Patent Application
20250198418
This application claims the benefit of Korean Patent Application No. 10-2023-0182057, filed on Dec. 14, 2023, and No. 10-2024-0033951, filed on Mar. 11, 2024, in the Korean Intellectual Property Office, the entire disclosures of which are incorporated herein by reference for all purposes.
One or more embodiments relate to an electronic device for predicting an occurrence of air leakage of an air compressor and a method of operating the same.
An air compressor may perform a role of sucking in outside air, generating compressed air, and supplying the compressed air to a source of demand. An air compressor may control an increase or decrease in pressure of emitted air by adjusting the amount of air sucked in through adjustment of an inlet guide vane (IGV). When the amount of air required at a source of demand decreases, the amount of air sucked into the air compressor needs to be reduced. To reduce the amount of air sucked, the air compressor may close the IGV.
Since there is a limit to the air compressor controlling the amount of air sucked by adjusting the IGV, the air compressor may open a bleed off valve (BOV) to emit compressed air generated. In particular, a turndown ratio is determined by an adjustable range of the IGV of the air compressor, and when the turndown ratio is exceeded, a surging phenomenon may occur, which may cause the air compressor to fail.
Controlling the amount of supply to reduce production rather than emitting compressed air due to an operation of the BOV may prevent financial losses for a supplier, but it may be difficult for the supplier to determine when it is appropriate to control the IGV. In addition, since the turndown ratio is set differently for each air compressor, it may not be appropriate to use a specific air compressor as a standard to stabilize the air compressor through the operation of the BOV.
To resolve the abnormal state of the air compressor, there may be a post-processing measure that the supplier checks values for the IGV and BOV after the abnormal situation occurs, cuts off the power supply to the air compressor in the abnormal state, and starts another air compressor. However, the post-processing measure may have a difficulty to identify whether the abnormal value for the BOV is simply a value for stabilizing the state of the air compressor, a value caused by a sudden decrease in demand from a source of demand, or a value caused by an increase in pressure or temperature of the air compressor.
Various embodiments may provide training a model for predicting an occurrence of air leakage of an air compressor in advance, based on data about the air compressor or a pneumatic chamber obtained from a plurality of sensors.
Various embodiments may provide training a model for determining whether air leakage occurs based on suction data and emission data of an air compressor, and predicting an occurrence of the air leakage in advance by matching whether the air leakage occurs to air leakage data related to the occurrence of the air leakage.
Various embodiments may provide a user with an advance notice of occurring air leakage by determining a possibility of the air leakage occurring using a model trained through air leakage data related to the occurrence of the air leakage.
Other objects and advantages of the present disclosure can be understood by the following description and will become more apparent by the embodiments of the present disclosure. In addition, it will be apparent that the objects and advantages of the present disclosure can be readily realized by the means and combinations thereof recited in the claims.
According to an aspect, there is provided an electronic device including a processor and a memory configured to store instructions, wherein the instructions, when executed by the processor, may cause the electronic device to obtain suction data about air sucked by an air compressor and emission data about air emitted from the air compressor, determine whether air leakage occurs in the air compressor, based on the suction data and the emission data, obtain air leakage data related to an occurrence of the air leakage from a plurality of sensors mounted on the air compressor, and train a model for predicting the occurrence of the air leakage of the air compressor in advance by matching whether the air leakage occurs to the air leakage data before a predetermined time from a time of the occurrence of the air leakage.
The air leakage data may be data about items in which a correlation coefficient between the emission data and pieces of data about a plurality of items obtained from the plurality of sensors is greater than a predetermined value, in a time interval where the air leakage has occurred.
The air leakage data may include data about temperature of air sucked by the air compressor, temperature and pressure of air emitted, temperature and pressure of oil inside the air compressor, and vibration and power consumption of the air compressor.
The correlation coefficient may represent a Spearman correlation coefficient.
The instructions, when executed by the processor, may cause the electronic device to determine that the air leakage has occurred when the air compressor emits air while sucking the air, based on the suction data and the emission data.
