菠菜表面农残的SERS无损检测方法研究

本文针对菠菜农药残留量超标问题,建立了一种能够快速、无损地对菠菜表面农药残留量进行检测的方法。利用表面增强拉曼光谱技术（SERS,Surface Enhanced Raman Scattering）采集含农药（溴氰菊酯）和不含的两组菠菜样本的SERS光谱,结合一阶导加Norris求导法、Savitzky-Golay卷积求导法进行光谱预处理,使用判别分析法和距离匹配法建立定性分析模型,成功区分两组菠菜样本,模型预测准确率最高可达到100%;使用偏最小二乘法建立了菠菜表面溴氰菊酯残留量的多个定量分析模型,研究发现差谱模型效果最好,其校正集相关系数（Rc）和预测集相关系数（Rp）分别为0.9908,0.9552,校正均方根误差（RMSEC）和预测均方根误差（RMSEP）分别为0.0802,0.23。结果表明,运用SERS方法能够很好地实现菠菜表面溴氰菊酯残留量的无损定性和定量检测分析,且无需任何前处理,对其它农产品中农药残留量的快速、无损检测具有借鉴意义。      

一种正方化有序树图布局算法

针对现有正方化树图有序性差、有序树图正方化性能差的问题,提出一种正方化有序树图布局算法——SOTLA,首先将多个节点捆绑组合构造大权值节点;然后按平均长宽比最接近1和沿最短边方向填充路径限制的原则对节点进行布局;通过对每一组合节点分治并重复上述过程进行布局,最终完成所有单个节点的布局.将该算法应用于50组具有层次结构和数据值大小悬殊特点的农药残留量检测数据中,得到了较好的可视化效果.将该算法与现有的Squarified,Pivot,Strip和Spiral 4种布局算法就平均长宽比、连续性、可读性、稳定性和稳定的平衡性5项评价指标进行对比,实验结果表明,文中算法在连续性和平均长宽比性能上具有明显优势,其可读性、稳定性和稳定的平衡性指标也表现良好.     

基于可变局部边缘模式的绿色植物物种识别

绿色植物物种识别在生态环境保护、中药制取、农业与园艺应用等方面有着重要的应用前景和潜在的经济价值。边缘是一种直观、简单、有效的对象识别特征,文中针对传统边缘算子方向少,尺度单一,操作不灵活等缺点,使用一种具有多尺度、多方向属性的圆形局部边缘模式算子(varied local edge pattern,VLEP)提取植物图像的边缘特征,同时考虑阈值细分的思想,在自建的绿色植物物种数据库上进行的实验结果表明,该算法不仅可以弥补传统算子由于边缘方向少、尺度单一导致丢失边缘信息的缺陷,同时可以有效用于绿色植物物种识别。     

基于参数化模型的FPGA时钟网络设计和优化

本文在FPGA时钟网络（Clock Distributed Network,CDN）关键结构尺寸的参数化建模基础上,提出一种针对全定制FPGA CDN的设计和优化方法.本文所建立的参数化模型将结构尺寸分为拓扑结构和电路与互连两类,分别给出了这两类尺寸参数的设计原则.在标准CMOS 0.13μm工艺下,对H树型、鱼骨型以及混合型三种类型时钟网络设计了2组结构参数,分别代表优化前和优化后,对比分析延时、偏斜、功耗和面积等性能参数.实验结果显示：混合型结构在绝对延时和时钟偏斜上减小最多,分别达到20.89%和63.20%;鱼骨型结构的面积减小达到50.14%;H树型结构的绝对延时和功耗则均降低了7.37%和8.33%.以上结果充分证明了本文所提设计优化方法的有效性.     

A heuristic master planning algorithm for supply chains that consider substitutions and commonalities

This study focuses on solving the master planning problem for supply chains by considering substitutions and common components. Such problems address the difficulties involved in synchronizing manufacturing processes and transporting of materials, semi-finished products, and final products along a supply chain and facilitate decision-making related to the effective and efficient use of production and transportation capacities over periods ranging from one month to one year. This study considers product structures with multiple final products, given substitutions and common components. For situations in which the capacity of the supply chain network partners is limited, the model constructed in this study is able to plan all demands and minimize delay costs, substitutions, and the costs of production, transportation, substitution, and inventory holding. Mixed integer programming is a popular way to solve supply chain master planning problems. However, as such problems increase in complexity, the MIP model becomes insolvable due to the time and computer resources it requires. Therefore, this study proposes a heuristic algorithm, called the Dynamic Search BOM Substitution Algorithm (DSBSA), to solve the supply chain master planning problem efficiently and effectively. DSBSA sorts demands according to the necessary final products, due dates, shared capacities, and substitution conditions, to name several possible criteria. Then, DSBSA plans the demands individually, using a minimum cost production tree. If the demand cannot be filled completely using the original BOMs (Bill of Materials), DSBSA substitutes another BOM to fill the demands. This study develops two algorithms to search the substitute BOM: one searches for substitutions in the BOM’s materials levels, this maintaining most of the materials in the original BOM; the other searches for substitutions and uses them to fill bottlenecks, allowing insufficient levels of materials to be detected and supplemented. To show the effectiveness and efficiency of DSBSA, a prototype was constructed and tested to demonstrate the power of DSBSA using complexity and computational analysis.     

