光伏发电系统发电功率预测

为解决光伏发电系统发电功率在不同条件下误差较大问题,提出光伏发电系统发电功率预测新方法。通过分析光伏发电系统结构,研究光伏发电系统发电功率影响因素;以季节和天气类型作为历史样本选取样本源,针对气象部门提供的预测日分时气象数据在历史数据库中寻找相似数据点作为历史样本;依据历史样本构建离线参数寻优数据总集,使用核函数极限学习机算法构建发电系统发电功率预测模型,通过粒子群算法优化模型参数。实验结果表明：所提方法在不同条件下预测太阳能光伏发电系统发电功率的平均绝对百分比误差分别为1.47%和6.39%,光伏组件在综合异常条件下发电功率预测误差相对变化均低于1%,证明所提方法满足实际预测要求。     

Air Quality Prediction Based on Kohonen Clustering and ReliefF Feature Selection

Air quality prediction is an important part of environmental governance. The accuracy of the air quality prediction also affects the planning of people’s outdoor activities. How to mine effective information from historical data of air pollution and reduce unimportant factors to predict the law of pollution change is of great significance for pollution prevention, pollution control and pollution early warning. In this paper, we take into account that there are different trends in air pollutants and that different climatic factors have different effects on air pollutants. Firstly, the data of air pollutants in different cities are collected by a sliding window technology, and the data of different cities in the sliding window are clustered by Kohonen method to find the same tends in air pollutants. On this basis, combined with the weather data, we use the ReliefF method to extract the characteristics of climate factors that helpful for prediction. Finally, different types of air pollutants and corresponding extracted the characteristics of climate factors are used to train different sub models. The experimental results of different algorithms with different air pollutants show that this method not only improves the accuracy of air quality prediction, but also improves the operation efficiency.     

基于数字原住民的敦煌新文创角色情感感知研究

目的 通过个案探讨数字原住民对具有传统文化背景与当代审美意味的角色设计的情感感知影响因素,以及年轻世代的审美态度、文化认同与价值趋向特征。方法 抓取在线评论中角色设计相关文本与数据,采用Nvivo 12 Plus质性分析工具,运用扎根理论方法构建敦煌新文创角色设计用户情感感知模型,利用ROST CM6软件进一步分析用户情感倾向与情感感知的高频因素。结果 归纳出审美颜值、场景代入、历史叙事三个类属是影响用户角色情感感知的核心因素,构建出基于主体、场景与内容的敦煌新文创角色情感感知影响因素模型。结论 从研究可知,把握极化的时代审美特征,塑造数字赋能下的场景沉浸式具身感知,实现文化认同下历史叙事的情感共鸣,对提升数字新文创角色设计的情感感知有显著影响。     

A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring

Data-driven process-monitoring methods have been the mainstream for complex industrial systems due to their universality and the reduced need for reaction mechanisms and first-principles knowledge. However, most data-driven process-monitoring methods assume that historical training data and online testing data follow the same distribution. In fact, due to the harsh environment of industrial systems, the collected data from real industrial processes are always affected by many factors, such as the changeable operating environment, variation in the raw materials, and production indexes. These factors often cause the distributions of online monitoring data and historical training data to differ, which induces a model mismatch in the process-monitoring task. Thus, it is difficult to achieve accurate process monitoring when a model learned from training data is applied to actual online monitoring. In order to resolve the problem of the distribution divergence between historical training data and online testing data that is induced by changeable operation environments, a robust transfer dictionary learning (RTDL) algorithm is proposed in this paper for industrial process monitoring. The RTDL is a synergy of representative learning and domain adaptive transfer learning. The proposed method regards historical training data and online testing data as the source domain and the target domain, respectively, in the transfer learning problem. Maximum mean discrepancy regularization and linear discriminant analysis-like regularization are then incorporated into the dictionary learning framework, which can reduce the distribution divergence between the source domain and target domain. In this way, a robust dictionary can be learned even if the characteristics of the source domain and target domain are evidently different under the interference of a realistic and changeable operation environment. Such a dictionary can effectively improve the performance of process monitoring and mode classification. Extensive experiments including a numerical simulation and two industrial systems are conducted to verify the efficiency and superiority of the proposed method.     

