然后對個像素中除中心像素以外的其他個像素做二值化處理，它能夠很好地解決灰度受光照影響的問題，第二由于以像素為單位計算值像素噪聲會造成值的噪聲，然后以個像素塊即個像素為單位計算值。
國際會議講稿
1 Forward
Mr. Chairman, thank you very much for your kind introduction. Ladies and gentlemen, good morning! My name is Wuyong Chen，come from Sichuan University. It is a pleasure for me to be here on our recent results. My topic is Pretanning Integrated Post Tanning Process.
2 Introduction
You know leather industry is very important in China.However, the traditional technology consumed a lot of water and produced heavy pollution, this lead to a negative image for leather industry.
3 Conventional chroming:
Usually, traditional chroming will result in higher contents of chlorides, sulfates and COD in the effluent. Also, chrome solid waste is produced as shavings.
4
We know post-tanning proceinvolves many operations, and consumes a large amount of chemicals. In addition, the chemicals uptake is lower, only about 60%-80%, so, a lot of chemical is drained with wastewater, and induced serious environmental problem. Furthermore, there are many steps of washing in the traditional post-tanning, so, we should not
ignore water consumption, as reported, this stage occupies about 40-45% water amount in leather manufacture.5
Actually, all of these are due to several unreasonable elements in an old process, so, we modified the traditional procefrom chroming to post-tanning and developed a new process, 6 Our Achievements:
This is the several achievements in the new process. You can see that the modified procedid well in energy-saving as well as emission-reduction.
7
Maybe you are thinking how can we do it? So, in the next part, I will show you the new process.
8
This is a ‘Pretanning Integrated Post-tanning Process’, well, you can see there are three innovations in the process. In other words, there are three considerations. They are: Integrated to One Bath Process, Adjust Chemicals Adding Sequence as Charge Pattern, Decrease Acid-Base Reactions, Lower the pH Variations.
Now, let us move on to the first consideration
9 First consideration:
This (upper) is a traditional procefrom bated skin to crust leather. You can see there are many steps in an old method, and almost every one or two operations there will be a washing (marked out with red color). So, there is no doubt that the procewill consume a lot of water. So, the first consideration is: will the water amount be reduced significantly while several operations are integrated in one bath? This (lower) graph is an integrated process. We can see that the new proceonly needs two washings.
Now, let us move on to the second consideration.
10 Second consideration:
Let us see the left chart, the green circle stands for positive chrome tannage, red for negative chemicals including syntans, dyes and fatliquors. Now I will show you the chemical adding sequence from tanning to post-tanning in traditional process, you can see positive (+) chrome is added firstly, while the subsequent chemicals are all negative(-), such as syntans, dyes and fatliquors, this will cause charge competition among these negative materials and lead to poor chemical uptake.
Then, our second consideration is: if we adjust the chemicals adding sequence according to their charges, will the chemicals absorption be improved? Well, I am going to show you the
chemical adding sequence. You can see that charge competition will be moderate in the new process.
11 Third consideration:
Fig. 1 shows the pH variations from bating to fixing in a traditional process. It is obvious that the old procesubjects the skin to wide pH changes. Usually, this needs a large amount of acids and alkalis and results in more TSS, COD, chlorides and sulfates in wastewater. So, our third consideration is: If the variations of pH in proceare decreased, will the pollutions in wastewater lower, and will the physical properties of final leather improve? Fig. 2 is the pH change in the new process, you can see the pH change reduced clearly.
12 Pretanning Integrated Post-tanning Process:
As discussed above, we developed a new way, that is the Pretanning Integrated Post-tanning Process. This is a flow chart of the new process. You can see the bated skin in the procewas pretanned directly by melamine resin, then split and shaved, and then treated with the proceof the Integrated Post-tanning, it includes dying, pre-fatliquoring, chroming, retanning and fatliquoring, all the operations were in one bath, of course, only need one washing.
