Modeling and simulation of solar pond floor heating system

This paper presents the development of modeling, simulation and analysis of a solar pond floor heating system. The developed computer simulation has been used to study the potential of using such a system under climatic conditions in Jordan. It was found that the solar pond heating system could meet most of the winter season in Jordan with Solar fraction in the range 80–100% for at least 2 months of the season. It must be emphasized that the feasibility of such a system is its utilization in district heating and not for individual households due to the limiting economical factors of high capital cost of the solar pond for small domestic applications.     

Minimum Initial Marking Estimation of Labeled Petri Nets Based on GRASP Inspired Method (GMIM)

Abstract

This paper deals with the problem of estimating the Minimum Initial Marking (MIM) of Labeled Petri Nets (L-PN). By the observation of a sequence of labels, we determine the set of possible MIMs related to a given L-PN through an approach based on GRASP (Greedy Randomized Adaptive Search Procedure) inspired method – GMIM. The objective is to get the maximum of feasible MIMs by exploring the search space and giving best solutions for real time cyber systems in short time. We consider four basic assumptions during the reasoning: (i) the L-PN structure is known; (ii) for each transition of L-PN, a label is associated, (iii) the label sequence is known, and (iv) all transitions of L-PN are observable. We show the validity and efficiency of our approach by applying the proposed GMIM metaheuristic to two validation examples: Initialization of two parallel machines (example widely cited in literature) and resources allocation in a monitoring problem via mobile robot network.     

Driver information system: a combination of augmented reality,deep learning and vehicular Ad-hoc networks

Improving traffic safety is one of the important goals of Intelligent Transportation Systems (ITS). In vehicle-based safety systems, it is more desirable to prevent an accident than to reduce severity of injuries. Critical traffic problems such as accidents and traffic congestion require the development of new transportation systems. Research in perceptual and human factors assessment is needed for relevant and correct display of this information for maximal road traffic safety as well as optimal driver comfort. One of the solutions to prevent accidents is to provide information on the surrounding environment of the driver. Augmented Reality Head-Up Display (AR-HUD) can facilitate a new form of dialogue between the vehicle and the driver; and enhance ITS by superimposing surrounding traffic information on the users view and keep drivers view on roads. In this paper, we propose a fast deep-learning-based object detection approaches for identifying and recognizing road obstacles types, as well as interpreting and predicting complex traffic situations. A single convolutional neural network predicts region of interest and class probabilities directly from full images in one evaluation. We also investigated potential costs and benefits of using dynamic conformal AR cues in improving driving safety. A new AR-HUD approach to create real-time interactive traffic animations was introduced in terms of types of obstacle, rules for placement and visibility, and projection of these on an in-vehicle display. The novelty of our approach is that both global and local context information are integrated into a unified framework to distinguish the ambiguous detection outcomes, enhance ITS by superimposing surrounding traffic information on the users view and keep drivers view on roads.     

Robust Platelet Logistics Planning in Disaster Relief Operations Under Uncertainty: a Coordinated Approach

Resource sharing, as a coordination mechanism, can mitigate disruptions in supply and changes in demand. It is particularly crucial for platelets because they have a short lifespan and need to be transferred and allocated within a limited time to prevent waste or shortages. Thus, a coordinated model comprised of a mixed vertical-horizontal structure, for the logistics of platelets, is proposed for disaster relief operations in the response phase. The aim of this research is to reduce the wastage and shortage of platelets due to their critical role in wound healing. We present a bi-objective location-allocation robust possibilistic programming model for designing a two-layer coordinated organization strategy for multi-type blood-derived platelets under demand uncertainty. Computational results, derived using a heuristic ε-constraint algorithm, are reported and discussed to show the applicability of the proposed model. The experimental results indicate that surpluses and shortages in platelets remarkably declined following instigation of a coordinated disaster relief operation.     

Optimal Newton–Secant like methods without memory for solving nonlinear equations with its dynamics

We construct two optimal Newton–Secant like iterative methods for solving nonlinear equations. The proposed classes have convergence order four and eight and cost only three and four function evaluations per iteration, respectively. These methods support the Kung and Traub conjecture and possess a high computational efficiency. The new methods are illustrated by numerical experiments and a comparison with some existing optimal methods. We conclude with an investigation of the basins of attraction of the solutions in the complex plane.     

