Source: http://www.google.com/patents/US7966280
Timestamp: 2017-10-18 13:08:22
Document Index: 789531010

Matched Legal Cases: ['Application No. 2007', 'Application No. 2006', 'Application No. 10', 'Application No. 070452316', 'Application No. 2008', 'Application No. 10']

Patent US7966280 - Automotive air conditioner and method and apparatus for controlling ... - Google Patents
An automotive air conditioner includes a storage unit which stores a plurality of pieces of state information as respective learned data; a learning unit which constructs a probabilistic model; a control information correcting unit which corrects setting information, etc., related to a vehicle occupant's...http://www.google.com/patents/US7966280?utm_source=gb-gplus-sharePatent US7966280 - Automotive air conditioner and method and apparatus for controlling automotive air conditioner
Publication number US7966280 B2
Application number US 12/069,579
Also published as DE102008008446A1, US20080195564
Publication number 069579, 12069579, US 7966280 B2, US 7966280B2, US-B2-7966280, US7966280 B2, US7966280B2
Inventors Yasufumi Kojima, Hiroshi Takeda, Kousuke Hara
Patent Citations (39), Non-Patent Citations (11), Referenced by (3), Classifications (14), Legal Events (2)
Automotive air conditioner and method and apparatus for controlling automotive air conditioner
US 7966280 B2
An automotive air conditioner includes a storage unit which stores a plurality of pieces of state information as respective learned data; a learning unit which constructs a probabilistic model; a control information correcting unit which corrects setting information, etc., related to a vehicle occupant's setting operation in accordance with a calculated probability so as to achieve a specific setting operation; and an air-conditioning control unit which controls an air-conditioning unit in accordance with the corrected setting information, etc. The learning unit includes a clustering subunit for classifying the plurality of learned data into at least first and second clusters and for determining first and second ranges for the value of the state information from the learned data included in the respective clusters, and a probabilistic model constructing subunit for constructing the probabilistic model by determining the probabilities for the state information contained in the first and second ranges, respectively.
an air-conditioning unit for supplying conditioned air into a vehicle;
an information acquiring unit for acquiring state information indicating a state related to said vehicle;
a storage unit for storing a plurality of pieces of said state information as respective learned data;
a learning unit, by using said learned data, for constructing a probabilistic model into which said state information is entered in order to calculate the probability of a vehicle occupant performing a specific setting operation;
a control information correcting unit for calculating said probability by entering said state information into the probabilistic model constructed by said learning unit, and for correcting setting information or control information related to the setting operation of said occupant in accordance with said calculated probability so as to achieve said specific setting operation; and
an air-conditioning control unit for controlling said air-conditioning unit in accordance with said corrected setting information or control information, wherein
said learning unit comprises:
a clustering subunit for classifying said plurality of learned data stored in said storage unit into at least a first cluster and a second cluster, and for determining a first range for a value of said state information from the learned data included in said first cluster and a second range for the value of said state information from the learned data included in said second cluster; and
a probabilistic model constructing subunit for constructing said probabilistic model associated with said specific setting operation by determining the probability of occurrence of the value of said state information contained in said first range and the probability of occurrence of the value of said state information contained in said second range.
2. The automotive air conditioner according to claim 1, wherein said clustering subunit has a first clustering condition and a second clustering condition that define different ranges for the value of said state information, and generates said clusters after determining the ranges of the clusters to which said respective learned data belong by using said plurality of learned data and said first and second clustering conditions.
said first clustering condition is that the Euclidean distance between each pair of learned data in said plurality of learned data is not greater than a first Euclidean distance, and
said second clustering condition is that the Euclidean distance between each pair of learned data in said plurality of learned data is greater than said first Euclidean distance but not greater than a second Euclidean distance.
4. The automotive air conditioner according to claim 1, wherein said clustering subunit determines said first range so as to contain all of said learned data included in said first cluster but not to contain any learned data included in said second cluster, and determines said second range so as to contain all of said learned data included in said second cluster but not to contain any learned data included in said first cluster.
said probabilistic model constructing subunit obtains from said plurality of learned data the number of times that said specific setting operation has been performed for the case where the value of said state information is contained in said first range as well as for the case where the value of said state information is contained in said second range, creates said conditional probability table by dividing said number of times by the total number of said plurality of learned data and thereby obtaining said recommended probability for the case where the value of said state information is contained in said first range as well as for the case where the value of said state information is contained in said second range, and stores said conditional probability table in said storage unit by associating said conditional probability table with said node.
