Decreasing luminance contrast on window walls as well as users’ interventions: Using LED lighting system

Mr Mehdi Amirkhani presented his research and case studies on improved lighting design in commercial spaces through daylight control and innovative lighting placement. This technical meeting was organised by IESANZ Queensland Chapter on April 7, 2016.

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

A vector measure for the intelligence of a question-answering (Q-A) system

The problem of quantification of intelligence of humans, and of intelligent systems, has been a challenging and controversial topic. IQ tests have been traditionally used to quantify human intelligence based on results of test designed by psychologists. It is in general very difficult to quantify intelligence. In this paper the authors consider a simple question-answering (Q-A) system and use this to quantify intelligence. The authors quantify intelligence as a vector with three components. The components consist of a measure of knowledge in asking questions, effectiveness of questions asked, and correctness of deduction. The authors formalize these parameters and have conducted experiments on humans to measure these parameters

Artificial intelligence approaches to network management: recent advances and a survey

With increased number of new services and users being added to the communication network, management of such networks becomes crucial to provide assured quality of service. Finding skilled managers is often a problem. To alleviate this problem and also to provide assistance to the available network managers, network management has to be automated. Many attempts have been made in this direction and it is a promising area of interest to researchers in both academia and industry. In this paper, a review of the management complexities in present day networks and artificial intelligence approaches to network management are presented. Published by Elsevier Science B.V.

Swarm intelligence algorithms for integrated optimization of piezoelectric actuator and sensor placement and feedback gains

Swarm intelligence algorithms are applied for optimal control of flexible smart structures bonded with piezoelectric actuators and sensors. The optimal locations of actuators/sensors and feedback gain are obtained by maximizing the energy dissipated by the feedback control system. We provide a mathematical proof that this system is uncontrollable if the actuators and sensors are placed at the nodal points of the mode shapes. The optimal locations of actuators/sensors and feedback gain represent a constrained non-linear optimization problem. This problem is converted to an unconstrained optimization problem by using penalty functions. Two swarm intelligence algorithms, namely, Artificial bee colony (ABC) and glowworm swarm optimization (GSO) algorithms, are considered to obtain the optimal solution. In earlier published research, a cantilever beam with one and two collocated actuator(s)/sensor(s) was considered and the numerical results were obtained by using genetic algorithm and gradient based optimization methods. We consider the same problem and present the results obtained by using the swarm intelligence algorithms ABC and GSO. An extension of this cantilever beam problem with five collocated actuators/sensors is considered and the numerical results obtained by using the ABC and GSO algorithms are presented. The effect of increasing the number of design variables (locations of actuators and sensors and gain) on the optimization process is investigated. It is shown that the ABC and GSO algorithms are robust and are good choices for the optimization of smart structures.

Patent intelligence and its implications for patent productivity

The prevailing hypercompetitive environment has made it essential for organizations to gather competitive intelligence from environmental scanning. The knowledge gained leads to organizational learning, which stimulates increased patent productivity. This paper highlights five practices that aid in developing patenting intelligence and empirically verifies to what extent this organizational learning leads to knowledge gains and financial gains realized from consequent higher patent productivity. The model is validated based on the perceptions of professionals with patenting experience from two of the most aggressively patenting sectors in today’s economy, viz., IT and pharmaceutical sectors (n=119). The key finding of our study suggests that although organizational learning from environmental scanning exists, the application of this knowledge for increasing patent productivity lacks due appreciation. This missing link in strategic analysis and strategy implementation has serious implications for managers which are briefly discussed in this paper.

Spectral-spatial MODIS image analysis using swarm intelligence algorithms and region based segmentation for flood assessment

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time-series analysis of satellite images utilizing pixel spectral information for image clustering and region based segmentation for extracting water covered regions. MODIS satellite images are analyzed at two stages: before flood and during flood. Multi-temporal MODIS images are processed in two steps. In the first step, clustering algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to distinguish the water regions from the non-water based on spectral information. These algorithms are chosen since they are quite efficient in solving multi-modal optimization problems. These classified images are then segmented using spatial features of the water region to extract the river. From the results obtained, we evaluate the performance of the methods and conclude that incorporating region based image segmentation along with clustering algorithms provides accurate and reliable approach for the extraction of water covered region.

Synthesis and luminescence properties of Sm3+ doped CaTiO3 nanophosphor for application in white LED under NUV excitation

CaTiO3:Sm3+ (1-11 mol%) nanophosphors were successfully synthesized by a low temperature solution combustion method LCS]. The structural and morphological properties of the phosphors were studied by using Powder X-ray diffractometer (PXRD), Fourier transform infrared (FTIR), X-ray photo electron spectroscopy (XPS), scanning electron microscope (SEM) and transmission electron microscopy (TEM). TEM studies indicate that the size of the phosphor is similar to 20-35 nm. Photoluminescence (PL) properties of Sm3+ (1-11 mol%) doped CaTiO3 for NUV excitation (407 nm) was studied in order to investigate the possibility of its use in White light emitting diode (WLED) applications. The emission spectra consists of intra 4f transitions of Sm3+, such as (4)G(5/2) -> H-6(5/2) (561 nm), (4)G(5/2) -> H-6(7/2) (601-611 nm), (4)G(5/2) -> H-6(9/2) (648 nm) and (4)G(5/2) -> H-6(11/2) (703 nm) respectively. Further, the emission at 601-611 nm show strong orange-red emission and can be applied to the orange-red emission of phosphor for the application for near ultra violet (NUV) excitation. Thermoluminescence (TL) of the samples irradiated with gamma source in the dose range 100-500 Gy was recorded at a heating rate of 5 degrees C s(-1). Two well resolved glow peaks at 164 degrees C and 214 degrees C along with shouldered peak at 186 degrees C were recorded. TL intensity increases up to 300 Gy and thereafter, it decreases with further increase of dose. The kinetic parameters namely activation energy (E), frequency factor (s) and order of kinetics were estimated and results were discussed in detail. (C) 2014 Elsevier B.V. All rights reserved.

A Novel and Scalable LED Communication System

We describe our novel LED communication infrastructure and demonstrate its scalability across platforms. Our system achieves 50 kilo bits per second on very simple SoCs and scales to megabits bits per second rates on dual processor based mobile phone platforms.

Emergent Intelligence Based QoS Routing in MANET

Mobile Ad hoc Networks (MANETs) are self-organized, infrastructureless, decentralized wireless networks consist of a group of heterogeneous mobile devices. Due to the inherent characteristics of MANE -Ts, such as frequent change of topology, nodes mobility, resource scarcity, lack of central control, etc., makes QoS routing is the hardest task. QoS routing is the task of routing data packets from source to destination depending upon the QoS resource constraints, such as bandwidth, delay, packet loss rate, cost, etc. In this paper, we proposed a novel scheme of providing QoS routing in MANETs by using Emergent Intelligence (El). The El is a group intelligence, which is derived from the periodical interaction among a group of agents and nodes. We logically divide MANET into clusters by centrally located static agent, and in each cluster a mobile agent is deployed. The mobile agent interacts with the nodes, neighboring mobile agents and static agent for collection of QoS resource information, negotiations, finding secure and reliable nodes and finding an optimal QoS path from source to destination. Simulation and analytical results show that the effectiveness of the scheme. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.ore/licenscs/by-nc-nd/4.0/). Peer-review under responsibility of the Conference Program Chairs