Patent ID: 12188683

DETAILED DESCRIPTION

Hereinafter, example embodiments of the present disclosure will be described in detail with reference to the drawings.

In describing the example embodiments, descriptions of technical contents that are well known in the art to which the present disclosure belongs and are not directly related to the present specification will be omitted. This is to more clearly communicate without obscure the subject matter of the present specification by omitting unnecessary description.

For the same reason, in the accompanying drawings, some components are exaggerated, omitted or schematically illustrated. In addition, the size of each component does not fully reflect the actual size. The same or corresponding components in each drawing are given the same reference numerals.

Advantages and features of the present disclosure and methods of achieving them will be apparent from the following example embodiments that will be described in more detail with reference to the accompanying drawings. It should be noted, however, that the present disclosure is not limited to the following example embodiments, and may be implemented in various forms. Accordingly, the example embodiments are provided only to disclose the present disclosure and let those skilled in the art know the category of the present disclosure. In the drawings, embodiments of the present disclosure are not limited to the specific examples provided herein and are exaggerated for clarity. The same reference numerals or the same reference designators denote the same elements throughout the specification.

At this point, it will be understood that each block of the flowchart illustrations and combinations of flowchart illustrations may be performed by computer program instructions. Since these computer program instructions may be mounted on a processor of a general-purpose computer, special purpose computer, or other programmable data processing equipment, those instructions executed through the computer or the processor of other programmable data processing equipment may create a means to perform the functions be described in flowchart block(s). These computer program instructions may be stored in a computer usable or computer readable memory that can be directed to a computer or other programmable data processing equipment to implement functionality in a particular manner, and thus the computer usable or computer readable memory. It is also possible for the instructions stored in to produce an article of manufacture containing instruction means for performing the functions described in the flowchart block(s). Computer program instructions may also be mounted on a computer or other programmable data processing equipment, such that a series of operating steps may be performed on the computer or other programmable data processing equipment to create a computer-implemented process to create a computer or other programmable data. Instructions for performing the processing equipment may also provide steps for performing the functions described in the flowchart block(s).

In addition, each block may represent a portion of a module, segment, or code that includes one or more executable instructions for executing a specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks may occur out of order. For example, the two blocks shown in succession may in fact be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the corresponding function.

According to various embodiments of the present disclosure, the term “ . . . part”, means, but is not limited to, a software or hardware component, such as a Field Programmable Gate Array (FPGA) or Application Specific Integrated Circuit (ASIC), which performs certain tasks. “ . . . part” may advantageously be configured to reside on the addressable storage medium and be configured to be executed on one or more processors. Thus, “ . . . part” may include, by way of example, components, such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. The functionality provided for in the components and “ . . . parts” may be combined into fewer components and “ . . . parts” or further separated into additional components and “ . . . parts”. In addition, the components and “ . . . parts” may be implemented such that they execute one or more CPUs in a device or a secure multimedia card.

The air handler of the present disclosure is for controlling air and may include an air purifier for performing an air purifying operation.

FIG.1is a diagram illustrating an air purifying operation of a purification part of an air handler according to an example embodiment.

An air handler of the present disclosure includes a purification part100. The purification part100may include a charging part100that ionizes at least a portion of substances included in air and a dust collecting part120that collects ionized substances.

Similar to an electrostatic precipitator using a typical photoionization method, the purification part100of the present disclosure ionizes particles and molecules of pollutants in the air in the charging part110and then collects the ionized particles and molecules in the dust collecting part120. Specifically, the purification part100may apply a voltage to the charging part110to ionize pollutants111and collect ionized pollutants112on dust collecting plates121and122of the dust collecting part120. According to an example embodiment, the charging part110of the present disclosure uses an electromagnetic wave of a low energy wavelength, so ozone is hardly generated. Also, the charging part110may charge pollutants in the air with negative charges as well as positive charges using a low wavelength of electromagnetic wave which has low energy, so that pollutant particles can be collected at both positively charged plate and negatively charged plate. Accordingly, pollutants112having negative charges may move to and be collected at a positively charged collecting plate121by electrostatic attraction. Also, the pollutants112having positive charges may move to and be collected at a negatively charged collecting plate122. Meanwhile, a clean gas113such as oxygen, which is not a pollutant in the air, is not ionized by the charging part110and does not have a specific charge. Thus, the clean gas113may pass without being collected in the dust collecting part120. As such, using the purification part100of the present disclosure, it is possible to ionize and collect only pollutant substances. The pollutant substances may be, for example, fine dust, volatile organic compounds (VOCs), and formaldehyde.

