外文文献原稿和译文

外文文献原稿和译文

原稿

Introduction

One of the hardest decisions an organization has to make is how to price its products. Price a product too low and you may not cover your costs or generate profits. Price the product too high and potential customers never turn into paying customers. Pricing strategies help a manager to answer such questions as:

How should I price my product?

How much will sales fall if I increase my price?

To whom would your product lose market share to if price changes appeared? Are there price thresholds?

Should I price products differently to achieve maxi-mum sales of the entire line?

Marketing research has long recognized the importance of price optimization. Survey research can help explore those pricing questions. Survey pricing evaluation can be thought of as a continuum that moves from quick and easy but less precise to complicated but more accurate methods. Among these methods are:

Direct methods, including willingness to pay (WTP), or what price would you pay eliciting, and incentive-aligned WTP techniques.

Indirect methods, such as Gabor-Granger, traditional and extended van Westendorp models.

Product/Price Mix methods, such as Discrete Choice, and Advanced Choice Models.

In fact, some of the best known econometricians have developed techniques to address these problems. For instance, Clive W. J. Granger, Nobel Prize winner in economics in 2003, is best known for his numerous papers and books on econometrics and time-series analysis. An area of his research that is less known was done in collaboration with AndréGabor on a pricing model project for the Nottingham University Consumer Study Group in the 1960's. Granger recalled in: During this

period I was also involved with André Gabor on some practical price research. To get data to test our theories and estimate models, we arranged with local supermarkets to conduct experiments in which we altered prices of popular products and recorded the change in sales. I believe that more economic micro-theory could be better tested by doing real world experiments rather than believing such an approach is impossible. Indeed, their collaboration had been prolific and together they created the method now known as Gabor-Granger price modeling. Most of their work has also been presented in a special issue, and incorporated into the monograph where Granger wrote the mathematical appendix. As noted in, the authors approach can be traced back to earlier work by Scitovsky , and even more to the French school's development on the so called psychological price presented by Stoetzel, Adam, and Fouilhé(republished also in). These works probably were inspired by the ideas of measuring subjective probability of a would-be bought product, developed at that time in game and utility theories. The Nottingham group's GG-models subsequently have been applied in numerous marketing research projects in different companies and countries. The GG-models have been used by other researchers as the foundation for other pricing optimization approaches. A popular technique based on GG-models (a development of approaches) was created by Dutch economist Peter H. van Westendorp (VW-model) and called price sensitivity meter.

GG-models are based on data elicited from respondents on their willingness to pay for a product innovation, a service, or concept at various price points. The approach is somewhat limited as price is not always a conscious variable, competitive price awareness isn't always high, and pricing often varies across distributors. In spite of these limitations, GG and VW techniques are popular due to the nature of their convenient descriptive data analysis and visualization especially when compared to numerous other more theoretically based price and cost models. These models serve as workhorse instruments in marketing research for empirical pricing of concepts and products.

In this work we consider some main features of the direct, indirect, and discrete choice models for finding optimal prices. The paper is organized as follows. Section 2 describes the direct price techniques, Sections 3and 4 concern the indirect methods of

Gabor-Granger and van Westendorp, respectively. Section 5 considers discrete choice models, and Section 6 summarizes.

Direct Price Techniques

Direct methods are based on willingness to pay estimation. A simplest approach to pricing research consists in asking the consumers to directly state their WTP for a specific product through an open-ended question format. The respondents answer to the question: What is the highest price you would be willing to pay for product X? An example of the elicited results is presented in Figure 1.

A modified version of WTP is called incentive-aligned WTP in which participants are obligated to purchase a product if the price drawn from a lottery is less than or equal to his/her stated WTP. Since real money is on the line, respondents have incentive to give realistic stated WTP. Obviously, this approach only works in special circumstances，easy to collect, and little prior knowledge required from the respondents. But these methods suffer serious disadvantages. Particularly, it was observed that respondents often overstate their price sensitivity. Also, though a little prior knowledge is typically required, if the new product concept is very unusual, respondents may not have any notion what price range is appropriate.