7. The automotive air conditioner according to claim 1, wherein said probabilistic model has a group of nodes consisting of a node that takes said state information as an input and that outputs a conditional probability of a specific event and at least one other node that takes the output of said node as an input and that outputs the probability of said occupant performing said specific setting operation, and said node has a conditional probability table that indicates said conditional probability for the case where the value of said state information is contained in said first range as well as for the case where the value of said state information is contained in said second range, and
said probabilistic model constructing subunit obtains from said plurality of learned data the number of times that said specific even has occurred for the case where the value of said state information is contained in said first range as well as for the case where the value of said state information is contained in said second range, creates said conditional probability table by dividing said number of times by the total number of said plurality of learned data and thereby obtaining said conditional probability for the case where the value of said state information is contained in said first range as well as for the case where the value of said state information is contained in said second range, and stores said conditional probability table in said storage unit by associating said conditional probability table with said node.
8. The automotive air conditioner according to claim 1, wherein said state information is said vehicle's current location information.
storing said state information as learned information in said storage unit;
selecting a plurality of learned data from said learned data stored in said storage unit;
classifying said plurality of learned data stored in said storage unit into at least a first cluster and a second cluster, and determining a first range for a value of said state information from the learned data included in said first cluster and a second range for the value of said state information from the learned data included in said second cluster; and
constructing said probabilistic model associated with said specific setting operation by determining the probability of occurrence of said state information contained in said first range and the probability of occurrence of said state information contained in said second range.
12. The method according to claim 11, wherein each time said specific setting operation is performed, said storing said state information stores said state information in said storage unit as the learned data related to said specific setting operation.
The present invention relates to an automotive air conditioner and a method and apparatus for controlling the automotive air conditioner, and more particularly to an automotive air conditioner that optimizes air conditioning state to match a vehicle occupant's sensitivity to temperature or to suit specific situations, and a method for controlling such an automotive air conditioner.
Generally, an automotive air conditioner automatically determines the temperature, airflow level, etc., of conditioned air discharged from selected air outlets by reference to various parameters such as temperature setting, outside temperature, inside temperature, and solar radiation. However, human sensitivity to temperature differs from one person to another (some are sensitive to heat, while others are sensitive to cold). As a result, the automatically determined temperature, airflow level, etc., of the conditioned air may not be optimum for every occupant. In that case, an occupant may adjust the air conditioner to raise or lower the temperature setting or to increase or reduce the airflow level by operating the operation panel. In view of this, an air conditioner has been developed that incorporates a learning control that corrects a relational equation for determining the temperature, airflow level, etc., of conditioned air by using relevant parameters when an occupant has changed the setting such as the temperature setting or airflow level by operating an operation panel (refer to Japanese Unexamined Patent Publication No. 2000-293204).
It is an object of the present invention to provide an automotive air conditioner that can accurately determine the range of each information value to match each specific situation that requires adjusting the setting of the air conditioner, and a method for controlling such an air conditioner.
An automotive air conditioner according to the present invention will be described below with reference to the drawings. However, it should be noted that the present invention is not limited by the description given herein, but embraces the inventions described in the appended claims and their equivalents.
In the present embodiment, a Bayesian network is used as the probabilistic model. A Bayesian network models probabilistic causality relationships among a plurality of events. Further, a Bayesian network is a network represented by a directed acyclic graph in which propagation between each node is obtained by a conditional probability. For the details of Bayesian networks, refer to “Bayesian Network Technology” by Yoichi Kimura and Hirotoshi Iwasaki, 1st Edition, Tokyo Denki University Press, July 2006, “Introduction to Bayesian Networks” by Kazuo Shigemasu et al., 1st Edition, Baifukan, July 2006, or “Pattern Recognition” translated by Morio Onoe, 1st Edition, Shin Gijutsu Communications, July 2001.