The instructions, when executed by the processor, may cause the electronic device to train the model through a classification algorithm for whether the air leakage occurs after the predetermined time from a time the air leakage data is obtained.
The instructions, when executed by the processor, may cause the electronic device to train the model by determining a case in which the air leakage occurs as a first value and a case in which the air leakage does not occur as a second value.
The instructions, when executed by the processor, may cause the electronic device to remove an outlier from the air leakage data, based on distribution of the air leakage data in a predetermined time interval.
According to another aspect, there is provided an electronic device including a processor and a memory configured to store instructions, wherein the instructions, when executed by the processor, may cause the electronic device to obtain air leakage data related to an occurrence of air leakage of an air compressor, determine a possibility of the air leakage occurring after a predetermined time from a time the air leakage data is obtained, using a model trained to predict the occurrence of the air leakage of the air compressor in advance, by matching whether the air leakage occurs to the air leakage data before the predetermined time from a time of the occurrence of the air leakage of the air compressor, and output the possibility of the air leakage occurring.
The instructions, when executed by the processor, may cause the electronic device to control an amount of air supplied to the air compressor, based on the possibility of the air leakage occurring.
According to another aspect, there is provided a method of operating an electronic device, the method including obtaining suction data about air sucked by an air compressor and emission data about air emitted from the air compressor, determining whether air leakage occurs in the air compressor, based on the suction data and the emission data, obtaining air leakage data related to an occurrence of the air leakage from a plurality of sensors mounted on the air compressor, and training a model for predicting the occurrence of the air leakage of the air compressor in advance by matching whether the air leakage occurs to the air leakage data before a predetermined time from a time of the occurrence of the air leakage.
The air leakage data may be data about items in which a correlation coefficient between the emission data and pieces of data about a plurality of items obtained from the plurality of sensors is greater than a predetermined value, in a time interval where the air leakage has occurred.
The air leakage data may include data about temperature of air sucked by the air compressor, temperature and pressure of air emitted, temperature and pressure of oil inside the air compressor, and vibration and power consumption of the air compressor.
The correlation coefficient may represent a Spearman correlation coefficient.
The determining of whether the air leakage occurs may include determining that the air leakage has occurred when the air compressor emits air while sucking the air, based on the suction data and the emission data.
The training of the model may include training the model through a classification algorithm for whether the air leakage occurs after the predetermined time from a time the air leakage data is obtained.
The training of the model may include training the model by determining a case in which the air leakage occurs as a first value and a case in which the air leakage does not occur as a second value.
The obtaining of the air leakage data may include removing an outlier from the air leakage data, based on distribution of the air leakage data in a predetermined time interval.
Additional aspects of embodiments will be set forth in part in the description which follows and, in part, will be apparent from the description, or may be learned by practice of the disclosure.
According to various embodiments, a model for predicting an occurrence of air leakage of an air compressor in advance may be trained, thereby allowing an operator to respond quickly by predicting the occurrence of the air leakage in advance rather than responding after the occurrence of the air leakage.
According to various embodiments, an occurrence of air leakage of an air compressor may be predicted through a trained model to allow immediate adjustment of supply, thereby preventing financial loss and increasing efficiency of compressed air supply.
According to various embodiments, a cause of abnormal values for a bleed off valve (BOV) may be probabilistically identified and an operator may be provided with help to adopt measures.
These and/or other aspects, features, and advantages of the invention will become apparent and more readily appreciated from the following description of embodiments, taken in conjunction with the accompanying drawings of which:
The following detailed structural or functional description is provided as an example only and various alterations and modifications may be made to the embodiments. Accordingly, the embodiments are not construed as limited to the disclosure and should be understood to include all changes, equivalents, and replacements within the idea and the technical scope of the disclosure.
As used herein, “A or B”, “at least one of A and B”, “at least one of A or B”, “A, B or C”, “at least one of A, B and C”, “at least one of A, B, or C”, and “one or a combination of at least two of A, B, and C,” each of which may include any one of the items listed together in the corresponding one of the phrases, or all possible combinations thereof. Although terms, such as first, second, and the like are used to describe various components, the components are not limited to the terms. These terms should be used only to distinguish one component from another component. For example, a first component may be referred to as a second component, and similarly the second component may also be referred to as the first component.