13 Tab.1:
We compared the new proceto traditional ones from leather properties, effluent as well as the amounts of water and chemicals. From Tab.1, we can see that the chemicals and water consumption in the new procedecreased by 40.29% and 42.73%, respectively, also, the time saved for 15 hours.14 Tab. 2:
We collected the spent liquors from tanning to post-tanning and mixed them together. Several pollution parameters were analyzed. Data is given in Tab. 2, we can see that the wastewater volume in the new procewas decreased by 43.78%, also, pollution parameters such as BOD5 load, COD load, TSS and Chroma were decreased significantly.15
Now we can see the photos of spent liquors from the two processes, it is evident that the new procedid well in reducing water pollution.
We know the new procegot good results in Energy-saving and Emission-reduction, however you may ask how about the quality of its final leather? Well, let me show you the results. 16 Tab. 3:
From this Tab, we can see that the experimental sample improved in tensile, tear and grain crack strength, and all
properties meet the national standard.
17 Fig. 3:
Fig. 3 shows the average rating of hand and visual properties. You can see the general appearance of experimental leather was comparable to control ones, but the experimental sample shows better fullneand surface color.
18 Tab. 4:
Tab. 4 presents the comparison of layer-wise distribution of chromium and oil content in leathers. The oil distribution is comparable for the two samples, but, the experiment ones show more uniform chromium distribution.
19 Conclusion
In this work, a modified procehas been developed for clean leather manufacture. The new proceresults in remarkable reduction in pollution. Also, the water and chemical consumption were reduced significantly. So, it provides an alternative procefor leathers industry in the future. 20 Thank you:
Now, I have finished my speech, I hope you will give me your comments and suggestions. Thank you!
英文國際會議講稿
PPT(1)
大家上午好！今天我匯報的主題是：基于改進型LBP算法的運動目標檢測系統。運動目標檢測技術能降低視頻監控的人力成本，提高監控效率，同時也是運動目標提娶跟蹤及識別算法的基矗圖像信號具有數據量大，實時性要求高等特征。隨著算法的復雜度和圖像清晰度的提高，需要的處理速度也越來越高。幸運的是，圖像處理的固有特性是并行的，尤其是低層和中間層算法。這一特性使這些算法，比較容易在FPGA等并行運算器件上實現，今天匯報的主題就是關于改進型LBP算法在硬件上的實現。
good morning everyone.
My report is about a Motion Detection System Based on Improved LBP Operator.
Automatic motion detection can reduce the human cost of video surveillance and improve efficiency ['f()ns]，it is also the fundament of object extraction, tracking and recognition
[rekg'n()n]. In this work, efforts ['efts] were made to establish the background model which is resistance to the variation of illumination. And our video surveillance system was realized on a FPGA based platform.
PPT(2)
目前，常用的運動目標檢測算法有背景差分法、幀間差分法等。幀間差分法的基本原理是將相鄰兩幀圖像的對應像素點的灰度值進行減法運算，若得到的差值的絕對值大于閾值，則將該點判定為運動點。但是幀間差分檢測的結果往往是運動物體的輪廓，無法獲得目標的完整形態。
Currently, Optic Flow, Background Subtraction and Inter-frame difference are regard as the three mainstream algorithms to detect moving object.
Inter-frame difference based method need not model ['mdl] the background. It detects moving objects based on the frame difference between two continuous frames. The method is easy to be implemented and can realize real-time detection, but it cannot extract the full shape of the moving objects [6].
PPT(3)
在攝像頭固定的情況下，背景差分法較為簡單，且易于實現。若背景已知，并能提供完整的特征數據，該方法能較準確地檢測出運動目標。但在實際的應用中，準確的背景模型很難建立。如果背景模型如果沒有很好地適應場景的變化，將大大影響目標檢測結果的準確性。像這副圖中，背景模型沒有及時更新，導致了檢測的錯誤。
The basic principle of background removal method is building a background model and providing a classification of the pixels into either foreground or background [3-5]. In a complex and dynamic environment, it is difficult to build a robust [r()'bst] background model.
PPT(4)
上述的幀間差分法和背景差分法都是基于灰度的。基于灰度的算法在光照條件改變的情況下，性能會大大地降低，甚至失去作用。
The algorithms we have discussed above are all based on grayscale. In practical applications especially outdoor environment, the grayscales of each pixel are unpredictably shifty because of the variations in the intensity and angle of illumination.
