2021年1月10日星期日

車牌號碼吉凶判斷

辦法一：將車牌號碼末4位數除以80，得到的小數部份乘以80後得到的數字對照「吉凶號碼表」即可。

例：YW-6675

將6675 ÷ 80 ＝ 83.4375,然後只取小數部份 0.4375 × 80 ＝ 35

餘數35對照表得知是「吉」，所以這個車牌號碼便是幸運號碼。

辦法二：將車牌號碼的每個數字加起來，得到的總和對照「吉凶號碼表」。
上例：6 ＋ 6 ＋ 7 ＋ 5 ＝ 24
總和24對照表得知是「吉」，所以這個車牌號碼便是幸運號碼。
看法一及看法二都認為這個車牌號碼是「吉數」，所以我們可以說這個車牌號碼是「大吉」。如果兩種看法都認為是「凶」，我們就可以說這個車牌號碼是「大凶」；如果一個看法是「吉」，一個看法是「凶」，就可以說是「半吉半凶」。
車牌數字以和數吉凶為區分數字最好由小到大排列為大吉
吉數;1.3.5.6.7.8.11.13.15.16.17.18.21.23.24.25.29.31.32.33.35
凶數;2.4.9.10.12.14.19.20.22.26.27.28.30.34

附件1 筆劃數理吉凶

筆劃	吉凶	筆劃	吉凶	筆劃	吉凶
1	吉	28	凶	55	吉帶凶
2	凶	29	吉(吉帶凶)	56	凶
3	吉	30	吉帶凶	57	凶帶吉
4	凶	31	吉	58	吉帶凶
5	吉	32	吉	59	凶
6	吉	33	吉	60	凶
7	吉	34	凶	61	吉帶凶
8	吉	35	吉	62	凶
9	凶	36	凶	63	吉
10	凶	37	吉	64	凶
11	吉	38	凶帶吉	65	吉
12	凶	39	吉	66	凶
13	吉	40	吉帶凶	67	吉

14	凶	41	吉	68	吉
15	吉	42	吉帶凶	69	凶
16	吉	43	吉帶凶	70	凶
17	吉	44	凶	71	吉帶凶
18	吉	45	吉	72	凶
19	凶	46	凶	73	吉(吉帶凶)
20	凶	47	吉	74	凶
21	吉	48	吉	75	吉帶凶
22	凶	49	凶(吉帶凶)	76	凶
23	吉	50	吉帶凶	77	吉帶凶
24	吉	51	吉帶凶(凶)	78	吉帶凶
25	吉	52	吉	79	凶
26	凶帶吉	53	吉帶凶	80	吉帶凶
27	吉帶凶	54	凶

