Ingeniarii intricata excogitant systemata vim electricam distribuendi . . .
. . . et actuaria aëris vehicula, sicut helicopterum hic visum.
Ars ingeniaria[1][2] est scientia et ars, quae praecipue principiis biologicis, chemicis, informaticis, et physicis una utuntur ad novas technologias adhibendas, sicut novas rationes, ingenia, instrumenta, et systemata cogitationis. Qui hanc disciplinam colat ingeniarius vel inventor dicitur.
Index
1Disciplinae ingeniariae
2Nexus interni
3Notae
4Bibliographia
Disciplinae ingeniariae |
Disciplinae ingeniariae principales sunt:
Ars aërospatialis
Ars chemica
Ars electrica (quae electronicam et informaticam comprehendit)
Ars energiam extrahendi (quae artem minerales extrahendi et artem nuclearem comprehendit)
Ars industrialis
Ars geotechnica
Ars mechanica (quoque scientia machinalis)
Biotechnologia
Ingeniaria civilis
Nexus interni
Destinatio urbana
Ethica ingeniariae
Ingeniaria forensis
Ingeniaria militaris
Ingeniaria petrolei
Ingeniaris structuralis
Ingeniarius
Mathematica
Scientia
Notae |
↑The Silent Sister, S. B. James auctore in periodico The Churchman, Londinium, 1883.
↑"Tabula expensarum pro unoquoque gradu academico", Universitatis Eblanae, anno 1908.
Bibliographia |
Blockley, David. 2012. Engineering: a very short introduction. Novi Eboraci: Oxford University Press. ISBN 9780199578696.
Dorf, Richard, ed. 2005. The Engineering Handbook. Ed. 2a. Boca Raton: CRC. ISBN 0849315867.
Billington, David P. 1996. The Innovators: The Engineering Pioneers Who Made America Modern. Ed. nova. Wiley. ISBN 0471140260.
Petroski, Henry. 1992. To Engineer is Human: The Role of Failure in Successful Design. Vintage. ISBN 0679734163.
Lord, Charles R. 2000. Guide to Information Sources in Engineering. Libraries Unlimited. doi:10.1336/1563086999. ISBN 1563086999.
Vincenti, Walter G. 1993. What Engineers Know and How They Know It: Analytical Studies from Aeronautical History. Baltimorae: The Johns Hopkins University Press. ISBN 0801845882.
Tabula multilinguis Rosettana in Museo Britannico ostenditur. Tabula Rosettana, [1] etiam titulo OGIS 90 agnita, est stela decreto de rebus sacris in Aegypto anno 196 a.C.n. lato inscripta. Tabula iuxta Rosettam Aegypti, urbem in delta Nili et ad oram maris Mediterranei iacentem, anno 1799 a milite Francico reperta est. Inventio stelae, linguis duabus scripturisque tribus inscriptae, eruditis Instituti Aegypti statim nuntiata est; ibi enim iussu imperatoris Napoleonis eruditi omnium scientiarum (sub aegide Commissionis Scientiarum et Artium) properaverant cum expeditione Francica. Qua a Britannis mox debellata, tabula Rosettana Londinium missa hodie apud Museum Britannicum iacet. Textus Graecus cito lectus interpretationi textuum Aegyptiorum (in formis hieroglyphica et demotica expressorum) gradatim adiuvit. Denique textum plene interpretatus est Ioannes Franciscus Champollion. Ab opere eruditorum cumulativo coepit hodiernus scripturae hieroglyphicae linguaeque Aegyptiae a...
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This is what I mean as document text image: I want to label the texts in image as separate blocks and my model should detect these labels as classes. NOTE: This is how the end result should be like: The labels like Block 1, Block 2, Block 3,.. should be Logo, Title, Date,.. Others, etc. Work done: First approach : I tried to implement this method via Object Detection, it didn't work. It didn't even detect any text. Second approach : Then I tried it using PixelLink. As this model is build for scene text detection, it detected each and every text in the image. But this method can detect multiple lines of text if the threshold values are increased. But I have no idea how do I add labels to the text blocks. PIXEL_CLS_WEIGHT_all_ones = 'PIXEL_CLS_WEIGHT_all_ones' PIXEL_C...
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I have this LSTM model model = Sequential() model.add(Masking(mask_value=0, input_shape=(timesteps, features))) model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2, return_sequences=False)) model.add(Dense(features, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) and shapes X_train (21, 11, 5), y_train (21, 5) . Each timestep is represented by 5 features and return_sequences is set to False because I want to predict one 5D array (the next timestep) for each input sequence of 11 timesteps. I get the error ValueError: y_true and y_pred have different number of output (5!=1) If I reshape the data as X_train (21, 11, 5), y_train (21, 1, 5) instead I get the error ValueError: Inva...