Meanwhile, because a high voltage of 8 to 15 kilovolts (kV) is applied to a charging part using a typical electrostatic precipitation method, plasma is formed around an electrode. Due to this, even oxygen, which is not a pollutant, may be ionized, and ozone may be generated as a byproduct. In contrast, the charging part110of the present disclosure uses an electromagnetic wave having a wavelength in a relatively low energy range and thus, may apply a voltage of 5 kV or less. Therefore, no plasma may be generated, and no or little amount of ozone may be generated. The charging part110according to an example embodiment of the present disclosure may ionize a substance using an electromagnetic wave having a wavelength of a range between 0.1 nanometers (nm) and 100 nm, inclusive. Specifically, the charging part110may use extreme ultraviolet (EUV) having a wavelength of 13.5 nm or a soft X-ray having a wavelength of a range between 0.1 nm and 10 nm, inclusive. In a case of using an electromagnetic wave having a wavelength of a low energy range, such as extreme ultraviolet or soft X-ray, a differential pressure and ozone may not be generated. Thus, it is possible to install the purification part100of the present disclosure in an existing air handler and achieve a high by-pass performance. For example, the extreme ultraviolet may have a by-pass performance of 50%, and the soft X-ray may have a by-pass performance of 75%.

FIG.2is a diagram illustrating an air handler according to an example embodiment.

An air handler200according to the present disclosure may include a purification part220to purify air, a first data collector210to acquire information associated with air flowing into the purification part220, and a second data collector230to acquire information associated with air discharged from the purification part220. Each of the first data collector210and the second data collector230may include at least one sensor to collect data on an air quality.

The purification part220according to an example embodiment may include at least one charging part240to ionize at least a portion of substances included in the air and include a dust collecting part250to collect substances ionized in the charging part240. The charging part240may include at least one tube241to emit an electromagnetic wave. According to an example embodiment, the tube241may be an X-ray tube that generates an X-ray through a collision between a metal target and an electron and may include an anode and a cathode. The tube241may further include a gate electrode as necessary. Here, the cathode that emits an electric field may include a field emitting element that is composed of a carbon nanotube (CNT) and emits a soft X-ray having a wavelength between 0.1 nm and 10 nm, inclusive. In this case, the field emitting element, for example, an emitter of the cathode may include a CNT structure including a plurality of unit yarns of a structure in which a plurality of CNTs is aggregated and extended in a first direction. According to an example embodiment, the charging part240may ionize substances using an electromagnetic wave having a wavelength of a range between 0.1 nm and 100 nm, inclusive. For example, the charging part240may photo-ionize pollutant substances using extreme ultraviolet or soft X-rays. The dust collecting part250may include at least one dust collecting plate251to collect ionized substances.

The air handler200according to the present disclosure may collect first data on an air quality of air flowing into the air handler200through the first data collector210. The first data may include at least one of information on an air pollutant such as fine dust, VOCs, and formaldehyde, carbon dioxide, a wind speed, a temperature, a humidity, and a size of a space in which the air handler200is installed, and may include all information associated with the air quality. The information on the fine dust may include a concentration of the fine dust and a concentration of ultrafine dust. The air may pass through the purification part220. In this instance, the purification part220may be controlled based on the collected first data. For example, the air handler200may be set to operate when, among parameters related to the air quality, a concentration of fine dust is 75 microgram per cubic meter (μg/m3) or more and a concentration of ultrafine dust is 30 μg/m3or more in the air. In this example, when a concentration of fine dust in the air according to the first data collected in the first data collector210is 80 μg/m3, the air handler200may operate.

According to an example embodiment, after the air passes through the purification part220, the second data collector230may collect second data on the air quality of air. The second data may include at least one of information on an air pollutant such as fine dust, VOCs, and formaldehyde, carbon dioxide, a wind speed, a temperature, a humidity, and a size of a space in which the air handler200is installed, and may include all information associated with the air quality. Once the air passes through the purification part220, a value indicated in the second data may be a value improved compared to a value indicated in the first data.