Fuzzy neural network based yield prediction model for semiconductor manufacturing system

Accurate die yield prediction is very useful for improving yield, decreasing cost and maintaining good relationships with customers in the semiconductor manufacturing industry. To improve prediction accuracy of die yield, a novel fuzzy neural networks based yield prediction model is proposed in which the impact factors of yield and critical electrical test parameters are considered simultaneously and are taken as independent variables. The mapping between these independent variables and yield is constructed in the fuzzy neural network (FNN). The lineal regression between FNN-based yield predicting output and actual yield demonstrates the effectiveness of the proposed approach by historical experimental data of semiconductor fabrication line in Shanghai. The comparison experiment verifies the proposed yield prediction method improves on three traditional yield prediction methods with respect to prediction accuracy.     

Self‐Assembly Approaches to Three‐Dimensional Photonic Crystals

A brief review of the historical development of photonic bandgap (PBG) materials is provided and the fabrication methods employed are discussed with emphasis on self‐assembly processes. The factors influencing the generation of a complete bandgap, from both an experimental and a calculational standpoint are then presented and discussed. The Figure shows a diamond‐like 3D periodic structure.     

Using AHP in determining the prior distributions on gas pipeline failures in a robust Bayesian approach

The paper proposes a robust Bayesian approach to support the replacement policy of low-pressure cast-iron pipelines used in metropolitan gas distribution networks by the assessment of their probability of failure. In this respect, after the identification of the factors leading to failure, the main problem is the historical data on failures, which is generally limited and incomplete, and often collected for other purposes. Consequently, effective evaluation of the probability of failure must be based on the integration of historical data and knowledge of company experts. The Analytic Hierarchy Process has been used as elicitation method of expert opinion to determine the a priori distribution of gas pipeline failures. A real world case study is presented in which the company expertise has been elicited by an ad hoc questionnaire and combined with the historical data by means of Bayesian inference. The robustness of the proposed methodology has also been tested.     

陕西社火脸谱传统造型因子提取与设计应用

目的 为了促进传统文化与现代设计的有机融合,提升产品的文化内涵,以陕西社火脸谱传统造型为基础,提出一种适用于现代设计的传统文化造型元素的演化方法,并在具体设计实例中验证方法的可行性。方法 在研究陕西社火脸谱史料及实物资料的基础上,对社会脸谱的谱式、色彩、纹饰进行整体特征分析;基于典型样本,提取出显性的谱式因子、色彩因子和纹饰因子,隐性的情感因子和历史文化因子。结果 依据产品的物理性要求和文化性要求,利用形状文法对关键性元素推演,设计出具有陕西社火脸谱韵味的十二生肖主题茶饮。结论 传统文化元素与现代设计手法有机融合是设计本土化的必然要求,具有文化性和人文情怀的产品,不仅提高了产品的价值内涵,也有利于传统文化的继承和现代设计的发展。     

一种小流量旋涡泵的汽蚀性能试验研究

对一种小流量旋涡泵的汽蚀性能进行了理论分析及试验研究.在试验中利用了LabVIEW软件进行数据的实时采集、存储,并用Access及Excel进行数据的记录处理,使用了精度较高的传感器并进行软件及硬件滤波,以克服流体脉动及随机因素造成的误差,使试验精度提高.提供了一份小流量旋涡泵汽蚀试验数据,可为小流量旋涡泵汽蚀性能的研究提供参考.     

Improvement in Preform Weights from a 48-Cavity PET Injection Molder

John Jones (pseudonym), injection engineer for Corporation X, gave our student project group from Iowa State University the challenge of determining the most influential variables affecting weights of preform plastic beverage bottle produced by injection-molding machines. In addition to determining which variables are most important, we were charged with identifying settings for the variables that will produce optimal preform weights. We benchmarked the current injection-molding process to get an idea to typical performance. This allowed us to compare later experimental results to typical process output.