It should be noted that if one component is described as being “connected”, “coupled”, or “joined” to another component, a third component may be “connected”, “coupled”, and “joined” between the first and second components, although the first component may be directly connected, coupled, or joined to the second component.
The singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises/comprising” and/or “includes/including” when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
Unless otherwise defined, all terms, including technical and scientific terms, used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure pertains. Terms, such as those defined in commonly used dictionaries, should be construed to have meanings matching with contextual meanings in the relevant art, and are not to be construed to have an ideal or excessively formal meaning unless otherwise defined herein.
Hereinafter, the embodiments will be described in detail with reference to the accompanying drawings. When describing the embodiments with reference to the accompanying drawings, like reference numerals refer to like components and a repeated description related thereto will be omitted.
Referring to
The air compressor 110 may generate compressed air by sucking in and compressing surrounding air and may provide the generated compressed air. In
Here, S in the capacity item may represent a screw-type air compressor, T may represent a turbo-type air compressor, and V may represent a variable-type air compressor. The air compressor 110 may be equipped with the plurality of sensors 115. The air compressor 110 may be equipped with a different number of sensors 115 depending on embodiments. The plurality of sensors 115 may include various types of Internet of Things (IoT) sensors.
The plurality of sensors 115 may obtain pieces of data about a plurality of items related to the air compressor 110. The pieces of data related to the air compressor 110 may include air leakage data related to an occurrence of air leakage in the air compressor 110. The electronic device 120 may obtain the pieces of data or air leakage data related to the air compressor 110 through the plurality of sensors 115.
The air leakage data may be data about items in which a correlation coefficient between emission data is greater than a predetermined value, among the pieces of data about a plurality of items related to the air compressor 110. The air leakage data may include data about temperature of air sucked by the air compressor 110, temperature and pressure of air emitted, temperature and pressure of oil inside the air compressor 110, and vibration and power consumption of the air compressor 110.
The electronic device 120 may represent a device for predicting an occurrence of air leakage in the air compressor 110. The electronic device 120 may communicate with the air compressor 110 or the plurality of sensors 115 via wires or wirelessly. The electronic device 120 may be mounted on or attached to the air compressor 110, or may be provided separately from the air compressor 110. In addition, the electronic device 120 may be equipped with a separate display to display data obtained from the plurality of sensors 115 or the air leakage data, or to display a possibility of the air leakage occurring after a predetermined time (e.g., 1 minute, 3 minutes, 5 minutes, etc.) or whether the air leakage occurs. The electronic device 120 may be equipped with a separate speaker to output a warning sound to notify the occurrence of the air leakage when the air leakage is expected to occur after the predetermined time.
The electronic device 120 may obtain suction data about air sucked into the air compressor 110 and emission data about air emitted from the air compressor 110. The suction data may represent the amount of sucked air regulated through an inlet guide vane (IGV) and the emission data may represent the amount of emitted air regulated through a bleed off valve (BOV).
The electronic device 120 may determine whether air leakage occurs in the air compressor, based on the suction data and the emission data. The electronic device 120 may train a model for predicting the occurrence of the air leakage in the air compressor in advance by matching whether the air leakage occurs to the air leakage data before a predetermined time from a time of the occurrence of the air leakage. In addition, the electronic device 120 may use the trained model to determine a possibility of the air leakage occurring after a predetermined time from a time the air leakage data is obtained.
A specific operation of the electronic device 120 training a model for predicting the occurrence of the air leakage in the air compressor 110 in advance and determining the possibility of the air leakage occurring using the trained model is described in detail with reference to
Referring to
The training phase of the electronic device 200 may represent an operation of training a model to predict in advance an occurrence of air leakage in an air compressor.
In operation 210, the electronic device 200 may obtain pieces of data about the air compressor, including air leakage data, from a plurality of sensors mounted on the air compressor. In addition, the electronic device 200 may obtain suction data about air sucked by the air compressor and emission data about air emitted from the air compressor.