附件2 號碼凶吉對照表

吉凶數字　解釋　　　　　　　批注

1　　　　大展鴻圖．可獲成功　吉

2　　　　一盛一衰．勞而無功　凶

3　　　　蒸蒸日上．百事順遂　吉

4　　　　坎坷前途．苦難折磨　凶

5　　　　生意欣榮．名利雙收　吉

6　　　　天降幸運．可成大功　吉

7　　　　和氣致祥．必獲成功　吉

8　　　　貫徹志望．成功可期　吉

9　　　　獨營無力．財利無望　凶

10　　　　空費心力．徒勞無功　凶

11　　　　穩健著實．必得人望　吉

12　　　　薄弱無力．謀事難成　凶

13　　　　天賦吉運．能得人望　吉

14　　　　是成是敗．惟靠堅毅　凶

15　　　　大事成就．一定興隆　吉

16　　　　成就大業．名利雙收　吉

17　　　　有貴人助．可得成功　吉

18　　　　順利昌隆．百事亨通　吉

19　　　　內外不合．障礙重重　凶

20　　　　歷盡艱難．焦心憂勞　凶

21　　　　專心經營．善用智能　吉

22　　　　懷才不遇．事不如意　凶

23　　　　名顯四方．終成大業　吉

24　　　　須靠自力．能奏大功　吉

25　　　　天時地利．再得人格　吉

26　　　　波瀾起伏．凌駕萬難　凶

27　　　　一盛一衰．可守成功　凶帶吉

29　　　　青雲直上．才略奏功　吉

30　　　　吉凶參半．得失相伴　凶

31　　　　名利雙收．大業成就　吉

32　　　　池中之龍．成功可望　吉

33　　　　智能慎始．必可昌隆　吉

34　　　　災難不絕．難望成功　凶

35　　　　中吉之數．進退保守　吉

36　　　　波瀾重疊．常陷窮困　凶

37　　　　逢凶化吉．風調雨順　吉

38　　　　名雖可得．利則難獲　凶帶吉

39　　　　光明坦途．指日可待　吉

40 一盛一衰．浮沉不定　吉帶凶

41　　　　天賦吉運．前途無限　吉

42　　　　事業不專．十九不成　吉帶凶

43　　　　忍耐自重．轉凶為吉　吉帶凶

44　　　　事難遂願．貪功好進　凶

45　　　　綠葉發枝．一舉成名　吉

46　　　　坎坷不平．艱難重重　凶

47　　　　有貴人助．可成大業　吉

48　　　　名利俱全．繁榮富貴　吉

49　　　　遇吉則吉．遇凶則凶　凶

50　　　　吉凶互見．一成一敗　吉帶凶

51　　　　一盛一衰．浮沉不常　吉帶凶

52　　　　雨過天青．即獲成功　吉

53　　　　盛衰參半．先吉後凶　吉帶凶

54　　　　雖傾全力．難望成功　凶

55　　　　外觀隆昌．內隱禍患　吉帶凶

56　　　　事與願違．終難成功　凶

57　　　　努力經營．時來運轉　吉

58　　　　浮沉多端．始凶終吉　凶帶吉

59　　　　遇事猶疑．難望成事　凶

60　　　　心迷意亂．難定方針　凶

61　　　　雲遮半月．百隱風波　吉帶凶

62　　　　煩悶懊惱．事事難展　凶

63　　　　萬物化育．繁榮之象　吉

64　　　　十九不成．徒勞無功　凶

65　　　　吉運自來．能享盛名　吉

66　　　　內外不和．信用缺乏　凶

67　　　　事事如意．富貴自來　吉

68　　　　不失先機．可望成功　吉

69　　　　動搖不安．常陷逆境　凶

70　　　　慘淡經營．難免貧困　凶

71　　　　吉凶參半．惟賴勇氣　吉帶凶

72　　　　得而復失．難以安順　凶

73　　　　安樂自來．自然吉祥　吉

74　　　　如無智謀．難望成功　凶

75　　　　吉中帶凶．進不如守　吉帶凶

76　　　　此數大凶．破產之象　凶

77　　　　先苦後甘．不致失敗　吉帶凶

78　　　　有得有失．華而不實　吉帶凶

79　　　　前途無光．希望不大　凶

80　　　　得而復失．枉費心機　吉帶凶

81　　　　最極之數．能得成功　吉

from : http://tanzih.blogspot.com/2013/09/4-80-80-yw-6675-6675-80-83.html

2020年12月27日星期日

using jquery to link dropdown to anchor text

use:

window.location.hash="someAnchor";

to jump to the anchor <a id="someAnchor" />

from : https://stackoverflow.com/questions/11147258/using-jquery-to-link-dropdown-to-anchor-text

2020年12月16日星期三

Terminology Explained: Percentile - P10, P50 and P90

In today’s Terminology Explained: P10, P50 and P90.

When working with Monte Carlo simulations, some parameters that show up quite a lot are the P10, P50 and P90.

What are these parameters? Why are they so important?

The large amount of data produced by statistical methods sometimes make it difficult to effectively use its results in the decision-making process. As we mentioned in our post about the Monte Carlo method, this methodology is based on “simulating” potential scenarios. An example of its use in the oil and gas industry is the estimation of potential lifecycle (i.e. a feasible representation of the life of the asset) to predict asset performance. Sometimes, when running models with a large variation, analysts will engage simulations that go beyond 1000 lifecycles. This number multiplied by the specified period required to simulate the asset performance (i.e. platform useful life = 10-20 years) means that we are effectively tracking tens of thousands of years of simulation.

The P10, P50 and P90 are useful parameters to understand how the numbers are distributed in a sample. Let’s try to explain that using an example.

Consider the following sample (list of observations). They could represent anything – oranges, bananas, production efficiency etc.:

There are several options to display this data. You could decide to group the observations within a certain range and create a frequency table (i.e. how frequently an observation appears in the sample). This is calculated by counting the observations with a specific value and dividing by the total number of observations (e.g. 93 appears one time in a sample which has ten observations so its frequency is 10%):

Data	Number	Frequency
93	1	10%
94	2	20%
95	4	40%
96	2	20%
97	1	10%

The frequency can also be used to create the famous “bell” curve:

Normal distribution i.e. Bell curve

With this distribution, there is no need to have access to all the data points in the sample to start the inference work. We can tell, by looking at the graph, that most observations are around 95.

What is Cumulative Frequency and probability of Exceedance and Non-Exceedance?