From company experience with the process, the team and Mr. Jones identified injection time, injection pressure, hold time, and hold pressure as candidates for the variables most influential on preform weight. The team devised a matrix of experimental conditions to study that consisted of combinations of high, low, and medium values of these process variables. After experimentation with the injection-molding process, regression analysis was used to help determine which factors are most influential in determining preform weight. The group determined that hold time and hold pressure are the two most influential factors in determining mean preform weight. The group also determined that hold pressure has the primary influence on the consistency of preform weight across the cavities in the mold. Using regression equations for mean weight and a within-die standard deviation, new machine settings for hold time and hold pressure were recommended. Additional runs were then performed to validate our recommendations. Ten runs were made with hold pressure set to 1140 psi and hold time set to 3.95 s. The average weight of 50 preforms from the verification experiment was 23.41 g with a standard deviation of 07 g. These results compare well to the engineering specifications of 23.9-22.9 g set on preform weights. A subsequent comparison of routine process monitoring data collected after implementing our recommendations with historical process data also confirms the substantial improvement provided by our analysis.     

Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Increasing energy demands due to factors such as population, globalization, and industrialization has led to increased challenges for existing energy infrastructure. Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable, cheap, and easily available sources of energy. Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions. But the integration of distributed energy sources and increase in electric demand enhance instability in the grid. Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid. Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control, reinforcement of the grid demand, and generation balancing with cost reduction. But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data. Machine learning and artificial intelligence have recognized more accurate and reliable load forecasting methods based on historical load data. The purpose of this study is to model the electrical load of Jajpur, Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression (GPR). The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past, current, and future dependent variables, factors, and the relationship among data. Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead. The study is very helpful in grid stability and peak load control management.     

Partition experimental designs for sequential processes: Part I—First‐order models

The output quality or performance characteristics of a product often depend not only on the effect of the factors in the current process but on the effect of factors from preceding processes. Statistically‐designed experiments provide a systematic approach to study the effects of multiple factors on process performance by offering a structured set of analyses of data collected through a design matrix. One important limitation of experimental design methods is that they have not often been applied to multiple sequential processes. The objective is to create a first‐order experimental design for multiple sequential processes that possess several factors and multiple responses. The first‐order design expands the current experimental designs to incorporate two processes into one partitioned design. The designs are evaluated on the complexity of the alias structure and their orthogonality characteristics. The advantages include a decrease in the number of experimental design runs, a reduction in experiment execution time, and a better understanding of the overall process variables and their influence on each of the responses. Copyright © 2001 John Wiley & Sons, Ltd.     

Language use reflects scientific methodology: A corpus-based study of peer-reviewed journal articles

Recently, philosophers of science have argued that the epistemological requirements of different scientific fields lead necessarily to differences in scientific method. In this paper, we examine possible variation in how language is used in peer-reviewed journal articles from various fields to see if features of such variation may help to elucidate and support claims of methodological variation among the sciences. We hypothesize that significant methodological differences will be reflected in related differences in scientists’ language style. This paper reports a corpus-based study of peer-reviewed articles from twelve separate journals in six fields of experimental and historical sciences. Machine learning methods were applied to compare the discourse styles of articles in different fields, based on easily-extracted linguistic features of the text. Features included function word frequencies, as used often in computational stylistics, as well as lexical features based on systemic functional linguistics, which affords rich resources for comparative textual analysis. We found that indeed the style of writing in the historical sciences is readily distinguishable from that of the experimental sciences. Furthermore, the most significant linguistic features of these distinctive styles are directly related to the methodological differences posited by philosophers of science between historical and experimental sciences, lending empirical weight to their contentions.     

基于用户评价的西京古道导视系统优化策略

目的 在广东省保护南粤古驿道线性遗产及开发古驿道旅游的背景下,空间导视系统作为古驿道游径建设的重要组成部分,通过对现有导视系统(记名类标识、定位类标识、引导类标识、解说类标识、管理类标识)的用户评价分析,探讨满足游客多维度需求的设计优化策略。方法 选取西京古道的14个样本点,运用SD-SBE法分别组织专家组、公众组对样本进行评价,利用SPSS软件进行统计分值对比分析,找出专家与公众差异性的原因及对策,使古驿道导视系统的设计更加科学合理。结论 评价结果表明专家组与公众组对导视系统样本的游客感知度、使用功能、方向指引、节点摆放、救助功能等相关因子的评价分值差异较大,在古驿道导视系统设计时应着重考虑以上5个因子,以更好地满足游客的使用需求。     

