The work reported in this paper aims to present possibility distribution model of soft data used for corporate client credit risk assessment in commercial banking by applying Type 2 fuzzy membership functions (distributions) for the purpose of developing a new expert decision-making fuzzy model for evaluating credit risk of corporate clients in a bank. The paper is an extension of previous research conducted on the same subject which was based on Type 1 fuzzy distributions. Our aim in this paper is to address inherent limitations of Type 1 fuzzy distributions so that broader range of banking data uncertainties can be handled and combined with the corresponding hard data, which all affect banking credit decision making process. Banking experts were interviewed about the types of soft variables used for credit risk assessment of corporate clients, as well as for providing the inputs for generating Type 2 fuzzy logic membership functions of these soft variables. Similar to our analysis with Type 1 fuzzy distributions, all identified soft variables can be grouped into a number of segments, which may depend on the specific bank case. In this paper we looked into the following segments: (i) stability, (ii) capability and (iii) readiness/willingness of the bank client to repay a loan. The results of this work represent a new approach for soft data modeling and usage with an aim of being incorporated into a new and superior soft-hard data fusion model for client credit risk assessment.

The main motivation for this paper is to apply LQ and LQG methodologies for quadcopter control system. The developed control system is for both the rectangular position (xy) and altitude (z) as well as the orientation (attitude - angles around the axes) based on 6-Degree of Freedom (6DOF) mathematical model. 6DOF refers to the model with 3 linear and 3 angular motions. The altitude and attitude controllers are designed and the results presented in both the continuous and the discrete time cases. For the controller design, a nonlinear mathematical model was obtained first for 6DOF. The next step was to linearize the nonlinear model in hovering mode, and the final step was the reduction of the resulted linear model to be used as starting model for the controller design. The reduced linear model was tested for controllablity and observability. The control goal was to track a spatial trajectory with the quadcopter center of gravity under environment disturbances and sensor measurement errors. For this purpose, designed LQ controller was augmented by Kalman Filter state observer. The resultant controllers provide precise and robust performance for an input reference signal and for a regulation problem. After the transient response (of order of few seconds) the tracking error is acceptable which provides safe handling even under disturbances and measurement noises. The transient response can be further reduced by controllers fine tuning.

This paper deals with the use of fuzzy logic as a support tool for evaluation of corporate client credit risk in a commercial banking environment. It defines possibilistic distribution of soft data used for corporate client credit risk assessment by applying fuzzy logic modeling, with a major goal to develop a new expert decisionmaking fuzzy model for evaluating credit risk of corporate clients in a bank. Currently, predicting a credit risk of companies is inaccurate and ambiguous, as well as affected by many internal and external factors that cannot be precisely defined. Unlike traditional methods for credit risk assessment, fuzzy logic can easily incorporate linguistic terms and expert opinions which makes it more adapted to cases with insufficient and imprecise hard data, as well as for modeling risks that are not fully understood. Fuzzy model of soft data, presented in this paper, is created based on expert experience of corporate lending of a commercial bank in Bosnia and Herzegovina. This market is very small and it behaves irrationally and often erratically and therefore makes the risk assessment and management decision making process very complex and uncertain which requires new methods for risk modeling to be evaluated. Experts were interviewed about the types of soft variables used for credit risk assessment of corporate clients, as well as for providing the inputs for generating membership functions of these soft variables. All identified soft variables can be grouped into following segments: stability, capability and readiness/willingness of the client to repay a loan. The results of this work represent a new approach for soft data usage/assessment with an aim of being incorporated into a new and superior soft-hard data fusion model for client credit risk assessment.

In this paper several methods of verification and validation process (V&V) of simulation models are discussed. In general, different set of techniques used for verification and validation vary from statistic to dynamic one. Theoretical methods presented in this paper are applicable in a real communication networks. V&V of simulation models have been mostly studied in the context of defense applications. In this paper, Kolmogorov-Smirnov test and Lilliefors test as non-parametric statistical tests are applied to the novel application layer communication protocol developed for wireless sensor networks for intruder tracking application. Protocol is tested on the newly established testbed at the Future Internet — Internet of Things Laboratory (FiT IoT-Lab) in Grenoble, France. The study demonstrated practical application of certain V&V methods.

In many systems sensors play an important role in observing environment and collecting data. This paper explains effective algorithm developed for energy savings in low power wireless sensor networks (WSNs). We consider a dynamic network, where sensors are geographically deployed in a field with different trajectories describing behavior of an object that is passing through the sensors field. The aim is to minimize number of sensors participating in object tracking to extend the network lifetime. We show significant energy savings when a binary search algorithm is used for different network scenarios. To support long lifetime of sensors with satisfactory intrusion tracking error, energy-efficient operation is the key, since energy is scarce resource. Our algorithm is suitable for implementation in the context of Internet of Things (IoT), especially in smart and green energy applications, as well as variety of campus and defense applications.

The objective of this paper is to present new and simple mathematical approach to deal with uncertainty transformation for fuzzy to random or random to fuzzy data. In particular we present a method to describe fuzzy (possibilistic) distribution in terms of a pair (or more) of related random (probabilistic) events, both fixed and variable. Our approach uses basic properties of both fuzzy and random distributions, and it assumes data is both possibilistic and probabilistic. We show that the data fuzziness can be viewed as a non uniqueness of related random events, and prove our Uncertainty Balance Principle. We also show how Zadeh’s fuzzy-random Consistency Principle can be given precise mathematical meaning. Various types of fuzzy distributions are examined and several numerical examples presented.

In this paper we extend our previous results in dual approach to analysis and simulation of a complex ecological system of preys and predators. We first define nonlinear dynamic equations Lotka-Volterra Model (LVM) with three preys and three predators and then simulate the equivalent situation with an Agent Based Model (ABM) which models a variety of species attributes and behaviors using NetLogo simulation environment for ABM model. The idea is that the LVM and ABM methods reinforce each other as the predator-prey models become more complex and their dimensionality rises. In particular LVM’s parameters, components of community matrix, can be fine tuned using ABM simulations. Dual approach may be able to answer and qualify some of the long standing ecological paradoxes.

Moving or stationary target Synthetic Aperture Radar (SAR) signatures can be analyzed and used to identify and classify target type against known stored target data base, consisting of full circle raw target data. Public target data base is used as data source, and additionally derived target signature characteristics are generated suitable for target identification. Pose, angle between position and velocity, can be derived in this process and it can be used to reduce search space and hence increase likelihood of Automatic Target Identification (ATR). Our goal in this paper is to define new methodology to analyze target data, using stored signatures as well as real time target signature, and generating variety of spatial statistics and correlations. Besides applications in defense area, there are numerous commercial applications in machine learning, augmented reality, traffic control, and facial recognition.