Performance evaluation of legacy QCN for multicast and multiple unicast traffic transmission

A multicast congestion control scheme is an interesting feature to control group communication applications such as teleconferencing tools and information dissemination services. This paper addresses a comparison between multiple unicast and multicast traffic congestion control for Carrier Ethernet. In this work, we proposed to study the quantized congestion notification (QCN), which is a layer 2 congestion control scheme, in the case of multicast traffic and multiple unicast traffic. Indeed, the QCN has recently been standardized as the IEEE 802.1Qau Ethernet Congestion Notification standard. This scheme is evaluated through simulation experiments, which are implemented by the OMNeT++ framework. This paper evaluates the reaction point start time congestion detection, feedback rate, loss rate, stability, fairness and scalability performance of the QCN for multicast traffic transmission and multiple unicast traffic transmission. This paper also draws a parallel between QCN for multicast traffic transmission and that for multiple unicast traffic transmission. Despite the benefit of integrating the multicast traffic, results show that performance could degrade when the network scales up. The evaluation results also show that it is probable that the feedback implosion problem caused by the bottlenecks could be solved if we choose to set the queue parameter Qeq threshold value at a high value, 75% of the queue capacity for instance. Copyright © 2016 John Wiley & Sons, Ltd.     

A 3.3 V, 10 Bits, Clock-Feedthrough Compensated Switched-Current Second Order Sigma-Delta Modulator

This article presents a low-pass sigma-delta modulator for Analogue-to-Digital conversion. The circuit uses a switched-current technique which presents a well known drawback called clock feedthrough. This phenomenon induces an error on the output signal value. In order to cancel the clock feedthrough effect, we use a new method based on a current feedback loop. The circuit is designed in 0.8 μm AMS “Austria Mikro Systems” single poly CMOS process. Measurements of the modulator are performed under A/D converters characterisation system, and show 55 dB dynamic range at 2.048 MHz sampling rate with 8 kHz input frequency bandwidth. These characteristics are suitable for audio applications.     

Fault diagnosis using partial syndromes: a modified Hopfield neural network approach

This paper presents a modified Hopfield neural network (HNN) for solving the system-level fault diagnosis problem which aims at identifying the set of faulty nodes. This problem has been extensively studied in the last three decades. Nevertheless, identifying the set of all faulty nodes using only partial syndromes, i.e. when some of the testing or comparison outcomes are missing prior to initiating the diagnosis phase, remains an outstanding research issue. The new HNN-based diagnosis algorithm does not require any prior learning or knowledge about the system, nor about any faulty situation, hence providing a better generalisation performance. Results from a thorough simulation study demonstrate the effectiveness of the HNN-based fault diagnosis algorithm in terms of diagnosis correctness, diagnosis latency and diagnosis scalability, for randomly generated diagnosable systems of different sizes and under various fault scenarios. We have also conducted extensive simulations using partial syndromes. Simulations showed that the HNN-based diagnosis performed efficiently, i.e. diagnosis correctness was around 99% when at most half of the test or comparison outcomes are missing, making it a viable alternative to existing diagnosis algorithms.     

Local commute-time guided MDS for 3D non-rigid object retrieval

In this paper, we propose a 3D non-rigid shape retrieval method based on canonical shape analysis. Our main idea is to transform the problem of non-rigid shape retrieval into a rigid shape retrieval problem via the well-known multidimensional scaling (MDS) approach and random walk on graphs. We first segment the non-rigid shape into local partitions based on its salient features. Then, we calculate a local MDS problem for each partition, where the local commute time distance is used as weighting function in order to preserve local shape details. Finally, we aggregate the set of local MDS problems as a global constrained problem. The constraint is formulated using the biharmonic function between local salient features. In contrast to MDS method, the proposed local MDS is computationally efficient, parameters free and gives isometry-invariant forms with minimum features distortion. Due to these advantageous properties, the proposed method achieved good retrieval accuracy on non-rigid shape benchmark datasets.     

Effect of Interlayer Thickness on Joint Formation Between Ti-6Al-4V and Mg-AZ31 Alloys

The joining of a Ti-6Al-4V alloy to a Mg-AZ31 alloy was performed using Ni electroplated coatings during the TLP bonding process. In this work, different coating thicknesses were used ranging from 1 to 20 μm. The effect of the coat thickness on microstructural developments and mechanical properties was studied. The bonded specimens were examined by metallographic examination, scanning electron microscopy, and X-ray diffraction analysis. It was observed that as the coat thickness increased from 1 to 12 μm, the joint shear strength increased from 9 to 47 MPa. A further increase in coat thickness had a detrimental effect on the bond strength, and a lower value of 11 MPa was recorded. The mechanism of joint formation includes three stages: solid-state diffusion, eutectic formation, and isothermal solidification.