Another option to display this distribution is using a “Cumulative Frequency” graph. This table is calculated by adding each frequency from a frequency distribution table to the sum of its predecessors.
One will notice that you can start from either the lower observation values to higher observation values or the opposite. So, we must introduce two new concepts:

Probability of exceedance: If you start from the left (i.e. lower observation values) of the bell curve to the right (i.e. higher observation values), you are building-up a probability of exceedance curve.
Probability non-exceedance: If you start from the right (i.e. higher observation values) of the bell curve to the left (i.e. lower observation values), you are building-up a probability of non-exceedance curve.

So, considering our sample, if we are preparing a probability of exceedance graph, we know that 97 appears 10% of the time and 96 appears 20% of the time, so for a Cumulative Frequency distribution, we will have 96 associated to 30%. It means that 30% of the observations will exceed the value of 96.

Data	Number	Frequency	Probability of Exceedance
93	1	10%	100%
94	2	20%	90%
95	4	40%	70%
96	2	20%	30%
97	1	10%	10%

Probability of Exceedance

Again, this graph adds up the frequency of occurrence as the value of the observation decreases i.e. 30% of our observations will be equal exceed the value of 96. This is what is called the probability of exceedance.

As mentioned before, another option is taking the opposite view – adding frequency of observations that will not exceed a certain value of observation.

So, considering our sample, if we are preparing a probability of non-exceedance graph, we know that 93 appears 10% of the time and 94 appears 20% of the time, so for a Cumulative Frequency distribution, we will have 94 associated to 30%. It means that 30% of the observations will be equal or not exceed the value of 94.

Probability of Non-Exceedance

Data	Number	Frequency	Probability of Non-Exceedance
93	1	10%	10%
94	2	20%	30%
95	4	40%	70%
96	2	20%	90%
97	1	10%	100%

You should also note that, for the same distribution, the P10 for the probability of exceedance is exactly the same as the P90 of the probability of non-exceedance. Another useful notion refers to the first and last value of these distributions. The first value for the Probability of exceedance and the last value for the Probability of Non-exceedance will always be equal to the total for all observations, since all frequencies will already have been added to the previous total. Again, this distribution can be extremely useful when dealing with large samples.

What about P10, P50 and P90?

Finally, in the P10, P50 and P90, the “P” stands for Percentile. The calculate value will depend on the type of distribution you have chosen to create. For example, if we decide to go for a probability of exceedance curve, when we state that a distribution has a P10 of X, we are saying “in this distribution, 10% of the observations will exceed the value of X”.

So, in our sample, P10 would be 96 – 10% of our observation will exceed the value of 96. And P90 will be 94 – 90% of our observation will exceed the value of 94.

Note that it does not mean that the estimate has a 90% chance of occurring – that is a very different concept. P50 is more likely to occur because it is closer to the mean. For this sample of observations, our P50 would be 95 which is exactly the mean (i.e. 95). There is a reason for this which is explained later in this article.

What does that mean for us working in the oil and gas sector?

Let’s try to give an example relating to the oil and gas industry.

We can never be sure exactly (this is an important word which is the core reason of why we use probabilistic approach) how much crude oil is available for production in the reserves. However, we can have a good estimate (another important word). Geologists and Reservoir Engineers working for the oil and gas industry have developed numerous methods and tools to calculate the potential production and get estimates of production rates from oil and gas reservoirs to obtain a high economic recovery.

The Securities and Exchange Commission (SEC) define the reserves and resources estimates in terms of P90/P50/P10 ranges as:

“The range of uncertainty of the recoverable and/or potentially recoverable volumes may be represented by either deterministic scenarios or by a probability distribution (see Deterministic and Probabilistic Methods, section 4.2).
When the range of uncertainty is represented by a probability distribution, a low, best, and high estimate shall be provided such that:
There should be at least a 90% probability (P90) that the quantities actually recovered will equal or exceed the low estimate.
There should be at least a 50% probability (P50) that the quantities actually recovered will equal or exceed the best estimate.
There should be at least a 10% probability (P10) that the quantities actually recovered will equal or exceed the high estimate.
When using the deterministic scenario method, typically there should also be low, best, and high estimates, where such estimates are based on qualitative assessments of relative uncertainty using consistent interpretation guidelines. Under the deterministic incremental (risk-based) approach, quantities at each level of uncertainty are estimated discretely and separately (see Category Definitions and Guidelines, section 2.2.2).”

The text gives us indication of what curve we should be using – actually recovered will equal or exceed – it means we should be using the probability of exceedance curve.

