第一篇：how to use QFD for standard making--國際會議最終演講稿

good afternoon ladies and gentlemen:

my name is shaoshasha,i am a senior in china jiliang university and majoring in business management.i am honored to be here to deliver an introduction of our group case study.this is our team member tanglihua ,sundan and me

as we all know.sony and lenovo both enjoy high reputation among the mass.therefore, as a famous brand over asia, sony competes with lenovo in domestic market only by obtaining the knowledge of the comparative disadvantage and corresponding betterment, and this is the primary issue we should to settle.taking the customers' demand as an base, we launched a market research and thereby get an understanding that the technical standard is the computer’s feature.since qfd is of great usefulness in analyzing dates, we apply it in our study in order to figure the approach of sony‘s improvement.in addition, quality function deployment is to help transform the customer wants into engineering characteristics for a product or service, prioritizing each product or service characteristic while simultaneously setting the development targets for the product or service.then according to the qfd analysis，we could get the new technical standards.based on market research we are able to get the customer satisfaction form.according to the form, we can figure that customers pay attention to laptop's processing speed, weight, memory mount and corresponding cognition and perception.technological indicators for processing speed are basic frequency and internal memory, for weight is material, size and thickness;for volume is hard disk.these are the features for these technical indicators, in term to lenovo and sony.then let me to explain qfd of snoy laptop.first we rank the significance degree of customer need thereby to conclude the planning matrix.after customer needs are known, we can get technical requirement quality table relevancy, relationship matrix and correlation matrix(影響程度劃分表)

after completing the above analysis, the house of quality is our final plan.from the key qualities characters setting, sony takes a leadership in basic frequency, material and hard disk.with drawbacks in size and thickness.while internal memory is neck and neck between us and lenovo.therefore we draw conclusions as follows:

1、in terms of basic frequency, lenovo performs better, however, the difficulty for improvement is relatively high.therefore, it is better to develop the attributes later rather than now.2、the amount of memory of sony and lenovo are equal;correspondingly we can take several steps to target a competitive advantage.3、as for material, sony has a patent for using light material and in contrast, lenovo does not.as a result, sony has an advantage.however, there are also some shortcomings, for instance: reliability.therefore.we should learn from the other competitors in order to improve our products.4、when it comes to the size, sony has some advantages, whereas size is the attribute which is of the lowest technological difficulty.hence in order to beat the competitors in the market, sony should be less than 11 inch.5、the thickness of sony is among the best in the opening market;however it is difficult for development.consequently ir would better be delayed the technological improvement.6、the most important one is hard disk, there is much potential for improvement ,therefore we want that the hard-disk can turn to 250g.from the improving goals, we can see the improvements we made in parameters of size and hard-disk to get the new technical standards

this is our qfd analysis.if you have any comments about our case analysis, you are welcome to contact us.thanks for your time.下午好，女士們，先生們：

我的名字是shaoshasha，我是中國計(jì)量學(xué)院高級和企業(yè)管理專業(yè)。我很榮幸能夠在這里實(shí)現(xiàn)了我們集團(tuán)的案例研究介紹，這是我們團(tuán)隊(duì)成員tanglihua，順和我大家都知道。sony和聯(lián)想都享有很高的聲譽(yù)之間的質(zhì)量。因此，作為整個亞洲的著名品牌，索尼的競爭對手聯(lián)想在國內(nèi)市場上唯一通過獲取的比較劣勢的知識和相應(yīng)的改善，這是首要問題，我們應(yīng)該解決。以客戶的需求為基礎(chǔ)，我們開展了市場調(diào)研，從而得到一個認(rèn)識，技術(shù)標(biāo)準(zhǔn)是計(jì)算機(jī)的功能。由于qfd在分析日期巨大作用，我們應(yīng)用我們的研究，以圖索尼的改進(jìn)方法。此外，質(zhì)量功能展開是幫助客戶需要轉(zhuǎn)變成一種產(chǎn)品或服務(wù)的工程特點(diǎn)，優(yōu)先考慮每一種產(chǎn)品或服務(wù)的特點(diǎn)，同時設(shè)置的產(chǎn)品或服務(wù)的發(fā)展目標(biāo)。然后根據(jù)qfd分析，我們可以得到新的技術(shù)標(biāo)準(zhǔn)。