When and where to transfer for Bayesian network parameter learning

Learning Bayesian networks from scarce data is a major challenge in real-world applications where data are hard to acquire. Transfer learning techniques attempt to address this by leveraging data from different but related problems. For example, it may be possible to exploit medical diagnosis data from a different country. A challenge with this approach is heterogeneous relatedness to the target, both within and across source networks. In this paper we introduce the Bayesian network parameter transfer learning (BNPTL) algorithm to reason about both network and fragment (sub-graph) relatedness. BNPTL addresses (i) how to find the most relevant source network and network fragments to transfer, and (ii) how to fuse source and target parameters in a robust way. In addition to improving target task performance, explicit reasoning allows us to diagnose network and fragment relatedness across Bayesian networks, even if latent variables are present, or if their state space is heterogeneous. This is important in some applications where relatedness itself is an output of interest. Experimental results demonstrate the superiority of BNPTL at various scarcities and source relevance levels compared to single task learning and other state-of-the-art parameter transfer methods. Moreover, we demonstrate successful application to real-world medical case studies.     

DPD: An intelligent parallel hybrid algorithm for economic load dispatch problems with various practical constraints

The Economic Load Dispatch (ELD) problem has attracted much attention in the field of electric power system. This paper proposes a novel parallel hybrid optimization methodology aimed at solving ELD problem with various generator constraints. The proposed approach combines the Differential Evolution (DE) and Particle Swarm Optimization (PSO). Initially the whole population (in increasing order of fitness) is divided into three groups - Inferior Group, Mid Group and Superior Group. DE is employed in the inferior and superior groups, whereas PSO is used in the mid-group. The proposed method is called DPD as it uses DE-PSO-DE on a population in parallel manner. Two strategies namely Elitism (to retain the best obtained values so far) and Non-redundant search (to improve the solution quality) have been employed in DPD cycle. Moreover, the suitable mutation strategy for both DEs used in DPD is investigated over a set of 8 popular mutation strategies. Combination of 8 mutation strategies generated 64 different variants of DPD. Top 4 DPDs are investigated through IEEE CEC 2006 functions. Based on the performance analysis, best DPD is reported and further used in solving four different typical test systems of ELD problem. Numerical and graphical results indicate the efficiency, convergence characteristic and robustness of proposed DPD.     

A perceptual computing-based method to prioritize failure modes in failure mode and effect analysis and its application to edible bird nest farming

A failure mode and effect analysis (FMEA) procedure that incorporates a novel Perceptual Computing (Per-C)–based Risk Priority Number (RPN) model is proposed in this paper. The proposed model considers linguistic uncertainties and vagueness of words, because it is more natural to use words, instead of numerals, for an FMEA user to express his/her knowledge when he/she provides an assessment. Therefore, it is important to consider the inherited uncertainties in words used by humans for assessment as an additional risk factor in the entire FMEA reasoning process. As such, we propose to use Per-C to analyze the uncertainties in words provided by different FMEA users. There are three potential sources of risks. Firstly, the risk factors of Severity (S), Occurrence (O), and Detection (D) are graded using words by each FMEA user, and indicated as interval type-2 fuzzy sets (IT2FSs). Secondly, the relative importance of S, O, and D are reflected by the weights given by each FMEA user in words, which are indicated as IT2FSs. Thirdly, the expertise level of each FMEA user is reflected by words, which are expressed as IT2FSs too. The proposed Per-C-RPN model allows these three sources of risks from each FMEA user to be considered and combined in terms of IT2FSs. A case study related to edible bird nest farming in Borneo Island is reported. The results indicate the effectiveness of the proposed model. In summary, this paper contributes to a new Per-C-RPN model that utilizes imprecise assessment grades pertaining to group decision making in FMEA.     

Measuring the performance improvement of a double generally weighted moving average control chart

This study extends the sum of squares generally weighted moving average (SS-GWMA) control chart by using the double generally weighted moving average (DGWMA) technique. The proposed expanded chart is called the sum of squares double generally weighted moving average (SS-DGWMA) control chart. Simulations are performed to evaluate the average run length (ARL) and standard deviation of run length (SDRL) of the SS-DGWMA, SS-DEWMA, and SS-GWMA charts. An extensive comparison shows that the optimal SS-DGWMA chart is superior to the optimal SS-GWMA and SS-DEWMA charts in all studied scenarios. The SS-DGWMA chart is also easy to implement and to interpret the abnormal signals.