In the present embodiment, the probabilistic model is generated for each user registered in the automotive air conditioner 1. Further, the probabilistic model is generated for each kind of setting operation (for example, lower or raise the set temperature Tset, adjust the airflow level W, set the air conditioner to the inside air recirculation mode, etc.). The structural information of each probabilistic model is stored in the storage unit 61 by being associated with the user information and setting operation. More specifically, the graph structure showing the connections between the nodes forming the probabilistic model, the type of input information given to the input node, and the conditional probability table (CPT) of each node are defined for each probabilistic model and stored in the storage unit 61. Further, the user identification number (ID), the setting operation number k that uniquely corresponds to the kind of setting operation, and the setting parameter to be corrected by the setting operation and its correction value (for example, (Tset, −3) when lowering the set temperature Tset by 3° C., (W, Wmax) when setting the air flow level W to maximum Wmax, etc.) are also defined for each probabilistic model and stored in the storage unit 61 by being associated with the probabilistic model.
A description will be given below by dealing with an example in which the set temperature Tset is lowered by 3° C. Here, the first threshold value Th1 is set to 0.9, and the second threshold value Th2 to 0.6.
FIG. 4 shows a graph structure for one example of the probabilistic model used for automatically adjusting the setting parameter of the automotive air conditioner 1. In the probabilistic model 101 shown in FIG. 4, three input nodes 102, 103, and 104 are connected to an output node 105. Day of week (X1), time segment (X2), and current location (X3) are given as state information to the respective input nodes 102, 103, and 104. The output node 105 outputs the probability of the set temperature Tset being lowered by 3° C.
When all of the information given to the respective input nodes is known, i.e., when the day of week is Saturday (X1=1), the time segment is daytime (X2=1), and the current location is the park (X3=1), for example, it can be seen from FIG. 5D that the probability, P(X4=1|X1=1, X2=1, X3=1), of the set temperature Tset being lowered by 3° C. is 0.95. Since the obtained probability is greater than the first threshold value Th1, the control information correcting unit 64 corrects the setting parameter so as to lower the set temperature Tset by 3° C.
On the other hand, when the day of week is Saturday (X1=1) and the time segment is daytime (X2=1), but it is not possible to determine the current location because power is not turned on to the navigation system 56, for example, then P(X4=1|X1=1, X2=1, X3) is calculated using the prior probability P(X3) shown in FIG. 5C for the case where the current location is the park. That is
P ( X 4 = 1 | X 1 = 1 , X 2 = 1 , X 3 ) = P ( X 4 = 1 | X 1 = 1 , X 2 = 1 , X 3 = 1 ) · P ( X 3 = 1 ) + P ( X 4 = 1 | X 1 = 1 , X 2 = 1 , X 3 = 0 ) · P ( X 3 = 0 ) = 0.95 · 0.15 + 0.55 · 0.85 = 0.61
Since the obtained probability is smaller than the first threshold value Th1 but greater than the second threshold value Th2, the control information correcting unit 64 queries the occupant via the A/C operation panel 59 or the like whether or not the set temperature Tset should be lowered by 3° C.
Further, when the day of week is Monday (X1=0), the time segment is nighttime (X2=0), and the current location is the office (X3=0), it can be seen from FIG. 5D that the probability, P(X4=1|X1=0, X2=0, X3=0), of the set temperature Tset being lowered by 3° C. is 0.1. Since the obtained probability is smaller than the first threshold value Th1 and the second threshold value Th2, the control information correcting unit 64 does not change the set temperature Tset, nor does it query the occupant whether to change the set temperature Tset.
T ao =k set T set −k r T r −k am T am −k s S+C
Do=aT ao +b
Do indicates the opening of the air mix door 28. Further, the coefficients kset, kr, kam, ks, C, a, and b are constants, and Tset, Tr, Tam, and S denote the set temperature, the inside temperature, the outside temperature, and the amount of solar radiation, respectively. When the set temperature Tset is corrected by the control information correcting unit 64, the temperature adjusting subunit 651 uses the corrected set temperature Tset. The opening Do of the air mix door 28 is 0% when the passage 32 passing through the heater core 29 is closed (that is, when providing only cooled air) and 100% when the bypass passage 30 is closed (that is, when providing only heated air). The coefficients kset, kr, kam, ks, and C in the temperature control equation and the coefficients a and b in the mathematical relationship for finding the opening of the air mix door are set as temperature control parameters for each registered user. These parameters are included in the personal setting information of the registered user.
Further, the number, iAK, of times that a particular occupant (for example, occupant A) has performed the setting operation α corresponding to the setting operation number k (for example, the operation for lowering the set temperature by 3° C. or setting the airflow level W to maximum) is also stored in the storage unit 61. The above learned information DAK is expressed, for example, by the following equation.
