Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2‐LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search‐based LBG (CS‐LBG), Firefly‐based LBG (FF‐LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2‐LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS‐LBG, FA‐LBG, and JPEG2000 methods. The experimental values revealed that the L2‐LBG process yielded effective compression performance with a better‐quality reconstructed image. 相似文献
We are investigating semantically configurable model-driven engineering (MDE). The goal of this work is a modelling environment that supports flexible, configurable modelling
notations, in which specifiers can configure the semantics of notations to suit their needs and yet still have access to the
types of analysis tools and code generators normally associated with MDE. In this paper, we describe semantically configurable code generation for a family of behavioural modelling notations. The family includes variants of statecharts, process algebras, Petri Nets,
and SDL88. The semantics of this family is defined using template semantics, which is a parameterized structured operational semantics in which parameters represent semantic variation points. A specific notation is derived
by instantiating the family’s template semantics with parameter values that specify semantic choices. We have developed a
code-generator generator (CGG) that creates a suitable Java code generator for a subset of derivable modelling notations.
Our prototype CGG supports 26 semantics parameters, 89 parameter values, and 7 composition operators. As a result, we are
able to produce code generators for a sizable family of modelling notations, though at present the performance of our generated
code is about an order of magnitude slower than that produced by commercial-grade generators. 相似文献
Plasma-sprayed molten molybdenum particles (∼40 μm in diameter) were photographed impinging at high velocity (∼140 m/s) on
a glass substrate at room temperature. An optical sensor detected thermal radiation emitted by a droplet as it approached
the substrate and activated a time delay unit. After a selected time interval, an Nd:YAG laser was triggered, emitting a 5
ns pulse that provided illumination for a charge-coupled device (CCD) camera to photograph the impacting droplet through a
long-range microscope. By varying the delay before pulsing the laser, different stages of droplet deformation were recorded.
Impacting droplets spread into a thin circular film that ruptured and broke into small fragments. An optical detector recording
thermal radiation from the impacting droplet gave a signal that increased as the droplet spread out, reached a maximum when
the liquid film began to rupture, and decreased as portions of the droplet recoiled because of surface tension and then flew
out of view of the photodetector. 相似文献
The surface force-pore flow (SF-PF) model of reverse osmosis transport and the extended and modified form (the MD-SF-PF model) have been employed to predict the performance of four aromatic polyamide (FilmTec, FT30)reverse osmosis membranes. The evaluation is based on a comparison of model predictions with experimental data. Dilute sodium chloride-water solution experimental data were used to estimate model parameters. The models are then used to predict flux and separation at various operating pressures and concentrations. Membrane performance (i.e., separation and permeate flux) can be well predicted by the MD-SF-PF model while the SF-PF model predicts the performance for the sodium chloride system less satisfactorily. 相似文献
Precipitation is one of the most important components of the hydrologic cycle as it is required for multi-objective applications including flood estimation, drought monitoring, watersheds management, hydrology, agriculture, etc. Therefore, its estimation and modeling via a suitable method is a challenging task for hydrologists. The present study seeks to model monthly precipitation at two stations located in Iran. Two artificial intelligence (AI)-based models consisting of multivariate adaptive regression splines (MARS) and k-nearest neighbors (KNN) were used as the modeling techniques. In doing so, nine single-input scenarios under limited climatic data are implemented using minimum, maximum, and mean air temperatures, dew point temperature, station pressure, vapor pressure, relative humidity, wind speed, and antecedent precipitation data. The attained results illustrate that the performance of single MARS and KNN is relatively poor when modeling the monthly precipitation. Additionally, this study develops hybrid models to enhance the precipitation modeling through combining the MARS and KNN models with three diverse types of the time series (TS) models, namely autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA). The most important justification for integrating the models applied is that the AI and TS-based models are respectively capable of modeling the non-linear and linear terms of the hydrological variables such as precipitation. It is therefore necessary to be considered both of the aforementioned terms in the modeling procedure. A performance comparison of the single and hybrid models denotes the higher accuracy of hybrid models than the single ones. However, the hybrid models generated by combining the KNN and the TS models used are the best-performing models.
Viscosity is a parameter that plays a pivotal role in reservoir fluid estimations. Several approaches have been presented in the literature (Beal, 1946; Khan et al, 1987; Beggs and Robinson, 1975; Kartoatmodjo and Schmidt, 1994; Vasquez and Beggs, 1980; Chew and Connally, 1959; Elsharkawy and Alikhan, 1999; Labedi, 1992) for predicting the viscosity of crude oil. However, the results obtained by these methods have significant errors when compared with the experimental data. In this study a robust artificial neural network (ANN) code was developed in the MATLAB software environment to predict the viscosity of Iranian crude oils. The results obtained by the ANN and the three well-established semi-empirical equations (Khan et al, 1987; Elsharkawy and Alikhan, 1999; Labedi, 1992) were compared with the experimental data. The prediction procedure was carried out at three different regimes: at, above and below the bubble-point pressure using the PVT data of 57 samples collected from central, southern and offshore oil fields of Iran. It is confirmed that in comparison with the models previously published in literature, the ANN model has a better accuracy and performance in predicting the viscosity of Iranian crudes. 相似文献
Eggs are a good source of high quality protein and knowing their quality (physical and chemical properties) during storage is of great importance. Thus, the aim of this research was to design a computer vision system to assess egg freshness during storage time. To this end, 210 intact eggs were collected and stored for 30 days under room conditions (25?±?2 °C and 20?±?3%). After imaging, every other day, some internal and external quality characteristics including yolk height, yolk and albumen pH, yolk and albumen density and Haugh unit (HU) were measured as destructive parameters and area index (D) egg weight as non-destructive parameters. Based on Pearson correlation coefficients, area index were significantly correlated with all destructive variables (p?<?0.05). In order to predict egg freshness, artificial neural network was trained by Levenberg–Marquardt, scaled conjugate gradient, Bayesian regulation, resilient and radial basis algorithms. The best result of artificial neural network for HU and albumen pH prediction was achieved by the Levenberg–Marquardt algorithm with the correlation coefficient of 0.93 and 0.87, respectively. 相似文献