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The isScorable attribute indicates whether the model is valid for scoring. If this attribute is true or if it is missing, then the model should be processed normally. However, if the attribute is false, then the model producer has indicated that this model is intended for information purposes only and should not be used to generate results. Patient-Specific Modeling Patientspecifik modellering Engelsk definition. The development and application of computational models of human pathophysiology that are individualized to.
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PMML 4.3 - Neural Network Models
D. b. sweeney. https://occupyheavy100.weebly.com/extracting-code-from-a-dmg.html. Neural Network Models for Backpropagation
The description of neural network models assumes that the reader has a general knowledge of artificial neural network technology. A neural network has one or more input nodes and one or more neurons. Some neurons' outputs are the output of the network. The network is defined by the neurons and their connections, aka weights. All neurons are organized into layers; the sequence of layers defines the order in which the activations are computed. All output activations for neurons in some layer L are evaluated before computation proceeds to the next layer L+1. Note that this allows for recurrent networks where outputs of neurons in layer L+i can be used as input in layer L where L+i > L. The model does not define a specific evaluation order for neurons within a layer.
Each neuron receives one or more input values, each coming via a network connection, and sends only one output value. All incoming connections for a certain neuron are contained in the corresponding Neuron element. Each connection Con of the element Neuron stores the ID of a node it comes from and the weight. A bias weight coefficient or a width of a radial basis function unit may be stored as an attribute of Neuron element.
All neurons in the network are assumed to have the same (default) activation function, although each individual layer may have its own activation and threshold that override the default. Given a fixed neuron j, and Wi representing the weight on the connection from neuron i, the activation for neuron j is computed using up to three steps as follows
- Compute a linear combination or euclidean distance using input activations and weights Wi. The input activations to the current neuron are the outputs of the connected neurons.
Z = see below - The activation function is applied to the result of step 1:
output(j) = activation( Z ) - A normalization method softmax ( pj = exp(yj) / Sumi(exp(yi) ) ) or simplemax ( pj = yj / Sumi(yi) ) can be applied to the computed activation values. The attribute normalizationMethod is defined for the network with default value none ( pj = yj ), but can be specified for each layer as well. Softmax normalization is most often applied to the output layer of a classification network to get the probabilities of all answers. Simplemax normalization is often applied to the hidden layer consisting of elements with radial basis activation function to get a 'normalized RBF' activation.
Saltmarsh dmg file d&d pdf. There are two groups of activation functions.
- Group 1 uses a linear combination of weights and input activations.
Z = Sum( Wi * output(i) ) + bias
Activation functions are:- threshold:
- activation(Z) = 1 if Z > threshold else 0
- logistic:
- activation(Z) = 1 / (1 + exp(-Z))
- tanh:
- activation(Z) = (1-exp(-2Z)/(1+exp(-2Z))
- identity:
- activation(Z) = Z
- exponential:
- activation(Z) = exp(Z)
- reciprocal:
- activation(Z) = 1/Z
- square:
- activation(Z) = Z*Z
- Gauss:
- activation(Z) = exp(-(Z*Z))
- sine:
- activation(Z) = sin(Z)
- cosine:
- activation(Z) = cos(Z)
- Elliott:
- activation(Z) = Z/(1+|Z|)
- arctan:
- activation(Z) = 2 * arctan(Z)/Pi
- rectifier:
- activation(Z) = max(0,Z)
- Group 2 computes a euclidean distance between weights and input activations (= outputs of other neurons)
Z = (Sumi (output(i)-Wi)2 )/(2*width2)
where the sum is taken over all input units, Wi are the coordinates of the center stored in Con elements in place of the weights, width is a positive number describing the width for the radial basis function unit stored either in Neuron element or in NeuralLayer or even in NeuralNetwork.
The only activation function in this group is 'radialBasis'.- radialBasis:
- activation = exp( f * log(altitude) - Z )
where f is the fan-in of each unit in the layer, that is the number of other units feeding into that unit, excluding bias, and the altitude is a positive number stored in Neuron or NeuralLayer or NeuralNetwork. The default is altitude='1.0', for that value the activation function reduces to the simple exp(-Z).
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The isScorable attribute indicates whether the model is valid for scoring. If this attribute is true or if it is missing, then the model should be processed normally. However, if the attribute is false, then the model producer has indicated that this model is intended for information purposes only and should not be used to generate results. In order to be valid PMML, all required elements and attributes must be present, even for non-scoring models. For more details, see General Structure.
NeuralInput defines how input fields are normalized so that the values can be processed in the neural network. For example, string values must be encoded as numeric values.
NeuralOutput defines how the output of the neural network must be interpreted.
NN-NEURON-ID is just a string which identifies a neuron. The string is not necessarily an XML ID because a PMML document may contain multiple network models where neurons in different models can have the same identifier. Within a model, though, all neurons (elements of NeuralInput and Neuron) must have a unique identifier.
