Hitachi, Ltd.

Hitachi Engineering Co., Ltd.

Mikio Yoda

Shoji Watanabe

Naoki Hara

Kenji Baba

Hayao Yahagi

Ichiro Enbutsu

Shunsuke Nogita

Motohisa Funabashi

Harumi Matsuzaki

Hiroshi Matsumoto

Shunji Mori

Fumio Maruhashi

Akira Maeda

Takushi Nishiya

Hiromasa Yamaoka

Kazuo Kera

Nobuyuki Fujikura

Hiromitsu Kurisu

Mitsuo Yanagi

Kazuo Tsutsui

Manabu Fukushima

Teruji Sekozawa

Masahiko Ishida

Takashi Iida

Kiyomi Mori

Shigeyuki Shimauchi

Masakatsu Hiraoka

Toshihide Ichimori

Akihiro Tanaka

Kazushi Tsumura

Yoshikatsu Asada

Yuzi Yamasawa

Toshimitu Akiyama

Takashi Katori

Kenzi Baba

Bunchi Kimura

Misako Obuchi

Masahiro Oba

A drawing management device for managing the drawing of an entire facility, such as a water supply facility, as digital information. This device includes a unit for storing drawings with priorities obtained by separating the entire facility drawing into sub-facility drawings located at plural levels, a selective display designating unit for displaying a desired sub-facility drawing obtained by selecting the number of levels considering the priority from the storing unit, and a display unit for displaying the desired sub-facility drawing in response to the signal from the selective display designating unit. By suitably selecting a desired level considering the priority, only a necessary and sufficient facility drawing can be displayed swiftly.

Drawing management device having ability to retrieve and display a desired amount of data within a desired display time

A method and a system for causing a neural circuit model to learn typical past control results of a process and using the neural circuit model for supporting an operation of the process. The neural circuit model is caused to learn by using, as input signals, a typical pattern of values of input variables at different points in time and, as a teacher signal, its corresponding values of the control variable. An unlearned pattern of input variables is inputted to the thus-learned neuron circuit model, whereby a corresponding value of the control variable is determined. Preferably, plural patterns at given time intervals can be simultaneously used as patterns to be learned.

Supporting method and system for process operation

A method for causing a neural circuit model to learn typical past control results of a process and using the neural circuit model for supporting an operation of the process. The neural circuit model is caused to learn by using, as input signals, a typical pattern of values of input variables at different points in time and, as a teacher signal, its corresponding values of the control variable. An unlearned pattern of input variables is inputted to the thus-learned neuron circuit model, whereby a corresponding value of the control variable is determined. Preferably, plural patterns at given time intervals can be simultaneously used as patterns to be learned.

Supporting neural network method for process operation

A pipeline breakage sensing system includes a plurality of pressure sensors installed on pipelines to measure pressure of a fluid. Each of these pressure sensors presumes a breakage location in cooperation with other neighboring sensors on the basis of a pressure change of the fluid and transmits information relating to the presumed breakage location. The pipeline breakage sensing system further includes a pressure sensor for detecting the occurrence of a seismic earthquake event, in addition to a managing center for sensing a breakage location on the basis of information transmitted from respective pressure sensors and by calculating a time correlated comparison between the superimposed pressure readings from neighboring pressure sensors used to detect the occurrence of such a fluid pressure change. Thereby, breakage of a pipeline network is sensed and the quantity of flow delivered through an affected pipeline is adjusted, stopped or regulated by control of one or more valves in accordance with a predetermined, weighted system of fluid flow requirements, customer ordered priorities and customer demand levels.

Pipeline breakage sensing system and sensing method

An information processing apparatus, which is easy to design and has functions of high speed processing, learning, and so on, includes a plurality of memories for storing sets of previously known input data (called response pattern data) and correct output data corresponding thereto; a comparator for providing input data supplied in actual operations to the plurality of sets and comparing the input data with the response pattern data in each set; an evaluation unit for evaluating the distance (degree of similarity) between the input data and the response pattern data; and a generator for generating final outputs by composing the evaluation result for each set and the output data in that set, for example, using a method which calculates weighted average values based on the evaluated distances.