基于样本的马勺脸谱造型因子提取

目的主要研究马勺脸谱造型因子提取方法及造型推演过程,并将因子提取应用于现代产品设计中,对相应设计方法和思路进行验证。方法首先在研究史料及实物资料基础上,从谱式、纹饰、色彩以及线条4个方面进行整体特征分析;其次,基于典型样本,用因子分析法提取谱式、色彩及纹样等关键造型要素,并在此基础上以形状文法进行形状推演。结论该方法解决了传统文化与现代产品设计的结合,并以实例验证了方法的可行性。     

Squeeze-and-breathe evolutionary Monte Carlo optimization with local search acceleration and its application to parameter fitting

Estimating parameters from data is a key stage of the modelling process, particularly in biological systems where many parameters need to be estimated from sparse and noisy datasets. Over the years, a variety of heuristics have been proposed to solve this complex optimization problem, with good results in some cases yet with limitations in the biological setting. In this work, we develop an algorithm for model parameter fitting that combines ideas from evolutionary algorithms, sequential Monte Carlo and direct search optimization. Our method performs well even when the order of magnitude and/or the range of the parameters is unknown. The method refines iteratively a sequence of parameter distributions through local optimization combined with partial resampling from a historical prior defined over the support of all previous iterations. We exemplify our method with biological models using both simulated and real experimental data and estimate the parameters efficiently even in the absence of a priori knowledge about the parameters.     

The disk drive industry

The size and structure of the moving head rigid disk drive and flexible disk drive industries are analyzed, with historical data and projections covering the span from 1976 to 1980. Products are analyzed in 12 categories, with discussion of underlying factors contributing to the growth or decline of individual product groups.     

Computer-Aided Design of Fractional Factorial Experiments Given A List of Feasible Observations

When attributes of experimental units serve as independent variables, locating the units possessing the required combinations of attribute values for an experimental design can be a serious practical problem. Often, however, data sets of observable experimental units exist. A computer-aided design methodology is presented which determines which two-level factorial and orthogonal fractional factorial designs are feasible, given the data set of observable experimental units. Contrary to usual practice, the number of factors to consider is an explicit experiment planning variable in the methodology. All combinations of ten and fewer factors and 2¹⁰ and fewer observations (in steps of powers of 2) are represented by a feasibility matrix. For a given set of observable experimental units, the design methodology attempts to map which cells of the matrix are feasible. Dependency relationships among feasibility matrix cells are stated which allow implicit enumeration of cells. An example taken from highway safety research is used to demonstrate use of the design methodology. Lastly, search times for random data sets indicate seven or fewer factors can be searched at low cost, but the cost for more than seven factors is dependent upon data-set size.     

Big data driven jobs remaining time prediction in discrete manufacturing system: a deep learning-based approach

Implementing advanced big data (BD) analytic is significant for successful incorporation of artificial intelligence in manufacturing. With the widespread deployment of smart sensors and internet of things (IOT) in the job shop, there is an increasing need for handling manufacturing BD for predictive manufacturing. In this study, we conceive the jobs remaining time (JRT) prediction during manufacturing execution based on deep learning (DL) with production BD. We developed a procedure for JRT prediction that includes three parts: raw data collection, candidate dataset design and predictive modelling. First, the historical production data are collected by the widely deployed IOT in the job shop. Then, the candidate dataset is formalised to capture various contributory factors for JRT prediction. Further, a DL model named stacked sparse autoencoder (S-SAE) is constructed to learn representative features from high dimensional manufacturing BD to make robust and accurate JRT prediction. Our work represents the first DL model for the JRT prediction at run time during production. The proposed methods are applied in a large-scale job shop that is equipped with 44 machine tools and produces 13 types of parts. Lastly, the experimental results show the S-SAE model has higher accuracy than previous linear regression, back-propagation network, multi-layer network and deep belief network in JRT prediction.