• Journal of Broadcast Engineering
• /
• v.27 no.2
• /
• pp.185-197
• /
• 2022

Recently, deep learning networks with high performance for object recognition are emerging. In the case of object recognition using deep learning, it is important to build a training data set to improve performance. To build a data set, we need to collect and label the images. This process requires a lot of time and manpower. For this reason, open data sets are used. However, there are objects that do not have large open data sets. One of them is data required for license plate detection and recognition. Therefore, in this paper, we propose an artificial license plate generator system that can create large data sets by minimizing images. In addition, the detection rate according to the artificial license plate arrangement structure was analyzed. As a result of the analysis, the best layout structure was FVC_III and B, and the most suitable network was D2Det. Although the artificial data set performance was 2-3% lower than that of the actual data set, the time to build the artificial data was about 11 times faster than the time to build the actual data set, proving that it is a time-efficient data set building system.

• Journal of Korean Society of Forest Science
• /
• v.111 no.2
• /
• pp.287-301
• /
• 2022

This study aims to propose a crane-tip control method to intuitively control the end-effector vertically or horizontally for improving the crane work efficiency and to confirm the control performance. To verify the control performance based on experimental variables, a laboratory-scale crane was manufactured using an electric cylinder. Through a forward and reverse kinematics analysis, the crane was configured to output the position coordinates of the current crane-tip and the joint angle at each target point. Furthermore, a method of generating waypoints was used, and a dead band using lateral boundary offset (LBO) was set. Appropriate parameters were selected using bang-bang control, which confirmed that the number of waypoints and LBO radius were associated with positioning error, and the cylinder speed was related to the lead time. With increased number of waypoints and decreased LBO radius, the positioning error and the lead time also decreased as the cylinder speed decreased. Using the proportional control, when the cylinder velocity was changed at every control cycle, the lead time was greatly reduced; however, the actual control pattern was controlled by repeating over and undershoot in a large range. Therefore, proportional control was performed by additionally applying velocity gain that can relatively change the speed of each cylinder. Since the control performed with in a range of 10 mm, it was verified th at th e crane-tip control can be ach ieved with only th e proportional control to which the velocity gain was applied in a control cycle of 20 ms.

• Journal of Practical Agriculture & Fisheries Research
• /
• v.24 no.2
• /
• pp.12-22
• /
• 2022

The purpose of this study is to design a farming model for Hanwoo start-up farmers. I prepared a Hanwoo production plan model according to the growth cycle of Hanwoo using EXCEL. The Hanwoo production plan model was simulated in two model: Model 1 (a model that only purchases Hanwoo calf) and Model 2 (a model that purchases both Hanwoo cow and Hanwoo calf). Next, I reviewed the profits and costs of two Hanwoo simulation models. As a result of the analysis, Model 2 has the following characteristics compared to Model 1. First, Model 2 requires a lot of initial investment. Second, Model 2 is advantageous in terms of farm cash balance because imports occur every year. Third, Model 2 can efficiently use facilities and machines.

• Journal of Convergence for Information Technology
• /
• v.11 no.5
• /
• pp.111-117
• /
• 2021

With the rapid development of advanced science and technology including the 4th industrial revolution, the future battlefield environment is evolving at a rapid pace. In order to actively respond to issues such as reduction of military resources and shortening of service period, and to emphasize the realization of human-centered values, the Ministry of National Defense is re-establishing the role of the Army in accordance with the defense reform and is promoting the Warrior Platform, a next-generation individual combat system. In this paper, we intend to present the optimal warrior platform architecture suitable for the Korean Army by realizing the concept of future ground operations and analyzing overseas cases. We analyze the essential abilities required of individual combatants and the abilities required for each unit type, and specifically presents a plan for integration and linkage of warrior platform equipment. We also propose an efficient business promotion direction by presenting the data flow and power connection diagram between the devices that need integration and interworking.

• Clean Technology
• /
• v.28 no.4
• /
• pp.293-300
• /
• 2022

The remarkable mechanical, electrical, and thermal properties of graphene have recently sparked tremendous interest in various research fields. One of the most promising methods to produce large quantities of graphene dispersion is liquid-phase exfoliation (LPE) which utilizes ultrasonic waves or shear stresses to exfoliate bulk graphite into graphene flakes that are a few layers thick. Graphene dispersion produced via LPE can be transformed into graphene ink to further boost graphene's applications, but producing high-quality graphene more economically remains a challenge. To overcome this shortcoming, an advanced LPE process should be developed that uses relatively cheap natural graphite as a graphene source. In this study, a flow-LPE process was used to exfoliate natural graphite to produce graphene that was three times cheaper and seven times larger than synthetic graphite. The optimal exfoliation conditions in the flow-LPE process were determined in order to produce high-quality graphene flakes. In addition, the structural and electrical properties of the flakes were characterized. The electrical properties of the exfoliated graphene were investigated by carrying out an ink formulation process to prepare graphene ink suitable for inkjet printing, and fabricating a printed graphene pattern. By utilizing natural graphite, this study offers a potential protocol for graphene production, ink formulation, and printed graphene devices in a more industrial-comparable manner.

