Fuzzy transfer point location problem: a possibilistic unconstrained nonlinear programming approach

Recently, the transfer point location problem with weighted demand points and uniformly distributed coordinates has been introduced. In the real world problems, such as disaster situations, different points of an area might be demand point locations with different possibility degrees. So, it is necessary to develop more applicable models for these kinds of problems. In this paper, a new transfer point location problem with weighted demand points and fuzzy coordinates is developed. The proposed model is formulated as a fuzzy unconstrained nonlinear programming in which decision variables are obtained as fuzzy numbers. Due to the complexity of the developed model, a new fuzzy logic controller is designed based on the derived fuzzy decision variables to infer the optimum or near-optimum values for decision variables. Finally, a numerical example is presented and its results are compared with the optimum solutions of the problem in order to illustrate the efficiency of the proposed model.     

A General Class of Heuristics for Minimum Weight Perfect Matching and Fast Special Cases with Doubly and Triply Logarithmic Errors

We give a class of heuristic algorithms for minimum weight perfect matching on a complete edge-weighted graph K(V) satisfying the triangle inequality, where V is a set of an even number, n, of vertices. This class is a generalization of the Onethird heuristics, the hypergreedy heuristic, and it possibly employs any given exact or approximate perfect matching algorithm as an auxiliary heuristic to an appropriate subgraph of K(V). In particular, by using the heuristic of Gabow et al. [3] as its auxiliary heuristic, our algorithm can obtain a solution whose weight is at most times the weight of the optimal solution in time, or a solution with an error of in O(n ² ) time. Received July 21, 1994; revised November 28, 1995.     

Design of CMOS Ternary Latches

This paper describes the design methodology of latches with three stable operating points. Open-loop analysis is used to obtain insight into how a conventional binary latch structure can be modified to yield a ternary latch. Four novel ternary latch structures, compatible with a standard CMOS process, are presented. Properties of each latch, including robustness of the ternary behavior, speed, and power dissipation, are described. Measurement results of four RS ternary flip-flops based on the proposed latch structures, fabricated in a standard 0.18-mum CMOS process, are presented. Maximum operating frequency and skew tolerance are reported for each of the four latches     

Highly Sensitive Humidity Sensor Using Pd/Porous GaAs Schottky Contact

This paper investigates the suitability of porous GaAs as a semiconductor material for sensing humidity. The authors have developed two types of sensors based on Pd/porous GaAs and Pd/GaAs Schottky contacts for humidity measurements. It was found that the porosity on GaAs wafer promoted the sensing properties of the contact used as highly sensitive humidity sensor toward different amounts of relative humidity operated at room temperature. On the contrary, the Pd/GaAs sample operated at room temperature exhibited negligible sensitivity to relative humidity. The advantages of using porous GaAs for Schottky humidity sensor are the following: high sensitivity, low response time, and insignificant dependence on temperature. Current-voltage (I-V) characteristics of the Pd/porous GaAs Schottky humidity sensor exhibited a saturation current value of 8.5times10^-10 A under dry condition (5% relative humidity). This was increased to 7.0times10^-9 A when submitted to a relative humidity of 25%. The saturation current was further increased considerably to 3.0times10^-7 A as the relative humidity was increased to 95%. This is more than two orders of magnitude increase in saturation current compared to dry condition. A parameter called humidity sensitivity was defined using the current value at a fixed forward voltage of 0.2 V to present the sensitivity of the sensor. Response times are reported to discuss the adsorption and desorption characteristics of the device. Pd/porous GaAs sensor operated at room temperature showed a fast response time of 2 s and a sensitivity value of 93.5% in the presence of 25% relative humidity. Furthermore, the influence of increase in relative humidity as well as heating effects on the responsivity of the sensor is described. Scanning electron microscopy analysis of the Pd/porous GaAs sample exhibited highly porous structures     

Deep HDR Video from Sequences with Alternating Exposures

A practical way to generate a high dynamic range (HDR) video using off‐the‐shelf cameras is to capture a sequence with alternating exposures and reconstruct the missing content at each frame. Unfortunately, existing approaches are typically slow and are not able to handle challenging cases. In this paper, we propose a learning‐based approach to address this difficult problem. To do this, we use two sequential convolutional neural networks (CNN) to model the entire HDR video reconstruction process. In the first step, we align the neighboring frames to the current frame by estimating the flows between them using a network, which is specifically designed for this application. We then combine the aligned and current images using another CNN to produce the final HDR frame. We perform an end‐to‐end training by minimizing the error between the reconstructed and ground truth HDR images on a set of training scenes. We produce our training data synthetically from existing HDR video datasets and simulate the imperfections of standard digital cameras using a simple approach. Experimental results demonstrate that our approach produces high‐quality HDR videos and is an order of magnitude faster than the state‐of‐the‐art techniques for sequences with two and three alternating exposures.     