Neural Network Input Neurons
An input neuron represents the normalized value for an input field. A numeric input field is usually mapped to a single input neuron while a categorical input field is usually mapped to a set of input neurons using some fan-out function. The normalization is defined using the elements NormContinuous and NormDiscrete defined in the Transformation Dictionary. The element DerivedField is the general container for these transformations.
Restrictions: A numeric input field must not appear more than once in the input layer. Similarly, a pair of categorical input field together with an input value must not appear more than once in the input layer.
Neural Network Neurons
Neuron contains an identifier id which must be unique in all layers. The attribute bias implicitly defines a connection to a bias unit where the unit's value is 1.0 and the weight is the value of bias. The activation function and normalization method for Neuron can be defined in NeuralLayer. If either one is not defined for the layer then the default one specified for NeuralNetwork applies. If the activation function is radialBasis, the attribute width must be specified either in Neuron, NeuralLayer or NeuralNetwork. Again, width specified in Neuron will override a respective value from NeuralLayer, and in turn will override a value given in NeuralNetwork.
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Weighted connections between neural net nodes are represented by Con elements.
Con elements are always part of a Neuron. They define the connections coming into that parent element. The neuron identified by from may be part of any layer.
NN-NEURON-IDs of all nodes must be unique across the combined set of NeuralInput and Neuron nodes. The from attributes of connections and NeuralOutputs refer to these identifiers.
Neural Network Output Neurons
In parallel to input neurons, there are output neurons which are connected to input fields via some normalization. While the activation of an input neuron is defined by the value of the corresponding input field, the activation of an output neuron is computed by the activation function. Therefore, an output neuron is defined by a Neuron. In networks with supervised learning the computed activation of the output neurons is compared with the normalized values of the corresponding target fields; these values are often called teach values. The difference between the neuron's activation and the normalized target field determines the prediction error. For scoring the normalization for the target field is used to denormalize the predicted value in the output neuron. Therefore, each instance of Neuron which represent an output neuron, is additionally connected to a normalized field. Note that the scoring procedure must apply the inverse of the normalization in order to map the neuron activation to a value in the original domain.
Connect a neuron's output to the output of the network.
For neural value prediction with back propagation, the output layer contains a single neuron, this is denormalized giving the predicted value.
For neural classification with backpropagation, the output layer contains one or more neurons. The neuron with maximal activation determines the predicted class label. If there is no unique neuron with maximal activation then the predicted value is the first output neuron with maximal activation.
Example model
HOME > COMPANY > About DMG MORI > History
DMG MORI's history
Business history
1948 | Began manufacture and sales of textile machine in Yamato-Koriyama City, Nara Prefecture |
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1958 | After textile machinery started manufacture and sale of machine tools (high-speed precision lathes) |
1970 | Constructed Iga Plant which has been in operation ever since |
1979 | Listed shares on the second section of the Osaka Securities Exchange |
1981 | Listed shares on the second section of the Tokyo Stock Exchange |
1982 | Established MORI SEIKI GmbH |
1983 | Established MORI SEIKI U.S.A., INC. (current DMG MORI U.S.A., INC.) Started actual operation at Iga No.1 Plant The Company was transferred to the first section of the Tokyo Stock Exchange and Osaka Securities Exchange |
1987 | Nara Plant (Current Nara Campus) Nara No.1 Plant |
1992 | Started operations at the Iga No.2 Plant |
1997 | Started operations at the Iga No. 2 Plant High-Precision Facility |
1999 | Completed construction of MORI001fSEIKI Nagoya building (current Nagoya Head Office) Acquired ISO9001 certification |
2000 | Established Digital Technology Laboratory (DTL) (current DMG001fMORI Digital Technology Laboratory Corporation) |
2001 | Acquired ISO14001 certification Established MORI SEIKI (SHANGHAI) CO., LTD. Consolidated TAIYO KOKI CO., LTD. as a subsidiary |
2002 | Started 24 hours a day, 365 days a year service support Took over operations from former HITACHI001fSEIKI Started operation as part of the MORI001fSEIKI Group Acquired OHSAS18001 certification |
2003 | Chiba Campus |
2004 | Established the Human Resources Development Center (current DMG001fMORI Academy) Transferred Head Office to Nagoya |
