to estimate the output weight performs well. This article introduces an alternative strategy for performing complete Bayesian inference (BI) of SCN networks. Different from the traditional way, the Bayesian training algorithm we proposed can obtain an entire probability distribution on the optimal output weight of the SCN networks, instead of a single pointwise estimate. The advantage of the Bayesian inference method lies in the possibility of introducing other prior knowledge during the training process, providing a way to measure uncertainty during the testing phase and the ability to automatically infer hyper‐parameters from given data. The BI algorithm proposed in this article for regression problem can be implemented through an iterative process under some practical assumptions. Experimental results show that our proposed Bayesian SCN algorithm performs well in solving data modeling problems with a large number of outliers.

Sustainable Development of Leisure Tourism Agriculture in Guangzhou Based on Data Hierarchical Modeling

Hierarchical modeling breaks down the problem of multiple input and multiple output modeling into a series of single input multiple output data modeling problems. This method can improve the efficiency and accuracy of multi-input multi-output system modeling. The model is suitable for describing a system with a complex structure and can contain more input variables, which is conducive to making full use of the existing system information. The problem of data modeling is to get this kind of mapping relationship. Usually a set of basic functions is selected for linear combination, an expression of F with a parameter is given, and then the coefficient of the parameter is determined by least square method or other methods. Urban agriculture was proposed in the process of industrialization and urbanization, with the rapid development of the urban economy, the increasingly severe environmental problems, and the deepening of the idea of sustainable development. Urban agriculture tourism is developed based on urban agriculture by developing various functions of urban agriculture. It makes the development of urban tourism more plentiful and novel, and further expands the space for agricultural development. Therefore, to find the efficient way, this paper proposes the pattern of the sustainable development of leisure tourism agriculture in Guangzhou based on data hierarchical modeling. Numerical analysis and the sample overview are provided to test he performance of the proposed methodology.

Socialbots: Impacts, Threat-Dimensions, and Defense Challenges

Online Social Networks (OSNs) are the modern communication media at the peak of Internet technology that facilitate users' ability to connect with friends and celebrities, and to disseminate breaking news and updates on real-life events. Connections and interactions among OSN users generate a massive amount of data containing a rich collection of knowledge that could be useful to various realworld data modeling and predictive analytics problems like open-source intelligence, business intelligence, event prediction, and product recommendations. On the Web, adversaries explore and target products and services for misuse and vulnerabilities, and OSNs are no exception. Their large user base, easy-to-use functionality, and open nature further attract antisocial elements to these OSNs. In OSNs, cybercrimes and illicit activities are generally committed using various forms of fake profiles, such as clone profiles,1 sybil,2 and bots.

Software and hardware for integrated ACS of casting quality

The implementation of the “Automated system for the operational control of castings production (AS OCCP)” is the first stage and the basis of the integrated automated production control system. It performs three main tasks: control and accounting (the course of production, products, materials, etc.), improvement of the casting quality and the operational control of technological processes. The second stage is based on mathematical processing and analysis of data coming from AS OCCP, which allows the optimal ranges of parameters of technological processes – “Automated system for optimization and analysis of production progress (AS OAPP)” to be determined. AS OAPP consists of two subsystems: quality analysis (QAS) and technology Control (TCS). The first one solves the problems of data analysis and modeling, the second one – real-time calculation of optimal process parameters and forecasting. The tasks of the first and second stages compete for access to different hardware resources. The most critical parameter for the AS OCCP is the performance of the server disk arrays, for the AS OAPP is the processor performance. In both cases, the scaling of the system is effectively solved by parallelizing operations on different servers that make up the cluster, and on different processors (cores) on the same server.

Students' Difficulties in Identifying the Use of Ternary Relationships in Data Modeling

The present study examines the difficulties novice data modelers face when asked to provide a data model addressing a given problem. In order to map these difficulties and their causes, two short data modeling problems were given to 82 students who had completed an introductory course in database modeling. Both problems involve three entity sets with relationships between them, either ternary or binary. The students' solutions were classified according to the types of errors they committed. More than half of the students provided faulty solutions. After an analysis of these results, open interviews were conducted with a selected group of students in order to figure out the reasons underlying the students' erroneous decisions regarding the data model. Among the reasons for their erroneous solutions were insufficient experience, lack of reflection on their solution, and lack of immediate feedback. In addition, the authors suggest instructional modifications derived from the research results.