In operation 220, the electronic device 200 may preprocess the obtained pieces of data. In other words, since data may be lost or abnormal data may be generated due to communication errors or device malfunctions, the electronic device 200 may preprocess the pieces of data to remove abnormal data and leave valid data.
According to an embodiment, the electronic device 200 may remove an outlier from the pieces of data, based on data distribution in a predetermined time interval. The electronic device 200 may list data in the predetermined time interval in a normal distribution form, may determine data outside a particular interval as an outlier, and may remove the outlier. For example, the electronic device 200 may determine distribution for a time interval of about 22 days to consider seasonality, may determine data in a range greater than or less than three times a standard deviation from an average for each time interval to be outliers and may remove the outliers. In addition, the electronic device 200 may remove an outlier using a linear regression method.
In operation 230, the electronic device 200 may determine whether air leakage occurs in the air compressor, based on the suction data and the emission data. The air leakage may represent a situation in which air flowing into the air compressor is discarded without reaching a source of demand. The electronic device 200 may determine that the air leakage has occurred in the air compressor when the air compressor emits air while sucking the air. In other words, the electronic device 200 may determine that the air leakage has occurred when values for an IGV and a BOV of the air compressor are not zero at the same time.
In operation 240, the electronic device 200 may determine a correlation coefficient between the emission data and the pieces of data obtained from the plurality of sensors. The electronic device 200 may determine which data is the air leakage data related to the occurrence of the air leakage through the determined correlation coefficient. Correlation may represent Pearson correlation, Kendall tau correlation, or Spearman correlation.
For each piece of data, even if the data is of the same item, the correlation coefficient may appear differently depending on a time interval of a normal state and a time interval of an abnormal state. The electronic device 200 may determine that the data is related to the occurrence of the air leakage and may determine the data as the air leakage data, when the absolute value of the correlation coefficient is greater than or equal to a predetermined value or a deviation according to a time interval is greater than or equal to a predetermined value. For example, the electronic device 200 may determine the data as the air leakage data, when the absolute value of the correlation coefficient is greater than or equal to about 0.4, which is in the top 10% of the data, or when the deviation according to the time interval is greater than or equal to about 0.5, which is in the top 10% of the data.
The electronic device 200 may determine the air leakage data related to the occurrence of the air leakage through correlation coefficient analysis. The air leakage data may include data about temperature of air sucked by the air compressor, temperature and pressure of air emitted, temperature and pressure of oil inside the air compressor, and vibration and power consumption of the air compressor.
In operation 250, the electronic device 200 may train a model for predicting the occurrence of the air leakage in the air compressor in advance by matching whether the air leakage occurs, which is determined in operation 230, to the air leakage data before a predetermined time from a time of the occurrence of the air leakage.
The inference phase of the electronic device 200 may represent an operation of determining a possibility of the air leakage occurring after a predetermined time from a time the air leakage data is obtained, using the model trained through operations 210, 220, 230, 240, and 250.
In operation 260, the electronic device 200 may obtain the air leakage data for a particular time. The electronic device 200 may obtain the air leakage data by receiving the air leakage data from a user of the electronic device 200 or by receiving the air leakage data periodically or aperiodically from the air compressor or the plurality of sensors.
In operation 270, the electronic device 200 may input the obtained air leakage data to the model trained in the training phase.
In operation 280, the electronic device 200 may determine a possibility of the air leakage occurring after a predetermined time from a time the air leakage data is obtained.
Referring to
The table 300 of
Referring to
Since there are many types of data obtained by a plurality of sensors mounted on an air compressor, there is a possibility that an electronic device may miscalculate unnecessary data to be related to an occurrence of air leakage. Therefore, the electronic device may train a model to predict air leakage situations in advance more accurately by searching for an indicator related to an actual cause of the air leakage.
In the table 400, the air leakage data may include first air temperature, second air temperature, third air temperature, oil temperature, oil pressure, BOV, first power load, second power load, third power load, first air pressure, second air pressure, and IGV.