The air handler200according to the present disclosure may generate a model by performing machine learning based on a setting value related to a control of the purification part220, the first data measured in the first data collector210, and the second data measured in the second data collector230and control the purification part220according to the generated model. In this instance, the control of the purification part220may indicate controlling a voltage applied to the charging part240and a voltage applied to the dust collecting part250. The voltage applied to the charging part240may correspond to a voltage applied to the tube241. According to an example embodiment, the tube241may include a carbon nanotube. Also, controlling the voltage applied to the charging part240may include an individual control of a voltage applied to the anode, the cathode, or a gate of the tube241. The air handler200may be driven based on an initial value before the machine learning is performed. The initial value may be previously set based on a minimum air quality that satisfies the law of indoor air quality management.

The air handler200according to the present disclosure may control the purification part220to perform an operation customized for a space in which the air handler200is installed. To this end, the air handler200may derive a threshold based on the first data and the second data on the air quality of the air passing through the purification part220. According to an example embodiment, the second data may be data on the air quality of the air passing through the purification part220set according to the initial value or the setting value related to the control of the purification part220, which is derived through the machine-learning model. The threshold may be a threshold for a parameter related to the air quality. The parameter may include at least one of information on an air pollutant such as fine dust, VOCs, and formaldehyde, carbon dioxide, a wind speed, a temperature, a humidity, and a size of a space in which the air handler200installed. Also, the threshold may be different for each space in which the air handler200is installed. In an example, when the air handler200is installed in two spaces, a vehicle that is periodically ventilated and a closed underground station, the two places may have different air quality data and thus, may have different control conditions of the purification part220. For example, since the underground station is an enclosed space, the threshold may be set to be relatively low, so that the air purification is frequently performed. In addition, the threshold may be determined based on a size of fine dust included in the second data. For example, when a particle size of the fine dust is less than or equal to 10 micrometers (μm) (e.g., PM 10), a criterion applied to the fine dust may be applied. Also, when a particle size of the fine dust is less than or equal to 2.5 μm (e.g., PM 2.5), a criterion applied to the ultrafine dust may be applied.

According to an example embodiment, the air handler200may derive a control condition of the purification part220such that the parameter reaches the threshold or more or less. Such control condition of the purification part220may include a voltage applied to the charging part240and a voltage applied to the dust collecting part250. The voltage applied to the charging part240may correspond to the voltage applied to the tube241. According to an example embodiment, the tube241may include a carbon nanotube. Also, controlling the voltage applied to the charging part240may include an individual control of a voltage applied to the anode, the cathode, or the gate of the tube241.

Meanwhile, the number of the tubes241used in the charging part240, a location of the tube241, and a size of the dust collecting part250(or a dust collecting plate251) and the like may vary based on product characteristics of the air handler200. According to this, the air handler200may previously input the number of at least one tube241that emits an electromagnetic wave in the charging part240, a location of the at least one tube241, and a size of the dust collecting part250before machine learning for controlling the purification part220is performed.

According to an example embodiment, the air handler200of the present disclosure may continuously update the machine-learning model even after operating the purification part220by deriving the control condition of the purification part220through the machine learning. The machine learning may be continually performed using a data set including the first data and the second data acquired while the purification part220is normally operated according to the control condition derived through the machine learning. When the air quality is improved even under the same control condition so that the air can be purified with less power consumption, an optimal threshold that reduces the voltage supplied to the purification part220while maintaining the air purification may be derived. When the air quality deteriorates under the same control condition, the air handler200may continue to run to adjust an air quality parameter below or above the threshold. In this case as well, the machine learning may be continually performed using a data set including the first data and the second data acquired while the purification part220is normally operated. The air handler200according to an example embodiment may collect the first data and the second data for the machine learning for a preset period of time in predetermined time units. For example, the air handler200may build a data set by collecting the first data and the second data for one week in units of five seconds.