Gabor-Granger Indirect Price Models

Indirect methods are generally more accurate than direct methods as respondents are faced with more realistic scenarios. These methods are quick and simple to ad-minister and also derive information on why respondents chose not to buy a product.

One such method that is widely recognized and use din the marketing science industry is the Gabor-Granger approach. It is a convenient and practical pricing technique to determine the highest price a respondent is willing to pay for a given product. In this approach, a set of price levels to test are first determined. Then a sample of respondents is gathered and the product is described to each respondent with a randomly chosen price from the predetermined list attached. The respondent is asked her willingness to purchase the product at that given price. If the respondent is willing to purchase the product at that price, the product is shown again but this time with a randomly chosen higher price from the predetermined price list and her

willingness to purchase asked. If the respondent is not willing to purchase the product at the first price shown, the product is again shown to her but with a randomly chosen lower price from the predetermined list and her willingness to purchase elicited. This pattern is point list until the highest price point a respondent is willing to pay is determined.

GG-model is suitable for a new product development. It aims to establish maximum price each respondent is willing to pay for a given product using a series of pre-determined price points, usually 5 or 7 of them.

A demand curve can be generated by calculating the cumulative frequency distribution of the highest prices respondents are willing to pay for a given product. An example with a real data from a marketing research project is presented in Figure 3.

By multiplying each price point by its corresponding

demand it is possible to consider the corresponding revenue curve -an example is given in Figure 4. There venue curve can be used to determine an acceptable price which maximizes a projected revenue value. Additional curve of a possible loss from optimal revenue is presented in Figure 5.

The results of GG-modeling can be summarized in the numerical Table 1 .

The method described above corresponds to just one of several different GG models which are applied when pricing established products. Another variation of the GG model is to simply ask respondents the highest price they would agree to pay and the lowest price they would find acceptable before suspecting a poor quality product. This approach does not assume a respondent buys at a single given price, but that lower prices are also acceptable (which can correspond to the case of the so called. To analyze such data, a frequency distribution of the willingness-to-buy is constructed a concave curve of reach percentage of the respondents agreeing to buy the product at that price. Among the advantages of the GG-models, we can mention that it is simple and. easy to complete the data eliciting and analysis, and the checked prices are clearly isolated. So it best suited for pricing situations that are later in the product development cycle and the clients have a clear idea of range of prices they want to use. Among the weaknesses of this approach are some bias towards overstating price and

lack of context.

Van Westedorp Price Sensitivity Models

An extension of the above described techniques is the van Westendorp psychological price modeling specifically focused on finding an acceptable price as a quality indicator. The VW approach takes into account concerns about low prices possibly indicating low quality as well as too high pricing. It is suitable for new product development, and it aims to establish limits of price elasticity, or price thresholds. The VW approach is based on the assumption that reasonable prices exist for consumers in every category and for each perceived level of quality within a category. Consumer price decisions are made by balancing value against price; and there is an upper and lower bound to the price a consumer will pay for a pro-duct or service. Data elicited in the VW process consists in the answers to four indirect questions to calibrate price from different perspectives: Cheap -at what price does this product start to seem cheap to you, that is, when does it start to seem like. a bargain? Expensive -at what price does this product start to seem expensive to you? Too Cheap -at what price does this product become too cheap, that is, so cheap that you would question its quality and not buy it? Too Expensive -at what price does this product become too expensive, that you would not consider buying it? For each of the four price questions the cumulative frequencies are plotted against the current price on the same graph (but the Too Cheap and Expensive curves are displayed in the reversed direction) see Figure 6.

The intersection of the reversed Too Cheap curve with Cheap curve according to VW is called the point of marginal cheapness. The intersection point of the reversed Expensive curve with Too Expensive curve is called marginal expensiveness. The range between these two points shows the area of the price acceptable for most consumers. The intersection of the Cheap and there versed Expensive curves also correspond to the indifference price point, where there are an equal number of respondents for both these questions. The intersection of the reversed Too Cheap and Too Expensive curves de-fines the point of optimal pricing. Among the advantages of this traditional approach -it permits to avoid imposing price points on respondents, it best suited for pricing situations that are very early in the product development cycle

and the client doesn't really have a clear idea of the price range to play in, it also is simple and easy to complete. On the other hand, respondents often overstate their price sensitivity, results can be unstable as even small changes in the sample can results in large changes in the price curves, and the range of acceptable prices can be quite large.

