• The KIPS Transactions:PartB
• /
• v.19B no.1
• /
• pp.27-30
• /
• 2012

One of the most fundamental functions required for an intelligent agent is to estimate its current position based upon uncertain sensor data. In this paper, we explain the implementation of a robot localization system using Particle filters, which are the most effective one of the probabilistic localization methods, and then present the result of experiments for evaluating the performance of our system. Through conducting experiments to compare the effect of the noise-free model with that of the noisy state transition model considering inherent errors of robot actions, we show that it can help improve the performance of the Particle filter localization to apply a state transition model closely approximating the uncertainty of real robot actions.

• Computers and Concrete
• /
• v.6 no.2
• /
• pp.133-153
• /
• 2009

Discrete element modeling (DEM) in concrete technology is concerned with design and use of models that constitute a schematization of reality with operational potentials. This paper discusses the material science principles governing the design of DEM systems and evaluates the consequences for their operational potentials. It surveys the two families in physical discrete element modeling in concrete technology, only touching upon probabilistic DEM concepts as alternatives. Many common DEM systems are based on random sequential addition (RSA) procedures; their operational potentials are limited to low configuration-sensitivity features of material structure, underlying material performance characteristics of low structure-sensitivity. The second family of DEM systems employs concurrent algorithms, involving particle interaction mechanisms. Static and dynamic solutions are realized to solve particle overlap. This second family offers a far more realistic schematization of reality as to particle configuration. The operational potentials of this family involve valid approaches to structure-sensitive mechanical or durability properties. Illustrative 2D examples of fresh cement particle packing and pore formation during maturation are elaborated to demonstrate this. Mainstream fields of present day and expected application of DEM are sketched. Violation of the scientific knowledge of to day underlying these operational potentials will give rise to unreliable solutions.

• Wind and Structures
• /
• v.5 no.6
• /
• pp.527-542
• /
• 2002

Wind-excited vibrations of slender structures can induce fatigue damage and cause structural failure without exceeding ultimate limit state. Unfortunately, the growing importance of this problem is coupled with an evident lack of simple calculation criteria. This paper proposes a mathematical method for evaluating the crosswind fatigue of slender vertical structures, which represents the dual formulation of a parallel method that the authors recently developed with regard to alongwind vibrations. It takes into account the probability distribution of the mean wind velocity at the structural site. The aerodynamic crosswind actions on the stationary structure are caused by the vortex shedding and by the lateral turbulence, both schematised by spectral models. The structural response in the small displacement regime is expressed in closed form by considering only the contribution of the first vibration mode. The stress cycle counting is based on a probabilistic method for narrow-band processes and leads to analytical formulae of the stress cycles histogram, of the accumulated damage and of the fatigue life. The extension of this procedure to take into account aeroelastic vibrations due to lock-in is carried out by means of ESDU method. The examples point out the great importance of vortex shedding and especially of lock-in concerning fatigue.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1993.10a
• /
• pp.240-247
• /
• 1993

The precise prediction of reserved carrying capacity of bridge as a system is extremely difficult especially when the bridges are highly redundant and significantly deteriorated or damaged. This paper is intended to propose a new approach for the evaluation of reserved system carrying capacity of bridges in terms of equivalent system-strength, which may be defined as a bridge system-strength corresponding to the system reliability of the bridge. This can be derived from an inverse process based on the concept of FOSM form of system reliability index. It may be emphasized that this approach is very useful for the evaluation of the deterministic system redundancy and reserve strength which are measured in terms of either probabilistic system redundancy factor and reserve factor or deterministic system redundancy factor and reserve factor. The system reliability of bridges is formulated as a parallel-series model obtained from the FAM(Failure Mode Approach) based on the major failure mechanisms. AFOSM and IST methods are used for the reliability analysis of the proposed models. The proposed approach and method for the system redundancy and reserve safety/strength are applied to the safety assessment of actual RC and steel box-girder bridges. The results of the evaluation of reserved system safety or bridge system-strength in terms of the system redundancy and the system safety/strength are significantly different from those of element reliability-based or conventional methods.