Граничні випадки критерію мінімуму протяжності

Представлені граничні випадки критерію мінімуму протяжності, які відбивають його зв'язок з вiдомими критерiями обробки даних. Установленi вiдповiдностi мiж критерієм мiнiмуму протяжностi й критерiями найменших квадратiв, найменших модулiв, максимуму правдоподiбностi в задачi оцiнювання параметра зсуву за умови фiксованого параметра масштабу для незалежних однаково розподiлених випадкових величин iз законами розподiлу Гаусса, Лапласа, Кошi, "гостровершинним" меридiанним законом розподiлу, законами узагальнених розподiлiв Гаусса й Кошi, критерієм узагальненої максимальної правдоподібності з вартісними функціями Мішалкіна й Демиденка, а також критеріїм максимуму гістограми. Крім цього, у рамках концепції функціонала протяжності представлено кілька варіантів граничного переходу від критерію мінімуму квазіпротяжності до критерію мінімуму строгої протяжності, де останній критерій для дискретного випадку дає постановку NP-складної задачі мінімізації квазінорми простору l0. Підкреслено, що більшість із зазначених критеріїв приводить до постановки задач оптимізації з неопуклою та неунімодальною цільовою функцією.

A Dirichlet Process Based Type-1 and Type-2 Fuzzy Modeling for Systematic Confidence Bands Prediction

This paper presents a new methodology for fuzzy logic systems modeling based on the Dirichlet process Gaussian mixture models (DPGMM). The proposed method simultaneously allows for the systematic elicitation of confidence bands as well as the automatic determination of model complexity. This work is new since existing fuzzy model elicitation techniques use ad hoc methods for confidence band estimations, which do not meet the stringent requirements of today's challenging environments where data are sparse, incomplete, and characterized by noise as well as uncertainties. The proposed approach involves an integration of fuzzy and Bayesian topologies and allows for the generation of confidence bands based on both the random and linguistic uncertainties embedded in the data. Additionally, the proposed method provides a “right-first time approach” to fuzzy modeling as it does not require an iterative model complexity determination. In order to see how the proposed framework performs across a variety of challenging data modeling problems, the proposed approach was tested on a nonlinear synthetic dataset as well as two real multidimensional datasets generated by the authors from materials science and bladder cancer studies. Results show that the proposed approach consistently provides better generalization performances than other well-known soft computing modeling frameworks—in some cases, improvements of up to 20% in modeling accuracy were achieved. The proposed method also provides the capability to handle uncertainties via the generation of systematic confidence intervals for informing on model reliability. These results are significant since the generic methodologies developed in this paper should help material scientists as well as clinicians, for example, assess the risks involved in making informed decisions based on model predictions.

Performance evaluation and prediction of rudders based on machine learning technology

Rudders are the important components of aircrafts, missiles and ships, and their traditional test equipment is not intelligent enough, so we have to evaluate their performance by observing every parameter manually. This situation makes it impossible to test the rudders rapidly and in quantity. In this paper, we present a new application in the field of rudder test based on machine learning (ML) and describe new methods for performance evaluation and state prediction. The main topics are concentrated on prediction-oriented problems of multiple performance data mining and modeling: analysis and extraction of data feature, performance scoring based on regression algorithm and cross-validation, screening of defective products and fault location based on classification algorithm and accuracy evaluation. Besides, we propose a new optimized decision tree algorithm (SFLA-MWDT) which solves the common decision difficulty in tree models caused by low-precision decision and high-vote competition. Here, through ‘automatic acquisition + intelligent analysis’, we break through the shortcomings of traditional rudder testing methods and technical bottleneck of low parameter testing efficiency. This test method is applicable to those rudders that have already produced but not yet in use. Also, it provides guidance for the production and practice of rudders.

