An artificial negotiating agent modeling approach embedding dynamic offer generating and cognitive layer

In this paper, a dynamic offer generating unit and cognitive layer are suggested for artificial agents based negotiation systems. For this purpose, first, adaptive time and behavior dependent tactics are developed taking advantages from time continuity and dynamics aspects (features) integrated in their modeling. Then, a negotiation strategy (bilateral over single issue) based on these two tactics is suggested. Second, a cognitive negotiation model for a negotiator agent is developed using Win-Lose and Win-Win orientations which will be formed based on personality factors. Afterwards, an experimental validation is conducted for testing applicability of time dependent tactics, the effect of offering time, and the effect of cognitive orientations (Win-Lose and Win-Win) on final negotiation outcomes. The results prove the applicability of the suggested time and behavior dependent tactics as well as the proposed cognitive negotiation model.     

Hot Corrosion Behavior and Microstructural Change of Al-Gradient CoNiCrAlYSi Coatings, Produced by LVPS and Diffusional Processes

Conventional and gradient CoNiCrAlYSi coatings were produced by using low vacuum plasma spray and an additional step of diffusional over aluminizing (pack cementation) techniques on an Inconel-738 substrate. Hot corrosion of these coatings was investigated using Na₂SO₄–20wt%NaVO₃ molten salt at 880?°C for 800?h. Hot corrosion rate was determined by measuring the weight gain of the specimens at regular intervals for a duration of 20?h. X-ray diffraction, field emission scanning electron microscopy and electron probe micro analysis techniques were used to determine the nature of phases, investigation of the thermally grown oxide, examination of the surface attack and determination of the elemental distribution. The gradient coating showed better performance by re-healing alumina scale due to its possession of more β phase as Al reservoir. Results indicated that pack cementation process caused an increase in amount of aluminum-rich β phase and better hot corrosion properties of gradient coatings owing to the Al enrichment in the outer layer and rapid formation of protective oxide on the surface.     

Knowledge Acquisition and Design Using Semantics and Perception: A Case Study for Autonomous Robots

Neural Processing Letters - The pervasive use of artificial intelligence and neural networks in several different research fields has noticeably improved multiple aspects of human life. The...     

Singular Value Decomposition Combined With Analytical Method for Tokamak Plasma Tomography

Several arrays of soft X-ray detectors are used with the Fourier expansion for angular direction and Zernicke polynomial on radial direction during an analytical method tomography. A truncation of two expansions is necessary. We used singular value decomposition in order to solve the set of equations. This technique reconstructs better images of tomography.     

Numerical simulation of direct methanol fuel cells using lattice Boltzmann method

In this study Lattice Boltzmann Method (LBM) as an alternative of conventional computational fluid dynamics method is used to simulate Direct Methanol Fuel Cell (DMFC). A two dimensional lattice Boltzmann model with 9 velocities, D2Q9, is used to solve the problem. The computational domain includes all seven parts of DMFC: anode channel, catalyst and diffusion layers, membrane and cathode channel, catalyst and diffusion layers. The model has been used to predict the flow pattern and concentration fields of different species in both clear and porous channels to investigate cell performance. The results have been compared well with results in literature for flow in porous and clear channels and cell polarization curves of the DMFC at different flow speeds and feed methanol concentrations.     

A machine learning based intelligent vision system for autonomous object detection and recognition

Existing object recognition techniques often rely on human labeled data conducting to severe limitations to design a fully autonomous machine vision system. In this work, we present an intelligent machine vision system able to learn autonomously individual objects present in real environment. This system relies on salient object detection. In its design, we were inspired by early processing stages of human visual system. In this context we suggest a novel fast algorithm for visually salient object detection, robust to real-world illumination conditions. Then we use it to extract salient objects which can be efficiently used for training the machine learning-based object detection and recognition unit of the proposed system. We provide results of our salient object detection algorithm on MSRA Salient Object Database benchmark comparing its quality with other state-of-the-art approaches. The proposed system has been implemented on a humanoid robot, increasing its autonomy in learning and interaction with humans. We report and discuss the obtained results, validating the proposed concepts.     

Hybrid credit ranking intelligent system using expert system and artificial neural networks

The main goal of all commercial banks is to collect the savings of legal and real persons and allocate them as credit to industrial, services and production companies. Non repayment of such credits cause many problems to the banks such as incapability to repay the central bank’s loans, increasing the amount of credit allocations comparing to credit repayment and incapability to allocate more credits to customers. The importance of credit allocation in banking industry and it’s important role in economic growth and employment creation leads the development of many models to evaluate the credit risk of applicants. But many of these models are classic and are incapable to do credit evaluation completely and efficiently. Therefore the demand to use artificial intelligence in this field has grown up. In this paper after providing appropriate credit ranking model and collecting expert’s knowledge, we design a hybrid intelligent system for credit ranking using reasoning-transformational models. Expert system as symbolic module and artificial neural network as non-symbolic module are components of this hybrid system. Such models provide the unique features of each components, the reasoning and explanation of expert system and the generalization and adaptability of artificial neural networks. The results of this system demonstrate hybrid intelligence system is more accurate and powerful in credit ranking comparing to expert systems and traditional banking models.     

A human-like visual-attention-based artificial vision system for wildland firefighting assistance

In this work we contribute to development of a “Human-like Visual-Attention-based Artificial Vision” system for boosting firefighters’ awareness about the hostile environment in which they are supposed to move along. Taking advantage from artificial visual-attention, the investigated system’s conduct may be adapted to firefighter’s way of gazing by acquiring some kind of human-like artificial visual neatness supporting firefighters in interventional conditions’ evaluation or in their appraisal of the rescue conditions of people in distress dying out within the disaster. We achieve such a challenging goal by combining a statistically-founded bio-inspired saliency detection model with a Machine-Learning-based human-eye-fixation model. Hybridization of the two above-mentioned models leads to a system able to tune its parameters in order to fit human-like gazing of the inspected environment. It opens appealing perspectives in computer-aided firefighters’ assistance boosting their awareness about the hostile environment in which they are supposed to evolve. Using as well various available wildland fires images’ databases as an implementation of the investigated concept on a 6-wheeled mobile robot equipped with communication facilities, we provide experimental results showing the plausibility as well as the efficiency of the proposed system.     

Autonomous knowledge acquisition based on artificial curiosity: Application to mobile robots in an indoor environment

This paper describes an autonomous system for knowledge acquisition based on artificial curiosity. The proposed approach allows a humanoid robot to discover, in an indoor environment, the world in which it evolves, and to learn autonomously new knowledge about it. The learning process is accomplished by observation and by interaction with a human tutor, based on a cognitive architecture with two levels. Experimental results of deployment of this system on a humanoid robot in a real office environment are provided. We show that our cognitive system allows a humanoid robot to gain increased autonomy in matters of knowledge acquisition.     

Experimental investigation of turbulent heat transfer and flow characteristics of SiO2/water nanofluid within helically corrugated tubes

Experiments were conducted to investigate the effect of nanofluid on turbulent heat transfer and pressure drop inside concentric tubes. Water and SiO₂ with mean diameter of 30 nm were chosen as base fluid and nano-particles, respectively. Experiments were performed for plain tube and five roughened tube with various heights and pitches of corrugations. Results show that adding the nano-particles in tube with high height and small pitch of corrugations augments the heat transfer significantly with negligible pressure drop penalty. It is discussed on relative Nusselt number and thermal performance of heat exchanger.