根據(jù)市場調(diào)研，我們能夠得到客戶的滿意度的形式。根據(jù)下表，我們可以計(jì)算，客戶要注意筆記本電腦的處理速度，重量，裝載存儲器和相應(yīng)的認(rèn)知和看法。處理速度技術(shù)指標(biāo)都基本頻率和內(nèi)存，重量是物質(zhì)的，大小和厚度，為卷是硬盤。這些是這些技術(shù)指標(biāo)的特點(diǎn)，在長期聯(lián)想和索尼。

然后讓我來解釋snoy筆記本qfd。首先，我們排名的客戶需要的重要性程度，從而結(jié)束了規(guī)劃矩陣。客戶的需求后，是已知的，我們可以得到的技術(shù)要求，質(zhì)量表的相關(guān)性，關(guān)系矩陣和相關(guān)矩陣(影響程度劃分表)

在完成上述分析，房子的質(zhì)量是我們的最終方案，從關(guān)鍵素質(zhì)人物設(shè)置，sony發(fā)生在基本頻率，材料和硬盤的領(lǐng)導(dǎo)，與大小和厚度的缺點(diǎn)。而內(nèi)部存儲器之間并駕齊驅(qū)我們和聯(lián)想。因此，我們得出結(jié)論如下：

1，在基本頻率方面，聯(lián)想性能更好，但是，改進(jìn)的難度也相對較高。因此，最好是以后發(fā)展的屬性，而不是現(xiàn)在。

2，索尼和聯(lián)想的內(nèi)存量是相等的，我們可以采取一些相應(yīng)措施，目標(biāo)競爭優(yōu)勢。

3，至于材料，sony有一個利用光材??料和對比專利，聯(lián)想不。因此，sony有優(yōu)勢。不過，也有一些缺點(diǎn)，例如：可靠性。因此，我們應(yīng)借鑒其他競爭對手，以便改進(jìn)我們的產(chǎn)品。

4，當(dāng)涉及到的大小，sony有一定的優(yōu)勢，而大小是屬性，最低的技術(shù)難度。因此，為了在市場上擊敗競爭對手，索尼應(yīng)該會比11英寸以下。

5，索尼厚度躋身于開放市場最好的，但它很難發(fā)展。因此ir將更好地被推遲了技術(shù)改進(jìn)。

6，最重要的是硬盤，還有許多需要改進(jìn)的潛力，因此我們希望，該硬盤可以打開250g。

從改善的目標(biāo)，我們可以看到我們的改進(jìn)在規(guī)模和硬盤參數(shù)作出來獲得新的技術(shù)標(biāo)準(zhǔn)

這是我們的qfd分析。如果您對我們的案例分析有任何意見，歡迎您與我們聯(lián)系。感謝您的時間。

第二篇：國際會議演講稿

Freeze–thaw cycle test and damage mechanics models of

alkali-activated slag concrete＇＇＇＇

Thank you for your invitation and warm hospitality.“Freeze–thaw cycle test and damage mechanics models of alkali-activated slag concrete” I would like to thank Professor Cui ,for inviting me to deliver this“Freeze-thaw cycle test and damage mechanics models of alkali-activated slag concrete”.Theplentiful studies on a new green binding material—alkali-activated concrete.The effect of freeze-thaw cycles on in concrete was studied by experiment.，I shall explore a possible agenda for analysis to enable understanding of the alkali-activated slag concrete.“new green binding material—alkali-activated cement”the introduction of Freeze–thaw cycle test and damage mechanics models of alkali-activated slag concrete.Now let's look at the ppt In recent years, there are plentiful studies on a new green binding material—alkali-activated cement, it can be prepared by wastes containing kaolinite(原文introduction第一句)The binding materials with three-dimensional network structure are yield by shrinking and polymerization reaction.With the arriving of low carbon economy time, international governments attach more importance to energy saving, emission reducing and cycled economics.(原文第二段)a genuine low carbon cement.（ppt第3頁）–I'd like to talk is the materialswe can see clear that the Slag used in this study was metallurgy blast furnace slag, was supplied by Jiangxi Building Materials Plant, PR China, its specific surface is 410 m2/kg.chemical compositions of slag are listed in.（ppt第4頁）

