Performance modeling of AMR refrigerators

A system modeling approach for predicting the performance of active magnetic regenerators using a one-phase approximation is presented. The approach is described for an arbitrary AMR device independent of the magnetic refrigerant, thermal losses or magnetic waveform. A general expression for magnetic work is derived which can be used for cycles where the low-field intensity is not zero. Additionally, a means of treating the varying magnetic field waveform as a single high and low field is described. The model is applied to a permanent magnet magnetic refrigerator using water–glycol as the heat transfer fluid. Simulated results are compared to experimental data which vary by heat load, frequency and utilization. A sensitivity analysis is performed using utilization, adiabatic temperature change, effective conductivity and particle size as independent variables. Comparisons to experimental data show that reducing the calculated magnetocaloric effect by 25% provides good agreement between simulations and experimental results.     

Elevated temperature low cycle fatigue of grey cast iron used for automotive brake discs

This paper evaluates the fatigue life properties of low carbon grey cast iron (EN-GJL-250), which is widely used for automotive brake discs. Although several authors have examined mechanical and fatigue properties at room temperatures, there has been a lack of such data regarding brake discs operating temperatures. The tension, compression and low cycle fatigue properties were examined at room temperature (RT) and at brake discs’ working temperatures: 500 °C, 600 °C and 700 °C. The microstructure of the material was documented and analysed. Tensile stress–strain curves, cyclic hardening/softening curves, stress–strain hysteresis loops, and fatigue life curves were obtained for all the above-mentioned temperatures. It was concluded, that Young’s modulus is comparable with both tension and compression, but yield its strength and ultimate strength are approximately twice as great in compression than in tension. All the mechanical properties remained quite stable until 500 °C, where at 700 °C all deteriorated drastically. During fatigue testing, the samples endured at 500 °C on average at around 50% of cycles at room temperature. Similar to other materials’ properties, the cycles to failure have dropped significantly at 700 °C.     

Constant and switching gains in semi-active damping of vibrating structures

A limit cycle is the stability boundary for linear and non-linear control systems. Hamiltonian mechanics and power flow control are employed to demonstrate this property of limit cycles. The presentation begins with the concept of linear limit cycles which is extended to non-linear limit cycles. Many examples are used to demonstrate these concepts including linear and non-linear oscillators, power engineering, and an extension to a class of plane differential systems. Power flow control based on Hamiltonian mechanics is shown to be applicable to a large class of non-linear systems. Finally, eigenanalysis and flight stability for linear systems are extended to non-linear systems and is referred to as ‘the power flow principle of stability for non-linear systems’.     

PRESSURIZED ELECTROOSMOTIC DEWATERING IN A FILTER CYCLE

This paper describes the role of pressurized electroosmotic dewatering (EOD) in a filter cycle. A laboratory-scale filter-press was used to filter a highly conductive silica suspension under constant pressure, followed by washing and mechanical precompression of the filter cake and finally by pressurized EOD at constant electric current. The influence of filter cycle parameters (filtration and washing pressure and duration, mechanical pressure, and electric field intensity) on the final cake dryness and energy consumption was studied. Electrodes of different materials, forms, and surfaces were used.

The optimal conditions of each filter cycle operation were found to minimize the energy consumption during EOD and maximize the cake dryness. With mechanical pressure and electric field intensity increasing, the total energy consumption increased, but the specific energy consumption (per kg of expressed water) decreased, and the final filter cake was dryer. The pressurized EOD also used less energy than thermal drying.     

Analysis of zeotropic mixtures used in low-temperature solar Rankine cycles for power generation

This paper presents the analysis of low-temperature solar Rankine cycles for power generation using zeotropic mixtures. Three typical mass fractions 0.9/0.1 (Ma) 0.65/0.35 (Mb), 0.45/0.55 (Mc) of R245fa/R152a are chosen. In the proposed temperature range from 25 °C to 85 °C, the three zeotropic mixtures are investigated as the working fluids of the low-temperature solar Rankine cycle. Because there is an obvious temperature glide during phase change for zeotropic mixtures, an internal heat exchanger (IHE) is introduced to the Rankine cycle. Investigation shows that different from the pure fluids, among the proposed zeotropic mixtures, the isentropic working fluid Mb possesses the lowest Rankine cycle efficiency. For zeotropic mixtures a significant increase of thermal efficiencies can be gained when superheating is combined with IHE. It is also indicated that utilizing zeotropic mixtures can extend the range of choosing working fluids for low-temperature solar Rankine cycles.     