The air compressor may be configured to compress air in one or more phases, depending on embodiments. Here, the first air temperature, the second air temperature, and the third air temperature may each represent temperature of air measured at different phases within the air compressor. The first power load, the second power load, and the third power load may each represent power load measured at different air compressors or under different load conditions. The first air pressure and the second air pressure may each represent pressure of air measured at different phases within the air compressor. For example, the first air temperature, the second air temperature, and the third air temperature may represent temperature of compressed air in a first phase, a second phase, and a third phase, respectively, for air sucked into the air compressor. The first power load, the second power load, and the third power load may represent power load at maximum load, minimum load, and no-load states, respectively. The first air pressure and the second air pressure may represent pressure of compressed air in a first phase and a second phase, respectively, for air sucked into the air compressor.
In addition, in the table 400, Binary_Target may represent a value determined by labeling an outlier as 1 and a normal value as 0 for each time interval.
Referring to the table 400, it may be learned that each piece of air leakage data is correlated with the BOV of each time interval and thus is related to the air leakage of the air compressor. For example, power load or air pressure that has a correlation coefficient relatively close to 1 or −1 may be highly correlated with the air leakage of the air compressor.
Referring to
The graphs 510, 520, 530, 540, 550, and 560 may represent first air temperature, second air temperature, third air temperature, oil temperature, oil pressure, and BOV values, respectively, for BOV values (shown as X marks). Here, each value of air leakage data is displayed as a point and a line.
Referring to
Referring to
The graphs 610, 620, 630, 640, 650, and 660 may represent first power load, second power load, third power load, first air pressure, second air pressure, and IGV values, respectively, for BOV values (shown as X marks). Here, each value of air leakage data is displayed as a point and a line.
Referring to
Referring to
The electronic device may determine whether the air leakage occurs 720 by using a first value as an abnormal situation in which the air leakage has occurred in an air compressor and a second value as a normal situation in which the air leakage has not occurred. For example, the first value may be 1 and the second value may be 0.
The electronic device may match whether the air leakage occurs 720 to each piece of air leakage data 710 obtained before the predetermined time. As shown in
The electronic device may train a model to predict the occurrence of the air leakage in advance by using the matched air leakage data 710 and whether the air leakage occurs 720.
Referring to
The electronic device may train the model by matching air leakage data obtained in a plurality of time intervals to whether the air leakage occurs.
The electronic device may train the model to predict the occurrence of the air leakage in advance using algorithms such as logistic regression, support vector machine (SVM), decision tree, random forest, naive bayes, k-nearest neighbor (KNN), ensemble models (e.g., eXtreme Gradient Boosting (XGBoost)), and deep neural network (DNN).
For example, the electronic device may train the model using a DNN algorithm such as the one below.
relu, Input_shape
[X_train.shape[1]]),
relu),
relu),
si(mold),
‘adam’
binary_crossent
[‘binary_accuracy’],
indicates data missing or illegible when filed
The electronic device may obtain air leakage data for a particular time and may use the trained model to determine a possibility of air leakage occurring after a predetermined time from the particular time at which the air leakage data is obtained. In addition, the electronic device may predict whether the air leakage occurs based on the possibility of the air leakage occurring. Thereafter, the electronic device may output information about the possibility of the air leakage occurring or the predicted occurrence of the air leakage on a display or the like, or may output a warning sound to notify of the occurrence of the air leakage through a speaker or the like.
A user of the electronic device or the electronic device may control the amount of air supplied to an air compressor, based on the possibility of the air leakage occurring or the predicted occurrence of the air leakage. For example, the user of the electronic device or the electronic device may prevent the occurrence of the air leakage in advance by reducing the amount of air supplied to the air compressor, when the air leakage is predicted to occur after the predetermined time. By controlling the amount of air, the user may prepare for the occurrence of the air leakage without monitoring the occurrence of the air leakage directly in real time and may reduce the cost associated with the air leakage.
In addition, since the electronic device may be used without being limited to type or performance of a particular air compressor, the electronic device may be connected to a plurality of air compressors to build an infrastructure for predicting and managing the occurrence of the air leakage.