To overcome these problems, the extended VW approach had been elaborated where the VW data can be considered in statistical regression modeling via a set of ordinal logistic regressions. If to use only two questions on lower and higher prices, VW model reduces to GG model of price as quality indicator. Statistical modeling suggested in can be seen as a developed approach de-scribed in. In EVW we get much more exact acceptable price estimates (the max reach of the OK curve), estimate confidence intervals for significance testing, and considering the OK price perception probability along with the probabilities of the other shares we can estimate acceptable prices for a variety of scenarios. The pricing analysis provides estimates of both share of consumers and revenue. The EVW can be adjusted to a broad span of products. First, researchers can set the range of prices acceptable. This approach is most relevant when the tar-get price is known in advance. A second approach is to let respondents set the price range: it enables consumers to fully express their price expectations, without any direction, resulting in a wider range of prices. Finally, respondents can evaluate manufacturer-chosen price points the concept shown to respondents is priced; then they are asked the price sensitivity questions. The first two alter-natives are usually preferred in practice.

Product/Price Mix Models

The next step for finding optimal prices is presented by various pricing techniques used in conjoint and discrete choice models (DCM). A simple DCM with price the sole variable, the so-called price challenger, is described in. General conjoint and DCM methods typically include additional variables taking into account covariates of brands, size, demographics, etc. DCM is best in the situations when simulating immediate response to competitive offerings, especially brand and price studies, decisions are made on the basis of relatively few, well-known, concrete attributes, consumers make these decisions on the basis of competitive differences

among attributes given, and we want to account for possible interactions between levels of different attributes. This is a realistic approach mimicking actual choices people are faced with in the store.

DCM studies for pricing can be performed as follows:

Ask a person to choose among competing products at different prices. Change the prices and/or product attributes and ask the respondent to choose again, etc. With such a data we build a model to predict the likelihood that a person will choose a specific product given the relative prices of the products in the test. Respondents are shown multiple scenarios at a time (task) and asked to pick the one they would choose/ purchase. Through experimental design, balanced (orthogonal) sets of choice tasks are produced. Typically, 12-l5 tasks are evaluated by each respondent. The advantages of this approach: it is reflective of real world marketplaces with competing products; it can accommodate conditional variables; brands can be customized to match market reality; it avoids impossible combinations and is easy to administer; it can capture interactions more efficiently than a full-profile design. Among the limitations, we can mention that it is impossible to handle too many attributes, to truly predict preference share (not market share), this approach. assumes same awareness and distribution, and the results are based on calibration which uses several assumptions that may not be realistic. Advances in DCM for pricing utilize only price changes have been shown to accurately reflect actual price elasticities in the market. Choice models with thresholds accurately predict in-market price elasticities and in fact seem to always increase model accuracy. Modern non-compensatory models started to make headway. Non-compensatory decision making is driven by a rule by which_ positive evaluation of an attribute does not compensate for a negative evaluation of the same object on some other attribute. Some non-compensatory models include lexicographic and disjunctive/conjunctive cut off models. Summary

Pricing research is one of the core methodologies in custom research. There are a multitude of approaches one can take and the exact method we recommend depends on the particular circumstances of the request. We de-scribed main features of Gabor-Granger, van Westedorp, and product/price mix models, their specifics and

areas of the application in practical marketing research for evaluation of optimal prices for different products and concepts. Direct/indirect methods are not the most accurate pricing techniques; they cannot really factor in competitive effects but only consider each product in isolation. The discrete choice techniques are much more flexible and accurate because they try to replicate real market conditions as close as possible, but on the other hand they are complicated and require nearly always a separate study because of the data setting and the needed intensive design support. The GG and VW models can also be used for each set of fixed covariates and produce an optimal price which then can be compared across all these combinations. It is the reason that the Gabor-Granger and van Westedorp models are widely used in practical marketing research for express analysis of optimal prices for different products and concepts.