Low-cost and compact design method for reversible sequential circuits

The Journal of Supercomputing - Reversible logic plays an important role in nanotechnology-based systems; therefore, it has become an interesting topic for many researchers in this field. Although...     

Deep Adaptive Sampling for Low Sample Count Rendering

Recently, deep learning approaches have proven successful at removing noise from Monte Carlo (MC) rendered images at extremely low sampling rates, e.g., 1–4 samples per pixel (spp). While these methods provide dramatic speedups, they operate on uniformly sampled MC rendered images. However, the full promise of low sample counts requires both adaptive sampling and reconstruction/denoising. Unfortunately, the traditional adaptive sampling techniques fail to handle the cases with low sampling rates, since there is insufficient information to reliably calculate their required features, such as variance and contrast. In this paper, we address this issue by proposing a deep learning approach for joint adaptive sampling and reconstruction of MC rendered images with extremely low sample counts. Our system consists of two convolutional neural networks (CNN), responsible for estimating the sampling map and denoising, separated by a renderer. Specifically, we first render a scene with one spp and then use the first CNN to estimate a sampling map, which is used to distribute three additional samples per pixel on average adaptively. We then filter the resulting render with the second CNN to produce the final denoised image. We train both networks by minimizing the error between the denoised and ground truth images on a set of training scenes. To use backpropagation for training both networks, we propose an approach to effectively compute the gradient of the renderer. We demonstrate that our approach produces better results compared to other sampling techniques. On average, our 4 spp renders are comparable to 6 spp from uniform sampling with deep learning‐based denoising. Therefore, 50% more uniformly distributed samples are required to achieve equal quality without adaptive sampling.     

Connectionist approaches for solubility prediction of n-alkanes in supercritical carbon dioxide

Carbon dioxide injection is a known promising and economical technology for improving oil recovery. Despite its immense effect on oil recovery, the application of this technique in modern recovery industry has been limited due to poor solubility of n-alkanes in supercritical CO₂. Therefore, it is very consequential to investigate the solubility of different n-alkanes in supercritical CO₂. Since experimental methods for measuring the solubility of n-alkanes in supercritical CO₂ at different temperatures and pressures are not economical and usually take a long time, feasibility of applying intelligent tools in the solubility prediction of different n-alkanes in supercritical CO₂ at pressures up to 45.9 MPa was conducted in this study. For this purpose, two models including an artificial neural network and an adaptive neuro-fuzzy interference system (ANFIS) both trained with particle swarm optimization (PSO) algorithm were used for simulating this process. Calculated mole fractions of n-alkanes in supercritical CO₂ from ANFIS–PSO model were excellently consistent with actual measured values. Moreover, comparison between these models and Chrastil semiempirical correlation show superiority and accuracy of the proposed ANFIS–PSO approach. Results of this study indicate that ANFIS–PSO method is a powerful technique for predicting solubility of n-alkanes in supercritical CO₂.

     

People Choice Modelling for Evacuation of Tall Buildings

Modelling people behaviour during emergencies has become an essential issue in attempting to increase safety aspects in buildings. This paper evaluates people’s choice behaviour for evacuation of tall buildings. A Stated Preference (SP) questionnaire was designed to understand underlying factors behind people behaviour and predict the likelihood of selecting evacuation lifts as opposed to stairs. Various scenarios including six different navigational cases, three levels for the density of people on stairs, three different number of people in the lift lobby and three vertical positions for refuge floors were administrated to 566 participants. A mixed logit model approach was then used to investigate how those factors influence the occupant’s decision-making as well as to capture the heterogeneity of different preferences among people. Traditionally, lifts were not allowed to be used in case of emergency, but the results indicate that people would tend to choose evacuation lifts in situations when they are suggested as the main exit option, and situations when stairs are overcrowded. Thus, if people are navigated by dynamic signs to use evacuation lifts, the percentage of lift users could go approximately from 70% to 80% for refuge floors between 15 and 55, respectively. In contrast, in situations when people have to make a decision between using lifts or stairs to evacuate, stairwells with fewer people as well as overcrowded refuge floors could lead to a decision in favour of stairs. This study represents the first SP experiment combining people decisions, pre-event opinions and beliefs related to evacuation lifts and stairs to understand their route choices for evacuation from tall buildings. The findings of this study can be used in the development of behavioural models for evacuation simulations of tall buildings.