Abstract: A system for tracking and managing baking pans and trays, comprising a set of baking pans or trays that includes a plurality of individual baking pans or trays, each of which includes a unique identification tag positioned thereon, wherein each unique identification tag includes information about the individual baking pan or tray on which the identification tag is positioned; and a data acquisition system that includes at least one sensor mounted in proximity to a production line conveyor for detecting baking pans or trays positioned on the conveyor; at least one tag-reading device that is activated when the at least one sensor detects a pan or tray on the conveyor, wherein the at least one tag reading device reads information from each identification tag and transmits the information to a controller; and a controller that includes hardware and software that controls the at least one sensor and the at least one tag reading device, and that receives, processes, and outputs the information read from the identif

Abstract: A system for tracking and managing leaking pans and trays, comprising a set of baking pans or trays that includes a plurality of individual baking pans or trays, each of which includes a unique identification tag positioned thereon, wherein each unique identification tag includes information about the individual baking pan or tray on which the identification tag is positioned; and a data acquisition system that includes at least one sensor mounted in proximity to a production line conveyor for detecting baking pans or trays positioned on the conveyor; at least one tag-reading device that is activated when the at least one sensor detects a pan or tray on the conveyor, wherein the at least one tag reading device reads information from each identification tag and transmits the information to a controller; and a controller that includes hardware and software that controls the at least one sensor and the at least one tag reading device, and that receives, processes, and outputs the information read from the identi

Abstract: A method and system for auto safety verification of automatic guided vehicle (AGV) sensors is presented. An auto safety verification test can be performed autonomously by the AGV to ensure that sensors on the AGV are configured correctly and working properly. The auto safety verification test avoids human errors that exist in manual verification tests and significantly reduces the amount of time required to verify sensors on the AGV.

Abstract: A parsing method and system. The method includes generating an n-gram model of a domain and computing a tf-idf frequency associated with n-grams of the n-gram model. A list including a frequently occurring group of n-grams based on the tf-idf frequency is generated. The frequently occurring group of n-grams is transmitted to a deep parser component and a deep parse output from the deep parser component is generated. The deep parse output is stored within a cache and a processor verifies if a specified text word sequence of the deep parse output is available in the cache.

Abstract: A parsing method and system. The method includes generating an n-gram model of a domain and computing a tf-idf frequency associated with n-grams of the n-gram model. A list including a frequently occurring group of n-grams based on the tf-idf frequency is generated. The frequently occurring group of n-grams is transmitted to a deep parser component and a deep parse output from the deep parser component is generated. The deep parse output is stored within a cache and a processor verifies if a specified text word sequence of the deep parse output is available in the cache.

Abstract: A method and system for auto safety verification of automatic guided vehicle (AGV) sensors is presented. An auto safety verification test can be performed autonomously by the AGV to ensure that sensors on the AGV are configured correctly and working properly. The auto safety verification test avoids human errors that exist in manual verification tests and significantly reduces the amount of time required to verify sensors on the AGV.

Abstract: A parsing method and system. The method includes generating an n-gram model of a domain and computing a tf-idf frequency associated with n-grams of the n-gram model. A list including a frequently occurring group of n-grams based on the tf-idf frequency is generated. The frequently occurring group of n-grams is transmitted to a deep parser component and a deep parse output from the deep parser component is generated. The deep parse output is stored within a cache and a processor verifies if a specified text word sequence of the deep parse output is available in the cache.

Abstract: A parsing method and system. The method includes generating an n-gram model of a domain and computing a tf-idf frequency associated with n-grams of the n-gram model. A list including a frequently occurring group of n-grams based on the tf-idf frequency is generated. The frequently occurring group of n-grams is transmitted to a deep parser component and a deep parse output from the deep parser component is generated. The deep parse output is stored within a cache and a processor verifies if a specified text word sequence of the deep parse output is available in the cache.

Abstract: A parsing method and system. The method includes generating an n-gram model of a domain and computing a tf-idf frequency associated with n-grams of the n-gram model. A list including a frequently occurring group of n-grams based on the tf-idf frequency is generated. The frequently occurring group of n-grams is transmitted to a deep parser component and a deep parse output from the deep parser component is generated. The deep parse output is stored within a cache and a processor verifies if a specified text word sequence of the deep parse output is available in the cache.

Abstract: A parsing method and system. The method includes generating an n-gram model of a domain and computing a tf-idf frequency associated with n-grams of the n-gram model. A list including a frequently occurring group of n-grams based on the tf-idf frequency is generated. The frequently occurring group of n-grams is transmitted to a deep parser component and a deep parse output from the deep parser component is generated. The deep parse output is stored within a cache and a processor verifies if a specified text word sequence of the deep parse output is available in the cache.

Abstract: A container that can be constructed from a blank is disclosed. The container can include four side walls and can be divided into multiple cells. The size or shape of the cell(s) can be substantially similar or can be varied. For instance, the cell(s) can be varied to correspond to the size or shape of a particular article of manufacture.

Abstract: A container that can be constructed from a blank is disclosed. The container can include four side walls and can be divided into multiple cells. The size or shape of the cell(s) can be substantially similar or can be varied. For instance, the cell(s) can be varied to correspond to the size or shape of a particular article of manufacture.