Information processing apparatus for obtaining output data from plural

An information processing apparatus is capable of achieving high-level functions such as high-speed processing and learning without requiring a complex algorithm based on, for example, knowledge processing. A plurality of sets of known input data and correct output data corresponding thereto are beforehand stored. The stored input data is called response pattern data. Data actually inputted in the system operation is supplied to the plural sets of data. For each set of data, the input data is compared with the response pattern data to evaluate a distance (degree of similarity) therebetween. Results of evaluations for the respective sets of data are combined with the associated output data, for example, according to weighted average values on the basis of the evaluated distance, thereby generating final outputs.

Information processing apparatus and monitoring apparatus

When amounts of operation control variables and/or operation state evaluation variables of a plant are calculated through a multilayered neural network based on data indicating a plant state, and the calculated results are indicated to an operator, a plant operation support system has functions to analyze an internal causality between neurons in the neural network, and display quantitative guidance by association in the neural network and also the analyzed result of the internal causality between the neurons as an association reason.

Plant operation support system

In a water supply plant which cleans water taken in from such sources of intake water as lakes and marshes, dams, rivers and so on and delivers the water as city water, obstructions to water treatment is predicted and methods for preventing the obstruction are decided, by measuring kinds and quantity of plankton and comparing the measured data with rules representing relations among kinds and quality of plankton, obstructions to water treatment and counteractions to such obstructions stored in a rule base in advance.

System for controlling water treatment based on plankton monitoring

In order to speed up and simplify automated learning of rules by a neural network making use of fuzzy logic, data from a system is analyzed by a teaching data creation means which groups the data into clusters and then selects a representative data item from each group for subsequent analysis. The selected data items are passed to a rule extraction means which investigates relationships between the data items, to derive rules, but eliminates rules which have only an insignificant effect on the system. The results are candidate rules which are stored in a first rule base. The candidate rules are then compared with rules in a second rule base to check for duplication and/or contradiction. Only those rules which are not duplicated and not contradictory are stored in the second rule base. Hence, when fuzzy inference is used to control the system on the basis of rules in the second rule base, only valid rules which provide a significant effect on the system are used.

Method of automated learning, an apparatus therefor, and a system

Drawing management device having ability to retrieve and display a

Suspended substances, such as active sludge, in a liquor is treated in an apparatus for treating the liquor by predicting a suspension state on the basis of an amount of existing suspended substances in the liquor.

Process for treating suspended substances in liquor

A symbolization apparatus which includes a membership function memory unit for storing a membership function used in fuzzy logic processing. In the symbolization apparatus a process condition change pattern is converted into an ambiguous symbol in order to build up a system capable of effecting an inference and decision approximate to a skilled operator against an object process. A process condition value memory is disclosed for holding a measurement of the process condition value and a process operation amount, a control history memory unit for storing operations and values converted into symbolic expressions, control units and a knowledge base for storing the information relating to the casual relations between the process control values and the process conditions. The process control system and a control support system comprise the function of condition identification approximate to that of a skilled operator by the use of the symbolization apparatus.

Symbolization apparatus and process control system and control support

A method and apparatus of a learning type decision support system are provided for improved acquisition of a priori knowledge from the system object and expression of non-linear structures in the object. The system is comprised of a learning module and an executing module for outputting advice and process manipulate command values to system users by receiving input data comprising on-line data and file data. The learning module comprises (1) a symbolized dictionary unit for generating a symbolized dictionary for giving a symbolic name in accordance with a pattern comprised of a combination of the values of the input data by giving examples of a pair of the pattern and the symbolic name; (2) a preprocessing unit for transforming at least a portion of the input data into the symbolic name with reference to the symbolic dictionary generated; and, (3) a model generating unit for determining an unknown parameter contained in a predetermined skeleton model to transform the preprocessed results into desired advice and manipulation command values. The executing module arithmetically processes the input data to output the advice and the manipulation command values by using the symbolized dictionary generated by the learning module and the generated model.

Learning type decision support system

A fish is bred in a transparent basin, into which water to be tested is supplied. A light source and a television camera are arranged in such a manner that images of the fish are taken by the camera as a silhouette. From the thus obtained fish images, the frequency distribution of the gravity centers of the fish images in the direction of the depth of water are obtained. The abnormality of the behavior of the fish is detected by comparison of the obtained frequency distribution with a reference distribution provided in advance.

Apparatus for monitoring the toxicant contamination of water by using

Growing community of inventors

Ibaraki, Japan

Mikio Yoda