According to an example embodiment, the air handler200of the present disclosure may generate a machine-learning model through reinforcement learning in which compensation is performed based on the parameter related to the air quality and the power consumption of the purification part220. Specifically, voltages of the charging part240and the dust collecting part250may be randomly selected at intervals of a preset period of time within a range derived through the machine learning, and the first data and the second data may be collected accordingly. Also, the reinforcement learning may be performed by assigning a score such that the power consumption of the air handler200is reduced while the parameter related to the air quality satisfies the threshold. For example, as initial conditions, a range of voltage applied to the charging part240may be set to a range of 4.0 to 4.8 kV, and a range of voltage applied to the dust collecting part250may be set to a range of 2.8 to 4.4 kV. In this example, air quality data of air before and after the air passes through the purification part220may be collected by randomly selecting each partial voltage at intervals of 20 seconds within a preset range of voltage. As a result of the reinforcement learning, an efficiency of removing the fine dust compared to the power consumption may be maximized when the voltage applied to the charging part240is 4.3 kV and the voltage applied to the dust collecting part250is 3.5 kV. Accordingly, thresholds may be set to be 4.3 kV and 3.5 kV. According to an example embodiment, the charging part240may include a plurality of tubes241. In this case, voltages applied to the respective tubes241may be slightly different. Thus, an average value may be obtained for the respective tubes241and reflected as the voltage of the charging part240. Alternatively, when a plurality of charging parts240is present in the purification part220, instead of performing the machine learning on each of the charging parts240, the machine learning may be performed by applying the same value of voltage to the charging parts240. For this, the number of charging parts240or the number of tubes241may be input before first machine learning is performed so as to be reflected in the machine learning.

Meanwhile, the machine-learning model may be generated through the reinforcement learning, and other machine learning algorithms may also be used to generate a model applied to the air handler200of the present disclosure. For example, various machine learning algorithms such as an unsupervised learning algorithm, a rule-based machine learning algorithm, and the like may apply to the present disclosure.

FIG.3is a diagram illustrating a purification part220according to an example embodiment. Referring toFIG.3, the purification part220may include a plurality of charging parts, for example, a first charging part240-1and a second charging part240-2and the dust collecting part250. Each of the charging parts240-1and240-2may be a module that ionizes pollutant substances in the air. A setting value according to machine learning may be equally applied to the first charging part240-1and the second charging part240-2, or voltage values having a neglectable difference therebetween may be applied to the first charging part240-1and the second charging part240-2.

Meanwhile, because air quality data changes based on locations of sensors of the first data collector210and the second data collector230, the control condition of the purification part220derived using a model trained based on the first data and the second data may also be changed. Accordingly, the locations of the sensors of the first data collector210and the second data collector230may be changed based on a structure, air quality characteristics, and the like of a space in which the air handler200is installed.

According to an example embodiment, a plurality of air handlers200may be installed in the same space. In this case, according to different machine learning models, the air handlers200may control purification parts220therein. That is, for each of the air handlers200, a threshold applied to an air quality parameter may be different.

When compared to a typical electrostatic precipitator using a corona discharge method, the air handler200of the present disclosure may have a high dust collection efficiency compared to the power consumption so a resulting ozone generation converges to zero. For example, a typical electrostatic precipitator may consume 110 watts (W) of power, have 10% of the dust collection efficiency compared to ionization, and generates approximately 65 parts per million (ppm) of ozone. In this case, the air handler of the present disclosure may consume 40 W of power, have 50% of the dust collection efficiency compared to ionization, and generates approximately 0 ppm of ozone.

The air handler200of the present disclosure may derive an optimal operation condition according to an environment and a space in which the air handler is installed and thus, may minimize energy consumption. In addition, since the machine learning is used, system configuration may not be possible unless a digital generating device capable of controlling energy in real time is provided. For this reason, it is difficult to configure the air handler200of the present disclosure using a typical ultraviolet or X-ray hot cathode tube. Instead, a carbon nanotube, which generates an electromagnetic wave using a relatively low energy and is easy to control energy, may be used to configure the air handler200of the present disclosure.

For example, a thermionic emission tube such as a typical ultraviolet or X-ray hot cathode tube may include a negative electrode made of a metal filament and a positive electrode that is a metal target, and may use a principle that electrons are emitted from a heated metal filament (negative electrode). That is, the metal filament of the thermionic emission tube may be heated to 1000 degrees Celsius (° C.) or more to emit electrons, and the electrons are accelerated by an applied electric field and attack the metal target, thereby generating an electromagnetic wave. The thermionic emission tube may have a limitation in that a response time is long because the metal filament has to be heated to a high temperature to emit the electrons.