• Earthquakes and Structures
• /
• v.18 no.1
• /
• pp.45-56
• /
• 2020

The application of critical excitation method with displacement-based objective function for multi degree of freedom (MDOF) systems is investigated. To this end, a new critical excitation method is developed to find the critical input motion of a MDOF system as a synthetic accelerogram. The upper bound of earthquake input energy per unit mass is considered as a new constraint for the problem, and its advantages are discussed. Considering this constraint, the critical excitation method is then used to generate synthetic accelerograms for MDOF models corresponding to three shear buildings of 10, 16, and 22 stories. In order to demonstrate the reliability of generated accelerograms to estimate dynamic response of the structures, three target ground motions with considerable level of energy contents are selected to represent "real critical excitation" of each model, and the method is used to re-generate these ground motions. Afterwards, linear dynamic analyses are conducted using these accelerograms along with the generated critical excitations, to investigate the key parameters of response including maximum displacement, maximum interstory drift, and maximum absolute acceleration of stories. The results show that the generated critical excitations can make an acceptable estimate of the structural behavior compared to the target ground motions. Therefore, the method can be reliably implemented to generate critical excitation of the structure when real one is not available.

• The Pure and Applied Mathematics
• /
• v.27 no.3
• /
• pp.137-155
• /
• 2020

Redundancy is commonly employed to improve system reliability. In most situations, components in the standby configurations are assumed statistically similar but independent. In many realistic models, all parts in standby are not treated as identical as they have different failure possibilities. The operational structure of the system has subsystem-1 with five identical components working under 2-out-of-5: G; policy, and the subsystem-2 has two units and functioning under 1-out-of-2: G; policy. Failure rates of units of subsystems are constant and assumed to follow an exponential distribution. Computed results give a new aspect to the scientific community to adopt multi-dimension repair in the form of the copula.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.1
• /
• pp.321-344
• /
• 2017

Smart homes are the new generation of homes where pervasive computing is employed to make the lives of the residents more convenient. Human activity recognition (HAR) is a fundamental task in these environments. Since critical decisions will be made based on HAR results, accurate recognition of human activities with low uncertainty is of crucial importance. In this paper, a novel HAR method based on a difference of convex programming (DCP) problem is represented, which manages to handle uncertainty. For this purpose, given an input sensor data stream, a primary belief in each activity is calculated for the sensor events. Since the primary beliefs are calculated based on some abstractions, they naturally bear an amount of uncertainty. To mitigate the effect of the uncertainty, a DCP problem is defined and solved to yield secondary beliefs. In this procedure, the uncertainty stemming from a sensor event is alleviated by its neighboring sensor events in the input stream. The final activity inference is based on the secondary beliefs. The proposed method is evaluated using a well-known and publicly available dataset. It is compared to four HAR schemes, which are based on temporal probabilistic graphical models, and a convex optimization-based HAR procedure, as benchmarks. The proposed method outperforms the benchmarks, having an acceptable accuracy of 82.61%, and an average F-measure of 82.3%.

• International Journal of Aeronautical and Space Sciences
• /
• v.11 no.4
• /
• pp.326-337
• /
• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• The Journal of Korea Robotics Society
• /
• v.12 no.4
• /
• pp.402-410
• /
• 2017

Acoustic based localization is essential to operate autonomous robotic systems in underwater environment where the use of sensorial data is limited. This paper proposes a localization method using artificial underwater acoustic sources. The proposed method acquires directional angles of acoustic sources using time difference of arrivals of two hydrophones. For this purpose, a probabilistic approach is used for accurate estimation of the time delay. Then, Gaussian sum filter based SLAM technique is used to localize both acoustic sources and underwater vehicle. It is performed by using bearing of acoustic sources as measurement and inertial sensors as prediction model. The proposed method can handle directional ambiguity of time difference based source localization by generating Gaussian models corresponding to possible locations of both front and back sides. Through these processes, the proposed method can provide reliable localization method for underwater vehicles without any prior information of source locations. The performance of the proposed method is verified by experimental results conducted in a real sea environment.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.12
• /
• pp.5422-5441
• /
• 2016

Despite the abundant literature in the field, there is still the need to find a time-efficient, highly accurate, easy to deploy and robust localization algorithm for real use. The algorithm only involves minimal human intervention. We propose an enhanced Received Signal Strength Indicator (RSSI) based positioning algorithm for Wi-Fi capable devices, called the Dynamic Weighted Evolution for Location Tracking (DWELT). Due to the multiple phenomena affecting the propagation of radio signals, RSSI measurements show fluctuations that hinder the utilization of straightforward positioning mechanisms from widely known propagation loss models. Instead, DWELT uses data processing of raw RSSI values and applies a weighted posterior-probabilistic evolution for quick convergence of localization and tracking. In this paper, we present the first implementation of DWELT, intended for 1D location (applicable to tunnels or corridors), and the first step towards a more generic implementation. Simulations and experiments show an accuracy of 1m in more than 81% of the cases, and less than 2m in the 95%.