As shown in
In the following embodiments, operations may be performed sequentially but not necessarily. For example, the order of the operations may change and at least two of the operations may be performed in parallel. Operations 910 to 940 may be performed by at least one component (e.g., a processor) of an electronic device.
In operation 910, the electronic device may obtain suction data about air sucked by the air compressor and emission data about air emitted from the air compressor.
In operation 920, the electronic device may determine whether air leakage occurs in the air compressor, based on the suction data and the emission data. The electronic device may determine that the air leakage has occurred when the air compressor emits air while sucking the air, based on the suction data and the emission data.
In operation 930, the electronic device may obtain air leakage data related to an occurrence of the air leakage from a plurality of sensors mounted on the air compressor. The electronic device may remove an outlier from the air leakage data, based on distribution of the air leakage data in a predetermined time interval.
In operation 940, the electronic device may train a model for predicting the occurrence of the air leakage in the air compressor in advance by matching whether the air leakage occurs to the air leakage data before a predetermined time from a time of the occurrence of the air leakage. The electronic device may train the model through a classification algorithm for whether the air leakage occurs after the predetermined time from a time the air leakage data is obtained. The electronic device may train the model by determining a case in which the air leakage occurs as a first value and a case in which the air leakage does not occur as a second value.
The air leakage data may be data about items in which a correlation coefficient between the emission data and pieces of data about a plurality of items obtained from the plurality of sensors is greater than a predetermined value, in a time section where the air leakage has occurred. The air leakage data may include data about temperature of air sucked by the air compressor, temperature and pressure of air emitted, temperature and pressure of oil inside the air compressor, and vibration and power consumption of the air compressor. The correlation coefficient may represent a Spearman correlation coefficient.
In the following embodiments, operations may be performed sequentially but not necessarily. For example, the order of the operations may change and at least two of the operations may be performed in parallel. Operations 1010 to 1030 may be performed by at least one component (e.g., a processor) of an electronic device.
In operation 1010, the electronic device may obtain air leakage data related to an occurrence of air leakage in an air compressor.
In operation 1010, the electronic device may determine a possibility of the air leakage occurring after a predetermined time from a time the air leakage data is obtained, using a model trained to predict the occurrence of the air leakage of the air compressor in advance, by matching whether the air leakage occurs to the air leakage data before the predetermined time from a time of the occurrence of the air leakage of the air compressor.
In operation 1030, the electronic device may output the possibility of the air leakage occurring. The electronic device may control the amount of air supplied to the air compressor based on the possibility of the air leakage occurring.
Referring to
The memory 1120 may store instructions (or programs) executable by the processor 1110. For example, the instructions may include instructions for executing an operation of the processor 1110 and/or an operation of each component of the processor 1110.
The processor 1110 may be a device that executes instructions or programs or controls the electronic device 1100 and may include, for example, various processors such as a central processing unit (CPU) and a graphics processing unit (GPU). The processor 1110 may obtain suction data about air sucked by the air compressor and emission data about air emitted from the air compressor. The processor 1110 may determine whether air leakage occurs in the air compressor, based on the suction data and the emission data. The processor 1110 may obtain air leakage data related to an occurrence of the air leakage from a plurality of sensors mounted on the air compressor. The processor 1110 may train a model for predicting the occurrence of the air leakage in the air compressor in advance by matching whether the air leakage occurs to the air leakage data before a predetermined time from a time of the occurrence of the air leakage.
The processor 1110 may determine that the air leakage has occurred when the air compressor emits air while sucking the air, based on the suction data and the emission data. The processor 1110 may train the model through a classification algorithm for whether the air leakage occurs after the predetermined time from a time the air leakage data is obtained. The processor 1110 may train the model by determining a case in which the air leakage occurs as a first value and a case in which the air leakage does not occur as a second value. The processor 1110 may remove an outlier from the air leakage data, based on distribution of the air leakage data in a predetermined time interval.