NaOH and Na2SiO3 sodium silicate multiplex solution was used as alkali activator, module of sodium silicate is 3.34.Sand with fineness modulus of 2.78 was used as fine aggregate.Limestone were used as crushed rock aggregate(5–20 mm:20–40 mm = 45:55).(引用原文Materials第二段結(jié)論)Mix proportion and specimen preparation ,.Mix proportion and specimen preparation.Mix proportion, workability and compressive strength at 28 d of ASC are listed in.It was prepared by a single decubital axis compellent beater with content of 60 L.the samples were demoulded and cured

under scheduled regimes.Thirty samples were tested for freeze–thaw cycle tests.Table 1.Mix proportion, workability and strength of ASC(引用原文第二部分第二小點(diǎn))（ppt第5頁）The Freeze–thaw resistance was tested according to ASTM C666 and GB/T 50082-2009 “Standard for test methods of long-term performance and durability of ordinary concrete”.Six samples of each batch were tested, the average value of 6 samples was served as the finial freeze–thaw resistance.Mass and dynamic elasticity modulus were tested once after an interval of 25 times cycles, maximal cycle times(when relative dynamic elasticity modulus was 60% and percentage of mass loss was 5% at lowest)can denote freeze–thaw resistance of ASC.TDR-16V computer controlled concrete fast freeze–thaw cycle testing machine and DT-10W dynamic elasticity modulus testing machine were used to conduct the tests.(原文2.3 /ppt第6頁)

–thaw resistance mechanism of ASC 2.Freeze–thaw resistance durability of ASC(ppt 第7頁)

Results of fast freeze–thaw cycle tests of ASC are listed in Table 3.As can be seen:(1)With the increase of freeze–thaw cycle times, relative dynamic elasticity modulus of ASC are descending slowly, this shows excellent ductility, relative dynamic elasticity modulus of A1–A5 are all about 90% at 300 times cycle（ppt第8頁）(2)It is improper to set mass loss of ASC as the evaluation index of freeze–thaw destroy, because mass loss of A1–A5 vary indistinctively in the progress of freeze–thaw, it cannot reflect the destroy degree of concrete exactly, thus it is improper to use it to test and evaluate the freeze–thaw damage of ASC(which is shown in Fig.1).（ppt第9頁）

The first is ASC used industrial waste – slag as raw materials, and it had excellent freeze–thaw resistance with frost-resisting grade of F300 at lowest, relative dynamic elasticity modulus were about 90% after 300 times freeze–thaw cycles, it also had little mass loss, surface freeze–thaw damage layers were very thin, which can effectively restrain freeze–thaw damage of concrete from worsening.(ppt第10頁)The second is Different from freeze–thaw cycle damage models of PC, dynamic elasticity modulus attenuation models were superior to accumulative freeze–thaw damage models, and power function models were superior to exponential function models with better precision and relativity.（ppt第10頁）

Thank you very much for the privilege of presenting this paper

第三篇：模擬國際會議演講稿

1.Introduction Thank you very much.Mr.Jiao, for your kind introduction.Ladies and gentlemen, Good afternoon!My name is Lijia, came from Harbin Engineering University.I am honored to have been invited to speak at this conference.Before I start my speech, let me ask a question.Do you know what can affect the properties of foam concrete? Do you think how to reinforced the properties of foam concrete？Most of the investigations on foam concrete in the past have been con?ned（被限于）to neat cement paste, cement paste with partial replacement with admixtures and to cement–sand mixes.Today, my topic is about In?uence of ?ller type on the properties of foam concrete.I want to share our interesting research result on reinforced concrete frame with you.The content of this presentation is divided into 4 parts: In section 1, I will introduce what is the foam concrete.In section 2, I will talk about Parameters investigated and mix compositions.In section 3, I will give E?ect of water–solids ratio on design density.And finally, I will make a conclusion.2.Body Section 1: the foam concrete Now, I will introduce the foam concrete.Pre-formed(成型的)foam concrete is manufactured（加工）by adding foam, prepared by aerating（充氣）a foaming agent solution, to cement paste or cement mortar（灰