• Journal of IKEEE
• /
• v.26 no.4
• /
• pp.633-638
• /
• 2022

Rencently, neuromorphic systems of spiking neural networks (SNNs) that imitate the human brain have attracted attention. Neuromorphic technology has the advantage of high speed and low power consumption in cognitive applications and processing. Resistive random-access memory (RRAM) for SNNs are the most efficient structure for parallel calculation and perform the gradual switching operation of spike-timing-dependent plasticity (STDP). RRAM as synaptic device operation has low-power processing and expresses various memory states. However, the integration of RRAM device causes high switching voltage and current, resulting in high power consumption. To reduce the operation voltage of the RRAM, it is important to develop new materials of the switching layer and metal electrode. This study suggested a optimized new structure that is the Metal/Al₂O₃/HfO_x/SWCNTs/N+silicon (MOCS) with single-walled carbon nanotubes (SWCNTs), which have excellent electrical and mechanical properties in order to lower the switching voltage. Therefore, we show an improvement in the gradual switching behavior and low-power I/V curve of SWCNTs-based memristors.

• KNOM Review
• /
• v.23 no.2
• /
• pp.18-28
• /
• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.8
• /
• pp.241-251
• /
• 2022

Diagnosis and management of customer's skin condition is an important essential function in the cosmetics and beauty industry. As the social media environment spreads and generalizes to all fields of society, the interaction of questions and answers to various and delicate concerns and requirements regarding the diagnosis and management of skin conditions is being actively dealt with in the social media community. However, since social media information is very diverse and atypical big data, an intelligent skin condition diagnosis system that combines appropriate skin condition information analysis and artificial intelligence technology is necessary. In this paper, we developed the skin condition diagnosis system SCDIS to intelligently diagnose and manage the skin condition of customers by processing the text analysis information of social media into learning data. In SCDIS, an artificial neural network model, AnnTFIDF, that automatically diagnoses skin condition types using artificial neural network technology, a deep learning machine learning method, was built up and used. The performance of the artificial neural network model AnnTFIDF was analyzed using test sample data, and the accuracy of the skin condition type diagnosis prediction value showed a high performance of about 95%. Through the experimental and performance analysis results of this paper, SCDIS can be evaluated as an intelligent tool that can be used efficiently in the skin condition analysis and diagnosis management process in the cosmetic and beauty industry. And this study can be used as a basic research to solve the new technology trend, customized cosmetics manufacturing and consumer-oriented beauty industry technology demand.

• Journal of Korea Society of Industrial Information Systems
• /
• v.27 no.4
• /
• pp.19-27
• /
• 2022

In general, the performance of ML(Machine Learning) application is determined by various factors such as the type of ML model, the size of model (number of parameters), hyperparameters setting during the training, and training data. In particular, the recognition accuracy of ML may be deteriorated or experienced overfitting problem if the amount of dada used for training is insufficient. Existing studies focusing on image recognition have widely used open datasets for training and evaluating the proposed ML models. However, for specific applications where the sensor used, the target of recognition, and the recognition situation are different, it is necessary to build the dataset manually. In this case, the performance of ML largely depends on the quantity and quality of the data. In this paper, training data used for motion recognition application is augmented using the kernel density estimation algorithm which is a type of non-parametric estimation method. We then compare and analyze the recognition accuracy of a ML application by varying the number of original data, kernel types and augmentation rate used for data augmentation. Finally experimental results show that the recognition accuracy is improved by up to 14.31% when using the narrow bandwidth Tophat kernel.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.8
• /
• pp.339-346
• /
• 2022

Recently, the resource depletion and climate change problem caused by the massive usage of fossil fuels for electric power generation has become a critical issue worldwide. According to this issue, interest in renewable energy resources that can replace fossil fuels is increasing. Especially, photovoltaic power has gaining much attention because there is no risk of resource exhaustion compared to other energy resources and there are low restrictions on installation of photovoltaic system. In order to use the power generated by the photovoltaic system efficiently, a more accurate photovoltaic power forecasting model is required. So far, even though many machine learning and deep learning-based photovoltaic power forecasting models have been proposed, they showed limited success in terms of interpretability. Deep learning-based forecasting models have the disadvantage of being difficult to explain how the forecasting results are derived. To solve this problem, many studies are being conducted on explainable artificial intelligence technique. The reliability of the model can be secured if it is possible to interpret how the model derives the results. Also, the model can be improved to increase the forecasting accuracy based on the analysis results. Therefore, in this paper, we propose an explainable photovoltaic power forecasting scheme based on BiLSTM (Bidirectional Long Short-Term Memory) and SHAP (SHapley Additive exPlanations).