PPT(5)
為了解決光照改變帶來的基于灰度的算法失效的問題，我們考慮用紋理特征來檢測運動目標。而LBP算法是目前最常用的表征紋理特征的算法之一。首先在圖像中提取相鄰9個像素點的灰度值。然后對9個像素中除中心像素以外的其他8個像素做二值化處理。大于
等于中心點像素的，標記為1，小于的則標記為0。最后將中心像素點周圍的標記值按統一的順序排列，得到LBP值，圖中計算出的LBP值為10001111。當某區域內所有像素的灰度都同時增大或減小一定的數值時，該區域內的LBP值是不會改變的，這就是LBP對灰度的平移不變特性。它能夠很好地解決灰度受光照影響的問題。
In order to solve the above problems, we proposed an improved LBP algorithm which is resistance to the variations of illumination.
Local binary pattern (LBP) is widely used in machine vision applications such as face detection, face recognition and moving object detection [9-11]. LBP represents a relatively simple yet powerful texture descriptor which can describe the relationship of a pixel with its immediate neighborhood. The fundamental of LBP operator is showed in Fig 1. The basic version of LBP produces 256 texture patterns based on a 9 pixels neighborhood. The neighboring pixel is set to 1 or 0 according to the grayscale value of the pixel is larger than the value of centric pixel or not. For example, in Fig1 7 is larger than 6, so the pixel in first row first column is set to 1. Arranging the 8 binary numbers in certain order, we get an 8 bits binary number, which is the LBP pattern we need. For example in Fig.1, the LBP is 10001111. LBP is tolerant ['tl()r()nt] against illumination changing. When the grayscales of pixels in a 9 pixels window are shifted due to illumination changing, the LBP value will keep unchanged.
PPT(6)
圖中的一些常見的紋理，都能用一些簡單的LBP向量表示，對于每個像素快，只需要用一個8比特的LBP值來表示。
There are some textures , and they can be represent by some simple 8bit LBP patterns. PPT(7)
從這幅圖也可以看出，雖然灰度發生了很大的變化，但是紋理特征并沒有改變，LBP值也沒有變化。
You can see, in these picture , although the grayscale change alot, but the LBP patterns keep it value.
PPT(8)
上述的算法是LBP算法的基本形式，但是這種基本算法不適合直接應用在視頻監控系統中。主要有兩個原因：第一，在常用的視頻監控系統中，特別是在高清視頻監控系統中，9個像素點覆蓋的區域很小，在如此小的區域內，各個像素點的灰度值十分接近，甚至是相同的，紋理特征不明顯，無法在LBP值上體現。第二，由于以像素為單位計算LBP值，像素噪聲會造成LBP值的噪聲。這兩個原因導致計算出的LBP值存在較大的隨機性，甚至在靜止的圖像中，相鄰兩幀對應位置的LBP值也可能存在差異，從而引起的誤檢測。
為了得到更好的檢測性能，我們采用基于塊均值的LBP算法。這種方法的基本原理是先計算出3×3個像素組成的的像素塊的灰度均值，以灰度均值作為該像素塊的灰度值。然后以3×3個像素塊（即9×9個像素）為單位，計算LBP值。
The typical LBP cannot meet the need of practical application of video surveillance for two reasons: Firstly, a “window” which only contains 9 pixels is a small area in which the grayscales of pixels are similar or same to each other, and the texture feature in such a small area is too weak to be reflected by a LBP. Secondly, pixel noise will immediately cause the noise of LBP, which may lead to a large number of wrong detection. In order to obtain a better performance, we proposed an improved LBP based on the mean value of “block”. In our algorithm, one block contains 9 pixels. Compared with original LBP pattern calculated in a local 9 neighborhood between pixels, the improved LBP operator is defined by comparing the mean grayscale value of central block
with those of its neighborhood blocks (see Fig.2).By replacing the grayscales of pixels with the mean value of blocks, the effect of the pixel noise is reduced. The texture feature in such a bigger area is more significant to be described by LBP pattern.