漿）.The composition（合成物）, physical properties（性能）and uses of foam concrete were discussed in detail（詳細(xì)的）by Valore, Short and Kinniburgh, Rudnai and Taylor.Although several investigations have been conducted on the properties of foam concrete, most of them deal with cement–sand mixes, neat cement paste with or without partial replacement（局部替換）using admixtures（摻合料）.Few studies report on the in?uence of ?ller type on the properties of foam concrete.By using ?y ash（粉煤灰）as ?ller(?ne aggregate細(xì)骨料)instead of sand, the high volume（體積）utilization（利用）of ?y ash becomes possible可能, thus providing a means of eco-nomic（經(jīng)濟(jì)）and safe disposal（處理）of this waste product.Comparison（比較）of strength of air-cured foam concrete made with cement-sand and cement–?y ash for masonry（砌體結(jié)構(gòu)）by Durack and Weiqing show that for products of comparable density（比較密度）, mixes with ?y ash as ?ne aggregate in place of sand gave relatively higher strength.Section 2: Parameters(參數(shù))investigated and mix compositions（組成成分）

So much for the foam concrete, now I will move on to Parameters(參數(shù))investigated and mix compositions.As the experimental programme(實(shí)驗(yàn)程序)was aimed at studying the e?ect of the ?llers on the properties like density（密度）, ?ow behaviour（流動特性）, water absorption（吸水率）and strength of foam concrete, the following mixes

were investigated by keeping the basic ?ller–cement ratio constant（恒定不變）at 1:1 by weight.The foam required for three densities（密度）of foam concrete viz.1000, 1250, 1500 kg/m3 were arrived at as per ASTMC 796-97.In the cement–sand–?y ash mixes 50% of the sand is replaced with ?y ash and in the cement–?y ash mixes all the sand is replaced with ?y ash.Section 3: E?ect of water–solids ratio（水砂比率）on design density That bring me to E?ect of water–solids ratio（水砂比率）on design density.I think this part is the most important in my presentation, I will explain in detail.As the foam is added to the wet foam concrete mix, the consistency（稠度）of the wet mix is very important to get the design density.Fig.2(a)and(b)show the variation of density ratio（密度變化率）(measured fresh density divided（分離）by design density)with water–solids ratio for mixes with di?erent ?ller type for each of the design densities, viz., 1000 and 1500 kg/m3, respectively（分別地）.It is observed that at lower water–solids ratios, i.e., at lower consistency, the density ratio is higher than unity（個體）.The mix is too sti?（嚴(yán)格地）to mix properly thus causing the bubbles（氣泡）to break during mixing resulting in increased density.At higher water–solids ratios there is also an increase in density ratio as higher water contents make the slurry（泥漿）too thin to hold the bubbles resulting in segregation（分離）of the foam from the mix along with segregation of the mix itself thus causing

an increase in measured density.Therefore, as shown in Fig.2(a)and(b), a density ratio of unity or nearly unity is achieved only at a particular consistency.This consistency requirement for the mix before adding foam to it can be expressed in terms of water–solids ratio.It is also observed that the water–solids ratio required to obtain a density ratio value of one, depends on the ?ller type.Section 4: Conclusion The conclusions drawn from this study and summarized below are applicable（合適的）to the characteristics of the materials（材料特性）used and the range of parameters（參數(shù)范圍）investigated:(i)the consistency of pre-formed foam concrete mixtures(de?ned as the water–solids ratio for achieving the target（目標(biāo)）density)mainly depends on the ?ller type, i.e., relatively higher for mixes with ?y ash as ?ller compared to mixes with sand;(ii)the ?ow behaviour mainly depends on the foam volume and as the foam volume increases the ?ow decreases.For a given density, foam concrete with ?y ash as ?ller showed relatively（相當(dāng)?shù)模﹉igher ?ow values;(iii)for a given density, an increase in ?y ash content of the mix results in increased strength.In comparison（比較）to cement–sand mixes, cement–?y ash mixes showed relatively higher water absorption（吸收）.That’s all.Thank you!Are there any questions?

The picture on this slide is

So much for......, now I will move on to......This slide shows the calculation result

As we know, the target user usually has taken a list of courses rather than a course, so we need to extent our probability calculation formulars.For example, suppose T={a,b}, Pr[x﹁T] the probability x occurs without either an a or b preceding it;Pr[x﹁T] the probability x occurs without either an a or b following it.This probability can be calculated exactly.So how to calculate it?

