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。     

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。     

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.     

考虑布线资源松弛的X结构Steiner最小树算法

为了进一步考虑X结构,并充分利用障碍内可用布线资源,文中提出考虑布线资源松弛的X结构Steiner最小树算法.为了能够求解离散问题,在粒子的更新操作中引入交叉算子和变异算子.通过构建查找表,为整个算法流程提供快速的信息查询.提出角点选取策略,通过引入一些障碍角点,使粒子满足约束.最后构建精炼策略,进一步提高最终布线树的质量.实验表明,文中算法充分利用障碍内可用布线资源,有效缩短总布线长度,取得较佳的总布线长度.     

Conductor selection and economic analysis of D.R. congo–guinea ±800 kV UHVDC transmission project

Africa is embracing new opportunities featured with industrialization, urbanization and regionalization. Based on co-development of ‘Electricity, Mining, Metallurgy, Industry and Trade’ and grids interconnection proposed by Global Energy Interconnection Development and Cooperation Organization (GEIDCO), the high-quality hydropower resource of the Congo River can be exploited in large scale under the wide-range interconnected framework of African Energy Interconnection (AEI), forging a new engine for Africa economy. The transmission distance of the Congo River hydropower reaches 6,000 km at its farthest end in North Africa, which brings forth challenges to economics of proposed projects. Under this novel continental energy interconnection scheme in Africa, economics of those projects have not yet been in detail studied. This paper has implemented China’s mature engineering experiences and analytical tools of UHVDC project planning into the AEI structure, through exploring the economic behavior of ultra-long distance UHVDC projects in the scope of conductor selection in the Congo River hydropower transmission for the first time, and has provided concerned parties with a technical and analytical results of their economics comparison. This paper has chosen the D.R. Congo - Guinea ±800 kV UHVDC project as a typical example. Its preliminary system planning is introduced and three types of conductor are selected for scheme comparison. Later in this paper, the transmission loss, total investment and equivalent annual cost of the project have been calculated and analyzed. In the final part, sensitivity analysis results of the annual cost to utilization hours, transmission loss, loss tariff and construction cost has been provided