Abstract:
The present invention relates to a sheet conveying apparatus comprising a conveyance belt, on which stripe shaped electrodes each fed to a voltage different from that fed to one another are alternatively arranged, for conveying a sheet in attracting the sheet with electrostatic force and power supplying means disposed on an inner side of the conveyance belt for supplying voltage to the electrodes.
Abstract:
A fluidized-bed drying and classifying apparatus is provided with a main body (10) in which a fluidized bed is formed to dry a granular material and to classify the same into fine particles and coarse particles. A perforated gas-distributing plate (12) is disposed in a lower part of a region in which a fluidized bed (14) is formed in the main body (10), a wind box having the shape of a hopper is disposed below the perforated gas-distributing plate. A dropped material discharge device (29) is connected to the lower end of the hopper-shaped wind box (16) to discharge the material dropped into the wind box (16). A gas supply system (110) is connected to the wind box (16) to supply a fluidizing gas that serves as a drying hot gas and a classifying gas into the wind box. A material supply opening portion (20) is mounted to the main body (10) to feed the granular material. A discharge chute (24) is connected to the main body (10) to discharge dried coarse particles. A gas discharge opening portion (56) is connected to the main body (10) to discharge an exhaust gas containing fine particles. The gas supply system (110) includes a flow controller (111) to control classification particle size by controlling the flow rate of the gas supplied into the wind box (16), and a temperature controller (112) to control drying degree through the adjustment of temperature of the hot gas supplied into the wind box (16) according to the adjusted flow rate.
Abstract:
An image processing apparatus which outputs an index print of information representing a plurality of pictures having picture size of different aspect ratios, with each of the plurality of pictures being respectively assigned a number in numerical order. The image processing apparatus detects size information for each of the plurality of pictures based on the respective aspect ratios, and a picture output order different from the numerical order is determined based on the detected size information for each of the plurality of pictures.
Abstract:
A fuzzy inference device determines an inference operational quantity in accordance with inference rules each constituted by an antecedent and a consequent. An inference rule division determiner which receives data of input variables and output variables so as to determine the number of the inference rules. An antecedent neural element obtains a membership value corresponding to an antecedent of a specific inference rule from the divided data of the input variables and the output variables. A situational change processor adaptively determines an inference quantity of a consequent of each inference rule in the case of a change of an initial state or inference situations. An inference operational quantity determiner receives outputs from the antecedent neural element and the situational change processor and performs fuzzy inference in accordance with the inference rules so as to determine the inference operational quantity. An evaluator evaluates, on the basis of an evaluation reference, the inference operational quantity outputted by the inference operational quantity determiner.
Abstract:
An apparatus and method for automatically generating and adjusting fuzzy reasoning, i.e. logic, rules based on changes in the reasoning error. The apparatus includes a fuzzy reasoning section that performs fuzzy logic based on the fuzzy reasoning rules stored in the a rule memory. The parameters of the fuzzy reasoning rules are adjusted in a parameter tuning section based on the output of the fuzzy reasoning section and predetermined input and output data. A reasoning error calculation section calculates a reasoning error and a change in the reasoning error based on the results from the fuzzy reasoning section and the predetermined input and output data. The reasoning error calculation section also disables the parameter tuning section when the calculated reasoning error is less than a predetermined first threshold value. When the calculated reasoning error is greater than or equal to the predetermined threshold value and the change in the reasoning error is less than a predetermined second threshold value, a rule generation section generates one or more fuzzy reasoning rules and regenerates the fuzzy reasoning rules stored in the rule memory based on the new fuzzy reasoning rule(s) so that optimal fuzzy reasoning rules are obtained.
Abstract:
An inference rule determining process according to the present invention sequentially determines, using a learning function of a neural network model, a membership function representing a degree which the conditions of the IF part of each inference rule is satisfied when input data is received to thereby obtain an optimal inference result without using experience rules. The inventive inference device uses an inference rule of the type "IF . . . THEN . . ." and includes a membership value determiner (1) which includes all of IF part and has a neural network; individual inference quantity determiners (21)-(2r) which correspond to the respective THEN parts of the inference rules and determine the corresponding inference quantities for the inference rules; and a final inference quantity determiner which determines these inference quantities synthetically to obtain the final results of the inference. If the individual inference quantity determiners (2) each has a neural network structure, the non-linearity of the neural network models is used to obtain the result of the inference with high inference accuracy even if an object to be inferred is non-linear.
Abstract:
The fuzzy inference apparatus is provided to effect the tuning operation of the fuzzy inference with the use of the descent steps from the input/output data to be obtained from the specialists, the inputs of the users, and to automatically produce the desired fuzzy inference rules not by the trial and error, whereby the optimum inference rule can be obtained by the descent steps, and the specialists' knowledge and khow-how can be easily mounted on the appliances as the inference rules.