Meanwhile, a field emission (FE) tube may emit electrons from a negative metal electrode by an applied electric field, but a temperature of an emitter is much lower than a temperature of the filament of the thermionic emission tube. That is, since the negative electrode is lower in temperature than the thermionic emission tube, the field emission tube may have a longer life and a shorter response time. In addition, it is possible to fine-tune a voltage control between electrodes, so it is easy to control an emission of an electromagnetic wave at a desired level. As the field emission tube, a CNT tube using a negative CNT electrode may be used.

Basically, the CNT tube may include an anode of a positive electrode, a cathode of a negative electrode, and a gate for inducing emission of electrons. According to this, a magnitude of voltage of the anode, the cathodes, or the gate may be individually set and learned. Due to the voltages applied to the anode and the cathode, current may flow in the tube, and current may be generated to correspond to a difference in voltage between the anode and the cathode. However, in practice, a high voltage may be applied to the tube to provide a magnetic field enough to eject electrons from the negative electrode, resulting in a leakage current at the gate. Accordingly, the anode, cathode, or gate voltage may be individually controlled to minimize gate leakage current while generating a desired amount of current in the tube.

FIG.4is a flowchart illustrating a control method of an air handler according to an example embodiment.

In operation S401, the method may collect first data on an air quality of air flowing into the air handler. According to an example embodiment, the first data may include at least one of information on air pollutants such as fine dust, VOCs, and formaldehyde, carbon dioxide, a wind speed, a temperature, a humidity, and a size of a space in which the air handler is installed.

In operation S402, the method may control a purification part based on the collected first data. According to an example embodiment, the purification part may include at least one charging part that ionizes at least a portion of substances included in the air and a dust collecting part that collects substances ionized in the charging part. The charging part may ionize the substances using an electromagnetic wave having a wavelength in a range between 0.1 nm and 100 nm, inclusive. Specifically, the charging part may photo-ionize pollutants in the air using extreme ultraviolet or soft X-rays.

In operation S403, the method may collect second data on an air quality of air passing through the purification part. According to an example embodiment, the second data may include at least one of information on air pollutants such as fine dust, VOCs, and formaldehyde, carbon dioxide, a wind speed, a temperature, a humidity, and a size of a space in which the air handler is installed.

In operation S404, the method may control the purification part according to a machine-learning model generated based on a setting value related to a control of the purification part, the first data, and the second data. According to an example embodiment, the setting value related to the control of the purification part may include setting values for a voltage applied to the charging part and a voltage applied to the dust collecting part. According to an example embodiment, the charging part may include at least one tube that emits an electromagnetic wave. The setting value for the voltage applied to the charging part may include a setting value for a voltage applied to each of a plurality of electrodes included in the tube. Accordingly, machine learning may be individual learning for each of the plurality of electrodes included in the tube. Meanwhile, the method may collect the first data and the second data for a preset period of time in predetermined time units to control the purification part according to the machine-learning model.

To control the purification part according to the machine-learning model, technology of the present disclosure may derive a threshold for a parameter related to an air quality based on the first data and the second data according to the setting value related to the control of the purification part and derive a control condition of the purification part in which the parameter decreases to be less than or equal to the threshold. The control condition of the purification part may include a voltage applied to the charging part and a voltage applied to the dust collecting part. According to an example embodiment, a control condition for the voltage applied to the charging part may include an individual control condition for the voltage applied to each of the plurality of electrodes included in the tube in the charging part. The parameter may include at least one of information on an air pollutant, carbon dioxide, a wind speed, a temperature, a humidity, and a size of a space in which the air handler is installed. The air pollutant may include, for example, one or more air substances harmful to the human body, such as fine dust, VOCs, and formaldehyde. In addition, the threshold may be different for each space in which the air handler is installed and determined based on a size of fine dust included in the second data. For example, different thresholds may be derived from ultrafine dust with a particle size of PM 2.5 and fine dust with a particle size of PM 10.

In relation to the machine learning performed in operation S404, a machine-learning model according to an example embodiment may be generated based on a number of at least one tube that emits an electromagnetic wave in the charging part, a location of the at least one tube, and a size of the dust collecting part as an input value. For this, the method may previously input the input value prior to the machine learning.