The air leakage data may be data about items in which a correlation coefficient between the emission data and pieces of data about a plurality of items obtained from the plurality of sensors is greater than a predetermined value, in a time section where the air leakage has occurred. The air leakage data may include data about temperature of air sucked by the air compressor, temperature and pressure of air emitted, temperature and pressure of oil inside the air compressor, and vibration and power consumption of the air compressor. The correlation coefficient may represent a Spearman correlation coefficient.
The processor 1110 may obtain air leakage data related to an occurrence of air leakage in an air compressor. The processor 1110 may determine a possibility of the air leakage occurring after a predetermined time from a time the air leakage data is obtained, using a model trained to predict the occurrence of the air leakage of the air compressor in advance, by matching whether the air leakage occurs to the air leakage data before the predetermined time from a time of the occurrence of the air leakage of the air compressor. The processor 1110 may output the possibility of the air leakage occurring.
The processor 1110 may control the amount of air supplied to the air compressor based on the possibility of the air leakage occurring.
In addition, the electronic device 1100 may process the operations described above.
The components described in the embodiments may be implemented by hardware components including, for example, at least one digital signal processor (DSP), a processor, a controller, an application-specific integrated circuit (ASIC), a programmable logic element, such as a field programmable gate array (FPGA), other electronic devices, or combinations thereof. At least some of the functions or the processes described in the embodiments may be implemented by software, and the software may be recorded on a recording medium. The components, the functions, and the processes described in the embodiments may be implemented by a combination of hardware and software.
The embodiments described herein may be implemented using a hardware component, a software component, and/or a combination thereof. A processing device may be implemented using one or more general-purpose or special-purpose computers, such as, for example, a processor, a controller and an arithmetic logic unit (ALU), a DSP, a microcomputer, an FPGA, a programmable logic unit (PLU), a microprocessor, or any other device capable of responding to and executing instructions in a defined manner. The processing device may run an operating system (OS) and one or more software applications that run on the OS. The processing device also may access, store, manipulate, process, and generate data in response to execution of the software. For purpose of simplicity, the description of a processing device is singular; however, one of ordinary skill in the art will appreciate that a processing device may include a plurality of processing elements and a plurality of types of processing elements. For example, the processing device may include a plurality of processors, or a single processor and a single controller. In addition, different processing configurations are possible, such as parallel processors.
The software may include a computer program, a piece of code, an instruction, or some combination thereof, to independently or collectively instruct or configure the processing device to operate as desired. Software and data may be stored in any type of machine, component, physical or virtual equipment, or computer storage medium or device capable of providing instructions or data to or being interpreted by the processing device. The software may also be distributed over network-coupled computer systems so that the software is stored and executed in a distributed fashion. The software and data may be stored in a non-transitory computer-readable recording medium.
The methods according to the above-described embodiments may be recorded in non-transitory computer-readable media including program instructions to implement various operations of the above-described embodiments. The media may also include, alone or in combination with the program instructions, data files, data structures, and the like. The program instructions recorded on the media may be those specially designed and constructed for the purposes of embodiments, or they may be of the kind well-known and available to those having skill in the computer software arts. Examples of non-transitory computer-readable media include magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as compact disc read-only memory (CD-ROM) discs and digital video discs (DVDs); magneto-optical media such as optical discs; and hardware devices that are specifically configured to store and perform program instructions, such as read-only memory (ROM), random access memory (RAM), flash memory, and the like. Examples of program instructions include both machine code, such as one produced by a compiler, and files containing higher-level code that may be executed by the computer using an interpreter.
The above-described hardware devices may be configured to act as one or more software modules in order to perform the operations of the above-described embodiments, or vice versa.
As described above, although the embodiments have been described with reference to the limited drawings, one of ordinary skill in the art may apply various technical modifications and variations based thereon. For example, suitable results may be achieved if the described techniques are performed in a different order, and/or if components in a described system, architecture, device, or circuit are combined in a different manner, and/or replaced or supplemented by other components or their equivalents.
Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 10-2023-0182057 | Dec 2023 | KR | national |
| 10-2024-0033951 | Mar 2024 | KR | national |