开放式windows 9x机房管理

介绍了一个开放式机房记账管理客户端软件，成功地解决了在Windows9x机房无法直接实现记账式管理的难题，实现了无管理员值守的记账式管理，使开放式机房实现了真正意义上的开放。这种管理模式完全由软件实现，配置的费用极低，提供多种保护方案，系统资源的占用极小，性能稳定，兼容性好，不对Windows9x系统和上机者作任何限制，不禁止使用各种工具软件，提供一个良好的开放式学习环境，同时还防范了各种绕过管理软件的企图，具有很高的管理安全性，是一种智能、高效、低成本的机房管理模式。     

Large scale automatic image annotation based on convolutional neural network

Automatic image annotation is one of the most important challenges in computer vision, which is critical to many real-world researches and applications. In this paper, we focus on the issue of large scale image annotation with deep learning. Firstly, considering the existing image data, especially the network images, most of the labels of themselves are inaccurate or imprecise. We propose a Multitask Voting (MV) method, which can improve the accuracy of original annotation to a certain extent, thereby enhancing the training effect of the model. Secondly, the MV method can also achieve the adaptive label, whereas most existing methods pre-specify the number of tags to be selected. Additionally, based on convolutional neural network, a large scale image annotation model MVAIACNN is constructed. Finally, we evaluate the performance with experiments on the MIRFlickr25K and NUS-WIDE datasets, and compare with other methods, demonstrating the effectiveness of the MVAIACNN.     

A high-throughput maize kernel traits scorer based on line-scan imaging

Maize kernel traits such as kernel length, kernel width and kernel number determine kernel weight and, consequently, maize yield. Therefore, the measurement of kernel traits is important for maize breeding and the evaluation of maize yield. The conventional method for measuring kernel traits is still manual, which is time consuming, costly and subjective. In this study, a novel maize kernel traits scorer (MKTS) was developed for the automatic measurement of 12 maize kernel traits based on line-scan imaging, image processing, and automatic control techniques. Here, total of 615 samples were measured to evaluate the system performance. The results showed that the MKTS was capable of evaluating maize kernel traits with the mean absolute percentage error of the manual and automatic measurements less than 5% and the measurement efficiency of approximately 72 s for the measurement of 6 ears. In conclusion, this high-throughput scorer will provide maize scientists with a novel tool to assist in maize functional genetics and maize breeding.     

Adaptive modulation recognition based on the evolutionary algorithms

Nowadays, the development of classification algorithms gives the ability to improve Automatic Modulation Recognition (AMR) effectively. This paper presents a novel modulation recognition algorithm based on clustering approach. Generally, we aim to distinguish multicarrier modulation OFDM from single-carrier modulations. In this regard, two statistics of the amplitude of the received signal are calculated at the output of a quadrature mixer as key features. The extracted features of training data points are submitted to the clustering algorithm, then, centroids for single-carrier and multicarrier modulations are assessed. Afterwards, each point of testing dataset is dedicated to its nearest centroid based on Euclidian distance and the recognition is accomplished. Simulation results demonstrate that the algorithm is beneficial in a wide range from low to high SNRs.     

聚驱后井网滞留区剩余油挖潜方式研究

根据试验区的动、静态数据，首先用Petrel地质建模软件建立试验区相控地质模型，然后应用CMG软件中的STARS模块进行数值模拟，在水驱阶段和聚驱阶段历史拟合的基础上，分析了聚驱后剩余油分布特征，针对聚驱后剩余油主要分布在聚驱井网分流线处的特征，选择组合轮换式周期注采方式，对分流线处的剩余油进行挖潜；设计了组合轮换式注采周期分别为4个月、6个月、8个月和10个月4种方案，对4种方案的剩余油挖潜效果进行预测。结果表明．聚驱后剩余油主要分布在聚驱井网分流线处，选择组合轮换式周期注采方式挖潜分流线处的剩余油，注采周期为6个月开采时，阶段采出程度为O．98％，净收益为1707．40万元，吨聚增油量为31．41t／t，投入与产出比为1：2．29，通过开发指标对比和经济效果评价，确定此方案为最佳方案。