PPT(9)
運用LBP描述背景，其本質上也是背景差分法的一種。背景差分法應用在復雜的視頻監控場景中時，要解決建立健壯的背景模型的問題。駛入并停泊在監控畫面中的汽車，被搬移出監控畫面的箱子等，都會造成背景的改變。而正確的背景模型是正確檢測出運動目標并提取完整目標輪廓的基矗如果系統能定時更新背景模型，將已經移動出監控畫面的物體“剔除”出背景模型，將進入監控畫面并且穩定停留在畫面中的物體“添加”入背景模型，會減少很多由于背景改變而造成的誤檢測。
根據前一節的介紹，幀間差分法雖然無法提取完整的運動目標，但是它是一種不依賴背景模型就能進行運動目標檢測的算法。因此，可以利用幀間差分法作為當前監控畫面中是否有運動目標的依據。如果畫面中沒有運動目標，就定期對背景模型進行更新。如果畫面中有運動目標，就推遲更新背景模型。這樣就能避免把運動目標錯誤地“添加”到背景模型中。 In practical application, the background is changing randomly. For traditional background subtraction algorithm the incapability of updating background timely will cause wrong detection. In order to solve this problem, we propose an algorithm with dynamic self updating background model. As we know, Inter-frame difference method can detect moving object without a background model, but this method cannot extract the full shape. Background subtraction method can extract the full shape but needs a background model. The basic principle of our algorithm is running a frame difference moving object detection proceconcurrently [kn'krntli] with the background subtraction process. What’s time to update the background is according to the result of frame difference detection.
PPT(10)
運動目標檢測系統特別是嵌入式運動目標檢測系統在實際應用中要解決實時性的問題。比如每秒60幀的1024×768的圖像，對每個像素都運用求均值，求LBP等算法，那么它的運算量是十分巨大的，為此我們考慮在FPGA上用硬件的方式實現。
If LBP algorithm is implemented in a software way, it will be very slow. FPGA have features of concurrent computation, reconfiguration and large data throughput. It is suitable to be built an embedded surveillance system. The algorithm introduced above is implemented on a FPGA board.
PPT(11)
這就是我們硬件實現的系統結構圖。首先輸入系統的RGB像素信號的濾波、灰度計算及LBP計算，得到各個像素塊的LBP值。然后背景更新控制模塊利用幀差模塊的檢測結果控制背景緩存的更新。區域判定模塊根據背景差模塊的輸出結果，結合像素塊的坐標信息，對前景像素塊進行區域判定。
The structure of the system is showed in this figure. In this system, a VGA signal is input to the development board. and the LBP pattern is calculated , Frame difference module also compares the current frame and the previous frame to determine whether there is a moving object in the surveillance vision. If the surveillance vision is static for a certain amount of frame, the background model will be updated.
PPT(12)
圖中是LBP計算模塊。圖中所示的窗口提取結構可以實現3×3像素塊窗口的提齲像素信號按順序輸入該結構，窗口中的數據就會按順序出現在Pixel1- Pixel9這9個寄存器中，
從而在最短的延時內提取出相鄰9個像素點的灰度值。行緩存的大小等于每一行圖像包含的像素個數減1。將9個像素點的灰度值通過求均值模塊，可以求出一個像素塊的像素均值。
將像素塊均值作為輸入再次通過類似的結構，可以提取出3×3個相鄰像素塊的灰度值。這時行緩存的大小為每一行包含的像素塊的個數減1。再用9個窗口的灰度值作為輸入，用比較器陣列計算出最終的LBP值。
To achieve real time computation of the LBP, a circuit structure is put forward as showed in Fig.5. Two line buffers and nine resisters are connected in the way showed in the figure. Nine neighbor pixels are extracted with minimum ['mnmm] delay, and the mean value of this block is calculated by the mean value calculate module which contains some adders and shifters. The mean values of the blocks are inputted to a similar structure and extracted in a similar way, and the LBP is calculated by the consequence LBP calculate module.