D Ak = ( d 11 k d 12 k d 13 k … d 1 lk d 21 k d 22 k … d 2 lk d 31 k ⋱ ⋮ d ijk d mlk d mlk )
where dijk represents the value of each piece of state information. Here, i indicates the number, iAK, of times that the operation has been performed. On the other hand, j is the state item number assigned to each value of the state information for convenience. In the present embodiment, the inside temperature Tr is assigned for j=1. Similarly, the outside temperature Tam is assigned for j=2, and the amount of solar radiation S for j=3. Then, the location information, the vehicle behavior information, the physiological information, etc. are assigned for j=4 and subsequent values of j. Further, k represents the setting operation number.
There are cases where information whose possible values cannot be limited to a given pattern or whose value varies continuously, for example, the vehicle's current location information, the temperature information such as the outside temperature and the inside temperature, the time information, etc., is used as the state information to be given to an input node of the probabilistic model. To construct a CPT that takes such information as an input parameter, how the input state information value is to be classified becomes an important issue. For example, consider the case of constructing a probabilistic model corresponding to the setting operation for lowering the set temperature by 3° C. in a specific situation where, after doing physical exercise, the occupant gets into the vehicle parked in the parking lot of the park, as shown in the example of FIG. 3. In this case, to use the vehicle's location information as one of the various pieces of state information to be given to the input nodes of the probabilistic model, it is important to classify the vehicle's location information by at least differentiating the case where the vehicle is located in the parking lot of the park from the case where the vehicle is located in some other place. Similarly, consider the case of constructing a probabilistic model corresponding to the setting operation for setting the air conditioner to the inside air recirculation mode in a specific situation where the vehicle is traveling on a certain national road. In this case, to use the vehicle's location information as one of the various pieces of state information to be given to the input nodes of the probabilistic model, it is important to differentiate the case where the vehicle is traveling on that national road from the case where the vehicle is located in some other place than that national road. As shown in these two examples, the vehicle's location information differs not only in terms of the location but also in terms of the range it covers between the case where the vehicle is located in the parking lot of the park and the case where the vehicle is traveling on the national road. It is therefore clear that the vehicle's location information cannot be classified in advance according to such specific situations.
D ( C 1 , C 2 ) = min x ∈ C 1 , y ∈ C 2 U d xy
where x and y are data contained in the respective clusters C1 and C2, and UDxy represents the Euclidean distance between x and y. Here, each data itself can be regarded as a cluster the number of data contained in which is 1.
The clustering subunit 662 calculates the centroids G1 to G4 of the respective clusters C1 to C4. Further, the clustering subunit 662 obtains the distances r1 to r4 to the data located farthest away from the respective centroids G1 to G4 in the respective clusters. Then, the clustering subunit 662 determines the ranges of the state information values corresponding to the respective clusters C1 to C4 as being the areas of radii r1 to r4 centered at the respective centroids G1 to G4 (step S209). The clustering subunit 662 stores the thus obtained centroids G1 to G4 and distances r1 to r4, as well as the number of data contained in the respective clusters, into the storage unit 61 by associating them with the respective clusters C1 to C4.
If it is considered that the number of data used for learning is not sufficient, the probabilistic model constructing subunit 663 may estimate the probability distribution using a beta distribution and use it to construct the CPT. If some of the input information values do no exist in the learned information DAK, that is, if there is unobserved data, the probabilistic model constructing subunit 663 estimates the probability distribution of the unobserved data. Then, the probabilistic model constructing subunit 663 calculates the corresponding conditional probability by calculating the expected value based on the estimated distribution. For the learning of such conditional probabilities, use can be made, for example, of the method described in “Introduction to Bayesian Networks” by Kazuo Shigemasu et al., 1st Edition, Baifukan, July 2006, pp. 35-38, 85-87.
AIC m=−2l m(θm |X)+2k m
Here, AICm is the ACI for the probabilistic model M. Further, θm represents a set of parameters of the probabilistic model M, lm(θm|X) the value of the maximum logarithmic likelihood of given data X in the probabilistic model M, and km the number of parameters of the probabilistic model M. Here, lm(θO|X) can be calculated by the following procedure. First, the learning unit 66 obtains the frequency of occurrence from the learned information DAK for each combination of parent node variables at each node. Then, the learning unit 66 multiplies the frequency of occurrence by the logarithmic value of the conditional probability. Finally, the learning unit 66 sums the resulting values to calculate lm(θm|X). On the other hand, km is obtained by adding together the number of combinations of the parent node variables at each node.