译文

导言

一个组织必须做出的最艰难的决定之一就是如何为其产品定价。如果你的产品价格太低，那么可能无法支付所需成本或产生利润。如果产品定价过高，潜在客户可能就永远都不会成为付费客户。定价策略有助于回答以下问题：应该如何给产品定价？

如果提高价格，销售量会下降多少？

如果价格出现变化，那么产品将失去市场份额给谁？有价格门槛吗？

我是否应该以不同的价格为产品定价，以达到整个生产线的最高销售额？

市场研究早已认识到了价格优化的重要性。调查研究可以帮助探索这些定价问题。调查定价评估可以被认为是一个连续的过程，从快速、容易但不太精确的方法转向复杂但更准确的方法。这些方法包括：

直接的方法，包括支付意愿，或者你会付出什么代价，以及引导消费的方式。

间接的方法，如哈伯-格兰格传统和扩展的模型。

产品/价格组合方法，如离散选择和高级选择模型。

事实上，一些最著名的计量经济学家已经开发了解决这些问题的技术。例如格兰杰，2003年诺贝尔经济学奖获得者，以他在计量经济学和时间序列分析方面的众多论文和著作而闻名。他研究的一个不太为人所知的领域是与哈博在60年代为诺丁汉大学消费者研究小组的一个定价模型项目上合作完成的。

格兰杰回忆道：在这期间，我还和哈博一起参与了一些实际的价格研究。为了获得数据来检验我们的理论和估计模型，我们与当地超市进行了实验，改变了流行产品的价格并记录了销售额的变化。我相信，更多的经济学微观理论可以通过做现实世界的实验得到更好的检验，而不是相信这样的方法是不可能的。事实上，他们的合作是多产的，他们共同创造了一种方法，现在被称为哈博罗格兰格价格模型。

他们的大部分工作也被刊登在特刊上，并被收入格兰杰撰写数学附录的专著中。正如在书中所指出的，作者的方法可以追溯到西托夫斯基早期的作品，甚至更多地追溯到法国学派在所谓的心理代价上的发展，这是由西托夫，亚当和福伊勒提出的。

这些作品的灵感可能来自于当时在游戏和实用理论中发展出来的测量潜在购买产品的主观概率的想法。诺丁汉集团的GG模型随后被应用在不同公司和国家的众多营销研究项目中。GG模型已被其他研究者用作其他定价优化方法的基础。荷兰经济学家皮特发明了一种基于GG模型（一种方法的发展）的流行技术，称为价格敏感度计。

GG模型是基于被调查者那里得到的数据，这些数据表明他们愿意在不同的价格点为产品创新、服务或概念付费。这种方法有些局限，因为价格并非总是有意识的变量，竞争性的价格意识并不总是很高，价格往往因经销商而异。尽管有这些限制，GG和VW技术由于其方便的描述性数据分析和可视化的性质而广受欢迎，特别是与众多其他基于理论的价格和成本模型相比。这些模型作为概念和产品经验定价的营销研究的工具。

在本文中，我们考虑了直接、间接和离散选择模型的一些主要特征，以寻找最优价格。论文的编排如下。第二节介绍了直接的价格方法，第三节和第四节分别介绍了哈博格兰格和范韦斯特多普的间接方法。第五节考虑离散选择模型，第六节概述。

直接价格模型

直接方法是基于支付意愿估计。定价研究的一个最简单的方法是要求消费者通过开放式的问题格式直接说明他们对特定产品的消费感受。被调查者回答的问题是：你愿意为产品支付的最高价格是多少？