PPT(13)
求均值模塊采用如圖3-12所示的四級流水方式實現。在算法的設計過程中，需要求出的是3×3像素塊中9個像素的均值。但是在硬件實現時，為了更合理地利用硬件資源，只計算剔除中心像素后的8個像素的均值。這樣做可以在不對計算結果造成太大影響的情況下減少加法器的使用。而且在求均值的最后一級流水，除8運算比除9運算更容易實現。因為8是2的整數冪，除8運算只需要將各個像素的和右移3位。而除9運算在FPGA中需要專用的DSP模塊來完成。
PPT(14)
如圖所示，塊均值計算模塊計算出的8個塊均值被圖3-11中的窗口提取模塊提取出來，并作為比較器陣列的輸入，比較器的輸出結果用0和1表示。最終的比較結果按一定的順序排列，重新拼接成一個8位的二進制數，即LBP值。LBP計算電路沒有采用流水結構，在一個時鐘周期內就能得到計算結果。
PPT(15)
這個是在系統測試中，實現對多個目標的檢測。
In this system test ,we achieve a multi-object detection.
PPT(16)
這個圖是對動態背景更新的測試，在監控區域中劃定一個目標區域，把一個靜止的物體放置到目標區域中。在前3分鐘內，系統會將其當做前景目標，矩形窗口會以閃爍的形式發出報警信號。3分鐘過后，由于物體一直處于靜止狀態，系統檢測到了10800個靜止幀，于是更新背景模型。靜止的物體被當做背景的一部分，此后窗口不再閃爍。經驗證，該系統能夠正確實現背景模型更新算法。
This is the test for the auto background update. We put a statics object in the surveillance area，at the beginningthis is trusted as a moving object . after 3 minutes , the system receive ten thousand static frames ,and then update the background model. Then this object is regard as a part of the background.
PPT(17)
此外為了驗證系統對室外光照變化抑制能力，我們選取了大量有光照變化，并且有運動目標的視頻對系統進行了測試。
In order to verify the resistance to the varation of illumination , a certification experiment is designed, and the ROC curves of the two algorithms based on LBP and grayscale are plotted and compared. A number of short video clips with shifty and fixed illumination, including positive
samples with moving objects and negative samples without moving objects .
PPT(18) 測試平臺如圖所示。用一臺PC機作為測試信號的輸出源，然后在PC機中播放視頻，并將視頻VGA信號發送給運動目標檢測系統，模擬真實的監控環境。FPGA將輸入信號和區域邊框圖形相疊加后在LCD上顯示。
The picture of the certification experiment is showed in this picture . A PC acts as the source of the test signal which is input to the FPGA in the form of VGA. Passing through the FPGA board, video signal is displayed on a LCD screen.
PPT(19)
并最終描繪了系統的ROC特性曲線。在沒有光照強度變化的情況下，采用基于灰度的運動目標檢測算法的性能略優于基于LBP值的運動目標檢測算法，兩種算法都能取得較好的檢測效果。但是在圖5-15中（測試集2），也就是在光照強度變化的情況下，畫面整體灰度發生較大的改變，基于灰度的檢測算法的性能大幅度下降，接近于失效。而采用LBP值的檢測算法卻能維持較好的性能。可見基于LBP的檢測算法對抑制光照強度變化造成的誤檢測有較好的效果。
This two figure are the ROC curves of the experiments using our algorithm and traditional grayscale-basedalgorithm . We can see in the Fig.1 which corresponds to the condition with fixed illumination, the performance of the grayscale-based algorithm is slightly better than these of LBP-based algorithm, they can both detect moving object effectively. But in Fig.2 which corresponds to the condition with shifty illumination, grayscale based algorithm deteriorates drastically and nearly lose efficacy ['efks]. But the improved LBP algorithm still keeps a good performance.
PPT(20)
謝謝大家！
Thanks for your attention
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 finenemodulus 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)（ppt第5頁） The Freeze–thaw resistance was tested according to ASTM C666 and GB/T 50082-2016 “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. Maand 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 malowas 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 maloof ASC as the evaluation index of freeze–thaw destroy, because maloof A1–A5 vary indistinctively in the progreof 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 maloss, 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
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