The probabilistic model evaluating subunit 664 determines whether the state information given to the input nodes of the selected tentative probabilistic model contains only the input parameters to the control equation such as the temperature control equation, i.e., the air conditioning information (inside temperature Tr, outside temperature Tam, and amount of solar radiation S). If the state information given to the input nodes contains only the air conditioning information, the probabilistic model evaluating subunit 664 does not add the probabilistic model as it is suspected that the temperature control is not optimized for the occupant's sensitivity to temperature. The control equation correcting subunit 665 then corrects the temperature control equation or the air conditioning control equation, as will be described later.
In step S115 in the above flowchart, the learning unit 66 may determine whether to construct or not to construct the probabilistic model by checking whether a first predetermined time (for example, one week or one month) has elapsed since the last time the probabilistic model associated with the same setting operation was constructed, rather than by comparing the number of times of the operation, iAK, with the prescribed number of times nl*j (j=1, 2, 3). In this case, the learning unit 66 constructs the probabilistic model only when the predetermine time has elapsed. In other words, the learning unit 66 proceeds to carry out the process from step S116 to S122. To determine whether to construct or not to construct the probabilistic model based on the elapsed time, the controller 60 stores the time and date of construction of the probabilistic model in the storage unit 61 by associating it with the probabilistic model. When calculating the elapsed time, the learning unit 66 acquires from the storage unit 61 the time and date of construction associated with the most recent probabilistic model among the probabilistic models related to the setting operation α, and calculates the elapsed time by obtaining the difference between the acquired time and the current time.
In the above embodiment, standard models having predetermined graph structures have been generated in advance for the construction of probabilistic models. However, instead of generating such standard models, the learning unit 66 may search a graph structure by using a K2 algorithm or a genetic algorithm. For example, when using a genetic algorithm, a plurality of “genes” are generated each serving as an element indicating the presence or absence of a connection between nodes. Then, the learning unit 66 calculates the fitness of each gene by using the previously described information criterion. After that, the learning unit 66 selects genes having fitness values greater than a predetermined value, and performs such manipulations as crossover and mutation to produce the next generation of genes. The learning unit 66 repeats such manipulations a plurality of times and selects genes having the best fitness. The learning unit 66 uses the graph structure described by the selected genes to construct the probabilistic model. The learning unit 66 may combine any of these algorithms with the method of constructing the probabilistic model from a standard model.
US4434932 Jul 6, 1982 Mar 6, 1984 Nippondenso Co., Ltd. Air-conditioner control system for automobiles
US5555495 Oct 25, 1993 Sep 10, 1996 The Regents Of The University Of Michigan Method for adaptive control of human-machine systems employing disturbance response
US6498958 Jan 28, 2000 Dec 24, 2002 Denso Corporation Apparatus and method for calculating an air-conditioning system controlled variable
US20030127527 Jan 9, 2003 Jul 10, 2003 Yoshinori Ichishi Vehicle air conditioner with automatic air-conditioning control
US20030136854 Jan 23, 2003 Jul 24, 2003 Shinji Aoki Vehicle air conditioner with automatic air-conditioning control
US20040102151 Nov 20, 2003 May 27, 2004 Kazushi Shikata Vehicle air conditioner
US20060149544 Jan 5, 2005 Jul 6, 2006 At&T Corp. Error prediction in spoken dialog systems
US20060195483 Apr 25, 2006 Aug 31, 2006 Bayerische Motoren Werke Aktiengesellschaft Method and device for adjusting user-dependent parameter values
US20070288413 Mar 7, 2005 Dec 13, 2007 Nobuhiro Mizuno Vehicle Information Processing System, Vehicle Information Processing Method, And Program
