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We develop an intelligent credit rating system that can provide debtors' rating information without involving credit rating agencies. Several models are used for credit scoring in our work, including the Duffie's model, logistic regression, and random forest. We compare the performance of these models and build an in-depth understanding of the evaluation of credit rating. Furthermore, we propose a...
Hardware-in-the-Loop (HIL) is a well-established concept for developing and testing embedded systems. While it is widely used in industrial automation and the automotive area, it is rarely applied to Building Automation Systems (BAS). This work proposes the interconnection of a prominent building automation protocol, namely Building Automation and Control network (BACnet), and a simulator, PowerDEVS,...
In this paper, we propose a classification model for learning state based on individual biometric data. In particular, we use the pupil size as a biometric data and the data has been collected from 72 participants. We also deploy the support vector machine (SVM) in conjunction with k-fold validation as an analysis tool. In order to improve the performance of the SVM, the we remove outliers from the...
In this paper an advanced iris-biometric comparator is presented. In the proposed scheme an analysis of bit-error patterns produced by Hamming distance-based iris-code comparisons is performed. The lengths of sequences of horizontal consecutive mis-matching bits are measured and a frequency distribution is estimated. The difference of the extracted frequency distribution to that of an average genuine...
In this PhD dissertation, we propose a new testing approach for effectively managing hardware development risks, producing hardware designs with enough quality and reliability. Our proposal is based on the combination of high-level modelling and a unit testing framework in order to generate real hardware implementations for validating the designer intent, in order to keep a high cycle-accuracy and...
Human action recognition has been extensively studied with a lot of real life application. Many methods have been proposed and achieved promising results when the input video captured from the same viewpoints. However, their accuracy decreases significantly under viewpoint changing. The reason is that action appearance is quite different when looking from a different angle. To overcome this problem,...
Credit scoring is explored to assess default risk of consumer behaviors for financial institutions, banks in particular. The advanced Bayesian algorithm is proposed for credit assessment. The new trial ensembles logistic regression analysis (LRA), cluster and MLP-NN in Bayesian approach as an advanced classifier. The investigation contain evidence that Bayesian ensemble technique optimizes LRA, cluster...
In recent years, model-based testing (MBT) of automation systems has gained in importance. However, increasing size and complexity of manufacturing plants also lead to larger models, which again cost time and manpower for modeling tasks. An approach to solve this problem is to subdivide the overall model into several separated models of usual components of automation systems which are reusable. Therefore,...
Nowadays a huge volume of biomedical data (images, genes, etc) are daily generated. The interpretation of such data involves a considerable expertise. The misinterpretation and/or misdetection of a suspicious clinical finding leads to increasing the negligence claims, and redundant procedures (e.g. biopsies). The analysis of biomedical data is a complex task which are performed by specialists on whose...
This study aims to present time series-based forecasting for Malaysian crude palm oil prices using neural network algorithms. Daily prices of soy bean oil and currency exchange rates are tested as input features, in addition to crude palm oil prices. Efforts are focused on finding the optimal network structures for the modelling of crude palm oil price forecasting. Neural network structures with an...
In this paper, the falsification of predefined requirements of a control system is formulated as an optimal control problem. We propose a framework that can be used to design input trajectories such that multiple requirements can be falsified by a single test trajectory. Furthermore, an algorithm based on gradient techniques is proposed for unknown nonlinear systems. A numerical example, treating...
In large-scale Earth System simulation codes, such asthe Accelerated Climate Model for Energy (ACME), complex user derived data types (containing large numberof variables) are designed to represent the interactionsof atmosphere, ocean, land, ice, and biosphere toproject global climate under a wide variety of conditions. The following is our proposed approach to restructurethe data architecture of...
The development of algorithms and models to be used for prediction of the reliability and health monitoring of components and sensors is of great importance in aerospace, automotive and power generation industry. For this purpose metamodels have been developed that are based on physical simulations and that are able to quantify the impact of uncertainties on system behavior. These surrogate metamodels...
In this paper, the problem of identifying correlated components in a high-dimensional Gaussian vector is considered. In the setup considered, instead of having to take a full-vector observation at each time index, the observer is allowed to observe any subset or full set of components in the vector, and he has the freedom to design his sampling strategies over time. The observer aims to find an optimal...
The move of cellular communications from existing frequencies predominantly below 3 GHz up to the lower end of the mmWave bands around 28 GHz and higher, is set to change everything about how we design, test and operate such systems. The successful launch of a new radio (NR) access system is critically dependent on a correct understanding of the quasi-optical propagation behaviour of narrow beamwidth...
This paper proposes a verification approach for testing the functionality of the algorithms in ECG bio-sensor. Algorithms functionality is achieved once the medical target of the algorithm is achieved correctly within an acceptable processing time. The Pan Tompkins algorithm is one of the well-known methods, which is used to detect the QRS wave in ECG signal. The QRS detection and other design goals...
We propose a Convolutional Neural Network model to learn spatial footstep features end-to-end from a floor sensor system for biometric applications. Our model's generalization performance is assessed by independent validation and evaluation datasets from the largest footstep database to date, containing nearly 20,000 footstep signals from 127 users. We report footstep recognition performance as Equal...
Wearable sensors have the potential to enable clinical-grade ambulatory health monitoring outside the clinic. Technological advances have enabled development of devices that can measure vital signs with great precision and significant progress has been made towards extracting clinically meaningful information from these devices in research studies. However, translating measurement accuracies achieved...
Documenting system behavior explicitely using graphical models (e.g. UML activity or sequence diagrams) facilitates communication about and understanding of software systems during development or maintenance. Creating graphical models manually is a time-consuming and often error-prone task. Deriving models from system-execution traces, however, suffers from the problem of model-size explosion. We...
Iris liveness detection methods have been developed to overcome the vulnerability of iris biometric systems to spoofing attacks. In the literature, it is typically assumed that a known attack modality will be perpetrated. Then liveness models are designed using labelled samples from both real/live and fake/spoof distributions, the latter derived from the assumed attack modality. In this work it is...
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