修改后的价格被称为激励一致的定价，如果从产品中抽取的价格低于或等于其确认的价格，参与者有义务购买产品。由于真实的金钱是存在一定风险的，被调查者有动机给现实的陈述的价格。显然，这种办法只在特殊情况下有效，便于收集，而且不需要答复者事先提供任何信息。但这些方法有严重的缺点。特别是有人指出，答复者往往夸大其价格敏感性。此外，虽然通常需要事先了解一些情况，但如果新产品的概念很不寻常，受访者可能不知道什么价格范围是合适的。间接价格模型

其中一种被广泛认可并在营销科学行业中使用的方法是GG模型方法。确定被调查者愿意为某一产品支付的最高价格是一种方便而实用的定价方法。在这种方法中，首先确定了一套要测试的价格水平。然后收集被调查者的样本，并向每个被调查者描述产品，从所附的预定清单中随机选择价格。被调查者被要求按给定的价格购买产品。如果被调查者愿意以这个价格购买产品，产品会再次显示，

但这次会从预定的价格清单中随机选择更高的价格，并询问她是否愿意购买。

如果被申请人不愿意按所显示的第一个价格购买产品，则再次向她展示产品，但从预定的清单中随机选择较低的价格，从而引起她的购买意愿。在确定被调查者愿意支付的最高价格点之前，这个模式是点数列表。

GG模型适合新产品开发。其目的是利用一系列预先确定的价格点，确定每个被调查者愿意为某一产品支付的最高价格。

需求曲线可以通过计算被调查者愿意为某一产品支付的最高价格的累积频率分布来产生。图3给出了一个实例，其中包含了营销研究项目的真实数据。

将每个价格点乘以相应的价格点

需求可以考虑相应的收入曲线。在那里，场地曲线可以用来确定一个可接受的价格，最大限度地提高一个预计的收入价值。图5显示了最佳收入可能造成的额外损失曲线。

上述方法仅对应于几种不同的GG模型中的一种，这些模型在为已建立的产品定价时应用。GG模型的另一种变体是，在怀疑产品质量不佳之前，简单地问被调查者他们同意支付的最高价格和他们认为可以接受的最低价格。这一办法并不假定被申请人以单一的价格购买，但较低的价格也是可以接受的（这可以对应于所谓的情况）。为了分析这些数据，将购买意愿的频率分布构造成凹形曲线，即同意以该价格购买该产品的受访者的百分比。在GG模型的优点中，我们可以提到它很简单。容易完成数据的提取和分析，并且检查的价格是明确的隔离。因此，它最适合在产品开发周期后期的定价情况，客户对他们想要使用的价格范围有明确的认识。这种做法的缺点之一是有些偏向于夸大价格和缺乏背景。

价格敏感性模型

以上描述的技术的延伸是范的心理价格建模，特别侧重于寻找一个可接受的价格作为质量指标。

大众的做法考虑到了对低价格的担忧，这可能意味着低质量和过高的价格。它适用于新产品的开发，其目的是建立价格弹性的限制，或价格阈值。大众的做法是基于这样的假设，即每个类别的消费者都有合理的价格，并且在一个类别内的每个感知的质量水平都有合理的价格。消费者价格的决定是通过价值与价格的平衡来做出的；并且对消费者支付的价格有一个上下限。在大众流程中得到的数据包括四个间接问题的答案，从不同的角度来校准价格：便宜。在什么价格这个产品开始看起来便宜，也就是说，什么时候开始看起来像价格过低？你认为这种