CN1389675A Jul 18, 2002 Jan 8, 2003 上海交通大学 Heat-comfortable fuzzily controlled air conditioner
CN1504354A Nov 20, 2003 Jun 16, 2004 株式会社电装 车用空调器
DE4426732A1 Jul 28, 1994 Feb 16, 1995 Volkswagen Ag Air-conditioning plant for automobile
DE10003548A1 Jan 28, 2000 Aug 10, 2000 Denso Corp Controlled variable calculating device for vehicle air conditioner
DE10202928A1 Jan 25, 2002 Jul 31, 2003 Opel Adam Ag System for adjusting settings for a motor vehicle, whereby a driver can connect to a central server for transfer of settings, conveniently input via a PC, so that they can be loaded into the onboard computer via a wireless link
DE10333181A1 Jul 22, 2003 May 12, 2005 Daimler Chrysler Ag Bayes-network-based diagnosis system e.g. for motor vehicles, has actual state of technical system formed with status/state parameters
DE10350715A1 Oct 30, 2003 Jun 2, 2005 Bayerische Motoren Werke Ag Verfahren und Vorrichtung zur Einstellung benutzerabhängiger Parameterwerte
DE19902201A1 Jan 21, 1999 Aug 10, 2000 Audi Ag Vehicle air conditioning control, includes look-ahead facility remotely determining air condition at pre-set destination, to control passenger compartment conditions accordingly
DE19904143A1 Feb 3, 1999 Aug 24, 2000 Daimler Chrysler Ag Regulating method for heating and/or air conditioning system involves automatically carrying out system settings related to control access associated with prevailing climatic situation
DE69811814T2 Oct 7, 1998 Dec 18, 2003 Valeo Climatisation Steueranordnung für Fahrzeugklimaanlagen, mit selbstlernenden Benutzerpräferenzen
DE69812525T2 Oct 6, 1998 Aug 14, 2003 Valeo Climatisation Steueranordnung einer Kraftfahrzeug-Klimaanlage, mit fahrerspezifischen Einstellungen
JP2000062431A Title not available
JP2000071060A Title not available
JP2000293204A Title not available
JP2002507793A Title not available
JP2003220816A Title not available
JP2005257270A Title not available
JP2006240387A Title not available
JPH05169963A Title not available
JPH05208610A Title not available
JPH08271026A Title not available
WO2005047062A1 Oct 6, 2004 May 26, 2005 Bayerische Motoren Werke Aktiengesellschaft Method and device for setting user-dependent parameter values
WO2005091214A1 Mar 7, 2005 Sep 29, 2005 Denso It Laboratory, Inc. Vehicle information processing system, vehicle information processing method, and program
1 Adams, Douglas,"Per Anhalter durch die Galaxis", novel, 14th edn., reprint of UB31070, Frankfurt am Main; Berlin, Ullstein, 1990, chapter 10, pp. 83-84, ISBN 3-548-22491-1 (English translation of the relevant part).
2 K. Shigemasu et al, "Overview of Bayesian Networks", Baifukan, Jul. 2006, pp. 4-7, 35-38, 54-63, 74-81, 85-87, and 102-110.
3 Office action dated Dec. 2, 2008 in Japanese Application No. 2007/032251.
4 Office Action dated Mar. 29, 2011 in corresponding Japanese Application No. 2006-260699.
5 Office Action dated Oct. 29, 2010, for corresponding German Application No. 10 2008 007 725.9, and English translation thereof.
6 Office action dated Sep. 21, 2010 in related German Application No. 070452316 and English translation.
7 Office action dated Sep. 25, 2009 in related Chinese Application No. 2008 10131658.0.
8 Office Action from corresponding German Patent Application No. 10 2008 008 446.8 dated Dec. 20, 2010 with English translation.
9 Richard O. Duda et al, "Pattern Classification", Second Edition, John Wiley & Sons, Inc., 2001, pp. 56-64.
10 U.S. Appl. No. 11/901,946, filed Sep. 19, 2007.
11 Y. Motomura and H. Iwasaki, "Technology of Bayesian Networks", Tokyo Denki University Press, Jul. 2006, pp. 9-26.
US9586459 * Jan 4, 2011 Mar 7, 2017 Ford Global Technolgies, Llc Method for motor vehicle interior climate control
US20110166711 * Jan 4, 2011 Jul 7, 2011 Ford Global Technologies, Llc Method for motor vehicle interior climate control
US20110172880 * Sep 26, 2008 Jul 14, 2011 Pioneer Corporation Air conditioning control device and air conditioning control method
U.S. Classification 706/62, 700/276
International Classification G05D23/00, G05B15/00, G06F15/18, G05B13/00, G01M1/38, G06F15/00
Cooperative Classification B60H1/00735, B60H1/00971, B60H1/00771
European Classification B60H1/00Y5, B60H1/00Y5F1, B60H1/00Y8
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:KOJIMA, YASUFUMI;TAKEDA, HIROSHI;HARA, KOUSUKE;REEL/FRAME:020745/0309