Moisture sensor and/or defogger with Bayesian improvements, and related methods
First Claim
1. A method of detecting moisture on a glass substrate, the method comprising:
- providing a parameterized model (M) for a possible moisture-related disturbance;
providing background information (I) concerning the model, I being known a priori;
calculating a prior probability of M given I, P(M|I);
collecting data from at least one sensor (D) connected to the substrate;
computing a probability of the model given D and I, P(M|D,I);
repeating the computing of P(M|D,I) as additional data is collected; and
accepting the model if P(M|D,I) meets or exceeds a predetermined threshold, and otherwise rejecting the model,wherein the glass substrate is a part of a vehicle window, building window, or merchandiser; and
wherein acceptance of the model triggers an action to be taken relative to the glass substrate, the action being selected from the group consisting of;
(i) causing a wiper to remove moisture from a vehicle window, (ii) heating the glass substrate, and (iii) defrosting the glass substrate.
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Accused Products
Abstract
In certain example embodiments, moisture sensors, defoggers, etc., and/or related methods, are provided. More particularly, certain example embodiments relate to moisture sensors and/or defoggers that may be used in various applications such as, for example, refrigerator/freezer merchandisers, vehicle windows, building windows, etc. When condensation or moisture is detected, an appropriate action may be taken (e.g., actuating windshield wipers, turning on a defroster, triggering the heating of a merchandiser door or window, etc.). Bayesian approaches optionally may be implemented in certain example embodiments in an attempt to improve moisture detection accuracy. For instance, models of various types of disturbances may be developed and, based on live data and a priori information known about the model, a probability of the model being accurate is calculated. If a threshold value is met, the model may be considered a match and, optionally, a corresponding appropriate action may be taken.
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Citations
16 Claims
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1. A method of detecting moisture on a glass substrate, the method comprising:
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providing a parameterized model (M) for a possible moisture-related disturbance; providing background information (I) concerning the model, I being known a priori; calculating a prior probability of M given I, P(M|I); collecting data from at least one sensor (D) connected to the substrate; computing a probability of the model given D and I, P(M|D,I); repeating the computing of P(M|D,I) as additional data is collected; and accepting the model if P(M|D,I) meets or exceeds a predetermined threshold, and otherwise rejecting the model, wherein the glass substrate is a part of a vehicle window, building window, or merchandiser; and wherein acceptance of the model triggers an action to be taken relative to the glass substrate, the action being selected from the group consisting of;
(i) causing a wiper to remove moisture from a vehicle window, (ii) heating the glass substrate, and (iii) defrosting the glass substrate. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 16)
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9. A method of detecting moisture on a glass substrate, the method comprising:
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providing a plurality of parameterized models (Mx) for different possible disturbances; providing background information (Ix) concerning each of the models; calculating a prior probability of Mx given Ix, P(Mx|Ix); collecting data from at least one sensor (D) connected to the substrate; computing a probability of each said model given D and Ix, P(Mx|D,Ix); repeating the computing of P(Mx|D,Ix) as additional data is collected; comparing the probability of each said model to a predetermined threshold; accepting or rejecting each said model based on the comparing; and when a particular model is accepted, causing an action to be taken relative to the glass substrate in dependence on the particular model that is accepted, the action being selected from the group consisting of;
(i) causing a wiper to remove moisture from a vehicle window, (ii) heating the glass substrate, (iii) defrosting the glass substrate, and (iv) turning on or off vehicle lights. - View Dependent Claims (10, 11, 12)
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13. An electronic device located in close relative proximity to a glass substrate, the device comprising:
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a first memory location storing a plurality of parameterized models (Mx) for different possible disturbances; a second memory location storing background information (Ix) concerning each of the models; at least one sensor configured to collect data from at least one sensor (D) connected to the substrate; and at least one processor configured to; calculate a prior probability of Mx given Ix, P(Mx|Ix); compute a probability of each said model given D and Ix, P(Mx|D,Ix); repeat computations of P(Mx|D,Ix) as additional data is collected by the at least one sensor; compare the probability of each said model to a predetermined threshold; and accept or reject each said model based on the comparison, wherein acceptance of a model triggers an action to be taken relative to the glass substrate, the action being selected from the group consisting of;
(i) causing a wiper to remove moisture from a vehicle window, (ii) heating the glass substrate, (iii) defrosting the glass substrate, and (iv) turning on or off vehicle lights. - View Dependent Claims (14, 15)
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Specification