That bring me to Recommendation Algorithms.I think this part is the most important in my presentation, I will explain in detail.In conclusion, we proposed a novel precedence mining model, developed

To sum up, first, I introduced the motivation and the outline of work;second, I gave the definition of precedence mining model;third, I described some new recommendation algorithms using precedence information;forth, I showed our experimental results to compare the new algorithms with traditional ones.Finally, I made a conclusion of our work..That’s all.Thank you!Are there any questions?

第四篇：模擬國際會議演講稿

Recsplorer:Recommendation Algorithms Based on Precedence Mining

1.Introduction Thank you very much, Dr.Li, for your kind introduction.Ladies and gentlemen, Good morning!I am honored to have been invited to speak at this conference.Before I start my speech, let me ask a question.Do you think recomemdations from others are useful for your internet shopping? Thank you.It is obvious that recommendations play an important role in our daily consumption decisions.Today, my topic is about Recommendation Algorithms Based on Precedence Mining.I want to share our interesting research result on recommendation algorithms with you.The content of this presentation is divided into 5 parts: in session 1, I will intruduce the tradictional recommendation and our new strategy;in session 2, I will give the formal definition of Precedence Mining;in session 3, I will talk about the novel recommendation algorithms;experimental result will be showed in session 4;and finally, I will make a conclusion.2.Body Session 1: Introduction The picture on this slide is an instance of recommemdation application on amazon.Recommender systems provide advice on products, movies,web pages, and many other topics, and have become popular in many sites, such as Amazon.Many systems use collaborative filtering methods.The main process of CF is organized as follow: first, identify users similar to target user;second, recommend items based on the similar users.Unfortunately, the order of consumed items is neglect.In our paper, we consider a new recommendation strategy based on precedence patterns.These patterns may encompass user preferences, encode some logical order of options and capture how interests evolve.Precedence mining model estimate the probability of user future consumption based on past behavior.And these probabilities are used to make recommendations.Through our experiment, precedence mining can significantly improve recommendation performance.Futhermore, it does not suffer from the sparsity of ratings problem and exploit patterns across all users, not just similar users.This slide demonstrates the differences between collaborative filtering and precedence mining.Suppose that the scenario is about course selection.Each quarter/semester a student chooses a course, and rates it from 1 to 5.Figure a)shows five transcripts, a transcript means a list of course.U is our target student who need recommendations.Figure b)illustrates how CF work.Assume similar users share at least two common courses and have similar rating, then u3 and u4 are similar to u, and their common course h will be a recommendation to u.Figure c)presents how precedence mining work.For this example, we consider patterns where one course follows another.Suppose patterns occour at least two transcrips are recognized as significant, then(a,d),(e,f)and(g,h)are found out.And d, h, and f are recommendation to u who has taken a, g and e.Now I will a probabilistic framework to solve the precedence mining problems.Our target user has selected course a , we want to compute the probability course x will follow, i.e., Pr[x|a].﹁howerve, what we really need to calculate is Pr[x|aX] rather than Pr[x|a].Because in our context, we are deciding if x is a good recommendation for the target user that has taken a.Thus we know that our target user’s transcript does not have x before a.For instance, the transcript no.5 will be omitted.In more common situation, our target user has taken a list of courses, T = {a,b,c,…} not

﹁just a.Thus, what really need is Pr[x|TX].The question is how to figure out this probability.I will answer it later.Session 2: Precedence Mining We consider a set D of distinct courses.We use lowercase letters(e.g., a, b, …)to refer to courses in D.A transcript T is a sequence of courses, e.g., a-> b-> c-> d.Then the definition of Top-k Recommendation Problem is as follows.Given a set transcripts over D for n users, the extra transcript T of a target user, and a desired number of recommendations k, our goal is to: 1.Assign a score score(x)(between 0 and 1)to every course x ∈ D that reflects how likely it is the target student will be interested in taking x.If x ∈ T , then score(x)= 0.2.Using the score function, select the top k courses to recommend to the target user.To compute scores, we propose to use the following statistics, where x, y ∈ D: f(x): the number of transcripts that contain x.g(x;y): the number of transcripts in which x precedes course y.This slide shows the calculation result of f(x)and g(x,y).For example, from the table, we know that f(a)is 10 and g(a,c)is 3.We propose a precedence mining model to solve the Top-k Recommendation Problem.Here are ﹁some notation: xy, which we have memtioned in session 1, refers to transcript where x occurs without a preceding y;x﹁y refers to transcript where x occurs without y following it.We use quantities f(x)and g(x,y)to compte probabilities that encode the precedence information.For instance, from formular 1 to 7.I would not tell the detail of all formulars.We just pay attention to