产品在什么价格上贵？太便宜了或太贵了以及这种产品在什么价格下会变得太贵，以至于你不会考虑买它？对于这四个价格问题中的每一个，累积频率都与同一图表上的当前价格作了对比（但过于廉价和昂贵的曲线显示在相反的方向上）。根据大众的说法，反向太廉价曲线与廉价曲线的交点称为边际廉价点。曲线过高的反向昂贵曲线的交点称为边际消耗。这两点之间的范围显示了大多数消费者可以接受的价格范围。便宜和有熟悉的昂贵曲线的交叉点也对应着冷漠的价格点，在那里对这两个问题的回答人数相等。反向的太便宜和太昂贵的交叉曲线降低了最优定价的点。这一传统做法的好处之一是，它允许避免对答复者强加价格点，它最适合在产品开发周期的早期定价，而且客户对价格范围没有一个明确的概念，它也很简单和容易完成。另一方面，答复者往往夸大其价格敏感性，结果可能不稳定，因为即使样本中的微小变化也可能导致价格曲线的巨大变化，而且可接受价格的范围也可能相当大。为了克服这些问题，我们提出了扩展的大众方法，在统计回归建模中，可以通过一组序数逻辑回归来考虑大众数据。如果只使用两个问题对更低和更高的价格，大众模型减少到GG模型的价格作为质量指标。所建议的统计建模可以看作是一种发展的方法。在目前，我们得到了更准确的可接受的价格估计，估计置信区间的意义测试，并且考虑到价格感知概率和其他不定的概率，我们可以对各种情况估计可接受的价格。价格分析提供了消费者份额和收入的估计。电动汽车可以调整到广泛的产品范围。首先，研究人员可以设定可接受的价格范围。这种方法在提前知道价格时最为重要。第二种方法是让被调查者确定价格范围：它使消费者能够在没有任何方向的情况下充分表达他们的价格预期，从而导致更广泛的价格范围。最后，被调查者可以对制造商选择的价格点进行评估，向被调查者展示的概念是定价的，然后被问及价格敏感性问题。

产品╱价格组合模式

下一步寻找最优价格是通过各种定价技术使用在联合和离散选择模型。一个简单的定价与价格唯一的变量，即所谓的价格挑战者，描述在一般的共伴和定价方法通常包括额外的变量，同时考虑到品牌、大小、人口等的共变量。定价在模拟对竞争性产品的即时反应时是最好的，特别是品牌和价格研究，决策是基于相对较少的，众所周知的，具体的属性，消费者根据所给的属性之间的竞争差异做出这些决策，我们要考虑不同属性层次之间可能的相互作用。这是一种现实的方法，模仿人们在商店里面对的实际选择。定价研究可按以下方式进行：要求一个人以不同的价格在竞争产品中做出选择。改变价格和/或产品属性，

要求被调查者重新选择，等等。有了这样的数据，我们建立了一个模型，根据测试中产品的相对价格来预测一个人选择特定产品的可能性。在一次任务中向被调查者展示多个场景，并要求他们选择/购买一个场景。通过实验设计，得到了平衡组的选择任务。典型的情况是，每个被调查者都要对任务进行评估。这种方法的优点是：它反映了现实世界市场与竞争产品；它可以容纳条件变量；品牌可以定制以适应市场现实；它避免不可能的组合，便于管理；它可以比全剖面设计更有效地捕捉交互。在限制因素中，我们可以提到，不可能处理太多的属性，不可能真正预测优惠份额（而不是市场份额），这种方法。假设相同的认识和分布，结果是基于标准，使用了几个可能不现实的假设。具有阈值的选择模型准确地预测了市场价格弹性，实际上似乎总是提高了模型的准确性。现代非补偿模式开始取得进展。非补偿性决策是由某项规则驱动的，根据该规则，对某属性的正向评价不补偿对于其他属性上同一对象的负评价。一些非补偿模型包括词典和析取/合取模型。

结论

定价研究是定制研究的核心方法之一。我们可以采取多种方法，我们推荐的具体方法取决于请求的具体情况。我们介绍了加博-格兰杰、范韦斯特多普和产品/价格组合模型的主要特点，它们的具体特点和在实际市场研究中的应用领域，以评估不同产品和概念的最佳价格。直接/间接方法并不是最准确的定价方法；它们不能真正考虑竞争效应，而只能孤立地考虑每种产品。离散选择技术的灵活性和准确性要高得多，因为它们试图尽可能接近地复制真实的市场条件，但另一方面，由于数据的设置和所需的密集设计支持，这些技术很复杂，几乎总是需要单独的研究。GG和VW模型也可以用于每一组固定的共变量，并产生一个最优价格，然后可以在所有这些组合中进行比较。这就是为什么哈博格兰格模型和范韦斯特多普模型在实际市场研究中被广泛应用，用于对不同产品和概念的最优价格进行快速分析。

