Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.     

煤与不同原料重整气化制甲醇对CO2排放的影响

通过介绍煤制甲醇对整个煤化工行业CO2排放的影响,选择以煤制甲醇工艺CO2排放为研究对象,分别叙述了煤气化制合成气,煤与天然气、煤与焦炉气以及煤与重质油重整气化制合成气联合生产甲醇过程中,吨甲醇产品工艺CO2排放情况,探讨不同煤气化形式对工艺CO2排放的影响因素,为如何通过气化工艺选型,降低煤制甲醇生产过程中工艺CO2排放量提供理论判断依据。     

Dynamic facility layout problem in footwear industry

Dynamic facility layout problem (DFLP) deals with the arrangement of machines in a site as to minimize the sum of materials handling and re-layout costs by considering multi periods. The DFLP studies in the literature provide several different algorithms and utilize the well known test problems to assess their performance. However, real life applications are overlooked. The industries such as footwear and clothing are prone to seasonal demand changes. Therefore, time horizons and layout/re-layout of the machines within the facility should be studied carefully. This study considers a footwear facility and several scenarios are generated by using the real life data. A clonal selection based algorithm is proposed to solve the real life DFLP. The performance of the algorithm, further the effect of time periods on solution quality and applicability of the results are tested and promising results are obtained.     

基于特征选择和深度信念网络的文本情感分类算法

由于人类语言的复杂性，文本情感分类算法大多都存在因为冗余而造成的词汇量过大的问题。深度信念网络（DBN）通过学习输入语料中的有用信息以及它的几个隐藏层来解决这个问题。然而对于大型应用程序来说，DBN是一个耗时且计算代价昂贵的算法。针对这个问题，提出了一种半监督的情感分类算法，即基于特征选择和深度信念网络的文本情感分类算法（FSDBN）。首先使用特征选择方法（文档频率（DF）、信息增益（IG）、卡方统计（CHI）、互信息（MI））过滤掉一些不相关的特征从而使词汇表的复杂性降低；然后将特征选择的结果输入到DBN中，使得DBN的学习阶段更加高效。将所提算法应用到中文以及维吾尔语中，实验结果表明在酒店评论数据集上，FSDBN在准确率方面比DBN提高了1.6%，在训练时间上比DBN缩短一半。     

Elitist clonal selection algorithm for optimal choice of free knots in B-spline data fitting

Data fitting with B-splines is a challenging problem in reverse engineering for CAD/CAM, virtual reality, data visualization, and many other fields. It is well-known that the fitting improves greatly if knots are considered as free variables. This leads, however, to a very difficult multimodal and multivariate continuous nonlinear optimization problem, the so-called knot adjustment problem. In this context, the present paper introduces an adapted elitist clonal selection algorithm for automatic knot adjustment of B-spline curves. Given a set of noisy data points, our method determines the number and location of knots automatically in order to obtain an extremely accurate fitting of data. In addition, our method minimizes the number of parameters required for this task. Our approach performs very well and in a fully automatic way even for the cases of underlying functions requiring identical multiple knots, such as functions with discontinuities and cusps. To evaluate its performance, it has been applied to three challenging test functions, and results have been compared with those from other alternative methods based on AIS and genetic algorithms. Our experimental results show that our proposal outperforms previous approaches in terms of accuracy and flexibility. Some other issues such as the parameter tuning, the complexity of the algorithm, and the CPU runtime are also discussed.     

Design of data feature-driven 1D/2D convolutional neural networks classifier for recycling black plastic wastes through laser spectroscopy

In this study, two types of convolutional neural network (CNN) classifiers are designed to handle the problem of classifying black plastic wastes. In particular, the black plastic wastes have the property of absorbing laser light coming from spectrometer. Therefore, the classification of black plastic wastes remains still a challenging problem compared to classifying other colored plastic wastes using existing spectroscopy (i.e., NIR). When it comes the classification problem of black plastic wastes, effective classification techniques by the laser spectroscopy of Fourier Transform-Infrared Radiation (FT-IR) with Attenuated Total Reflectance (ATR) and Raman to analyze the classification problem of black plastic wastes are introduced. Due to the strong ability of extracting spatial features and remarkable performance in image classification, 1D and 2D CNN through data features are designed as classifiers. The technique of chemical peak points selection is considered to reduce data redundancy. Furthermore, through the selection of data features based on the extracted 1D data with peak points is introduced. Experimental results demonstrate that 2DCNN classifier designed with the help of 2D data feature selection as well as 1DCNN classifier shows the best performance compared with other reported methods for classifying black plastic wastes.     

Development of a conceptual model for the selection of intelligent building systems

With the availability of a myriad of intelligent building components or products in the market, the decision to choose between them becomes significant and crucial in the configuration of building alternative. This results in placing the decision makers in the selection ‘dilemma’. This paper presents the development of a conceptual model for the selection of intelligent building systems which aims at assisting the decision makers to select the most appropriate combination of intelligent building components. The paper commences by reviewing the literature on intelligent building research. A survey is conducted to examine the criticality of selection attributes. Findings of this survey enrich the field of intelligent building research in at least two ways. Firstly, it widens the understanding of the factors, as well as their degree of importance, in affecting the selection of intelligent building systems and components. Second, the identified selection attributes form a conceptual framework which can be used to guide the selection of intelligent building components.     

从企业总部地址迁移变化看CBD的发展态势

当前的企业总部的迁移有以下几个方面特征:欠发达区域的知名企业总部向中心城市或者省会城市聚集;跨国公司在中国的中心城市设立具有总部功能的办公机构;中心城市特别是CBD已经形成总部经济的空间聚集形态.在集聚效应规律的作用下促使CBD出现规模扩大化、办公空间扩散化、职能高级化等趋势.     

南太行山山地民居解读

地处南太行山的高家台村及周围地区的自然风光壮美秀丽,民居建筑恬静自然,环境极富诗情画意,犹如世外桃源.着重论述了对村落的选址、建筑环境的形式美、村居和环境及园林的关系的理解和认识.     

A 5-GHz LC VCO with digital AAC and AFBS for 2.4 GHz ZigBee transceiver applications

A 5 GHz LC VCO (voltage-controlled oscillator) with automatic amplitude control (AAC) and automatic frequency-band selection (AFBS) for 2.4 GHz ZigBee transceivers is presented. Instead of continuous feedback loop, an alternative amplitude calibration scheme is proposed in this paper to alleviate the deficiencies inherent in the conventional approach. It helps to keep the VCO at optimum amplitude to avoid saturation of the cross-coupled transistors and therefore stabilizes the phase noise performance over process, voltage and temperature variations. For the ZigBee application with 16 frequency channels, a coarse tuning loop is added in this work to implement the frequency-band selection using the AFBS mechanism. The VCO core and the digital AAC, AFBS modules have been fully integrated in a 2.4 GHz ZigBee transceiver which was fabricated in a 0.18 μm RF-CMOS technology. The current consumption is 4.7 mA at 4.85 GHz with 1.8 V power supply and a chip area of about 0.285 mm² is occupied. The VCO is capable of operating from 4.67 GHz to 5.18 GHz and the measured phase-noise level is –120 dBc/Hz at 1 MHz offset from a 4.85 GHz carrier. The tuning sensitivity K_VCO of the VCO is about 78 MHz/V with 0.9 V control voltage.