﹁formular 5, note that this quantity above is the same as: Pr[x﹁y |yx] which will be used to compute score(x).As we know, the target user usually has taken a list of courses rather than a course, so we need to

﹁extent our probability calculation formulars.For example, suppose T={a,b}, Pr[xT] the probability x occurs without either an a or b preceding it;Pr[x﹁T] the probability x occurs without either an a or b following it.This probability can be calculated exactly.So how to calculate it?

Session 3: Recommendation Algorithms Let’s review session 2.The main goal of the recommendation algorithms is to calculate the score(x), and then select the top k courses based on these scores.Traditional recommendation algorithms compute a recommendation score for a course x in D only based on its frequency of occurence.It does not take into account the courses taken by the target user.Our recommendation algorithms called SingleMC conquer the shortcoming of the traditional ones.It computes the score(x)using the formular 5.The detail is as follows: a student with a transcrip T of taken courses, for the course y ∈ T, if y and x appear together in transcripts satisfies the

﹁threshold θ, then compute the Pr[x﹁y |yx], reflecting the likelihood the student will take course x

﹁and ignoring the effect of the other courses in T;finally the maximum of Pr[x﹁y |yx] is choosen as the score(x).Here is the calculation formular of score(x)of SignleMC.For example, with the higer score, d will be recommended.Another new recommendation algorithm named Joint Probabilities algorithm, JointP for short, is proposed.Unlike SingleMC, JointP takes into account the complete set of courses in a transcript.In formular 12, we cannot compute its quantity exactly, Remember this problem we have mentioned.Our solution is to use approximations.This slide is about the first approximating formular.And this the second approximating formular.The system is courseRand, and data set for experiment contains 7,500 transcripts.This slide shows the new recommendation algoritms with black color and the traditional ones with blue color.The chart on this slide indicates our new recommendation algorithms beat the traditional ones in precision, because the former ones exploit patterns across all users, while the latter ones just use the similar users.The chart on this slide points out our new recommendation algorithms also beat the traditional ones in coverage for the same reason.Session 5: Conclusion and Summary In conclusion, we proposed a novel precedence mining model, developed a probabilistic framework for making recommendations and implemented a suite of recommendation algorithms that use the precedence information.Experimental result shows that our new algorithms perform better than the traditional ones, and our recommendation system can be easily generalized to other scenarios, such as purchases of books, DVDs and electronic equitment.To sum up, first, I introduced the motivation and the outline of work;second, I gave the definition of precedence mining model;third, I described some new recommendation algorithms using precedence information;forth, I showed our experimental results to compare the new algorithms with traditional ones.Finally, I made a conclusion of our work..That’s all.Thank you!Are there any questions?

第五篇：國際會議英語演講稿

下面請看參加國際會議的英文版演講稿

On the International Day of United Nations peacekeepers, we honour the sacrifices of the men and women who lost their lives while serving under the UN flag.This year's commemoration is a somber one.The past 14 months have been especially deadly for UN peacekeeping.Ambushes in Darfur...Terrorism in Kabul...And a plane crash in Haiti...These were among the tragedies that struck peacekeeping last year, killing 121 people.That toll was nearly matched in a few seconds with the devastating earthquake that struck Haiti last January.The United Nations Stabilization Mission in Haiti lost 96 peacekeepers--the biggest single loss of life in peacekeeping history.But that dark day also became one of our finest hours, as the men and women of MINUSTAH set aside their own trauma, got the mission quickly back on its feet, and helped the people of Haiti cope with the horrific aftermath.As we honour such moving displays of courage and dedication, we also pay tribute to the more than 122,000 military, police and civilian personnel who serve with distinction in our operations across the world.Their efforts directly help millions of people...By providing security and promoting reconciliation...By clearing land-mines and demobilizing combatants...By strengthening institutions and the rule of law...By delivering aid and repatriating refugees and displaced persons

By supporting democratic elections, reforming the security sector...and so much more.peacekeeping is an indispensable part of the UN’s work for a better world.Let us give it the support it needs to succeed.