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B16.5.html ASME B16.5 WN # NPS Class O tf(min) X A B(min) Y1 Y2 R B.C.D Bolt Hole Bolt Size(Metric) Bolt Size(Imperial) Bolt Length 0.5150* 1/2 150 88.9 9.7 30.2 21.3 22.4 46 14.2 35.1 60.5 15.9 1/2x13UNC M12-4 70 0.75150 3/4 150 98.6 11.2 38.1 26.7 27.7 50.8 14.2 42.9 69.9 15.9 1/2x13UNC M12-4 75 1150** 1 150 108 12.7 49.3 33.5 34.5 53.8 15.7 50.8 79.2 15.9 1/2x13UNC M12-4 75 1.25150 1-1/4 150 117.3 14.2 58.7 42.2 43.2 55.6 19.1 63.5 88.9 15.9 1/2x13UNC M12-4 85 1.5150* 1-1/2 150 127 15.7 65 48.3 49.5 ...

Video  and AI  video understanding / frame / persistence of vision.  one more dimension than figure - time. I (x,y,t) action / action recognition  challenges of action recognition  background noise : filming distance , light (rightness), angle, shadow lots of noise  same stuff but different meaning (shaving bears) not significant , can not be clearly defined (eating foods) less data (13320 videos in youtube , 14000 thousands figures in ImageNet ) How to defined features for video AI ? motion  OPTICAL FLOW - 3D motion's projection.

Voice and AI (transform music to data) speech and music cock-tail party effect 85-1100 Hz for Human Steps from Human voice to MP3  VOICE > SENSOR > VOLTAGE SAMPLING (computer cannot manage continuous signal) : DISCRETE (time) QUANTIZATION : DISCRETE (amplitude) ENCODING TIME SERIES WAVEFORM SAMPLING RATE (for MP3 is 44100 Hz , the reason is human is not sensitive to high frequency voice ) Characteristics of Voice frequency spectrum (x - frequency ; y - amplitude) with log coordinate Amplitude (Loudness) Frequency (Pitch) Timbre (related with harmonics) Classic Voice Features -MFCC MFCC - Mel-Frequency Cepstral Coefficients Formant Vowel Steps of using MFCC 26 dimensions vector  Mel-Frequency, mel(f) = 1125 *ln(1+f/700) good classification at low frequency , bad at high frequency (similar as human) Calculate Its CEPSTRAL the purpose of cepstral is converting 26 dimensions into 13 dimensions. window width window pitch finally g...

Recap :What are AI good at ? Classification ! Two steps of classification : feature extract and feature classify What is image mean to computer ? pixel , matrix , resolution  Black and white , 0 - 255 Colorful (0-255,0-255,0-255), RGB , tensor  Channel number (# dimension) Tensor  0 order : scalar 1 order : vector 2 order : matrix Before deep learning (human designed feature) graphical feature is defined by computer vision, edge texture after combined with machine learning  object recognization object detection Convolution vector, matrix, tensor can be applied. steps of convolution : repeat (slide, extract, inner product) convolution kernel  histogram of oriented gradients (HOG) - to get the edge of figure  step 1 : using convolution calculation get the edge of the figure  step 2 : divide figure into multiple zones  step 3 : according to edge feature , amplitude, orientation conduct statistic.  step 4 :...

購屋指南 看房子Check List指點 部分摘錄自 : https://goo.gl/AX16VM https://goo.gl/0HfNA https://goo.gl/nmW5Zr 棟房子在不同時間點呈現的真實屋況 (晴天雨天，白天晚上) 至少看完30間再購買 房子的四周環境  治安死角 捷運站 高鐵站 醫院 賣吃的 學區 市場 便利店 高速公路 基地台 墓地 加油站 高壓電塔 車流量，塞車情形 工業區附近 ? 住宅附近的鄰居  是否能接受小孩哭聲 養寵物 出入人口複雜等 住宅附近的點燈率 (晚上看) 隔音效果好不好 亂路邊停車 房屋的背景調查  是否為凶宅 屋主為何要出售 過去是否有淹水的紀錄 磁磚是否剝落 管線是否正常 屋齡為何 管理費用 是否有車位 公設比 相關尺寸空間 手機訊號 附近有無變電箱，鴿舍 除了搭電梯也要看樓梯間 公佈欄住戶欠管理費的情形 注意大樓之間的距離 避免中繼水箱樓層 二樓通常會有交會的排水管線 房屋的風水調查  房屋是否寬正 是否通風 採光是否好 是否有壁癌 (漏水，最好連續三四天的下雨天後去看，有沒有霉味，接縫是否完整，突起，龜裂) 是否需要大規模翻修 是否鄰近馬路有噪音 房屋樓板高度 路衝，壁刀 西曬，房屋面向 淋浴間可以轉身? 兩房們對開相撞? 床鋪旁是水管? 附近的房屋近幾年的售價，成交價，成交量 議價空間至少一成 (多看多比較) 與房仲培養良好的關係  議價空間 (etp.cpami.gov.tw/)，可以查詢各路段成交行情。另外可以上網多和網友討論「買房經」，參考網友買房的議價實戰經驗。 掌握「投資客拋售」的時機點 (新成屋戶數多、供給量大，急著用錢的投資客較容易議價、議價空間大。) 掌握房市利空的時機點，像是歐債風暴、奢侈稅、景氣衰退等時間點出門買屋議價，通常也會獲得不錯的價格。如果真的遇到屋主急著賣屋，尤其是因為怕被課稅，或是資金週轉不靈的投資客，相對的也比較容易議價，議價空間可能可以拉高到二成。 議價時記得多嫌棄對方的房屋，例如碰到頂樓或二樓的房子，就可以抱怨頂樓容易漏水、二樓容易水管堵塞。風...