Translating all these terms, the amount thought to be in the reserves is generally estimated as three figures:

Proved (P90): The lowest figure. It means that 90% of the calculated estimates will be equal or exceed P90 estimate.
Median (P50): This is the median.
Possible (P10): The highest figure, it means that 10% of the calculated estimates will be equal or exceed P10 estimate.

For example, if a geologists’ calculation estimates that there is a 90% chance that an oil field contains 50 million barrels and another estimate says there is a 10% chance of producing another 20 million barrels in addition to the 50 million barrels. So, we would refer to:

P10 as the highest figure – it is possible that we can produce up to 70 million barrels.
P90, the lowest figure – is it proved that we can produce up to 50 million barres.

Remember that the production profile is extremely important for Maros as it is the reference point for the entire analysis, as described here.

What is the best estimate?

There is extensive discussion on what is the best estimate – mean, P50, P90 and P10?

A lot of people would insist that taking the mean is better.

This argument says that the mean will incorporate both the higher and the lower observations which will smooth the differences when added together. Comparing to the P10, which could potentially give estimates that are over-optimistic, and the P90, a conservative estimate which could potentially leave too much oil, both providing confusing future trends.

The next step would be discussing P50 and mean – this is a hard one.

It is a common misunderstanding that the P50 is a synonym of mean. This will be true is the probability distribution function for the observations were symmetrical. In this case, the mode, mean and P50 would all be the same. In Maros and Taro, we call the P50 by an alternative name to avoid confusion: Median. For distributions where the values tend to be skewed, the mode, P50, and the mean begin to diverge.

So, what’s the best? The argument for the mean works well for distributions that are symmetrical but if the distribution has a degree of skewness it might be better to reconsider and perhaps look at the P50.

from : https://blogs.dnvgl.com/software/2016/12/p10-p50-and-p90/

2020年4月14日星期二

Missing write access in mac to /usr/local/lib/node_modules

Run these commands in a terminal window:

sudo chown -R $USER ~/.npm
sudo chown -R $USER /usr/lib/node_modules
sudo chown -R $USER /usr/local/lib/node_modules

from : https://stackoverflow.com/questions/54802330/missing-write-access-in-mac-to-usr-local-lib-node-modules

2020年4月9日星期四

JSPaint 經典「小畫家」繪圖模擬器，沒裝軟體也能畫！

網站名稱：JSPaint
網站網址：http://jspaint.ml

from : https://briian.com/49549/

訂閱：文章 (Atom)

Linux/Apache/PHP+MySQL/Smarty/JQuery/RubyOnRails

2021年1月10日星期日

車牌號碼吉凶判斷

2020年12月27日星期日

using jquery to link dropdown to anchor text

2020年12月16日星期三

Terminology Explained: Percentile - P10, P50 and P90

In today’s Terminology Explained: P10, P50 and P90.

What are these parameters? Why are they so important?

What is Cumulative Frequency and probability of Exceedance and Non-Exceedance?

What about P10, P50 and P90?

What does that mean for us working in the oil and gas sector?

What is the best estimate?

2020年4月14日星期二

Missing write access in mac to /usr/local/lib/node_modules

2020年4月9日星期四

JSPaint 經典「小畫家」繪圖模擬器，沒裝軟體也能畫！

wibiya widget

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##EasyReadMore##

暫存區

Linux/Apache/PHP+MySQL/Smarty/JQuery/RubyOnRails

2021年1月10日 星期日

車牌號碼吉凶判斷

2020年12月27日 星期日

using jquery to link dropdown to anchor text

2020年12月16日 星期三

Terminology Explained: Percentile - P10, P50 and P90

In today’s Terminology Explained: P10, P50 and P90.

What are these parameters? Why are they so important?

What is Cumulative Frequency and probability of Exceedance and Non-Exceedance?

What about P10, P50 and P90?

What does that mean for us working in the oil and gas sector?

What is the best estimate?

2020年4月14日 星期二

Missing write access in mac to /usr/local/lib/node_modules

2020年4月9日 星期四

JSPaint 經典「小畫家」繪圖模擬器，沒裝軟體也能畫！

wibiya widget

搜尋Vegeee網誌

網站規劃,網頁製作,網站建置推薦

DesignPattern

我家寶貝

專案管理

程式教學手冊

投資理財網站

訂閱

最新文章

最新回應

我常逛的網站

文章分類

網誌存檔

關於我自己

訪客足跡

馬來西亞

##EasyReadMore##

暫存區

2021年1月10日星期日

2020年12月27日星期日

2020年12月16日星期三

2020年4月14日星期二

2020年4月9日星期四