Rapid determination of the fracture toughness of metallic materials using circumferentially notched bars

Three types of material whose fracture toughness tests were previously performed by using circumferentially notched bars, namely (1) a dual-phase steel with three different morphologies; (2) an Al-Zn-Mg-Cu-wrought alloy; and (3) Al-Si-cast alloys with three different Si contents, were investigated in terms of accuracy and reliability of the testing method. Also, the advantages of using circumferentially notched bars for fracture toughness determination of metallic materials were discussed. With the help of stress concentration factors, which are associated with the bluntness of the notch, correction factors for the fracture toughness calculations are derived. The corrected fracture toughness values are found to be close to the uncorrected ones, implying that the testing procedure is reliable.     

Salt and pepper noise filtering with fuzzy-cellular automata

A new image denoising algorithm is proposed to restore digital images corrupted by impulse noise. It is based on two dimensional cellular automata (CA) with the help of fuzzy logic theory. The algorithm describes a local fuzzy transition rule which gives a membership value to the corrupted pixel neighborhood and assigns next state value as a central pixel value. The proposed method removes the noise effectively even at noise level as high as 90%. Extensive simulations show that the proposed algorithm provides better performance than many of the existing filters in terms of noise suppression and detail preservation. Also, qualitative and quantitative measures of the image produce better results on different images compared with the other algorithms.     

Sharing in teams of heterogeneous,collaborative learning agents

This paper is focused on the effects of sharing knowledge and collaboration of multiple heterogeneous, intelligent agents (hardware or software) which work together to learn a task. As each agent employs a different machine learning technique, the system consists of multiple knowledge sources and their respective heterogeneous knowledge representations. Collaboration between agents involves sharing knowledge to both speed up team learning, as well as refine the team's overall performance and group behavior. Experiments have been performed that vary the team composition in terms of machine learning algorithms, learning strategies employed by the agents, and sharing frequency for a predator‐prey cooperative pursuit task. For lifelong learning, heterogeneous learning teams were more successful than homogeneous learning counterparts. Interestingly, sharing increased the learning rate, but sharing with higher frequency showed diminishing results. Lastly, knowledge conflicts are reduced over time the more sharing takes place. These results support further investigation of the merits of heterogeneous learning. © 2008 Wiley Periodicals, Inc.     

High-performance temperature-programmed microfabricated gas chromatography columns

This paper reports the first development of high-performance, silicon-glass micro-gas chromatography (/spl mu/GC) columns having integrated heaters and temperature sensors for temperature programming, and integrated pressure sensors for flow control. These 3-m long, 150-/spl mu/m wide and 250-/spl mu/m deep columns, integrated on a 3.3 cm square die, were fabricated using a silicon-on-glass dissolved wafer process. Demonstrating the contributions to heat dissipation from conduction, convection, and radiation with and without packaging, it is shown that using a 7.5-mm high atmospheric pressure package reduces power consumption to about 650 mW at 100/spl deg/C, while vacuum packaging reduces the steady-state power requirements to less than 100 mW. Under vacuum conditions, 600 mW is needed for a temperature-programming rate of 40/spl deg/C/min. The 2300 ppm//spl deg/C TCR of the temperature sensors and the 50 fF/kPa sensitivity of the pressure sensors satisfy the requirements needed to achieve reproducible separations in a /spl mu/GC system. Using these columns, highly resolved 20-component separations were obtained with analysis times that are a factor of two faster than isothermal responses.     

Dynamics of temperature dependent optical properties of tissue:dependence on thermally induced alteration

Thermal damage in heated bovine myocardial tissue is assessed from measured changes in total reflection and transmission of light. Mathematical expressions, based on random walk analysis of light propagation within tissue slabs, are used to relate the diffuse reflection and transmittance to the absorption coefficient, μ_a , and effective scattering coefficient, μ'_s for samples of myocardial tissue which were subjected to rapid step changes in temperature. Time-dependent changes in μ'_s indicate two processes, one with a fast and temperature-dependent rate the other with a slow and apparently temperature-independent rate. For final temperatures above 56.8°C and for the first 500 s after the temperature change, the optical parameters are well fit by exponential forms that exhibit temperature-dependent time constants as predicted by Arrhenius reaction rate theory of thermal damage. The scattering changes are associated with an apparent activation energy, ΔE, of 162 kJ/mole and a frequency constant, A, of 3×10²³ s^-1 . This method provides a means for estimating optical coefficients which are needed to assess laser tissue dosimetry     

Phylogenetic and Ontogenetic Learning in a Colony of Interacting Robots

The objective of this paper is to describe the development of a specific theory of interactions and learning among multiple robots performing certain tasks. One of the primary objectives of the research was to study the feasibility of a robot colony in achieving global objectives, when each individual robot is provided only with local goals and local information. In order to achieve this objective the paper introduces a novel cognitive architecture for the individual behavior of robots in a colony. Experimental investigation of the properties of the colony demonstrates its ability to achieve global goals, such as the gathering of objects, and to improve its performance as a result of learning, without explicit instructions for cooperation. Since this architecture is based on representation of the likes and dislikes of the robots, it is called the Tropism System Cognitive Architecture. This paper addresses learning in the framework of the cognitive architecture, specifically, phylogenetic and ontogenetic learning by the robots. The results show that learning is indeed possible with the Tropism Architecture, that the ability of a simulated robot colony to perform a gathering task improves with practice and that it can further improve with evolution over successive generations. Experimental results also show that the variability of the results decreases over successive generations.     

Tropism-based cognition: a novel software architecture for agents in colonies

A novel architecture for agents in colonies has been developed in order to investigate certain forms of group interaction. Specifically, we are interested in the extent to which overall goals for a colony can be achieved when each agent is only aware of limited local goals, whether the architecture allows for emergence of unexpected behaviour, and whether explicit communication among agents facilitates or hinders task performance. The architecture supports several forms of learning. Large colonies of agents (as many as 100) have been studied in simulation experiments, where they carried out fetch-and-carry tasks in the presence of predators and with limited energy reserves. In addition, a physical colony of four agents has been fabricated with the same architecture, to ensure that the behaviours observed in simulation were also present in the hardware implementations.