2005 | Completed construction of the Iga Campus Heat Treatment Plant |
2006 | Completed construction of the Iga Campus Casting Plant |
2007 | Established AKISHINO MOLD LABORATORY, LTD. (current DMG MORI MOLD LABORATORY CO., LTD.) Consolidated DIXI machines as a subsidiary |
2008 | TOBLER S.A.S. Consolidated B.U.G., Inc. (current DMG MORI B.U.G. CO.,001fLTD.) |
2009 | Established the Tokyo branch Started capital and business collaboration with DMG of Germany |
2010 | Acquired the measuring equipment business of Sony Manufacturing Systems Corporation, and consolidated as a subsidiary named Magnescale Co., Ltd |
2011 | Established MORI SEIKI SALES AND SERVICE CO., LTD. (current DMG MORI SALES AND SERVICE CO., LTD.) |
2012 | Established the Iga Campus Bed / Column Precise Processing Plant Established North American Factory in Davis city, California |
2013 | Established Tianjin Factory in China |
Products history
1960 | Began export of high-speed precision lathes |
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1968 | Began manufacture and sales of numerically controlled lathes |
1976 | NCPL-300 |
1977 | Developed SL-2 |
1981 | MV-40 |
1983 | Began manufacture and sales of horizontal machining centers |
1994 | SH-50 |
2000 | MT2500SZ Expanded MT Series line-up Started use of CAPS-NET |
2003 | Developed DCG (Driven at the Center of Gravity) Developed DDM (Direct Drive Motor) Developed NV 4000 DCG and NH 4000 DCG Introduced a machine equipped with a HEIDENHAIN CNC into the European market |
2004 | Developed the NL Series with BMT (Built-in Motor Turret) |
2005 | NVD1500 DCG Developed NT Series |
2006 | Developed NMH 6300 DCG Developed NMV 5000 DCG |
2007 | NZ2000 T3Y3 |
2009 | MAPPS integrated operation panel completely revamped, and started installing on new models as MAPPS Ⅳ |
2010 | Developed the X-class machines (NLX, NVX, NHX, NTX) |
2011 | Developed NTX 2000 Developed NZX Series Jointly developed MILLTAP 700 with DMG |
2012 | NVX5080 II |
2013 | Developed NHC 4000 and NHC001f5000 |
DMG MORI AG's history
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1870 | Friedrich Gildemeister founded GILDEMEISTER & Comp. in Bielefeld |
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1906 | Wilhelm Berg took over the company management and started mass production of machine tools |
1910 | Concentrated on its flagship products: turret lathes, multi-spindle automatic lathes, milling machines, and vertical and horizontal milling machines |
1928 | Released the POX multi-spindle automatic lathe |
1950 | Exhibited the RV50 turret lathe at Hanover trade fair |
1961 | Built a new manufacturing plant in Sennestadt and started operation (in 1965) |
1975 | Exhibited the company's first NC lathe (NEF) at EMO |
1995 | Acquired DECKEL MAHO AG, and put the milling and drilling machine business on track ※DECKEL AG and MAHO AG merged in 1993 |
1998 | Sales exceeded one billion Deutschmarks for the first time in its history (1998 average exchange rate: 1DM = ¥70) |
1999 | Entered the laser technology sector with the takeover of LCTec GmbH (present SAUER) |
2000 | Repurchased its former subsidiary GILDEMEISTER Italiana |
2001 | Entered the field of the ultrasonic machining technology by the investment in SAUER GmbH & Co. KG |
2002 | The new plant in Seebach won the '”Best Factory TM - Industrial Excellence Award 2002” |
2003 | DMG Nippon K.K. opened a technology center in Yokohama Established in Shanghai the first production plant in Asia |
2005 | DMG Asia established the spare parts center |
2008 | Adopted new design |
2011 | Opened the HSC center |
2012 | Expanded the plant in Seebach |
DMG MORI's history
2009 | Collaboration started March Started business collaboration with DMG |
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2010 | September / October Hosted a joint exhibition booth at IMTS and JIMTOF |
2012 | January Established DMG MORI SEIKI Europe AG in Switzerland (current DMG MORI Europe AG) Started joint sales and service across Europe |
2013 | Company names unified September Unveiled CELOS and machines with premium design as the World Premium at EMO Hannover 2013 October Company names unified |
2014 | March Became the Exclusive Premium Partner of Porsche team July Tokyo Global Headquarters started operation |
2015 | Started integrated management as a consolidated entity April Adopted IFRS Changed accounting period (irregular accounting period from April to December) Consolidated DMG Established DMG MORI WASINO, LTD. June Developed NRX 2000 Developed A 150 Series Developed G 100 Series July Opened the world’s largest Global Solution Center in Iga September Ulyanovsk Plant (Russia) started operation December Developed NLX 6000 |
2016 | CMX 1100 V January Established the System Solution Plant in Nara February Released Technology Cycles August Domination, Profit and Loss Transfer Agreement came into effect September Released CMX V Series Agreed on technological cooperation with Microsoft Japan Concluded partnership agreement with TOYOTA for the FIA World Rally Championship (WRC) |
2017 | February Introduced powder bed technology to additive manufacturing segment |
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