Manufacturing quality prediction using smooth spatial variable selection estimator with applications in aerosol jet® printed electronics manufacturing

Additive manufacturing (AM) has advantages in terms of production cycle time, flexibility, and precision compared with traditional manufacturing. Spatial data, collected from optical cameras or in situ sensors, are widely used in various AM processes to quantify the product quality and reduce variability. However, it is challenging to extract useful information and features from spatial data for modeling, because of the increasing spatial resolutions and feature complexities due to the highly diversified nature of AM processes. Motivated by the aerosol jet® printing process in printed electronics, we propose a smooth spatial variable selection procedure to extract meaningful predictors from spatial contrast information in high-definition microscopic images to model the resistances of printed wires. The proposed method does not rely on extensive feature engineering, and has the generality to be applied to a variety of spatial data modeling problems. The performance of the proposed method in prediction and variable selection through simulations and a real case study has proven to be both accurate and easy to be interpreted.

SOFTWARE AND HARDWARE AUTOMATED SYSTEM OF CASTS DEFECTS NON-DESTRUCTIVE MONITORING

Introduction of the “Automated system for operational control of casts production (OCCP AS)” makes the basis of an integrated automated production control system (APCS). It performs three main tasks: control and recording (production, products, materials, etc.), improving quality of casts and operational management of technological processes. Solution of these tasks was accomplished through automating data collection in real time for all production operations, recording material flows, creating operational communication channels, as well as centralized collection, processing and representation of data by the process information server. The next step in building an effective automated control system is to stabilize product quality in changing external conditions, for example, quality of materials, and to optimize production (technology change in order to reduce costs for constant or higher product quality). The second stage is based on mathematical processing and analysis of data coming from OCCP AS, it allows to determine optimal ranges of parameters of technological processes – “Automated system for optimization and analysis of production progress (OAPP AS)”. OAPP AS consists of two subsystems: quality analysis and technology management. The first solves the problem of data analysis and modeling, the second – calculation of real-time optimal process parameters and real time prediction. The stages tasks compete for access to different hardware resources. The most critical parameter for OCCP AS is performance of server disk arrays, for OAPP AS it is processor performance. In either case, system scaling is effectively solved by parallelizing operations across different servers, forming a cluster, and across different processors (cores) on the same server. To process defect images and to obtain cause-and-effect characteristics, you can use OpenCV software package, which is an open source computer vision library. In course of processing, Sobel operator, Gauss filter and binarization were used. They are based on processing pixels using matrices. Operations on pixels are independent and can be performed in parallel. The task of clustering is reduced to definition of an expert method or using various mathematical algorithms for defects belonging to a specific cluster (data block) through a set of values of dependent factors. Thus, data blocks are formed by the criterion of the defect cause. Calculation of a data block to which a product defect belongs can be very resource-intensive operation. To increase efficiency of image recognition systems and parallelization ofsearch operations, it makes sense to place data clusters on different servers. As a result, there is a need for a distributed database. This is a special class of DBMS, which requires appropriate software. Generation of OAPPAS based on a multi-node cluster with ApacheCassandra DBMS installed and using Nvidia video cards supporting CUDA technology on each node will be the cheapest and most effective solution. Video card is selected based on required number of graphics processors on the node.

Bayesian frameworks for traffic scenes monitoring via view-based 3D cars models recognition

Traffic Scenes Monitoring has been a topic of large research in the last decade. An important step is the recognition of cars. Indeed, recognizing 3D models of cars could allow efficient tracking and detection. In this work we propose to develop new flexible and powerful nonparametric frameworks for the problem of data modeling and 3D recognition. In particular, we propose a Bayesian inference method via scaled Dirichlet mixture models. The consideration of scaled Dirichlet mixture is encouraged by its flexibility recently obtained in several real-life applications. Moreover, the consideration of Bayesian learning is attractive in several ways. It makes it possible to take uncertainty into account by introducing prior information on the parameters, it permits to overcome learning issues regarding the under and/or over-fitting. and it permits simultaneous parameters estimation and model selection. We investigate in this work the integration of both Markov Chain Monte Carlo (MCMC) and reversible jump MCMC (RJMCMC) techniques for learning the resulting models. Detailed experiments have been conducted to demonstrate the advantages of our Bayesian frameworks.

Graph Modeling of Data for Assembly Process of GTE Units in Conditions of Production Digitalization

The problems of data modeling for assembly process of GTE units are considered on the basis of graph approach for solving the production digitalization problem. With the help of the graph model, the assembly workplaces and new states of the assembled GTE units are determined.