英文文献翻译

中等分辨率制备分离的 快速色谱技术 W. Clark Still,* Michael K a h n , and Abhijit Mitra Departm(7nt o/ Chemistry, Columbia Uniuersity,1Veu York, Neu; York 10027 ReceiLied January 26, 1978 我们希望找到一种简单的吸附色谱技术用于有机化合物的常规净化。这种技术是适于传统的有机物大规模制备分离，该技术需使用长柱色谱法。尽管这种技术得到的效果非常好，但是其需要消耗大量的时间，并且由于频带拖尾经常出现低复原率。当分离的样本剂量大于1或者2g时，这些问题显得更加突出。近年来，几种制备系统已经进行了改进，能将分离时间减少到1-3h，并允许各成分的分辨率ΔR f≥（使用薄层色谱分析进行分析）。在这些方法中，在我们的实验室中，媒介压力色谱法1和短柱色谱法2是最成功的。最近，我们发现一种可以将分离速度大幅度提升的技术，可用于反应产物的常规提纯，我们将这种技术称为急骤色谱法。虽然这种技术的分辨率只是中等（ΔR f≥），而且构建这个系统花费非常低，并且能在10-15min内分离重量在的样本。4 急骤色谱法是以空气压力驱动的混合介质压力以及短柱色谱法为基础，专门针对快速分离，介质压力以及短柱色谱已经进行了优化。优化实验是在一组标准条件5下进行的，优化实验使用苯甲醇作为样本，放在一个20mm*5in.的硅胶柱60内，使用Tracor 970紫外检测器监测圆柱的输出。分辨率通过持续时间（r）和峰宽（w,w/2）的比率进行测定的（Figure 1），结果如图2-4所示，图2-4分别放映分辨率随着硅胶颗粒大小、洗脱液流速和样本大小的变化。

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Introduction Introduction With the continuous development of our economy, rapidly rising living standards, people working and living environment have become increasingly demanding, while the lighting system requirements have become more sophisticated, the traditional lighting technology has been a strong blow. On the one hand because of information technology and computer technology changes in lighting technology, providing technical support; the other hand, due to energy shortage, the state more and more attention on energysaving lighting, new lighting control technology to develop rapidly to meet with By energy conservation, comfort, convenience requirements. Lighting control lighting control from the traditional manual method, automated lighting control to today's intelligent lighting control. Intelligent lighting control system is based on computercontrolled alldigital platform, modular, distributed bus control system, the central processor modules communicate directly through the network bus, the bus makes use of lighting, dimming, blinds, scene control to achieve intelligent, and become a complete bus system. Can be based on changes in the external environment in the device automatically adjust the status of the bus to reach safety, energy conservation, human effects, and can use in the future, in accordance with the requirements of users through the computer Way to increase or modify the system's functionality, without having to relaying of cables, intelligent lighting control system, high reliability, flexible control, lighting control is the traditional way can not be done. The basic components and monitoring the contents of the system System The basic components and monitoring the contents of the system System components Intelligent lighting control system is usually dimmer module, switch module, input module, the control panel, liquid crystal display touch screen, smart sensors, PC interface, time management module, handheld programmer, monitoring computer (need to bridge a large network connection) and other components composition.

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(2) w 12… . (3) R 1,2, … (4) Δ ? ? ?others toprojectQ rcer humanresou i k 01 (5) . I t I t . (6) △ I ’s a .( ’t .) (7) (5) t I △ ,( △ ). , – a . (8) (6) (7), I ( = △* △ ). (9) =ηi / * , ηi I ; * I , * =∑=R k ki 1 δ . , . , , . 3.3 , , : = ∑∑==N i i N i Ci 11 ω i i N i i N i c t ??∑∑==1 1 ω (2) ∑∑ ==N i i N i 1 1 ω ) E i R i ki i t - ?? ∑=1 δη i c ? 2F Z 2()i t ? ) E i R i ki i t - ??∑=1 δη (3) () ,(N j i K 3,2,1,=?) (4)

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