The emergence of remote sensing images with high spatial resolution has increased the advancement of image-based information extraction methods. One of the rapidly developing approaches for mapping and analyzing high spatial resolution images is the object-based approach, also known as geographic object-based image analysis (GEOBIA). This development makes it possible to quickly and accurately distinguish between vegetated and non-vegetated objects in vegetation study. This study aims to (1) create a ruleset to discriminate vegetated and non-vegetated objects from a high spatial resolution image, (2) apply the GEOBIA approach to map vegetated and non-vegetated objects, and (3) calculate the accuracy of the mapping results. The GEOBIA approach was applied to a WorldView-2 image (2 m pixel size and eight multispectral bands) of the Clungup Mangrove Conservation area, Malang, East Java, Indonesia. We assessed the ability of all of the WorldView-2 image bands for discriminating the targeted objects. The segmentation process in GEOBIA used a multi-resolution segmentation algorithm using the normalized difference vegetation index (NDVI), and the image classification used a rule-based classification technique. The green, red, and near-infrared bands can effectively distinguish the targeted objects based on the developed ruleset. The classification result shows that the vegetated and non-vegetated classes fall within their corresponding objects on the image. We implemented an area-based accuracy assessment that assesses both positional and thematic accuracy of the mapping result, based on the visual interpretation of the pansharpened WV-2 image (0.5 m pixel size) as a reference for the accuracy assessment. This process results in a 74,06% accuracy, meaning that the combination of GEOBIA and WorldView-2 image produces high accuracy of vegetated and non-vegetated objects map.

This paper proposes an anonymity based mechanism for providing privacy in IoT environment. Proposed scheme allows IoT entities to anonymously interacting and authenticating with each other, or even proving that they have trustworthy relationship without disclosing their identities. Authentication is based on an anonymous certificates mechanism where interacting IoT entities could unlinkably prove possession of a valid certificate without revealing any incorporated identity-related information, thereby preserving their privacy and thwarting tracking and profiling attacks. Through a security analysis, we demonstrate the reliability of our solution.

This paper deals with the robust stabilization of infinite dimensional systems subjected to stochastic and deterministic perturbations. First, we give conditions providing the stability of the parameterized system. Then, we investigate the maximization of the stability radius by state feedback. We establish conditions for the existence of suboptimal controllers. Using these conditions we characterize the supreme achievable stability radius via an infinite dimensional Riccati equation.

Soil-structure interaction is the key to study the behavior of the structure under static or dynamic loading. The pile foundation is adopted to transfer the load from the structure to the soil when the structure is embedded in a weak soil stratum. Soil-pile system has a nonlinear behavior, thus, it is more complicated to understand. This study focuses on the numerical investigations of interaction soil–pile–structure system (ISPS) and interaction soil–pile system (ISP) under lateral loads. The nonlinear static analysis is carried out considering the lateral capacity of ISPS and ISP system under lateral loading using the pushover analysis, with a parametric study. The results show that in most cases, the lateral capacity of the fixed system is low compared to lateral capacity in ISPS system when increasing the axial load, pile diameter and length of the pile in all types of sand.

One of the main data-driven challenges when assessing bearing health is that training and test samples must be drawn from the same probability distribution. Indeed, it is difficult and almost rare to witness such a phenomenon in practical applications due to the constantly changing working conditions of rotating machines. In addition, collecting sufficient deterioration samples from the bearing life cycle is not possible due to the huge memory requirements and processing costs. As a result, accelerated life tests are believed to be the primary alternatives to such a situation. However, and unfortunately, the recorded samples always are subject to lack of real patterns. Therefore, in this paper, a transfer learning approach is performed to solve such kind of problem where PRONOSTICO dataset is used to assess the current procedures.

The issue of robustness of stability has been prominent in the literature on control theory over the last two decades. An important state-space approach to robustness analysis is the stability radii theory. The robust stability problems of infinite dimensional systems subjected to stochastic bounded structured perturbations have been studied using the stability radius approach. In the applications it is important to study these problems in the case where the perturbations operator structure are unbounded, because it covers the case of partial differential equations with boundary and ...

Multi-agent systems research community has developed solutions to model dynamic and complex systems. Those solutions have generated a growing interest in a variety of domains such as robotics or distributed controls. It is therefore important to build solutions to analyze the basic mechanisms of multi-agent systems such as interaction. In this paper, we formalize the epistemic interaction, we propose to use default theory to describe state transitions. The introduction of such semantics allows characterizing the verification of agent behavior when dealing with new information. Therefore, we introduce semantics to establish a basis for exploiting the properties of the agent behavior and its messages.We propose to formalize and verify the response of an agent when it receives a new information from another agent of its environment. Our approach extends the actions initiated in the mA ^* [5] language by applying the Reiter’s default rules [32]. A simple example is used to illustrate our approach.

In the petroleum industry, equipments must be maintained properly to meet the adequate reliability standards in order to achieve the desired business goals in terms of productivity, safety and environmental protection. This article offers a new approach focused on risk analysis to select a better maintenance strategy. The proposed approach consists of three stages. In the first step, we identify the accident scenarios that could lead to the loss of production and damage to the environment. In the second step, we estimate the frequency of occurrence of these scenarios. In the third step, we calculate the economic losses and environmental taxes. Finally, an appropriate maintenance strategy is proposed, taking into account the evaluation results obtained by the previous steps. A case study illustrates the proposed approach and shows that the latter constitutes an important decision support tool to improve the existing maintenance strategy to comply with regulations and standards in term of productivity, reduction of costs and environmental protection

This paper proposes a new approach of multi-spectral image compression based on the combination of the particle swarm optimization (PSO) and the discrete wavelet transforms (DWT). In the first stage, the PSO is used to reduce the redundancies in the spectral domain. In fact, the PSO transforms a given multispectral image to optimize the energy in the first band. Despite to the complexity of this kind of approach, the transformed multispectral image is easily computed by multiplying a de-correlation matrix and the input multispectral image. The de-correlation matrix is estimated via a PSO evolution derived by a defined fitness function. In the second stage, the compressed data, related to the input multispectral image, is computed from the transformed multispectral image using an efficient 2D-DWT based algorithm. In addition to this compression approach, the original multispectral image can be recovered using a decompression algorithm. Experimental results show the validity of our proposed approach. These significant results are evaluated according to Peak signal-to-noise ratio (PSNR), compression ratio (CR) and bits per pixel (bpp) metrics.

As a key wind turbine element, the blade is a determining factor for reliability and efficiency of the turbine system and a main source of complicated and critical loads. In this present investigation, the strength behavior of a small composite blade for H-type Darrieus wind turbine was studied. Firstly, three-dimensional (3D) modelling of the blade structure with NACA 0018 airfoil profile was established. Secondly, the Finite element analysis (FEA) technique was conducted to perform the strength analysis of the blade structure subjected to extreme climatic conditions by means SOLIDWORKS SIMULATION software. This analysis was performed to identify the resistance, stiffness and reliability of the composite blade structure. The results from FEA identify that the structure of the blade will not be subjected to structural failure during WT operation (0 ~ 19 m/s) according to maximum principal stress equivalent (von Mises) and maximum displacements.