Hitachi, Ltd.

Hitachi Engineering Co., Ltd.

Yoshiyuki Satoh

Haruki Inoue

Masakazu Yahiro

Motohisa Funabashi

Tadaaki Kitamura

Yoshiki Kobayashi

Masao Takatoo

Kazuyoshi Asada

Kunio Nakanishi

Toshiro Shibata

Takeshi Horie

Katsuyuki Yamamoto

Kazuya Takahashi

Hiroyuki Ichikawa

Hideo Yoshida

Noboru Abe

Yasuhiro Terada

Akemi Ohtsuki

A traffic flow monitor apparatus which can monitor a traffic flow at high precision over a wide range from the position near a TV camera to the distant position regardless of traffic conditions. An image analysis area crossing a road of an overlooked road image received every predetermined cycle is analyzed, the characteristic portion of the front (rear) portion of the vehicle is registered as a template, the vehicle is tracked by a pattern-matching process while updating the template, and the size of the template is reduced (enlarge) depending on a change in apparent width of a lane during template updating. Since the characteristic portion of the front (rear) portion of the vehicle is registered, and the vehicle is tracked by the pattern-matching process, no reference image of a road portion on which no vehicle exists is required, and influence of approaching or overlapping of another vehicle can be reduced. A traffic flow can be monitored at high precision regardless of deserted traffic or crowded traffic. Since the size is reduced (enlarged) during template updating, a vehicle can be stably tracked by using a template having an appropriate size in the range from the near position to the distant position.

Traffic flow monitor apparatus

An object recognition system using the image processing in which an area having a unique feature is extracted from an input image of an object, the unique image is registered in a shade template memory circuit as a shade template, the input image is searched for an image similar to the shade template registered by a shade pattern matching circuit, the position of an object is determined for each template, the speed and direction of movement of the object is determined from the positional information, and the results thereof are integrated by a separation/integration circuit, thereby recognizing the whole of the moving object.

Object recognition system and abnormality detection system using image

A planning system for quickly forming the optimum plan for a given planning problem, includes a setting unit for accepting the given planning problem; an optimization unit for creating an objective function which expresses an item intended to be minimized or maximized in the planning problem, and for executing a process which minimizes or maximizes a value of the created objective function; a storage unit for storing therein, at least, constant values which are required for the optimizing process; and a display unit for displaying a processed result of the optimization unit. The optimization unit performs a first function of calculating a difference value between the values of the objective function assumed in a last formed plan and a newly formed plan, and performs a second function of comparing the difference value of the objective function with the constant value set in the storage unit, and then substituting the last formed plan by the newly formed plan and setting the latter as a candidate for an optimum plan, on condition that the difference value of the objective function is smaller than the constant value.

Planning method and system

A process control system for controlling a process exhibiting both linear behavior and non-linear behavior with a plurality of control quantities has a knowledge base storing therein universal facts, production rules including expert's empirical rules, meta rules describing flows of inferences, algorithm methods by mathematical expressions and membership functions referenced in fuzzy inference and a complex inference mechanism. The complex inference mechanism is composed of a complex fuzzy mechanism for inferring directly predicted values on a multi-dimensional space generated by a plurality of elements for evaluation and predicted control objectives evaluated previously with fuzzy quantities, a predicting fuzzy mechanism having the input supplied with the predicted values for determining arithmetically correlation to the membership functions determined previously with the fuzzy quantities and a satisfaction grade obtained by imparting weight to the correlation for each of the control objectives to thereby determine such a combination of the control quantities for control effectors to vary the operation states thereof from the current operation states that the satisfaction grade becomes maximum, and a main inference mechanism having the input supplied with process data to thereby compare selectively the process data with the knowledge stored in the knowledge base for thereby making decision of the process behavior through a forward fuzzy inference with the aid of the production rules based on the empirical knowledge and manage the whole process control system including the complex fuzzy inference mechanism, the predicting fuzzy inference mechanism and the knowledge base. The system can be advantageously employed for intra-tunnel ventilation control and other processes exhibiting both linear and non-linear behaviors.

Method and system for process control with complex inference mechanism

A prediction control of a process containing a non-linear behavior is effected by predicting a transition of process quantities (control quantities) of an object to be controlled a predetermined time period after the current time to provide predicted values and by determining manipulation quantities of at least a control effector in accordance with a difference between the predicted values and predetermined target values of the controlled object. A quantitative operation is carried out arithmetically identifying a process as a linear behavior, and a fuzzy inference or qualitative operation, including a fuzzy rule based on empirical knowledge, is carried out for simulating a process, wherein for input process information and current manipulation quantities to be maintained, both operations parallelly determine predicted values of control quantities. A process behavior determination is arithmetically carried out to determine whether the process linearly behaves (i.e. the normal state or the state dominant to statistic distribution) or not (i.e. the extreme transition state such as a transient response, or an indeterminate state) from the specified process information on the basis of a process behavior determination rule. As a result, a predicted value acquired by the quantitative operation is selected in response to a determination of a linear behavior, and a predicted value acquired by the fuzzy inference operation is selected in response to a determination of a non-linear behavior.

Growing community of inventors

Hitachi, Japan

Yoshiyuki Satoh