An architectural counterpoint in Universal Everything: Fluid Bodies exhibition

ABSTRACT

This exhibition review focuses on the quest for weaving boundaries between body-form-space-and material relationship using immersive technologies. Emerging as an architectural counterpoint, Universal Everything: Fluid Bodies exhibition held in Borusan Contemporary focuses on how we perceive the motion and form in relation to it, as well as fusing senses of kinaesthetic and synaesthetic through a data driven and motion-based visual representation. Through these algorithms, the exhibition displays how neuroarchitecture reminds us of the senses of perception. The architectural counterpoint, intention as an interaction and encounter of the body with ‘the machine’ as the ‘voyeur body’ and how this observational dialogue becomes a research methodology in understanding the nature of movement in space through digital tools.     

Medical image segmentation with transform and moment based features and incremental supervised neural network

In this study, a novel incremental supervised neural network (ISNN) is proposed for the segmentation of medical images. Performance of the ISNN is investigated for tissue segmentation in medical images obtained from various imaging modalities. Two feature extraction methods based on transform and moments are comparatively investigated to segment the tissues in medical images. Two-dimensional (2D) continuous wavelet transform (CWT) and the moments of the gray-level histogram (MGH) are computed in order to form the feature vectors of ultrasound (US) bladder and phantom images, X-ray computerized tomography (CT) and magnetic resonance (MR) head images. In the 2D-CWT method, feature vectors are formed by the intensity of one pixel of each wavelet-plane of different energy bands. The MGH represents the tissues within the sub-windows by using the spatial variation of image intensities. In this study, the ISNN and Grow and Learn (GAL) network are employed for the segmentation task. It is observed that the ISNN has significantly eliminated the disadvantages of the GAL network in the segmentation of the medical images.     

ADALINE based robust control in robotics: a Riemann-Liouville fractional differintegration based learning scheme

This paper presents an approach to improve the performance of intelligent sliding model control achieved by the use of a fundamental constituent of soft computing, named Adaptive Linear Element (ADALINE). The proposed scheme is based on the fractional calculus. A previously considered tuning scheme is revised according to the rules of fractional order differintegration. After a comparison with the integer order counterpart, it is seen that the control system with the proposed adaptation scheme provides (1) better tracking performance, (2) suppression of undesired drifts in parameter evolution and (3) a very high degree of robustness and insensitivity to disturbances. The claims are justified through some simulations utilizing the dynamic model of a two degrees of freedom (DOF) direct drive robot arm and overall, the contribution of the paper is to introduce the fractional order calculus into a robust and nonlinear control problem with some outperforming features that are absent when the integer order differintegration operators are adopted.     

On the design considerations of solid-state power amplifiers for satellite communications: A systems perspective

Conventional solid-state power amplifier (SSPA) design approach isolates radio frequency (RF) design from communication theory. In this paper, a unified SSPA design approach is proposed, which optimizes SSPA parameters (bias voltage and input RF signal power) to minimize total DC power consumption while satisfying received SNR constraint specified by the link budget. The effect of SSPA nonlinearity is quantified by the error vector magnitude measured at its output and the corresponding received SNR degradation is analyzed. Using the quantitative metrics for received SNR, it is possible to evaluate highly nonlinear SSPA classes such as Class-B or deep-Class AB, which are normally not considered in conventional SSPA design approach to be used in satellite communication applications.     

Integrated Risk of Progress-Based Costs and Schedule Delays in Construction Projects

This article aims to explore the impact of the integration of risk factors into delayed milestones for construction projects. A simulation model was developed to determine the impact of schedule variability on cost estimation. To generate random scenarios a Monte Carlo Simulation (MCS) technique was applied. The developed model computes the cost impact of delayed milestone in the expected budget. Using a risk integration approach revealed the critical time frame that may lead to a budget deficit for a project. As a result, a number of cost-sensitive risk factors and schedule delays were identified for the critical time period where the risk of budget deficit increases. The method of integration proposed in this article highlights the priority of risk factors and schedule delays for construction contracts involving Payments at Event Occurrences (PEO). Consequently, the developed method can be useful for practitioners in anticipation of potential increase of costs, hence, prevention of failure due to budget deficit.     

Effect of magnetic field and temperature on the voltage-current characteristics of YBCO superconductor

Voltage-current characteristics of YBCO superconductor was studied under magnetic field up to 0.4 T at different temperatures below T_c. The critical temperature decreases and the transition width broadens under magnetic field. V-I data below T_c were fitted to a power law expression V I^(T,B) in which (T,B) is found to decrease with increase of magnetic field and temperature, gradually approaching unity as T approaches T_c, being independent of magnetic field. Similarly, (T,B) approaches unity as magnetic field increases being independent of temperature.     

Rule extraction from artificial neural networks to discover causes of quality defects in fabric production

In this paper, a novel classification rule extraction algorithm which has been recently proposed by authors is employed to determine the causes of quality defects in a fabric production facility in terms of predetermined parameters like machine type, warp type etc. The proposed rule extraction algorithm works on the trained artificial neural networks in order to discover the hidden information which is available in the form of connection weights in them. The proposed algorithm is mainly based on a swarm intelligence metaheuristic which is known as Touring Ant Colony Optimization (TACO). The algorithm has a hierarchical structure with two levels. In the first level, a multilayer perceptron type neural network is trained and its weights are extracted. After obtaining the weights, in the second level, the TACO-based algorithm is applied to extract classification rules. The main purpose of the present work is to determine and analyze the most effective parameters on the quality defects in fabric production. The parameters and their levels which give the best quality results are tried to be discovered and evaluated by making use of the proposed algorithm. It is also aimed to compare the accuracy of proposed algorithm with several other rule-based algorithms in order to present its competitiveness.