According to an example embodiment, the method of the present disclosure may update the machine-learning model based on the first data collected in operation S401and the second data according to the control condition of the purification part derived in operation S404. For example, the machine-learning model may be continuously corrected based on the first data acquired by sensing the air quality of the air flowing into the purification part and the second data acquired by setting the purification part with the setting value derived through the machine learning and sensing the air quality of the air passing through the purification part.

According to an example embodiment, the machine-learning model may be generated through reinforcement learning in which compensation is performed based on the parameter related to the air quality and power consumption of the purification part. For example, in an environment in which the parameter satisfies the threshold, as the power consumption of the purification part decreases, a larger score may be assigned. Through this, the model may be generated to minimize the power consumption and increase a purification efficiency.

According to an example embodiment, a plurality of air handlers may be installed. In this case, according to different machine-learning models, the respective air handlers may control purification parts therein.

FIG.5is a block diagram illustrating an air handler500according to an example embodiment. Referring toFIG.5, the air handler500may include a purification part510, a first data collector520, a second data collector530, and a controller540.

The first data collector520may include at least one sensor and acquire information associated with air flowing into the purification part510. For example, the first data collector520may be located before the purification part510and collect first data on an air quality of air before the air passes through the purification part510. According to an example embodiment, the first data collector520may be located before the charging part of the purification part510or located after the charging part and before the dust collecting part to sense air. This is a factor that can be changed according to a characteristic of a space where the air handler500is installed and the needs of a user of installation.

The second data collector530may include at least one sensor and acquire information associated with air discharged from the purification part510. For example, the second data collector530may be located after the purification part510and collect second data on an air quality of air passing through the purification part510.

The purification part510may include at least one charging part that ionizes at least a portion of substances included in the air and a dust collecting part that collects ionized substances. By controlling voltages applied to the charging part and the dust collecting part, it is possible to increase an air purification efficiency per power consumption of the air handler500.

The controller540may collect first data on an air quality acquired through the first data collector520and control the purification part510based on the first data. In addition, through the second data collector530, the controller540may collect second data on an air quality of air passing through the purification part510. Also, the controller540may control the purification part510according to a machine-learning model generated based on a setting value related to a control of the purification part510, the first data, and the second data.

According to an example embodiment, based on the machine-learning model, the controller540may derive a threshold for a parameter related to the air quality based on the first data and the second data according to the setting value related to the control of the purification part510. Also, the controller540may derive a control condition of the purification part510in which the parameter decreases to be less than or equal to the threshold, thereby controlling the purification part510. Specifically, the machine-learning model may be generated through reinforcement learning in which compensation is performed based on the parameter related to the air quality and power consumption of the purification part510. For example, as the power consumption of the purification part510decreases while the parameter related to the air quality satisfies the threshold, a larger score may be assigned to the model to perform the reinforcement learning on the model.

In an example, when an air handler to which the machine learning of the present disclosure is not applied operates at a maximum performance, a voltage of 4.8 kV may be applied to the charging part and a voltage of 4.4 kV may be applied to the dust collecting part. In this case, total power consumption may be 20 W and an average fine-dust removal efficiency may be 15%. That is, when a maximum performance operation condition is applied, a fine-dust removal efficiency per power consumption may correspond to 0.75%/W. In contrast, when an air handler to which the machine learning of the present disclosure is applied operates at an optimal performance, a voltage of 4.3 kV may be applied to the charging part and a voltage of 3.5 kV may be applied to the dust collecting part. In this case, total power consumption may be 8 W and an average fine-dust removal efficiency may be 14%. That is, when an optimal performance operation condition is applied, a fine-dust removal efficiency per power consumption may correspond to 1.75%/W, which is higher compared to a case in which the machine learning is not applied. Accordingly, a fine-dust removal performance is not reduced despite the decrease in power consumption, so that the fine-dust removal efficiency per power consumption may be increased.

The air handler500ofFIG.5is merely an example, and may further include other components in addition to the components shown inFIG.5. Also, the air handler500may implement the above-described example embodiments through the components.

The present specification and drawings have been described with respect to the example embodiments of the present disclosure. Although specific terms are used, it is only used in a general sense to easily explain the technical content of the present disclosure and to help the understanding of the invention, and is not intended to limit the scope of the specification. It will be apparent to those skilled in the art that other modifications based on the technical spirit of the present disclosure may be implemented in addition to the embodiments disclosed herein.