Psychrometric Calculations Psychrometric Calculations [Maintenance: BingHung ; Ver: 20180416_R0] Inputs Unit Chosen: SI Parameter Name Value Unit Dry Bulb Temp.: Wet Bulb Temp.: Relat. Humidity:    Dew Point Temp Altitude Outputs   Atmospheric Press Sat. Vapor Press. Partial Vapor Press. Humidity Ratio ...

Volumetric Flow Rate Calculations Volumetric Flow Rate Calculations [Maintenance: BingHung ; Ver: 20180611_R0]  Item Unit Nomenclature Equation Value   [INPUT] Flow rate (total) Nm3/hr q#(qNm3)   Molar mass of dry air kg/kmol md   Molar mass of water vapor kg/kmol mw   Molar volume of ideal gas at 0oC Nm3/kmol vmo   Volume mole fraction (total) % Xt   [INPUT] Volume mole fraction (dry air) % Xd   Volume mole fraction (water vapor) % Xw   Density of dry air kg/Nm3 dd md/vmo Density of water vapor kg/Nm3 dw mw/vmo Density of wet air kg/Nm3 d (dd*Xd+dw*Xw)/100 [OUTPUT] Flow rate (total) kg/hr q q#*d [OUTPUT] Flow rate (dry air) kg/hr qd q#*Xd*dd /100 [OUTPUT] Flow rate (water vapor) kg/hr qw q#*Xw*dw /100

Unit Converter Unit Converter [Maintenance: BingHung ; Ver: 20180618_R0]   Length   Weight   Pressure   Energy   Viscosity   Temperature   mm g Pa J cp oC cm kg kPa kJ kg/m_sec oF m lb MPa btu lb/ft_sec K inch N kg/cm2 kcal         feet oz psi hp                 bar                     mmAq                     atm             mm cm m inch ft g kg lb N oz Pa kPa MPa kg_cm2 psi bar mmAq atm J kJ btu kcal hp cp kg/m-sec lb/ft-sec oC oF K             ...

AI Development History 1950 : Alan Turing , Thesis - Computing Machinery and Intelligence , Turning Test 1951 : Marvin Minsky, Princeton Master Student, SNARC (1969 Turning Award) 1955 : Allen Newell/ Herbert Simon/ Cliff Shaw, Logic Theorist, Reasoning    1956 : John McCarthy/ Claude Shannon/ Nathan Rochester, AI Artificial Intelligence First Period (1956-1974) 1963 : APPA, Project MAC, MIT CSAIL 1964-1966, Joseph Weizenbaum, ELIZA, MIT 1967-1972, Human Robot. Japan Second Period (1980-1987) 1960s, Edward Feigenbaum, expert system 1970s, Stanford, MYCIN 1980s, CMU,DEC,XCON,expert system Expert system Artificial neural network 1982, John Hopfield, Hopfield net, Associative memory 1986, David Rumelhart/ Geoffrey Hinton/ Ronald Williams, Thesis-Learning representations by back-propagating errors, prove backpropagation by experiment 1982,Japan, 5g calculator, over ten years investment, the project finally fail. Third Period (2011-Now) Math Based and S...

LMTD Calculations LMTD Calculations [Maintenance: BingHung ; Ver: 20180611_R0] Item Unit Nomenclature Value Hot Side Temperature In oC Thin Hot Side Temperature Out oC Thout Cold Side Temperature In oC Tcin Cold Side Temperature Out oC Tcout LMTD Calculation - Co-current flow dT1 oC dT2 oC LMTD - Co-Current Flow oC LMTD Calculation - Counter-current flow dT1 oC dT2 oC LMTD - Counter-Current Flow oC